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[ "# An object with a specific mass will weigh ______.\n\nThis question was previously asked in\nRRB ALP Electrician 22 Jan 2019 Official Paper (Shift 3)\nView all RRB ALP Papers >\n1. zero on the Earth\n2. less on the Earth than on the Moon\n3. more on the Earth than on the Moon\n4. equal on the Earth and the moon\n\nOption 3 : more on the Earth than on the Moon\nFree\nRRB ALP CBT I 9 Aug 2018 Shift 2 Official Paper\n70.1 K Users\n75 Questions 75 Marks 60 Mins\n\n## Detailed Solution\n\nConcept:\n\nWeight:\n\n• It is the measure of the force of gravity acting on a body.\n• Formula: Weight, W = mg, where m = mass, a = acceleration\n• So it can be said that the weight of an object is directly proportional to its mass.\n• As weight is a force its SI unit is also the same as that of force, SI unit of weight is Newton (N).\n\nAcceleration due to gravity:\n\n• It is the acceleration gained by an object due to gravitational force.\n• Its SI unit is m/s2.\n• It has both magnitude and direction, hence, it’s a vector quantity.\n• Acceleration due to gravity is represented by g.\n• The standard value of g on the surface of the earth at sea level is 9.8 m/s2.\n• The acceleration on the moon is one-sixth of the acceleration due to gravity on the earth.\n\nExplanation:\n\nWeight, W = mg\n\nOn the moon, g' = g/6, where, g = acceleration due to gravity on the surface of earth.\n\n• Hence, the weight is directly proportional to the acceleration due to gravity.\n• The acceleration due to gravity on the moon is less as compared to the surface of earth.\n• Therefore, the weight on the surface of earth is more than that on the moon's surface." ]

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[ "", null, "Home Intuitionistic Logic ExplorerTheorem List (p. 121 of 127) < Previous  Next > Bad symbols? Try the GIF version. Mirrors  >  Metamath Home Page  >  ILE Home Page  >  Theorem List Contents  >  Recent Proofs       This page: Page List\n\nTheorem List for Intuitionistic Logic Explorer - 12001-12100   *Has distinct variable group(s)\nTypeLabelDescription\nStatement\n\nTheoremneii2 12001* Property of a neighborhood. (Contributed by NM, 12-Feb-2007.)\n((𝐽 ∈ Top ∧ 𝑁 ∈ ((nei‘𝐽)‘𝑆)) → ∃𝑔𝐽 (𝑆𝑔𝑔𝑁))\n\nTheoremneiss 12002 Any neighborhood of a set 𝑆 is also a neighborhood of any subset 𝑅𝑆. Similar to Proposition 1 of [BourbakiTop1] p. I.2. (Contributed by FL, 25-Sep-2006.)\n((𝐽 ∈ Top ∧ 𝑁 ∈ ((nei‘𝐽)‘𝑆) ∧ 𝑅𝑆) → 𝑁 ∈ ((nei‘𝐽)‘𝑅))\n\nTheoremssnei 12003 A set is included in any of its neighborhoods. Generalization to subsets of elnei 12004. (Contributed by FL, 16-Nov-2006.)\n((𝐽 ∈ Top ∧ 𝑁 ∈ ((nei‘𝐽)‘𝑆)) → 𝑆𝑁)\n\nTheoremelnei 12004 A point belongs to any of its neighborhoods. Property Viii of [BourbakiTop1] p. I.3. (Contributed by FL, 28-Sep-2006.)\n((𝐽 ∈ Top ∧ 𝑃𝐴𝑁 ∈ ((nei‘𝐽)‘{𝑃})) → 𝑃𝑁)\n\nTheorem0nnei 12005 The empty set is not a neighborhood of a nonempty set. (Contributed by FL, 18-Sep-2007.)\n((𝐽 ∈ Top ∧ 𝑆 ≠ ∅) → ¬ ∅ ∈ ((nei‘𝐽)‘𝑆))\n\nTheoremneipsm 12006* A neighborhood of a set is a neighborhood of every point in the set. Proposition 1 of [BourbakiTop1] p. I.2. (Contributed by FL, 16-Nov-2006.) (Revised by Jim Kingdon, 22-Mar-2023.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝑆𝑋 ∧ ∃𝑥 𝑥𝑆) → (𝑁 ∈ ((nei‘𝐽)‘𝑆) ↔ ∀𝑝𝑆 𝑁 ∈ ((nei‘𝐽)‘{𝑝})))\n\nTheoremopnneissb 12007 An open set is a neighborhood of any of its subsets. (Contributed by FL, 2-Oct-2006.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝑁𝐽𝑆𝑋) → (𝑆𝑁𝑁 ∈ ((nei‘𝐽)‘𝑆)))\n\nTheoremopnssneib 12008 Any superset of an open set is a neighborhood of it. (Contributed by NM, 14-Feb-2007.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝑆𝐽𝑁𝑋) → (𝑆𝑁𝑁 ∈ ((nei‘𝐽)‘𝑆)))\n\nTheoremssnei2 12009 Any subset 𝑀 of 𝑋 containing a neighborhood 𝑁 of a set 𝑆 is a neighborhood of this set. Generalization to subsets of Property Vi of [BourbakiTop1] p. I.3. (Contributed by FL, 2-Oct-2006.)\n𝑋 = 𝐽       (((𝐽 ∈ Top ∧ 𝑁 ∈ ((nei‘𝐽)‘𝑆)) ∧ (𝑁𝑀𝑀𝑋)) → 𝑀 ∈ ((nei‘𝐽)‘𝑆))\n\nTheoremopnneiss 12010 An open set is a neighborhood of any of its subsets. (Contributed by NM, 13-Feb-2007.)\n((𝐽 ∈ Top ∧ 𝑁𝐽𝑆𝑁) → 𝑁 ∈ ((nei‘𝐽)‘𝑆))\n\nTheoremopnneip 12011 An open set is a neighborhood of any of its members. (Contributed by NM, 8-Mar-2007.)\n((𝐽 ∈ Top ∧ 𝑁𝐽𝑃𝑁) → 𝑁 ∈ ((nei‘𝐽)‘{𝑃}))\n\nTheoremtpnei 12012 The underlying set of a topology is a neighborhood of any of its subsets. Special case of opnneiss 12010. (Contributed by FL, 2-Oct-2006.)\n𝑋 = 𝐽       (𝐽 ∈ Top → (𝑆𝑋𝑋 ∈ ((nei‘𝐽)‘𝑆)))\n\nTheoremneiuni 12013 The union of the neighborhoods of a set equals the topology's underlying set. (Contributed by FL, 18-Sep-2007.) (Revised by Mario Carneiro, 9-Apr-2015.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝑆𝑋) → 𝑋 = ((nei‘𝐽)‘𝑆))\n\nTheoremtopssnei 12014 A finer topology has more neighborhoods. (Contributed by Mario Carneiro, 9-Apr-2015.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       (((𝐽 ∈ Top ∧ 𝐾 ∈ Top ∧ 𝑋 = 𝑌) ∧ 𝐽𝐾) → ((nei‘𝐽)‘𝑆) ⊆ ((nei‘𝐾)‘𝑆))\n\nTheoreminnei 12015 The intersection of two neighborhoods of a set is also a neighborhood of the set. Generalization to subsets of Property Vii of [BourbakiTop1] p. I.3 for binary intersections. (Contributed by FL, 28-Sep-2006.)\n((𝐽 ∈ Top ∧ 𝑁 ∈ ((nei‘𝐽)‘𝑆) ∧ 𝑀 ∈ ((nei‘𝐽)‘𝑆)) → (𝑁𝑀) ∈ ((nei‘𝐽)‘𝑆))\n\nTheoremopnneiid 12016 Only an open set is a neighborhood of itself. (Contributed by FL, 2-Oct-2006.)\n(𝐽 ∈ Top → (𝑁 ∈ ((nei‘𝐽)‘𝑁) ↔ 𝑁𝐽))\n\nTheoremneissex 12017* For any neighborhood 𝑁 of 𝑆, there is a neighborhood 𝑥 of 𝑆 such that 𝑁 is a neighborhood of all subsets of 𝑥. Generalization to subsets of Property Viv of [BourbakiTop1] p. I.3. (Contributed by FL, 2-Oct-2006.)\n((𝐽 ∈ Top ∧ 𝑁 ∈ ((nei‘𝐽)‘𝑆)) → ∃𝑥 ∈ ((nei‘𝐽)‘𝑆)∀𝑦(𝑦𝑥𝑁 ∈ ((nei‘𝐽)‘𝑦)))\n\nTheorem0nei 12018 The empty set is a neighborhood of itself. (Contributed by FL, 10-Dec-2006.)\n(𝐽 ∈ Top → ∅ ∈ ((nei‘𝐽)‘∅))\n\n6.1.6  Subspace topologies\n\nTheoremrestrcl 12019 Reverse closure for the subspace topology. (Contributed by Mario Carneiro, 19-Mar-2015.) (Proof shortened by Jim Kingdon, 23-Mar-2023.)\n((𝐽t 𝐴) ∈ Top → (𝐽 ∈ V ∧ 𝐴 ∈ V))\n\nTheoremrestbasg 12020 A subspace topology basis is a basis. (Contributed by Mario Carneiro, 19-Mar-2015.)\n((𝐵 ∈ TopBases ∧ 𝐴𝑉) → (𝐵t 𝐴) ∈ TopBases)\n\nTheoremtgrest 12021 A subspace can be generated by restricted sets from a basis for the original topology. (Contributed by Mario Carneiro, 19-Mar-2015.) (Proof shortened by Mario Carneiro, 30-Aug-2015.)\n((𝐵𝑉𝐴𝑊) → (topGen‘(𝐵t 𝐴)) = ((topGen‘𝐵) ↾t 𝐴))\n\nTheoremresttop 12022 A subspace topology is a topology. Definition of subspace topology in [Munkres] p. 89. 𝐴 is normally a subset of the base set of 𝐽. (Contributed by FL, 15-Apr-2007.) (Revised by Mario Carneiro, 1-May-2015.)\n((𝐽 ∈ Top ∧ 𝐴𝑉) → (𝐽t 𝐴) ∈ Top)\n\nTheoremresttopon 12023 A subspace topology is a topology on the base set. (Contributed by Mario Carneiro, 13-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐴𝑋) → (𝐽t 𝐴) ∈ (TopOn‘𝐴))\n\nTheoremrestuni 12024 The underlying set of a subspace topology. (Contributed by FL, 5-Jan-2009.) (Revised by Mario Carneiro, 13-Aug-2015.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝐴𝑋) → 𝐴 = (𝐽t 𝐴))\n\nTheoremstoig 12025 The topological space built with a subspace topology. (Contributed by FL, 5-Jan-2009.) (Proof shortened by Mario Carneiro, 1-May-2015.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝐴𝑋) → {⟨(Base‘ndx), 𝐴⟩, ⟨(TopSet‘ndx), (𝐽t 𝐴)⟩} ∈ TopSp)\n\nTheoremrestco 12026 Composition of subspaces. (Contributed by Mario Carneiro, 15-Dec-2013.) (Revised by Mario Carneiro, 1-May-2015.)\n((𝐽𝑉𝐴𝑊𝐵𝑋) → ((𝐽t 𝐴) ↾t 𝐵) = (𝐽t (𝐴𝐵)))\n\nTheoremrestabs 12027 Equivalence of being a subspace of a subspace and being a subspace of the original. (Contributed by Jeff Hankins, 11-Jul-2009.) (Proof shortened by Mario Carneiro, 1-May-2015.)\n((𝐽𝑉𝑆𝑇𝑇𝑊) → ((𝐽t 𝑇) ↾t 𝑆) = (𝐽t 𝑆))\n\nTheoremrestin 12028 When the subspace region is not a subset of the base of the topology, the resulting set is the same as the subspace restricted to the base. (Contributed by Mario Carneiro, 15-Dec-2013.)\n𝑋 = 𝐽       ((𝐽𝑉𝐴𝑊) → (𝐽t 𝐴) = (𝐽t (𝐴𝑋)))\n\nTheoremrestuni2 12029 The underlying set of a subspace topology. (Contributed by Mario Carneiro, 21-Mar-2015.)\n𝑋 = 𝐽       ((𝐽 ∈ Top ∧ 𝐴𝑉) → (𝐴𝑋) = (𝐽t 𝐴))\n\nTheoremresttopon2 12030 The underlying set of a subspace topology. (Contributed by Mario Carneiro, 13-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐴𝑉) → (𝐽t 𝐴) ∈ (TopOn‘(𝐴𝑋)))\n\nTheoremrest0 12031 The subspace topology induced by the topology 𝐽 on the empty set. (Contributed by FL, 22-Dec-2008.) (Revised by Mario Carneiro, 1-May-2015.)\n(𝐽 ∈ Top → (𝐽t ∅) = {∅})\n\nTheoremrestsn 12032 The only subspace topology induced by the topology {∅}. (Contributed by FL, 5-Jan-2009.) (Revised by Mario Carneiro, 15-Dec-2013.)\n(𝐴𝑉 → ({∅} ↾t 𝐴) = {∅})\n\nTheoremrestopnb 12033 If 𝐵 is an open subset of the subspace base set 𝐴, then any subset of 𝐵 is open iff it is open in 𝐴. (Contributed by Mario Carneiro, 2-Mar-2015.)\n(((𝐽 ∈ Top ∧ 𝐴𝑉) ∧ (𝐵𝐽𝐵𝐴𝐶𝐵)) → (𝐶𝐽𝐶 ∈ (𝐽t 𝐴)))\n\nTheoremssrest 12034 If 𝐾 is a finer topology than 𝐽, then the subspace topologies induced by 𝐴 maintain this relationship. (Contributed by Mario Carneiro, 21-Mar-2015.) (Revised by Mario Carneiro, 1-May-2015.)\n((𝐾𝑉𝐽𝐾) → (𝐽t 𝐴) ⊆ (𝐾t 𝐴))\n\nTheoremrestopn2 12035 If 𝐴 is open, then 𝐵 is open in 𝐴 iff it is an open subset of 𝐴. (Contributed by Mario Carneiro, 2-Mar-2015.)\n((𝐽 ∈ Top ∧ 𝐴𝐽) → (𝐵 ∈ (𝐽t 𝐴) ↔ (𝐵𝐽𝐵𝐴)))\n\nTheoremrestdis 12036 A subspace of a discrete topology is discrete. (Contributed by Mario Carneiro, 19-Mar-2015.)\n((𝐴𝑉𝐵𝐴) → (𝒫 𝐴t 𝐵) = 𝒫 𝐵)\n\n6.1.7  Limits and continuity in topological spaces\n\nSyntaxccn 12037 Extend class notation with the class of continuous functions between topologies.\nclass Cn\n\nSyntaxccnp 12038 Extend class notation with the class of functions between topologies continuous at a given point.\nclass CnP\n\nSyntaxclm 12039 Extend class notation with a function on topological spaces whose value is the convergence relation for limit sequences in the space.\nclass 𝑡\n\nDefinitiondf-cn 12040* Define a function on two topologies whose value is the set of continuous mappings from the first topology to the second. Based on definition of continuous function in [Munkres] p. 102. See iscn 12048 for the predicate form. (Contributed by NM, 17-Oct-2006.)\nCn = (𝑗 ∈ Top, 𝑘 ∈ Top ↦ {𝑓 ∈ ( 𝑘𝑚 𝑗) ∣ ∀𝑦𝑘 (𝑓𝑦) ∈ 𝑗})\n\nDefinitiondf-cnp 12041* Define a function on two topologies whose value is the set of continuous mappings at a specified point in the first topology. Based on Theorem 7.2(g) of [Munkres] p. 107. (Contributed by NM, 17-Oct-2006.)\nCnP = (𝑗 ∈ Top, 𝑘 ∈ Top ↦ (𝑥 𝑗 ↦ {𝑓 ∈ ( 𝑘𝑚 𝑗) ∣ ∀𝑦𝑘 ((𝑓𝑥) ∈ 𝑦 → ∃𝑔𝑗 (𝑥𝑔 ∧ (𝑓𝑔) ⊆ 𝑦))}))\n\nDefinitiondf-lm 12042* Define a function on topologies whose value is the convergence relation for sequences into the given topological space. Although 𝑓 is typically a sequence (a function from an upperset of integers) with values in the topological space, it need not be. Note, however, that the limit property concerns only values at integers, so that the real-valued function (𝑥 ∈ ℝ ↦ (sin‘(π · 𝑥))) converges to zero (in the standard topology on the reals) with this definition. (Contributed by NM, 7-Sep-2006.)\n𝑡 = (𝑗 ∈ Top ↦ {⟨𝑓, 𝑥⟩ ∣ (𝑓 ∈ ( 𝑗pm ℂ) ∧ 𝑥 𝑗 ∧ ∀𝑢𝑗 (𝑥𝑢 → ∃𝑦 ∈ ran ℤ(𝑓𝑦):𝑦𝑢))})\n\nTheoremlmrcl 12043 Reverse closure for the convergence relation. (Contributed by Mario Carneiro, 7-Sep-2015.)\n(𝐹(⇝𝑡𝐽)𝑃𝐽 ∈ Top)\n\nTheoremlmfval 12044* The relation \"sequence 𝑓 converges to point 𝑦 \" in a metric space. (Contributed by NM, 7-Sep-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n(𝐽 ∈ (TopOn‘𝑋) → (⇝𝑡𝐽) = {⟨𝑓, 𝑥⟩ ∣ (𝑓 ∈ (𝑋pm ℂ) ∧ 𝑥𝑋 ∧ ∀𝑢𝐽 (𝑥𝑢 → ∃𝑦 ∈ ran ℤ(𝑓𝑦):𝑦𝑢))})\n\nTheoremlmreltop 12045 The topological space convergence relation is a relation. (Contributed by Jim Kingdon, 25-Mar-2023.)\n(𝐽 ∈ Top → Rel (⇝𝑡𝐽))\n\nTheoremcnfval 12046* The set of all continuous functions from topology 𝐽 to topology 𝐾. (Contributed by NM, 17-Oct-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌)) → (𝐽 Cn 𝐾) = {𝑓 ∈ (𝑌𝑚 𝑋) ∣ ∀𝑦𝐾 (𝑓𝑦) ∈ 𝐽})\n\nTheoremcnpfval 12047* The function mapping the points in a topology 𝐽 to the set of all functions from 𝐽 to topology 𝐾 continuous at that point. (Contributed by NM, 17-Oct-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌)) → (𝐽 CnP 𝐾) = (𝑥𝑋 ↦ {𝑓 ∈ (𝑌𝑚 𝑋) ∣ ∀𝑤𝐾 ((𝑓𝑥) ∈ 𝑤 → ∃𝑣𝐽 (𝑥𝑣 ∧ (𝑓𝑣) ⊆ 𝑤))}))\n\nTheoremiscn 12048* The predicate \"the class 𝐹 is a continuous function from topology 𝐽 to topology 𝐾\". Definition of continuous function in [Munkres] p. 102. (Contributed by NM, 17-Oct-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌)) → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑦𝐾 (𝐹𝑦) ∈ 𝐽)))\n\nTheoremcnpval 12049* The set of all functions from topology 𝐽 to topology 𝐾 that are continuous at a point 𝑃. (Contributed by NM, 17-Oct-2006.) (Revised by Mario Carneiro, 11-Nov-2013.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝑃𝑋) → ((𝐽 CnP 𝐾)‘𝑃) = {𝑓 ∈ (𝑌𝑚 𝑋) ∣ ∀𝑦𝐾 ((𝑓𝑃) ∈ 𝑦 → ∃𝑥𝐽 (𝑃𝑥 ∧ (𝑓𝑥) ⊆ 𝑦))})\n\nTheoremiscnp 12050* The predicate \"the class 𝐹 is a continuous function from topology 𝐽 to topology 𝐾 at point 𝑃\". Based on Theorem 7.2(g) of [Munkres] p. 107. (Contributed by NM, 17-Oct-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝑃𝑋) → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑦𝐾 ((𝐹𝑃) ∈ 𝑦 → ∃𝑥𝐽 (𝑃𝑥 ∧ (𝐹𝑥) ⊆ 𝑦)))))\n\nTheoremiscn2 12051* The predicate \"the class 𝐹 is a continuous function from topology 𝐽 to topology 𝐾\". Definition of continuous function in [Munkres] p. 102. (Contributed by Mario Carneiro, 21-Aug-2015.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       (𝐹 ∈ (𝐽 Cn 𝐾) ↔ ((𝐽 ∈ Top ∧ 𝐾 ∈ Top) ∧ (𝐹:𝑋𝑌 ∧ ∀𝑦𝐾 (𝐹𝑦) ∈ 𝐽)))\n\nTheoremcntop1 12052 Reverse closure for a continuous function. (Contributed by Mario Carneiro, 21-Aug-2015.)\n(𝐹 ∈ (𝐽 Cn 𝐾) → 𝐽 ∈ Top)\n\nTheoremcntop2 12053 Reverse closure for a continuous function. (Contributed by Mario Carneiro, 21-Aug-2015.)\n(𝐹 ∈ (𝐽 Cn 𝐾) → 𝐾 ∈ Top)\n\nTheoremiscnp3 12054* The predicate \"the class 𝐹 is a continuous function from topology 𝐽 to topology 𝐾 at point 𝑃\". (Contributed by NM, 15-May-2007.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝑃𝑋) → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑦𝐾 ((𝐹𝑃) ∈ 𝑦 → ∃𝑥𝐽 (𝑃𝑥𝑥 ⊆ (𝐹𝑦))))))\n\nTheoremcnf 12055 A continuous function is a mapping. (Contributed by FL, 8-Dec-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       (𝐹 ∈ (𝐽 Cn 𝐾) → 𝐹:𝑋𝑌)\n\nTheoremcnf2 12056 A continuous function is a mapping. (Contributed by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝐹 ∈ (𝐽 Cn 𝐾)) → 𝐹:𝑋𝑌)\n\nTheoremcnprcl2k 12057 Reverse closure for a function continuous at a point. (Contributed by Mario Carneiro, 21-Aug-2015.) (Revised by Jim Kingdon, 28-Mar-2023.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ Top ∧ 𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃)) → 𝑃𝑋)\n\nTheoremcnpf2 12058 A continuous function at point 𝑃 is a mapping. (Contributed by Mario Carneiro, 21-Aug-2015.) (Revised by Jim Kingdon, 28-Mar-2023.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃)) → 𝐹:𝑋𝑌)\n\nTheoremtgcn 12059* The continuity predicate when the range is given by a basis for a topology. (Contributed by Mario Carneiro, 7-Feb-2015.) (Revised by Mario Carneiro, 22-Aug-2015.)\n(𝜑𝐽 ∈ (TopOn‘𝑋))    &   (𝜑𝐾 = (topGen‘𝐵))    &   (𝜑𝐾 ∈ (TopOn‘𝑌))       (𝜑 → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑦𝐵 (𝐹𝑦) ∈ 𝐽)))\n\nTheoremtgcnp 12060* The \"continuous at a point\" predicate when the range is given by a basis for a topology. (Contributed by Mario Carneiro, 3-Feb-2015.) (Revised by Mario Carneiro, 22-Aug-2015.)\n(𝜑𝐽 ∈ (TopOn‘𝑋))    &   (𝜑𝐾 = (topGen‘𝐵))    &   (𝜑𝐾 ∈ (TopOn‘𝑌))    &   (𝜑𝑃𝑋)       (𝜑 → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑦𝐵 ((𝐹𝑃) ∈ 𝑦 → ∃𝑥𝐽 (𝑃𝑥 ∧ (𝐹𝑥) ⊆ 𝑦)))))\n\nTheoremssidcn 12061 The identity function is a continuous function from one topology to another topology on the same set iff the domain is finer than the codomain. (Contributed by Mario Carneiro, 21-Mar-2015.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑋)) → (( I ↾ 𝑋) ∈ (𝐽 Cn 𝐾) ↔ 𝐾𝐽))\n\nTheoremicnpimaex 12062* Property of a function continuous at a point. (Contributed by FL, 31-Dec-2006.) (Revised by Jim Kingdon, 28-Mar-2023.)\n(((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝑃𝑋) ∧ (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ∧ 𝐴𝐾 ∧ (𝐹𝑃) ∈ 𝐴)) → ∃𝑥𝐽 (𝑃𝑥 ∧ (𝐹𝑥) ⊆ 𝐴))\n\nTheoremidcn 12063 A restricted identity function is a continuous function. (Contributed by FL, 27-Dec-2006.) (Proof shortened by Mario Carneiro, 21-Mar-2015.)\n(𝐽 ∈ (TopOn‘𝑋) → ( I ↾ 𝑋) ∈ (𝐽 Cn 𝐽))\n\nTheoremlmbr 12064* Express the binary relation \"sequence 𝐹 converges to point 𝑃 \" in a topological space. Definition 1.4-1 of [Kreyszig] p. 25. The condition 𝐹 ⊆ (ℂ × 𝑋) allows us to use objects more general than sequences when convenient; see the comment in df-lm 12042. (Contributed by Mario Carneiro, 14-Nov-2013.)\n(𝜑𝐽 ∈ (TopOn‘𝑋))       (𝜑 → (𝐹(⇝𝑡𝐽)𝑃 ↔ (𝐹 ∈ (𝑋pm ℂ) ∧ 𝑃𝑋 ∧ ∀𝑢𝐽 (𝑃𝑢 → ∃𝑦 ∈ ran ℤ(𝐹𝑦):𝑦𝑢))))\n\nTheoremlmbr2 12065* Express the binary relation \"sequence 𝐹 converges to point 𝑃 \" in a metric space using an arbitrary upper set of integers. (Contributed by Mario Carneiro, 14-Nov-2013.)\n(𝜑𝐽 ∈ (TopOn‘𝑋))    &   𝑍 = (ℤ𝑀)    &   (𝜑𝑀 ∈ ℤ)       (𝜑 → (𝐹(⇝𝑡𝐽)𝑃 ↔ (𝐹 ∈ (𝑋pm ℂ) ∧ 𝑃𝑋 ∧ ∀𝑢𝐽 (𝑃𝑢 → ∃𝑗𝑍𝑘 ∈ (ℤ𝑗)(𝑘 ∈ dom 𝐹 ∧ (𝐹𝑘) ∈ 𝑢)))))\n\nTheoremlmbrf 12066* Express the binary relation \"sequence 𝐹 converges to point 𝑃 \" in a metric space using an arbitrary upper set of integers. This version of lmbr2 12065 presupposes that 𝐹 is a function. (Contributed by Mario Carneiro, 14-Nov-2013.)\n(𝜑𝐽 ∈ (TopOn‘𝑋))    &   𝑍 = (ℤ𝑀)    &   (𝜑𝑀 ∈ ℤ)    &   (𝜑𝐹:𝑍𝑋)    &   ((𝜑𝑘𝑍) → (𝐹𝑘) = 𝐴)       (𝜑 → (𝐹(⇝𝑡𝐽)𝑃 ↔ (𝑃𝑋 ∧ ∀𝑢𝐽 (𝑃𝑢 → ∃𝑗𝑍𝑘 ∈ (ℤ𝑗)𝐴𝑢))))\n\nTheoremlmconst 12067 A constant sequence converges to its value. (Contributed by NM, 8-Nov-2007.) (Revised by Mario Carneiro, 14-Nov-2013.)\n𝑍 = (ℤ𝑀)       ((𝐽 ∈ (TopOn‘𝑋) ∧ 𝑃𝑋𝑀 ∈ ℤ) → (𝑍 × {𝑃})(⇝𝑡𝐽)𝑃)\n\nTheoremlmcvg 12068* Convergence property of a converging sequence. (Contributed by Mario Carneiro, 14-Nov-2013.)\n𝑍 = (ℤ𝑀)    &   (𝜑𝑃𝑈)    &   (𝜑𝑀 ∈ ℤ)    &   (𝜑𝐹(⇝𝑡𝐽)𝑃)    &   (𝜑𝑈𝐽)       (𝜑 → ∃𝑗𝑍𝑘 ∈ (ℤ𝑗)(𝐹𝑘) ∈ 𝑈)\n\nTheoremiscnp4 12069* The predicate \"the class 𝐹 is a continuous function from topology 𝐽 to topology 𝐾 at point 𝑃 \" in terms of neighborhoods. (Contributed by FL, 18-Jul-2011.) (Revised by Mario Carneiro, 10-Sep-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝑃𝑋) → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑦 ∈ ((nei‘𝐾)‘{(𝐹𝑃)})∃𝑥 ∈ ((nei‘𝐽)‘{𝑃})(𝐹𝑥) ⊆ 𝑦)))\n\nTheoremcnpnei 12070* A condition for continuity at a point in terms of neighborhoods. (Contributed by Jeff Hankins, 7-Sep-2009.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       (((𝐽 ∈ Top ∧ 𝐾 ∈ Top ∧ 𝐹:𝑋𝑌) ∧ 𝐴𝑋) → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝐴) ↔ ∀𝑦 ∈ ((nei‘𝐾)‘{(𝐹𝐴)})(𝐹𝑦) ∈ ((nei‘𝐽)‘{𝐴})))\n\nTheoremcnima 12071 An open subset of the codomain of a continuous function has an open preimage. (Contributed by FL, 15-Dec-2006.)\n((𝐹 ∈ (𝐽 Cn 𝐾) ∧ 𝐴𝐾) → (𝐹𝐴) ∈ 𝐽)\n\nTheoremcnco 12072 The composition of two continuous functions is a continuous function. (Contributed by FL, 8-Dec-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐹 ∈ (𝐽 Cn 𝐾) ∧ 𝐺 ∈ (𝐾 Cn 𝐿)) → (𝐺𝐹) ∈ (𝐽 Cn 𝐿))\n\nTheoremcnptopco 12073 The composition of a function 𝐹 continuous at 𝑃 with a function continuous at (𝐹𝑃) is continuous at 𝑃. Proposition 2 of [BourbakiTop1] p. I.9. (Contributed by FL, 16-Nov-2006.) (Proof shortened by Mario Carneiro, 27-Dec-2014.)\n(((𝐽 ∈ Top ∧ 𝐾 ∈ Top ∧ 𝐿 ∈ Top) ∧ (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ∧ 𝐺 ∈ ((𝐾 CnP 𝐿)‘(𝐹𝑃)))) → (𝐺𝐹) ∈ ((𝐽 CnP 𝐿)‘𝑃))\n\nTheoremcnclima 12074 A closed subset of the codomain of a continuous function has a closed preimage. (Contributed by NM, 15-Mar-2007.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐹 ∈ (𝐽 Cn 𝐾) ∧ 𝐴 ∈ (Clsd‘𝐾)) → (𝐹𝐴) ∈ (Clsd‘𝐽))\n\nTheoremcnntri 12075 Property of the preimage of an interior. (Contributed by Mario Carneiro, 25-Aug-2015.)\n𝑌 = 𝐾       ((𝐹 ∈ (𝐽 Cn 𝐾) ∧ 𝑆𝑌) → (𝐹 “ ((int‘𝐾)‘𝑆)) ⊆ ((int‘𝐽)‘(𝐹𝑆)))\n\nTheoremcnntr 12076* Continuity in terms of interior. (Contributed by Jeff Hankins, 2-Oct-2009.) (Proof shortened by Mario Carneiro, 25-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌)) → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑥 ∈ 𝒫 𝑌(𝐹 “ ((int‘𝐾)‘𝑥)) ⊆ ((int‘𝐽)‘(𝐹𝑥)))))\n\nTheoremcnss1 12077 If the topology 𝐾 is finer than 𝐽, then there are more continuous functions from 𝐾 than from 𝐽. (Contributed by Mario Carneiro, 19-Mar-2015.) (Revised by Mario Carneiro, 21-Aug-2015.)\n𝑋 = 𝐽       ((𝐾 ∈ (TopOn‘𝑋) ∧ 𝐽𝐾) → (𝐽 Cn 𝐿) ⊆ (𝐾 Cn 𝐿))\n\nTheoremcnss2 12078 If the topology 𝐾 is finer than 𝐽, then there are fewer continuous functions into 𝐾 than into 𝐽 from some other space. (Contributed by Mario Carneiro, 19-Mar-2015.) (Revised by Mario Carneiro, 21-Aug-2015.)\n𝑌 = 𝐾       ((𝐿 ∈ (TopOn‘𝑌) ∧ 𝐿𝐾) → (𝐽 Cn 𝐾) ⊆ (𝐽 Cn 𝐿))\n\nTheoremcncnpi 12079 A continuous function is continuous at all points. One direction of Theorem 7.2(g) of [Munkres] p. 107. (Contributed by Raph Levien, 20-Nov-2006.) (Proof shortened by Mario Carneiro, 21-Aug-2015.)\n𝑋 = 𝐽       ((𝐹 ∈ (𝐽 Cn 𝐾) ∧ 𝐴𝑋) → 𝐹 ∈ ((𝐽 CnP 𝐾)‘𝐴))\n\nTheoremcnsscnp 12080 The set of continuous functions is a subset of the set of continuous functions at a point. (Contributed by Raph Levien, 21-Oct-2006.) (Revised by Mario Carneiro, 21-Aug-2015.)\n𝑋 = 𝐽       (𝑃𝑋 → (𝐽 Cn 𝐾) ⊆ ((𝐽 CnP 𝐾)‘𝑃))\n\nTheoremcncnp 12081* A continuous function is continuous at all points. Theorem 7.2(g) of [Munkres] p. 107. (Contributed by NM, 15-May-2007.) (Proof shortened by Mario Carneiro, 21-Aug-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌)) → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ (𝐹:𝑋𝑌 ∧ ∀𝑥𝑋 𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑥))))\n\nTheoremcncnp2m 12082* A continuous function is continuous at all points. Theorem 7.2(g) of [Munkres] p. 107. (Contributed by Raph Levien, 20-Nov-2006.) (Revised by Jim Kingdon, 30-Mar-2023.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       ((𝐽 ∈ Top ∧ 𝐾 ∈ Top ∧ ∃𝑦 𝑦𝑋) → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ ∀𝑥𝑋 𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑥)))\n\nTheoremcnnei 12083* Continuity in terms of neighborhoods. (Contributed by Thierry Arnoux, 3-Jan-2018.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       ((𝐽 ∈ Top ∧ 𝐾 ∈ Top ∧ 𝐹:𝑋𝑌) → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ ∀𝑝𝑋𝑤 ∈ ((nei‘𝐾)‘{(𝐹𝑝)})∃𝑣 ∈ ((nei‘𝐽)‘{𝑝})(𝐹𝑣) ⊆ 𝑤))\n\nTheoremcnconst2 12084 A constant function is continuous. (Contributed by Mario Carneiro, 19-Mar-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝐵𝑌) → (𝑋 × {𝐵}) ∈ (𝐽 Cn 𝐾))\n\nTheoremcnconst 12085 A constant function is continuous. (Contributed by FL, 15-Jan-2007.) (Proof shortened by Mario Carneiro, 19-Mar-2015.)\n(((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌)) ∧ (𝐵𝑌𝐹:𝑋⟶{𝐵})) → 𝐹 ∈ (𝐽 Cn 𝐾))\n\nTheoremcnrest 12086 Continuity of a restriction from a subspace. (Contributed by Jeff Hankins, 11-Jul-2009.) (Revised by Mario Carneiro, 21-Aug-2015.)\n𝑋 = 𝐽       ((𝐹 ∈ (𝐽 Cn 𝐾) ∧ 𝐴𝑋) → (𝐹𝐴) ∈ ((𝐽t 𝐴) Cn 𝐾))\n\nTheoremcnrest2 12087 Equivalence of continuity in the parent topology and continuity in a subspace. (Contributed by Jeff Hankins, 10-Jul-2009.) (Proof shortened by Mario Carneiro, 21-Aug-2015.)\n((𝐾 ∈ (TopOn‘𝑌) ∧ ran 𝐹𝐵𝐵𝑌) → (𝐹 ∈ (𝐽 Cn 𝐾) ↔ 𝐹 ∈ (𝐽 Cn (𝐾t 𝐵))))\n\nTheoremcnrest2r 12088 Equivalence of continuity in the parent topology and continuity in a subspace. (Contributed by Jeff Madsen, 2-Sep-2009.) (Revised by Mario Carneiro, 7-Jun-2014.)\n(𝐾 ∈ Top → (𝐽 Cn (𝐾t 𝐵)) ⊆ (𝐽 Cn 𝐾))\n\nTheoremcnptopresti 12089 One direction of cnptoprest 12090 under the weaker condition that the point is in the subset rather than the interior of the subset. (Contributed by Mario Carneiro, 9-Feb-2015.) (Revised by Jim Kingdon, 31-Mar-2023.)\n(((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ Top) ∧ (𝐴𝑋𝑃𝐴𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃))) → (𝐹𝐴) ∈ (((𝐽t 𝐴) CnP 𝐾)‘𝑃))\n\nTheoremcnptoprest 12090 Equivalence of continuity at a point and continuity of the restricted function at a point. (Contributed by Mario Carneiro, 8-Aug-2014.) (Revised by Jim Kingdon, 5-Apr-2023.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       (((𝐽 ∈ Top ∧ 𝐾 ∈ Top ∧ 𝐴𝑋) ∧ (𝑃 ∈ ((int‘𝐽)‘𝐴) ∧ 𝐹:𝑋𝑌)) → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ↔ (𝐹𝐴) ∈ (((𝐽t 𝐴) CnP 𝐾)‘𝑃)))\n\nTheoremcnptoprest2 12091 Equivalence of point-continuity in the parent topology and point-continuity in a subspace. (Contributed by Mario Carneiro, 9-Aug-2014.) (Revised by Jim Kingdon, 6-Apr-2023.)\n𝑋 = 𝐽    &   𝑌 = 𝐾       (((𝐽 ∈ Top ∧ 𝐾 ∈ Top) ∧ (𝐹:𝑋𝐵𝐵𝑌)) → (𝐹 ∈ ((𝐽 CnP 𝐾)‘𝑃) ↔ 𝐹 ∈ ((𝐽 CnP (𝐾t 𝐵))‘𝑃)))\n\nTheoremcndis 12092 Every function is continuous when the domain is discrete. (Contributed by Mario Carneiro, 19-Mar-2015.) (Revised by Mario Carneiro, 21-Aug-2015.)\n((𝐴𝑉𝐽 ∈ (TopOn‘𝑋)) → (𝒫 𝐴 Cn 𝐽) = (𝑋𝑚 𝐴))\n\nTheoremcnpdis 12093 If 𝐴 is an isolated point in 𝑋 (or equivalently, the singleton {𝐴} is open in 𝑋), then every function is continuous at 𝐴. (Contributed by Mario Carneiro, 9-Sep-2015.)\n(((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑌) ∧ 𝐴𝑋) ∧ {𝐴} ∈ 𝐽) → ((𝐽 CnP 𝐾)‘𝐴) = (𝑌𝑚 𝑋))\n\nTheoremlmfpm 12094 If 𝐹 converges, then 𝐹 is a partial function. (Contributed by Mario Carneiro, 23-Dec-2013.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐹(⇝𝑡𝐽)𝑃) → 𝐹 ∈ (𝑋pm ℂ))\n\nTheoremlmfss 12095 Inclusion of a function having a limit (used to ensure the limit relation is a set, under our definition). (Contributed by NM, 7-Dec-2006.) (Revised by Mario Carneiro, 23-Dec-2013.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐹(⇝𝑡𝐽)𝑃) → 𝐹 ⊆ (ℂ × 𝑋))\n\nTheoremlmcl 12096 Closure of a limit. (Contributed by NM, 19-Dec-2006.) (Revised by Mario Carneiro, 23-Dec-2013.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐹(⇝𝑡𝐽)𝑃) → 𝑃𝑋)\n\nTheoremlmss 12097 Limit on a subspace. (Contributed by NM, 30-Jan-2008.) (Revised by Mario Carneiro, 30-Dec-2013.)\n𝐾 = (𝐽t 𝑌)    &   𝑍 = (ℤ𝑀)    &   (𝜑𝑌𝑉)    &   (𝜑𝐽 ∈ Top)    &   (𝜑𝑃𝑌)    &   (𝜑𝑀 ∈ ℤ)    &   (𝜑𝐹:𝑍𝑌)       (𝜑 → (𝐹(⇝𝑡𝐽)𝑃𝐹(⇝𝑡𝐾)𝑃))\n\nTheoremsslm 12098 A finer topology has fewer convergent sequences (but the sequences that do converge, converge to the same value). (Contributed by Mario Carneiro, 15-Sep-2015.)\n((𝐽 ∈ (TopOn‘𝑋) ∧ 𝐾 ∈ (TopOn‘𝑋) ∧ 𝐽𝐾) → (⇝𝑡𝐾) ⊆ (⇝𝑡𝐽))\n\nTheoremlmres 12099 A function converges iff its restriction to an upper integers set converges. (Contributed by Mario Carneiro, 31-Dec-2013.)\n(𝜑𝐽 ∈ (TopOn‘𝑋))    &   (𝜑𝐹 ∈ (𝑋pm ℂ))    &   (𝜑𝑀 ∈ ℤ)       (𝜑 → (𝐹(⇝𝑡𝐽)𝑃 ↔ (𝐹 ↾ (ℤ𝑀))(⇝𝑡𝐽)𝑃))\n\nTheoremlmff 12100* If 𝐹 converges, there is some upper integer set on which 𝐹 is a total function. (Contributed by Mario Carneiro, 31-Dec-2013.)\n𝑍 = (ℤ𝑀)    &   (𝜑𝐽 ∈ (TopOn‘𝑋))    &   (𝜑𝑀 ∈ ℤ)    &   (𝜑𝐹 ∈ dom (⇝𝑡𝐽))       (𝜑 → ∃𝑗𝑍 (𝐹 ↾ (ℤ𝑗)):(ℤ𝑗)⟶𝑋)\n\nPage List\nJump to page: Contents  1 1-100 2 101-200 3 201-300 4 301-400 5 401-500 6 501-600 7 601-700 8 701-800 9 801-900 10 901-1000 11 1001-1100 12 1101-1200 13 1201-1300 14 1301-1400 15 1401-1500 16 1501-1600 17 1601-1700 18 1701-1800 19 1801-1900 20 1901-2000 21 2001-2100 22 2101-2200 23 2201-2300 24 2301-2400 25 2401-2500 26 2501-2600 27 2601-2700 28 2701-2800 29 2801-2900 30 2901-3000 31 3001-3100 32 3101-3200 33 3201-3300 34 3301-3400 35 3401-3500 36 3501-3600 37 3601-3700 38 3701-3800 39 3801-3900 40 3901-4000 41 4001-4100 42 4101-4200 43 4201-4300 44 4301-4400 45 4401-4500 46 4501-4600 47 4601-4700 48 4701-4800 49 4801-4900 50 4901-5000 51 5001-5100 52 5101-5200 53 5201-5300 54 5301-5400 55 5401-5500 56 5501-5600 57 5601-5700 58 5701-5800 59 5801-5900 60 5901-6000 61 6001-6100 62 6101-6200 63 6201-6300 64 6301-6400 65 6401-6500 66 6501-6600 67 6601-6700 68 6701-6800 69 6801-6900 70 6901-7000 71 7001-7100 72 7101-7200 73 7201-7300 74 7301-7400 75 7401-7500 76 7501-7600 77 7601-7700 78 7701-7800 79 7801-7900 80 7901-8000 81 8001-8100 82 8101-8200 83 8201-8300 84 8301-8400 85 8401-8500 86 8501-8600 87 8601-8700 88 8701-8800 89 8801-8900 90 8901-9000 91 9001-9100 92 9101-9200 93 9201-9300 94 9301-9400 95 9401-9500 96 9501-9600 97 9601-9700 98 9701-9800 99 9801-9900 100 9901-10000 101 10001-10100 102 10101-10200 103 10201-10300 104 10301-10400 105 10401-10500 106 10501-10600 107 10601-10700 108 10701-10800 109 10801-10900 110 10901-11000 111 11001-11100 112 11101-11200 113 11201-11300 114 11301-11400 115 11401-11500 116 11501-11600 117 11601-11700 118 11701-11800 119 11801-11900 120 11901-12000 121 12001-12100 122 12101-12200 123 12201-12300 124 12301-12400 125 12401-12500 126 12501-12600 127 12601-12632\n Copyright terms: Public domain < Previous  Next >" ]

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[ "# I don't like curry\n\nI don't like curry. Help me reverse the effects of this evil question - Make me some curry - by uncurrying functions.\n\n• Given a blackbox curried function, output its uncurried equivalent.\n• The curried function will take a single argument and output either another curried function or a value of another type.\n• The uncurried function you create will take the same number of arguments as the curried function, but all at once.\n\n# Rules\n\n• In languages without first-class functions, you can use an object with a method in place of a curried function - but please specify this.\n• The curried function may also output a function pointer instead.\n• If your language does not allow function pointers or makes passing around functions hard to work with, but allows evaluating strings to make functions (e.g. APL), you can use strings to represent both curried functions and your uncurried output function.\n• You can also take the number of arguments of the functions as an input to help you determine the depth of currying.\n• You may choose to return a function that takes a tuple, list, or other single value containing all the arguments.\n• You may constrain the arguments and return type to a data type of your choosing, but that type should have more than 1 possible value.\n• If f is the inputted curried function and g is your uncurried function, f(a1)(a2)...(aN) must always equal g(a1, a2, ..., aN).\n• The order of arguments in the uncurried function should be the same as in the original curried function.\n\nFor example, given the function a -> b -> c -> a + b + c, you can return a function that looks like (a, b, c) -> a + b + c or arr -> arr + arr + arr -> ... but not (c, b, a) -> ... (wrong order). If you also take the number of arguments of the function as input, then (a, b) -> c -> ... would be an acceptable output given the input 2 (2 arguments are curried).\n\n# Bounty\n\nSince Rust is the language of the month, I will be offering a 50 rep bounty on Rust answer to this question if you haven't used Rust before.\n\n• Is there a limit to the number of arguments we must support? Haskell has a hard limit on tuple size (23 I think?). Dec 28, 2020 at 2:32\n• By \"Given a blackbox curried function, output its uncurried equivalent\": The uncurry function receives two arguments, a function and its arguments, currying.\n– tsh\nDec 28, 2020 at 3:06\n• What should happen in languages that allow polymorphic returns where the polymorphism may cover function types? For example, in Haskell, id :: a -> a can be also be called at the type id :: (a -> b) -> a -> b. Does id have one argument or two (or three or four or...)? Dec 28, 2020 at 4:56\n• @vrintle that's not a curried proc... this is... (what i mean is, the builtin .curry is not what the question means by a curried proc.) Dec 28, 2020 at 10:49\n• @user Would it? Or would it be the unchanged a -> a? Or would it be ((a -> b -> c), a, b) -> c? Or ((a -> b -> c -> d), a, b, c) -> d, or...? But okay, if I can expect the caller to specify the number of arguments, then the goal is more clear. Dec 28, 2020 at 16:33\n\n# Haskell, 129 127 bytes, thanks to Wheat Wizard\n\n{-#Language GADTs#-}\ndata N c u o where Z::N o()o;S::N c u o->N(i->c)(i,u)o\n(!)::N c u o->c->u->o\n(Z!o)_=o\n\n# C++ (gcc), 189 bytes\n\n#define T template\nT<class F>struct U{F f;auto operator()(){return f;}T<class A,class...B>auto operator()(A a,B...b){return U<decltype(f(a))>{f(a)}(b...);}};T<class F>U<F>u(F f){return{f};}\n\n\nTry it online!\n\nFunction u takes in a curried callable object, and returns another callable object which is uncurried.\n\n(I don't see any allowance for a restriction on the return type of the curried function, just on the argument types. If it were allowed, we could save two bytes by replacing both instances of auto with int`.)" ]

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[ "• 先到先得。\n• 先到先得。\n\n• 提前15分钟发放免费票。\n• 先到先得。\n\n• 先到先得。\n\n• 提前15分钟发放免费票。\n• 先到先得。\n\n• 先到先得。\n• 先到先得。\n\n• 先到先得。\n• 先到先得。\n\n• 先到先得。\n• 先到先得。\n• 注册已关闭\n\n• 先到先得。\n\n• 注册已关闭\n• 先到先得。\n• 先到先得。\n• 注册已关闭\n\n• 先到先得。\n\n• 提前15分钟发放免费票。\n• 致电本图书馆报名。\n• 先到先得。\n\n• 先到先得。\n• 先到先得。\n• 先到先得。\n\n• 先到先得。\n• 先到先得。\n• 先到先得。\n\n• 事件被取消\n• 注册已关闭\n\n• 先到先得。\n• 注册已关闭\n• 先到先得。\n• 先到先得。\n\n• 先到先得。\n• 先到先得。\n• 先到先得。\n• 先到先得。" ]

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[ "", null, "Solve problem \"Multiply two matrices\" online - Learn Python 3 - Snakify\n\n# Multiply two matrices\n\n## Statement\n\nGiven three positive integers $$m$$, $$n$$ and $$r$$, $$m$$ lines of $$n$$ elements, giving an $$m \\times n$$ matrix $$A$$, and $$n$$ lines of $$r$$ elements, giving an $$n \\times r$$ matrix $$B$$, form the product matrix $$A B$$, which is the $$m \\times r$$ matrix whose $$(i,k)$$ entry is the sum $$A[i] * B[k] + \\cdots + A[i][n] * B[n][k]$$ and print the result.\nIn all the problems input the data using input() and print the result using print()." ]

[ null, "https://mc.yandex.ru/watch/34045460", null ]

[ "# Quasi-Monte-Carlo-Methods\n\n## for the valuation of financial instruments\n\n``", null, "``\n\n### Quasi-Monte Carlo Methods\n\n#### Generating of Quasi-Monte Carlo sequences\n\nAim of the Monte-Carlo method is in the end to explore an unknown result space. The problem with Monte Carlo is that this exploration is random. This means that a specific area in the result space might be explored multiple times other areas are not explored at all. This problem can be seen by visualizing the generated random numbers.\n\n``", null, "``", null, "There are clusters in some areas other areas are white. An alternative to random numbers are determistic number sequences which explore the result space systematicly. An example for such low discrepancy sequences are van-der Corput and Halton sequences. Following Ötken (1999) these sequences can be implemented in Mathematica as\n\n``", null, "``\n\nThe first argument defines which element of the row will be calculated the second argument defines the basis to which the sequence is generated. Extensions of the van-der-Corput sequences are the Halton sequences (For further details on these sequences see Ötken (1999)).\n\n``", null, "``", null, "#### Using the low discrepancy sequences\n\nThe simplest method to use the low discrepancy sequences is to overwrite the build in random generator of Mathematica.  The sequence can be generated once up front as an array.\n\n``", null, "``\n\nEach call to Random returns a number of the sequence. The global variable \\$RandomSequence makes the necessary bookkeeping to assure that the returned number is always the next within the sequence.\n\n``", null, "``\n`", null, "`\n``", null, "``", null, "#### Using the low discrepancy sequences together with the normal distribution\n\nMathematica uses a variant of the Box-Muller method to generate normal distributed random numbers. Within this method two equally distributed random numbers are used. Therefore a sequence of 2000 numbers is needed to generate 1000 normal distributed random numbers.\n\n``", null, "``", null, "`", null, "`\n\nThe advocated approach does not always work with RandomArray. The reason for this is that Mathematica automaticly compiles the function connected with RandomArray when the package is loaded (see the comments from the original developer in the package Statistics`NormalDistribution`). This problem can be circumvated if the build-in random generator is overwritten before the package is loaded.\n\n#### Using the low discrepancy sequences together with the normal distribution\n\nFollowing the described approach for low discrepancy sequences the implementation of a simple option is the same as with the normal Monte-Carlo method.\n\n``", null, "``\n`", null, "`\n\n#### Undoing the changes to Random[]\n\nIf it is necessary to recover the build-in random generator of Mathematica within a session the overwriting can be undone with the following commands.\n\n``", null, "``\n\n### Literature\n\nBoyle, Philm P., 1977, Options: A Monte Carlo Approach, in: Journal of Financial Economics, 4/1977, p.141-158.\nClewlow, L./Strickland, C., 1998, Implementing Derivatives Models, John Wiley & Sons Ltd.\nÖtken, Giray, 1999, Quasi-Monte Carlo Methods in Option Pricing, in: Mathematica in Education and Research, Vol. 8, No. 3-4, p. 52-57.\nShaw, William: Modelling Financial Derivatives with Mathematica, Cambridge 1998.\n\nConverted by Mathematica      October 1, 2000" ]

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[ "# Einstein Relatively Easy\n\n### What's Up", null, "If you like this content, you can help maintaining this website with a small tip on my tipeee page", null, "Our aim is to get more familiar with the Riemann curvature tensor and to calculate its components for a two-dimensional surface of a sphere of radius r.\n\nFirst let's remark that for a two-dimensional space such as the surface of a sphere, the Riemann curvature tensor has only one not null independent component.\n\nActually as we know from our previous article The Riemann curvature tensor part III: Symmetries and independant components, the first pair and last pair of indices must both consist of different values in order for the component to be (possibly) non-zero. Therefore, in two dimensional space where each indice could only take the values 0 and 1, the only possibility for each pair is to contain these distinct indices 0 and 1, which represent the coordinates θ and φ in polar coordinates.\n\nSo, over 2^4 = 16 components of the Riemann tensor in two-dimensional space, only one component is independent and not null.\n\nIf we choose this component to be Rθφθφ for example, then we can easily deduce the three other not null components by using the Riemann tensor symmetries:", null, "Therefore, we only have to calculate the first term Rθφθφ", null, "Indeed, we recall from our article The Riemann curvature tensor for the surface of a sphere that the spacetime interval on the surface of a sphere of radius r in polar coordinates is:\n\nds2 = r22 + r2sin2θdΦ2\n\nSo that we get as the corresponding metric gij:", null, "which means that gθφ=0 and that gθθ=r2\n\nAs the expression of the Riemann tensor as deduced in The Riemann curvature tensor part II: derivation from the geodesic deviation is given by", null, "If we substitute the indices for Rθφθφ ,the above equation becomes", null, "We now sum over dummy indice m to give", null, "Now it is just a matter of calculating all these connection coefficients. We are lucky as we have already calculated them in our previous article Christoffel symbol exercise: calculation in polar coordinates part II; we have found that the eight Christoffel symbols at a given point on the surface of the sphere were:", null, "We can therefore simplify our Riemann tensor expression to", null, "So that finally", null, "##### Ricci tensor\n\nWe can now find the Ricci tensor", null, "So we get by summing over indices a and b", null, "", null, "And finally the last two components of the Ricci tensor:", null, "##### Ricci scalar\n\nThe last quantity to calculate is the Ricci scalar R = gabRab\n\nWe sum over the a and b indices to give", null, "meaning the Ricci scalar decreases as the radius increase and tends to zero for large radii.\n\n### Quotes\n\n\"The essence of my theory is precisely that no independent properties are attributed to space on its own. It can be put jokingly this way. If I allow all things to vanish from the world, then following Newton, the Galilean inertial space remains; following my interpretation, however, nothing remains..\"\nLetter from A.Einstein to Karl Schwarzschild - Berlin, 9 January 1916\n\n\"Quantum mechanics is certainly imposing. But an inner voice tells me that it is not yet the real thing. The theory says a lot, but does not really bring us any closer to the secret of the 'old one'. I, at any rate, am convinced that He is not playing at dice.\"\nEinstein to Max Born, letter 52, 4th december 1926" ]

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[ "", null, "# Are they same - I\n\nGiven two string A and B with backspace character. Output 1 if both strings would end up to be same, 0 otherwise.\n\nNote:\n\n1. You are Not allowed to use extra memory.\n2. “\\B” denotes the backspace character.\n\nInput Format:\n\n`````` First argument of input contains a string A.\nSecond argument of input contains a string B.\n``````\n\nOutput Format:\n\n`````` return an integer 1 if strings would be same, 0 otherwise.\n``````\n\nConstraints:\n\n`````` 1 <= |A| , |B| <= 1,000,000\nstring consist of smaller case english alphabets and \"\\B\".\n``````\n\nFor Example:\n\n``````Input 1:\nA = \"abc\\Bbd\\Be\", B = \"abbe\\Be\"\nOutput 1:\n1\nExplanation:\nboth strings would end up to be \"abbe\".\nInput 2:\nA=\"a\\Bb\", B=\"b\\B\"\nOutput 2:\n0\n``````\nNOTE: You only need to implement the given function. Do not read input, instead use the arguments to the function. Do not print the output, instead return values as specified. Still have a doubt? Checkout Sample Codes for more details.", null, "" ]

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[ "# Line slope calculator 6x+y=26\n\nEnter coordinates of two different points:", null, "Straight line given by points A[3; 8] and B[4; 2]\n\n## Calculation:\n\nSlope-intercept form of line: y = -6x+26\n\nCanonical form of the line equation: 6x+y-26 = 0\n\nParametric form of the line equation:\nx = t+3\ny = -6t+8      ; t ∈ R\n\nSlope: m = -6\n\nSlope angle of line: φ = -80°32'16″ = -1.4056 rad\n\nX intercept: x0 = 4.3333\n\nY intercept: y0 = q = 26\n\nDistance line from the origin: d0 = 4.2744\n\nThe length of the segment AB: |AB| = 6.0828\n\nVector: AB = (1; -6)\n\nNormal vector: n = (6; 1)\n\nMidpoint of the segment AB: M = [3.5; 5]\n\nPerpendicular Bisector equation: x-6y+26.5 = 0\n\nVector OA = (3; 8) ;   |OA| = 8.544\nVector OB = (4; 2) ;   |OB| = 4.4721\nScalar product OA .OB = 28\nAngle ∠ AOB = 42°52'44″ = 0.7484 rad" ]

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[ "# What Is a Discounting Factor?\n\nJim B.\n\nThe discounting factor is the amount that a future sum of money must be multiplied by to calculate its value at the present time. This calculation is a product of the time-value theory of money that states that money loses value over time. To perform the equation for the discounting factor, the discount interest rate, which will be affected by inflation, and the time in years between the future date and the present must both be known. In most cases, the factor is used to calculate what a dollar in the future will be worth in the present time.", null, "The discounting factor is the amount that a future sum of money must be multiplied by to calculate its value at the present time.\n\nThere is an old adage that says that a dollar today can't buy nearly as much as it did in the past. That is because of the time-value theory of money. A dollar will always be worth more in the present time than it will be in the future according to this theory. For that reason, investors can use the discounting factor to try and judge just how much promised money in the future is actually worth to them in the present time.\n\nCalculating the discounting factor requires knowing both the discount interest rate and the time in years between the future time being studied and the present. For example, imagine that the interest rate is five percent and the future time in question is two years from now. The pertinent equation states that the present value is equal to one plus the interest rate, which is raised to the power of the time in years, and finally divided by 1.\n\nIn this case, the interest rate of 5 percent, or .05 for mathematical purposes, is added to one, yielding a sum of 1.05. This sum is then raised to the second power, since the time is two years in the future. The resulting discounting factor of 1.1025 is then divided by one, which yields a quotient of 0.907. Using that projected inflation rate, this means that one dollar in the future is worth about 91 cents in the present.\n\nBy doing simple multiplication, the discounting factor can then be used to measure the value of any future sum of money in the present day. Using the example above, \\$1,000 US Dollars (USD) two years from now would be worth approximately \\$910 USD. For that reason, investors might consider it wiser to take \\$1,000 USD and invest it now rather than wait on a promised \\$1,000 USD return down the line.\n\n## You might also Like", null, "" ]

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[ "# Interpolated table lookups using SSE2 [1/2]\n\nSomething that you are very likely to encounter, when writing SSE2 assembler is the issue of using tables for looking up data. While this is a very simple operation in C, it presents a challenge with a lot of pitfalls, when using SSE2.\n\nWhen to use lookup tables?\n\nIn general, avoid them, if it is within the possibility. Let’s have a look at a simple example:\n\n```const float ushort_to_float;\n\nfloat convert_unsigned_short_to_float(unsigned short value) {\nreturn ushort_to_float[value];\n}```\n\nThis is quite tempting in C – especially if you can do some transformations on the value, by modifying the lookup table, if you need something expensive like the square root, use the inverted value. But lets look at the drawbacks.\n\nThe table is 256KB, so you will run out of level 1 cache on most CPUs. In C the penalty for a value in L1 cache this is usually a couple of cycles per value, but if it has to be fetched from L2, we are talking about 15 cycles or more on most platforms. But if it saves you a division and a square root or something similar the trade-off may be ok for you.\n\nIn SSE2, the trade-off picture is changed. Here the penalty per pixel for doing the lookup remains the same (or even rises), while calculating the value is usually only about 25% of cost of what the C-code. So you can see – we can do a lot more calculations in SSE2 before using a table should even be considered.\n\nWhat if I have to use lookup tables?\n\nThere might be cases, where using tables are the only way, because calculating the values simply aren’t practically possible in SSE2.  A practical example of this from Rawstudio is the curve correction, that involves mapping input values to output values based on an n-point 2D curve.", null, "Curve Correction: Nearly impossible without using lookup tables.\n\nIn the “old days”, we used a table with 65535 16 bit entries, that simply gave the output value, much as the example above. The table was 128KB, so again, we had a lot of L1 cache misses.\n\nWhen we re-wrote colour correction, everything internally was converted to float, which made the table 256KB, and even slower. Furthermore it also made sure that we lost a lot of the float precision, by having to fit the float values into 65535 slots.\n\nSo we got two problems:\n\n• Table too large for L1 cache\n\nUsing lookup tables for float data\n\nSince most of these lookups are fairly linear, without too sudden peaks, usually we are fairly in the clear if we reduce the precision of the table, and do linear interpolation between them instead. This avoids posterizing the image data, because all float values will have a unique output. Here is an example, where we use a curve correction with only 256 input/output values, and interpolate them instead.\n\n```float curve_samples;\n\n// Make sure we have an extra entry, so we avoid an\n// \"if (lookup > 255)\" later.\ncurve_samples = curve_samples;\n\nfor (all pixels...) {\n// Look up and interpolate\nfloat lookup = CLAMP(input * 256.0f, 0.0f, 255.9999f);\nfloat v0 = curve_samples[(int)lookup];\nfloat v1 = curve_samples[(int)lookup + 1];\nlookup -= floorf(lookup);\noutput = v0 * (1.0f - lookup) + v1 * lookup;\n}```\n\nSo now the table is only 1KB in size and fits very nicely within the Level 1 cache. This approach solves both our problems, but at the expense of some calculations. Whether 256 entries are enough is up for you to decide, but the principle applies for all sizes. The “floor” function can be quite slow on some compilers/CPUs, but it is here to demonstrate the algorithm. We will touch this later.\n\nIn part two, I will look at the actual implementation – there are still a lot of pitfalls in making this work in real life.\n\n### 7 responses to “Interpolated table lookups using SSE2 [1/2]”\n\n1.", null, "Wes says:\n\nI can’t say I know too much about the technical details of image processing, but I do appreciate the insight post like these give. Thanks!\n\nIt seems like you could reduce the size further is you used a different data type for the LUT, for instance a 16-bit or 8-bit int. With my monitor at 1024 pixels high, I couldn’t imagine being able to enter a curve at more than 10-bits precision, and that’s if the screen is maximized. Sounds like the image data is in float, but does it take too many cycles to cast an int to a float? I wonder if the loss of precision would be noticable. Anyways, these are just questions floating in my head, and I’m not trying to criticize your implementation, you folks know way more than I ever will about this stuff.\n\n•", null, "Klaus Post says:\n\nThe game is a little different when you are dealing with Raw data, because you operate in a linear colorspace. Because of this you need additional precision, since errors become very visible in dark areas.\n\nSee this PDF on why you need at least 16 bit precision for Raw images:\n\n2.", null, "Tor Lillqvist says:\n\nYes, I enjoyed this posting very much. Thanks!\n\n3.", null, "LukasT says:\n\nThanks, I enjoy this kind of blog posts! Keep them coming :)\n\n4. Blog says:\n\n[…] Continued from part 1… […]\n\n5.", null, "edouard gomez says:\n\nThe problem with that approach is that decreasing the table precision leads to at least two problems.\n\nFirst, the curve downscaler 65536->256 has to be chosen carefully.\n\nAnd then such a big downscale can’t be reasonably inversed using a simple linear interpolation w/o introducing aliasing problems.\n\nDo you have some PSNR numbers comparing the original results using interpolation between the 65536 sampling points of the curve, and the faster 256 entry table interpolation ?\n\n•", null, "Klaus Post says:\n\nHi Edouard!\n\n>First, the curve downscaler 65536->256 has to be chosen carefully.\n\nThe curve isn’t downscaled as such – the curve is simply sampled with 256 entries instead of 65536. So we get 256 exact values, and not some that are derived from 65536.\n\n>And then such a big downscale can’t be reasonably inversed using a simple linear interpolation w/o introducing aliasing problems.\n\nHow do you get to that result? The curve is an input->output mapping, where neutral is input=output (straight line). You would have to have a curve that has variations where the correction is changed at a 1/128th pixel resolution, which simply wouldn’t make sense.\nSo unless you have information with a frequency close to or above the nyquist frequency, a linear interpolation should not generate aliasing.\n\nIf you consider the image of the curve, you have approximately the horizontal resolution of the image (the image is ~290 pixels wide). The vertical sample point have float point precision, so the vertical resolution is very precise. The vertical resolution is the reason there is no precision issues, even with such small tables.\n\n>Do you have some PSNR numbers comparing the original results using interpolation between the 65536 sampling points of the curve, and the faster 256 entry table interpolation ?\n\nI’ll make a few calculations that calculates the min, max and average error on “realistic” and some “extreme” curves.\n\nKlaus Post on October 28, 2010", null, "" ]

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[ "## Friday, March 22, 2013\n\n### This figure is not drawn to scale\n\nBoth the ACT and the SAT math sections are filled with figures and diagrams.  Can you trust what you see?  That depends on the test.\n\nOn the ACT none of the figures are drawn to scale.  They make this disclaimer at the beginning of the math section.  You can still trust certain aspects of the diagram.  For example, if it shows that two line segments intersect, then they really do.  Sometimes they will give you facts about the diagram that you should assume.  However, sometimes they can be mean, and looks can be deceiving.  Here are some things you CANNOT assume about the diagram. Consider the diagram below:\n\nUnless it is stated or you can prove it given what is stated, you CANNOT assume any of the following:\n\n• that segment AC is tangent to the circle\n• that point O is the center of the circle\n• that D, O and F are collinear (in other words, angle DOF could be 179°)\n• that any angle shown is a right angle, an acute angle or an obtuse angle\n• that any segment is longer than, shorter than, or the same length as another based on how they look\n\nFor the SAT on the other hand, you should assume that every figure is drawn precisely to scale unless there is a note under the figure that says otherwise.  If the figure IS drawn to scale, you can use that information to eliminate some of the answer choices. Again considering the figure above:  If you are told that segment OB is 3 inches long and asked the length of segment EF you can eliminate any answer choice that reads 3 inches or longer, since EF is clearly shorter than OB." ]

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[ "# Numerical quadrature in Discontinuous Galerkin\n\nI would like to know which is the best way to integrate numerically Legendre polynomials. I am building up a Discontinuous Galerkin CFD code for which Legendre polynomials are used as basis functions in the spectral projection.\n\nIn previous codes, I was used to employ Gauss-Legendre numerical quadrature, where only $k$ points allowed to obtain the exact integral of a polynomial of degree $2k−1$. Now, as such quadrature points are the roots of Legendre polynomials, I need use $k+1$ points to integrate the $k$-th Legendre polynomial (otherwise the polynomials are evaluated at their roots and all higher order ingegrals are zero). Is there any other efficient way to do it?\n\nThank you very much for your help.\n\n• If I remember correctly, assembling the elemental matrices for a Galerkin method requires integrating a function where two polynomials are multiplied together. Thus, for a 1D element, f(x) = N_i(x) * N_j(x). Assuming N_i and N_j are 2nd order polynomials, f(x) is a 4th order polynomial, so you need to use (4*2)-1 = 7 points to integrate the function exactly. I don't think you'll run into the problem you think you'll run into. – cbcoutinho Jan 20 '17 at 15:11\n\nWhich basis you use for your finite element space does not matter for quadrature in general. If you use, for example, polynomials of degree $k$ (whether the Lagrange basis, or the Legendre basis, or monomials, or anything else) then the integrand of the mass matrix has polynomial degree $2k$, and you need a Gauss quadrature with $k+1$ points to integrate that correctly. That is because such Gauss quadratures can integrate any polynomial of degree $2k+1$ exactly, regardless of how you actually represent that polynomial in terms of bases.\n• I thought you needed to have $2k+1$ points where $k$ is the polynomial degree of the combined integrand, which using degree $k$ polynomials would be $k^2$, so you would actually need $2(k^2) + 1$ points to accurately integrate the function - correct? – cbcoutinho Jan 20 '17 at 22:44\n• @cbcoutinho if the polynomials have degree $k$, then the mass matrix integrand has degree $k + k = 2k$, not $k^2$. And $k$ Gauss quadradure points are sufficient to integrate polynomials of degree $2k-1$ exactly, as explained here. – GoHokies Jan 20 '17 at 22:50" ]

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[ "# If (x+iy)^1/3 =(a+ib),prove that (x/a+y/b)=4(a^2-b^2)?\n\n• 13\n\n(x+iy)^1/3 =(a+ib)^3                                                                                                                                                                          CUBING BOTH THE SIDES                                                                                                                                    (x+iy )=a^3+i^3b^3+3a^2ib+3ai^2b^2                                                                                                                                     x+iy=a^3-ib^ +3a^2ib-3ab^2                                                                                                                                                   COMPAIRING BOTH THE SIDES                                                                                                                                         x=a^3-3ab^2                ..................1                                                                                                                                         y=3a^2b-b^3                ..................2                                                                                                                                         DIVIDING 1 by 'a'    AND   2 by 'b'                                                                                                                                            x/a=a^2-3b^2  ...........3    ,   y/b=3a^2-b^2 .........4                                                                                                                       ADD (3+4)\n\n• 18\n\nPlease can u do it properly its really confusing\n\n:(\n\n• 2\nWhat are you looking for?" ]

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[ "# Concentration Problem\n\n## Homework Statement\n\nQuestion 9)", null, "## Homework Equations\n\n$$K=\\frac{[P]}{[R]}$$\n\n## The Attempt at a Solution\n\nWhat is confusing me is the wording. It merely says for the concentration of chromate. All this time we've been doing the equilibrium concentrations. Doing mole-mole with the Cr2O7 yields the answer to be D. I then assumed that it meant that they were the concentrations at equilibrium, which gave C to be the correct answer after preforming the equilibrium constant equation. Thanks for reading.\n\nBorek\nMentor\nNot sure what you mean by \"doing mole-mole\". I read the question as if 0.100 M was the equilibrium concentration, not the initial.\n\nNot sure what you mean by \"doing mole-mole\". I read the question as if 0.100 M was the equilibrium concentration, not the initial.\n\nForgive me. Mole to mole ratio is what I meant to convey. Thank you for your input.\n\nBorek\nMentor\nIn other words you tried to calculate equilibrium concentration following the stoichiometry?\n\nWhere did you got OH- from?\n\nQuantum Defect\nHomework Helper\nGold Member\n\n## Homework Statement\n\nQuestion 9)\n\nView attachment 77944\n\n## Homework Equations\n\n$$K=\\frac{[P]}{[R]}$$\n\n## The Attempt at a Solution\n\nWhat is confusing me is the wording. It merely says for the concentration of chromate. All this time we've been doing the equilibrium concentrations. Doing mole-mole with the Cr2O7 yields the answer to be D. I then assumed that it meant that they were the concentrations at equilibrium, which gave C to be the correct answer after preforming the equilibrium constant equation. Thanks for reading.\n\nWhat is the expression for the equilibrium constant for this particular equilibrium?\n\nTrue, it has [products] in the numerator and [reactants] in the denominator, but what do you do with the stoichiometric coefficients?\n\nAs Borek notes, they are giving you equilibrium values for the concentrations. If they weren't they would say something like \"... the inital concentration of dichromate beofre equilibrium is reached is blah blah...\"\n\nAs Borek notes, they are giving you equilibrium values for the concentrations. If they weren't they would say something like \"... the inital concentration of dichromate beofre equilibrium is reached is blah blah...\"\n\nYes. The issue was if they were equilibrium values or not. Usually it explicitly states.\n\nQuantum Defect\nHomework Helper\nGold Member\nYes. The issue was if they were equilibrium values or not. Usually it explicitly states.\nWhat is the expression for the equilibrium constant, using concentrations? Where do the stoichiometric coefficients come in?" ]

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[ "# Introduction to calculus\n\nAn Introduction to CalculusShairy Balicao2016Merced CollegeMerced College, 2016Some rules of differentiation and how to use themDerivatives can be used in economics whenever we want to talk about a rate of change or wantto maximise or minimise something. For example, marginal cost is the rate of change of totalcost with respect to output, so marginal cost can expressed as the derivative of total cost.In the following notes, we will give a few rules so that you can find derivatives of simplepolynomial functions and then discuss how they can be used.Notation We will use y to represent our dependent variable and q as our independent variable,so we will differentiate with respect to q.We may use functional notation and write y = f(q) which means that y is a function of q. So, y= f(q) + g(q) means that y is the sum of two functions of q, one called f(q) and one called g(q).We will express the derivative of y with respect to.dydfIf we use functional notation and write y = f(q) then = . That is, the derivativedqdqof y with respect to q can be written as the derivative of f with respect to q.Some rules of differentiationRule 1. If y = c, where c is an arbitary constant, then.For example, if p = 2000, then.Rule 2. If y = kqn where n is any number and k is a constant, then.For example, if TC = 2q2, then.Rule 3. If y = f(q) + g(q), then.For example, if TC = 12q + 2q2, thenThese are not the only rules for differentiation, but if we consider … Purchase document to see full attachment\n\nHello Good Friend\n40% OFF on your Assignments." ]

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[ "# Space before referencing an equation\n\nwhen I label an equation in the equation environment and refer to it via \\ref{eq1} and when I write in the text\n\n Eq. \\ref{eq1}\n\n\nthere is a one more white space between Eq. and the reference.\n\n• Welcome to TeX.SX! You can have a look on our starter guide to familiarize yourself further with our format- in particular, could you explicitly say what your question is? – cmhughes Oct 20 '13 at 15:51\n• I dont want the additional space between Eq and the number of the equation – lambda1990 Oct 20 '13 at 15:59\n\nThis is most likely a visual difference due to the Eq. being considered an end-of-sentence space. You can avoid this by forcing a regular space (that is, Eq.\\ \\ref{eq1}), or even better, use Eq.~\\ref{eq1}, or even more better, add \\usepackage{amsmath} to your document preamble and use Eq.~\\eqref{eq1}.\n\nHere are the options, visually:", null, "\\documentclass{article}\n\\usepackage{amsmath}% http://ctan.org/pkg/amsmath\n\\begin{document}\n\\begin{equation}\nf(x) = ax^2 + bx + c \\label{quadratic}\n\\end{equation}\n\n• @lambda1990: That's unfortunate. Do you use any cross-referencing packages that could influence the way \\ref works? Like varioref, cleveref, hyperref, ...? – Werner Oct 20 '13 at 16:54\n• @lambda1990: Why not just do a search-and-replace for Eq. to Eq.~? – Werner Oct 20 '13 at 17:02\n• Do a global search and replace of Eq. \\ref -> Eq.~\\ref. – Mico Oct 20 '13 at 17:02\n• @lambda1990: A really ambitious attempt would be to use \\let\\oldref\\ref \\def\\ref{\\unskip\\nobreakspace\\oldref} in your preamble. But then you can't use \\eqref, and other things may break... undoubtedly. – Werner Oct 20 '13 at 17:30" ]

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[ "# Momentum\n\n• Saber\n\n##### Citation preview\n\nMomentum :1- A deer with a mass of 146kg is running head-on toward you with a speed of 17m/sec. you are going north. Find the momentum of the deer. Given :- m = 146 kg , v = 17 m/sec Solution:- p = m v = 146 . 17 = 2482 kg.m/sec The direction of deer is south. 2- A 21kg child on a 5.9kg bike is riding with a velocity of 4.5m/sec to the northwest. a- What is the total momentum of the child and the bike together? b- What is the momentum of the child? c- What is the momentum of the bike? Given : mchild = 21 kg , mbike = 5.9 kg , v = 4.5 m/sec Solution: a- pT = (mchild + mbike) v = ( 21 + 5.9) . 4.5 = 121.05 kg.m/sec b- pchild = mchild v = 21 . 4.5 = 94.5 kg.m/sec c- pbike = mbike v = 5.9 . 4.5 = 26.55 kg.m/sec Force and impulse :A 0.5 kg football is thrown with a velocity of 15m/sec to the right. A stationary receiver catches the ball and brings it to rest in 0.02 sec. What is the force exerted on the ball by the receiver? Given : m = 0.5 kg , vi = 15m/sec , vf = 0 (\n\nSolu. :\n\n,\n\nΔt = 0.02 sec (\n\n)\n\n)\n\nThen , the force exerted on the ball is F = 375 N Stopping distance : A 2500kg car traveling to the north is slowed down uniformly from an initial velocity of 20m/sec by a 6250N braking force acting opposite the car's motion. Use the impulse-momentum theorem to answer the following questions: How long does it take the car to come to a complete stop? Given : m = 2500 kg ,\n\nvi = 20 m/sec , f = - 6250 N ,\n\nSolu. : But ,\n\n(\n\n)\n\n(\n\n)\n\nvf = 0\n\nMomentum and impulse review :1- The speed of a particle is doubled. a- By what factor is its momentum changed? b- What happens to its kinetic energy ? Given: v2 = 2v1 Solution : a) p2 = mv2 = m (2v1) = 2 mv1 = 2p1 Thus, the momentum will double. b)\n\nThus, the kinetic energy\n\nwill be increased to 4 -fold 2- A pitcher claims he can throw a 0.145kg baseball with as much momentum as a speeding bullet. Assume that a 3gm bullet moves at a speed of 1.5× 103m/sec. a- What must the baseball's speed be if the pitcher's claim is valid? b- Which has greater kinetic energy, the ball or the bullet? Given: mball = 0.145kg , mbullet = 3gm = 0.03kg ,\n\nvbullet = 1.5×103m/sec\n\nSolu. : a)\n\nb)\n\nThus, 3- A 0.42 kg soccer ball is moving downfield with a velocity of 12m/sec. A player kicks the ball so that is has a final velocity of 18m/sec downfield. a- What is the change in the ball's momentum? b- Find the constant force exerted by the player's foot on the ball if the two are in contact for 0.02 sec. Given: m = 0.42kg , vi = 12m/sec , vf = 18m/sec a) b)\n\n(\n\n)\n\nConservation of momentum :1- A 63 kg astronaut is on a spacewalk when the tether line to the shuttle breaks. The astronaut is able to throw a spare 10 kg Oxygen tank in a direction away from the shuttle with a speed of 12m/sec, propelling the astronaut bake to the shuttle. Assuming that the astronaut starts from rest with respect to the shuttle, find the astronaut's final speed with respect to the shuttle after the tank is thrown. Given: mTotal = 63kg , mtank =10kg , vtank = -12m/sec Sol.: After throwing the reservoir tank its momentum will be equal to the momentum of the astronaut in opposite direction ,\n\n2- An 85 kg fisherman jumps from a dock into a 135kg rowboat at rest on the west side of the dock. If the velocity of fisherman is 4.3m/sec to the west as he leaves the dock, what is the final velocity of fisherman and the boat? Given: mman = 85kg , mboat =135kg , vi ,boat =0 , vi , man =4.3m/sec Sol.: momentum primary (the fisherman) = final momentum (the fisherman + boat)\n\nPerfectly inelastic collisions :1- A 1500kg car traveling at 15m/sec to the south collides with a 4500kg truck that is initially at rest at a stoplight. The car and truck stick together and move together after the collision. What is the final velocity of the two-vehicle mass? Given: m1 = 1500kg , v1 = 15m/sec , m2 = 4500kg , v2 = 0 Sol.:\n\n2- A grocery shopper tosses a 9kg bag of rice into a stationary 18kg grocery cart. The bag hits the cart with a horizontal speed of 5.5m/sec toward the front of the cart. What is the final speed of the cart and bag? Given: m1 = 9kg , m2 = 18 , v1 = 5.5m/sec , v2 = 0 Sol.:\n\nKinetic energy in perfectly inelastic collisions:1- A 0.25kg arrow with a velocity of 12m/sec to the west strikes and pierces the center of a 6.8kg target. a- What is the final velocity of combined mass? b- What is the decrease in kinetic energy during the collision? Given: m1 = 0.25 kg , v1 = 12m/sec , m2 = 6.8kg , v2 = 0 Solu.: a) b)\n\n2- During practice, a student kicks a 0.4kg soccer ball with a velocity of 8.5m/sec to the south into a 0.15kg bucket lying on its side. The bucket travels with the ball after the collision. a- What is the final velocity of the combined mass? b- What is the decrease in kinetic energy during the collision? Perfectly elastic collisions:A 4kg bowling ball sliding to the right at 8m/sec has an elastic head-on collision with another 4kg bowling ball initially at rest. The first ball stops after the collision. a- Find the velocity of the second ball after the collision. b- Verify your answer by calculating the total kinetic energy before and after the collision. Given: m1 = 4 kg , v1 = 8 m/sec , m2 = 4 kg , v2 = 0 Sol.:\n\n, v'1 =0\n\nProblems homework : 1- Calculate the momentum of the following objects: a- A 10kg ball moving 15 m/s b- A 10 kg ball at rest. c- A 10 kg ball moving 1.5×103m/s d- A 1.5× 104 kg wrecking ball moving 1 m/s 1.5×104 kg*m/s e- A 747 jumbo jet at rest. 2- What is the velocity of a 0.025 g bullet that has a momentum of 31.4 kg*m/s 3- Calculate the mass of Oil Tanker traveling at 0.045 m/s. The momentum of the tanker is 1.46 x 104 kg*m/s 4- A 63 kg astronaut is on a spacewalk when the tether line to the shuttle breaks. The astronaut is able to throw a 10 kg oxygen tank in a direction away from the shuttle with a speed of 12 m/s, propelling the astronaut back to the shuttle. a- Assuming that the astronaut starts from rest, find the final speed of the astronaut after throwing the tank. b- Determine the maximum distance the astronaut can be from the craft when the line breaks in order to return to the craft within 60 sec. 5- A 2500 kg car traveling to the north is slowed down uniformly from an initial velocity of 20 m/s by a 6250 N braking force acting opposite the car’s motion. (Hint – force is negative 6250 not positive). Use the impulse momentum theorem to answer the following questions:\n\na- What is the car’s velocity after 2.5 sec? b- How far does the car move during 2.5 sec? c- How long does it take the car to come to a complete stop (final velocity now equals zero)? 6- A boy stands at one end of a floating raft that is stationary relative to the shore. He then walks in a straight line to the opposite end of the raft, away from the shore. Does the raft move? Explain. What is the total momentum of the boy and the raft before the boy walks across the raft? 7- What centripetal force is needed to make a 65 Kg rider go 15 m/s on the edge of a ride with a radius of 4.5 m? 8- There is a coefficient of friction of .34 between a 1200 kg car and the pavement. What is the force of friction between the road and the tires, and what is the car's maximum speed around a 73 m radius corner? 9- A 2.0 gram penny is on a turntable 13 cm from the center. As you\n\ngradually speed up the turntable, the penny flies off when it is turning one revolution per second. What is the centripetal acceleration of the penny, and what is the coefficient of friction between the penny and the turntable? (Hint - set the force of friction equal to the centripetal force) 10- For each of the following situations, identity the specific force that is providing the centripetal force: a) A car drives around on a flat track. b) A child rides on a merry-go-round . c) A child is spun in a circle until their feet leave the ground . d) A child on a tire swing swings in a circular path. e) The clothes in your clothes washer go through the spin cycle. f) your dog runs in circles around you while you hold onto the leash.\n\nCircular motion :Tangential Speed:-\n\nTangential speed (v t ) object’s speed along an imaginary line drawn tangent to the circular path. Tangential speed depends on the distance from the object to the center of the circular path. Tangential speed constant à in uniform circular motion.\n\nCentripetal Acceleration:-\n\nThe acceleration of an object moving in a circular path at constant speed is due to a change in direction. An acceleration of this nature is called a centripetal acceleration.\n\nCentripetal acceleration is always directed toward the center of a circle.\n\nCentripetal acceleration results from a change in direction. Acceleration due to a change in speed is called tangential acceleration.\n\nCentripetal Force :-\n\nIf the centripetal force vanishes, the object stops moving in a circular path. A ball that is on the end of a string is whirled in a vertical circular path.\n\nEx: A rope attaches a tire to an overhanging tree limb. A girl swinging on the tire has a centripetal acceleration of 3m/sec2 . If the length of the rope is 2.1m, what is the girl's tangential speed? √\n\nQ: As a young boy swing a yo-yo parallel to the ground and above his head, the yo-yo has a centripetal acceleration of 250m/sec2. If the yo-yo's string is 0.5m long, what is the yo-yo's tangential speed?\n\nEx.: A 2.1 m rope attaches a tire to an overhanging tree limb. A girl swinging on the tire has a tangential speed of 2.5m/sec. if the magnitude of the centripetal force is 88N , what is the girl's mass?\n\nQ: A piece of clay sits 0.16m from the center of a potter's wheel. If the potter spins the wheel at an angular speed of 20.2 rad/s, what is the magnitude of the centripetal acceleration of the piece of clay on the wheel? Q: A bicyclist is riding at a tangential speed of 13.2 m/s around a circular track with a radius of 40.0 m. if the magnitude of the force that maintains the bike's circular motion is 377 N, what is the combined mass of the bicycle and rider? Q: A dog sits 1.5 m from the center of a merry-go-round with an angular speed of 1.2 rad/s. If the magnitude of the force that maintains the dog's circular motion is 40.0 N, what is the dog's mass? Q: A 905 kg test car travels around a 3.25 km circular track. if the magnitude of the force that maintains the car's circular motion is 2140 N, what is the car's tangential speed?\n\nNewton's Law of Universal Gravitation :-" ]

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[ "# Move all zeroes to end of the array using List Comprehension in Python\n\nGiven a list of numbers, move all the zeroes to the end using list comprehensions. For example, the result of [1, 3, 0, 4, 0, 5, 6, 0, 7] is [1, 3, 4, 5, 6, 7, 0, 0, 0].\n\nIt's a single line code using the list comprehensions. See the following steps to achieve the result.\n\n• Initialize the list of numbers.\n\n• Generate non-zeroes from the list and generate zeroes from the list. Add both. And store the result in a list.\n\n• Print the new list.\n\n## Example\n\n# initializing a list\nnumbers = [1, 3, 0, 4, 0, 5, 6, 0, 7]\n# moving all the zeroes to end\nnew_list = [num for num in numbers if num != 0] + [num for num in numbers if num == 0]\n# printing the new list\nprint(new_list)\n[1, 3, 4, 5, 6, 7, 0, 0, 0]\n\nIf you run the above code, you will get the following result.\n\n## Output\n\n[1, 3, 4, 5, 6, 7, 0, 0, 0]\n\n## Conclusion\n\nIf you have any queries regarding the tutorial, mention them in the comment section." ]

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[ "", null, "Total Ionospheric Conductance: Summation of Sources\n•", null, "•", null, "•", null, "• +1", null, "", null, "", null, "", null, "## Abstract\n\nConductivity of the ionosphere allows the complex system of magnetospheric currents to flow through. Conductivity is governed by several factors including electron density and temperature, whose influence is highly dynamic during geomagnetic storm events. Thus, it is a crucial parameter that must be determined for space weather modeling to specify the coupling between the magnetosphere, ionosphere and thermosphere systems. Major sources of ionospheric conductivity are solar EUV and particle precipitation which includes Diffuse (Diff.), Monoenergetic (ME) and Broadband (BB) precipitations. Conductance Σ is the height integrated version of conductivity. Empirically, total ionospheric conductance (Hall and Pedersen) is known to be the root sum square of individual conductance terms [Wallis and Budzinski, 1981], considering that conductivity resulting from different processes are not linearly additive and corresponding ionization rates shall be added at each altitude and then integrated over the desired altitude range. With the inclusion of the less energetic broadband precipitation that was found to cause ionization in the bottom-side F region, the expression for the total ionospheric conductance was modified by the linear addition of the contribution of the broadband precipitation to the total Hall and Pedersen conductance[Zhang et al., 2015].In this study, using a 3-dimensional global physics based model GITM (Global Ionosphere Thermosphere Model), the validity of this combination of vector and linear addition of individual source terms to the total ionospheric conductance is examined and the more accurate expression for the summation of sources contributing to the total conductance is quantified. GITM is employed to calculate the Hall and Pedersen conductance using the average energy, potential and energy flux for each of the sources of conductance. Several scenarios are simulated where the different sources of precipitation are paired with solar EUV radiation, and the total conductance is obtained. Linear and vector summation of conductance resulting from combinations of sources and individual sources indicate that the contribution of broadband precipitation to the total conductance also follows vector addition. To quantify the result that the total conductance is the vector sum of individual sources, error histograms are plotted and a set of metrics including RMSE, mean error, standard deviation, correlation coefficient and fractional error are enumerated for both linear and vector summation of individual sources to produce the total conductance." ]

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[ "# Tip Calculator\n\nHow much is a 10 percent tip on \\$27?\n\nTIP:\n\\$ 0\nTOTAL:\n\\$ 0\nTIP PER PERSON:\n\\$ 0\nTOTAL PER PERSON:\n\\$ 0\n\n## How much is a 10 percent tip on \\$27? How to calculate this tip?\n\nAre you looking for the answer to this question: How much is a 10 percent tip on \\$27? Here is the answer.\n\nLet's see how to calculate a 10 percent tip when the amount to be paid is 27. Tip is a percentage, and a percentage is a number or ratio expressed as a fraction of 100. This means that a 10 percent tip can also be expressed as follows: 10/100 = 0.1 . To get the tip value for a \\$27 bill, the amount of the bill must be multiplied by 0.1, so the calculation is as follows:\n\n1. TIP = 27*10% = 27*0.1 = 2.7\n\n2. TOTAL = 27+2.7 = 29.7\n\n3. Rounded to the nearest whole number: 30\n\nIf you want to know how to calculate the tip in your head in a few seconds, visit the Tip Calculator Home.\n\n## So what is a 10 percent tip on a \\$27? The answer is 2.7!\n\nOf course, it may happen that you do not pay the bill or the tip alone. A typical case is when you order a pizza with your friends and you want to split the amount of the order. For example, if you are three, you simply need to split the tip and the amount into three. In this example it means:\n\n1. Total amount rounded to the nearest whole number: 30\n\n2. Split into 3: 10\n\nSo in the example above, if the pizza order is to be split into 3, you’ll have to pay \\$10 . Of course, you can do these settings in Tip Calculator. You can split the tip and the total amount payable among the members of the company as you wish. So the TipCalc.net page basically serves as a Pizza Tip Calculator, as well.\n\n## Tip Calculator Examples (BILL: \\$27)\n\nHow much is a 5% tip on \\$27?\nHow much is a 10% tip on \\$27?\nHow much is a 15% tip on \\$27?\nHow much is a 20% tip on \\$27?\nHow much is a 25% tip on \\$27?\nHow much is a 30% tip on \\$27?" ]

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[ "#### Abstract\n\nIn this research work, finite element method (FEM) and 2D Plaxis were employed to generate numerical values for bilayered soils bearing a strip footing of width B and depths, h and H, in order to predict the ultimate bearing capacity (UBC) of the strip footing underlain by layered soil profile. Several research works have tried to solve bearing capacity problems using limit equilibrium (LE) techniques. But, the LE techniques have limitations in terms of soil properties and profile arrangement. The need, however, for using constitutive models or numerical methods powered by FEM and discrete element method (DEM) has been on the rise due to the versatility and robustness of these techniques to accommodate erratic soil behaviors. Multiple numerical data were generated for the case under study and artificial intelligence (AI)-based techniques; generalized reduced gradient (GRG), genetic programming (GP), artificial neural network (ANN), and evolutionary polynomial regression (EPR) were used to predict the UBC. In order to conduct the parametric analysis to investigate the effect of different soil layers, footing width and overburden pressure at foundation level on the ultimate bearing capacity sets of finite element models were prepared. This was executed using the following soil properties: soil type of top layer (from S1 to S6), soil type of bottom layer (from S1 to S6), strip footing width (B) (from 1.0 to 5.0 m), thickness of the top layer (h) (from 0.5 B to 1.0 B), overburden pressure (from 1.0 m to 3.0 m multiplied by the γt), and the parameters combination of each finite model in the set is randomly selected. The results of the FEM/Plaxis parametric study produced the 2D-model, deformed shape, stress distribution, and plastic point (failure point) models. The loading produced appreciable deformation on both x- and y-axes of the soil profile with the y-axis showing a scattered failure configuration. The AI-based prediction produced UBC equations which performed at over 90% accuracy with ANN (99.9%; 6.3%) outperforming other techniques followed by GRG, GP, and EPR.\n\n#### 1. Introduction\n\nThe study of soil-structure interaction has been of great interest to geotechnical engineers and soil mechanics due to its obvious connection with the ultimate bearing capacity of footings underlain by soil . This is the interface between soils and superstructures. Meanwhile, ultimate bearing capacity constitutes the maximum pressure a soil can withstand prior to failure. This has always been estimated using the limit equilibrium methods in constitutive modeling exercises . Constitutive models in soil mechanics have been proposed by researchers as mathematical relationships related to the stress-strain behavior of soils and the application of the geometrical and property changes with response to loading with which earthwork problems are solved. Ultimate bearing capacity, slope stability, and landslide problems have been solved by relating the stress-strain Mohr-Coulomb criterion in proposing related equations or models . Limit equilibrium conditions have always governed the classical techniques that have been in use example of which include the Terzaghi method of LE, Prandtl technique, Meyerhoff, and Hansen methods of determining constitutive relationships for ultimate bearing capacity . These LE techniques have been explored with the assumption that the underlying soil mass is homogeneous and infinite . Obviously, however, multilayered soil conditions are experienced in practice, which is the reason for many earthwork failures and low performance of geotechnical engineering infrastructure around the world. It is important to note that the estimation of the ultimate bearing capacity of foundations underlain by multilayered soil strata is needed in the safety assessment of foundation structures .\n\nThere have been previous attempts made to study the bearing capacity analysis of foundations on layered soils. Mosallanezhad and Moayedi studied the bearing capacity of footing on layered soils by conducting a comparative analysis of different approaches for strip footing by conducting a large number of experimental, LE, and FE analyses. It was found that the first layer thickness (h) to the foundation width (B) ratio (h/B) was the critical point and more influential than other factors. Park and Lee also studied the bearing capacity for multilayered clay deposits with geosynthetic reinforcement using the discrete element method (DEM). In this stability analysis of reinforced foundation soil, a multilayered soil reinforced with a horizontal strip of geosynthetics was evaluated for its ultimate bearing capacity and the distribution of tensile forces. Several bearing capacity theories and plate loading tests were used to verify the developed model. Shoaei et al. reviewed available approaches for the evaluation of the ultimate bearing capacity of two-layered soils. These works extensively presented different classical and limit equilibrium (LE) techniques that have evolved over the years. This review work by focused on the importance of different failure strains and load spread angles in the analysis of foundations on multilayered soils. The computer simulation of the bearing capacity of multilayer soils in Baotou was also conducted by . Various other approaches have been employed to evaluate the ultimate bearing capacity of strip foundations on multilayered soils . However, in addition to LE and numerical approaches to determining the ultimate bearing capacity of strip foundations underlain by multilayered soils, AI-based techniques such as ANN and GP have also been employed in this case study with great results. In previous research works, ANN and some metaheuristic approaches were used to predict the ultimate bearing capacity of strip foundations over multilayered soils . In another development, dragonfly algorithm, Harris hawks optimization, and sparse polynomial chaos expansions were employed to predict the ultimate bearing capacity of strip footing underlain by multilayered soil [17, 18] as well as other relevant research papers on the geotechnics of strip and reinforced footing on soft clays [19, 20]. Also, genetic programming (GP) was used to predict the ultimate bearing capacity of shallow foundations with good performance accuracy . In another study, Dutta et al. studied the bearing capacity of footing on a two-layered arrangement of sand and clay under inclined loading. It was observed that the bearing capacity of this studied arrangement compared well with lower angles of inclination and overestimated for higher ones. All the above-reviewed LE and numerical approaches to determining the ultimate bearing capacity of strip footing underlain by multilayered soils, and none was able to couple the use of LE and numerical (FE and AI) techniques to formulate numerical data and at the same time predict AI-based models for the ultimate bearing capacity (UBC). In this work, FEM/Plaxis 2D was used to generate multiple numerical data from bilayered soils selected by permutation from six (6) different soil properties and thicknesses. Subsequently, the numerical data were deployed in an AI-based predictive model exercise to predict the ultimate bearing capacity.\n\n#### 2. Methodology\n\n##### 2.1. FEM/PLAXIS Analysis\n\nGeotechnical engineers often deal with layered foundation soil, which is nonhomogeneous in nature but can be simplified in representation as distinct homogeneous layers for engineering purposes. The failure mechanism of layered soil depends on the thickness and soil properties of each layer. In some cases where the top layer is relatively thick and consists of weak soil, the failure mechanism may be limited to the top layer only, and the strength of the remaining lower layers has no influence. In many other cases, however, the failure mechanism may involve two or more layers. The parametric study was carried out using PLAXIS 20 to evaluate the ultimate bearing capacity of a strip footing resting on two different soil layers.\n\nThe following Figure 1 shows the 2-dimensional plane strain model; a footing with a width B is rested on a half-space soil layer with thickness (h) followed by another soil layer with thickness (H).\n\nDue to symmetry, only half of the problem is modeled. The model has a dimension of 10 B in both X and Y directions. The size of the finite element model is large enough to keep the boundary conditions at the bottom and the right side from restricting the soil movement due to the footing load. Furthermore, a 15-node element was used in the finite element mesh. Soil layers were modeled using Mohr-Coulomb’s constitutive law with parameters shown in Table 1.\n\nA parametric analysis was conducted to investigate the effect of different soil layers, footing width, and overburden pressure at foundation level on the ultimate bearing capacity; therefore, sets of finite element models were arranged using the following parameter ranges: soil type of top layer (from S1 to S6), soil type of bottom layer (from S1 to S6), strip footing width (B) (from 1.0 to 5.0 m), thickness of the top layer (h) (from 0.5 B to 1.0 B), overburden pressure (from 1.0 m to 3.0 m multiplied by the γt), and the parameters combination of each finite model in the set are randomly selected. Samples of the generated FEM models and its output are shown in Figure 2. This shows the 2D model, deformed shape, stress distribution, and points of failure for the foundation under loading considerations.\n\n##### 2.2. FEM/PLAXIS-Generated Database, Statistical, and Correlation Analyses\n\nIn the course of the constitutive phase of the research work, 156 PLAXIS models were developed to generate the required dataset for the regression process, and each record in the dataset contains the following fields:(i)Cohesion of top layer (Ct) kN/m2(ii)Tangent of friction angle of top layer tan (φt)(iii)Effective density of top layer (γt) kN/m3(iv)Thickness of top layer (h) m(v)Cohesion of bottom layer (Cb) kN/m2(vi)Tangent of friction angle of bottom layer tan (φb)(vii)Effective density of bottom layer (γb) kN/m3(viii)Width of strip footing (B) m(ix)Effective overburden pressure at foundation level kN/m2(x)The ultimate bearing stress of strip footing kN/m2\n\nThe generated records were divided into training and validation sets (110 (70%) and 46 (30%) records, respectively). Tables 2 and 3 show the statistical characteristics of the generated dataset and the correlation matrix while Figure 3 presents the statistical distribution (histograms) for input and output parameters.\n\n##### 2.3. Research Program\n\nBesides the well-known generalized reduced gradient (GRG) technique, another three (AI) techniques were used to predict the ultimate bearing capacity of strip footing using the generated database. The used techniques are evolutionary polynomial regression (EPR), artificial neural network (ANN), and genetic programming (GP). These models were developed to predict the ultimate bearing stress of strip footing using the soil parameters of both layers (Ct), (Cb), tan (φt), tan (φb), (γt), (γb), and geometrical parameters (h), (B), and . The accuracy of each model was evaluated using the sum of squared errors (SSE).\n\n#### 3. Results and Discussion, and Prediction of Ultimate Bearing Capacity\n\n##### 3.1. Model (1)—Evolutionary Polynomial Regression (EPR)\n\nA pentagonal level EPR model was developed, for 9 inputs; there are 1287 possible combinations (792 + 330 + 120 + 36 + 8 + 1 = 1287) as follows:\n\nThe most effective 39 terms were selected by the GA technique. The developed model to predict the bearing capacity is presented in (2), while its fitness is shown in Figure 4(a). The determination factor (R2) and average error values are 0.939 and 27.2%, respectively.\n\n##### 3.2. Model (2)—Artificial Neural Network (ANN)\n\nA single hidden layer (ANN) with a nonlinear activation function (hyper-tan) was trained using the backpropagation technique to predict bearing capacity. The layout of the developed ANN and their connotation weights is shown in Figure 5 and Table 4. The average error was 6.3%, and the R2 was 0.997. The relations between calculated and predicted values are illustrated in Figure 4(b).\n\n##### 3.3. Genetic Programming (GP) Prediction of Ultimate Bearing Capacity\n\nFive levels of complexity (GP) model (63 genes per chromosome) was developed to predict the bearing capacities values of generated database records. This model was developed using a population size of 100000 chromosomes, a survivor size of 25000 chromosomes, and 250 generations. Figure (3) presents the generated formula for the ultimate bearing capacity, while Figure 4(c) shows its fitness. The average error and (R2) values for this model were 11.7% and 0.989, respectively.\n\n##### 3.4. Generalized Reduced Gradient (GRG) Prediction of Ultimate Bearing Capacity\n\nA GRG model was developed using the MS EXCEL add-in solver module. The selected structure for the bearing capacity formulas and optimized coefficients is shown in (5), and its fitness is illustrated in Figure 4(d). The corresponding (R2) value is 0.992 while the average error is 10%. The accuracies of developed models are compared in Table 5.where\n\n#### 4. Conclusions\n\nThis research was concerned in predicting the ultimate bearing capacity of strip footing rested on bilayered soil profile using the following four techniques: EPR, ANN, GP, and GRG. These techniques were applied to a generated dataset of 165 records, and each record contains data for soil parameters of both layers (Ct), (Cb), tan (φt), tan (φb), (γt), (γb), and geometrical parameters (h), (B), and besides the corresponding ultimate bearing capacity . The used database was generated using the well-known PLAXIS software. The output of each technique could be evaluated as follows:(i)The first technique (EPR) generated an optimized pentagonal polynomial with 39 terms selected out of 1287 possible terms. Its accuracy was lower among all developed models with an average error percent of 27.2%. Besides that, it includes only five parameters (Ct), tan (φt), tan (φb), (h), and (γb) and neglects the effect of the rest (Cb), (γt), (B), and .(ii)The second technique (ANN) showed the highest accuracy with an average error percent of 6.3%, and it utilized all the input parameters. The importance of each parameter is illustrated by the size of its input blocks in Figure 2, which indicates that all parameters have almost the same impact on the ultimate bearing capacity except (h), (Ct), and tan (φt) which have a slightly higher impact.(iii)The GP technique generated a complicated 3rd-degree exponential formula with a very good accuracy and an average error percent of 11.7%. All input parameters were used except the top layer thickness (h); besides that, using tan (φt) and tan (φb) as exponent assured their significant impact on the ultimate bearing capacity.(iv)Although GRG is not an advanced (AI) technique, it showed a very good accuracy with average error of 10%. The main advantage of this technique is the chosen structure of the generated formula which matched the well-known general bearing capacity equation. In addition, all input parameters were used. On a practical need, these models can be applied in predicting the ultimate bearing capacity of a similar soil layered arrangement under loading for design and foundation performance and behavior purposes.\n\n#### 5. Recommendations\n\nBased on the previous discussion, the following points could be recommended:(i)It is not recommended to use the EPR formula due to its low level of accuracy and the fact that ignored the impact of almost half of the input parameters.(ii)Although the ANN model showed a perfect accuracy and accounts for all input parameters, its output is a weight matrix which is very difficult to be handled manually.(iii)Both GP and GRG models showed the same level of accuracy, and both generated a closed-form formula that could be used manually. However, the absence of (h) parameter in the GP model is a significant shortage. On the other hand, the well-known structures of the GRG formula are an important advantage.(iv)For computerized calculations, it is recommended to use the ANN model, while the minimum of the GP and GRG models is recommended for manual calculations.(v)All generated models are valid within the considered range of input parameter values, beyond this range; the prediction accuracy should be verified.\n\n#### Data Availability\n\nAll data, models, and code generated or used during the study appear in the submitted article.\n\n#### Conflicts of Interest\n\nThe authors declare that they have no conflicts of interest." ]

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[ "Subgroup Type Coordinates and the Separation of Variables in Hamilton-Jacobi and Schrödinger Equations\n\nE G Kalnins, Z Thomova, P Winternitz\n2005 Journal of Nonlinear Mathematical Physics\nSeparable coordinate systems are introduced in the complex and real four-dimensional flat spaces. We use maximal Abelian subgroups to generate coordinate systems with a maximal number of ignorable variables. The results are presented (also graphically) in terms of subgroup chains. Finally, the explicit solutions of the Schr\\H{o}dinger equation in the separable coordinate systems are computed." ]

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[ "# How to Sort a HashMap by Value in Java?\n\nPublished on August 3, 2022", null, "By Jayant Verma\nDeveloper and author at DigitalOcean.", null, "While we believe that this content benefits our community, we have not yet thoroughly reviewed it. If you have any suggestions for improvements, please let us know by clicking the “report an issue“ button at the bottom of the tutorial.\n\nHashMap in java provides quick lookups. They store items in “key, value” pairs. To get a value from the HashMap, we use the key corresponding to that entry.\n\nHashMaps are a good method for implementing Dictionaries and directories.\n\nKey and Value can be of different types (eg - String, Integer).\n\nWe can sort the entries in a HashMap according to keys as well as values.\n\nIn this tutorial we will sort the HashMap according to value.\n\nThe basic strategy is to get the values from the HashMap in a list and sort the list. Here if the data type of Value is String, then we sort the list using a comparator. To learn more about comparator, read this tutorial.\n\nOnce we have the list of values in a sorted manner, we build the HashMap again based on this new list.\n\nLet’s look at the code.\n\n## Sorting HashMap by Value Simple Example\n\nWe first get the String values in a list. Then we sort the list.\n\nTo sort the String values in the list we use a comparator. This comparator sorts the list of values alphabetically.\n\n`````` Collections.sort(list, new Comparator<String>() {\npublic int compare(String str, String str1) {\nreturn (str).compareTo(str1);\n}\n});\n``````\n\nOnce, we have sorted the list, we build the HashMap based on this sorted list.\n\nThe complete code is as follows :\n\n``````package com.journaldev.collections;\n\nimport java.util.ArrayList;\nimport java.util.Collections;\nimport java.util.Comparator;\nimport java.util.HashMap;\nimport java.util.Map;\nimport java.util.Map.Entry;\n\npublic class Main {\npublic static void main(String[] args) {\nHashMap<String, String> map = new HashMap<>();\nArrayList<String> list = new ArrayList<>();\nmap.put(\"2\", \"B\");\nmap.put(\"8\", \"A\");\nmap.put(\"4\", \"D\");\nmap.put(\"7\", \"F\");\nmap.put(\"6\", \"W\");\nmap.put(\"19\", \"J\");\nmap.put(\"1\", \"Z\");\nfor (Map.Entry<String, String> entry : map.entrySet()) {\n}\nCollections.sort(list, new Comparator<String>() {\npublic int compare(String str, String str1) {\nreturn (str).compareTo(str1);\n}\n});\nfor (String str : list) {\nfor (Entry<String, String> entry : map.entrySet()) {\nif (entry.getValue().equals(str)) {\nsortedMap.put(entry.getKey(), str);\n}\n}\n}\nSystem.out.println(sortedMap);\n}\n}\n\n``````\n\nOutput :\n\n``````{8=A, 5=B, 3=D, 7=F, 10=J, 2=W, 1=Z}\n``````\n\nHashMap entries are sorted according to String value.\n\n## Another Example of Sorting HashMap by Value\n\nIf values in the HashMap are of type Integer, the code will be as follows :\n\n``````package com.JournalDev;\n\nimport java.util.ArrayList;\nimport java.util.Collections;\nimport java.util.Comparator;\nimport java.util.HashMap;\nimport java.util.Map;\nimport java.util.Map.Entry;\n\npublic class Main {\npublic static void main(String[] args) {\nHashMap<String, Integer> map = new HashMap<>();\nArrayList<Integer> list = new ArrayList<>();\nmap.put(\"A\", 5);\nmap.put(\"B\", 7);\nmap.put(\"C\", 3);\nmap.put(\"D\", 1);\nmap.put(\"E\", 2);\nmap.put(\"F\", 8);\nmap.put(\"G\", 4);\nfor (Map.Entry<String, Integer> entry : map.entrySet()) {\n}\nCollections.sort(list);\nfor (int num : list) {\nfor (Entry<String, Integer> entry : map.entrySet()) {\nif (entry.getValue().equals(num)) {\nsortedMap.put(entry.getKey(), num);\n}\n}\n}\nSystem.out.println(sortedMap);\n}\n}\n\n``````\n\nOutput :\n\n``````{D=1, E=2, C=3, G=4, A=5, B=7, F=8}\n``````", null, "Here HashMap values are sorted according to Integer values.\n\n## Sorting the HashMap using a custom comparator\n\nIf you notice the above examples, the Value objects implement the Comparator interface. Let’s look at an example where our value is a custom object.\n\nWe can also create a custom comparator to sort the hash map according to values. This is useful when your value is a custom object.\n\nLet’s take an example where value is a class called ‘Name’. This class has two parameters, firstName and lastName.\n\nThe code for class Name is :\n\n``````package com.JournalDev;\n\npublic class Name {\nString firstName;\nString lastName;\nName(String a, String b){\nfirstName=a;\nlastName=b;\n\n}\npublic String getFirstName() {\nreturn firstName;\n}\n\n}\n\n``````\n\nThe custom comparator will look like :\n\n``````Comparator<Name> byName = (Name obj1, Name obj2) -> obj1.getFirstName().compareTo(obj2.getFirstName());\n``````\n\nWe are sorting the names according to firstName, we can also use lastName to sort. Rather than using a list to get values from the map, we’ll be using LinkedHashMap to create the sorted hashmap directly.\n\nThe complete code is :\n\n``````public static void main(String[] args) {\nHashMap<Integer, Name> hmap = new HashMap<Integer, Name>();\nName name1 = new Name(\"Jayant\", \"Verma\");\nName name2 = new Name(\"Ajay\", \"Gupta\");\nName name3 = new Name(\"Mohan\", \"Sharma\");\nName name4 = new Name(\"Rahul\", \"Dev\");\n\nhmap.put(9, name1);\nhmap.put(1, name2);\nhmap.put(6, name3);\nhmap.put(55, name4);\n\nComparator<Name> byName = (Name obj1, Name obj2) -> obj1.getFirstName().compareTo(obj2.getFirstName());\n\n.sorted(Map.Entry.<Integer, Name>comparingByValue(byName))\n.collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue, (e1, e2) -> e1, LinkedHashMap::new));\n\n//printing the sorted hashmap\nSet set = sortedMap.entrySet();\nIterator iterator = set.iterator();\nwhile (iterator.hasNext()) {\nMap.Entry me2 = (Map.Entry) iterator.next();\nSystem.out.print(me2.getKey() + \": \");\nSystem.out.println(hmap.get(me2.getKey()).firstName + \" \"+hmap.get(me2.getKey()).lastName );\n\n}\n}\n``````\n\nOutput :\n\n``````1: Ajay Gupta\n9: Jayant Verma\n6: Mohan Sharma\n55: Rahul Dev\n``````", null, "## Conclusion\n\nThis tutorial covered sorting of HashMap according to Value. Sorting for String values differs from Integer values. String values require a comparator for sorting. Whereas, Integer values are directly sorted using Collection.sort().", null, "Developer and author at DigitalOcean." ]

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[ "## 8.3. Arithmetic Instructions\n\nThe IA32 ISA implements several instructions that correspond with arithmetic operations performed by the ALU. Table 1 lists several arithmetic instructions that one may encounter when reading assembly:\n\nTable 1. Common instructions for multiplication and division.\nInstruction Translation\n\n`add S, D`\n\n`S + D``D`\n\n`sub S, D`\n\n`D - S``D`\n\n`inc D`\n\n`D + 1``D`\n\n`dec D`\n\n`D - 1``D`\n\n`neg D`\n\n`-D``D`\n\n`imul S, D`\n\n`S * D``D`\n\n`idiv S`\n\n`%eax/S` : `Q``%eax`, `R``%edx`\n\nThe `add` and `sub` instructions correspond to addition and subtraction and take two operands each. The next three entries show the single register instructions for the increment (`x++`), decrement (`x--`) and negation (`-x`) operations in C. The multiplication instruction operates on two operands and places the product in the destination `D`. If the product requires more than 32 bits to represent, the value will be truncated to 32 bits.\n\nThe division instruction works a little differently. Prior to the execution of the `idiv` instruction, it is assumed that register `%eax` contains the dividend. Calling `idiv` on operand `S` divides the contents of `%eax` by `S` and places the quotient ( `Q` ) in register `%eax` and the remainder ( `R` ) in register `%edx`.\n\n### 8.3.1. Bit Shifting Instructions\n\nBit shifting instructions enable the compiler to perform bit shifting operations. Multiplication and division instructions typically take a long time to execute. Bit shifting offers the compiler a shortcut for multiplicands and divisors that are powers of 2. For example, to compute `77 * 4`, most compilers will translate this operation to `77 << 2` to avoid the use of an `imul` instruction. Likewise, to compute `77 / 4`, a compiler typically translates this operation to `77 >> 2` to avoid using the `idiv` instruction.\n\nKeep in mind that left and right bit shift translate to different instructions based on whether the goal is an arithmetic (signed) or logical (unsigned) shift:\n\nTable 2. Bit Shift Instructions\nInstruction Translation Arithmetic or Logical?\n\n`sal v, D`\n\n`D << v``D`\n\narithmetic\n\n`shl v, D`\n\n`D << v``D`\n\nlogical\n\n`sar v, D`\n\n`D >> v``D`\n\narithmetic\n\n`shr v, D`\n\n`D >> v``D`\n\nlogical\n\nEach shift instruction take two operands, one which is usually a register (denoted by `D`) and the other which is a shift value (`v`). On 32-bit systems, the shift value is encoded as a single byte (since it doesn’t make sense to shift past 31). The shift value `v` must either be a constant or be stored in register `%cl`.\n\n Different versions of instructions help us distinguish types at an assembly level At the assembly level, there is no notion of types. However, recall that shift right works differently depending on whether or not the value is signed. At the assembly level, the compiler uses separate instructions to distinguish between logical and arithmetic shifts!\n\n### 8.3.2. Bitwise Instructions\n\nBitwise instructions enable the compiler to perform bitwise operations on data. One way the compiler uses bitwise operations is for certain optimizations. For example, a compiler may choose to implement 77 mod 4 with the operation `77 & 3` in lieu of the more expensive `idiv` instruction.\n\nThe table below lists common bitwise instructions:\n\nTable 3. Bitwise Operations\nInstruction Translation\n\n`and S, D`\n\n`S & D``D`\n\n`or S, D`\n\n`S | D``D`\n\n`xor S, D`\n\n`S ^ D``D`\n\n`not D`\n\n`~D``D`\n\nRemember that bitwise not is distinct from negation (`neg`). The `not` instruction flips the bits but does not add `1`. Be careful not to confuse these two instructions.\n\n Use bitwise operations only when needed in your C code! After reading this section, it may be tempting to replace common arithmetic operations in your C code with bitwise shifts and other operations. This is NOT recommended. Most modern compilers are smart enough to replace simple arithmetic operations with bitwise operations when it makes sense, making it unnecessary for the programmer to do so. As a general rule, programmers should prioritize code readability whenever possible and avoid premature optimization.\n\nWhat’s lea got to do (got to do) with it?\n\n~with apologies to Tina Turner\n\nWe finally come to the load effective address or `lea` instruction, which is probably the arithmetic instruction that causes students the most consternation. The `lea` instruction is traditionally used as a fast way to compute the address of a location in memory. The `lea` instruction operates on the same operand structure that we’ve seen thus far but does not include a memory lookup. Regardless of the type of data contained in the operand (whether it be constant values or addresses), `lea` simply performs arithmetic.\n\nFor example, suppose register `%eax` contains the constant value `5`, register `%edx` contains the constant value `4`, and register `%ecx` contains the value `0x808` (which happens to be an address). Table 4 gives some example `lea` operations, their translations, and corresponding values:\n\nTable 4. Example lea operations\nInstruction Translation Value\n\n`lea 8(%eax), %eax`\n\n`8 + %eax``%eax`\n\n`13``%eax`\n\n`lea (%eax, %edx), %eax`\n\n`%eax + %edx``%eax`\n\n`9``%eax`\n\n`lea (,%eax,4), %eax`\n\n`%eax * 4``%eax`\n\n`20``%eax`\n\n`lea -0x8(%ecx), %eax`\n\n`%ecx - 8``%eax`\n\n`0x800``%eax`\n\n`lea -0x4(%ecx, %edx, 2), %eax`\n\n`%ecx + %edx*2 - 4``%eax`\n\n`0x80c``%eax`\n\nIn all cases, the `lea` instruction performs arithmetic on the operand specified by source `S` and places the result in destination operand `D`. The `mov` instruction is identical to the `lea` instruction except that the `mov` instruction is required to treat the contents in the source operand as a memory location. In contrast, `lea` performs the same (sometimes complicated) operand arithmetic without the memory lookup, enabling the compiler to cleverly use `lea` as a substitution for some types of arithmetic." ]

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[ " Statistics Other Preferences\n\n# Statistics Other Preferences\n\nTop  Previous  Next", null, "Statistics, particularly secondary school statistics, is particularly localised.  The “right” way of doing something in one location is often very different to the “right” way of doing it in another location.  The system options allow you to customise you version of FX Stat to suit you local conditions.  System options can be accessed through the Edit menu.\n\nWith all the system options it is vital that you TRY each setting and pick the one that suits your situation.  Unfortunately there are no standard names for different methods of calculation.\n\n## Calculating Quartiles\n\nEfofex has found three common methods of calculating quartiles.\n\n1, 3, 3, 4, 5, 6, 6, 8, 9, 10, 10, 11, 13, 14, 16, 18, 20\n\nIn the above example, 9 is the median.  The lower quartile is the median of the bottom half of the scores BUT does the lower half include the 9 or exclude it?  Depending on your answer, the lower quartile would be 4.5 or 5.\n\nThis gives us two of the three options for calculating quartiles.  The third is to use the 75th and 25th percentiles.  The results of this option depends on how you calculate percentiles.\n\n## Seasonalised Data\n\nMethod One is to calculate the average residual between a point in the cycle and the moving point average at that point.  This average residual is then removed from the data to obtain the seasonalised figure.\n\nMethod Two is to calculate the average deviation between a point in the cycle and the mean of the data set.  This average deviation is then removed from the data to obtain the seasonalised figure.\n\n## Calculating Percentiles\n\nThe interpolated method of calculating percentiles is the same as the method in Excel .\n\nIf the data point 13 indicates the 20th percentile and data point 14 is the 30th percentile, the 21st percentile is 13.1, the 22nd is 13.2, the 23rd 13.3 and so on.  The interpolated method interpolates values for the percentiles that are not in the original data.\n\nThe discrete method of calculating percentiles would produce 13 as the value of the percentile for the 20th up to the 29th percentile.  Each percentile is guaranteed to be a number in the data set." ]

[ null, "https://efofex.com/documentation/clip1504.png", null ]

[ "mfbt/WrappingOperations.h\n author Justin Wood Mon, 15 Apr 2019 22:53:10 -0400 changeset 474153 59a1393697e0ac36ee62be71adf58a51904e02b6 parent 454520 5f4630838d46dd81dadb13220a4af0da9e23a619 child 483832 04b82bfc269eb6c01b0ab4024402e48b8de17b76 permissions -rw-r--r--\nBug 1547730 - Use new inspection methods introduced in py3 but work in py2.7 for functions r=#build Differential Revision: https://phabricator.services.mozilla.com/D28112\n\n/* -*- Mode: C++; tab-width: 8; indent-tabs-mode: nil; c-basic-offset: 2 -*- */\n/* vim: set ts=8 sts=2 et sw=2 tw=80: */\n/* This Source Code Form is subject to the terms of the Mozilla Public\n* License, v. 2.0. If a copy of the MPL was not distributed with this\n* file, You can obtain one at http://mozilla.org/MPL/2.0/. */\n\n/*\n* Math operations that implement wraparound semantics on overflow or underflow.\n*\n* While in some cases (but not all of them!) plain old C++ operators and casts\n* will behave just like these functions, there are three reasons you should use\n* these functions:\n*\n* 1) These functions make *explicit* the desire for and dependence upon\n* wraparound semantics, just as Rust's i32::wrapping_add and similar\n* functions explicitly produce wraparound in Rust.\n* 2) They implement this functionality *safely*, without invoking signed\n* integer overflow that has undefined behavior in C++.\n* 3) They play nice with compiler-based integer-overflow sanitizers (see\n* build/autoconf/sanitize.m4), that in appropriately configured builds\n* verify at runtime that integral arithmetic doesn't overflow.\n*/\n\n#ifndef mozilla_WrappingOperations_h\n#define mozilla_WrappingOperations_h\n\n#include \"mozilla/Attributes.h\"\n#include \"mozilla/TypeTraits.h\"\n\n#include <limits.h>\n\nnamespace mozilla {\n\nnamespace detail {\n\ntemplate <typename UnsignedType>\nstruct WrapToSignedHelper {\nstatic_assert(mozilla::IsUnsigned<UnsignedType>::value,\n\"WrapToSigned must be passed an unsigned type\");\n\nusing SignedType = typename mozilla::MakeSigned<UnsignedType>::Type;\n\nstatic constexpr SignedType MaxValue =\n(UnsignedType(1) << (CHAR_BIT * sizeof(SignedType) - 1)) - 1;\nstatic constexpr SignedType MinValue = -MaxValue - 1;\n\nstatic constexpr UnsignedType MinValueUnsigned =\nstatic_cast<UnsignedType>(MinValue);\nstatic constexpr UnsignedType MaxValueUnsigned =\nstatic_cast<UnsignedType>(MaxValue);\n\n// Overflow-correctness was proven in bug 1432646 and is explained in the\n// comment below. This function is very hot, both at compile time and\n// runtime, so disable all overflow checking in it.\nMOZ_NO_SANITIZE_UNSIGNED_OVERFLOW\nMOZ_NO_SANITIZE_SIGNED_OVERFLOW static constexpr SignedType compute(\nUnsignedType aValue) {\n// This algorithm was originally provided here:\n// https://stackoverflow.com/questions/13150449/efficient-unsigned-to-signed-cast-avoiding-implementation-defined-behavior\n//\n// If the value is in the non-negative signed range, just cast.\n//\n// If the value will be negative, compute its delta from the first number\n// past the max signed integer, then add that to the minimum signed value.\n//\n// At the low end: if |u| is the maximum signed value plus one, then it has\n// the same mathematical value as |MinValue| cast to unsigned form. The\n// delta is zero, so the signed form of |u| is |MinValue| -- exactly the\n// result of adding zero delta to |MinValue|.\n//\n// At the high end: if |u| is the maximum *unsigned* value, then it has all\n// bits set. |MinValue| cast to unsigned form is purely the high bit set.\n// So the delta is all bits but high set -- exactly |MaxValue|. And as\n// |MinValue = -MaxValue - 1|, we have |MaxValue + (-MaxValue - 1)| to\n// equal -1.\n//\n// Thus the delta below is in signed range, the corresponding cast is safe,\n// and this computation produces values spanning [MinValue, 0): exactly the\n// desired range of all negative signed integers.\nreturn (aValue <= MaxValueUnsigned)\n? static_cast<SignedType>(aValue)\n: static_cast<SignedType>(aValue - MinValueUnsigned) + MinValue;\n}\n};\n\n} // namespace detail\n\n/**\n* Convert an unsigned value to signed, if necessary wrapping around.\n*\n* This is the behavior normal C++ casting will perform in most implementations\n* these days -- but this function makes explicit that such conversion is\n* happening.\n*/\ntemplate <typename UnsignedType>\nconstexpr typename detail::WrapToSignedHelper<UnsignedType>::SignedType\nWrapToSigned(UnsignedType aValue) {\nreturn detail::WrapToSignedHelper<UnsignedType>::compute(aValue);\n}\n\nnamespace detail {\n\ntemplate <typename T>\nconstexpr T ToResult(typename MakeUnsigned<T>::Type aUnsigned) {\n// We could *always* return WrapToSigned and rely on unsigned conversion to\n// undo the wrapping when |T| is unsigned, but this seems clearer.\nreturn IsSigned<T>::value ? WrapToSigned(aUnsigned) : aUnsigned;\n}\n\ntemplate <typename T>\nprivate:\nusing UnsignedT = typename MakeUnsigned<T>::Type;\n\npublic:\nMOZ_NO_SANITIZE_UNSIGNED_OVERFLOW\nstatic constexpr T compute(T aX, T aY) {\n}\n};\n\n} // namespace detail\n\n/**\n* Add two integers of the same type and return the result converted to that\n* type using wraparound semantics, without triggering overflow sanitizers.\n*\n* For N-bit unsigned integer types, this is equivalent to adding the two\n* numbers, then taking the result mod 2**N:\n*\n* WrappingAdd(uint32_t(42), uint32_t(17)) is 59 (59 mod 2**32);\n* WrappingAdd(uint8_t(240), uint8_t(20)) is 4 (260 mod 2**8).\n*\n* Unsigned WrappingAdd acts exactly like C++ unsigned addition.\n*\n* For N-bit signed integer types, this is equivalent to adding the two numbers\n* wrapped to unsigned, then wrapping the sum mod 2**N to the signed range:\n*\n* WrappingAdd(int16_t(32767), int16_t(3)) is\n* -32766 ((32770 mod 2**16) - 2**16);\n* WrappingAdd(int8_t(-128), int8_t(-128)) is\n* 0 (256 mod 2**8);\n* WrappingAdd(int32_t(-42), int32_t(-17)) is\n* -59 ((8589934533 mod 2**32) - 2**32).\n*\n* There's no equivalent to this operation in C++, as C++ signed addition that\n* overflows has undefined behavior. But it's how such addition *tends* to\n* behave with most compilers, unless an optimization or similar -- quite\n* permissibly -- triggers different behavior.\n*/\ntemplate <typename T>\nconstexpr T WrappingAdd(T aX, T aY) {\n}\n\nnamespace detail {\n\ntemplate <typename T>\nstruct WrappingSubtractHelper {\nprivate:\nusing UnsignedT = typename MakeUnsigned<T>::Type;\n\npublic:\nMOZ_NO_SANITIZE_UNSIGNED_OVERFLOW\nstatic constexpr T compute(T aX, T aY) {\n}\n};\n\n} // namespace detail\n\n/**\n* Subtract two integers of the same type and return the result converted to\n* that type using wraparound semantics, without triggering overflow sanitizers.\n*\n* For N-bit unsigned integer types, this is equivalent to subtracting the two\n* numbers, then taking the result mod 2**N:\n*\n* WrappingSubtract(uint32_t(42), uint32_t(17)) is 29 (29 mod 2**32);\n* WrappingSubtract(uint8_t(5), uint8_t(20)) is 241 (-15 mod 2**8).\n*\n* Unsigned WrappingSubtract acts exactly like C++ unsigned subtraction.\n*\n* For N-bit signed integer types, this is equivalent to subtracting the two\n* numbers wrapped to unsigned, then wrapping the difference mod 2**N to the\n* signed range:\n*\n* WrappingSubtract(int16_t(32767), int16_t(-5)) is -32764 ((32772 mod 2**16)\n* - 2**16); WrappingSubtract(int8_t(-128), int8_t(127)) is 1 (-255 mod 2**8);\n* WrappingSubtract(int32_t(-17), int32_t(-42)) is 25 (25 mod 2**32).\n*\n* There's no equivalent to this operation in C++, as C++ signed subtraction\n* that overflows has undefined behavior. But it's how such subtraction *tends*\n* to behave with most compilers, unless an optimization or similar -- quite\n* permissibly -- triggers different behavior.\n*/\ntemplate <typename T>\nconstexpr T WrappingSubtract(T aX, T aY) {\nreturn detail::WrappingSubtractHelper<T>::compute(aX, aY);\n}\n\nnamespace detail {\n\ntemplate <typename T>\nstruct WrappingMultiplyHelper {\nprivate:\nusing UnsignedT = typename MakeUnsigned<T>::Type;\n\npublic:\nMOZ_NO_SANITIZE_UNSIGNED_OVERFLOW\nstatic constexpr T compute(T aX, T aY) {\n// Begin with |1U| to ensure the overall operation chain is never promoted\n// to signed integer operations that might have *signed* integer overflow.\nstatic_cast<UnsignedT>(aY)));\n}\n};\n\n} // namespace detail\n\n/**\n* Multiply two integers of the same type and return the result converted to\n* that type using wraparound semantics, without triggering overflow sanitizers.\n*\n* For N-bit unsigned integer types, this is equivalent to multiplying the two\n* numbers, then taking the result mod 2**N:\n*\n* WrappingMultiply(uint32_t(42), uint32_t(17)) is 714 (714 mod 2**32);\n* WrappingMultiply(uint8_t(16), uint8_t(24)) is 128 (384 mod 2**8);\n* WrappingMultiply(uint16_t(3), uint16_t(32768)) is 32768 (98304 mod 2*16).\n*\n* Unsigned WrappingMultiply is *not* identical to C++ multiplication: with most\n* compilers, in rare cases uint16_t*uint16_t can invoke *signed* integer\n* overflow having undefined behavior! http://kqueue.org/blog/2013/09/17/cltq/\n* has the grody details. (Some compilers do this for uint32_t, not uint16_t.)\n* So it's especially important to use WrappingMultiply for wraparound math with\n* uint16_t. That quirk aside, this function acts like you *thought* C++\n* unsigned multiplication always worked.\n*\n* For N-bit signed integer types, this is equivalent to multiplying the two\n* numbers wrapped to unsigned, then wrapping the product mod 2**N to the signed\n* range:\n*\n* WrappingMultiply(int16_t(-456), int16_t(123)) is\n* 9448 ((-56088 mod 2**16) + 2**16);\n* WrappingMultiply(int32_t(-7), int32_t(-9)) is 63 (63 mod 2**32);\n* WrappingMultiply(int8_t(16), int8_t(24)) is -128 ((384 mod 2**8) - 2**8);\n* WrappingMultiply(int8_t(16), int8_t(255)) is -16 ((4080 mod 2**8) - 2**8).\n*\n* There's no equivalent to this operation in C++, as C++ signed\n* multiplication that overflows has undefined behavior. But it's how such\n* multiplication *tends* to behave with most compilers, unless an optimization\n* or similar -- quite permissibly -- triggers different behavior.\n*/\ntemplate <typename T>\nconstexpr T WrappingMultiply(T aX, T aY) {\nreturn detail::WrappingMultiplyHelper<T>::compute(aX, aY);\n}\n\n// The |mozilla::Wrapping*| functions are constexpr. Unfortunately, MSVC warns\n// about well-defined unsigned integer overflows that may occur within the\n// constexpr math.\n//\n// https://msdn.microsoft.com/en-us/library/4kze989h.aspx (C4307)\n// https://developercommunity.visualstudio.com/content/problem/211134/unsigned-integer-overflows-in-constexpr-functionsa.html\n// (bug report)\n//\n// So we need a way to suppress these warnings. Unfortunately, the warnings are\n// issued at the very top of the `constexpr` chain, which is often some\n// distance from the triggering Wrapping*() operation. So we can't suppress\n// them within this file. Instead, callers have to do it with these macros.\n//\n// If/when MSVC fix this bug, we should remove these macros.\n#ifdef _MSC_VER\n# define MOZ_PUSH_DISABLE_INTEGRAL_CONSTANT_OVERFLOW_WARNING \\\n__pragma(warning(push)) __pragma(warning(disable : 4307))\n# define MOZ_POP_DISABLE_INTEGRAL_CONSTANT_OVERFLOW_WARNING \\\n__pragma(warning(pop))\n#else\n# define MOZ_PUSH_DISABLE_INTEGRAL_CONSTANT_OVERFLOW_WARNING\n# define MOZ_POP_DISABLE_INTEGRAL_CONSTANT_OVERFLOW_WARNING\n#endif\n\n} /* namespace mozilla */\n\n#endif /* mozilla_WrappingOperations_h */" ]

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[ "Figure me out\n\nPurpose\n\nThis unit is designed to engage your class for at least the first week of the school year. It provides students an opportunity to work collaboratively and independently on challenging mathematical tasks. It also provides you, their teacher, with opportunities for you to learn about their current level of achievement.\n\nSpecific Learning Outcomes\n• Find whether a given whole number is prime or non-prime (composite) and whether the number is a multiple of three.\n• Use exponents, square roots, factorials and place value to write expressions for whole numbers.\n• Represent category data using bar charts and interpret those charts.\n• Calculate common measures of centrality; mean, median and mode.\n• Use scale maps to identify the distance from home to school.\n• Represent a three-dimensional object using plan views.\n• Use trials and theoretical models (tables, tree diagrams) to estimate or find the probability of an event.\nDescription of Mathematics\n\nThe mathematics in the unit is varied. However, there is a general requirement for students to think multiplicatively rather than additively. The shift from additive thinking to integration of additive and multiplicative thinking is an essential requirement at Level 4. Indicators of multiplicative thinking are:\n\n• Use of the properties of whole numbers under multiplication and division, which includes:\n• The distributive property, e.g. 6 x 24 = 6 x 20 + 6 x 2 so 144 ÷ 6 = 120 ÷ 6 + 24 ÷ 6;\n• The associative property, e.g. 15 x 36 = 3 x 5 x 36 = 3 x 180;\n• Inverse, e.g. If 57 ÷ 3 = 19 then 19 x 3 = 57.\n• Integration of all four operations accepting the conventional order of operations:\n12 + 7 x 16 - 20 = 12 + 112 – 20 = 104\n• Flexibility with the use of notation for powers (using exponents), square roots and factorials (roots are also powers), and multiplication and division by decimals:\n• 2.4 ÷ 0.6 = 24/10 ÷ 6/10 = 4\n• 5! = 1 x 2 x 3 x 4 x 5 = 120\n• 63 = 6 x 6 x 6 = 216\n\nSpecific Teaching Points\n\nMost of the tasks are open ended so students can operate at a level that suits them. Encourage students to experiment with expressions are much as possible rather than operate ‘in the known’.\n\nAlso encourage students to work in systematic ways. To identify an unknown student which clues will be most useful? Why?\n\nHow will students check to see that they have identified the correct student? What will they do when several students have similar data?\n\nRequired Resource Materials\n• Calculators\n• Connecting cubes\n• Copymasters, PowerPoint and animation as listed below.\nActivity\n\nPrior Experience\n\nIt is expected that you will use the sessions to informally assess students' mathematical skills and understanding at the start of the school year.\n\nSession One\n\nWelcome your new class to the first week of mathematics adventures. The goal by the end of the week is to provide a classmate with a poster that will allow them to figure out who you are and learn a bit about you. That is why the name of the unit is Figure me out. A template to fill in is included as Copymaster One.\n\nToday you will add two clues to your poster; Your gender and your date of birth. That sounds easy!\n\nFor your gender, you might put male or female. That would be a little easy for the person getting your poster. Look at the poster template which has a key; If a prime then male, if a multiple of three then female.\n\nSuppose a classmate wrote 51 in that space. Would they be male or female?\n\nSome students may know what prime numbers are. You may need to research primes to find they are whole numbers with only two factors, one and themselves. Is this person male?\n\nAnother way to look at the problem is to find if two factors, other than 1 x 51, have a product of 51. Let the students discuss this in pairs. Talk about the ways to check if 51 is prime:\n\n• Get 51 objects and try to sort them into equal sets. That would work but could be time consuming.\n• Go through a times table chart to see if 51 occurs in the products. Why won’t that strategy always work? (51 will not show if a factor is greater than 10 or 12)\n• Use a calculator. Nice idea but how do you use the calculator? You could try all the multiples of two, then three, then four, etc. That would take a long time. Maybe a student might suggest using division. 51 ÷ 2 = 25.5, there is a remainder (0.5) so two is not a factor of 51. But 51 ÷ 3 = 17, there is no remainder so 3 x 17 = 51. Therefore, the person is a female.\n\nSuppose a classmate wrote 47 in that space. Would they be male or female?\n\nLet the students work in pairs to establish if the person is male or female. 47 is prime so the person is male.\n\nIf students are not familiar with divisibility by three you may want to use elements from the Nines and threes activity from the Numeracy Project series.\n\nIn the case of a student who does not want to identify with being either male or female, they could choose a number that is neither prime nor a multiple of three.\n\nInvite the students to make their first entry onto their poster in the Gender space. The number they put must be greater than twenty but less than 100, so the problem is sufficiently challenging. Allow students to use calculators to test out numbers they select. This time provides opportunities to assess students’ multiplicative thinking.\n\n• Do they need materials, like counters, to check for factors?\n• Do they use division rather than trial and error to find if a number has factors?\n• Do they recognise that a decimal in the quotient (division answer) indicates a remainder?\n• Do they know that a remainder indicates non-divisibility?\n\nAt this point you need to create a spreadsheet to gather data on your students. You may have a speedy typist in the room who can enter the data or rely on each student to enter their own. The spreadsheet will be needed on the final day of the unit.\n\nMove on to date of birth. If a person wrote 12/04/11 for their DoB, what would that mean? Students should recognise that the date of birth (DoB) is 12th of April, 2011. Taking out the forwards slashes this date of birth can be written as 120411.\n\nHowever, on their poster a person might write one of the following:\n\n• (12 x 10 000) + (4 x 100) + 11\n• 3332 + 9 522   or (310 x 2) – 38 – 36 – 397\n• (9! ÷ 3) – 549\n\nDiscuss what each expression means. Important points are:\n\nWhat is 10 x 10 000? (100 000)? So what is 12 x 10 000? (120 000)\n\nWhat does 3332 mean? (333 x 333 or two 333’s multiplied together) You may need to show simpler examples of exponents like 42, 53 and 24.\n\nWhat does 9! Mean? (9 factorial which is the product of 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9)\n\nGet the students to use calculators to check that each expression equals 120411. Watch for students to recognise the importance of order of the operations. The expressions included parentheses (brackets) that are unnecessary since exponents precede multiplication and division in operational order, that precede addition and subtraction.\n\nLet the students come up with an expression that calculates to their DoB and add it to their poster. Encourage them to be creative and use some of the ideas in the three expressions shown. Also update the spreadsheet with the dates of birth.\n\nFor early finishers pose this problem:\n\nA classmate gives you this clue for their date of birth. Use your calculator and research skills to find their DoB.\n\n310 + 76 – (2 x 55) + 43          (59049 + 117 649 – 6250 + 64 = 170512)\n\nSession Two\n\nIn this session students develop two more expressions for the number of people that normally live in their household and the distance of their home from school. Begin with the PowerPoint. Slide One shows a graph.\n\nNumber of People per Household\n\nThis graph is something about the homes that students in another class live.\n\nWhat might the graph be about? Can we give the graph a title and some labels?\n\nLet your students discuss what the graph might be about. It is likely they will realise that the size of the numbers prohibits ideas like letter box numbers or number of cars. The graph is about number of people that normally live in each student’s house. Discuss a good graph title and labels for the axes. Slide Two provides those additions. The vertical axis shows frequency, the number of data items in each category. Ensure students know this.\n\nWhat is the average number of people that live in Room 5 households?\n\nThe term average refers to some measure of middle or centrality. Common averages are the mean, median and mode. The mode for Room 5 data is four, the category with the highest frequency.\n\nHow might we find the median? (The median is the middle number of people)\n\nYou could mimic writing all the data points in the graph, like this:\n\n3, 3, 3, 3, 3, 4, 4, 4, 4, ….6, 6, 6, 7, 7, 8+\n\nHow will we find the middle number? You could count in one point at a time from the top and bottom. However, halving the total number of data points will give the location of the median. There are 5 + 9 + 7 + 3 + 2 + 1 = 27 data points (adding the frequencies) and 27 ÷ 2 = 13.5 so median lies between the 13th and 14th data points. Both points lie in the bar for a four-person household, so the median is four.\n\nHow might we find the mean? (The total of all data points divided by the number of data points)\n\nIt is easier to calculate the total of scores as (5 x 3) + (9 x 4) + (7 x 5) + (3 x 6) + (2 x 7) + 8 = 126. Note that 8+ is treated as eight though the actual number might be more than eight. 126 ÷ 27 = 4.66…\n\nTherefore, the mean is closer to five people per household which reflects the fact that the median was ‘at the end’ of the four-person bar. It is interesting to reflect on the fact that two-thirds of a person is a mathematical rather than real idea, but it does suggest the centre is closer to five people than four.\n\nUse post-it notes to gather the data for your class. Get each student to provide data about how many people normally reside in their household and write the number on a note. You may need to discuss ‘normally’ as some households are very transient. Get students to organise the notes into a bar graph on the whiteboard. Find the mode, median and mean for your class and compare the distributions.\n\nFinally, ask students to add to their poster by giving a clue about the number of people who live in their household. Encourage them to use what they have learned about averages.\n\nFor example, if five people live in a student’s household their clue might be:\n\n“The median of 2, 9, 5, 8, 6, 3 and 4”, or “The mean of 1, 3, 4, 7, 7, 8.”\n\nDistance from home to school\n\nIn the second part of the lesson students provide a clue about the location of their home. To do so they need to work out the distance of their home from school. Provide students with a photocopied map of the local area complete with scale. This video gives an example of working out the distance between home and school. Useful questions are:\n\nHow far is 53mm in real life? (53 ÷ 32 = 1.67 and 1.67 x 200 = 334 metres in real life)\n\nHow far do you estimate the total journey is from Kiwi Street to Hilltop School? (1.6 – 1.8 kilometres)\n\nNote that metres can be converted into kilometres. Do your students know how to do that?\n\nOnce students have established their home to school distance ask them to create an expression for that section of the poster. For example, 1.6 kilometres might be written as 402 metres or as 4002 cm\n\nSession Two allows you opportunity to assess students’ understanding of the following concepts:\n\n• Multiplicative thinking – Do they apply multiplication and division to find averages and create expressions?\n• Proportional thinking – Do they apply scale correctly to work out distances from a map?\n• Measurement system – Do they convert easily among metres, kilometres, and centimetres?\n\nSession Three\n\nIn this session student investigate two more clues to add to their poster. First, they investigate the Scrabble total for their Christian name. Then they create a personal icon, a sculpture made from connecting cubes. The icon will be used in Session Five to check that the person you identify is who you think they are.\n\nScrabble Total\n\nIntroduce the famous ‘four fours’ puzzle. In the puzzle you find expressions that contain four fours, with any number and operation symbols you like, that represent the numbers 0-100. For example, 4 + 4 – 4 – 4 is an expression for zero and 44 ÷ 4 + 4 = 15. Remind the students that the rules for order of operations must be followed, e.g. multiplication and division before addition and subtraction. Students also need to recognise that division can be expressed as fraction notation, e.g. 44/4 = 44 ÷ 4 = 11.\n\nChallenge your students to come up with four fours expressions for the numbers 1-10. Expect answers like:", null, "The options for ten bring out an interesting idea about dividing by decimals. Four divided by point four (4 ÷ .4) is equivalent to asking, “How many lots of four tenths fit into four?” Since four equals forty tenths the question can be reworded as, “How many lots of four tenths fit into forty tenths?” Note that the quotient is ten which is larger than the dividend of four. This will be challenging for many students.\n\nCopymaster Two contains a graphic of the tiles used in the game of Scrabble. Some students may not be aware of how the game is played so you might use the paper version to show them. Ask the students to create their Christian name with scrabble tiles. Here are some examples:", null, "Zoe’s letter score is 10 + 1 + 1 = 12. Hinemoa has a letter score of 4 + 1 + 1 + 1 + 1 + 1 + 1 = 10 and Kevin has a score of 5 + 1 + 4 + 1 + 1 = 12.\n\nOnce they make their name students need to create a ‘four fours’ clue for that total to put on their poster. For example, Kevin or Zoe might write (44 + 4) ÷ 4.\n\nThe Icon\n\nGive each student 15 connecting cubes to create their personal sculpture. The icon must be able to sit on a desk, so it can be drawn from different viewpoints, and it must be asymmetrical. You may need to discuss with students that an asymmetrical figure has no symmetry, either reflective or rotational. Copymaster Three has three views of an icon. Invite the students to recreate this icon with their cubes. Look to see if your students:\n\n• Start with one view and create an icon that fits.\n• Move progressively to the two other views, one at a time, to create an icon that works.\n\nNext, ask your students to form their own asymmetric icon and draw three different views of it for their poster. The icon should be stored in a desk or tote tray, so it can be used in Session Five.\n\nSession Four\n\nIn this session students investigate a game involving chance. They play the game 25 times and express the result of their trial using a fraction, decimal or percentage.\n\nTo play the game ‘Odds and Evens’ students need a partner. One player wins if the product is odd. The other wins if the product is even. This is how play proceeds.\n\nEach player chooses a digit from 0-9, this could be by drawing numbers from a hat or by randomly picking a digit card. For example Player A chooses 3 and Player B chooses 9.\n\nThe players reveal their chosen digits and multiply the numbers, e.g. 3 x 9 = 27.\n\nIf the answer is odd, e.g. The ‘odd’ player wins, and the even player loses that round.\n\nAfter 25 games each player records their success rate on the poster as a fraction, decimal or percentage. For example, if the player was ‘odd’ they might get 10 out of 25 wins. 10/25 = 2/5 = 0.4 = 40%. They also record their raw score, e.g. 10, in the spreadsheet.\n\nAfter students have played the game gather the class.\n\nWhat did you notice as you played the game? (Students should notice that the ‘even’ person wins more than the ‘odd’ person)\n\nWhy does the ‘even’ person win more? (There are more possible outcomes that give an even product)\n\nHow might we find out the actual chances of an odd or even win?\n\nStudents may suggest that there are three events that might happen, odd x odd, even x odd, and even x even.\n\nWhat are the products of these events, even or odd? (odd x odd = odd, even x odd = even, and even x even = even). Students might conclude that the chances of an odd product are one out of three or one third. While some good reasoning is involved to get to this point the conjecture is incorrect since even x odd can occur in two different orders. To establish the probabilities students must consider all the outcomes that contribute to each event.\n\nOne way to do that is to create a model. Students may want to create a 10x10 array where they can work out which of the possible products are odd. There are 100 outcomes but only 25 of them produce odd products. The actual probability of getting an odd product is 25/100 = 1/4. Students that are more comfortable with the properties of numbers may identify that they only need to use a 2x2 array identifying whether each factor is odd or even.\n\nStudents might like to compare their success rate with the theoretical chances. They may observe that the results vary a lot from the predictions.\n\nFinish the lesson with a challenge:\n\nKeep the two players secretly entering two digits, possibly using two calcularors or digit cards. However, change the rules so the game is much fairer. How will you do that?\n\nStudents might suggest that the game will be more even if:\n\n• Player A wins when the product is less than 20. Player B wins if the product is 20 or more.\n• Player A wins when the product is the result of even and odd digits. Player B wins if the product is the result of odd x odd or even x even.\n• Add the digits instead of multiplying them. Player A wins if the sum is odd. Player B wins if the sum is even.\n\nSession Five\n\nIn this session students receive the poster of another student, solve the clues and identify the unknown student in their class. Students will need access to copies of the spreadsheet. Once they think they know who the person is they confirm the identity by matching the three views with the icon in the classmate’s desk or tote tray.\n\nDuring the session students might identify several students.\n\nYou might extend the unit by asking:\n\n• What else could we find out about each other?\n• How might we record clues about that information?\n\nLog in or register to create plans from your planning space that include this resource." ]

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[ "# Theory of relativity/Kerr–Newman metric\n\nThe spacetime metric is, in Boyer-Lindquist coordinates,\n\n$ds^{2}={\\frac {\\Delta ^{2}}{\\rho ^{2}}}(dct-a\\,\\sin ^{2}\\theta \\,d\\phi )^{2}-{\\frac {\\sin ^{2}\\theta }{\\rho ^{2}}}[(r^{2}+a^{2})d\\phi -a\\,dct]^{2}-{\\frac {\\rho ^{2}}{\\Delta ^{2}}}dr^{2}-\\rho ^{2}d\\theta ^{2}$", null, "where\n\n$\\Delta ^{2}\\equiv a^{2}+r^{2}\\alpha$", null, "$\\alpha =1-{\\frac {2GM}{rc^{2}}}+{\\frac {e^{2}}{r^{2}}}$", null, "$\\rho ^{2}\\equiv r^{2}+a^{2}\\cos ^{2}\\theta$", null, "$a\\equiv {\\frac {J}{Mc}}$", null, "$e\\equiv {\\frac {\\sqrt {k_{e}G}}{c^{2}}}q$", null, "This represents the exact solution to General relativity/Einstein equations for the stress-energy tensor for an electromagnetic field from a charged rotating black hole. Defining three more functions of the coordinates\n\n$\\Sigma ^{2}\\equiv {\\sqrt {\\left(r^{2}+a^{2}\\right)^{2}-a^{2}\\Delta ^{2}\\sin ^{2}\\theta }}$", null, "$\\varpi \\equiv {\\frac {\\Sigma ^{2}}{\\rho }}\\sin \\theta$", null, "$\\omega \\equiv a{\\frac {\\left(r^{2}+a^{2}-\\Delta ^{2}\\right)}{\\Sigma ^{4}}}c$", null, "The solution can now be written\n\n$ds^{2}=\\left({\\frac {\\Delta ^{2}-a^{2}\\sin ^{2}\\theta }{\\rho ^{2}}}\\right)dct^{2}+2{\\frac {\\omega }{c}}\\varpi ^{2}dctd\\phi -\\varpi ^{2}d\\phi ^{2}-{\\frac {\\rho ^{2}}{\\Delta ^{2}}}dr^{2}-\\rho ^{2}d\\theta ^{2}$", null, "$ds$", null, "is an invariant line element, a measure of spacetime displacement between neighboring events. The displacement four vector between those events is $dx^{\\mu }$", null, ", and being a four-vector, would yield an invariant scalar for the inner product of it with itself using the metric tensor $g_{\\mu \\nu }$", null, "as a spacetime inner product operator as $g_{\\mu \\nu }dx^{\\mu }dx^{\\nu }$", null, ". We call that invariant scalar $ds^{2}$", null, ".\n\n$ds^{2}=g_{\\mu \\nu }dx^{\\mu }dx^{\\nu }$", null, "So though technically it is the set of elements$\\left[g_{\\mu \\nu }\\right]$", null, "that is the metric tensor, since its elements can be directly read off of this line element as the coefficients of the coordinate differentials, in jargon $ds^{2}$", null, "is often referred to as just \"the metric\". In the case that the charge $q$", null, "is zero it becomes an exact vacuum solution to Einstein's field equations and is called just \"the Kerr solution\".\n\n## Gravitational Red Shift Factor\n\nThe solution\n\n$ds^{2}=\\left({\\frac {\\Delta ^{2}-a^{2}\\sin ^{2}\\theta }{\\rho ^{2}}}\\right)dct^{2}+2{\\frac {\\omega }{c}}\\varpi ^{2}dctd\\phi -\\varpi ^{2}d\\phi ^{2}-{\\frac {\\rho ^{2}}{\\Delta ^{2}}}dr^{2}-\\rho ^{2}d\\theta ^{2}$", null, "may also be written as\n\n$ds^{2}=R^{2}dct^{2}-\\varpi ^{2}\\left(d\\phi -{\\frac {\\omega }{c}}dct\\right)^{2}-{\\frac {\\rho ^{2}}{\\Delta ^{2}}}dr^{2}-\\rho ^{2}d\\theta ^{2}$", null, "where\n\n$R\\equiv {\\sqrt {{\\frac {\\Delta ^{2}-a^{2}\\sin ^{2}\\theta }{\\rho ^{2}}}+{\\frac {\\omega ^{2}}{c^{2}}}\\varpi ^{2}}}$", null, "Lets say something neutral is equatorially orbiting in this spacetime with an angular velocity of $\\omega$", null, ", then in using the solution in describing its path through spacetime, or world line, the $\\left(d\\phi -{\\frac {\\omega }{c}}dct\\right)$", null, "term vanishes and it is said to be \"locally nonrotating\". If it emits according to its local free fall frame a frequency $f_{0}$", null, ", then the frequency received by a remote observer $f'$", null, "will be red shifted by\n\n$f'=Rf_{0}$", null, "## Mathematical Surfaces\n\nThere are three important mathematical surfaces for this line element, the static limit and the inner and outer event horizons. The static limit is the outermost place something can be outside the outer horizon with a zero angular velocity. It is\n\n$r_{s}={\\frac {GM}{c^{2}}}+{\\sqrt {\\left({\\frac {GM}{c^{2}}}\\right)^{2}-a^{2}\\cos ^{2}\\theta -e^{2}}}$", null, "The event horizons are coordinate singularities in the metric where $\\Delta =0$", null, ".\n\nThe outer event horizon is at\n\n$r_{+}={\\frac {GM}{c^{2}}}+{\\sqrt {\\left({\\frac {GM}{c^{2}}}\\right)^{2}-a^{2}-e^{2}}}$", null, "and the inner horizon is at\n\n$r_{-}={\\frac {GM}{c^{2}}}-{\\sqrt {\\left({\\frac {GM}{c^{2}}}\\right)^{2}-a^{2}-e^{2}}}$", null, "An external observer can never see an event at which something crosses into the outer horizon. A remote observer reckoning with these coordinates will reckon that it takes an infinite time for something infalling to reach the outer horizon even though it takes a finite proper time till the event according to what fell in.\n\n## Kerr-Newman Equatorial Geodesic Motion\n\nThe exact equations of equatorial geodesic motion for a neutral test mass in a charged and rotating black hole's spacetime are\n\n${\\frac {dt}{d\\tau }}={\\frac {\\gamma \\left(r^{2}+a^{2}+2a^{2}{\\frac {GM}{rc^{2}}}-a^{2}{\\frac {e^{2}}{r^{2}}}\\right)-{\\frac {al_{z}}{c}}\\left({\\frac {2GM}{rc^{2}}}-{\\frac {e^{2}}{r^{2}}}\\right)}{r^{2}-{\\frac {2GMr}{c^{2}}}+a^{2}+e^{2}}}$", null, "${\\frac {d\\phi }{d\\tau }}={\\frac {{\\frac {l_{z}}{c}}\\left(1-{\\frac {2GM}{rc^{2}}}+{\\frac {e^{2}}{r^{2}}}\\right)+\\gamma a\\left({\\frac {2GM}{rc^{2}}}-{\\frac {e^{2}}{r^{2}}}\\right)}{r^{2}-{\\frac {2GMr}{c^{2}}}+a^{2}+e^{2}}}c$", null, "${\\frac {1}{2}}\\left({\\frac {dr}{d\\tau }}\\right)^{2}+V_{eff}=0$", null, "$V_{eff}=-{\\frac {GM}{r}}+{\\frac {e^{2}c^{2}}{2r^{2}}}+{\\frac {1}{2}}{\\frac {l_{z}^{2}}{r^{2}}}+{\\frac {1}{2}}\\left(1-\\gamma ^{2}\\right)c^{2}\\left(1+{\\frac {a^{2}}{r^{2}}}\\right)-\\left({\\frac {GM}{r^{3}c^{2}}}-{\\frac {e^{2}}{2r^{4}}}\\right)\\left({\\frac {l_{z}}{c}}-a\\gamma \\right)^{2}c^{2}$", null, "where $\\gamma$", null, "is the conserved energy parameter, the energy per $mc^{2}$", null, "of the test mass and $l_{z}$", null, "is the conserved angular momentum per mass $m$", null, "for the test mass.\n\n## Kerr-Newman Polar Geodesic Motion\n\nThe exact equations of polar geodesic motion for a neutral test mass in a charged and rotating black hole's spacetime are\n\n${\\frac {dt}{d\\tau }}=\\gamma \\left({\\frac {a^{2}+r^{2}}{a^{2}+e^{2}+r^{2}-{\\frac {2GMr}{c^{2}}}}}\\right)$", null, "${\\frac {1}{2}}\\left({\\frac {dr}{d\\tau }}\\right)^{2}-{\\frac {{\\frac {GM}{rc^{2}}}-{\\frac {e^{2}}{2r^{2}}}}{1+{\\frac {a^{2}}{r^{2}}}}}c^{2}={\\frac {\\gamma ^{2}-1}{2}}c^{2}$", null, "where $\\gamma$", null, "is the conserved energy parameter, the energy per $mc^{2}$", null, "of the test mass.\n\n## Wormhole Structure", null, "Penrose diagram for coordinate extension of a charged or rotating black hole\n\nAbove we see a Penrose diagram representing a coordinate extension (1) for a charged or rotating black hole. The same way as mapping Schwarzschild coordinates onto Kruskal-Szekeres coordinate reveals two seperate external regions for the Schwarzschild black hole, such a mapping done for a charged or rotating hole reveals an even more multiply connected region for charged and rotating black holes. Lets say region I represents our external region outside a charged black hole. In the same way that the other external region is inaccesible as the wormhole connection is not transversible, external region II is also not accessible from region I. The difference is that there are other external regions VII and VIII which are ideed accesible from region I by transversible paths at least one way. One should expect this as the radial movement case of geodesic motion for a neutral test particle written above leads back out of the hole without intersecting the physical singularity at $r=0,\\theta ={\\frac {\\pi }{2}}$", null, "." ]

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[ "# Question:Use This Information To Answer Questions 3, 4, And 5: The Equilibrium Constant (K) Of The Reaction Below Is K = 6.0 X 102, With Initial Concentrations As Follows: H2 1.0 X 102 M, [N2] = 4.0 M, And [NH3] = 1.0 X 104 M. 2NH3(g) N2(g)3H2 (g) 3. Consider The Chemical Reaction: N2 +3H2 Yiel Ds 2NH3. If The Concentration Of The Reactant H2 Was Increased From ...\n\n## This problem has been solved!", null, "" ]

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[ "# tunablePID\n\nTunable PID controller\n\n## Syntax\n\n```blk = tunablePID(name,type) blk = tunablePID(name,type,Ts) blk = tunablePID(name,sys) ```\n\n## Description\n\nModel object for creating tunable one-degree-of-freedom PID controllers. `tunablePID` lets you parametrize a tunable SISO PID controller for parameter studies or for automatic tuning with tuning commands such as `systune`, `looptune`, or the Robust Control Toolbox™ command, `hinfstruct`.\n\n`tunablePID` is part of the family of parametric Control Design Blocks. Other parametric Control Design Blocks include `tunableGain`, `tunableSS`, and `tunableTF`.\n\n## Construction\n\n`blk = tunablePID(name,type)` creates the one-degree-of-freedom continuous-time PID controller:\n\n`$blk={K}_{p}+\\frac{{K}_{i}}{s}+\\frac{{K}_{d}s}{1+{T}_{f}s},$`\n\nwith tunable parameters `Kp`, `Ki`, `Kd`, and `Tf`. The `type` argument sets the controller type by fixing some of these values to zero (see Input Arguments).\n\n`blk = tunablePID(name,type,Ts)` creates a discrete-time PID controller with sample time `Ts`:\n\n`$blk={K}_{p}+{K}_{i}IF\\left(z\\right)+\\frac{{K}_{d}}{{T}_{f}+DF\\left(z\\right)},$`\n\nwhere IF(z) and DF(z) are the discrete integrator formulas for the integral and derivative terms, respectively. The values of the `IFormula` and `DFormula` properties set the discrete integrator formulas (see Properties).\n\n`blk = tunablePID(name,sys)` uses the dynamic system model, `sys`, to set the sample time, `Ts`, and the initial values of the parameters `Kp`, `Ki`, `Kd`, and `Tf`.\n\n### Input Arguments\n\n`name`\n\nPID controller `Name`, specified as a character vector such as `'C'` or `'PI1'`. (See Properties.)\n\n`type`\n\nController type, specified as one of the values in the following table. Specifying a controller type fixes up to three of the PID controller parameters.\n\nValue for `type`Controller TypeEffect on PID Parameters\n`'P'`Proportional only`Ki` and `Kd` are fixed to zero; `Tf` is fixed to 1; `Kp` is free\n`'PI'`Proportional-integral`Kd` is fixed to zero; `Tf` is fixed to 1; `Kp` and `Ki` are free\n`'PD'`Proportional-derivative with first-order filter on derivative action`Ki` is fixed to zero; `Kp`, `Kd`, and `Tf` are free\n`'PID'`Proportional-integral-derivative with first-order filter on derivative action`Kp`, `Ki`, `Kd`, and `Tf` are free\n\n`Ts`\n\nSample time, specified as a scalar.\n\n`sys`\n\nDynamic system model representing a PID controller.\n\n## Properties\n\n`Kp, Ki, Kd, Tf`\n\nParametrization of the PID gains `Kp`, `Ki`, `Kd`, and filter time constant `Tf` of the tunable PID controller `blk`.\n\nThe following fields of `blk.Kp`, `blk.Ki`, `blk.Kd`, and `blk.Tf` are used when you tune `blk` using a tuning command such as `systune`:\n\nFieldDescription\n`Value`Current value of the parameter.\n`Free`\n\nLogical value determining whether the parameter is fixed or tunable. For example,\n\n• If `blk.Kp.Free = 1`, then `blk.Kp.Value` is tunable.\n\n• If `blk.Kp.Free = 0`, then `blk.Kp.Value` is fixed.\n\n`Minimum`\n\nMinimum value of the parameter. This property places a lower bound on the tuned value of the parameter. For example, setting ```blk.Kp.Minimum = 0``` ensures that `Kp` remains positive.\n\n`blk.Tf.Minimum` must always be positive.\n\n`Maximum`Maximum value of the parameter. This property places an upper bound on the tuned value of the parameter. For example, setting ```blk.Tf.Maximum = 100``` ensures that the filter time constant does not exceed 100.\n\n`blk.Kp`, `blk.Ki`, `blk.Kd`, and `blk.Tf` are `param.Continuous` objects. For general information about the properties of these `param.Continuous` objects, see the `param.Continuous` (Simulink Design Optimization) object reference page.\n\n`IFormula, DFormula`\n\nDiscrete integrator formulas IF(z) and DF(z) for the integral and derivative terms, respectively, specified as one of the values in the following table.\n\nValueIF(z) or DF(z) Formula\n`'ForwardEuler'`\n\n`$\\frac{{T}_{s}}{z-1}$`\n\n`'BackwardEuler'`\n\n`$\\frac{{T}_{s}z}{z-1}$`\n\n`'Trapezoidal'`\n\n`$\\frac{{T}_{s}}{2}\\frac{z+1}{z-1}$`\n\nDefault: `'ForwardEuler'`\n\n`Ts`\n\nSample time. For continuous-time models, `Ts = 0`. For discrete-time models, `Ts` is a positive scalar representing the sampling period. This value is expressed in the unit specified by the `TimeUnit` property of the model. Unspecified sample time (`Ts = -1`) is not supported for PID blocks.\n\nChanging this property does not discretize or resample the model.\n\nDefault: `0` (continuous time)\n\n`TimeUnit`\n\nUnits for the time variable, the sample time `Ts`, and any time delays in the model, specified as one of the following values:\n\n• `'nanoseconds'`\n\n• `'microseconds'`\n\n• `'milliseconds'`\n\n• `'seconds'`\n\n• `'minutes'`\n\n• `'hours'`\n\n• `'days'`\n\n• `'weeks'`\n\n• `'months'`\n\n• `'years'`\n\nChanging this property has no effect on other properties, and therefore changes the overall system behavior. Use `chgTimeUnit` to convert between time units without modifying system behavior.\n\nDefault: `'seconds'`\n\n`InputName`\n\nInput channel name, specified as a character vector. Use this property to name the input channel of the controller model. For example, assign the name `error` to the input of a controller model `C` as follows.\n\n`C.InputName = 'error';`\n\nYou can use the shorthand notation `u` to refer to the `InputName` property. For example, `C.u` is equivalent to `C.InputName`.\n\nInput channel names have several uses, including:\n\n• Identifying channels on model display and plots\n\n• Specifying connection points when interconnecting models\n\nDefault: Empty character vector, `''`\n\n`InputUnit`\n\nInput channel units, specified as a character vector. Use this property to track input signal units. For example, assign the concentration units `mol/m^3` to the input of a controller model `C` as follows.\n\n`C.InputUnit = 'mol/m^3';`\n\n`InputUnit` has no effect on system behavior.\n\nDefault: Empty character vector, `''`\n\n`InputGroup`\n\nInput channel groups. This property is not needed for PID controller models.\n\nDefault: `struct` with no fields\n\n`OutputName`\n\nOutput channel name, specified as a character vector. Use this property to name the output channel of the controller model. For example, assign the name `control` to the output of a controller model `C` as follows.\n\n`C.OutputName = 'control';`\n\nYou can use the shorthand notation `y` to refer to the `OutputName` property. For example, `C.y` is equivalent to `C.OutputName`.\n\nInput channel names have several uses, including:\n\n• Identifying channels on model display and plots\n\n• Specifying connection points when interconnecting models\n\nDefault: Empty character vector, `''`\n\n`OutputUnit`\n\nOutput channel units, specified as a character vector. Use this property to track output signal units. For example, assign the unit `Volts` to the output of a controller model `C` as follows.\n\n`C.OutputUnit = 'Volts';`\n\n`OutputUnit` has no effect on system behavior.\n\nDefault: Empty character vector, `''`\n\n`OutputGroup`\n\nOutput channel groups. This property is not needed for PID controller models.\n\nDefault: `struct` with no fields\n\n`Name`\n\nSystem name, specified as a character vector. For example, `'system_1'`.\n\nDefault: `''`\n\n`Notes`\n\nAny text that you want to associate with the system, stored as a string or a cell array of character vectors. The property stores whichever data type you provide. For instance, if `sys1` and `sys2` are dynamic system models, you can set their `Notes` properties as follows:\n\n```sys1.Notes = \"sys1 has a string.\"; sys2.Notes = 'sys2 has a character vector.'; sys1.Notes sys2.Notes```\n```ans = \"sys1 has a string.\" ans = 'sys2 has a character vector.' ```\n\nDefault: `[0×1 string]`\n\n`UserData`\n\nAny type of data you want to associate with system, specified as any MATLAB® data type.\n\nDefault: `[]`\n\n## Examples\n\nTunable Controller with a Fixed Parameter\n\nCreate a tunable PD controller. Then, initialize the parameter values, and fix the filter time constant.\n\n```blk = tunablePID('pdblock','PD'); blk.Kp.Value = 4; % initialize Kp to 4 blk.Kd.Value = 0.7; % initialize Kd to 0.7 blk.Tf.Value = 0.01; % set parameter Tf to 0.01 blk.Tf.Free = false; % fix parameter Tf to this value blk```\n```blk = Parametric continuous-time PID controller \"pdblock\" with formula: s Kp + Kd * -------- Tf*s+1 and tunable parameters Kp, Kd. Type \"pid(blk)\" to see the current value and \"get(blk)\" to see all properties. ```\n\nController Initialized by Dynamic System Model\n\nCreate a tunable discrete-time PI controller. Use a `pid` object to initialize the parameters and other properties.\n\n```C = pid(5,2.2,'Ts',0.1,'IFormula','BackwardEuler'); blk = tunablePID('piblock',C)```\n```blk = Parametric discrete-time PID controller \"piblock\" with formula: Ts*z Kp + Ki * ------ z-1 and tunable parameters Kp, Ki. Type \"pid(blk)\" to see the current value and \"get(blk)\" to see all properties. ```\n\n`blk` takes the value of properties, such as `Ts` and `IFormula`, from `C`.\n\nController with Named Input and Output\n\nCreate a tunable PID controller, and assign names to the input and output.\n\n```blk = tunablePID('pidblock','pid') blk.InputName = {'error'} % assign input name blk.OutputName = {'control'} % assign output name```\n\n## Tips\n\n• You can modify the PID structure by fixing or freeing any of the parameters `Kp`, `Ki`, `Kd`, and `Tf`. For example, `blk.Tf.Free = false` fixes `Tf` to its current value.\n\n• To convert a `tunablePID` parametric model to a numeric (nontunable) model object, use model commands such as `pid`, `pidstd`, `tf`, or `ss`. You can also use `getValue` to obtain the current value of a tunable model.\n\n## Version History\n\nIntroduced in R2016a\n\nexpand all\n\nBehavior changed in R2016a" ]

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[ "```// this is an example of how i can create a\n// function to make my draw() loop look much simpler\n// ie: instead of having 3 for loops in draw() - one for each shape\n// i've put the instructions for drawing the shape in an easy-to-use\n// function .. twister()\n// - [email protected]\n\nvoid setup() {\nsmooth();\nsize(400,400);\nbackground(100);\nnoFill();\nnoLoop();\n}\n\nvoid draw() {\nbackground(100);\nstroke(255,0,0); //red\ntwister(200,200,50,50);\nstroke(0,0,255); //blue\ntwister(100,150,80,50);\nstroke(#FF15B9); //pink\ntwister(200,200,90,10);\n\n}\n\n// below, i'm declaring my function...\n// i use void because it doesn't actually RETURN anything\n// and i specify each of the arguments that will be given\n// when it's called in the draw() loop above\n\n// this function makes a spirograph-looking shape\n// hub specifies the spacing of the elements\n// size is the size of the individual rectangles that make up the shape\nvoid twister(int x, int y, int hub, int size) {\nfor (int i=1; i<=36; i++) {\n// i'm using a for loop to create a repetitive spiro-shape\npushMatrix();\ntranslate(x,y);" ]

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[ "", null, "# Math Subtraction Worksheets For 1st Grade\n\nStudents use the 3 numbers provided to write 4 different families. Our grade 1 subtraction worksheets provide practice in solving basic subtraction problems exercises begin with simple subtraction facts using pictures or number lines followed by 1 digit subtraction facts and and progress to subtraction of 2 digit numbers in columns and subtraction word problems.", null, "1st Grade Subtraction Worksheets Free Printables Education Com Subtraction Worksheets Addition And Subtraction Worksheets Math Subtraction\n\n### Both one and two digit numbers are covered in this printable addition and subtraction worksheet.", null, "Math subtraction worksheets for 1st grade. Mixed operation addition and subtraction worksheets first grade math packet this handy packet provides practice adding and subtracting within 100 as well as word problems and cut and paste math puzzles. Both addition and subtraction skills are tested here. Practice using fact families with numbers from 1 to 10 in this printable math worksheet.\n\nA list of free printable math worksheets for first grade subtraction including single digit subtraction missing numbers subtracting whole tens and double digit subtraction without borrowing. Finish the fact families. As students become more fluent they can try subtraction with larger numbers and move from no regrouping to the regrouping worksheets.\n\nYour first graders will explore subtraction with the help of manipulatives number lines simple equations and even word problems. Grade 1 subtraction worksheets. These subtraction worksheets are made for first graders who are ready to move on to a little more challenging subtraction math facts practice.\n\nWorksheets math grade 1 subtraction. Turtle tracker worksheets 1 10 students will practice mental math when they identify sums and differences within twenty. Randomly generated you can print from your browser.\n\nTurtle tracker worksheets 11 20 students will improve their. Our library of first grade subtraction worksheets and printables offers your students an opportunity to strengthen and challenge their math skills with interactive engaging activities.", null, "", null, "", null, "", null, "1st Grade Math Worksheets Best Coloring Pages For Kids Math Addition Worksheets Free Printable Math Worksheets Kindergarten Math Worksheets Free", null, "Pin On New Zealand Mathematics", null, "First Grade Subtraction Timed Tests Subtraction Within 10 Math Fact Fluency Subtraction Worksheets Kindergarten Subtraction Worksheets Subtraction", null, "", null, "First Grade Math Subtraction Worksheets Math Worksheets First Grade Math Worksheets Free Math Worksheets", null, "", null, "", null, "", null, "", null, "", null, "", null, "Spring Math Worksheets For First Grade Spring Subtraction Fun Math Worksheets First Grade Math Worksheets Math Worksheets", null, "Free Addition And Subtraction Worksheets For Kids Basic Math Worksheets Addition And Subtraction Worksheets 1st Grade Math Worksheets", null, "1st Grade Math Worksheets Best Coloring Pages For Kids First Grade Math Worksheets Printable Math Worksheets 1st Grade Math Worksheets", null, "Pin By Allison Mayes On Free Printables Resources Pre Kinder 1st Reading Writing Math Abc S Numbers Kindergarten Subtraction Worksheets Math Subtraction Worksheets Subtraction Kindergarten", null, "Worksheetfun Free Printable Worksheets Kindergarten Addition Worksheets 1st Grade Math Worksheets Kindergarten Subtraction Worksheets", null, "Previous post Algebra Word Problems Worksheet With Answers Pdf", null, "Next post Printable Math Worksheets Grade 2 Addition" ]

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[ "# Hands-on ActivityCreate a Safe Bungee Cord for Washy!\n\n### Quick Look\n\nTime Required: 1 hours 30 minutes\n\n(can be split into two 45-minute sessions)\n\nExpendable Cost/Group: US \\$1.00\n\n(Cost is for washers, which are reusable)\n\nGroup Size: 4\n\nActivity Dependency: None\n\nSubject Areas: Data Analysis and Probability, Physical Science, Physics\n\nNGSS Performance Expectations:", null, "### Summary\n\nStudents learn about the role engineers and mathematicians play in developing the perfect bungee cord length by simulating and experimenting with bungee jumping using washers and rubber bands. Working as if they are engineers for a (hypothetical) amusement park, students are challenged to develop a show-stopping bungee jumping ride that is safe. To do this, they must find the maximum length of the bungee cord that permits jumpers (such as brave Washy!) to get as close to the ground as possible without going \"splat\"! This requires them to learn about force and displacement and run an experiment. Student teams collect and plot displacement data and calculate the slope, linear equation of the line of best fit and spring constant using Hooke's law. Students make hypotheses, interpret scatter plots looking for correlations, and consider possible sources of error. An activity worksheet, pre/post quizzes and a PowerPoint® presentation are included.\nThis engineering curriculum aligns to Next Generation Science Standards (NGSS).\n\n### Engineering Connection\n\nEngineers are involved in the planning and design of large and complex construction projects such as airports, tunnels, bridges, skyscrapers and amusement parks, and must effectively interact with and articulate their ideas and designs to other engineers. When designing a bungee cord ride, engineers must have a full understanding of the forces and materials as well as the concepts of overextension and tension in order to create designs that guarantee jumper safety. In this activity, students play the role of engineers as they design and test simulated bungee cords. Like engineers who put their math skills to work in their designs, students use linear equations when making plans for bungee cord use.\n\n### Learning Objectives\n\nAfter this activity, students should be able to:\n\n• Utilize collected data to construct scatter plots and find lines of best fit.\n• Calculate the slope of a line, linear equation and spring constant (using Hooke's law).\n• Measure displacement.\n\n### Educational Standards Each TeachEngineering lesson or activity is correlated to one or more K-12 science, technology, engineering or math (STEM) educational standards. All 100,000+ K-12 STEM standards covered in TeachEngineering are collected, maintained and packaged by the Achievement Standards Network (ASN), a project of D2L (www.achievementstandards.org). In the ASN, standards are hierarchically structured: first by source; e.g., by state; within source by type; e.g., science or mathematics; within type by subtype, then by grade, etc.\n\n###### NGSS: Next Generation Science Standards - Science\nNGSS Performance Expectation\n\nMS-ETS1-4. Develop a model to generate data for iterative testing and modification of a proposed object, tool, or process such that an optimal design can be achieved. (Grades 6 - 8)\n\nDo you agree with this alignment?\n\nClick to view other curriculum aligned to this Performance Expectation\nThis activity focuses on the following Three Dimensional Learning aspects of NGSS:\nScience & Engineering Practices Disciplinary Core Ideas Crosscutting Concepts\nDevelop a model to generate data to test ideas about designed systems, including those representing inputs and outputs.\n\nAlignment agreement:\n\nModels of all kinds are important for testing solutions.\n\nAlignment agreement:\n\nThe iterative process of testing the most promising solutions and modifying what is proposed on the basis of the test results leads to greater refinement and ultimately to an optimal solution.\n\nAlignment agreement:\n\n###### Common Core State Standards - Math\n• Model with mathematics. (Grades K - 12) More Details\n\nDo you agree with this alignment?\n\n• Look for and make use of structure. (Grades K - 12) More Details\n\nDo you agree with this alignment?\n\n• Reason abstractly and quantitatively. (Grades K - 12) More Details\n\nDo you agree with this alignment?\n\n• Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n• Investigate patterns of association in bivariate data. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n• Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n• Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n###### International Technology and Engineering Educators Association - Technology\n• Students will develop an understanding of the attributes of design. (Grades K - 12) More Details\n\nDo you agree with this alignment?\n\n• Students will develop an understanding of the relationships among technologies and the connections between technology and other fields of study. (Grades K - 12) More Details\n\nDo you agree with this alignment?\n\n• Create solutions to problems by identifying and applying human factors in design. (Grades 6 - 8) More Details\n\nDo you agree with this alignment?\n\n###### New York - Math\n• Model with mathematics. (Grades Pre-K - 12) More Details\n\nDo you agree with this alignment?\n\n• Look for and make use of structure. (Grades Pre-K - 12) More Details\n\nDo you agree with this alignment?\n\n• Reason abstractly and quantitatively. (Grades Pre-K - 12) More Details\n\nDo you agree with this alignment?\n\n• Investigate patterns of association in bivariate data. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n• Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n• Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n• Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. (Grade 8) More Details\n\nDo you agree with this alignment?\n\n###### New York - Science\n• Develop a model to generate data for iterative testing and modification of a proposed object, tool, or process such that an optimal design can be achieved. (Grades 6 - 8) More Details\n\nDo you agree with this alignment?\n\nSuggest an alignment not listed above\n\n### Materials List\n\nEach group needs:\n\n• a washer (affectionately called \"Washy\") with a weight of ~100 g*; such as a 1-1/8-inch USS flat washer (1-1/4-inch inner diameter, 2-3/4-inch outer diameter and 11/64-inch thickness); alternatively, use two ¾-inch USS flat washers (13/16-inch inner diameter, 2-inch inner diameter and 5/32-inch thickness); *washer weights may vary and the activity will still work\n• 15 - 20 rubber bands\n• 1 large piece of paper, such as chart paper, at least 54 inches (137 cm) long\n• measuring tape, at least 5 feet (152 cm) long\n• (optional) calculator\n• (optional) bin or box to organize materials by group\n• (optional) paper and pencil for taking notes\n• Pre-Activity Quiz, one per student\n• Activity Worksheet, one per student\n• Post-Activity Quiz, one per student\n\nTo share with the entire class:\n\n### Pre-Req Knowledge\n\nStudents should have experience plotting points on x-y coordinate planes and calculating the equation of a line using y = mx + b and the slope of a line using: m = (y2-y1)/(x2-x1).\n\n### Introduction/Motivation\n\n(Present to the class the following hypothetical story along with the online video and presentation, as described in the Procedure section.)\n\nIzzy and Samayrus are studying to become engineers and are interning at a local theme park called Crazy Town. The executives at Crazy Town want Izzy and Samayrus to develop a show-stopping bungee jumping ride that is safe.\n\nNow let's watch a video on bungee jumping. As you watch, record your observations: What do you see? What do you think about that? What does it make you wonder? (Show students the 5:11-minute video, Forces and Motion – Bungee Jumping at https://www.youtube.com/watch?v=RoLjKlHYvzA. While students are watching, circulate and read their answers.)\n\n(Stop the video at 1 minute and 20 seconds.) Force is a push or a pull on an object. It results from one object's interaction with another. Unless it is opposed, a force changes the motion of an object. Write down the definition.\n\nWhat information and/or physics concepts might be necessary to know in order to develop a show-stopping bungee jumping ride that is safe? Turn and talk to a partner. Jot down all your ideas. After three minutes, we will have a class discussion.\n\n(Circulate and listen to student discussions. If students are off task, ask them to explain their ideas. After three minutes, begin a class discussion.)\n\nWhat are your ideas? (Call on several students to share their ideas.)\n\nNow back to our story. One issue that Izzy and Samayrus are concerned with is the impact of the bungee cord length on the distance of the jumper's fall. (Write \"the impact of the bungee cord length on the distance of the jumper's fall\" on the classroom board.) How does this concept relate to force?\n\n(THINK: Have students write down the following question.) Write this down: Do you think that bungee cord length is an issue to consider when designing a bungee jump ride for Crazy Town? Please explain in writing, why or why not. (Circulate while students are writing. Ask students to explain their answers.)\n\n(PAIR) Now pair up with another student and discuss your thoughts on this potential issue.\n\n(Continue with the story.) Today, the executives want you to predict and measure the distance that a jumper will fall for different bungee lengths. Remember, your bosses want this ride to be thrilling and safe!\n\nYour task is to find the maximum length of the bungee cord that permits jumpers to get as close to the ground as possible (thrilling) without going \"splat\" (so they are safe)! You will record your data and use this information to find the total force acting on the bungee cord. Then you will use an equation we call Hooke's law to calculate the spring constant. You will also use scatter plots and linear equations to predict the maximum length of bungee cord for a safe fall and return.\n\n### Procedure\n\nTeacher Background and Concepts\n\n\"Linear equation\" has the word \"line\" in it; it is the equation for a straight line. An example linear equation is: y = 2x + 1. See Figure 1.\n\nStudents and teachers need to understand how to plot linear equations by plugging in x and y values. For example, given the equation y = 2x + 1:\n\n1 = 2(0) + 1 when x is 0, y is 1.\n\n3 = 2(1) + 1 when x is 1, y is 3\n\n5 = 2(2) + 1 when x is 2, y is 5\n\nScatter plots are similar to line graphs in that data points are plotted on a grid with x- and y-axes. Scatter plots show how one variable is related to another variable, which is called correlation. A positive correlation exists when the pattern of data points appears to have a positive slope (as the x-values increase, the y-values also increase; see Figure 2). In contrast, a negative correlation exists when the pattern of data points appears to have a negative slope (as the x-values increase, the y-values decrease; see Figure 3).\n\nA high correlation exists when the pattern of data points looks like a straight line with either a positive or negative slope. A low correlation exists when the data points are more spread out, but still resemble a positive or negative slope. We say there is no correlation when no pattern exists between the variables (see Figure 4).\n\nA line of best fit is a straight line on a scatter plot of data. The line may pass through all of the points, some of the points or none of the points, depending on the data. A line of best fit enables you to predict values not displayed on the scatter plot using the linear equation from the line of best fit.\n\nExamine the incorrect and correct example lines of best fit in Figures 5, 6 and 7:\n\n• Although the straight line in Figure 5 goes through numerous points on the scatter plot, it is not a good example of a line of best fit because the line is placed below a majority of the points.\n• Although the line in Figure 6 has approximately the same number of points below the line as above the line, it is not a good line of best fit because the slope of the line does not follow the trend of the data points.\n\nIn this activity, students create scatter plots and find the lines of best fit in order to understand the patterns in the data and be able to predict future values.\n\nIn bungee jumping, a bungee cord acts like a spring. To understand the properties of a spring, it helps to be familiar with Hooke's law, which is a linear equation. Hooke's law states that the force of an elastic object (such as a spring) is directly related to the displacement of the spring (how far the spring was stretched).\n\nThe equation for Hooke's law is F = -kx, where F = force (N), k = spring constant (N/m), and x = length of the displacement for the spring (m). The spring constant, k, characterizes the rigidity of the spring. A spring with a high k is considered highly rigid.\n\nWhen a spring's elastic limit is reached, the spring becomes permanently distorted and no longer \"springs\" back. When relating bungee jumping to Hooke's law, both the cord strength and cord (maxed out) length must be considered. (Source: Physics 24/7; see the References section.)\n\nThe difference between tension (pulling) and extension is that tension leads to a more rigid elasticity of the spring and extension can lead to distortion of the string. For example, a stretched rubber band (adding tension) returns to its original shape. But if the rubber band is stretch using a great amount of force, it stretches so much that it is unable to return to its original shape. The amount of force required depends on the elasticity of the object.\n\nBefore the Activity\n\nWith the Students—Pre-Quiz and Motivation (20 minutes)\n\n1. Administer the pre-activity quiz as described in the Assessment section. Collect the quizzes for a quick review. (3-5 minutes)\n2. (slides 1 and 2) Begin by presenting to the class the Introduction/Motivation section content, which is a hypothetical story that gives context for the engineering challenge. Make sure students have paper and pencils for writing down observations, ideas and notes. Then show the class a five-minute online video about bungee jumping, during which they write down their see/think/wonder ideas.\n3. (slide 3) Pause the video at 1 minute and 20 seconds. Clarify the definition of a force and have students write it down. (Optional: Play the rest of the video to further introduce students to air resistance and the equation: speed = distance / time.)\n4. (slide 4) After watching the video, direct students to turn and talk to a partner, brainstorming about what information and physics concepts might be necessary to know in order to develop a show-stopping bungee jumping ride that is safe. Have them jot down their ideas. Then lead a class discussion, calling on several students to share their ideas.\n5. (slide 5) Continue with the story. Write the key issue on the classroom board. How does the bungee cord length impact the distance of the bungee jump fall? How does this concept relate to force? Direct students in a think-pair-share approach to consider the issue, as guided by the slide text.\n6. (slide 6) Then continue with the story, which presents the engineering design challenge: To find the maximum length of the bungee cord that permits jumpers to get as close to the ground as possible (thrilling) without going \"splat\" (so they are safe)! As engineers, this requires some experimentation with the materials and some calculations and graphing.\n\nWith the Students—Introduction (25 minutes)\n\n1. (slides 7-10) Recap and discuss the objectives. Explain as necessary and have students take notes.\n2. (slide 10) Ask students to answer the following questions, then turn and talk to a partner about their answers: What do you know? What do you want to know?\n3. (slides 11 and 12) Quick notes and discussion: Introduce and explain some important concepts that students must know. Start by defining a linear equation and providing an example.\n4. (slides 13-17) Define scatter plots and correlations (positive, negative, none) with some examples (provided on the slides). Make the point that correlation does not imply causation.\n5. (slides 18-19) Describe Hooke's law and explain the equation.\n6. (slide 20) Describe and compare tension and overextension.\n7. (slide 21) Direct students in a think-pair-share examination of the concepts and vocabulary just presented.\n\nWith the Students—Experiment & Analysis (45 minutes)\n\n1. Divide the class into groups of four students each. Hand out the worksheets and supplies.\n2. Direct students to each formulate a hypothesis and write a prediction on the worksheet.\n3. While students follow the worksheet to run the experiment, circulate the room to help them along the way. Review their pre-quiz answers to learn of the strengths and weaknesses in their base knowledge. Below is an overview of the procedure that students follow to complete the experiment activity. See the worksheet for photographs, tables, graphs and more details.\n• Set up the chart paper and measuring tape on the wall.\n• Weigh the washer (in kilograms). Record the weight in Table 2.\n• Attach two rubber bands to the washer as shown in the worksheet.\n• Run the experiment for two rubber bands by having one student hold one end of the rubber band with one hand and drop the washer with the other hand from the jump line (top of the measuring tape). The other students in the group carefully observe the drop so as to note the farthest distance Washy falls and mark that distance on the chart paper.\n• To obtain accurate data, students run several trials for each number of rubber bands and determine the average displacements. They record the displacements in centimeters in Table 1.\n• Add additional rubber bands, running the experiment and recording the data for each trial in Table 1.\n• Calculate force, using weight x gravity, and record the results in Table 2. (Gravity is 9.81 m/s2.)\n• Convert the displacement for each trial from centimeters to meters and record the results in Table 2.\n• Calculate the spring constants using Hooke's law and record the results in Table 2.\n• Find the absolute values for the spring constants.\n• Create a scatter plot using the number of rubber bands for the x-values and displacement in centimeters as the y-values (Table 1 data), and answer a few questions based on the scatter plot.\n• Read about the line of best fit, including correct and incorrect examples. Then draw the lines of best fit on the scatter plots.\n• Answer the five questions about slope and equations of lines and the six questions about interpreting data.\n• Create a second scatter plot using displacement in meters for the x-values and the absolute value of the spring constant as the y-values (Table 2 data), and answer a few questions based on the scatter plot.\n1. (slides 22-27) Engage the class in a discussion to review the worksheet questions/answers on pages 6-8, as described in the Assessment section. Use the slides to help lead the discussion.\n2. (3-5 minutes) Exit slip: Administer the post-activity quiz, described in the Assessment section.\n\n### Vocabulary/Definitions\n\ndisplacement: A measure of the change in an object's position. How far an object is out of place.\n\nforce: A push or a pull on an object. Unless it is opposed, a force changes the motion of an object.\n\nHooke's law: The relationship between the force applied to a spring and the displacement, which is the amount the object is stretched relative to its initial position. Fspring = -kx, where k is the spring constant and x is the displacement; k = pushing, while –k = pulling.\n\nline of best fit: A way of representing scatter data using a straight line though the points for the purpose of predicting values.\n\noverextension: A process that occurs when a certain threshold has been exceeded. For example, a spring that is stretched to a length from which it cannot return to its original shape.\n\nscatter plot: Unconnected data points plotted on x- and y-axes that show values for two variables.\n\nspring constant: A constant, abbreviated as \"k\" (with newton/meter for units) that defines spring stiffness. A stiff spring has a high spring constant, which means it takes a large amount of force to cause a small displacement.\n\ntension: Pulling that leads to a more rigid elasticity of a spring.\n\n### Assessment\n\nPre-Activity Assessment\n\nPre-Quiz: To find out what students already know about the basic mathematical and scientific topics pertinent to this activity, administer the three-question Pre-Activity Quiz, which asks them to calculate the slope of a line, determine the equation of the line and describe what they know about force. Understanding how much students know about the basics of linear equations and slopes helps you anticipate how much assistance students may need when they are asked to perform related tasks during the activity. Force is a basic concept that students must understand in order to complete the activity. Refer to the Pre-Activity Quiz Answer Key.\n\nActivity Embedded Assessment\n\nCirculate and Track: During the activity, when students are working in groups of four, observe and informally assess their progress. As guided by the worksheet, students calculate Hooke's constant, graph their data as scatter plots, describe the correlation of the plots, draw lines of best fit, calculate the slopes and equations of the line, and analyze the data. At activity end, review the activity and cold call on several students to explain their answers. As a tracking device, attach the Teacher's Tracking System Handout on a clipboard and record small rating notes as you evaluate student understanding. Expect some students to require more time than others to complete the task. Be ready with related reading/mathematical analysis material for students who complete the task quickly.\n\nWorksheet: As students are working, guided by the Activity Worksheet instructions, informally check on their progress by reading their answers to the worksheet questions. Ask students additional questions based on the work you observe. At activity end, review the answers as a class (refer to the Activity Worksheet Answer Key), and individually assess student comprehension by reviewing their worksheets.\n\nPost-Activity Assessment\n\nExit Slip: To determine what students learned about the activity's basic mathematical and scientific topics, administer the two-problem Post-Activity Quiz, which asks them to determine a spring constant using Hooke's law, graph a line of best fit and determine the equation of the line. Review students' answers to gauge their depth of comprehension. Refer to the Post-Activity Answer Key.\n\n### Safety Issues\n\n• Rubber bands can be dangerous if they break and hurt nearby students. Watch that students do not get off task by using the rubber bands in distracting and/or harmful ways.\n• Watch that the washers are not thrown or placed in mouths.\n\n### Activity Scaling\n\n• For lower grades, make the scatter plots together as a class.\n• For higher grades, have students work in groups on the scatter plots.\n\nDuring the Introduction/Motivation section, show students a five-minute video on bungee jumping: Forces and Motion – Bungee Jumping at https://www.youtube.com/watch?v=RoLjKlHYvzA.\n\n### Subscribe\n\nGet the inside scoop on all things TeachEngineering such as new site features, curriculum updates, video releases, and more by signing up for our newsletter!\nPS: We do not share personal information or emails with anyone.\n\n© 2015 by Regents of the University of Colorado; original © 2015 Polytechnic Institute of New York University\n\n### Contributors\n\nMarc Frank; Ramona Fittipaldi\n\n### Supporting Program\n\nSMARTER RET Program, Polytechnic Institute of New York University\n\n### Acknowledgements\n\nThis activity was developed by the Science and Mechatronics Aided Research for Teachers with an Entrepreneurial ExpeRience (SMARTER): A Research Experience for Teachers (RET) Program in the School of Engineering funded by National Science Foundation RET grant no. 1132482. However, these contents do not necessarily represent the policies of the NSF, and you should not assume endorsement by the federal government." ]

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[ "# peek\n\n## Syntax\n\n``out = peek(asyncBuff)``\n``out = peek(asyncBuff,numRows)``\n``out = peek(asyncBuff,numRows,overlap)``\n``[out,nUnderrun] = peek(___)``\n\n## Description\n\n````out = peek(asyncBuff)` returns all unread samples from the buffer, `asyncBuff`, without changing the number of unread samples in the buffer.```\n\nexample\n\n````out = peek(asyncBuff,numRows)` returns `numRows` samples from each channel (column) of the buffer.```\n````out = peek(asyncBuff,numRows,overlap)` returns `numRows` samples from each channel and overlaps previously read samples by `overlap`.```\n````[out,nUnderrun] = peek(___)` also returns the number of zero-padded rows if underrun occurred, using any of the previous arguments.```\n\n## Examples\n\ncollapse all\n\nRead data from the async buffer without changing the number of unread samples using the `peek` function.\n\nCreate a `dsp.AsyncBuffer` System object™. The input is a column vector of 100 samples, 1 to 100. Write the data to the buffer.\n\n`asyncBuff = dsp.AsyncBuffer`\n```asyncBuff = AsyncBuffer with properties: Capacity: 192000 NumUnreadSamples: 0 ```\n```input = (1:100)'; write(asyncBuff,input);```\n\nPeek at the first three samples. The output is [1 2 3]'.\n\n`out1 = peek(asyncBuff,3)`\n```out1 = 3×1 1 2 3 ```\n\nThe `NumUnreadSamples` is 100, indicating that the `peek` function has not changed the number of unread samples in the buffer.\n\n`asyncBuff.NumUnreadSamples`\n```ans = int32 100 ```\n\nAfter peeking, read 50 samples using the `read` function. The output is [1:50]'.\n\n`out2 = read(asyncBuff,50)`\n```out2 = 50×1 1 2 3 4 5 6 7 8 9 10 ⋮ ```\n\nThe `NumUnreadSamples` is 50, indicating that the `read` function has changed the number of unread samples in the buffer.\n\n`asyncBuff.NumUnreadSamples`\n```ans = int32 50 ```\n\nNow peek again at the first three samples. The output is [51 52 53]'. Verify that the `NumUnreadSamples` is still 50.\n\n`out3 = peek(asyncBuff,3)`\n```out3 = 3×1 51 52 53 ```\n`asyncBuff.NumUnreadSamples`\n```ans = int32 50 ```\n\nRead 50 samples again. The output now contains the sequence [51:100]'. Verify that `NumUnreadSamples` is 0.\n\n`out4 = read(asyncBuff)`\n```out4 = 50×1 51 52 53 54 55 56 57 58 59 60 ⋮ ```\n`asyncBuff.NumUnreadSamples`\n```ans = int32 0 ```\n\n## Input Arguments\n\ncollapse all\n\nAsync buffer, specified as a `dsp.AsyncBuffer` System object.\n\nNumber of samples peeked from each channel (column) of the buffer, specified as a positive integer. This operation does not change the number of unread samples in the buffer. If the requested number of samples is greater than the number of unread samples, the output is zero-padded.\n\nNumber of samples overlapped, specified as an integer. The function returns `numRows` samples from each channel and overlaps previously read samples by `overlap`. The total number of samples peeked is `numRows` × NumChann, where NumChann is the number of channels in the buffer. The total number of new samples peeked is (`numRows``overlap`) × `NumChann`. If the overlap portion contains samples that are overwritten, and are therefore not contiguously written, the output is zero-padded.\n\n## Output Arguments\n\ncollapse all\n\nData peeked from the buffer, returned as an array of `numRows` × NumChann samples. If `overlap` is specified, the function returns (`numRows``overlap`) × NumChann samples. If the requested number of samples is greater than the number of unread samples, the output is zero-padded.\n\nData Types: `double`\nComplex Number Support: Yes\n\nNumber of zero-padded samples in each channel (column) if underrun occurred. Underrun occurs if you attempt to peek more samples than available. Samples that are zero-padded in overlapped portions are not counted as underrun.\n\nData Types: `int32`\n\n### Objects\n\nIntroduced in R2018b\n\n## Support", null, "Get trial now" ]

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[ "## SHOW FUNCTIONS\n\nSynopsis.\n\n`SHOW FUNCTIONS [ LIKE? pattern? ]? `\n SQL match pattern\n\nDescriptionList all the SQL functions and their type. The `LIKE` clause can be used to restrict the list of names to the given pattern.\n\n```SHOW FUNCTIONS;\n\nname | type\n----------------+---------------\nAVG |AGGREGATE\nCOUNT |AGGREGATE\nMAX |AGGREGATE\nMIN |AGGREGATE\nSUM |AGGREGATE\nSTDDEV_POP |AGGREGATE\nVAR_POP |AGGREGATE\nPERCENTILE |AGGREGATE\nPERCENTILE_RANK |AGGREGATE\nSUM_OF_SQUARES |AGGREGATE\nSKEWNESS |AGGREGATE\nKURTOSIS |AGGREGATE\nDAY_OF_MONTH |SCALAR\nDAY |SCALAR\nDOM |SCALAR\nDAY_OF_WEEK |SCALAR\nDOW |SCALAR\nDAY_OF_YEAR |SCALAR\nDOY |SCALAR\nHOUR_OF_DAY |SCALAR\nHOUR |SCALAR\nMINUTE_OF_DAY |SCALAR\nMINUTE_OF_HOUR |SCALAR\nMINUTE |SCALAR\nSECOND_OF_MINUTE|SCALAR\nSECOND |SCALAR\nMONTH_OF_YEAR |SCALAR\nMONTH |SCALAR\nYEAR |SCALAR\nWEEK_OF_YEAR |SCALAR\nWEEK |SCALAR\nABS |SCALAR\nACOS |SCALAR\nASIN |SCALAR\nATAN |SCALAR\nATAN2 |SCALAR\nCBRT |SCALAR\nCEIL |SCALAR\nCEILING |SCALAR\nCOS |SCALAR\nCOSH |SCALAR\nCOT |SCALAR\nDEGREES |SCALAR\nE |SCALAR\nEXP |SCALAR\nEXPM1 |SCALAR\nFLOOR |SCALAR\nLOG |SCALAR\nLOG10 |SCALAR\nMOD |SCALAR\nPI |SCALAR\nPOWER |SCALAR\nRANDOM |SCALAR\nRAND |SCALAR\nROUND |SCALAR\nSIGN |SCALAR\nSIGNUM |SCALAR\nSIN |SCALAR\nSINH |SCALAR\nSQRT |SCALAR\nTAN |SCALAR\nASCII |SCALAR\nCHAR |SCALAR\nBIT_LENGTH |SCALAR\nCHAR_LENGTH |SCALAR\nLCASE |SCALAR\nLENGTH |SCALAR\nLTRIM |SCALAR\nRTRIM |SCALAR\nSPACE |SCALAR\nUCASE |SCALAR\nSCORE |SCORE```\n\nThe list of functions returned can be customized based on the pattern.\n\nIt can be an exact match:\n\n```SHOW FUNCTIONS LIKE 'ABS';\n\nname | type\n---------------+---------------\nABS |SCALAR```\n\nA wildcard for exactly one character:\n\n```SHOW FUNCTIONS LIKE 'A__';\n\nname | type\n---------------+---------------\nAVG |AGGREGATE\nABS |SCALAR```\n\nA wildcard matching zero or more characters:\n\n```SHOW FUNCTIONS LIKE 'A%';\n\nname | type\n---------------+---------------\nAVG |AGGREGATE\nABS |SCALAR\nACOS |SCALAR\nASIN |SCALAR\nATAN |SCALAR\nATAN2 |SCALAR\nASCII |SCALAR```\n\nOr of course, a variation of the above:\n\n```SHOW FUNCTIONS '%DAY%';\n\nname | type\n---------------+---------------\nDAY_OF_MONTH |SCALAR\nDAY |SCALAR\nDAY_OF_WEEK |SCALAR\nDAY_OF_YEAR |SCALAR\nHOUR_OF_DAY |SCALAR\nMINUTE_OF_DAY |SCALAR```" ]

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[ "## Zeta functions and transfer operators for piecewise monotone transformations.(English)Zbl 0703.58048\n\nGiven a piecewise monotone transformation T of the interval and a piecewise continuous complex weight function g of bounded variation, the authors prove that the Ruelle zeta function $$\\zeta$$ (z) of (T,g) extends meromorphically to $$\\{| z| <\\theta^{-1}\\}$$ (where $$\\theta =\\lim_{n\\to \\infty}\\| g\\circ T^{n-1}\\cdot...\\cdot g\\circ T\\cdot g\\|_{\\infty}^{1/n})$$ and that z is a pole of $$\\zeta$$ if and only if $$z^{-1}$$ is an eigenvalue of the corresponding transfer operator $${\\mathcal L}$$. They do not assume that $${\\mathcal L}$$ leaves a reference measure invariant.\nReviewer: G.M.Rassias\n\n### MSC:\n\n 37C25 Fixed points and periodic points of dynamical systems; fixed-point index theory; local dynamics 37C80 Symmetries, equivariant dynamical systems (MSC2010) 30D50 Blaschke products, etc. (MSC2000)\nFull Text:\n\n### References:\n\n Baladi, V., Eckmann, J.-P., Ruelle, D.: Resonances for intermittent systems. Nonlinearity2, 119–135 (1989) · Zbl 0673.58030 Dunford, N., Schwartz, J. T.: Linear operators, part one. New York: Wiley 1957 · Zbl 0128.34803 Eckmann, J.-P.: Resonances in dynamical systems, Preprint. University of Geneva (1988) · Zbl 0726.70017 Haydn, N. T. A.: Meromorphic extension of the zeta function for Axiom A flows. Preprint (1987) · Zbl 0694.58035 Hofbauer, F.: On intrinsic ergodicity of piecewise monotonic transformations with positive entropy. Israel. J. Math.34, 213–237 (1979) · Zbl 0422.28015 Hofbauer, F.: Periodic points for piecewise monotonic transformations. Ergod. Th. Dynam. Sys.5, 237–256 (1985) · Zbl 0572.54036 Hofbauer, F.: Piecewise invertible dynamical systems. Probab. Th. Rel. Fields72, 359–386 (1986) · Zbl 0591.60064 Hofbauer, F., Keller, G.: Ergodic properties of invariant measures for piecewise monotonic transformations. Math. Z.180, 119–140 (1982) · Zbl 0485.28016 Hofbauer, F., Keller, G.: Zeta-functions and transfer-operators for piecewise linear transformations. J. reine angew. Math.352, 100–113 (1984) · Zbl 0533.28011 Kato, T.: Perturbation theory for linear operators. Berlin, Heidelberg, New York: Springer 1976 · Zbl 0342.47009 Keller, G.: On the rate of convergence to equilibrium in one-dimensional systems. Commun. Math. Phys.96, 181–193 (1984) · Zbl 0576.58016 Keller, G.: Markov extensions, zeta-functions, and Fredholm theory for piecewise invertible dynamical systems. Preprint (1986), to appear in Trans. Am. Math. Soc. Landau, E.: Darstellung und Begründung einiger neuerer Ergebnisse der Funktionentheorie. New York: Chelsea 1946 · JFM 46.0469.02 Milnor, J., Thurston, W.: On iterated maps of the interval. In: Dynamical systems (Lecture Notes in Mathematics vol.1342) pp. 465–564. Berlin, Heidelberg, New York: Springer 1988 · Zbl 0664.58015 Mori, M.: On the Fredholm determinant of a piecewise linear transformation. Preprint, National Defense Academy of Japan (1987) · Zbl 0689.58024 Pollicott, M.: Meromorphic extensions of generalised zeta functions. Invent. Math.85, 147–164 (1986) · Zbl 0604.58042 Preston, C.: What you need to know to knead, Preprint, University of Bielefeld (1988) · Zbl 0701.58032 Ruelle, D.: Zeta functions for expanding maps and Anosov flows. Invent. Math.34, 231–242 (1976) · Zbl 0329.58014 Ruelle, D.: Thermodynamic formalism. Reading MA: Addison-Wesley 1978 · Zbl 0401.28016 Ruelle, D.: One-dimensional Gibbs states and Axiom A diffeomorphisms. J. Diff. Geom.25, 117–137 (1987) · Zbl 0628.58043 Ruelle, D.: The thermodynamic formalism for expanding maps. Preprint (1989), Bowen lectures given at U.C. Berkeley in November 1988 · Zbl 0702.58056 Rychlik, M.: Bounded variation and invariant measures. Studia Math.LXXVI, 69–80 (1983) · Zbl 0575.28011 Walters, P.: A variational principle for the pressure of continuous transformations. Am. J. Math.97, 937–971 (1976) · Zbl 0318.28007\nThis reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching." ]

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[ "This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate WATER INTELLIGENCE PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Chi-Square1,2,3,4 and conclude that the LON:WATR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:WATR stock.\n\nKeywords: LON:WATR, WATER INTELLIGENCE PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.\n\n## Key Points\n\n1. What are the most successful trading algorithms?\n2. Is now good time to invest?\n3. What is prediction model?", null, "## LON:WATR Target Price Prediction Modeling Methodology\n\nStock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We consider WATER INTELLIGENCE PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:WATR stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4\n\nF(Chi-Square)5,6,7= $\\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \\dots & {p}_{1n}\\\\ & ⋮\\\\ {p}_{j1}& {p}_{j2}& \\dots & {p}_{jn}\\\\ & ⋮\\\\ {p}_{k1}& {p}_{k2}& \\dots & {p}_{kn}\\\\ & ⋮\\\\ {p}_{n1}& {p}_{n2}& \\dots & {p}_{nn}\\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+3 month) $\\stackrel{\\to }{R}=\\left({r}_{1},{r}_{2},{r}_{3}\\right)$\n\nn:Time series to forecast\n\np:Price signals of LON:WATR stock\n\nj:Nash equilibria\n\nk:Dominated move\n\na:Best response for target price\n\nFor further technical information as per how our model work we invite you to visit the article below:\n\nHow do AC Investment Research machine learning (predictive) algorithms actually work?\n\n## LON:WATR Stock Forecast (Buy or Sell) for (n+3 month)\n\nSample Set: Neural Network\nStock/Index: LON:WATR WATER INTELLIGENCE PLC\nTime series to forecast n: 16 Sep 2022 for (n+3 month)\n\nAccording to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:WATR stock.\n\nX axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)\n\nY axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)\n\nZ axis (Yellow to Green): *Technical Analysis%\n\n## Conclusions\n\nWATER INTELLIGENCE PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Chi-Square1,2,3,4 and conclude that the LON:WATR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:WATR stock.\n\n### Financial State Forecast for LON:WATR Stock Options & Futures\n\nRating Short-Term Long-Term Senior\nOutlook*B1B1\nOperational Risk 4939\nMarket Risk4882\nTechnical Analysis7355\nFundamental Analysis5748\nRisk Unsystematic8572\n\n### Prediction Confidence Score\n\nTrust metric by Neural Network: 91 out of 100 with 549 signals.\n\n## References\n\n1. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.\n2. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer\n3. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010\n4. Bessler, D. A. S. W. Fuller (1993), \"Cointegration between U.S. wheat markets,\" Journal of Regional Science, 33, 481–501.\n5. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32\n6. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37\n7. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier\nFrequently Asked QuestionsQ: What is the prediction methodology for LON:WATR stock?\nA: LON:WATR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Chi-Square\nQ: Is LON:WATR stock a buy or sell?\nA: The dominant strategy among neural network is to Buy LON:WATR Stock.\nQ: Is WATER INTELLIGENCE PLC stock a good investment?\nA: The consensus rating for WATER INTELLIGENCE PLC is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.\nQ: What is the consensus rating of LON:WATR stock?\nA: The consensus rating for LON:WATR is Buy.\nQ: What is the prediction period for LON:WATR stock?\nA: The prediction period for LON:WATR is (n+3 month)\n\n## People also ask\n\nWhat are the top stocks to invest in right now?\nOur Mission\n\nAs AC Investment Research, our goal is to do fundamental research, bring forward a totally new, scientific technology and create frameworks for objective forecasting using machine learning and fundamentals of Game Theory.\n\n301 Massachusetts Avenue Cambridge, MA 02139 667-253-1000 [email protected]\n\nFollow Us | Send Feedback" ]

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[ "### Physics 406, Fall 2002: Statistical and Thermal Physics\n\nRoom: 331 Dennison\nTime: MWF 9-10am\n\nInstructor: Mark Newman\nOffice: 277 West Hall\nOffice hours: Thursdays 2-4pm\nEmail: [email protected]\n\nOffice: 1476 Randall Lab\nOffice hours: Wednesdays 1-3pm\nEmail: [email protected]\n\nDescription: This course provides an introduction to the fundamentals of thermal physics including classical thermodynamics (the three laws, temperature, internal energy, and entropy) and statistical mechanics (microscopic entropy, classical and quantum thermal distributions, ideal gases, Fermi and Bose gases, thermal radiation, electrons in metals, Bose-Einstein condensation).\n\nTextbook (required): Thermal Physics, 2nd edition, C. Kittel and H. Kroemer (Freeman, New York, 1980). ISBN 0-7167-1088-9.\n\nCourse pack (required): A required course pack for this course is available from Ulrich's Bookstore on S. University. Ask for Physics 406, Prof. Newman, Bin #1087. Price is \\$9.10. The course pack consists of four chapters from the book Equilibrium Thermodynamics, 3rd edition, C. J. Adkins (Cambridge University Press, Cambridge, 1984). ISBN 0-5212-7456-7. This book is not required, but if you want more detail on the thermodynamics part of the course you may wish to look at it. A copy is on reserve at Science Library Reserves in the Shapiro Library.\n\nSupplementary texts (not required):\n\nGrading: There will be weekly problem sets handed out Friday and due a week later. The first problem set will be handed out on Friday 13th September. There will be one mid-term and a final. Grade will be 50% on the problem sets, 20% on the mid-term, and 30% on the final.\n\n### Problem sets:\n\n• Homework 1 - First Law of Thermodynamics\n• Homework 2 - Second Law of Thermodynamics\n• Homework 3 - Maxwell relations, multiplicity, and microcanonical entropy\n• Homework 4 - More entropy, Sterling's approximation, the Planck distribution function\n• Homework 5 - Integral approximations and the perfect gas\n• Homework 6 - Thermal radiation\n• Homework 7 - Phonon specific heat and chemical potential\n• Homework 8 - Quantum gases in the classical limit\n• Homework 9 - Fermi gases\n• Homework 10 - Bose gases, phase transitions\n• Homework 11 - The maximum entropy principle, Monte Carlo simulation\n\n### Syllabus:\n\n1. First handout: Introduction to statistical physics, percolation, random walks, entropy.\n2. Course pack (Adkins) 1.5 and 2.1-2.6: Introduction to classical thermodynamics. Intensive and extensive thermodynamic variables, conjugate pairs. The zeroth law of thermodynamics, the derivation and definition of temperature.\n3. Course pack (Adkins) 1.9: Mathematical preliminaries, partial derivatives, the chain rule, the reciprocal and reciprocity theorems.\n4. Course pack (Adkins) 3.1-3.7 (excluding 3.5.3): The first law of thermodynamics, conservation of energy, heat and work, work done by pressure, surface tension, in a magnetic field. Heat capacity and enthalpy.\n5. Course pack (Adkins) 4.1-4.3, 4.5, 4.6, 4.8: The second law, Clausius' statement, heat engines, the Carnot engine, irreversibility of heat flow. Carnot's Theorem, the definition of thermodynamic temperature, refrigerators and heat pumps.\n6. Course pack (Adkins) 5.1-5.6.1: Clausius' Theorem, derivation of entropy, law of increase of entropy. Entropy form of the first law, degradation of energy, heat capacities, free energy, Maxwell relations, free expansion of a gas.\n7. Kittel and Kroemer, Ch. 1: Counting quantum states, simple binary models, spin models, binary alloys. Spin excess, multiplicity, width of the distribution, multiplicity as a function of energy.\n8. Kittel and Kroemer, Ch. 2: Fundamental assumption of the microcanonical ensemble, many-systems view, the ergodic hypothesis. Systems in equilibrium, the derivation of temperature and entropy, Boltzmann's constant. Properties of entropy, the law of increase of entropy (again), maximization of entropy at equilibrium.\n9. Kittel and Kroemer, Ch. 3, part 1: Derivation of the Boltzmann distribution and the partition function. Entropy of the Boltzmann distribution, Shannon's formula for the entropy, Helmholtz free energy. Minimization of the free energy.\n10. Kittel and Kroemer, Ch. 3, part 2: A particle in a box, many particles in a box, the perfect gas. Entropy of a perfect gas, the Gibbs correction, derivation of the equation of state. Sterling's approximation, the Sackur-Tetrode equation, entropy of mixing.\n11. Kittel and Kroemer, Ch. 4: The Planck distribution, black-body radiation and the Stefan-Boltzmann law. Color of thermal radiation. Phonon spectra, the Debye theory of the phonon specific heat.\n12. Kittel and Kroemer, Ch. 5: Gases with varying numbers of particles, chemical potential, generalization of the first law, chemical potential of the perfect gas, barometric pressure. The Gibbs distribution, the grand partition function, the grand potential.\n13. Kittel and Kroemer, Ch. 6: Quantum gases 1, the Fermi-Dirac distribution, the Bose-Einstein distribution, the classical limit, chemical potential, energy, pressure, and the ideal gas again.\n14. Kittel and Kroemer, Ch. 7: Quantum gases 2, the quantum limit. Fermi gases, electron gases, electronic heat capacity, astrophysical examples. Bose gases, Bose-Einstein condensation, liquid helium, superfluidity.\n15. Advanced topics (time permitting): Phase transitions, ferromagnetism, Landau theory; semiconductors, donors and acceptors, p-n junctions; spin models, Ising model, percolation; computer simulation methods, Monte Carlo methods; information theory.\n\nSome notes on Lagrange multipliers are here.\n\nMark Newman" ]

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[ "#### Authors\n\nCommunication overhead is one of the key challenges that hinders the scalability of distributed optimization algorithms. In this paper, we study local distributed SGD, where data is partitioned among computation nodes, and the computation nodes perform local updates with periodically exchanging the model among the workers to perform averaging. While local SGD is empirically shown to provide promising results, a theoretical understanding of its performance remains open. In this paper, we strengthen convergence analysis for local SGD, and show that local SGD can be far less expensive and applied far more generally than current theory suggests. Specifically, we show that for loss functions that satisfy the Polyak-Kojasiewicz condition, $O((pT)^{1/3})$ rounds of communication suffice to achieve a linear speed up, that is, an error of $O(1/pT)$, where $T$ is the total number of model updates at each worker. This is in contrast with previous work which required higher number of communication rounds, as well as was limited to strongly convex loss functions, for a similar asymptotic performance. We also develop an adaptive synchronization scheme that provides a general condition for linear speed up. Finally, we validate the theory with experimental results, running over AWS EC2 clouds and an internal GPUs cluster." ]

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[ "# Video: Finding the Set of Zeros of a Quadratic Function\n\nBethani Gasparine\n\nWhat are the zeros of the function 𝑓(𝑥) = 2(𝑥 − 1)² − 7?\n\n00:44\n\n### Video Transcript\n\nWhat are the zeros of the function 𝑓 of 𝑥 equals two times 𝑥 minus one squared minus seven.\n\nIf we’re finding the zeros of this function, we need to set it equal to zero and solve. So the first thing we should do is add seven to both sides. And now we can divide both sides by two. So 𝑥 minus one squared is equal to seven-halves and now we can square root both sides which is equal to 𝑥 minus one equals plus or minus the square root of seven-halves. And now we add one to the other side of the equation to the right. So we have one plus or minus the square root of seven-halves.\n\nSeparating now, we could say 𝑥 equals one plus square root of seven-halves and 𝑥 equals one minus square root of seven-halves.\n\nNagwa uses cookies to ensure you get the best experience on our website. Learn more about our Privacy Policy." ]

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[ "# 8 16 21 triangle\n\n### Obtuse scalene triangle.\n\nSides: a = 8   b = 16   c = 21\n\nArea: T = 56.43998005316\nPerimeter: p = 45\nSemiperimeter: s = 22.5\n\nAngle ∠ A = α = 19.61659069161° = 19°36'57″ = 0.34223621615 rad\nAngle ∠ B = β = 42.17772350071° = 42°10'38″ = 0.73661316203 rad\nAngle ∠ C = γ = 118.2076858077° = 118°12'25″ = 2.06330988719 rad\n\nHeight: ha = 14.10999501329\nHeight: hb = 7.05499750664\nHeight: hc = 5.37114095744\n\nMedian: ma = 18.23545825288\nMedian: mb = 13.73295302177\nMedian: mc = 7.05333679898\n\nInradius: r = 2.50766578014\nCircumradius: R = 11.91549357563\n\nVertex coordinates: A[21; 0] B[0; 0] C[5.92985714286; 5.37114095744]\nCentroid: CG[8.97661904762; 1.79904698581]\nCoordinates of the circumscribed circle: U[10.5; -5.63216688535]\nCoordinates of the inscribed circle: I[6.5; 2.50766578014]\n\nExterior(or external, outer) angles of the triangle:\n∠ A' = α' = 160.3844093084° = 160°23'3″ = 0.34223621615 rad\n∠ B' = β' = 137.8232764993° = 137°49'22″ = 0.73661316203 rad\n∠ C' = γ' = 61.79331419232° = 61°47'35″ = 2.06330988719 rad\n\n# How did we calculate this triangle?\n\nNow we know the lengths of all three sides of the triangle and the triangle is uniquely determined. Next we calculate another its characteristics - same procedure as calculation of the triangle from the known three sides SSS.", null, "### 1. The triangle circumference is the sum of the lengths of its three sides", null, "### 2. Semiperimeter of the triangle", null, "### 3. The triangle area using Heron's formula", null, "### 4. Calculate the heights of the triangle from its area.", null, "### 5. Calculation of the inner angles of the triangle using a Law of Cosines", null, "### 6. Inradius", null, "### 7. Circumradius", null, "### 8. Calculation of medians", null, "#### Look also our friend's collection of math examples and problems:\n\nSee more informations about triangles or more information about solving triangles." ]

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[ "# Program to Calculate the Edge Cover of a Graph\n\nGiven the number of vertices N of a graph. The task is to determine the Edge cover.\n\nEdge Cover: Minimum number of edge required to cover all vertex is known as Edge Cover.\n\nExamples:\n\n```Input : N = 5\nOutput : 3\n\nInput : N = 4\nOutput : 2\n```\n\n## Recommended: Please try your approach on {IDE} first, before moving on to the solution.\n\nExample 1: For N = 5 vertices,", null, "Edge Cover is: 3 (Choosing the edges marked in Red, all of the vertices will get covered)", null, "Example 2: For N = 8 vertices,", null, "Edge Cover is: 4 (Choosing the edges marked in Red, all of the vertices will get covered)", null, "Formula:\n\n```Edge Cover = ceil (no. of vertices / 2)\n```\n\nBelow is the implementation of the above approach:\n\n## C++\n\n `// C++ program to find Edge Cover ` `#include ` `using` `namespace` `std; ` ` `  `// Function that calculates Edge Cover ` `int` `edgeCover(``int` `n) ` `{ ` `    ``float` `result = 0; ` ` `  `    ``result = ``ceil``(n / 2.0); ` ` `  `    ``return` `result; ` `} ` ` `  `// Driver Code ` `int` `main() ` `{ ` `    ``int` `n = 5; ` ` `  `    ``cout << edgeCover(n); ` ` `  `    ``return` `0; ` `} `\n\n## Java\n\n `// Java program to find Edge Cover ` `import` `java.util.*; ` `import` `java.lang.*; ` `import` `java.io.*; ` ` `  `class` `GFG{ ` `// Function that calculates Edge Cover ` `static` `int` `edgeCover(``int` `n) ` `{ ` `    ``int` `result = ``0``; ` `  `  `    ``result = (``int``)Math.ceil((``double``)n / ``2.0``); ` `  `  `    ``return` `result; ` `} ` `  `  `// Driver Code ` `public` `static` `void` `main(String args[]) ` `{ ` `    ``int` `n = ``5``; ` `  `  `    ``System.out.print(edgeCover(n)); ` `} ` ` `  `} `\n\n## Python3\n\n `# Python 3 implementation of the above approach. ` ` `  `import` `math ` ` `  `# Function that calculates Edge Cover  ` `def` `edgeCover(n): ` `     `  `    ``result ``=` `0` `     `  `    ``result ``=` `math.ceil(n ``/` `2.0``) ` `     `  `    ``return` `result ` `     `  `     `  `# Driver code       ` `if` `__name__ ``=``=` `\"__main__\"` `:  ` `   `  `    ``n ``=`  `5` `   `  `    ``print``(``int``(edgeCover(n))) ` ` `  `# this code is contributed by Naman_Garg `\n\n## C#\n\n `// C# program to find Edge Cover ` `using` `System; ` ` `  `class` `GFG ` `{ ` `// Function that calculates Edge Cover ` `static` `int` `edgeCover(``int` `n) ` `{ ` `    ``int` `result = 0; ` ` `  `    ``result = (``int``)Math.Ceiling((``double``)n / 2.0); ` ` `  `    ``return` `result; ` `} ` ` `  `// Driver Code ` `static` `public` `void` `Main () ` `{ ` `    ``int` `n = 5; ` `     `  `    ``Console.Write(edgeCover(n)); ` `} ` `} ` ` `  `// This code is contributed by Raj `\n\n## PHP\n\n ` `\n\nOutput:\n\n```3\n```\n\nDon’t stop now and take your learning to the next level. Learn all the important concepts of Data Structures and Algorithms with the help of the most trusted course: DSA Self Paced. Become industry ready at a student-friendly price.\n\nMy Personal Notes arrow_drop_up", null, "Check out this Author's contributed articles.\n\nIf you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks.\n\nPlease Improve this article if you find anything incorrect by clicking on the \"Improve Article\" button below.\n\nArticle Tags :\nPractice Tags :\n\nBe the First to upvote.\n\nPlease write to us at [email protected] to report any issue with the above content." ]

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[ "# Cobalt Chloride And Lechatlier S Principle\n\n### Question Description\n\nObjective: To gain an understanding of LeChatlier’s principle through the observation of the effect of induced perturbations on the equilibrium distribution of various cobalt(II) complexes.\n\nBackground: Cobalt(II) does not exist in aqueous solution as a free ion, but forms a complex ion where 6 water molecules, acting as a Lewis bases (electron pair donors), donate electrons into the vacant orbitals of the cobalt (II). This results in the pink complex ion Co(H2O)6+2. In the presence of chloride ions, a different complex forms, the blue CoCl4-2 complex ion. We use their different colors to indicate the equilibrium concentrations for the following reaction.\n\nCo(H2O)6+2 + 4Cl – <==> CoCl4-2 + 6H2O\n\nIn this simulation you not only observe the equilibrium concentrations through their colors, but also directly read their concentrations. Note how the stock solutions exist as their ions (Co(No3)2(aq) exist as Co(H2O)6+2 and NO3-2.\n\nAssignment: Use the equilibrium concentrations after each step to determine the K for the above equation. Be sure to include appropriate dilution factors (as they will not cancel).\n\n1.) Add 25 mL of [Co(H2O)6]+2 to an empty Erlenmeyer flask. Now add 12 M HCl in 1mL increments until the equilibrium color has changed. ( Hint: Type in 1 for the volume to be transferred, and then keep clicking “pour” until you see a change, counting clicks to determine total volume added.)\n\n2.) Predict the effect of removing chloride ions. Now remove some of the free chloride ions by adding some silver nitrate (Hint: add 1 mL amounts of the silver nitrate successively until the equilibrium has been shifted instead of a whole bunch at once).\n\nNote by the above equation how Ag+ scavenges free chloride ions by tying them up in a precipitate, and thus removes them from the solution.\n\nAgNO3(aq) <==> Ag+ + NO3-\n\nAg++ Cl – <==> AgCl(s)\n\nAgNO3(aq) + Cl – <==> AgCl(s) + NO3-\n\n3.) Predict the effect of adding HCl to the reaction. Now do so in very small incremental steps until the equilibrium has shifted.\n\n4.) Is the reaction as written endo or exothermic? Right click on the flask and choose “thermal properties”. You can now change the temperature between 0 and 99 deg C. Heat or cool the system until you have perturbed the equilibrium. Then apply LeChatlier’s principal to determine if it is exothermic or endothermic.\n\n5.) Allow the system to reach thermal equilibrium (constant temperature). Use the concentration values to determine K. Now go to the thermal properties, change the temperature and click on the thermally isolated system option. Determine the new K at the new temperature. From the new K at the new temperature, determine if the system is endothermic or exothermic.\n\nhttp://chemcollective.org/activities/info/85\n\nObjective: To gain an understanding of LeChatlier’s principle through the observation of the effect of induced perturbations on the equilibrium distribution of various cobalt(II) complexes. Background: Cobalt(II) does not exist in aqueous solution as a free ion, but forms a complex ion where 6 water molecules, acting as a Lewis bases (electron pair donors), donate electrons into the vacant orbitals of the cobalt (II). This results in the pink complex ion Co(H2O)6+2. In the presence of chloride ions, a different complex forms, the blue CoCl4-2 complex ion. We use their different colors to indicate the equilibrium concentrations for the following reaction. Co(H2O)6+2 + 4Cl – <==> CoCl4-2 + 6H2O In this simulation you not only observe the equilibrium concentrations through their colors, but also directly read their concentrations. Note how the stock solutions exist as their ions (Co(No3)2(aq) exist as Co(H2O)6+2 and NO3-2. Assignment: Use the equilibrium concentrations after each step to determine the K for the above equation. Be sure to include appropriate dilution factors (as they will not cancel). 1.) Add 25 mL of [Co(H2O)6]+2 to an empty Erlenmeyer flask. Now add 12 M HCl in 1mL increments until the equilibrium color has changed. ( Hint: Type in 1 for the volume to be transferred, and then keep clicking “pour” until you see a change, counting clicks to determine total volume added.) 2.) Predict the effect of removing chloride ions. Now remove some of the free chloride ions by adding some silver nitrate (Hint: add 1 mL amounts of the silver nitrate successively until the equilibrium has been shifted instead of a whole bunch at once). Note by the above equation how Ag+ scavenges free chloride ions by tying them up in a precipitate, and thus removes them from the solution. AgNO3(aq) <==> Ag+ + NO3-Ag++ Cl – <==> AgCl(s) AgNO3(aq) + Cl – <==> AgCl(s) + NO3- 3.) Predict the effect of adding HCl to the reaction. Now do so in very small incremental steps until the equilibrium has shifted. 4.) Is the reaction as written endo or exothermic? Right click on the flask and choose “thermal properties”. You can now change the temperature between 0 and 99 deg C. Heat or cool the system until you have perturbed the equilibrium. Then apply LeChatlier’s principal to determine if it is exothermic or endothermic. 5.) Allow the system to reach thermal equilibrium (constant temperature). Use the concentration values to determine K. Now go to the thermal properties, change the temperature and click on the thermally isolated system option. Determine the new K at the new temperature. From the new K at the new temperature, determine if the system is endothermic or exothermic. http://chemcollective.org/activities/info/85" ]

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[ "# Simplifying Rational Expressions\n\n### Popular Tutorials in Simplifying Rational Expressions\n\n• #### How Do You Reduce Common Factors in a Rational Expression?\n\nMultiplying rational expressions? Want to cancel common factors out to make things easier to work with? In this tutorial, you'll see how to cancel out common factors in order to find the simplified product of two rational expression. Check it out!\n\n• #### How Do You Divide Two Polynomials by Factoring and Canceling?\n\nSimplifying a rational expression? You could factor the numerator and denominator and then cancel like factors. Learn what to do in this tutorial!\n\n• #### How Do You Simplify a Polynomial Over a Polynomial Using Opposite Binomial Factors?\n\nSimplifying a rational expression? You could factor the numerator and denominator and then cancel like factors. Learn what to do in this tutorial!\n\n• #### How Do You Find Excluded Values?\n\nExcluded values are values that will make the denominator of a fraction equal to 0. You can't divide by 0, so it's very important to find these excluded values when you're solving a rational expression. Follow along with this tutorial and learn how to find these excluded values!\n\n• #### How Do You Divide Monomials Using the Greatest Common Factor?\n\nSimplifying a rational expression? You could divide the numerator and denominator by the greatest common factor (GCF). In this tutorial, you'll learn what you need to do to simplify a rational expression by factoring out the GCF!\n\n• #### What's a Rational Expression?\n\nGot a fraction with a polynomial in the numerator and denominator? You have a rational expression! Learn about rational expressions in this tutorial.\n\n• #### What's an Excluded Value?\n\nExcluded values are simply that: values that are excluded, or left out. These are values that will make the denominator of a rational expression equal to 0. Remember, you're not allowed to divide by 0, so these values are important to identify and exclude while solving. This tutorial shows you all about excluded values!" ]

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[ "# What is the difference between a BarChart and a Histogram ?\n\nA Histogram represents the distribution of a numerical variable.  A bar-chart is typically used to compare numeric values corresponding to categorical variables.\n\nTo construct a histogram:\n\n• X-axis: Usually the range of values is binned. In other words, the entire range is divided into a series of intervals and each interval occupies a slot on the X axis.\n• Y-axis: The number of times a value occurred in each interval is shown on the Y axis.\n\nExample: Histogram of number of people who arrived at various time intervals", null, "To construct a Vertical Barchart :\n\nX axis: Categorical Labels\n\nY-axis : Numeric values corresponding to the categorical variable\n\nExample: Comparing the percentage of population of various ethnicities", null, "" ]

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[ "Home » Blog » Find the smallest value of a constant making a function meet given conditions\n\n# Find the smallest value of a constant making a function meet given conditions\n\nConsider the function", null, "Find the minimum value of", null, "such that", null, "for all", null, ".\n\nFor this problem, first we want to find where the function has a minimum. Then, we’ll set this minimum equal to 24 to solve the problem.\n\nTo find the minimum we take the derivative of", null, ",", null, "Setting this equal to 0 we have", null, "So,", null, "has a minimum at this value of", null, ". Now we plug this value of", null, "into", null, "and set it equal to 24 (so that", null, "at its minimum).", null, "Thus,", null, "for all", null, "." ]

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[ "# WBUT, Instrumentation Engineering, 2nd sem., Mechanical Sciences (ME 201) Papers\n\nTopics taught in this paper:\n\nThe topics which are covered under this paper are force on a static body, force exerted by a moving body, displacement, Newton’s law, etc.\n\nDivision of paper:\n\nThe question paper is divided into three sections as Section A, Section B and Section C. Section A of the paper contains objective type questions, Section B of the paper contains short answer type questions and the Section C of the paper contains long answer type questions.\n\nPaper Pattern:\n\nThe total marks contained by this paper are 70 and the total time duration provided to the candidates to solve the question paper is of three hours. There are total 11 questions in this paper. Section A of the paper comprises of 15 questions which are objective type of which only 10 are to be answered correctly. Section B of the question paper contains questions which are short answer type carrying 5 marks each. There are total 5 questions in this paper of which only 3 are required to be answered correctly and the total marks for this section is 15. Section C of the paper contains total 5 questions of which only three are required to be answered correctly and the total marks contained by this section is 45.\n\nQuestions will be answered on our Forum section\n\n### One Response to “WBUT, Instrumentation Engineering, 2nd sem., Mechanical Sciences (ME 201) Papers”\n\n1. 1\nabhijeet:\n\ni need last year question papers of theory of machine,fluids and manufacturing process!!!!" ]

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[ "Skill 3E\nSimple Algebra: Subtraction\n\nHere, we are teaching how to use the opposite operation and fact families to solve a horizontal subtraction problem. This is really the same as \"counting-up\" to find the answer and the preferred method for this type of problem.\n\n 12 ─ 7 =", null, "7 +", null, "= 12\n\n 15 ─ 9 =", null, "9 +", null, "= 15" ]

[ null, "http://freemathprogram.com/members1/G12345-18/Grade2/test2/graphics/03E/box.gif", null, "http://freemathprogram.com/members1/G12345-18/Grade2/test2/graphics/03E/box.gif", null, "http://freemathprogram.com/members1/G12345-18/Grade2/test2/graphics/03E/box.gif", null, "http://freemathprogram.com/members1/G12345-18/Grade2/test2/graphics/03E/box.gif", null ]

[ "# plotIndiv: Plot of Individuals (Experimental Units) In mixOmics: Omics Data Integration Project\n\n## Description\n\nThis function provides scatter plots for individuals (experimental units) representation in (sparse)(I)PCA, (regularized)CCA, (sparse)PLS(DA) and (sparse)(R)GCCA(DA).\n\n## Usage\n\n ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36``` ```## S3 method for class 'pls' plotIndiv(object, comp = NULL, rep.space = NULL, ind.names = TRUE, group, col.per.group, style = \"ggplot2\", ellipse = FALSE, ellipse.level = 0.95, centroid = FALSE, star = FALSE, title = NULL, subtitle, legend = FALSE, X.label = NULL, Y.label = NULL, Z.label = NULL, abline = FALSE, xlim = NULL, ylim = NULL, col, cex, pch, pch.levels, alpha = 0.2, axes.box = \"box\", layout = NULL, size.title = rel(2), size.subtitle = rel(1.5), size.xlabel = rel(1), size.ylabel = rel(1), size.axis = rel(0.8), size.legend = rel(1), size.legend.title = rel(1.1), legend.title = \"Legend\", legend.title.pch = \"Legend\", legend.position = \"right\", point.lwd = 1, background = NULL, ... ) ## S3 method for class 'mint.spls' plotIndiv(object, comp = NULL, study = \"global\", rep.space = NULL, group, col.per.group, style = \"ggplot2\", ellipse = FALSE, ellipse.level = 0.95, centroid = FALSE, star = FALSE, title = NULL, subtitle, legend=FALSE, X.label = NULL, Y.label = NULL, abline = FALSE, xlim = NULL, ylim = NULL, col, cex, pch, layout = NULL, size.title = rel(2), size.subtitle = rel(1.5), size.xlabel = rel(1), size.ylabel = rel(1), size.axis = rel(0.8), size.legend = rel(1), size.legend.title = rel(1.1), legend.title = \"Legend\", legend.position = \"right\", point.lwd = 1, ... ) ## S3 method for class 'sgcca' plotIndiv(object, comp = NULL, blocks = NULL, ind.names = TRUE, group, col.per.group, style = \"ggplot2\", ellipse = FALSE, ellipse.level = 0.95, centroid = FALSE, star = FALSE, title = NULL, subtitle, legend = FALSE, X.label = NULL, Y.label = NULL, Z.label = NULL, abline = FALSE, xlim = NULL, ylim = NULL, col, cex, pch, pch.levels, alpha = 0.2, axes.box = \"box\", layout = NULL, size.title = rel(2), size.subtitle = rel(1.5), size.xlabel = rel(1), size.ylabel = rel(1), size.axis = rel(0.8), size.legend = rel(1), size.legend.title = rel(1.1), legend.title = \"Legend\", legend.title.pch = \"Legend\", legend.position = \"right\", point.lwd = 1, ... ) ```\n\n## Arguments\n\n `object` object of class inheriting from any mixOmics: `PLS, sPLS, PLS-DA, SPLS-DA, rCC, PCA, sPCA, IPCA, sIPCA, rGCCA, sGCCA, sGCCDA` `comp` integer vector of length two (or three to 3d). The components that will be used on the horizontal and the vertical axis respectively to project the individuals. `rep.space` For objects of class `\"rcc\"`, `\"pls\"`, `\"spls\"`, character string, (partially) matching one of `\"X-variate\"`, `\"Y-variate\"` ,or `\"XY-variate\"`, determining the subspace to project the individuals. Defaults to `\"X-variate\"` `\"pca\"` object and for `\"plsda\"` objects. For objects of class `\"pls\"` and `\"rcc\"`,defaults, the tree subspaces represent the individuals. For objects of class `\"rgcca\"` and `\"sgcca\"`, numerical value indicating the block data set form which to represent the individuals. `blocks` integer value of name of a block to be plotted using the GCCA module. See examples. `study` Indicates which study-specific outputs to plot. A character vector containing some levels of `object\\$study`, \"all.partial\" to plot all studies or \"global\" is expected. Default to \"global\". `ind.names` either a character vector of names for the individuals to be plotted, or `FALSE` for no names. If `TRUE`, the row names of the first (or second) data matrix is used as names (see Details). `group` factor indicating the group membership for each sample, useful for ellipse plots. Coded as default for the supervised methods `PLS-DA, SPLS-DA,sGCCDA`, but needs to be input for the unsupervised methods `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` `col.per.group` character (or symbol) color to be used when 'group' is defined. Vector of the same length than the number of groups. `style` argument to be set to either `'graphics'`, `'lattice'`, `'ggplot2'` or `'3d'` for a style of plotting. Default set to 'ggplot2'. See details. `3d` is not available for MINT objects. `ellipse` boolean indicating if ellipse confidence region plots should be plotted. In the non supervised objects `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` ellipse plot is only be plotted if the argument `group` is provided. In the `PLS-DA, SPLS-DA,sGCCDA` supervised object, by default the ellipse will be plotted accoding to the outcome `Y`. `ellipse.level` Numerical value indicating the confidence level of ellipse being plotted when `ellipse =TRUE` (i.e. the size of the ellipse). The default is set to 0.95, for a 95% region. `centroid` boolean indicating whether centroid points should be plotted. In the non supervised objects `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` the centroid will only be plotted if the argument `group` is provided. The centroid will be calculated based on the group categories. In the supervised objects `PLS-DA, SPLS-DA,sGCCDA` the centroid will be calculated according to the outcome `Y`. `star` boolean indicating whether a star plot should be plotted, with arrows starting from the centroid (see argument `centroid`, and ending for each sample belonging to each group or outcome. In the non supervised objects `PCA, sPCA, IPCA, sIPCA, PLS, sPLS, rCC, rGCCA, sGCCA` star plot is only be plotted if the argument `group` is provided. In the supervised objects `PLS-DA, SPLS-DA,sGCCDA` the star plot is plotted according to the outcome `Y`. `title` set of characters indicating the title plot. `subtitle` subtitle for each plot, only used when several `block` or `study` are plotted. `legend` boolean. Whether the legend should be added. Default is FALSE. `X.label` x axis titles. `Y.label` y axis titles. `Z.label` z axis titles (when style = '3d'). `abline` should the vertical and horizontal line through the center be plotted? Default set to `FALSE` `xlim,ylim` numeric list of vectors of length 2 and length =length(blocks), giving the x and y coordinates ranges. `col` character (or symbol) color to be used, possibly vector. `cex` numeric character (or symbol) expansion, possibly vector. `pch` plot character. A character string or a vector of single characters or integers. See `points` for all alternatives. `pch.levels` Only used when `pch` is different from `col` or `col.per.group`, ie when `pch` creates a second factor. Only used for the legend. `alpha` Semi-transparent colors (0 < `'alpha'` < 1) `axes.box` for style '3d', argument to be set to either `'axes'`, `'box'`, `'bbox'` or `'all'`, defining the shape of the box. `layout` layout parameter passed to mfrow. Only used when `study` is not \"global\" `size.title` size of the title `size.subtitle` size of the subtitle `size.xlabel` size of xlabel `size.ylabel` size of ylabel `size.axis` size of the axis `size.legend` size of the legend `size.legend.title` size of the legend title `legend.title` title of the legend `legend.title.pch` title of the second legend created by `pch`, if any. `legend.position` position of the legend, one of \"bottom\", \"left\", \"top\" and \"right\". `point.lwd` `lwd` of the points, used when `ind.names = FALSE` `background` color the background by the predicted class, see `background.predict` `...` external arguments or type par can be added with `style = 'graphics'`\n\n## Details\n\n`plotIndiv` method makes scatter plot for individuals representation depending on the subspace of projection. Each point corresponds to an individual.\n\nIf `ind.names=TRUE` and row names is `NULL`, then `ind.names=1:n`, where `n` is the number of individuals. Also, if `pch` is an input, then `ind.names` is set to FALSE as we do not show both names and shapes.\n\n`plotIndiv` can have a two layers legend. This is especially convenient when you have two grouping factors, such as a gender effect and a study effect, and you want to highlight both simulatenously on the graphical output. A first layer is coded by the `group` factor, the second by the `pch` argument. When `pch` is missing, a single layer legend is shown. If the `group` factor is missing, the `col` argument is used to create the grouping factor `group`. When a second grouping factor is needed and added via `pch`, `pch` needs to be a vector of length the number of samples. In the case where `pch` is a vector or length the number of groups, then we consider that the user wants a different `pch` for each level of `group`. This leads to a single layer legend and we merge `col` and `pch`. In the similar case where `pch` is a single value, then this value is used to represent all samples. See examples below for object of class plsda and splsda.\n\nIn the specific case of a single 'omics supervised model (`plsda`, `splsda`), users can overlay prediction results to sample plots in order to visualise the prediction areas of each class, via the `background` input parameter. Note that this functionality is only available for models with less than 2 components as the surfaces obtained for higher order components cannot be projected onto a 2D representation in a meaningful way. For more details, see `background.predict`\n\nFor customized plots (i.e. adding points, text), use the style = 'graphics' (default is ggplot2).\n\nNote: the ellipse options were borrowed from the ellipse, see `?ellipse` for more details about how the confidence region is calculated.\n\n## Author(s)\n\nIgnacio González, Benoit Gautier, Francois Bartolo, Florian Rohart\n\n`text`, `background.predict`, `points` and http://mixOmics.org/graphics for more details.\n ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205``` ```## plot of individuals for objects of class 'rcc' # ---------------------------------------------------- data(nutrimouse) X <- nutrimouse\\$lipid Y <- nutrimouse\\$gene nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008) # default, only in the X space plotIndiv(nutri.res) ## Not run: # ellipse with respect to genotype in the XY space, # names also indicate genotype plotIndiv(nutri.res, rep.space= 'XY-variate', ellipse = TRUE, ellipse.level = 0.9, group = nutrimouse\\$genotype, ind.names = nutrimouse\\$genotype) # ellipse with respect to genotype in the XY space, with legend plotIndiv(nutri.res, rep.space= 'XY-variate', group = nutrimouse\\$genotype, legend = TRUE) # lattice style plotIndiv(nutri.res, rep.space= 'XY-variate', group = nutrimouse\\$genotype, legend = TRUE, style = 'lattice') # classic style, in the Y space plotIndiv(nutri.res, rep.space= 'Y-variate', group = nutrimouse\\$genotype, legend = TRUE, style = 'graphics') ## End(Not run) ## plot of individuals for objects of class 'pls' or 'spls' # ---------------------------------------------------- data(liver.toxicity) X <- liver.toxicity\\$gene Y <- liver.toxicity\\$clinic toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50), keepY = c(10, 10, 10)) #default plotIndiv(toxicity.spls) ## Not run: # two layers legend: a first grouping with Time.Group and 'group' # and a second with Dose.Group and 'pch' plotIndiv(toxicity.spls, rep.space=\"X-variate\", ind.name = FALSE, group = liver.toxicity\\$treatment[, 'Time.Group'], # first factor pch = as.numeric(factor(liver.toxicity\\$treatment\\$Dose.Group)), #second factor pch.levels =liver.toxicity\\$treatment\\$Dose.Group, #levels of the second factor, for the legend legend = TRUE) ## End(Not run) # indicating the centroid plotIndiv(toxicity.spls, rep.space= 'X-variate', ind.names = FALSE, group = liver.toxicity\\$treatment[, 'Time.Group'], centroid = TRUE) # indicating the star and centroid plotIndiv(toxicity.spls, rep.space= 'X-variate', ind.names = FALSE, group = liver.toxicity\\$treatment[, 'Time.Group'], centroid = TRUE, star = TRUE) # indicating the star and ellipse plotIndiv(toxicity.spls, rep.space= 'X-variate', ind.names = FALSE, group = liver.toxicity\\$treatment[, 'Time.Group'], centroid = TRUE, star = TRUE, ellipse = TRUE) # in the Y space, colors indicate time of necropsy, text is the dose plotIndiv(toxicity.spls, rep.space= 'Y-variate', group = liver.toxicity\\$treatment[, 'Time.Group'], ind.names = liver.toxicity\\$treatment[, 'Dose.Group'], legend = TRUE) ## plot of individuals for objects of class 'plsda' or 'splsda' # ---------------------------------------------------- data(breast.tumors) X <- breast.tumors\\$gene.exp Y <- breast.tumors\\$sample\\$treatment splsda.breast <- splsda(X, Y,keepX=c(10,10),ncomp=2) # default option: note the outcome color is included by default! plotIndiv(splsda.breast) # also check ?background.predict for to visualise the prediction # area with a plsda or splsda object! ## Not run: # default option with no ind name: pch and color are set automatically plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2)) # default option with no ind name: pch and color are set automatically, with legend plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2), legend = TRUE) # trying the different styles plotIndiv(splsda.breast, ind.names = TRUE, comp = c(1, 2), ellipse = TRUE, style = \"ggplot2\", cex = c(1, 1)) plotIndiv(splsda.breast, ind.names = TRUE, comp = c(1, 2), ellipse = TRUE, style = \"lattice\", cex = c(1, 1)) # changing pch of the two groups plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2), pch = c(15,16), legend = TRUE) # creating a second grouping factor with a pch of length 3, # which is recycled to obtain a vector of length n plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2), pch = c(15,16,17), legend = TRUE) #same thing as pch.indiv = c(rep(15:17,15), 15, 16) # length n plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2), pch = pch.indiv, legend = TRUE) # change the names of the second legend with pch.levels plotIndiv(splsda.breast, ind.names = FALSE, comp = c(1, 2), pch = 15:17, pch.levels = c(\"a\",\"b\",\"c\"),legend = TRUE) ## End(Not run) ## plot of individuals for objects of class 'mint.plsda' or 'mint.splsda' # ---------------------------------------------------- data(stemcells) res = mint.splsda(X = stemcells\\$gene, Y = stemcells\\$celltype, ncomp = 2, keepX = c(10, 5), study = stemcells\\$study) plotIndiv(res) ## Not run: #plot study-specific outputs for all studies plotIndiv(res, study = \"all.partial\") #plot study-specific outputs for study \"2\" plotIndiv(res, study = \"2\") ## End(Not run) ## variable representation for objects of class 'sgcca' (or 'rgcca') # ---------------------------------------------------- ## Not run: data(nutrimouse) Y = unmap(nutrimouse\\$diet) data = list(gene = nutrimouse\\$gene, lipid = nutrimouse\\$lipid, Y = Y) design1 = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE) nutrimouse.sgcca <- wrapper.sgcca(X = data, design = design1, penalty = c(0.3, 0.5, 1), ncomp = 3, scheme = \"horst\") # default style: one panel for each block plotIndiv(nutrimouse.sgcca) # for the block 'lipid' with ellipse plots and legend, different styles plotIndiv(nutrimouse.sgcca, group = nutrimouse\\$diet, legend =TRUE, ellipse = TRUE, ellipse.level = 0.5, blocks = \"lipid\", title = 'my plot') plotIndiv(nutrimouse.sgcca, style = \"lattice\", group = nutrimouse\\$diet, legend = TRUE, ellipse = TRUE, ellipse.level = 0.5, blocks = \"lipid\", title = 'my plot') plotIndiv(nutrimouse.sgcca, style = \"graphics\", group = nutrimouse\\$diet, legend = TRUE, ellipse = TRUE, ellipse.level = 0.5, blocks = \"lipid\", title = 'my plot') ## End(Not run) ## variable representation for objects of class 'sgccda' # ---------------------------------------------------- ## Not run: # Note: the code differs from above as we use a 'supervised' GCCA analysis data(nutrimouse) Y = nutrimouse\\$diet data = list(gene = nutrimouse\\$gene, lipid = nutrimouse\\$lipid) design1 = matrix(c(0,1,0,1), ncol = 2, nrow = 2, byrow = TRUE) nutrimouse.sgccda1 <- wrapper.sgccda(X = data, Y = Y, design = design1, ncomp = 2, keepX = list(gene = c(10,10), lipid = c(15,15)), scheme = \"centroid\") # plotIndiv # ---------- # displaying all blocks. bu default colors correspond to outcome Y plotIndiv(nutrimouse.sgccda1) # displaying only 2 blocks plotIndiv(nutrimouse.sgccda1, blocks = c(1,2), group = nutrimouse\\$diet) # with some ellipse, legend and title plotIndiv(nutrimouse.sgccda1, blocks = c(1,2), group = nutrimouse\\$diet, ellipse = TRUE, legend = TRUE, title = 'my sample plot') ## End(Not run) ```", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "" ]

[ null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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VeWSCqVJisv79ixo7W1FQAQCoXC4fC4B2EYhmXZiew5LpZluUUI+Xx+6gGLioo6OzvNZvPUiyjRNJ0TiZ6Lc1qu6kyDEDIMkxMLrtM0DSHMiavKsiyEkKKoTAcyPi7InLiqDMNgGIbj09w/zLLsyPf/NJyjtrZWpVIdOHBgz549p59++tQPOEUOh4Om6ZKSkmHbxWKxRqOx2+058X5FEASZnqHBL7/8cigU+slPfiISibgt55577rnnngsAePbZZ8cuysxJJBKRSGQie457nMHBQRzHzWbzyEe5drrH40muITsJEMJYLJZ8pdksFoslEompX9VZwDAMRVFCoTDTgYwvGo0yDJMTV5WiKAghn8/PdCDj49rOOXFVE4kEjuPTPq+CZVmCIIZtnIZzfPXVVwRB3HfffVM/1NQRBFFYWDhG/4NCoZDL5bMZEoIgyORMQ4JubW3t6ur68Y9/DAC44IILVq5cOfVjThpBEFVVVWPsACF0u92zFs+kxWKx1tbWefPmKZXK1O39/f0ul2vx4sWZCgxBkFkzDQl6w4YNUz/IrOnv7zeZTPPmzSssLMx0LGPh8/lCobCtra2xsTGZo/v7+41GY2lpaWZjQxBkdszZiSppcZO/CYIwmUw0TWc6nLHgON7Y2CiXy9va2nw+H/hfdtbpdBUVFZmODkGQ2XBqJWhu8veCBQsYhrFarZkOZxypObqnp4fLzpWVlZmOC8lSh/qDN/2l3RvJ6pYHclJOoQQdCoW4yd9SqVSn09lstmg0mumgxsHlaIFA4HA48vPzUXZGRsOw8MnPrL1D0Rd22zIdCzJtTqEE3dvbm5z8zVXnMBgMmQ5qfHa7PRqNCgQCt9vN9XUgyEjvtLiM7ujaOvX7R4e6B1FdsDniVEnQg4ODqZO/cRyvqKjweDxerzfToY0l2e+8bNmy1P5oBEkVjDEv7xn4VkPeT9ZW6JTC339uhZkOCZkWp0SCZhjGaDTm5eWpVKrkRq1WK5fLDQYDhFn6Zk5m58rKypH3DBEk6YXdtgTDfu+sEh6B3XOOrtUe+qw7q1seyASdEgnaarVSFDWyA7eqqioSidjt9oxENbZIJDLsrmAyR3d2dmY2NiSrGN2xd1uH1p9epJHyAAAr9YoVFfJnv+iP0TlQLgYZ29xP0LFYzGazKZXKeDzuOxFN0zKZzGKxZGF1DrFYvHjx4mEfKlyOXrBgQaaiQrLQpp3WAhn/20uOlwDbuKrUHab+dsCZwaiQaTH3l+nzeDwsy3q93jG6m30+X35+/mxGNRFSqXTkRhzHxWLx7AeDZKcv+nx7zYHfXKznk8cbWxVq4ZUL87fud6ytUWilc/9/fA6b+3+8oqKisYv0YxiWJYsMIMhJoRj43Bf9S0tlq+ephj1024rijzo9L+wZeOQbaN5pDpv7CRrDsJyokYYgJ+udFpfVF08w8Ja/dYx8lIXg027ftYs0TSU5UM0OSWvuJ2gEmatqtOLL5mtGe7ROKwYAKkXofzyHzZ0/XjQaHRoaQoWEkFPHohLpopI0NyqSuHrQsxYPMu3mToLu6enx+/1CoTALb/chCIJMwhwZZudyubjsbDQac2K1QARBkHHNhQTNsqzJZFIqlY2NjYlEor+/P9MRIQiCTIO5kKD7+/vj8bherxeLxcXFxVarNR6PZzooBEGQqcr5BM01mYuLi7nlJsvKygiCMBqNmY4rPbfb3dvb297ebjQaQ6FQpsNBECSr5XyC5mrwJwdvkCRZXl7OdUlnNrBhGIY5evRoe3u73+9nWdblch0+fDhrP0gQBMkGuT2KIxAIDA4OVldX83i85MbCwsKBgQGDwZBVK6v29fUFAoGmpiauoh6E0GazGY1GsVhcUFCQ6egQBMlGud2CNhgMYrF42PKvGIbp9fpQKOR0ZkuxGJqmBwcHy8rKkvVOMQzT6XRqtTo7a+khCJINcrgF7XQ6g8GgRqOx2dKs8cPn800mk0ajIQhilgPz+/3hcBjDMIlEIpfLAQCxWAxCqFAohu2pUCjMZvMsh4cgSK7I4QQdj8dJkuQKh6bdAcfxRCIhEolmLaRYLNbd3e33+7l1WyCEKpWqpqaG+3XkAG2WZXE8t7/EIAgyc3I4QZeVlZWVlQEAIpFI2gqcZrPZ5XJx+8wClmVbW1sBAE1NTUqlEgDg8Xh6e3vb2toWLlxIkuTQ0FBqIxpCODQ0JJPJZic8BEFyTs4334LB4MGDB0dOTgmFQlar1WKxhMPh2YnE5XLFYrHGxkaVSoVhGIZheXl59fX1oVDI6/WWlpba7XaTycQtDhCNRjs6OiKRyKx9fiAIknNyuAXN6evrAwBYLBatVsvnHy+raDAYuCqjfX19s7MESSAQEIvFw9rycrmcz+cHg8GKigqWZa1Wq9VqxXGcZVk+n19XV8d1UiMIclIoBu4x+c+uUmKZjmRG5XYLmrtPWFdXh+O4yWRKbufGQev1+srKSr/fPzQ0NAvBQAjTdijjOM5VFCsrK1u2bFldXV15eXlDQ8OyZcs0mlFrRSIIMoajA6H/916fzTfH5wzncAuaYRiTyZSXl5efn59IJAwGQ1FRkUwmS5bm4BZSUalUBoNBrVbP9O04iUTicrkoikodlB2LxWKxWLJZLRAIULE9BJk6hgUAAHauF1PNjRZ0NBr1eDzDNlqt1kQiwc3y4KZ6GwwGkFKag9tNr9cnEom0Q/GmV35+PoZhnZ2diUSC2xKLxTo7O/l8PmopIwgyCbmRoH0+X1tbW+qcjlgsxt0YDAQC4H+TUwKBgN1uTy3NAQAQi8VFRUUWi2WmKyjx+fz6+vpwOLx///7m5uYjR44cPHgwkUg0NDTM/lhsBEHmgNzo4igqKopEItz9wOLiYgBAZ2cnN8q4vLyc24fr0zCZTKmlOThcdQ6TyVRbWzujcapUqmXLljkcjlAohGFYfn5+YWEhys4IgkxObiRoAEBVVRX435gNhmGCwaBEImloaEjtWdZqtR6PR61Wp/YCAwBIkiwrK+vr6ysqKprpURMkSep0uhk9BYKcgp7Zad3Vd7wCWpyGAIB73+oh8ePjOC6dr7nptMI0T85ZOZOgQUqOxjCMIIja2tpkby/HarUCADweT3Nz87Bbgtw4CqPRuHDhwlkMGUGQ6bFEJ+MTx/+pHYHEx12eFRVyufB4EmsolGQitBmUSwkaAKBSqQYGBiCEDMMcOnRotN3i8fiwGd4YhimVyrQTDhEEyX7nVCnPqVImfz1gCX7c5bl+aUGZSpjBqGZaLiVoj8fT0dHBFYTzeDzFxcV5eXlp95RIJMN6ORAEQXJOziRoLjsrlcr6+nocx/v6+ux2u0gk4u4ZIgiCzD25kaB9Pl97e7tara6vr+cqwyX7owmCQAXvx+X3+6VS6eTGk0QiEZIkU6fRIwgyO3JjHDTLsoWFhfX19YODg11dXVzdzqqqqrKyMgghhLC7u3tgYCDTYWYpCGFLS4vb7Z7c07u7u1HRaiTbKEQEj8Ak/Dk+hjU3WtBqtZqbty0QCIaGhiiK4gbYlZeXQwg7Ojo8Hk9WT6Fmabb/MOsxYSIlXtSEyYtm8+TcCBY42Umx3EfgtEaEIFM1L1/86fcX84i5XSspRxJ0klKpbGhoaG9vb29vb2howDCMy8719fXJ1aSyDWtvobb/ih0yAJwALANwklz6bd7Z3wd4jl18BMkqcz47g5xL0AAAlUqVzNEYhnm93vr6+tGGc2Qc9Nvib96Na/SCG7bgBfUwHmKa36J2vwgYirfmoUxHhyBIVsu9BA0AUKlU9fX17e3tEMJszs4AAHr/XzC+SHD1ZsAXAwAwkYJccSvAMOqr58kVt2JidaYDRBAke+VkgoYQOhwOAACGYQ6HYxZKiU4aa2/BK8/isnMSUbeW+vKP7MBRouqcmTipy+Uym83wf7iLYzQaLRZLch+5XJ62MkkkEunu7k7tdI5EIrFY7PDhw8fjJ4jGxkZUYwRBZlruJejkXUFuQHSyPzo7czRkKJw3YqYTTwQAAAw1QycVCoVKpZKbb8kwDI/H42pSp86ulEqlaZ9LkqRUKk1N0LFYjMfjpe5PkmR2Xm0EmWNyLEGnZmeuZyP1nmEWZg1cXcn2HwEAAnD8hgZrPQQAwPMqZ+ikMpmMW4s2FoslEgmpVOp0OgsKCiYyYJzP51dXV6duCYVCEolk3rx5MxQtgiCjybqMNjar1ZqancH/7hn6/f7U7++zo6ura3BwcOx9yIVXsK5u6ovnku1ldrCL2vE0rluC5elnPkYEQXJYjrWgtVqtSqXimodJKpVq4cKFJDmrr8Xlcg0ODno8HpVKNUbdD7xiBe/s71NfPc+0/wcvqIdRHztwFM+r5F/0y9mMFkGQXJRjCVooFHJrdQ8zWo/qDGFZ1mg0yuXycDhsNpuH9QkMQy6/Ba9axbS+Cz0mTKnjNV1CNq0DOLrDhiDIOHIsQY8tEAhgGDasfT0THA5HIpFoampyu91ms7moqCi5wlZauEaPr35gpqMaDVc+e9LfMEiSnOVvJwiCcHKsD3oMNE23t7e3tbXRND2jJ4rH406ns7i4WCwW63Q6oVDILVabtTAMW7FixaRHizc0NFRWztT9TARBxjB3ErTFYuFGlXHrqswQ6DEZW/cCliktUAEAMAyrrKz0+XxDQ0Mzd9Kpm+j4FirG9H1BH/gr0/4f6D+2DjpBEFwFQQRBZtmMf3VlGCYWi427G03TLMtOZM+0YrGY3W4vKioCANhsNpVKlbarekr8/XDHkyGva6jx3nLjG/TB/2OW3gSW3iSRSBQKhcFgEIvF2TbOj6KoiV9VaP4a7Pg9CDoATwSoKMBJsPBq7IwNs9NdzrIswzCzcKKpm+J7dTYxDAMh5Ko/Zjnui2+uXFUMw6b9mzrLsiP/UjOeoHEcn+DiJhRFTXoZlJ6eHj6fX1paimGY2+3u7++vr69PPkrT9BR7UWHUT79zD0YK+hc9ICaEeWvuIno/Zva+QlAR4px7Kysrm5ubXS5Xti0Xy/3JJ3JVoauH+s//Ybol5OXPYHmVIBFmDr/BfP0STpLEyrtnIVQuO+fEOjg5FCqGYRDCnAiVy3c5ESoAAMfxaZ9Jy7LsyBbejCdo7g7VuLtxH0qTe80ej8fn89XV1XFF5SsqKrq6ugKBAFffLhQKHTlypLa2dir1SKnmN2E87PvWH0JWx/y6Glwg4Os20kIZtfsF3mk3ymT5RUVFNputqKgoqwrb4zg+wauaOLAVkxUKr3gGEDwAABDJiTNvpyBN79vKW3EbJpiNQTIsy+bE9HEcxyGEOREqy7K5EirXjZYToTIMMxMJOm1HYnZ9JZ8ECKHBYJDL5cn8q9Vq5XK5wWDg5iv39fVBCI1G41S+6LH9B7GKM8wON47jVqu1u7u7tbW1Q7iwu+bO1rb21tbWYDDIMEzuFrZnB44SVWcfy87/Q9ScB5gEHOzOVFTI3MOwsM0R/rDdvc8cCMZyo1Mrg3J++JTdbo9Go8Pq/uTn53OLFvJ4vEAgUFVVZTAYrFZreXn5JE9DxYBCqlarua+3DMMQBAEInKEjOKQxkiRJUiAQ5PCq4SwNSMGwbRi3hZ2pmiHIqabZFvrdpxaDO8r9KhUQd5xRfM1ibWajyma5naApirJYLIWFhaljn6PRqMFgIEnSYrHgOK5Wq4uLi2OxWH9/fzgcbmhomMSJMGUpcByt+dZPAMAghLFYTCQSsdZD8d4/C057GS+pH/8Q2Q3L07PWg8M2MtYDAGDYjNUMQU4phqHoxrd75uWL/nRNba1W7Aol/nFo8OkdVgjBtUtQjk4vt7s4zGYzTdMSiWQoRWdnJ47jNE3TNJ1IJPR6PQBAp9NBCN1ut9PpnMSJyPnr2KE+es/LAB7rJ4GhQeqz32N5lXjxgul8SRlCLrqatbdQX24+XjPEepD+cjMx71xMiv55kGnw2r4BjYT33FU1i0qkIh5ephI+fF7ZZQvyX/7aTjFoTbX0crsF7fV6AQB9fX2j7UCSJFdj0263c1uMRqNGoznZDgwTjWEAACAASURBVH68fDm54lbqqxfpzu148QIQ8cUs+zC+mH/VswDL7Q85DlG9irfyLmr3S8zRf2OaahB2s0O9eMlC3tqfZDo0ZI44Ygt9qy5PQJ7w/3JBvfrdFlfvULRsVos15IzcTtDLli1LHTwLIWxubiZJsqmpqbOz0+fzcXcOY7GYzWaDEJIkSVGU1WqtqKg42XPxVn6PqDqHPvwGHOwCPCm57AbeaTcB/lgzvHMLueI2Yt5quu0D6DFjJQvIM24jas9PrZKKIFORoKGEP7w1wy3LHadZ9E5LK7cTNIZhqQOcbTZbNBpdtGhRJBLxer1qtdrhcBQXFxuNRgghn89PJBIAAKvVWlBQkFq9foLwoiZ+URPXB807+adnM4ZhbDZbIBBhFOeIi8RFRUWzXH8KmfNKlYKjjvCwja0DYRwDpUoBAImMRJXl5sLXcw5N0xaLpaCgQCaT9fX1SSQSbmR0V1eXx+MBADQ1Ncnlcm7i8sxVz2BZdmhoyGKx2O32YDA4Q2eZXuFw+MCBA1arFcMwoVDo8/mOHDky+/W1kblt3XzNboP/3RZXsr+5ezDy8h77mZWKPEluzE+Zfbndgk5lMpkghBUVFW63OxQKAQB2794NAIjH4wAACOGhQ4eSO3s8Hq/Xy81kmQiWZd1udyQSIQhCJpONNhvF7/f39PREo1Eej8cwDMuyGo1m3rx52VwNDkLY2dnJ4/EWLVokEAi4LWaz2Ww2KxQKhUKR6QCROeLiRk3bQPi3n1rePOLiRnEc6g9V5gl/eP5kB7+OiYVwjzHQOxQlcayuQLy0dMaLXM6E7E0cJyUcDjscjoqKCj6fr1Aokusz+Xw+l8sFAKisrOSypMPhCIfDIpHIYDAsWbJkImWA/H5/d3c3tzQfVzJCoVA0NDQMS7vxeLytrU0ikTQ0NIjFYgihy+Xq6+vr7u6e3Ni+2REMBiORyIIFC7jsDADAMIz7nHM4HChBI9MFx8D/faP8G3XqD9vdFm9MI+E9sLr0svkaAp/+3ue+oejP/2vqcUVkQoJmYJRil5bKHv1WhVaaRRN9JyJXE7Tf72dZNtkE7uvrwzCMz+dzVeW41MkwjNvt5nbAMKywsBAAoFarDxw4IBKJhoaGHA4HV19pDFzalUqlTU1NXLe1y+Xq7e3t7OxsampK7ub1ejs6OjAMa2xs5M6OYZhWqwUAdHV1RSKRrJ3DEo1GAQAji2jLZLJIJJKJiJC5bFmpbNkMN2bDCea+t3vkQvKVb9c1FEogAHuM/sc/MT/8775Xr6/Dc6o0Y04maJqmOzo6WJZdtmwZn89nGCYYDLIs29XVNdpTrFZrIBDgfhYKhW63G8dxs9ms1WrHHnJnt9sxDEttL2s0mkQiYTAYQqFQ8k5aIpFgGCYvL29Ys5qrwhwMBrM2QXP1WWiaHtZvQ9N0thXnQ5CJ+KDN7YvSL3+7rkDGBwBgAJxZqfjFhfq73uj62hQ4s3IGvxR6ItRf9ju/f3YJOU1fC3IyQXOln3EcN5lMNTU1BEGcccYZI0tt9PT0DA0NSSQSkiRTywOSJMlVEZLL5Vy9jjGEQiGFQjEs7SqVSu6hYUMdRnaYcFvGPUsGyeVyDMMGBwdTS/ElEgmfz1dSUpLBwBBkcjqdkXn5Yi47Jy0qkcqFZIczMqMJusMZ+cch57WLtYXy6elLyb0EHYlE7HZ7WVkZQRAGg6GoqEgmk+E4Pqy5xzBMIBAQCATFxcUjD8Lj8YaGhgoKCka7fUdR1KFDh7hZiKlpd2BgwGq1NjY2gnRpNxAIDKsZ6PP5AABjL4iVWQKBoKioyGQyMQzDXRC/3280GgmCSHvpECTLsRCSRJoGLIljLJu9TaW0ci9BGwwGPp9fUlKC47jD4TAYDAsXLhy5WywW4wqr9/T0jHaoUCg02kJQPB5PoVB0dXUplUquvxvH8YGBgd7eXqVSaTKZwP96ulNRFNXd3V1dXc09FAwGe3t75XL5LCyTOBV6vZ4kyf7+/uTQOu5Ga64U5811EACGhdP1pRip0oh29Pp8UVopIgEAdn/8rwedzf0hT4Q66gjbfPES5fC6YFkrxxI0Nzyurq6O6zjW6/VHjx51uVwjaz1LJJKzzjprKueqra3FMMzlcnED0bhlUzAM8/l8GIZhGNbV1WW329VqNYZh3MA+jUbj8Xj27t3L4/EghIlEQiqV1tXVTSWMWYBhWHl5eVFRUTgcpmlaLBZnc5N/7vntJ+ZWe3jLDfW8dO0+5GRd1Jj3532On3xgeOSbFS320K+3m8V8nGKAmE8Y3bHr/9L+o2+Uf7NOnekwJySXEvTI0s8qlUqtVhuNRoZh4vH45KuJpoNhWE1NDQDA5XJ5vV5uQAiO4wzDiMVikiSDwWAgEAiFQlwFdwCA1+vl1rDgpizm5eXV1dXlyq02Pp+fVasNnCK6BiPvHx1iIfjXkcFvLy3IdDhzgVrMe3xd1c8/NF792lGGgSI+4YsypUrBYxfri5WC32w3P/axeVGJdFgn9eTEaNYdPl6Pl/vZEUwwKf2fBTL+pL8e5VKC5mZyD2uQ6vX6Q4cO9fb2QghVKpVcLp/GM3I5OhqNBoNBbg0FrVYrk8mkUik35M5kMtlstuXLl7tcru7u7uXLl0MITSbTwMAAVzxvz57dJdhQkWcfxtJ4fg2x6GpMhEYWI8dAAJ763FqqEjYUSl7dO/Ct+jyVOJf+JbPWslLZP29u/OVHpi/6fGvmqRaUSL9Zp+a+oDx8XtnOPt8nXd4blk3Dx+EP/t17wDJ8wvBdb5wwnOzaJdr7VpVO7vg5827gihwVFhYOGzghEomEQmEkEhGJRH19fYsXLwYAQAg7OjqKi4u54RZT4XQ6g8GgSCSKRqMqlUqv13O1OLhH8/LyrFZrcrwwhPDo0aPhcLi8vFylUjGOjsHWz/vzTo/waqv8e6n9W+mDf+Nd/Gui4owpRoXMDds7PS320FOXV9dpJV/2+V7cbft/MzOt7hQk5hMaKb9cLfzR2hMuqVRAlKsE/b7pWZ32h+eV2wPx5K9tjvALX9l/dkGlOuWDtkoz+bo9OZOgTSYTTdMCgcDhcKRuj8fjXH4UCAQ+n8/hcBQWFtpsNm5m9tKlSycyV3A0Doejp6ensLCwurp69+7dHo9ncHAwtb972Cg6t9sdCATmz5+vVCphPBj/6Idlmipp+eW9FqLsrCsleIL6z0+p9/8Pv+1tTJwbXWDIzInT7J922c6qVJxRoQAA3Hx60eZd/evm59cXZOmQ+Zwj4uFpV9UKxBgRf3pWFCxRClJvOSYYCABYWCydrmF2udE9CgDg7sKZzeaeEyUHHsRiMY1GYzKZIpGIxWJRKpXRaNRms036jMnsPG/ePAzDZDIZSZLd3d3cZEUO1+mcLIzn8/nEYjHXbGe6PoGxIP/CXxWWVRME4fP5MLGad+EvIB1nOv47+QuBzBVb9zncYereVceGn1+zWKtTCp/ZYc2xgWBZbIlONhSm9pj8qRv3mgPOYGKpLqsHViXlTAua67sYZnBwsKurq6mpiZvzHY/HDxw40NHRAQCora01m80Wi0Wr1U7i3hdFUT09PUVFRdXV1dyW4uLijo4OsVjc3d3NBeNyubgFt5Lj7RiGSQ5Ng24jpijBZFoAAEmS3DQZTKzC8yqh2ziJK4DMJc5g4m8Hndcs1paphNwWHoHdfXbJw+/1fd7tXVMz0TJeyBiWV8iXlcoe+cB4+xlFZ1cpAQC7DP6X99gX62Rn6nPjVlDOJOiRWJY1mUxqtTpZkUMgEOTl5blcLqlUyufzKyoqhoaGzGZzsnbSxPF4vCVLlqSONtNoNJWVlWazGcOwzs5Omqbj8bhGo+Ems6jV6tra2nA47Pf7IYQYhgGCB+gYAICiqEQiIRQe+z+EVGzY4tnIKejZL/pFfPyW5SeUgjm7SrmiQv6HL/rP1CuEZM58u81aGACPXVK1eZft2S9sm3b2AwBwDKxr0nz/bF2ujGdM8yagKOrJJ5+c/VBOltVqTS45mMTN3AuFQpFIhMfjlZaWOhwOrnvkZI0cC6zT6ZYsWaLT6YRCoVarnT9/fn19PTeKjsfjabVarVZLURQ3jQUvXgBDLrr/SF9fH47j3IwY1tkJvZa5sYwhMmkt9tBn3d7vnlkiFQzvCd24qtQdpv52cDIrZyIjSQXEw+eV/fuO+c9cMe+ZK+b9+/YF/+/88pGXfbqUq4TLymTK6RuKk+ZAPB5v9+7dV1555STWhZo18Xi8v7+/uLg4dWEUi8VCUZRCofD7/V1dXYsXLy4pKXE4HH19fWlnG06CSCQqLS3lVvUe+ahEIqmoqDCZTF6vV6Uqpepv83VZE3xF7bxqHNKmw7sDTjOz6IdirLw4GMzy6YXIzHlptx0C8NyX/X/c1T/yURaC1/c7blhaIECN6GmikfA0s7ImgE4pePbKmmk8YPpML5PJli5detpppyWTyJtvvjmNZ506k8nEsmwkEuF6nAEA3LhjDMN4PB43tc/j8ajVar1e39bWlna24UwoLS1VKpVWq3VoyI3lL5EF+gpb/gRbyf21dzKESEHyhUXzguHIkSNHSktLs/kjEJk5N55WeHr5WAP2lSISZWcEjJagb7nllltuuWWWQzkpPB5PKpVSFEVRx6bxcGOT+Xx+LBaTSCSxWKyvr0+pVHKd1EajUa1Wn+xi3pMjk8lSKvQvZxcsPdw7QEK4sDxfpDuP22qxWLglSya+qgsyZywvly8fM0EjCCd9gj777LN7enq4lJdIJB544IEvv/xydgMbx7Cu52g0euDAARzHk01+kiS5tfVEIhE3Edxut5eWTnI+z1SERcURZnD+/PmilFkzZWVl3IoBKEEjCDKa9An6u9/97hdffGGz2U4//fTDhw9/5zvfmeWwTpbdbgcASKXSZNFnAACfz+dWEcRxXKlUntRgu2MjMabDGEuWjFxVFka8TO8O6LVgYjVetgwvqJ+WGBAEyUXpE/S2bdusVuuPfvSj66+/XigUPvLII7Mc1kmJxWIOh2Nkly5FUQcOHBCLxZMYZnf48OHCwsJpKYjM9avQND2sg4VhmOFb2v9DffZ7GA9h8kIY8YKdfyAaL+Kv/REgUA0jBDkVpb8Rwa0f2tjYuH///rq6ujFKKmcDg8FAEERRURF9IgzDiouLJzfMLrV3e4pkMhmGYU7nCQOnEomEx+NJXZKV7T+U+PBneOVZwu9tF97xnujenby1P2K6tlOfPz0tYSAIknPSt6AvvfTSyy677Lnnnrv44ou5CXWzHNbExWIxrhDovn37RtvHZrPV1tbOYlAn4JYX4Jbp4qYdBgIBo9GI43jqmlL0/tdxTRX/wp8DDAcAAAwnF1wOIl5q94vkyrswIbqnhCCnnPQJesOGDYlEoqKiYvPmzTt37vzTn/40y2FNnFAobGxsHLkgYaphBfCmBxWlD/2DtbfAWBDP0xOLrsK1o45/rKioIAjCarX29x8b96pQKBoaGlKXLGEHO4n6C45l5//Bq88BuzZDVy9WumT6XwKCINlt1Ba0SqVav379ddddd84558xyTCdLrU5TGQ5CGIlEIIRisXi0kvnBYLClpWX+/PknXUXaY4xt+wGM+ojSpZi8kDHvpVvf5Z21gVxxa9rdMQwrKyvjliyhKCr9kiUQAGzkKED8f48hCDIlNAut3vhgKKFTCIoUgpxYYix9gu7u7j58+PAbb7yxcuXK+vr69evXr1u3bpYjmzQIoc1m47oUAAAYhpWUlHCLzA7bs6+vj2XZ3t7e+vp6p9OZuggswzA+ny+1Yc71VAAAAGTBR49iAqngupcwRTEAALA09dXz1K7NeMlCvHTpaIHxeLwx6lPj2nmsaQ84+y4Ajr9xWNNugBOYpuokrwGCICf4yuh/+nOrzX+sdnNDoeShNWXZX9l11DnjixcvXrhw4YUXXvjII49cddVV3BpOOaG3t9fpdBYVFWk0GhzHPR5Pf39/KBSaP39+6m6Dg4PBYLCsrMxisQwMDHg8ntQEzU1TTH3VyQTN2luBx8S75k/HsjMAACd5Z3+f6dlBt/6bP3qCHhu59Pr4m3dT2x/jnbsR8CUAQKbzY+qrF4jGizDRVJcdQJBT2Rd9vv97v++0MvkPv1FeKOMb3NGX99i//2bXS9fVTaWa/ixIn6A/+OCDd99998MPP5w/f/769evfeeedWQ5r0sLhsMPhqK6uTt7YlMlkcrn86NGjQ0NDGo2G28gwjNFozMvLKy8vj0Qig4ODy5YtS12le+/evYWFhWkXOYReCwAAL5p/4mYML54Ph/omHTlevpz3jR9SO56h2z/AVWUw7IYRD1Gzhr/mB5M+JoIgAIBnv+hfXq548vJq7supTik4vVz+nb+0v7jb/tt1Wf31NH2C/sMf/nDNNdf89re/Tdu9m21omqYoiqte5PV6cRwvLCxM3UGlUonFYq/Xm0zQVquVoqjKykoAgF6vP3DggNVq5X4FAAAAARUDzChfGkgBAAAmwhhPeML2eAgM23KSyIVXEvqzma6PodcMxHlE2Wk4ujeIIFPjCCT6ffG7TywxKiTxbzXkvb7fMerTskP6BP3RRx/NchxT0dHREQ6HuSYwwzAkSY6cBMjj8ZKTDGOxmM1m0+l0XE4XCAQ6nY5b8JDbAkNDMBGCAQcAaWa44CWLAIYz7R+Qpx2fYAnDQ4x5L7n026zlAOs2YAIpXtSEqcpO9rVgMi257IaTfRaCIKOJJBgAgEI0PNepRGSMYlkI8WmaMzwTcrhgP8ftdvt8PgzDzGZzVVWVQCDg5pikjmDjRnQUFBxbxJeb2JJal6O0tNTpdBoMhsbGRu4Z3NPSnhGTaUHTZdSXmyEVIxsvxkQKxnqI2vkMRvDYnh30nlcAwQMMBTCcXHAZb/WDXIsbQZCM0Mr4BI71uCKLSk4Ybts9GCmU87M5O4NcT9AQQqPRqFQqFQoFt/qUWq3GcdxgMNTU1CTb0WazmaIortyoz+dzu901NTWpgzpwHK+oqOjq6vJ6vVz1IgxCMMYfbuU9pFhJ791C737x2BGKm2DYDXBC8O1X8KImQMfoo+9TXz4HqRj/wl/M2AVAEGQcUgFxtl7x532O08vk5epjnZAHLMEP2t03nVY49nMzbvwEzTDM0NBQsvmZDSKRCEmSfD6/v78/FovV19eLRCKuCTx//vzq6uru7u5gMJiXl4dhmNfrDYVCZYV5UokYQmgwGKRSafLlQL+dG4yh1WoHBgYMBsOSJUsAAJW9f5GVfm/UCHCSd/b3yCXXss4OEA9ieVVM18eM28S/evOxERd8CbnkOkDwqI8fh2fegSkzUEUPQRDOg6vLvvdm102vt59TpSyU8w1Dsa9N/sU62XfmQII2mUzV1dVwlO/7s4+iqObmZrFYXF9fz3Ucc5M+KisrOzo63G63VquVSqVms3loaIibqLJgwQLB1ssS5af7VzwQDodxHN+zZw8AANBxSMcxgZGbv8eyLMuyLpcrXwxkwT7+eAXTMUkeoV95LKrPn8TLThs2Ho6o/Qb18WOsvZVACRpBMkcj5b1+U8PfDw1+bfK3O8I6pfD/vlF+UaMm++eqjJ+g9Xr95Nb0myFms5lhmEAg0NHRgWFYciScRqNRKBQGg4Ebs1Fff0KhTuabjyS2/UiECysX3nYsHZv3Mda9ePlyoqyRtexjTHu5PflHfhmLewAAiXcfTD0CUXUO//KnRg2LoQA5fAjHsWEezPQUXUIQZNL4JL7+9ML1p6dpMtMsJLM1VQ9P0FdffXXa/bJkyatIJOJwOMrLy71er9/vr6ysTL0ZWFVVdfjwYbvdrtPphj2RqFnDv/g3iW0/0rJR/sW/oQ/8ldrzLHn6et5ZVwAAoJhmjt8/WA7jQXrns1x5DRpiJAYBALi6EowOz9Mzln2AZQB+vGubtR4CAGB5Yz0RQZCMGAwl/rTLttcc8EXoIoVgbZ36O6cVinjZtdLY8AS9YcOGjMQxQQaDQSAQlJSUDA0NYRiWWp4fACCRSAoLCy0Wi1arHVmeP5mj41tvYIf6yNPX8865h3sIU1eQ6orknjA0SO98lqg4g6hetW/37nnz5o27niGx8Ar66HvUJ7/lrX6AGw3NDhkSn/wWL6jDi5qm/sIRBJlG3YOR773ZLeLhly/Iz5fye12Rvx10ftHre+Ha2plb83sShifo8847DwBAUdTLL7/82Wef0TR97rnn3nXXXZmIbbihoSGv19vQ0DA0NBQKheRyuc1mk8vlqbk4Ly/P4XCYTKaamjS15YiaNUTdWqb9P5i6nLdy/BcFIWQYZtjHQFp4YQPv/P9Hff4k0/s5XlAP4yHW0YYpdfxLHhtWoA5BkIz73aeWQjn/+WtqpQLCH6NN7miRjGd0x27Y2vbds0ouaMjLki6P9H3Qd999d19f34033sjj8bZu3Xr06NGXXnppliMbBkJoMpmUSmVeXl5zczMAIBAIAADa2tpG7jw4OFhdXT2yiB29789M+3+I6nMZw67Eth/xL/4NwKdtoCG58EqiYgXd8g4cMmBKHa/hQnL+pYCYjcXeEQSZOGcw0eYI/+LCSqmA6BuK3v2vbpqF59eoRPyIyRP71UemT7u9v11XlQ0d0+nT03//+9/e3l6ue/faa69N2xqdZckRdQCAuro6bqE/j8djs9n0ev2w6p08Hi9tdqa+eJbr2WC6P0ts+9G052hMUcI7++7kryzLosYzgmQbd5gCAOiUQgDAr7eb1GLec1fVqMTkPw45N++yPXFp9cPv9b3d7LpmsTbTkY6y5BVJkh6Ph/vZ6/UOK20x+xKJROqIOoFAoFQqlUqlXq+XSqVOp1OhUChTjKy2TO95ifriWfLMO7l+Z6JmDf/iXzO9OxMf/ATA4cX+MYEUU+ow+ZRedSQS2b17dzwen8pBEASZdty0b1coYfHGOpyRO88sVolJAIAjmFCJeGfpFSv1io86PZkOE4DRWtDLli1raGhYu3YthmHbt29fuXIlt27sxo0bk/WGZpPJZGJZNi8vb+SAP61WazAYnE7n2J8idMs7vDPvJM+8M7mFqDmPf/FvqI9+CYODyVwcCoUcDgeEEKx6AvgB9PUAAAYHB1PPKxKJ8vLyxo2ZoigIIU3T3AKPCIJkiRKFoDJP+LeDTm6iCldxdDCU+G+757xaFQCgOl90dCCc4SgBAKMl6FWrVq1Zsyb5cywWEwqFAIDUgpxJDMM8/fTT4XC4rKzslltumfYQWZYdHByEEB49enS0fRwOx9gJWvjd/4zcSNScR9Scl7olHo8Hg8Fhu8Xj8dTK/dxHxbhhQyoCAGCOvsfIFLhuyRTb4wiCTKOH1pTd93bPU59bAQBf9nkpFvz9oFNA4rcuLwIAeCJ0lozlSJ+gt27d+uqrrzY0NAAAvvzyyw0bNqS9F8f5+uuvKyoqrrrqqieeeKK/v3/kGOQpwnF82bJlI4dSsCzb2dmZl5dXUFAwclDd5OTl5aUmXwjhrl27SktLeTweV+WD2xiLxcY+DtO5nfr6n6DqZrr57UTACEg+efrNvDPvAGMV+EAQZJYs0clevq7u6Z39dn/8D1/YeAS2tk5911kleRKeO0x91u39Zl1WVFpOn6A3bdq0fv36yy+/3Gaz7du3b+vWrWMcore3d/ny5QCApqam3t7eaU/QAACu/T6MxWKJx+MOh0On001Xgk6L6/fQ6XQpBaPHwlr2JT74CVZ/FQBAcMMWIQnpfVvp3S9hpJA8/TvjPh1BkFlQoxX/6eqa91qHHvvEfFqZ/Bu1am+U3m30v/L1AI/AvpNuzuHsS5+gly9f/uijj65fv54giA8//HDp0rGWcQqFQmKxGAAgEomSfbVbtmz59NNPAQDV1dU+n2/cOLgRxxPZk0NRlNVqValUwWCwu7s7tXboNOIqkLhcLhzH7Xa7RCLhPglYlh159y8QCEQiEQAA3ncQ1NwY150L/H6j0UiSJMhfgy2S4xaLsNAkk8/e+lUsy0IIJ35VMwhCOJGvJtmA6+/KlasKAODellmOe69S1GzXRTinlCTXFL6wb+i+t3u4LSvKJHeenkdS4dH+wtxVHVl0fopYlh3ZT5A+Qd94440ul+vw4cOBQOD2229fvXr1448/PtpxJRIJ9w6IRqPJ4RMrVqzgmtJHjhzhquCPjabpeDw+kT05AwMDOI5XVlZ6vV6j0VhUVCSTySb4XAAAYGkQ9QHJODc8ub8EwzCNjY3d3d2Dg4Pz5s3j3kYj2+xut5sbmg0ExUAgYyMRAEA4HD72hxSXAyDND3pEBUUnEefUUBRF0/TEr2oGsSzLMEzqxP2slUgkWJZN+60u2zAMAyFMe+so23AtnozcUT+vTrS6VjMQSHgidJlKoBCO0/tM0zSGYSMXoZ4ilmVHHjP9X+7888+/+eabuZ+/+uqr3/zmN2Mcd968eR0dHXV1dR0dHddccw23sa6urq6uDgDQ0dExkYuOYRhFURP88wQCgaGhoerqaolEIpFIXC6XxWJZvHjxxD/T6EPv0Pv+LNzw4di7cW8alUqlVqsrKyu7u7sjkYhSqQyHwz6fb1izvaqqqqqqimGY2HPnClbeFa6+qKWlpampifvQYm3N8b/fL1jwN3wW34IQQpZlc2IYCcMwE38DZBb3WZIToXJDiWa0A3C6cI3HDF5VvVCgn9ieGIbhOD7tH3ssy47MYOnHQd98882vvPLKHXfcEQ6Hjxw58uijj45x3BUrVlgslieeeEKj0cxEB/RIBoNBLBYnh21UVVWFw2Gn03kSh6CigIqOvQuMB/v2/pdg4qWf3x97+TJ15z9kUonBYIAQ+v3+/v7+Yfs7nc4DBw7s3r378JJfNwcVx1rT/8Na9gOSj6tm4/pMHdfhMPHtCILMhPQfAo8++ui+fftMJhOGYQ888MDq1at//vOfj3YIgiA2btw4YxEO53Q6g8FgU1NT8tNGLpdrNBqTyaTRaKbrYw0GHO53H3FX3qTHp0cDqwAAIABJREFULJKzboc+C33kX6V5He3lNwwMDIzcv7u72+l05ufnl5aWMv1HXHa7ySRPPsr07qD2/ZlsvATwxNMS3kwzm80ul2vBggWpLZp4PN7a2qrRaCoqKjIX2twXTjA9ruiw9ZmQU1P6FvQbb7zxr3/9q7S0VCwWb9++/bXXXpvlsEbDMAyXiLmFqZL0ej3DMFardbpORH2xyVz4DYlIWHzODeSSa3lrfiC8+R+SgEFDD1gsltRh0QAAv9/vdDrnzZtXV1dXUFBQvHRtgzyaN3QAABD65KnYK1ck3n2IKF3KO/f+6QpvphUWFkIIW1pakvdC4/F4S0sLy7IZn1Y65z31ufX7b3Z1OHPgzh4y09In6Hg8nrydGo/Hs+d+iMVioShqZAuOW5nbZrNxNTqmikk4vdGIqKhqXm2ynY4pSsjFV5d0boUQDuu+cLvdAoEgJXNhvFX3Fi46DwAQEumIyjP5V2ziX/EMV4Y0JwiFwoULFwIAmpubY7EYl50hhAsWLMieN8Oc1OEM/7fDTRL4MzusqC8JGbWa3Te/+U2Px/Pkk09u2bLlnnvumeWwQCLCDnbiuiWp2yiKstttGABHjx5lGIZhmNS7H1z3qMViqa2tHXk8asczMOg4vrPHBKhY4v0fHt8Dw8nTbsIL6gEAdNjTX7JWTMLkGDXudKy4ihbp5FKx1x8kCCI5JiwajY68uaGqmM9z7sV15/ImNno62wgEggULFrS0tDQ3N3OfUig7zzQIwKad/WUq4b2rdA+80/txp2dtdkyXQDIlfYJ+8MEHFy9evGPHDoqitmzZMvY46JnAmL9O/Pth3uoHyKXXJzcSwYHigc8ZaSFefN7AwAA31EkqPaGrTqlMP8oYxgMhhvDzi4rD7QAASMUAgDCW2hDGAJ3gfgrEAM2T0TRobW098TA8UHUD8AcBAAzD7N+/P/Uxp9OZurQuN7I7J26gj0YgENTV1TU3N6O28+z4qMPTbAs9ffm8FRXyMysVz33Zf3aVMtvW+EBm06i31NasWZMsxzH7iHlryOU3U58/BegEufxmAAD0WhJvbCjiCQXfet7kCgMAuFkqdXV1Exk8y1v7iOnw4XA4rFhxvVqtpve+Ru/7s+DqzWl3VmsLF335BIwF+Jc+wa00GIlE6HiY2vU8LtXgS653uVyBQICbCg8ACIfDBoOBYZjUg1gsFgihWp3DLaB4PN7Z2cl9xnR1daEcPaNiNPv8V7aVesWKCjkA4P5zS6/f2vbXA47bzyjOdGhIxmTvCHausDL15XMAAKJmTfyfGwBPKLj2+Tgpt9t7dDpdUVHRgQMHLBZLVVXVuEdzOBzhcFgkEnGryqbdhx0ysJZ9MOTClDrxsusS7z4I/nkruexGXFVGekz0/q0gFhBc/ypQa8PhcDAYTLbWuZHRfX19gUBArVazLMst/lJWVpYTk0TSSu13xjCspaWlpaUF5eiZs3WfwxOh7jlnHverTim4apH29QPOixo1RfIc/h6GTEX2JmiQkqPpvVuARC249nlMqu1rayNJUqfTEQRRWlpqNpuTdaJHQ9O02WzWarU6ne7w4cM2m234QASWoXY8RR9+E+AkJlHD4CDgicjTb2RtLdQnjwPIAgwnKs/krX4AU5WlHQhcU1Mjl8utVqvL5cIwTCwWNzQ0TKToXXYaeVdw/vz5LS0tra2tw8beIdPCGUz8/aDzmsXaMtXxz7/bVhRt7/Rs3tX/ywsnOIUCmWvGSdCBQODf//73pZdeKpfLx95zhpBN6+jDb8BEiFx8FSbV+nw+j8dTW1vLzYksKSlxOp0Gg2H+/PljHMRisTAMU1FRIRAICgoKLBaLGidTO/ao3S/QR/7FW7WRXHItwEkYC1A7N9FfvcS/4hni0t/DwACmKAbkOFmpsLCwsLCQYRhuotE0vPjMsdvtw/qdhUIhd8/QbrdPsGgUMnF/2Nkv4uM3n35CGQAJn7j9jOLffmK+YkFwse5kKhkgc8U4Cfree++tra3duHFjRoZCQ68l/s8NmERDNK6g924BfJmBN18mk2m1x5aiwXG8oqKio6PD7XaP1lyNRCJ2u72srIxr91VUVAwNDVmhppz439dGOkEf/Ae59Hpy2Q3cBkwo53/zJ3GPid73Z0J/FpZ3EvmI++RgGMblckUiEQzDZDJZRlY5mIqKioqysrJhlQGEQuHSpUtz/bMnCx3uD37W472kSdPhHF4kvkjOV4nJTTv7X72+PgtWyENm2wkJmmGYvr6+1BUIOzs7t2zZwi31Pcu47Mz1O2NSLeCLbd3N4Qr9okWLUnfjJq1wPctpc4fBYODz+SUlJdyvPB6vtLTUaKSLL/sj1zhkvWZARYiqs098HkZUnUPteTltbGKxeIy7fz6fr7u7O5FICIVClmX7+/ulUml9fX0O9d6OVgtm2gvEIACA/3Z4AADvHx16/+hQ2h28EdrojnILfyCnlBMSNIZhmzZtwnH8xz/+MTftor6+/o477kgOV5g10G+P//O7gCcSXPsCJs0HAGBnbHCAXZqh/aLeAbD4GgAAy7IdHR0lJSV6vf7QoUN2u31kJRCPx+P1euvq6lIzS0lJicPhMLgiC4/dHocAAICNSO4YNnK5Qo5CoRhtQl0ikWhvb5dKpcn+gUAg0NXV1d7eflLlnJBTx/2rSy9fkD/GDmI+nto3jZw6TkjQOI7/8Y9/7Ozs/MEPfqDX6x966KGXXnpp9+7dK1asmOWwYGAAU+r4F/+Gy86Am0MIiKimqT0Qx1pbAQCxWCwWi/l8PplMhuO4xWIpLCxMrcUBITQYDHK5PD//hHc/hmF6vb6trc3lcuXn5+PKMkAKGNPXeMkJbXPG+DWuTTPnZWwOhwMA0NDQkIxELpfX1NS0tLT4fL7RBpAgpzIhidcV5EaRFmSWpemDrqure+211/bt27d+/frVq1dv2LBh9qv04qVLBde9lLpFIpGk9uRyJfN5PB5X8pjr34jFYhDCZGFou90ejUbFYrHRaBx5CpIkjUZjXl4ezhOSC6+k923FlTqi8SIAMEDFqN0vspZ9/EtGrYI9mlAoJJPJhtVsUigUBEGEw2GUoBEEmbjhCXrfvn233347AOC+++57++2333vvvSuvvPL666+/7rrrMhHecQUFBanz9Lq7uwmCWLp0qdlsHhwcbGpqisViLS0tGIYtW7aM+0RJJBIkSfr9fr/fn/aYGIbRNM3n83nn3A0j3sSHP8N2/gGTaVmvBTA07+y7idrzT3gCZKGvHxICwBt1TAvqxEAQZLoMT9C//vWv33rrLb1ev2jRoltvvXXdunUXXXTRli1bMhEbAABACEemvFAo5Pz/7d13fBTF2wDw2d3bu/Tee4EQCJ0QejX0SCcggnSFKAhiRVFfRSw/pIkI0hQFFbAAAiItMZCEJqEFQki/S7305HK3bd4/Do9wOVLgciV5vh/+SObm9p4sy8NmduaZwsLg4GCapgMCAoqLi7OyssrKynieJ0kyKyurffv2CKHAwMCmTgijxOJxHwvdp/JZSbi6iO4wggoZTjjUqcfPKtmLe7ir+xCrRAghaxd+0MtU56j6m8Da2NioKzrV/bWjvLyc53mtVekAANAw7QTt6+v7119/de3aVbP0g6KoBQsWGDgsjLFMJisoKFAqlSKRyN7ePiAgQLMqLz093crKytPTEyEkEon8/PwyMjIQQmKxmGXZgoICT0/PJ8iGpHc30rubrmgE1W+vCvk3Rb1mUn4RAlvL3j7O/PWRqCyXHhSj1dfDw0MqlaakpLRv3169VaN6Uoetre3j6oQAUBcn4A2xuVO6ugbBtI02TztBf/rpp2vXrr158+a+ffuMEhBCSBCEmzdvVlVVubm5eXt7syxbWFj477//hoWFOTg4FBUVVVZW1i3Y7+7unpGRQRCEerWbIAjp6enqapl6waeeEnL/lUR/TfpFIIQIjFnvCNo1mE3aJeo6kbB/pFQCTdNhYWGpqalXr16VSCSCILAsa29vr7PGHgD1/Xq9+LfrxfeLa7dN79DE8TIB442xUjFFdPexHRhk37LxAQPSTtC2trbTp0+vra0NDAy8fPny8ePHQ0NDo6OjDTm0mp+fX1VV1bVrV83yRR8fn1u3bqWlpfXq1SsrK8vJyanu07b09HSEEMa4pqbG39///v376k0L9bU8hM+4QLq1V2dnDVH482ziTj4rSdRtslZ/Ozu7Xr16yeXympoaiqJsbW3h2SBoovJabldSXoib1Y286tOppSM6NKnY1pGb8oPJRQRCx1JKDs7rbCOB6eqthPbk348++mjs2LHvvPPOnDlz5syZo1Ao1q1bt2rVKkPGJJfLnZyc6i4uJ0nS399fqVSmp6czDBMU9LA0gUKhKCoqEolEjo6OmZmZrq6u1tbWIpEoIyNDa9+TBjTSU1lJ2LhpN4qtCbE1qtW9MztJkm5uboGBgX5+fpCdQdNtT8jjBfTlxHb9A+2/+kdayzZ+DVer+O0JeZY0hRGqUfE7k/IMECcwDO0EvX379tu3b8fGxspksvfff//zzz8/derUTz/9ZMiYGIapXwRO3VJYWEhR1P3792/+Jzk5GSEkkUgYhlGpVHl5eUFBQRzHMQxTf19XnTiOu3z5clZW1uM6EDZuQmn2g/Us/8HVxVhVTdi6P+5dADRXRknt0VvyuREeLtb0iqG+5bXcvisFjb5rZ1JejYqvZfmuXtYiijiUXJxRoo99hZ7U7zeKr+RUaTUynLA9Ia+gkjFKSOZLO0GLRCL147WFCxcOGjQIISQWiw1cfkEkEmm2wtNQqVQYYzs7u7pP/1iW5XmeoiiapmmadnBwsLCwcHBwcHFxIQgiJyen/nHqy8nJUWfzx22XRYVG4vJc7trBh00Cz8ZtRmJLMmhgA0fGijI+/R/u+m9C9iXENR4JaOM2xUk9bMUzerqjOuVG8xtMatmlyl+TiyU0OSDQ/oPRgTxGFjS5MbZJtyYt5Jq0+rU/0hKzHs5tZTjhraPp+68WVqk4IwZmjrTHoCdPnjxs2LAPPvhg5syZCKGrV6++//77Y8aMMWRMTk5OUqlUoVCoZ0Go5ebmisXiLl26aEbDMcZJSUkIIR8fn7o95XK5g4NDSUkJxjgrK6vhp3PqUko+Pj7FxcWZmZk6F7WTfhGiblPYM1/w6f9Q/hGYqUWpp/iyHPGYDwnLxz6Q4a7uZy9sR8yD8jeEnScd+TYVNKA5ZwK0IWfTyi5lV34+PpimHlzhTSk3uilOKhGRSlZYNsTHy14yo6fbvisFl3MqL2RWDAg0ztPC1aMCaln+rSPpa6OCBwbZszxedSzjam7Vp1HB7V1hwWTzaCfoDRs2/PXXX5o5dgUFBRMnTpw/f74hY/Lx8ZHL5cnJyT4+PjY2NizLqh8bduzYse6zyvz8fI7jEELZ2dmPO5RcLm84QatLKfn5+VlbW6emppaVlekcMqZHvEMG9OEu/cAm7CREYuTeSRL1SQMLwbl/f2HPrRf1iBb1nk3Yugvy+2zcZuaPlZLndpCeDVVGBW0Ty+NvzsvCfW0HBz+ci6kpNzqlW3V3bx3TRi9kViRmVdAkoSkkPTfC83hKCcsJG2NzI/yMUyKYpoi1UcGr/kxf9Wf6x2ODjt6WX8qu/DQqeABML2k+HUu9R48erfl63LhxBgzmAYqiunXrlp2dnZubq358p649ZG//yF8wTdNaLVoIgmh4s5XSonxNKSU3N7f8/PyMjIyePXvqnLJCtR9OtR+OEMIYK5VKsoGtUgSeS9xBdRlPP/OmuoF0DZFM3qTa+zyXuFM8eVMDIYG2ad+VgrwK5rNntS/X8Z1dDt8s3hCbu2dmKPnoZckJeHOc1N5SRCA0r8+DQtKWNLlkgPfHJ7OqVPyh5KKpXY2z45o6R799NH3Vn+kEgT5/th1k5ydjojuqiESi4ODg4OBgpVJJ07TOKpeurq5aVZCaBWOcfvOKrYWF5iDBwcHXrl3Lz8/38mp8FzihOA0XpyGCJN1CCOdHfgPFZTm4tlwUOuqRN5AU1SGSu/LjEwcMWquSGnbv5QInK9Gh5KL6r4op8kZe9fGUkqiwR6aNHrhWJC1TYoTejPSvO69uTCfn364X55QrdyXlDwu2dbIy2r9xdZVIAiFB1yZEoClMNEFr1K+hzJ75grB1F0XMecoj5+XlKWn7dnyapsXGxsbNzS07O9vV1bWB+lC4uhj9vVaVeV7TQoWOpJ95k7B88Msp5hmEEKLr3WKLrTAHT7GBthqGD3S2FDC+W6TQ2SHU3YrlH8lxFUpuz8V8giQQxr8lF/12vbjuq5VKrkrJI4T2XC5aOcQIe86yPH7nz/QrOZVro4KOpZS8eyzjk3FBg4JhJW2z6U7Qv/zyy9ixY9Vl4aqrq48dOzZ9+nTDBqabkHOFu3YAUTTVfhjh6PfEx2FZNicnx7Xilo39I1txBwYGlpSUNLQRrcCxv72KakrFUWupoAEYC8L9ODZ2I/P7a5LndqqLSpMOPogUCbLrpFfXR94qTSadA5445idTXV1dUVHh6+vbeFdgJH6OFrueC23WW2pUvJutWFqmEhDKLFXW7yAiCWsxxfJNXQqgR+rsrBl37h9o/+6xDMjRT0Z3gl63bl3fvn3VCbq8vHzdunUmkaCxwJ77knQPxYpS9tx68eSNT3ykrKwsjLF3cTyy71+3XSwWN7wRLX/vjFCchqbtoPx7IIQIhKiwKMLWTXUghs9MfDBJQ2xNhY7kknaTHp1I317q0LnkX/m0c3TkW08c85OpqqqqrKyEBN3KeNlL9s1ufBsNlmV17nHcoj4+mXU5p+p/E9r18bdDCNEU8cm4oLePpr97LGPnjNAQN5jI0Qy6E/Tly5c1X/v4+NT91oi4678JxfclM77FVQXMsdV8ZgIV2F+rD8aYYZiGt51mGKawsBBjnNw+BiGE4uPr98nNzQ0N1XFTI0ivITtPJLZWb/WtbiT9IggrJ0GWrJlFRz/zBlORp/plMenRkbDzEIrv47JcUddJ9deFA9DK9PSxmdDFpZfvw11uaYr47Nng7y7m21ua+piqqdE+XxzH7d69OzMzc/z48f369VM3rlq1au3atQaP7RFYWcld2EZ1HEX69EAIk9d/Y8+tp/wjEPnIj5CZmZmfn9+rV6/6g9e4QsZd/gEJQhnpSEo6+bEZOP82YeVA2Hs/7CS2pNoNQYjQcfvMqdiLe7gbvyFBQPtmKiU2ov4vinpMRySFEEJi67pLUQiJreS5Hfydv/jMRFxdTPmFU6PeI3166vOMAGCSJuravoumiEX9jTAabu60lwjGxMTs27fP0dFx4cKFR44cUTd+8803Bg9MG5ewA7O19KCXEUIIEfSwlbgsh7t2oG4f9aoTQRB0bqGCFWVCQYpQeIerKuER6Zz3j0vRBZeCeBfZWc0f14ILHm5uHh4emm1ZNO9mDr/BXd5L+vRCFI3GrKU6jmZjN7KnP0MI4eoiXCGrt/k3QXUcIx77kST6G3rEKv1mZ1whU24diat0PPQHALQa2nfQR48evXv3rr29/axZswYNGtSvX7+nmcqmL7gkk0s+SPdbQNg9mO9JuodSYVFcwg6q4xjC6sHSEvWqEx8fn/T09IqKCq1Z0qRnZ8msvQghUWEhundPMnMXs3MCFTqSHry00QD49PN8ZoJ40nrSvaNy12R055go6mPCJZg9/QUZOpJL2kNIbKgQw+19jmtKsKIU15YRttpVnFQqFcuy6q8ZhuE4ThCE6upqTQeC0PX7AQDA9GgnaDs7O57nEUJeXl6rVq2KiYk5ePCgrjcaFBu7gbB2EoXPrttID35Fee8Md2EbPeIdVGcDb1dX16KiovT0dD3uoi1kJRL23lTwYIQQPfYj9sSHqp0TSa8uiCDZ35YjkUT87GeEhXEWbtWFMb5y5Ur94nzXrl2r+22nTp2cnZ0NGBcA4EloJ+iYmJi+ffsuWrTojTfeWLBgwalTp8aNG6dU6pjHYzB8ejyfmSCOWovoR4aVCSsnus889vxWqvsUwqV93Q28g4KCrl+/rt5aRd05MzOTYZgnrpqPlVWEzYNlAlS7oezz+6mUw7joLqJowj1EPHFDA0U5DIkgiO7du9e9gy4sLFQoFHV/cIIg6pZyBQCYLO0E/eqrr0ZEROTn56u/3b9//6FDh+rWXzY4zMZtRATJ3fiDu/mH9os8i7DAxm0u7vt2bW2tJg2pM7V6yYlIJCoqKiotLeV5Xj02rVAoEEJZWVm862ASeZKZmQRBqAtJPy4Iws5dyE5CAv/gkaCVEz0oBisr+a0jqdDRJpKd1er+FEqlsqysTKlUwm5brVVhFbPs17T3Rvp38YIdL1shHbNeNJM3EEIkSUZHR0dHRxswJC0E5d8Hu7Z/3MtUh0jepUNubq7Wk73AwMCrV6/m5OQEBQWVlJSoq5XK5XKEkHoMp6SkBNt3RIQlIZcjhCwtLRtI0FSHEdylvWziDnrASw+aBJ6N24hIimo/VD8/aIOEvBuC9BrCAmZrCbE1rsxHCPG3jgo2SZo+pG8v0rOzAYIBpmNLvDSnTLnuXO6emR1Jfe96VK3iOQE71JsbJ69hbSSUhcigVYjbJjOYlqgpOfQ4aWlpuLjY39+/bqNEIvH29lYn7o4dO969e5dhmK5duyKECgsL792716tXLyRPI2zdNUu0G0C6dRD1W8glfCtkXSQD+iJGpcq+IJRk0iPe0bHZSgvgU07wd08insEcQ0hskMAhhPjbf2rmYiOEqJoSSNBtSrKs+kxq2bgw52O3S/68LR/fWT97vGl8fV52PqN867QOvg4PFxbcLqhZ/lvarHCPOREe+v04UJ8ZJOiG1dTUFBYWenl5CYKgNVbu4uKSl5eXmZkZFham870N1Autj+7/IuXTk724h79+CBEU4dFJMub/SPfmrdB9YnTkW3TkW9zlH9iE7RavnBXybqj2zxdP3675EbCqmhDpWp4DGwW0UgJGm+Ny27larRrhX8sK2y7Ihrd31O9uhIv6eV6XVS3+JXXL1JBAZwv0X3b2d7SY2t34k7vagscm6F27diUlJW3cuPHOnTvh4eGGjKlZZDIZxlgmk8lkMp0dSktLMzIyamtrOY5Tb4KlnnOWl5fn6emps07e45B+4RK/cHW5UXED5UaNgfn9NYR5yZSvkPjhUlqclWB/eT/ddXYDbwRm6ugt+Z1CxdZpISRBLBvsM+O723su5i8d7KPHj3CyordMDXnl0L1XDt3bMjVExQkrfk/zcZBsmNTeWgz70hqC7gT9/vvvX7p0KSsriyCI1157bdiwYf/3f/9n4MiayN/f38lJu+gtx3FZWVma2WaFhYWCIGCMc3Nz0X9bxEqlUicnp7pbsZg1etDLql+Xqn5dqsnRfMZ5dOI9O++eHt20F8QDc6dg+B2Jec+EOPbwsUUIuduKZ/Zy/+FKwYQuLurK/fqiydGLD6TyAvZ1lGyeHGJrAdnZQHQn6AMHDly5cmXSpElWVlZ///13SEiIySZoiUSis/KGh8cjA2R1x6CLiopSU1MjIiIMvNdiiyK9u0mmblEdWqo69Ipk6le87Dpz5E3Cpyf7zIeIemzpVGCmdiXlV6v4lwc9vF9+IcLjWErJ5n+k6ya00+9nOVnRy4f4rvgjjUTEyqF+kJ0NSXeCrrsaTaVS1a9rYdacnJw6depkFtmZjd+Cyx9uAIpLsxHPMkffxsoKhBD7z2bCwl4UPov06IQQIr26SqZ+pTq0VPnjXFwpo3zDhVFrkBHqTYKWJS1XHUwumt3bw9NOrGmUiMglA70/PJGZmFXRL0Cf8z7vFireO54R5GzJ8vito+ma8WhgALqT1CuvvDJq1KjMzMwvv/xy4MCBS5c2vhjajIhEInNZR4cVZVhZ+fAPp0QIY2UlwphwCkRYwLXliH34aJT06koPeBGXZhKURDzm/5BI3MDBgZnaGJvrYCmaFf7wd0RewPP238ksqe3ubbMpTsoJeisxerdQsey3e972kq1TO2ydFmJvSb1y6F5miTFXrrUpuu+gV65c2aNHj9jYWJZlv/vuu169ehk4LKAmHrW67rfqWRySaVsf15/PTGDjvya9ugjyDNUfK1HU/xCC8Y1WJVlWfSGzwsFSFHMwVdNYpuAKq5jUQoWzNS2vYf+8XTKxix6m3KUU1Lz6W5q/o8XGye1tJBRC1ObJITEHU5f9dm/btA7eDg0V9QV6ofsO+uWXXx4+fPhHH3309ttv9+rVa+7cuYaNqklUKlVKSgrDNHUTKX3V5TBZfGYCc/h1yjdcEr1dMvUrQZ6Oj6wkWN27KAFTdj6joobhdb7kbiue3M11aDuHUDcr9Z8gZ4tSBettL7GgSYokJnZ1DXHVzxSjM/fKgpwt/8vOCCHkYkNvndbBVkKdz6zQy0eAhmnfQa9Zs2bfvn05OTlnz55Vt/A83/Dm2caSkZFRUlIiEolCQkIa7ezj41O/hFBrwmdcYA6/QflHiCd8gSgx6dVVMmWz6tel1Im30fRvdGyQCEzV5ZzKNw7fn9DF5e1I//qvetqJ3xj+yGZv/zubIyKJbdEdrsmq3j+eOSjIvpOHfqoV6py052JD739B98ICoHfad9ArVqw4f/58VFTU+f8kJiYmJSXpfLMRVVRUyOVye3v7wsLCqqqqRvvb2Ni07gpB3JUfNdlZ3UJ6dyOe/ZIozeRzrhg3NtB0vIA3xkotafLorZJ7j9lDtq7MEuXhm/K5EZ4uNnRkB6fu3jYbYnO1dpgF5ks7QVtbWzs7O//0009yuVwqlUql0oyMjKFDhxojtoZkZGTY2Nh06dLF2to6PT3d2OEYCiUmSN1jypKpX4knb9Rk5wc8u7Av/E4FDzJEbEAffrtRnFlS+78J7XwcJF+ey2000W6Ky/WwFc/o6YYQIhBaMdQ3r0L16/XGd3I4nlJy4Bps+GDqdI9Bv/TSS+PHjx8wYMCKFStGjhzZs6dp7dWUn59fXV1aaUVfAAAgAElEQVQdFBREEERQUFBVVVVRUZu41ERdJkhm7tL9GvmYRaGPawemp0rJ70rMH93JuZev7dLBPjfyqs/eK2ugf+z98ovZlUsH+4j/q1sU4mY1tpPLzsT8khq2gTcWVjH/O5Oz+R9pRkmtPn8AoG+6E/Sff/55+/btmJiY9evXJyYm5uXlGTisBvA8n5OT4+rqqh4Zd3BwcHJyyszMVNeoawzm75xQ7ZtTu2mwcvtY5ujbuCynpQPWJ9qCcH5Q+pW9sI1PO2fccIB+bU+QqTjhpf5eCKGBQfZ9A+y++keq5HQ/O2F5vDVeGu5rO6TdI9W+YgZ6EwTamZTfwAd9HS+zoElXG3pTnLSBbsDodCdo9dq8sLCwy5cvh4aGpqWlGTaqhmRnZ3McFxj4cAPA4OBgTZ2NBmHmz3eZY6sJK2e6/0Kq0zih4I7yu+l8xvkWDbiF8PfOCLkmMbgs5N9GuF4S4VmhKFVXd6BbZonyj5vyuX083W0fjFO9OsS3pIbdf7VQZ//9VwtlFcyrQ3212h2tRHMiPI7cLL5bqHsI+1Z+zenU0hf7ey0d5HMpuzI+vVyPPwXQL92//06YMGHixIlbtmyJiopKS0vT7EtidLW1tXl5eb6+vnWXd1tYWHh5eUmlUnd39wYWPfKpp/m7f4vHraE6jla30P0WMYdfZ//6iHrxKNJZCg40BtfIVfvnUZ2jxCPfe1j7lGeYw2/wuf9aLourWxAVNGBTXK67rfi5ng+r1wY4WUzu5rr3UsG4Ts6arK1Wywp7LxdQJHr7iI4HMJyABYx2JeWtHas9D0TAaP253EBny/GdXSiS6HnddtM/0r4B9jTVyiehmindCXrjxo03btwICAjYunVrXFzc0+zqzXFcbW3j41w8zwuC0GjPtLQ0mqadnZ21erq6uhYWFt6/fz84OPixb075C7l3ZAKGoLrv7fsS+mlObXoi8uvTaJAamnXwxoQxx3Fcg2eMZVme55ty/p8caY2Gvcmf+6KW49GwNxFBIp5Ff61GuZfQ6I9rlU0tdioIAs/zGJvB9AOWZZtyrTbLhayqi9mVH4704VlVbZ2La1Z3p5N3SrbE5ax6xrtufwGjWT2dq1QNzRzt5G6pUqnQfztUqB2/U3ansOaLcX6MSokQWtLP7aVDGT9eks7oruda0s3FcRxCqGWvVT3heZ4gCL0nAUEQ6k8F1k7Qs2fP/uKLL95885Ea+atXr/7hhx+e7FNFIpFlEypzqvefbrhnWVlZRUWFpaWluiidFoqiysrKWJZ93HQ6laKEcAnWLhPqGVqLEK0qFTW5fKg6idQNVShIwdXFVLshTTyCXigJghKJ6AbDJgiCYZimnP+n0nMqLxYzJ9dQFCWOfIv560M+95J4/OfqPXabiOd5lmXNpeoLz/N6PKssj7cnpXvZS5xsLW8Va/+zHxDkcCKlZGyY6/W86gV9vTS3uvP6aQ9u6Dgyy2KMxeIHd98Khv/uinxYe8dBIQ+qOXf2sRzf2fnHf0ue7erhYm3MRafq3NTi16o+MAxDkqRIpOfH74Ig1C8QpP0Zs2bNsrOzM82lgwRBqPfWU/9nq8XCwqLhv13Cwg5XF2s1qlsIi6daicPf+EMoTGnZBI0FQXYd8XX+9bK1uKpQyL70sMXKkXz83mAtiuo8XowQc3KNMvsSVpQ0Nzu3calFCmm5CiG07NfHPuzZcl6WIa+1oqkXnmIfkz0XCyqUXMzAR27GXxrgffpe2bcJeatG6FgXA4xLO0GPGjWKZdnk5OSVK1caJaAGODg4PM3mp2RgfzZ2g1CUWncjFe7Kj0gkIX2fckcCjFr4d3M+K4n5dZl2Y2UBnxb78HuSslwaa6xFg1THMeS/PwtF90j/CCpooFFiMFOdPa0PL+rSwOqS+3LFqqMZ3vaS7y7lj+3k7GLzJLe6snLVL9cKnw/38HGQlCpYGzGlnpznYCma38dzS7x0UleXju76WYII9EXHXTpN0wkJCVOmTAkICDB4PC1I1HUyf+uI6ucX6YgXSO8eWFXF3zrK34+lhy43qW25daIC+0nm/lz3Dpo58hbpGSbq/YKmhbCwN9qSbp5ljrwllGSKes7grh1gTq4Rj3oPHg82nZvNY+sOYoQ+Ppnl52jx9bSQmd+nfHNBtnpUwBN8xFfxUpbHeRWqNSezTqWWutuKe/k+2GSZEzDGaHOc9JvoZmwCBwxA9zCKra1tr169evfurdkn++DBgwaMqmWIxOLp27kL29jEnepMR9h5iqPWUqEjjR1ZUxCky6OF2EUSwsqJdO9opHjq4FnmyFt8VqJ6ZIN0C2FOrmEQghytFyfvlF6XVW+Y1N7Jip7f13NjbO7kbq5hza+2YUVToe5WOWXKoiqG4XFuuYoiCQv6wV9QB3cr/e5nCPRCd4KeN2/evHnzDByKARASW3r4G/SQ5bg8F4ltCFtDbMjdygk8c/h1PvuyeMI6KmgAUo9HY8T8vYYlKXrku8aOz7ypOGHbBdmAIPu+AXYIoSndXA/flG+Mzf12Rmhzp8W9PzoAIZRVqpz9Q8r8vp7/3C+nSGLXcx1JmF9nwnQn6EGDBqWlpak3yWYY5rXXXouPjzdsYC2JojXr8Z4AVlWjHVG1TI1We+26OgPZJCUe83+EgzfhFEhIbJ74s0wfVlYKpTniCf9TZ2c1qst4MYHYyz/SWICb6Kex91JBqYJdNvjBs1+KJFYM9V36672Td0pHd9TeirMpNsXlOlvTs3t79PCxXXro3omUknFh5rF5RdukO0G/9NJL//zzj0wmi4iIuHbt2gsvvKCzW9tESKzR0Ndp9HA4mE85gSvyRP0WIIQQwoL0mnA/jjn2HkIIESTVOYoetJSwcjROuC2MsHK0WPh7/Xaq83iq83jDx9OaFFYx+68WRnd3q7sPbLif7cAg+63npUPaOVjSzfvP70JGRVJW5ZpxQRYiMtzXdnCww9bzsqHtHWCLbpNlfrU4TACB2keKuk7W/CGdAghLB/XXuLKQTzlJdhghmb5d8sI+euhyIS2WOfAS4pq6ZKOJRJ3GkYEDGu8HzNbmf6SWYnJuH+11vMuH+JbXcj9cLmjW0Vgeb/5H2sXLZnjIg3uFZUN8qhl+bzOPAwxJ9x103VocixYtMqlaHKYMVxdzl/aK+i2gByxWt5BuHUj/CNXeWdyN30Q9n9PjZ4n6zNXj0YCpSZZVn71X1tPH9vcb2pP3EUK+Dhb7rhaO7+ziYdfUbScPJBdJy5UfjumoGXP2tpfM6OH209XCqDAXX9i/yiSZWS0OEyfkXkECJ+oRXbeRdGlH+vQUspKQXhM0aN2k5Uo7C1GaXJEm113zyIomZRWqJiboMgX33cX8Zzu7dHS3qts+t4/n8TslX8dLP3v28TUSgPHoTtCLFy9mGEYvtTjaFKyqQQRJWGqvpiGsnXElDBOBZogKc4kK01t9jO0JMo7Hz/V0r1JqV+WdFe6xMTb3Sk5VuJ+tvj4O6Mtj76AdHR3nzJkzY8aMwYNhzW4jCKcAglMhhAg7T4QFoSRDa84yLk4jjLQIG4AKJXf0VomA8Yzvbz+uz76rBZCgTZDuBH3v3r1r164dOHBg4MCBHTt2nDNnzvjxpvhEHmPMsqymFoyxiMKfV39B+YUT1i7s2fWSSesQ/eB3Se7fnwT5ffEQ7YXaABiGvYVo/aR2NaqGdrQIdDaDKkVt0GMLMvXo0aNbt25jx45dvXr11KlTGYYxZFhNlJ6eXlxcHB4eTtPGLMT1kEgiHvt/qj9WKndNodoPQ2IbQXpVkF0X9YimAvsbOzjQdvXxb807JrdiuqfZHTt2bNGiRX5+fmvXrp0zZ05hoe49HYyrpqamoKCA47isrCxjx/IQ6d/HYt4hMrAfn3OZT/mToK3EUzbTz7zZ+DsB0BOWx4lZFcaOAuiB7jvozZs3R0dHf/75505OT7JayTDS09MlEom7u3t2dranq6O1mCSstKPFijKClmhGGwyDsPMQj3rfkJ8IQF0/XC7YkZi3ZlzQ4MDWvIq1LdB9B33y5MkFCxY0bRtW4yguLq6oqAgODvb19bW0tLx/84rquxmC/H7dPoI8XfXddDZhp7GCBKClqQtJ11VUzfxwuUAiIrfES1WP2XAWmIuGloqa5oNBhJAgCFlZWer9vAmCCAwMrELWZe59mANLhOIHa2oEeTpzYDFh5SjqPfuR9+bfEmTJxogaAD27kFExbc+tv++W1m3c8o9MLCI3T2kvr2Z/+lfHIhdgRsyykI1UKlWpVEFBDwoeOTs7Ozo65vqMw7aezMEYoTgNl2YxB2OQhb142latIhjcpe+5xF3GiBoAfWJ5vCkul0Do63hZLfvgTlm9Y/fiAV5dvWymdHPd929xYZUJ7J8JnlRDCXrhwoXqL+pvZWhEDMNIpVJPT09r64clcYOCghiWK+r/LmHtwhx4SfnTAiSxlUzfTljXq9SFMUZmsDNpo4S8G1hZWa8ZC9mXkGC6Y1NAX365ViirUH00Nqislv3xcgFCSMBoQ+yDHbsRQgv6elnS5LdJUGrDjGkn6JkzZ5aVlam/XrBgAUIoIyPDpNaqZGZmEgTh5+dXt9HKysrT01NaIBeGvoVUCqSqpiPf0pGdWxHm+PvMLy/h2vI6bZg98z/VwZeFkgyjhQUMokzBfX+pYHwX18gOjtHd3X68WphfyRxPkacU1Lw6xIciCYSQrQU1P8L9zL3yZFm1seMFT0g7QXt5eXXv3v3UqVPqb3fs2NGnT5+oqCiDB6ZbZWVlUVGRj48PQRDco7y9vQmEM28kIDtP0tGf/XOVZjy6VZJM2oAVpaqfF+GaEoQQQpg9u467dpAe/rqxto4FBrP1vAxjtLCvJ0Jofl9POwm1KS53+4W8Ye0dI+pMeR7f2SnI2WJDbK7QGn5pbIu0p9mtW7duypQpCxYsGDZsWHZ2dmlpaXx8fGhoqFGCq08mkyGEsrKyHjf3ucShc/CgKWKxmDkYwxyMEU/b2lqzFeEcKInepjqwWHVgsST6G+7iHu7fA/Tw10U9pxs7NNCy7hUpjqfIlw72cbamEUJWYmpRf69PT2WLKEJrx26SIJYO8lr+R8bxlJIoKMxvhnTMg+7Xr98nn3wyffp0W1tbk8rOCKGAgABXV9f67biqkEv4FklsxANekji4I4TE07YyB5YwBxZjRBD44Rg6ZhQIIeWW4Q/fTIokM3cRDr4tHr2+aXK0ck80UlZBdm4jNsTmetlLpnR7uGFbD29bgkASisirUOZXPpx4x3EcQqidi+W2C7Lh7R2soDC/udFO0KWlpStWrLh48eL58+ezsrJGjBixbNmy119/naJM4q/W0tLS0lJH0QAu7XeeKxZP+lAzZ4OwchRHf8McepmwcaV8emp68rePYYETdZnw8M0iCWHj3sKBtxTCOYDyj+BSTiBrZ6pDpLHDAS3uVGppsqz6y4ntaOrhZoKx98swRjWM8Opv9x/3xiu5VYODtessAhOnnaDDwsKio6OvXbtmaWkZERExcODA+fPnHzp06PLly0aJr4lE/RaJ+i3SaiSsHCUv7NdqFPJuIk4piphjqNBaFGbPruNS/qL7zOduHVYdWCyJ3ta6H422cUpO+DpeFuZh7e9kIat4eKc8pJ1DmKf1tguy7FLllqkhmjtljuMwxjRNkwTR9NL+j/PFmRySQK8N86u7z2xRNfPO0YxF/bzU29oC/dJO0Pv37x82bJjmWy8vrxMnTmzdutWwUYGmwOzZdZpxZ6rTmP/GoyFHt1rn7pUVVjGFVczU3bce1+e6rHpq9wejHyxLYoz1Ve6xi6f1mr+zVBx+Z4S/OkcXVTMxB++xHPZ3smjs3eBJaCfoutlZjSCIl19+2VDxgKZiz23g/j1AP/OGegMXwjlQPHULc2AJc+gVyXM7kdi60SMAszMsxNFKTPGPn5NBkkTvFivrPKaTM4/Rp6eyEMLvjAiQ1zAvH7zHcvjraSGeT317DnR6bLlRYOJwWQ4d+aao+zRNC+naXhz9DXvyY6woJSBBt0YWInJIO2OOI6ungnx6KkvJCneKFOrs7AP7GbaYNpegCQdvglUaOwo9EE/eWL+RdG0vmbXX8MGAtiMqzLlSxW2Jk1rQ5HfPd4Ls3KLMshbH06CHrqBHvGPsKAAwV0XVzO/Xi20kIhUn/HA5H5bAtKg2dwcNAHhiRdXMywfvMRzePTM0WVb96akshNA7IwLqzusAegQJGgDQJJrsrB53Vg9uQI5uUZCgAQBN8sWZHObRp4JRYc4I4U9PZff2sxsZarq7L5kvSNDNhmvk6NZxtroAWTpQvr1Iv3BjRwSAISwb7GMlplysH9mgOSrMpZ2LFcyDbiGQoJuHv3mYOfclEjjBwQ8rSrnEHVTwYPG4NUhs0G0PAajvdkHNrbya6T3dGu/6RPwcdWfhUHe4+FsKJOhmEKT/MifXUJ2f5fstkdi7IoT41NPMyY+YU5+Kx31s7OhAm8by+KO/snLKlCFulj18WmqtCjCwNjfN7mlwV38iXYLoke8i8YPNkqkOkfTAl/m7J3GN3LixgTbuUHKRtFzpbS/ZECsVMMx9ayUgQTeDUHSPDOiLiEdOGhXUH2GhdW8OAExcmYLbfTE/Kszlg9EB94sVR26VGDsioB+QoJuDIFD9exOMEUIEgklGwGi2J8gEjBb19+riZfNMB8ftF2TVKtiXsjWABN0MpFsHPjMB4Ue20OXT/0EESbiFGCsq0MalFSuO3iqZ39dTPb9i6WAfFSfsvphv7LiAHkCCbgZR71m4LIc99h6qVe+ri/lbR9kL26jOzxJWMAkUGMeGWKmnvTj6vxKjbjbimb3cD1wryiipbeBdsnJVdmlrKErTusEsjmYgPbuIx33Mnv4c3TujtPPEtWWIqaU6jhI/86axQwNt1Jl7ZdekVf+b8MgGK7N7exxLKdkUJ133bIDOd7E8XvF7mpITfpnb2ZKGuzTTBQm6eajQUYRfH9XdU1SljLByIn17kZ5hxg4KtFEqTvg6XhruZzswyL5uu0RExgz0fv94ZmJWZV9/HVPuDiQXySpUFEnsvVzwUn8vQ8ULmg0SdLMRlvaoYxSta2tEAAzpp6uF+ZVMqLv11vMyrZcwRhYi8usL+b19bbReKlNw313MH9/ZxVpC7btSMK6TM5QMNVmQoAEwV/Ia1taCupJbeSVXx6u0iOB4XMPwlo8uAPzmggxjtLCflyVNnrxT+s152SdRQYYJGDQXJGgAzNXrw/1eH+7XQAeWZfGjE0PvFSmO3Za/MsjH2ZpGCL3Y32vtqexL2ZUR/rDlqymC5wMAtCEbYnO97CWaXWXHhbl0dLfeFCdtYJ9DYESQoB/CVYVYVW3sKABoKadSS5Nl1cuH+mqmfJAEWjHUJ7Ok9ugtqFVgimCIAyFWyV7czf37M2IUCCHCOYgevJQKHmTssADQJxUnbD0v6+1nNyDwkSkfXbxsIjs4fXMhb3iIo50FJATT0ub/PgROdTBGKEoV9ZxOendHjIK7dYT5fQX9zJuiHtHGDg4Avdl3pbCgkpnZy/1yTqXWSxH+tqdTS/dcLHh1iI9RYgOP09YTNHfziJB/SzJjB+ndTd1CdRzNnlrL/vMV1XE0YQFPTkArcfxOCUJo/TldEz4QQgidSCmBBG1q9JCgFQrFpk2bGIaxt7dfsmSJRGJOcyqFjHjSp4cmO6uJ+szjrv8m5FyhQoYbKzAA9Gt7dIfiaraBDs6PbpUCTIEeEvSpU6e6du06bty4n376KT4+PjIy8umPaTBYUU44+mo1ErbuCBH4QcENAFoDZ2saUrDZ0UOCDg0NdXNzQwjZ2NjQtJldAYSNKy7T/qUPl+UihAkbV6OEBAAAanpI0B06dEAIXblyJTExcfXq1erGzz777NChQwihsWPHyuVNncHT9J76IvKKkMR9Vnb1MO8/4EGTwFvEbaAktuXWgfjx8dTU1BgoxKdm+LP6xKqrzWaao1IJpeD0r7a2oQp8rZsgCCyrPQb15An69OnTt2/f7tu3b58+fXbu3FldXf3ee+9Z/lehYvny5TExMQih7777ztnZudGjMQxTW1trb2/faE89c5rEShMszq2hQkcQ3j2Qqlq4e0IoyaLHfeLkrvuBCcZYpVJZWJjBNsZKpZJlWVtbM9ihjud5juPM4gFGbW2tIAjW1tbGDqRx6pWEYrHY2IE0TqFQIISsrMxg/1mGYUiSFIn0PMNCEIT6IxBP/hmRkZHq4eYLFy5QFLV8+fK6r1pYWKhTGEEQBNH4biPqPk3pqWcEJZ64jrv5B3f1Z3zvDEFbkp5dJGM/Jt06NPI+w4fafEY7q80HobYEMwpVzSxCJf6j98PWb9TDfwI3b95MTU199913EUJjxowZOHDg0x/ToAhS1HWyqOtkxLOIMrMxdABAK6aHBL148eKnP4hJgOwMADAlUIsDAABMFCRoAAAwUZCgAQDAREGCBgAAEwUJGgAATBQkaAAAMFFtvdzoExCK01D+Xd7SmnQPJey9jR0OAKDVggTdDLiygD39KZ9xASHEIIQIUtR5PD1sBRKbwapfAIDZgQTdZJxKdTAGcSr62c9Yj+4WNMWnnmLjt+CaYvHkTcYODgDQCkGCbiru9jFcLrWY+zNyCmSVSsLSUtQjmrB2Zo68JeTdIL26GjtAAEBrAw8Jm0qQJZPuoYRzUN1Gqv0wRFsK0mRjRQUAaMUgQTcZzyBxvVqIBEnQFoiD0sAAAP0zswSNa8v5+7FG+WjC0Q8X3UPsI7kYl+VgRRnhFGCUkAAArZuZJWj2zBfMH68LuVcN/9GisCjM1jKn1iL2waYPuKaE+esjwtqFCh5k+HgAAK2eOT0kFGTJ/N1TSGzNnv1S8sKPiDDo/y6Eo5945HvMqbVCVhLy7MJgjpdeI0QS8YT/IdrSkJEAANoI87mDxgJ7bj3p2l4yeaNQnMbd+MPwIVBh4yzmHaQ6jkaqGoSQKHyWZP6vpHd3w0cCAGgLzOYOmrt1RChIkUzfTvr0oEJHcPFbqA6RhIWdgcMg7L1EQ1dwSqXYEu6aAQAty0zuoBkFd34b1SGS9O2FEKKHLMc8yyXuNHZYAADQgswjQbOJO7Cqkh68TP0tYesm6j2bu/aLIM8wbmAAANByzCBB43Ip9+8vovDZhL2XppGOmEPYuLHnvjRiYAAA0KLMIEGz59YTlg50nzmPtIok9OBXhOyL6tJFAADQ+pj6Q0IhK4lP/4fqNJbPSNB+jSAJKyc2dj0V0AeRpv6DAABAc5l6XuPvxyGE+JTjfMpx3T0UZUJJJuna3qBhAQBAyzP1BE0Pf100cEkDHQiSgnLMAIBWydQTNCIpw092BgAAU2AGDwkBAKBtggQNAAAmChI0AACYKEjQAABgoiBBAwCAiYIEDQAAJgoSNAAAmChI0AAAYKIgQQMAgImCBA0AACYKEjQAAJgoSNAAAGCiIEEDAICJggQNAAAmyuTLjQKeFYrTcLmUsPMgXUMQbWHsgAAABgIJ2qQJOZeZ05/h0mz1t4S1Cz1sBRU6yrhRAQAMAxK06RLybqh+XUZ6dRGPWk04BeDKAu7ibubP98SIoEJHGjs6AECLgzFo08Ve2E46BUqmbSW9uxOWDqR7qHj8F1TwIDZ+i7FDAwAYAiRok4UF6TWq42itDcupzs/iijxcWWCssAAABgMJ2lQJPOJZZGGj1UxIbBFCiK01QkgAAMOCBG2qSBFh74nzU7SahfxbiBITdh5GCQoAYEiQoE2XqPN4LuUYfz9O0yLk3+Iu76VCRyDa0oiBAQAMA2ZxmC5RxAtCwW3mj5WkV1fSOUiozBNyrpDuofSwlcYODQBgCJCgTRglFk/awKee5lNPCYV3CDsPOvJtUdeJiIDfewBoEyBBmzqqQyTVIdLYUQAAjADuxQAAwERBggYAABPV4kMcHMfV1NQ02o3neUEQmtLTFHAcJwiCsaNonDpOszirGGOe53meN3YgjeM4DmNsFmdVEASMMcuyxg6kceogzeKs8jxPEARJ6vnuVhCE+td/iydokUhkbW3daDeGYRQKRVN6Gh3GWKlUWlqawUQ3pVLJMIxZnFWe51mWtbAwg1p9tbW1PM+bxVllWRZjLBaLjR1I49Sp2SzOKsMwJEmKRHpOnoIgUBSl1QhDHAAAYKIgQQMAgImCBA0AACYKEjQAAJgoSNAAAGCiIEEDAICJggQNAAAmChI0AACYKEjQAABgoiBBAwCAiYIEDQAAJgoSNAAAmChI0AAAYKIgQQMAgImCBA0AACYKEjQAAJgoSNAAAGCiIEEDAICJggQNAAAmChI0AACYKEjQAABgoiBBAwCAiYIEDQAAJgoSNAAAmChI0AAAYKIgQQMAgImCBA0AACYKEjQAAJgoSNAAAGCiIEEDAICJggQNAAAmChI0AACYKEjQAABgoiBBAwCAiYIEDQAAJgoSNAAAmChI0AAAYKLMLUGzCtXPi4SsJGPHAQAALc7MEjSbtEeQXmNOf454xtixAABAyzKnBI0rZNzVfVS7obhCxl392djhAABAyzKnBM3GbiTENuKx/yfqOpFN2olr5MaOCAAAWpDZJGgh5zKfdo4evBSJrUUDlhAExZ7fauygAACgBZlJgsYCe2496R5KhY1DCBFWjqJ+C/lbfwoFKcaODAAAWop5JGju+q9C8X162EpEPAhY1HMG6RzAnl2HEDZubAAA0ELMIEFjZSV3YTvVcTTp0+NhK0nRw1YKeTf4u38bLzQAAGhBZpCguYRvMVtLD3pZq53070MFDWDjNiG21iiBAQBAixIZO4BG4AoZl3yIkNgwJz/S8XJNCa4q4v79WdRnnsFDAwCAlmXqCRqJLKgOI5DA6n7Vwo5yDiScgwwbEwAAGIKpJ2jC2lk87mNjRwEAAEZgBmPQAADQNkGCBgAAEwUJGgAATJQ+E3RVVZUejwYAAG2c3hL0uXPnduzYoa+jAQAA0M8sjsLCwh6AaE0AAAqBSURBVLi4ODs7O01LVlZWUVERQohhGJZ9zCS5OjiOwxg3pafRYYx5njeLUHmeFwTBLEIVBAHOqt5xHIcQMotQBUFAZhKq+gLAWM9FJnQeUw8JWhCE7777bvbs2YcPH9Y0xsbGnjlzBiHUrl27mpqaRg+iznpN6WkKBEFQX/omTv1XbhZnFWNsLv9Dq1OJuZxVhBBBEMYOpHHqa5XneWMH0rgWOqvqexStxidP0KdPn759+3bfvn2zs7NHjhxpa2tb99W5c+fOnTsXIfTVV185ODg0ejSGYRQKRVN6Gh3GWKlUWlpaGjuQximVSoZh6v5mY7LUt88WFhbGDqRxtbW1PM/b2NgYO5DGsSyLMRaLxcYOpHHq//Csra2NHUjjGIYhSVIk0vMiEkEQ6h/zyT8jMjIyMjISIXTnzp0///xTpVLJZLITJ06MGTPmqcIEAACAENLLEIf6TrmoqOjHH3+E7AwAAPqit1kcbm5ur732mr6OBgAAABaqAACAiYIEDQAAJqrFq9nJ5fKvvvqq0W4Mw9TW1trb27d0PHrBcZzen+G2BJVKpVKpzGIWB8ZYEASKoowdSOMUCgXP81rTlkyTekYgSZrBfZhCoUAIWVlZGTuQxrXQWcUYOzo6ajUSep9u/WTOnz+/e/fu3bt3GzuQVuXIkSNxcXFffvmlsQNpVfbu3ZuTk/Pee+8ZO5BWZfPmzRRFvfyy9sZJbZwZ/NcKAABtk6n8nu7h4TFkyBBjR9HaBAQEqFQqY0fR2rRv397FxcXYUbQ2nTt3NosVjwZmKkMcAAAAtMAQBwAAmCgjD3EoFIpNmzYxDGNvb79kyRKJRIIQksvlK1eudHNzQwitWLHCy8vLuEGaEZ7nN2zYUFNT4+fnN2/evAYaQdPBVdoSdJ5AuFa1UB9++KERP/748eOenp6LFi3KzMyUy+VBQUEIoezsbDs7uxUrVtSvwQQalpiYKBKJYmJiTp8+7e/vr55gp7MRNB1cpS1B5wmEa1WLkYc4QkND+/fvjxCysbGhaVrdWFhYmJeXt2XLltjYWGMGZ4bu37/fuXNnhFDnzp3v37/fQCNoOrhKW4LOEwjXqhYjJ+gOHTo4OjpeuXIlMTExIiJC3WhlZdW1a9cZM2acOXPm5s2bxo3QvFRXV6un+ltaWlZXVzfQCJoOrtKWoPMEwrWqxThj0Jpa0n369Nm5c2d1dfV7772nqbCs+TcwfPjw1NTULl26GCVIc2Rtba1ekVVbW6sprauzETQLXKV6p/MEwrWqxTh30JGRka+++mqfPn0uXLhAUdTy5cvrLvHct2+f+n/U7OxsT09Po0Roptq3b3/nzh2E0J07d9q3b99AI2g6uEpbgs4TCNeqFiPPg962bVtqaqr6uh8zZoybm9vff/89derUrVu3YoxdXFyWLVsG09ebjuf5LVu2MAzj7u7+wgsv3Lt37++//16yZEndRmPHaH7gKm0JBQUFdU9gWloaXKv1wUIVAAAwUbBQBQAATBQkaAAAMFGQoAEAwERBggagEYIg8Dxv7ChAWwQJGjyVs2fPenl5lZaWqr9duXLlc88998RHO3ny5OzZs+u319TUrFmzpilHqKmpGTFiRAMdNm/eHBoa6uXl9dJLL3EchxBq166d5X8SEhKqqqqWLl06YcKES5cuqd+ydOnS9PT05v802s6cOTNt2rSnPw5oOyBBg6cyfPjwadOmrVixAiGUlJR06NChrVu3PtmhWJYdOHDgF198Uf8lpVK5b9++Ro+wa9euESNGyOXyx3VITEz86quvEhIS7ty5c+fOnd27dwuCUFtH//79165dGxISsm7duoULFyKE7t69S5JkSEjIk/1Q+sWyrLFDAAYFCRo8rc8+++zixYu///77/Pnzd+7cqbWv2qhRo44dO6b+Wr0MYfny5YGBgZ6eni+++CLGOC4ubt68eVOmTNm0aVNSUtKbb76JMdbqExMTk5OTo94Paf369UFBQaGhoR988IFWJK6urlOnTq3b0rt3719++UXzbX5+/osvvujk5GRvbx8VFZWZmSmVSr29veu+RS6Xd+3aVV0RieO4Tz755N1339X5g2tFsmfPnlmzZiGELl682Ldv361bt06fPj04ONjf33/x4sUN5NZmnaIG/iJAK4QBeGpJSUk0TcfExNR/aefOnQsXLsQY//vvvxEREcnJyWPHjmVZlmGYkJCQu3fvxsbGOjk5ZWZmYoxPnz49a9as+n3kcnloaCjG+OzZs7179y4tLa2qqho1atTOnTu1Pu7u3bvdu3fXfJuVlVVZWVk/qry8vLCwsISEhNjY2ICAgMjISA8Pj1deeYXjuLS0tIiIiM6dO+/YsSMhIeH999/X+SPrjGTkyJFHjhwJDw+/devWtm3bnJyc8vPzGYYZPnz41q1bT58+PXXq1Kc8RaBNMZUtr4BZy8nJsbW1zc7OVn+7a9eunTt3IoSOHDkyadKkDz74QBCEAwcOzJkzp1u3bl9//fX+/ftv376dl5enVCoRQr179w4ICNAcTWcftXPnzpWWlkZHRyOEcnNzExMTFyxY0EBg/v7+9Rt37979ySefrF+/vl+/fjdv3ly5cuWSJUvKysrGjx+/Y8eOxYsXX7x4Ud1z8uTJ33//vc4j64zk22+/7dGjx+uvvx4WFnb+/PkRI0Z4eHgghGbPnn3s2LHHjZM8wSkCbQQkaPC08vLyli9fHh8fv3Dhwh07dixatGjBggV182aXLl0uXLhw+PDh8+fPx8fHL1my5NVXX50xY4bmKZxWPWWdfdSsrKxiYmJee+01hJBCocDNXAcrCMLzzz/PcVxiYqK6VHxYWJh6NzwXF5fJkyffuHFD0/nIkSPDhg2zsrKaP39+SkpKaGjo5s2bNRWKdUZSVVVFUZRMJlP3IckHQ4g0TasfSOrk5OTU3FME2ggYgwZPa/78+cuWLevUqdPu3btXrVqVkZGh1SE6Onr16tUdO3Z0cnKKjY0dO3bsokWLLC0tk5OTGYapf0CdfdQT3SIjI/fs2VNRUaFUKkePHq0urNMAqVRat2rlr7/+WllZefDgQXV2Rght3LhxypQpLMuWl5cfOXJkwIAB6nae53fu3Ll48WJ1paSkpKQOHTocPnxYc6j6kXAct2DBghMnTly6dCk+Ph4h9PfffxcXF3Mct3fv3qFDh6rfiDFOT0/XmrfX3FME2ghI0OCpfP3113K5/PXXX0cIhYaGrly58oUXXhAEoW6fiRMnJiQkzJkzByE0e/bsGzdudO/effXq1bNnz1bfgWqp38fBwcHOzm7evHnh4eGLFi0KDw8PCQkZOnRoeHh4w+FNmjRJ8/wNIRQfH3/27FnH/6xateqVV17x9/fv1KlTt27dhg0b9vzzz6t7fv/99zNnzqRpum/fvhKJ5JlnnklNTR0/frzmUPUjWbt2rfqLbdu2LV68WKlUDhw4cMKECSEhIT4+PosXL1a/UaFQtGvXTmuqSXNPEWgjoFgSAC1i+/btUqn0448/NnYgwIzBHTQAAJgouIMGoEWUl5dzHOfi4mLsQIAZgwQNAAAmCoY4AADAREGCBgAAEwUJGgAATBQkaAAAMFH/D7A39GQyR94wAAAAAElFTkSuQmCC", null, 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", null, 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OlnnnnG4r0QjKDBSjB8Bgfm7++fmpoaFham1WoTEhKslM4EAQ22gU8LwZHI5fLU1FQbdMTz76ROp/vggw9s0Dc4Kt7hM8bUAObiCWipVHrmzJlbt27ZvBhwBAhiAEvhn+Lw9PTs37//gAEDPD09uS3ffPONDasCx4SJDgCz8Ad0dHR0dHS0jUsBx4AIBrAU/lOaBg0a1KZNG29vb29vb7lcHh8fb+OyAACAfwT95ptv/vDDDyUlJUFBQRcuXJg5c6aNywIAAP6APnjw4J07d1auXPnaa681a9YsISHBxmU1TvVqcFj4DQAcAH9Au7m5EUJ69+59/vz5mJiYa9eu2baqxqi5Vif3GDFNrbrO9MheHGDjSgBoxh/Q48ePnzBhwubNm8eMGXPt2rW2bdvauCxz8a6kjDWUrQfnY4DzKHqo2XH296KH6hZyaUQPn5HP+disa/6Ajo2N1Wq1fn5+ycnJp06d2rJlS6M70Ov1SqXS9P11Op3BYDB3LY66lm8wq2tCiF6vZ1nW3GdZkE6nE7B3QohKpTLxzQ/ZkJ/xhkKo3i3OyQ89y7JqtVrAN1+n01ljGRaGYZrYwrrvi/b/Ulq9ZNGZ3x6n5d7d+XovuastFo2pcwTdokWLqKioadOmvfTSS03qwMXFrCVgGrdYkrqO7eauPuPkiyWp1Wq5XN7g70n1BIXFSzWld95Kamrc0J5bLMlpD71Go5HJZI63WFIT2/ym4MG+n0uNNpY8qnrjq8LPZ/Rq8OnDhw9v3br1559/Tgg5e/bspEmTevX6z7O+/vrrFi1aNNgC/y/D1atXU1JS7ty5ExoaOnny5AMHDjTYEDghXDQIju2zc/d4t98oVd97rK3/ueXl5Uql8sKFC1rtf/YcPXp0+p9MSWdSz4L9AQEBa9as+eSTT8rKyqZMmWJKWwLinWvGBLQ1IJTBeTxS6+v6VtZvD+t/7r59+8aOHfviiy+eOHGi0QXwB/ShQ4diYmI6duy4du3aqKio+/fvN7oDmzGKY6SzNdROZ+S1pQz7tDBkQz7eT7rUvVp+g+vo79mzZ+LEiWPGjPn222+5LYcPH46IiIiIiPj73/9uYv/8c9AfffRRZGTkunXrfHxs93ll0zlzKOO0CrtWM5dxKOnh1UxSXscg+oUu9d1U8NGjR6dOnYqPj1epVOfPn09JSSGEjB492txFSvkD+tixY2a1AjSw9i92XYO7RvTLu79aXddnvQ6O9/8SZDQNXg9ssymruPb2Dt7NOng1q+eJBw4cWLZs2T/+8Q9CSGRk5OnTp5s1q2//uuCmsY6g+jdcqH+Q8Y85OKTXAn2HK3yMTilr5S799NWe9T9xz549r7zyCvd4ypQp1bMc5sIdVcBUtcd0XC5jrAcO7G+jOk99vvWO3HvFj7VeMsmQbt6RAb4NPqvmmW+RkZHcLWKDg4PN7b3hgGYYprS01Ne34ZrA2ngXGzEavdrsv+Oaw3ahMhp/G8AGevm6vz++myBdNzzFcevWrTZt2tigFKiHOinQaLER3qvbhYIpDgBraDigu3TpUllZaYNSoBGEuvsfEtmCcpb2r/mvgNGX4MyMpziqJ7aN4JZXFKLqI0GceNBEGW8oBLzUG+hkHNCxsbGC1AGWhbgEcADGAT106FBCiE6nS0tLO3HihF6vDwsLmzdvnhC1QQMEieB6hu34qwBgWfxncSxYsODGjRvTp0+XSqU7d+68ePGiuRfAgGXJVuRR8qkgIhjAZvgD+ujRo9evX5dKpYSQqVOn9ujRw7ZVAY+aGe3MF7UD2J7h3iW25ALr1d6lUxBxldusX/6AdnFxKS8v5859fvjwIU6zowRyGcDGDDdzqg6tIpoK7kudSOIS8Io0fIVteucP6MDAwF69eg0fPlwkEh0/fjw0NJS7b+zixYtbtmxpm8oAAIRluHepau8iwtZYuo5l9D9+SaqU0lGJ9Txx//79y5Yt69Kli0ajCQ4OTkpKMlqwf8OGDf7+/g0WwB/QgwcPDg8Pr36s0Wi4lT5cXHBpOAA4C93Rvz2Vzn/S/3pQOvSt+uc6Zs6cmZiYSAh54YUXrl+/Tiy4mt3OnTu3bdvGhX1WVlZsbOylS5fMahcAwN4ZHt3h/wbL6q9879JnbIMtlJSUaLXadu3alZWVNaIA/oDeuHFjVFTUxIkTS0pKzp07t3PnzkY0DQBg35g676giUpfX/9SdO3eePn36yZMnnTp14u60yS3Yz33XxHsS8gd0cHDw6tWro6KiJBLJkSNH+vfHmVUA4HykMqJT8X5H1KZ3/U+tnuKIj4/fu3dv165dLTbFMX369AcPHly4cKGiomLu3LlDhgz55z//aVa7AAD2TtL1RaYwnecbrnJxR1OvS5BIJI2+iJ9/saRhw4YdO3asU6dOffr0yc7Olsttd94fAAAlXEf+TeTRynirSOw2dl2Dz/38889HjRoVFhb2ww8/jBw5khBy9OjRUX8y8a5V/CPoWbNmbd26NTc391//+tfly5dXr15tSlsAAA7FxbVZ7BHdiST95SOkqlLk4ipq2d1t3DpSO7WfNn78+PHjx9fcEhwcfOdOHR851tM/79bVq1efO3fu1q1bIpFo2bJlQ4YM+dvf/mZu05SreeU0LgABgLpIw1fY7MoUI/xTHF9//fWePXs6dOggl8uPHz++fft2G5dlbUbrWlCyzAUAQE38AV1VVaXT6aofN+5+tPYFGQ0AtOEP6AULFowYMeLmzZsffPBBaGjowoULbVyWVSGLAcAu8M9BL1++PCAg4OTJkzqdbseOHTgP2qqe+oMx75RwhQAAXepcWyM8PLx6OQ4HQ8/ayqTWcF68ZTDBJ5YAQAgx5aaxTkKQEzl4/07Q88cDAITlpAEtW5FXM5Fxmh0AUKiB5UMrKir2798/fvz45s2b26YgW2pKLuPmJgBgbQ2MoBctWlRcXLx48WLbVGMvas5CqJMCGz0pgXAHgHo8FdAMw1y9erXmlsLCwnfeeef27du2rYpq1p4jRmoDAOepKQ6RSLRx40axWLxq1SruPoTPPfdcTExM9W1arIoLPhEhmqe320VgqZMCG1en0bOUSqWFKgIAu/dUQIvF4o8//riwsPCtt97q0qXLihUrUlNTz5w5M3DgQKHqqwfvSNYu0hwAwBQ8HxIqFIrt27efO3cuKipqyJAhsbGxUqnU9pXZF/xhAACLM/6Q8Ny5c3379n3++ecLCwv37t3bqVOnyZMnf/nll4IURydkMQDYhnFAr1mz5ttvv71w4cKGDRsIIePGjfvuu+8wMWrEKKMR2QBgDcZTHB06dDh69Gjfvn2r79EikUjmzJlj88Joh1AGAGszHkG/9957d+/e/eKLL7744gtBCgIAAI7xCPqXX3557733CCEMw6SkpPz888+hoaGvvfaaELU1BndqB4a3AOAAjAN66NCharWaELJ48eIff/xx0qRJKSkpV65cscEtr7hUValUMplMJBI1pSnENAA4gDrX4tizZ8/Fixdbtmw5d+7cgIAACu9JWDN/sQIcADieOtfi8Pf39/HxIYS4u7vb6XnQSG0AsGvGAf3ss88+++yzQUFBN27cSE5OZll26tSpY8aMEaQ4AABnZjzFcf36dYPBUFxcfPPmTR8fH5Zl7feehILPQdc1hBe8MACwCzxz0GKxuGPHjh07duS+XLZsmW1Lagyq7mIFAGARDSzYb0eMMrrpo1SjxMewFwBszAIBzTDMhg0blEplx44do6Ojm95go1kwQ2uPxxu9oCgAQOPwB/RXX301evRoT09PQkhlZeWhQ4emTp1aVxO5ubl+fn5TpkxZv359cXFx+/btrVUsgNMI2ZDPuz1naX8bV+JsioqKQkND79y5Y9Ve1q1b16xZswZvVsV/ml1SUlJ5eTn3+NGjR0lJSfU0cf36dX9/f0KIv7//9evXG1UtAAAY4x9Bnz9/vvpx+/bta35ZW2VlpVwuJ4TIZLLKykpu444dO77//ntCSLdu3R49emR6QQaDQavVmr6/ZRkMBkKITqdz4/uuWS+EEMLbSP3tGAwGnU5nVi+WVVFRIVTXBoOhqqpKwN6J+YfYsgWYcuitVCHLshUVFU28grfRDAaDSCSyRu+W+m368MMPN2/e7OrqOnXqVO6qvYSEhH//+99t2rTx8vJatGjRiBEjjPbJzs5et27d48eP7969279//927dxsMhiVLlhw8eNDX17dbt25BQUEN9msc0Hq9ftu2bTdv3hw3blxISAi3ceXKlWvXrq2rCXd3d5VKRQhRq9XVa+CFhYVxN8rKysqq3miKqqoqV1dXoX5QqqqqWJZt1qyZnu+7Zr0QQghZnMO7uZ6Jf41G06xZM/N6sZzHjx/LZDKxuIFbCVuJ4IfeYDDIZDJBeicmH3qzfwhNU1FRIZfLhTr0Wq3WxcXFGr27uFjgY7bMzMwvv/wyPz9fKpVOmTJl69at7du3z8zMLCwsLC4u9vf3X7RoUe19FArF4cOHr1271qlTp/Dw8BMnTvz2228XL168evXqw4cP+/Tp05iAjouLu3Llyssvvzx37tz33ntv3LhxhJAtW7bUE9Ddu3e/fPmyQqG4fPlyZGQkt9HPz8/Pz48QcvbsWbMuRNTpdFKpVKjfUp1Ox7KsVCqV1jpvzzafEGq1WmGv25RKpUL9lur1eiv9lprYOyFEwDffxENvvQpdXFwkEomVGq8fwzBW6t0iSZKZmVleXs6F2507d3Jyclq0aDFx4kQXFxc/P79Bgwbx7qNQKEJCQjp37kwICQgIKCsry8zMnDlzpqurq6+v7/Dhw03p2jig/+///q+wsNDLy2v69OmDBg0KCQlp1apV/U0MHDhw8+bN69ev9/X1daRPCLlExskbAE5OLpfHxcVxV4SoVCqWZePj46ujn5t0qr1PQUFBy5Yta7YjFourxx9eXl6mdG08WmnevDnDMISQdu3arVy5Mi4ursEmJBLJ4sWL33rrrZkzZ5rSpX1BOgM4uWHDhm3fvv3x48cajWbkyJGXL18ODw/ft28fwzBFRUV5eXm8+9RuJywsbNeuXTqd7uHDh/v37zela54pjoEDB8bExLz11ltz5sxJT09/+eWXNRpN018kAJgOp9MJ6N69ex06dOAe+/n5ZWVlxcTEBAYGVlVVzZo1KzAwkBCSnZ2tUCh69+49ePBgLy+vwMBAo32ys7ONmo2Ojr5w4YJCoWjVqlX1bHAD2FrOnDnz7bffco8Zhvnqq68WLFhQezcTffTRR2btr1QqDQZDo7trIqVSWVlZKVTvLMsK2/uDBw8YhhGqd5VKJWzvT548Eap3VuhDX1paqtfrhepdrVZbqXdz88dEZ8+e5VpWq9UKhaK0tNQavbAsy/MRZ/XJG4QQsVgcGRlpatgDADgBf3//1NTUsLAwrVabkJDwzDPPWKkjx1mLAwDANuRyeWpqqg06EuaUJgCrqus6aQD7UmdAb926NSYmRqlUcp9RAgCAjfEH9OrVq7/55pusrCyRSLRs2bLExEQblwXQaNzwGYNocAD8Af3111/v2bOnQ4cOcrn8+PHj27dvt3FZAE2HjAZ7xx/QVVVV1YuMVFVVCbg6BIBZEMrgSPgDesGCBSNGjLh58+YHH3xgv/ckBGdTO52R12DX+E+zW758eUBAwMmTJ3U63Y4dO/r3x0VNAAC2xh/Q8+fP//jjj8PDw7kvZ82atWPHDtsVBWC+ugbLIRvycdk02CnjgH733Xe/+OKL27dvnzhxgtvCMIyJCy8BAIAFGQf00qVL582bFxcXl5ycXL3R29vbtlUBmA3DZLCGulaUZlm2wecWFBT4+/u7uLgUFxcPGDCAWyJ/586d3bt3N7F344B2d3d3d3f/97//fe3aNW4RO61Wu2zZsqysLBNbBNPVvne4mBA11jgFsHMGg+H8+fOzZs3Ky8tzcXEpKiqaP39+fHy8ue3wz0G/+eabP/zwQ0lJSVBQ0IULFxxyoWcAACtRKpUnT57kbtNDCLl169bVq1djYmLCwsJef5dv9AYAABRlSURBVP1109vhP83u4MGDly5diouL+/DDD3Nycu7evWuBkgGAejgx0SI8PT3/53/+h5vTIIQ0b948PDw8MTFx+/btmZmZprfDH9Bubm6EkN69e58/f16hUFy7dq3JBQMA7XCVvJWMHTt21qxZ7du3j4qKys3NNf2J/AE9fvz4CRMmDB48eOPGjW+//Xbbtm0tVCf8V+0JaABwSKtXrz558iQh5JdffjH9E0JSV0D/61//WrNmjZ+fX3Jysqen55YtWyxSJQBQK/SjgurHGERbVlRU1Nq1ayMiIh48eDBp0iTTn2j8IeGMGTPef//9t99+u+bGhISEXbt2WaBMAACnkZ6ezj3o2rXr8ePHG9GCcUBPnz69efPms2bNamJlAGBHxuy8ZbQFV2ByTDnf2XqMA3rEiBE6na6goGD58uWCFAQ4CRpsDFfJU4tnDloqlZ45c+bWrVs2LwYAAP6L/0IVT0/P/v37DxgwwNPTk9vyzTff2LAqALCR+j8PxCBaWPwBHR0dHR0dbeNSoH51nZaHKRFoipyl/evKaESz4PgDetCgQViLw9p4g1WpVNq+EgCgE9biAAAeGD7TAGtxADi72lmMdKYE/wi65locMTExWIsDwLEdnOnn7e0tkUiELgSewh/Q3FocmzdvHjNmzLVr1xxyLQ51UiA+XgMAE3Er99v4uhX+gI6NjdVqtdxaHKdOnXK8tTiwUBEAmKjmTVXMiunqO6oYDIbly5ffunXrmWeeSU1NresuLbXVuZrdG2+8kZyc7O/vn5CQUL2qqYNBTANA/UwP05oMBsPZs2dfffXVqqoqQsj+/ft9fX2/++67kSNH3rx50/R2+AP66tWrKSkpd+7cCQ0NnTx58oEDBxpRIrXsNJcxIQNAiQZT2+iOKmfOnGFZdvbs2RUVFV26dDG9I/4pDkJIQEDAX/7yl9GjRyckJEyZMkWr1ZreqB1xgJloB3gJTqWuq0Iy3lDYuBKwHu6OKhkZGdyXjx49KioqWr9+/fz58zt27Dhs2DAT2+EfQR86dCgmJqZjx45r166Nioq6f/++ZaqmQO3hs50OqAFAKOZ+VOjl5TV79uxOnTq9+uqr58+fN/2J/AH90UcfDRw48Oeffz5y5Eh0dHSLFi3MqgZsCcNnAMoNGDAgLy+PEHLhwoWuXbua/kT+gD527NicOXMYhrFMddSoa7CMQTQA8GJZ1mi83Igz7SZMmFBQUBAWFnb37t3Jkyeb/kT+gOaMGzfO3Dpo5gApXHuwjOGzHcF9pOwXW4Ppz0pPT3d3dyeEuLm57dmz5+TJk7t37zbraqA6PyR0PI6RZY7xKpwQ0hkaob4R9Ny5c7kHBoPBJsUAODjENJjFOKBfe+21hw8fco/nzJlDCPntt99eeuklW9cF4ECQy9A4xlMc7dq169evX1paWkREBCEkNTV15cqVuD8hgAXxrhWHpcChNuOATkpKmjx58pw5c4YMGVJUVFReXp6VlaVQ4BR6gEaqPXzGfaTARDwfEoaEhKxZs2bq1Kmenp5IZwAAoRjPQZeXl0dFRb3zzjunT5/esmVLRETEunXrHO+EaADbqGv2GbPSYArjEXTv3r0jIyMvXLggk8mCgoJCQ0Nnz569Z88esy5PBKHgxrJUQQo7EpFIZOPFoEntgN69e/eQIUOqv2zXrt2RI0eSk5NtWxWAI8BEs4OxfUYbT3HUTGeOSCSaP3++reoBAKBO41aFLigo4FYcfe+990JCQkJCQhQKxVtvvWV6C/VdqAIAALXvqNIgowX733nnnZycnJycnODg4DfffNP0rq1+qbfBYNDpdKbvzzCMTqdr3N+rpjMYDCzLmlWwxQuwRu+mt6nT6cRiYf5sc4dewN6t9OabSNjeCSF6vV6oa4YZhhGJRNbo3RozEqZMdBgt2M85d+6ch4dHt27dTO8LI2gAgDo1brDILdhvdLPADRs2JCQkmNWO1UfQYrFYKpWavr9Op5NKpQ2+KUanK1jqLAWdTseyrFkFW5ZWq21K7/o6tpveplQqFWoMq9frhe2dmPNGWVwTD33Tubi4mLXQmgUxDGOl3q30v3gjPi0sKytTqVRt2rQx61l2OYLGXVEAwAYsmO9Hjx4NDw8391lOtNyoM8D5zgCWUn86mzuIPnToUHx8vLk1IKABAHg0/QPG9PT06se7d+9uRAt2OcUBAOAMENAAAJSyy4DGrfkAwBnY6xw0EhkAHJ5djqABAJwBAhoAgFIIaAAASiGgAcAOhGzId8IbICCgAcBuOFtGI6ABgHbOlsvVENAAYE+cKqwR0ABANadKZCMIaACgF286O09kI6ABACiFgAYAStUzUnaSQbS9rsUBAA4vZ2l/oUsQGEbQAACUQkADAFAKAQ0AQCnMQQuj+jbkWNgaAOqCgLaw6uQ1Uh3ERjuokwLtPaMd4CUA0AlTHMKrK9Ptgl0XD0A5BDRYAGIawBoQ0NB4yGUAq3L2OWjjiIn7QaBC7B5mogEszqlH0LUHgKLklwSpxB5h+AxgbU4d0LzEWwZbtX2jYaZsRZ7DDDwR2QCW5exTHIJwgESuK4sx0QFgQQhoC3OGeMJIGcA2ENDGDPNOCV0C7ZzhjxAADZx6Drp20LA4iwMAqOHsI2ijjFapVEJVAgBgxKlH0AAANENAAwBQCgENAEApBDQAAKUQ0AAAlEJAAwBQCgENAEApBDQAAKUQ0AAAlEJAAwBQCgENAEApBDQAAKUQ0AAAlEJAAwBQCgENAEApC6wHrVKpNm7cqNVqvby85s2b5+bm1vQ2AQDAAiPo9PT0vn37JiYm+vr6ZmVlNb1BAAAgFhlBKxSK1q1bE0I8PDykUim38ffff3/48CEhRK/X6/V601szGAx6vV4kEjW9sEYwGAwsy5pVsMULELB3QoherxeLhZn44l67gL0L++YLfugZhmFZVpCuDQaDlXoX6hVZigUCumfPnoSQvLy8nJychIQEbuO+ffuOHDlCCAkICHjy5InprbEsq9Ppml5V43CHU8DfE2H/PBBClEqlUF2zLKvVagXsnWVZs35WLV6AsD94lZWVQg2MWJa1UtfC/jY1XeMDOiMj49KlSwMHDgwODk5LS6usrIyPj5fJZNx3Y2NjY2NjCSGbNm1q0aKF6c2qVCqZTCbUD4pKpWJZ1t3dXZDeCSFKpVLA3ktLS728vIQaw6rVajc3NwF7ZxjGw8NDkN6J0Ie+rKzMy8tLIpEI0rtGo5FKpdbovfp/ejvV+IAeNmzYsGHDCCHZ2dkSiWTJkiWWqwoAACwxxfHLL79cuXJl1apVhJBRo0aFhoY2vU37pU4KrL3R6N7hAACmsEBAc1MZAABgWbhQBQCAUghoAABKIaABACiFgAYAoBQCGgCAUghoAABKWeA0O6gJpzwDgKVgBA0AQCkENAAApRDQAACUQkADAFAKAQ0AQCkENAAApRDQAACUQkADAFAKAQ0AQCkENAAApRDQAACUQkADAFAKAQ0AQCkENAAApRDQAACUQkADAFAKAQ0AQCkENAAApRDQAACUQkADAFAKN421P+qkwOrHuEctgAPDCNrO1Ezn2l8CgCNBQNsT3jhGRgM4KgQ0AAClMAftyOoaXGPmGsAuYAQNAEApBLQ94R35YjgM4KgwxWFnuDjm5i4QzQCODQFtlxDNAM4AUxwAAJRCQAMAUAoBDQBAKcxBOzJMVQPYNYygAQAohYAGAKAUAhoAgFIIaAAASiGgAQAohYAGAKAUAhoAgFIIaAAASiGgAQAohYAGAKCU1S/11uv1SqXS9P11Op3BYBCJRNYrqR56vZ5lWbMKtiydTidg74QQlUol4JvPMAwOvSBYllWr1QK++TqdTiy2/HiRYRiLt2lLVg9oFxcXd3d30/dXqVQymUyoHxSVSsWyrFkFW5ZSqRSwd7VaLZfLrfF7YmLvbm5uAvbOMIzTHnqNRiOTySQSiVC9S6VSa/Qu1CuyFExxAABQCgENAEApBDQAAKUQ0AAAlEJAAwBQCgENAEApBDQAAKUQ0AAAlEJAAwBQCgENAEApBDQAAKUQ0AAAlLL6YkkAZlEnBfJul63Is3ElAILDCBoAgFIIaAAASiGgAQAohYAGAKAUAhoAgFIIaAAASiGgAQAohfOggS443xmgGkbQAACUQkADAFAKAQ0AQCkENAAApRDQAACUQkADAFAKAQ0AQCkENAAApRDQAACUQkADAFAKAQ0AQCkENAAApRDQAACUsvpqdqWlpZs2bTJ9f71e7+Ii2Bp7KpXKYDB4eHgIVYCwL7+srKxFixZisTB/toV97Wq1Wq/Xe3p6ClWAsC+/vLzcy8tLIpEI0jvDMGKxWCQSWbxlNzc3i7dpSyKWZYWugSKffvppRUXFihUrhC5EGCEhIQcPHnzmmWeELkQAu3btunnz5urVq4UuRBjh4eGfffZZhw4dhC4EnoIpDgAASmHB/qcoFAqNRiN0FYKZMGFCs2bNhK5CGD169PDx8RG6CsGMGTPG3d1d6CrAGKY4AAAohSkOAABKYYrjKSqVauPGjVqt1svLa968efb+EbDpGIbZsGGDUqns2LFjdHS00OXYmtMe95qePHki4EkswAsj6Kekp6f37ds3MTHR19c3KytL6HJsJzc318/PLzExsbS0tLi4WOhybM1pj3u1zMzM1NRUoasAYxhBP0WhULRu3ZoQ4uHhIZVKhS7Hdq5fvx4cHEwI8ff3v379evv27YWuyKac9rhz7t+/f+rUqebNmwtdCBjDCPopPXv2bNGiRV5eXk5OTlBQkNDl2E5lZaVcLieEyGSyyspKocuxNac97oQQg8GwY8eOGTNmCF0I8MAImhBCMjIyLl26NHDgwODg4LS0tMrKyvj4eJlMJnRdtuPu7q5SqQgharXaOU+3cs7jTgjZs2fP8OHDMftMJwQ0IYQMGzZs2LBhhJDs7GyJRLJkyRKhK7K17t27X758WaFQXL58OTIyUuhybM1pjzshRKVSHTx4sKqqqqSk5MiRI6NGjRK6IvgvnAf9lJSUlCtXrnD/7I8aNSo0NFToimyEYZjNmzdrtVpfX9+ZM2cKXY6tOe1xr/bHH398/vnny5YtE7oQeAoCGgCAUviQEACAUghoAABKIaABACiFgAa7ZzAYGIYRugoAy0NAO50TJ060a9euvLyc+3L58uWvvvpqo1s7duwY7zUOSqXy3XffNaUFpVIZERFR13fXrFkj+5Orq+vLL7/85MmThQsXjh8//ty5c9w+CxcuvHHjRuPqr+n7779/5ZVXmt4OgKUgoJ1OeHj4K6+8snTpUkJIbm7unj17kpOTG9eUTqcLDQ19//33a39Lo9F88cUXDbawdevWiIiI0tLSunZYtWqV+k+vv/764sWL165d26NHj6SkpLlz5xJCCgsLxWJxjx49GvcSLEun0wldAjgUBLQz+uc//3n27Nnvvvtu9uzZaWlpLVq0qPndESNGHDp0iHvMXcCyZMmSzp07t23b9o033mBZ9tSpU9HR0ZMnT964cWNubu7bb7/NsqzRPnFxcbdv354/fz4h5MMPP+zSpYtCoUhMTDSqpFWrVlOmTKm5ZcCAAV999VXtmjMyMsRi8fDhw0tLS/v27dulSxdCiF6vX7NmzapVq3hfplG/27dvnz59OiHk7NmzAwcOTE5Onjp1ateuXTt16hQbG1tPtpr1htT3vgOYiwWnlJubK5VK4+Lian8rLS1t7ty5LMv++OOPQUFBBQUFo0eP1ul0Wq22R48ehYWFJ0+e9PHxuXnzJsuyGRkZ06dPr71PaWmpQqFgWfbEiRMDBgwoLy9/8uTJiBEj0tLSjLorLCzs169f9Ze3bt2qqKgw2odhmKCgoJKSEpZlr127FhQU5O/vn5qaeubMmdWrV/O+QN5+hw8ffuDAgcDAwIsXL6akpPj4+Ny7d0+r1YaHhycnJ2dkZEyZMqWJbwiABeFSbyd1+/ZtT0/PoqIi7sutW7empaURQg4cODBx4sTExESDwfD1119HRUX95S9/+fjjj3fv3n3p0qW7d+9ytwQbMGCAn59fdWu8+3AyMzPLy8u5y8fv3LmTk5MzZ86cegrr1KlT7Y179+7t169fu3btCCHdunU7e/Yst33SpEmfffYZbzu8/X766acBAQErVqzo3bv36dOnIyIi2rRpQwiZMWPGoUOH6ponacQbAmARmOJwRnfv3l2yZElWVlZ5eTm3CvCcOXNycnJycnJatWrl4+PTp0+f7Ozs/fv3T5s2LSsra8yYMVVVVdOmTQsMDORaMFpbh3cfjlwuj4uLS09PT09P//HHHxs3CbB79+4JEyYYbTxw4MCQIUPkcvns2bMHDhw4a9asioqK+vt98uSJRCIpKSnh9hGL//PzL5VK9Xp9Xb034g0BsAgEtDOaPXv2okWLevXqtW3btpUrV/72229GO0RGRiYkJDz33HM+Pj4nT54cPXp0TEyMTCYrKCjQarW1G+Tdhzv1bdiwYdu3b3/8+LFGoxk5cuTly5frr624uNhovVOVSpWVlTVkyJCaGxmGSUtLi42N5dY5ys3N7dmz5/79+6t3qN2vXq+fM2fOkSNHzp07x63Kf/z48QcPHuj1+p07d4aFhXFPZFn2xo0bRuftmfuGAFgEAtrpfPzxx6WlpStWrCCEKBSK5cuXz5w502Aw1NxnwoQJZ86ciYqKIoTMmDHj559/7tevX0JCwowZM3jX06m9j7e3d/PmzaOjowMDA2NiYgIDA3v06BEWFmY0vq5t4sSJ1Z/IcY4ePfrCCy8Y3W78s88+e+2116RS6cCBA93c3IYOHXrlypVx48ZV71C737Vr13IPUlJSYmNjNRpNaGjo+PHje/To0b59+9jYWO6JKpWqW7duRieWmPuGAFgEFksCJ/XJJ58UFxf/4x//ELoQgDphBA0AQCmMoMFJPXr0SK/Xt2zZUuhCAOqEgAYAoBSmOAAAKIWABgCgFAIaAIBSCGgAAEr9Pzy5Qlj2wqNYAAAAAElFTkSuQmCC", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", 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[ "Select Page\n\n# For loop\n\nIt takes  the following form:\n\n```for (initialization block; termination condition; update clause ) {\nstatements\n}\n```\n\nThere are of three parts in the round brackets or parentheses () of the for loop:\n\n• Initialization block\n\nThis executes only once. One or multiple variables can be declared and initialized, but they have to be the same type.\n\n• Termination condition\n\nIf the condition evaluates to true, then the iteration continues, otherwise it stops.\n\n• Update clause, executable statement(s)\n\nThis consists of one or more executable statement(s). They are usually used to update the variables defined in the initialization block.\n\nStatement(s) within the for loop\n\nThere can be zero, one or more statement(s) within the curly brackets or braces {} of the for loop. It is valid to have an empty block within the for loop. A loop which does nothing.", null, "Note that the three components in the round brackets are optional! The two semi-columns ( ; ; ) have to be present between the round brackets.\n\nThe initialization block can be before and outside the for loop. An empty condition is valid and implies an infinite loop. The increment or decrement of the index in the update clause is not obliged.\n\nA for loop example\n\n```public class ForLoopTest2 {\npublic static void main(String[] args) {\n/*\n* Not the third component update clause has to be an assignment.\n* For example\n* i++, i--, ++i, i=i+2\n* Not: for example i+2\n*/\n// for (int i=0; i <10; i+2) { // compilation error,\n// third component is not an assignment!\n// for (int i=0; i <10; i=i+2) { // valid\n// for (int i=0; i <10; ++i) { // valid\nfor (int i = 0; i < 10; i++) { // valid, same result as with ++i\nSystem.out.println(i);\n}\n}\n}\n```\n\nSome examples of the for loops. You can declare multiple initialization variables, but they have to be of the same types.\n\n```public class ForLoopTest1 {\npublic static void main(String[] args) {\n/*\n* Note that all three parts of a for statement are optional.\n* - Initialization block\n* - Termination condition\n* - Update clause, executable statement(s)\n*\n* It is possible to specify the initialization part outside the for loop.\n*/\n\nint i = 0; // this is visible in every for loop,\n// so it cannot be declared again in a for loop\nfor (; i < 5; i++) { // valid\nSystem.out.println(i);\n}\n\n// also valid to put the increment or decrement within the loop\ni = 0;\nfor (; i < 5;) { // valid\nSystem.out.println(\"test2: i = \" + i);\ni++;\n}\n\n// what will be the value of 1 after the loop ?\n// if i = 9, then the condition check is true, i++ set i to 10\n// then, the third component i++ is executed and changes i to 11.\n// It will exit the loop, and i = 11 is printed!\ni = 0;\nfor (i = 1; i < 10; i++) {\ni++;\nSystem.out.println(\"test3: i = \" + i);\n}\nSystem.out.println(\"test3 after for loop: i = \" + i);\n\n// No statement within the for loop\ni = 0;\nfor (i = 1; i < 10; i++) {\n// valid to have an empty block\n}\nSystem.out.println(\"test: multiple variables in the initialization\");\n// all the variables in the initialization block have to be the same type\n// In each part there can be multiple statements or conditions\nfor (int m = 0, n = 1; n <= 3 && m <= 5; m++, n++) {\nSystem.out.println(\"m = \" + m + \", n = \" + n);\n}\n\n// Not valid to have different types in the initialization block\n// It is already a syntax error\n// for (int m = 0, long n = 1; n <= 3 && m <= 5 ; m++, n++) { // not valid\n// System.out.println(\"m = \" + m + \", n = \"+ n);\n// }\n\n}\n}\n```\n\nOutput\n\n```\\$ java ForLoopTest1\n0\n1\n2\n3\n4\ntest2: i = 0\ntest2: i = 1\ntest2: i = 2\ntest2: i = 3\ntest2: i = 4\ntest3: i = 2\ntest3: i = 4\ntest3: i = 6\ntest3: i = 8\ntest3: i = 10\ntest3 after for loop: i = 11\ntest: multiple variables in the initialization\nm = 0, n = 1\nm = 1, n = 2\nm = 2, n = 3\n```\n\n# Enhanced for loop\n\nIt is also called the for-each loop and is introduced since Java 5.\n\n## Advantages over the regular for loop\n\nIt is mainly used for iterating through a collection or list with a short and simple syntax. It is also easy to use to iterate through non-nested or nested array.\n\n```for (int element : intArray) {\nSystem.out.println(element);\n}\n```\n\n## Some differences compared to the regular for loop\n\nNote that the declaration of the looping variable MUST be declared within the enhanced for loop, while it is not the case for the regular for loop. See example below:\n\n```public class EnhancedForLoopTest1 {\npublic static void main(String[] args) {\n/*\nNOTE, unlike the for loop\nthe declaration of the variable element MUST be in the\nbrackets of the enhanced for loop !!!!\n*/\nint[] intArray = {0,1,2,3,4,5};\n//int element;\n// enhanced for loop or for each loop\n// for (element : intArray) { // compilation error,\n// the declaration of variable element must be in the brackets !!!\nfor (int element : intArray) { // valid\nSystem.out.println(element);\n}\n}\n}\n```\n\nThe looping variable can also be declared as final, because it is created in each iteration! While it is not the case in the regular for loop. See example below:\n\n```public class FinalIndexElementTest {\npublic static void main(String[] args) {\n\nint[] intArray = { 1, 2, 3, 4, 5 };\n// if the element (this is not an index of the array) is final\n// in an enhanced for loop.\n// that is fine! Because the new variable element is\n// created in each iteration!\nfor (final int element : intArray) {\nSystem.out.println(element);\n}\n\n// what happened if the variable i is final\n// compilation error, you cannot assign a value to\n// a final variable i, at i++\n// for (final int i=0; i <5; i++) { // final is not valid\nfor (int i = 0; i < 5; i++) {\nSystem.out.println(i);\n}\n\n}\n}\n```\n\n## Limitations\n\nIt is enhanced in the context for iterating through the elements due to the compact syntax, but it is not so powerful as the regular for loop. Because the enhanced for loop cannot do the following (without adaptation of the original enhanced for loop construction):\n\n• It cannot be used to initialize an array and modify its elements\n• It cannot be used to delete or remove the elements in a collection\n• It cannot be used to iterate over multiple collections or arrays in the same loop. Because you can only define one looping variable in the enhanced for loop, while you can define multiple looping variables in the regular for loop.\n\nIn general the enhanced for loop should be used to iterate over collections or arrays. Don’t use it to initialize, modify or delete them.\n\n# While loop\n\nThe loop checks the condition first before it executes the statement(s) in the loop.\n\n```public class WhileLoopTest1 {\npublic static void main(String[] args) {\nint i = 0;\nwhile (i < 10)\ni++;\nSystem.out.println(i); // 10\n\ni = 0;\nwhile (i < 10) {\n++i;\nSystem.out.println(\"test i: \" + i);\n}\n//\nSystem.out.println(i); // 10 , still 10\n\n}\n}\n```\n\nOutput:\n\n```\\$ java WhileLoopTest1\n10\ntest i: 1\ntest i: 2\ntest i: 3\ntest i: 4\ntest i: 5\ntest i: 6\ntest i: 7\ntest i: 8\ntest i: 9\ntest i: 10\n10\n```\n\n# Do-While loop\n\nThis executes at least once. The condition is evaluated at the end.\n\n```public class DoWhileTest2 {\npublic static void main(String[] args) {\n// will this print at least once?\n// Yes!\n// So the while condition is evaluated after the execution of the do block\n// first.\nint i = 0;\ndo {\nSystem.out.println(\"i= \" + i);\ni++;\n} while (i < 0);\n}\n}\n```\n\nOutput:\n\n```\\$ java DoWhileTest2\ni= 0\n```\n\nIn the following example the variable i is not visible for the while condition.\n\n```public class DoWhileTest1 {\npublic static void main(String[] args) {\n// will this work?\n// it will not compile, because it is not visible\n// for the while condition!!\ndo {\nint i = 0;\nSystem.out.println(\"i= \" + i);\ni++;\n} while (i < 10); // cannot find variable i\n}\n}\n```\n\nDon’t forget the semicolon (;) at the end of the do-while loop", null, "# Break and Continue in loop\n\n## Break statement\n\nIt is used to exit the for, enhanced-for, while and do-while loops, as well as the switch statement.\n\n```public class BreakTest1 {\npublic static void main(String[] args) {\n//\nloop1: for (int i = 0; i < 4; i++) {\nSystem.out.println(\"1st loop, i = \" + i);\n\nfor (int j = 0; j < 4; j++) {\nSystem.out.println(\"2nd loop, j = \" + j);\nif (j > 1) {\nbreak; // break only this loop\n}\n}\n}\n}\n}```\n\nOutput:\n\n```\\$ java BreakTest1\n1st loop, i = 0\n2nd loop, j = 0\n2nd loop, j = 1\n2nd loop, j = 2\n1st loop, i = 1\n2nd loop, j = 0\n2nd loop, j = 1\n2nd loop, j = 2\n1st loop, i = 2\n2nd loop, j = 0\n2nd loop, j = 1\n2nd loop, j = 2\n1st loop, i = 3\n2nd loop, j = 0\n2nd loop, j = 1\n2nd loop, j = 2\n```\n\n### Labeled break statement\n\nYou can use a labeled break statement to exit the outer loop. An example of a break to the outer loop with label loop1.\n\n```public class BreakTest1 {\npublic static void main(String[] args) {\n//\nloop1: for (int i = 0; i < 4; i++) {\nSystem.out.println(\"1st loop, i = \" + i);\n\nfor (int j = 0; j < 4; j++) {\nSystem.out.println(\"2nd loop, j = \" + j);\nif (j > 1) {\nbreak loop1; // break to the labeled loop\n}\n}\n}\n}\n}\n```\n\nOutput:\n\n```\\$ java BreakTest1\n1st loop, i = 0\n2nd loop, j = 0\n2nd loop, j = 1\n2nd loop, j = 2\n```\n\n### Break in a Switch statement\n\n```public class SwitchTest1 {\npublic static void main(String[] args) {\n// enum has to be defined in similar way as a class\n// enum Gender {FEMALE, MALE, UNISEX};\nGender gender = Gender.MALE; // has to be initialized\n// Gender gender = xx; // compile error\n\n/*\n* switch works with the byte, short, char, int primitive data types and their\n* wrapper classes, enum types String\n*\n* But NOT with Boolean !!!\n*\n*/\nswitch (gender) {\ncase FEMALE:\nSystem.out.println(\"Gender is Female.\");\nbreak;\ndefault: // default can be placed everywhere, but only once !\nSystem.out.println(\"Not a predefined gender\");\ncase MALE:\nSystem.out.println(\"Gender is male.\");\nbreak;\n// so, without break, the statement below will be executed as well\ncase UNISEX:\nSystem.out.println(\"Gender is Unisex.\");\nbreak;\n\n}\n}\n}\n```\n\nOutput:\n\n```\\$ java SwitchTest1\nGender is male.\n```\n\n## Continue statement\n\nIt is used to continue to the start of the next iteration. It cannot be used in the switch statement.\n\n```public class ContinueTest1 {\npublic static void main(String[] args) {\n// continue can only use in a loop construct\nfor (int i = 0; i < 10; i++) {\nif (i % 2 == 0)\ncontinue;\n// means that the statement after continue\n// with not be executed in the iteration\nSystem.out.println(\"i= \" + i);\n}\n}\n}\n```\n\nOutput:\n\n```\\$ java ContinueTest1\ni= 1\ni= 3\ni= 5\ni= 7\ni= 9\n```" ]

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[ "# Percent Change Worksheet Story Problems Worksheets Free Percentage Percent Change Word Problem Worksheet Finding Percentage Increase And Decrease Worksheet", null, "percent change worksheet story problems worksheets free percentage percent change word problem worksheet finding percentage increase and decrease worksheet.\n\npercent of change worksheet algebra 1 activity worksheets finding percentage increase and decrease grade for all download,percent change worksheet word problems finding percentage increase and decrease percentages worksheets,finding percentage change worksheet percent increase and decrease new of images,quick tips for calculating percentages in excel learning finding percent change worksheet of algebra one word problems,percent change worksheets middle school activity of worksheet 8th grade calculating,percentage change worksheet activity sheet foundation finding increase and decrease percent of algebra one answer key,percent of change worksheet 8th grade finding proportions word problem freebie by math on the move free algebra one answer key,percent change worksheet word problems of 8th grade the best worksheets image algebra 1,percent change and error notes task cards a problem solving of worksheet algebra 1 worksheets middle school finding percentage increase decrease,percent of change worksheet algebra one word problems answer key chapter 3 percents per cent definition out.\n\nPosted on" ]

[ null, "http://activepatience.com/wp-content/uploads/2018/08/percent-change-worksheet-story-problems-worksheets-free-percentage-percent-change-word-problem-worksheet-finding-percentage-increase-and-decrease-worksheet.jpg", null ]

[ "Abstract: 本文介绍多项式求值的相关内容。\nKeywords: 多项式求值，霍纳方法\n\n# 多项式求值\n\n## 三种方法\n\n$$P(x)=2x^4+3x^3-3x^2+5x-1$$\n\n$$P(\\frac{1}{2})=2\\times \\frac{1}{2}\\times \\frac{1}{2}\\times \\frac{1}{2}\\times \\frac{1}{2} +3\\times \\frac{1}{2}\\times \\frac{1}{2}\\times \\frac{1}{2} -3\\times \\frac{1}{2}\\times \\frac{1}{2} +5\\times \\frac{1}{2}-1$$\n\n$$x^4=x\\times x^3\\\\ x^3=x\\times x^2\\\\ \\vdots$$\n\n$$\\frac{1}{2}\\times \\frac{1}{2}$$\n\n$$\\frac{1}{2}\\times \\frac{1}{2}\\times \\frac{1}{2}=\\frac{1}{2}\\times a$$\n\n\\begin{aligned} P(x) &=2x^4+3x^3-3x^2+5x-1\\\\ &=-1+x(5-3x+3x^2+2x^3)\\\\ &=-1+x(5-x(3+3x+2x^2))\\\\ &=-1+x(5-x(3+x(3+2x)))\\\\ \\end{aligned}\n\n## 深入理解这个例子\n\n• 计算机在简单计算的时候速度极快\n• 简单计算会被多次重复执行，所以高效的简单计算非常重要\n• 最好的计算方法并不是最显而易见的那种。\n\n20世纪后五十年，结合计算机硬件，常见问题已经开发出了许多有效的求解方法（也就是利用计算机帮我们高效的完成数学计算）。\n\n$$c_1+x(c_2+x(c_3+x(\\dots)))$$\n\n$$c_1+(x-r_1)(c_2+(x-r_2)(c_3+(x-r_3)(\\dots)))$$\n\n## 编程实现", null, "Matlab的代码比较简单，虽然我们写过matlab的代码，但是这段似乎挺简单，但是这语法，写C太久，看别的语言总感觉语法混乱/(ㄒoㄒ)/~~\n\nMatlab还可以同时输入多个 $x$ 真的很神奇 😓", null, "$$P(x)=1+x(\\frac{1}{2}+(x-2)(\\frac{1}{2})+(x-3)(-\\frac{1}{2}))$$\n\n0%" ]

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[ "# How Do You Multiply Two Numbers Using Scientific Notation?\n\n### Note:\n\nMultiplying together two really large numbers? What about two really small numbers? How about one of each? Scientific notation to the rescue! Watch this tutorial and learn how to multiply using scientific notation.\n\n### Keywords:\n\n• problem\n• scientific notation\n• multiplication\n• multiply\n• multiply scientific notation\n• associative property\n• power\n• powers\n• exponent\n• exponents" ]

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[ "Vous êtes sur la page 1sur 2\n\n# Printed Pages : 3\n\n## END SEMESTER EXAMINATION, 2015–16\n\nM.TECH.-CE (STRUCTURAL ENGG.) (SEMESTER-02)\nCVL 622 : THEORY OF ELASTICITY AND PLASTICITY\n\n## Time : 03 Hours Maximum Marks : 100\n\nNote : 1. Attempt all sections.\n2. All questions carry equal marks.\n\nSECTION-A\n\n## Q.1 Answer all parts of the following: (5 4 20)\n\n(i) Define principal plane and principal stress.\n\n## (iii) Show the stress-strain behavior of the material which is rigid\n\nwith stain hardening properties?\n\ntensor?\n\n## (v) Define Modulus of Rigidity.\n\n4\n1 [Contd…….\nQ.2 Answer all parts of the following: (5 4 20) Q.5 Answer both parts of the following: (2 10 20)\n\n(i) Define Hook’s law. (i) Find the stress components of a circular bar when subjected to\ntorsional moment M. Cross section of the equation is given by\n(ii) What are the boundary conditions in terms of stresses?\nx2 + y2 = r2.\n(iii) Write a short note on Tresca’s failure theory.\n(ii) Derive the torsion equation of a hollow cylinder.\n(iv) Define: Poisson’s Ratio and Factor of safety.\n(v) Define Torsional rigidity. Q.6 Answer both parts of the following: (2 10 20)\n\nQ.3 Answer all parts of the following: (5 4 20) (i) A rectangular beam having linear stress‐strain behavior is\n6 cm wide and 8 cm deep. It is 3 m long, simply supported at\n(i) What are stress invariants and strain invariants?\nthe ends and carries a uniformly distributed load over the\n(ii) What are stress formulations? whole span. The load is increased so that the outer 2 cm depth\n(iii) Define Polar Modulus and Moment of Inertia. of the beam yields plastically. If the yield stress for the\nbeam material is 240 MPa, plot the residual stress distribution\n(iv) Give Equilibrium equation in 3D form in Cartesian coordinate?\nin the beam.\n(v) Explain St. Venant’s Principle.\n(ii) Derive the torque equation of a prismatic bar subjected to turst\nT, according to St. Venant’s theory.\n\nSECTION-B\n\nSECTION-C\nNOTE : ATTEMPT ANY TWO QUESTIONS.\nQ.4 Answer both parts of the following: (2 10 20) Q.7 A cantilever beam of length ‘L’ is loaded at the end. Using Inverse\n(i) Derive the equation of equilibrium for polar coordinate system. method, obtain the expression for normal stress and shear stress.\n(ii) The displacement field in a body is specified as:\n2 3 2 3 3\n_________________________________\nUx x 3 10 , Uy 3 y 10 , U z x 3z 10 , determine\nthe strain components at appoint whose coordinates are (1, 2, 3)?\n\n2 [Contd……. 3" ]

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[ "The crazy place of all quizzes\nSolve tons of quizzes. Solve till u fall!\n\nYou are here: Home » CBSE Science Class 10 Index » Chapter 10: Light Reflection and Refraction\n\n# Light Reflection and Refraction: FIB Notes\n\nLight Reflection and Refraction is a chapter of CBSE class 10 Science textbook. This is second part of learning notes on FIBs of Light Reflection and Refraction. These notes give the correct answer to the Fill in the blanks questions. Learn these Science notes on Light Reflection and Refraction to score full marks in CBSE Class 10 board exams. Our notes are designed and thoroughly checked by experts and are completely error-free. Quizcrazy.in and team is proud to present Light Reflection and Refraction chapter FIB notes to you.\n\n Light Reflection and RefractionCBSE Science Class 10 FIB Test 6)   A spherical mirror whose reflecting surface is curved outwards, is called a ______ mirror. Answer)\n\n Light Reflection and RefractionCBSE Science Class 10 FIB Test 7)   The centre of the reflecting surface of a spherical mirror is a point called the ______. Answer)\n\n Light Reflection and RefractionCBSE Science Class 10 FIB Test 8)   The reflecting surface of a spherical mirror is part of a sphere and has a centre. This point is called the centre of ______ of the spherical mirror. Answer)\n\n Light Reflection and RefractionCBSE Science Class 10 FIB Test 9)   The centre of curvature of a concave mirror lies behind it. (State true or false) Answer)\n\n Light Reflection and RefractionCBSE Science Class 10 FIB Test 10)   A straight line passing through the pole and the centre of curvature of a spherical mirror is called the ______. Answer)\n\nwww.quizcrazy.in\nCBSE Class 10 Science" ]

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[ "M.Sc Student Tuito Nati Naftali Maximum Conditional Probability Stochastic Controller for Linear Systems with Additive Cauchy Noises Department of Aerospace Engineering Professor Moshe Idan\n\nAbstract\n\nThe majority of practical estimation and control solutions are based on system models with additive Gaussian noises. Gaussian distribution, being light tailed, does not capture significant fluctuations that occur in many engineering applications, such as atmospheric noises and air turbulence, underwater acoustic noises and image processing. It was shown that those noises are better described by heavy tailed distributions that exhibit significant impulsive characteristics. The heavy tailed distributions are characterized by probability density functions with tails that are not exponentially bounded. Named after the French mathematician, Augustin Louis Cauchy, the heavy-tailed Cauchy distribution has been shown to much better represent this type of fluctuations. The challenge of using this distribution is that it does not have a moment generating function. Specifically, its first moment is not well defined and its second and higher moments are infinite.\n\nIn the recent years, progress was reported in the area of estimation of linear systems with additive Cauchy noises. As part of its solution, the estimator computes explicitly the conditional probability density function (pdf) of the state given the measurement history, or its characteristic function. Those were used to derive a stochastic optimal-predictive controller for the system. Although demonstrating good performance characteristics, the proposed controller was shown to entail high numerical complexity. This motivated the alternative approach presented in the current study.\n\nIn this work a stochastic controller, inspired by the sliding mode control methodology, is proposed for linear systems with additive Cauchy distributed noises. The design goal is to maximize the prior probability of the system state or its linear combination to be within a given bound around the regulation point. The control law utilizes the time propagated pdf of the system state given measurements that is computed by the Cauchy estimator. The single-state controller was derived using two equivalent implementations: one that relies directly on the above mentioned prior pdf while the second uses the characteristic function of that pdf. The latter was addressed mainly because in the multi-state case only the characteristic function can be determined by the respective Cauchy estimator. The controller performance was evaluated numerically, and compared to an alternative approach presented recently and to a Gaussian approximation to the problem. A fundamental difference between the Cauchy and the Gaussian controllers is their response to noise outliers. While all controllers respond to process noises, even to the outliers, the Cauchy controllers drive the state faster towards zero after those events. On the other hand, the Cauchy controllers do not respond to measurement noise outliers, while the Gaussian does. The newly proposed Cauchy controller exhibits similar performance to the previously proposed one, while requiring lower computational effort, and is much easier to implement." ]

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[ "", null, "# Factorial – Explanation & Examples\n\nIn probability theory, there are many scenarios in which we have to calculate all the possible arrangements of a given set.\n\nFor example, if we toss a coin $10$ times, what would be the size of sample space?\n\nOr if we want to select a team of $5$ people from $10$ available members, how many different teams can we make? Factorial is a mathematical operation that helps us in figuring out such arrangements and hence plays an important role in probability theory.\n\nA factorial (denoted by ‘ ! ‘) is defined as the product of all positive integers that are less than or equal to a given positive integer.\n\n1. Factorial\n2. How to calculate factorials\n3. How factorials are used to evaluate permutations\n4. How factorials are used to evaluate combinations\n5. How factorials are used to find probabilities\n\nIt would be advisable to refresh the following topics:\n\n## What Is a Factorial\n\nThe factorial of a positive integer $n$ is defined as the product of all positive integers that are less than or equal to $n$. The factorial is denoted by that integer and an exclamation mark, i.e., “!“.\n\nExample 1:\nWhat is the factorial of $6?$\n\nSolution:\nThe numbers less than or equal to $6$ are $6,\\,5,\\,,4,\\,3\\,,2\\,,1$. Therefore,\n\n$6! = \\large {6 \\times 5 \\times 4 \\times 3 \\times 2 \\times 1 }$ = $720$.\n\nExample 2: Evaluate $\\large {\\frac{7!}{4!} }$.\n\nSolution:\n\n$\\frac{7!}{4!}$ = $\\large {\\frac{7 \\times 6 \\times 5 \\times 4 \\times 3 \\times 2 \\times 1}{4 \\times 3 \\times 2 \\times 1}}$\n\n= $\\large{ (7 \\times 6 \\times 5)}$\n\n= $210$\n\n### Factorial Formula:\n\nFor any integer $n$, we can define a formula for factorial as follows:\n\n$n! = n \\times (n-1) \\times (n-2) \\times \\cdots \\times 2 \\times 1$.\n\nWe can also use the product notation, i.e., $\\prod$ to succinctly write the formula as follows\n\n$n! = \\prod_{k=0}^{n-1} (n-k)$\n\n## Factorial Rules\n\nLet us tabulate the factorials of the first few numbers in the table below:\n\nIt becomes obvious from the above table that the factorial of any integer $n$ can be calculated from the factorial of the previous integer, i.e., $n-1$. We can state the factorial rule as follows:\n\n$n! = n \\times (n-1)!$\n\nThe factorial rule simplifies the calculations of complex expressions involving factorials. An interesting application of the factorial rule is in the evaluation of $0!$. If we want $1$ to follow the factorial rule, then:\n$1! = 1 \\times 0!$.\n\nHowever, from the formula of the factorials, it is obvious that $1! = 1$. Hence,\n\n$1 = 1 \\times 0!$.\n\nThis means that $0!=1$. Note that factorial is not defined for negative integers, and hence factorial rule cannot be applied on $0$.\n\n## How to Do Factorials\n\nWhen doing factorials, it is advisable to apply the factorial rule whenever possible to simplify expressions, as shown in the examples below\n\nExample 3: Evaluate $\\large{\\frac{6!}{3 \\times 4!}}$.\n\nSolution:\n\nUsing the factorial rule, we can write\n$6! = 6 \\times 5!$\nHowever, we have $4!$ in the denominator, so we apply factorial rule again on $5!$ to get\n$6! = 6 \\times 5 \\times 4!$. Accordingly,\n\n$\\large{\\frac{6!}{3 \\times 4!} = \\frac{6 \\times 5 \\times 4!}{3 \\times 4!} = \\frac{6 \\times 5}{3} = 10}$.\n\nExample 4: Evaluate $\\large{\\frac{11!}{5!\\times 8!}}$.\n\nSolution:\n\n= $\\large {\\frac{11 \\times 10 \\times 9 \\times 8!}{5!\\times 8!} }=\\large{ \\frac{11 \\times 10 \\times 9}{5.4.3.2.1}}= \\large{\\frac{990}{120}}= 8.25$.\n\n### Applications of Factorials:\n\nIn probability theory, factorials are extensively used in the evaluation of permutations and combinations. We discuss both these applications below:\n\nFactorials and Permutations:\n\nThe possible arrangements of a given set of objects are called permutations. For instance, let’s suppose we have a set of three numbers $\\{1,2,3\\}$. There are six possible permutations (i.e., arrangements) of the these three numbers, i.e., $\\{1,2,3\\}, \\, \\{1,3,2\\}, \\, \\{3,1,2\\}, \\, \\{3,2,1\\}, \\{2,3,1\\}$ and $\\{2,1,3\\}$. Generally, in problems involving permutations, we are given a set of $n$ objects, and we are asked to find all possible arrangements if $k$ objects are chosen out of the total $n$ objects and are referred to as “$n$ choose $k$” permutations. For instance, we might be given $10$ numbers from $0$ t0 $9$, and we could be asked to find all possible ways in which we can select three numbers out of the given $10$ numbers. There are two different types of permutations that can be performed on any given set.\n\n1. Permutations in which we can repeat the objects from the available set.\n2. Permutations in which repetitions are not allowed.\n\nWe will focus on the second type of permutations only as these are the ones that involve factorials.\n\nPermutations Without Repetition:\n\nLet us suppose we have $n$ objects, and we have to find all possible arrangements when $k$ out of $n$ objects are chosen without repetition. As shown in the figure below, there are $n$ possible ways to select the first item.\n\nOnce the first item has been selected, there are $n-1$ objects left as we are not allowed repetitions. Hence there are $n-1$ ways to select the second item, as shown in the figure below.\n\nSimilarly, there are $n-2, n-3$ possibilities for the third and fourth selection, and finally, we will have $n-(k-1)$ possible selections for the last $k$th item.\n\nUsing the fundamental counting principle, the total permutations are\n\n$\\textrm{Total permutations for n choose k} = P(n,k) = n \\times n-1 \\times n-2 \\times \\cdots \\times n-(k-1)$.\n\nWe multiply and divide the above expression with $(n-k)!$ to get\n\n$P(n,k) = \\frac{n \\times n-1 \\times n-2 \\times \\cdots \\times n-(k-1) \\times (n-k)!}{(n-k)!}$.\n\nFrom the factorial rule discussed above, we note that\n\n$n \\times n-1 \\times n-2 \\times \\cdots \\times n-(k-1) \\times (n-k)! = n!$\n\nHence, the formula for $n$ choose $k$ permutations, without repetition is given as\n\n$P(n,k) = \\frac{n!}{(n-k)!}$.\n\nExample 5: How many 4 letters words can be formed out of the letters of the word “Employ” when repetition is not allowed?\n\nSolution:\nThe word “employ” has six letters so $n=6$ and we have to form all possible four-letter words without repetition hence $r=4$. Therefore, possible permutations can be calculated as:\n\n$P(n,r) = \\large{\\frac{n!}{(n-r)!}}$\n$P(6,4)=\\large{\\frac{6!}{(6-4)!}}$\n$P(6,4)=\\large{\\frac{6\\times 5\\times 4\\times 3\\times 2 \\times 1}{(2\\times 1)}}$\n$P(6,4)=360$\n\nExample 6: How many $5$-digits numbers can be formed by using the digits $1,2,3,4,5,6,7$ only once?\n\nSolution:\n\nWe have seven digit in total and we choose 5 digits for the permutations, hence\n\n$P(7,5)=\\large{\\frac{7!}{(7-5)!}}$\n$P(7,5)=\\large{\\frac{7\\times 6\\times 5\\times 4\\times 3\\times 2\\times1}{(2\\times1)}}$\n$P(5,3)=2520$\n\nPermutations with non-distinct objects:\n\nIn some situations, we have a set of non-distinct objects, and we are interested in finding the possible permutations. For instance, in how many different ways can we arrange the letters in the word MISSISSIPPI? In total we have 11 letters, so $n=11$. Since we are interested in the arrangements of all the letters so $r=11$ in this case. For simplicity, we limit our discussion to such cases only where $n=r$.\n\nIf all letters had been distinct, we would have gotten $P(n,r) = \\frac{n!}{(n-r)!} = n!$ permutations. However, “S” appears four times, “I” appears four times, and “P” appears twice. So, there would be $4!$ permutations of “S” that would result in the same arrangement of the $11$ letters of MISSISSIPPI. Similarly, there would be $4!$ arrangements of “I” and $2!$ arrangements of “P” that would result in the same permutation. Therefore, we can write\n$\\textrm{Permutations if letters had been distinct} =\\textrm{Permutations of non distinct letters} \\times 4! \\times 4! \\times 2!$\n\nRearranging the above equation\n\n$\\textrm{Total permutations of non distinct letters} =\\large{ \\frac{n!}{4! 4! 2!}}$.\n\n$\\textrm{Total permutations of non distinct letters} = \\large{\\frac{11!}{4! \\times 4! \\times 2!} }$= $34650$.\n\nGenerally, if we have a set of $n$ objects and there are $n1$ items that are the same, and $n2$ items that are the same but different from $n1$ and $n3$ similar items that are different from $n1$ and $n2$ etc., then the formula for permutations can be written as\n$\\textrm{permutations of non distinct items} = \\large{\\frac{n!}{n1! \\times n2! \\times n3!}}$\n\nExample 7:\nIn how many different ways can we arrange the letters in the word $\\text{TOOTH}$?\n\nSolution:\nThere are $5$ letters in $\\text{TOOTH}$ so $n=5$. “T” appears twice so $n1=2$ and “O” appears twice to $n2=2$. Hence\n\n$\\textrm{Permutations of the Word TOOTH} = \\frac{n!}{(n1! \\times n2!)}$\n\n$\\textrm{Permutations of the Word TOOTH} = \\frac{5!}{(2! \\times 2!)} = \\frac{5\\times4\\times3}{2} = 30$.\n\nExample 8:\nHow many $6$ letter words can be formed using the letters in the word $\\text{BANANA}$?\n\nSolution:\n\nIn this case, $n=6$, $n1=3$ (i.e., three “A”s) and $n2=2$ (i.e., two “Ns”). The total permutations are\n$\\textrm{Permutations of the word BANANA} = \\frac{6!}{ 3! \\times 2!}$\n\n$\\textrm{Permutations of the word BANANA} = \\frac{6 \\times 5 \\times 4 \\times 3!}{3! \\times 2!}$\n\n$\\textrm{Permutations of the word BANANA} = \\frac{6 \\times 5 \\times 4}{2!} = 60$\n\nFactorials and Combinations:\n\nThe evaluation of possible combinations is another interesting application of the factorial. Similar to permutations, combinations are possible arrangements of a set of given items. The difference between permutations and combinations is that, in combinations, the order of the arrangements does not matter, whereas, in permutations, the order of the arrangement matters.\n\nFor instance, let us suppose we are interested in possible arrangements of the numbers $1,2,3, 4$ using three numbers only. If we treat $1,2,4$ as one possible arrangement and $4,2,1$ as another arrangement, then we are dealing with permutations. However, if we treat $1,2,4$ and $4,2,1$ and  $2,1,4$, etc., as the same arrangement, i.e., we disregard the order and only focus on the elements, then we are dealing with combinations.\n\nThere are many scenarios when order naturally does not matter when arranging a set of objects. For example, let’s say we have a group of four people. Let’s call those Jack, Jill, Liz, and Henry. We wish to make the team consisting of three people using the available group. How many distinct teams can we make? Note that in this scenario, it does not make sense to treat $\\{Jack Jill Liz\\}$ as one team and $\\{Jill Jack Liz\\}$ as another team. Obviously, no matter how we order three people, it would results in the same team. Such situations are handled by the concept of combinations and not by permutations.\n\nCombinations Formula:\n\nLet us suppose we have a set of $n$ distinct objects, and we are interested in choosing $k$ objects out of those $n$ objects. How many possible arrangements of $k$ objects can we make if the order does not matter?\n\nSimilar to the case of permutations, there are $n$ ways to fill the first slot out of the available $k$ slots. Then there are $n-1$ ways to fill the second slot and so on. Finally, we will have $n-k$ ways to fill the $k$th slot. Now we have a collection of $k$ objects, and these $k$ objects can be rearranged in $k!$ different ways among themselves. Since we are disregarding order in this scenario so the $k!$ re-arrangements would result in the same combination. Hence, we can write\n\n$n \\times n-1 \\times \\cdots (n-k) = \\textrm{Total number of distinct Combinations} \\times k!$.\n\nTherefore,\n\n$\\textrm{Total number of distinct Combinations} = \\frac{n \\times n-1 \\times \\cdots (n-k)}{k!}$.\n\nNow multiplying and dividing the right-hand-side of the equation by $(n-k)!$, we get\n\n$\\textrm{Total number of distinct Combinations} = \\frac{n \\times n-1 \\times \\cdots (n-k) \\times (n-k)!}{k!(n-k)!}$.\n\nUsing the factorial rule, we can write\n\n$\\textrm{Total number of distinct Combinations} = \\frac{n!}{k!(n-k)!}$.\n\nWe will use the notation $C(n,r)$ to denote the possible combinations of $n$ distinct objects when we choose $k$ objects out of the $n$ available objects.\n\nExample 8: We have a group of four people. Let’s call those Jack, Jill, Liz, and Henry. We wish to make the team consisting of three people using the available group. How many distinct teams can we make?\n\nSolution:\nWe have already discussed that in this scenario, order of the arrangements does not matter and hence we are dealing with possible combinations with $n=4$ and $k=3$. Using the formula for combinations, we get\n\n$C(4,3) = \\frac{4!}{3!(4-3)!} = \\frac{4!}{3!1!} = \\frac{4 \\times 3!}{3!} = 4$.\n\nExample 9: A soccer team consisting of $11$players is to be formed from a pool of $15$ players. In how many ways can a team be selected if the Captain is to be included in every team?\n\nSolution:\nSince we have to include the captain in every team, so essentially, we have a pool of 14 players and 10 places in the team. When we are selecting a team of individuals, it does not matter in which order the members were selected, and we get the same team if we have the same members. Therefore, we are dealing with combinations in this question, and the possible ways in which a team can be selected are $C(14,10)$\n\n$(C14,10) = \\frac{14!}{10!(14-10)!} = \\frac{14 \\times 13 \\times 12 \\times 11 \\times 10!}{10!4!}$\n\n$C(14,10) = \\frac{14 \\times 13 \\times 12 \\times 11}{4!} = 1001$\n\nFactorials and Probabilities\n\nFactorials help us evaluate permutations and combinations. Let us see some examples of how permutations and combinations (and hence factorials) appear in probability questions.\n\nExample 10:\nSuppose we randomly pick four alphabets without repetition to form a word. What is the probability that the word does not contain a vowel?\n\nSolution:\nSince we are randomly picking up alphabets, so all four-letter words (whether meaningful or not) are equally likely. Let us first calculate how many possible words we can make, i.e., the total number of elements in the sample space of our experiment. Since there are $26$ alphabets in the English language and we are choosing $4$ of them without repetition; hence the total number of possible worlds would be $P(26,4)$.\n\n$\\textrm{Total number of possible words} = P(26,4) = \\frac{26!}{(26-4)!} = \\frac{26 \\times 25 \\times 24 \\times 23 \\times 22!}{22!} = 358800$.\n\nNow we want to find the total number of words without vowels. There are $5$ vowels in the alphabets. If we discard the vowels, we are left with $21$ alphabets to make the four-letter words. There will be $P(21,4)$ words that do not contain the vowels, i.e.,\n\n$\\textrm{Total number of words without vowels} = P(21,4) = \\frac{21!}{(21-4)!} = \\frac{21 \\times 20 \\times 19 \\times 18 \\times 17!}{17!} = 143640$.\n\nHence the probability of randomly choosing a four letter word that does not contain a vowel is\n\n$P(\\textrm{Choosing a random word without vowels}) = \\frac{143640}{358800} = 0.4 = 40\\%$\n\nExample 10:\nWe have a class of nine students, five boys and four girls. We randomly choose to make a team of 4 people. What is the probability that the team will contain only one boy?\n\nSolution:\nLet us first find the total number of possible teams, i.e., the number of elements in our sample space. Since we are dealing with a team of people, so the order in which the members are chosen does not matter, and we would get the same team. Hence, the total number of possible teams would be $C(9,4)$, i.e.,\n\n$C(9,4) = \\frac{9!}{4!(9-4)!} = \\frac{9 \\times 8 \\times 7 \\times 6 \\times 5!}{4!5!} = \\frac{3024}{24} = 126$.\n\n1.\n\na) 24\nb) 40320\nc) 1680\nd) 0.875\n\n2. 24\n\n3.\na) 35\nb) 840\n\n4. 0.36%\n\n5. 99.5%" ]

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[ "# zbMATH — the first resource for mathematics\n\nDiophantine approximation on linear algebraic groups. Transcendence properties of the exponential function in several variables. (English) Zbl 0944.11024\nGrundlehren der Mathematischen Wissenschaften. 326. Berlin: Springer. xxiii, 633 p. (2000).\nThis extensive monograph gives an excellent report on the present state of the art of some of the most important parts in modern transcendence theory. In the following, it will be tried to provide the reader with an idea of what he may find in this book.\nThe first four chapters (1: introduction and historical survey, 2: transcendence proofs in one variable, 3: heights of algebraic numbers, 4: criterion of Schneider-Lang) may serve as an introduction to the subject. For instance, the first three of these do not require much preliminary knowledge and include already complete proofs of a number of classical transcendence results. The first (of three) proof(s) of Baker’s theorem on linear independence of logarithms of algebraic numbers, in Ch. 4, follows an argument of Bertrand and Masser who derived it from the Schneider-Lang criterion concerning algebraic values of meromorphic functions on cartesian products.\nPart II (linear independence of logarithms and measures) comprises chapters 5-7 on zero estimate (by D. Roy), linear independence of logarithms of algebraic numbers, homogeneous measures for linear independence. In particular, in Ch. 6 Schneider’s method is extended (as in Ch. 9 for the inhomogeneous case), and in Ch. 7 a relatively simple proof for a linear independence measure is explained.\nPart III (multiplicities in higher dimension) contains multiplicity estimates (again by D. Roy), refined measures, Baker’s method as chapters 8-10 where in the last one Baker’s argument extending Gelfond’s solution of Hilbert’s seventh problem is explained.\nA central result in this book, entitling Part IV, is the so-called linear subgroup theorem (LST) which occurs in two forms. The qualitative one (Ch. 11: points whose coordinates are logarithms of algebraic numbers) gives a lower bound for the dimension of the $${\\mathbb C}$$-vector subspace of $${\\mathbb C}^d$$ spanned by points whose coordinates are either algebraic numbers or logarithms of such numbers. Finally, lower bounds for the rank of matrices (title of Ch. 12) are deduced from the LST.\nThe (last) Part V, simultaneous approximation of values of the exponential function in several variables, starts with Ch. 13: a quantitative version of the LST. This version, by no means a simple statement, includes a lot of diophantine estimates, as shown in Ch. 14: applications to diophantine approximation. Ch. 15 deals with algebraic independence (criteria, small and large transcendence degrees).\nAltogether, the author’s emphasis is not only on the results, but also on the methods; this is why he gives sometimes several proofs of the same result. An original feature is certainly the systematic use of Laurent’s interpolation determinants instead of the classical construction of auxiliary functions via Thue-Siegel’s lemma.\nWhat is excluded from the presentation? Not considered are elliptic curves, abelian varieties, and more generally nonlinear algebraic groups. Not discussed is Nesterenko’s breakthrough concerning the algebraic independence of $$\\pi$$ and $$e^\\pi$$; excluded are also elliptic, theta, and abelian functions as well as all kinds of non-archimedean considerations.\nThe reader having enough time and energy may learn from this carefully written book a great deal of modern transcendence theory from the very beginning. In this process, the many included exercises may be very helpful. Everybody interested in transcendence will certainly admire the author’s achievement to present such a clear and complete exposition of a topic growing so fast. The value of Waldschmidt’s new monograph for the further development of the subject cannot be overestimated.\n\n##### MSC:\n 11J81 Transcendence (general theory) 11-02 Research exposition (monographs, survey articles) pertaining to number theory 20G15 Linear algebraic groups over arbitrary fields 20G30 Linear algebraic groups over global fields and their integers" ]

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[ "Other\n\nDescription:\n\nIn this activity, students roll a die that will advance either a blue or red car in a race to the finish. Students can adjust which numbers on the die make each car move, changing the probability that each car will win. This activity allows students to explore experimental and theoretical probability by running multiple races at once and examining how often each car wins and comparing that to how often each car is expected to win. This activity includes supplemental materials, including background information about the topics covered, a description of how to use the application, and exploration questions for use with the java applet.\n\nSubjects:\n\n• Mathematics > General\n\nKeywords:\n\nInformal Education,Vocational/Professional Development Education,Middle School,NSDL,Upper Elementary,High School,Elementary School Programming,NSDL_SetSpec_ncs-NSDL-COLLECTION-000-003-112-016,Elementary School,oai:nsdl.org:2200/20120614151507118T,High School Programming,Mathematics,Middle School Programming\n\nEnglish\n\nAccess Privileges:\n\nPublic - Available to anyone\n\nCreative Commons Attribution Non-Commercial Share Alike\n\nCollections:\n\nNone\nUpdate Standards?\n\nCCSS.Math.Content.7.SP.C.5: Common Core State Standards for Mathematics\n\nUnderstand that the probability of a chance event is a number between 0 and 1 that expresses the likelihood of the event occurring. Larger numbers indicate greater likelihood. A probability near 0 indicates an unlikely event, a probability around 1/2 indicates an event that is neither unlikely nor likely, and a probability near 1 indicates a likely event.\n\nCCSS.Math.Content.7.SP.C.7b: Common Core State Standards for Mathematics\n\nDevelop a probability model (which may not be uniform) by observing frequencies in data generated from a chance process.\n\nCCSS.Math.Content.HSS-IC.A.2: Common Core State Standards for Mathematics\n\nDecide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.\n\nCCSS.Math.Content.HSS-IC.B.5: Common Core State Standards for Mathematics\n\nUse data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.\n\nCCSS.Math.Content.HSS-CP.B.6: Common Core State Standards for Mathematics\n\nFind the conditional probability of A given B as the fraction of B's outcomes that also belong to A, and interpret the answer in terms of the model.\n\nCCSS.Math.Content.HSS-CP.B.9: Common Core State Standards for Mathematics\n\n(+) Use permutations and combinations to compute probabilities of compound events and solve problems.\n\nCCSS.Math.Content.HSS-MD.A.3: Common Core State Standards for Mathematics\n\n(+) Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value.\n\nCCSS.Math.Content.HSS-MD.B.6: Common Core State Standards for Mathematics\n\n(+) Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator).\nCurriki Rating\n'NR' - This resource has not been rated\nNR\n'NR' - This resource has not been rated\n\nThis resource has not yet been reviewed.\n\nNot Rated Yet.\n\nNon-profit Tax ID # 203478467" ]

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[ "## What is zeros of polynomial with example?\n\nZeros of a polynomial are nothing but the roots of the polynomial. Zeros or roots of a polynomial are those values of the variable (x) which make the polynomial equal to 0. For example- For the polynomial x2+7x-18, the zero or the root will be 2, because (2)2+72-18=4+14-18=0.\n\n## What is number of real zeros the polynomial?\n\nAccording to Descartes’ Rule of Signs, the number of positive real zeros within a polynomial P(x) is equal to the number of changes in sign or an even number subtracted from it.\n\nDoes the polynomial have real zeroes?\n\nA polynomial of degree n≥1 can have at most n real zeroes. A quadratic polynomial can have at most two real zeroes .\n\n### What is the degree of a zero polynomial class 9 with examples?\n\nDegree of a Zero Polynomial A zero polynomial is the one where all the coefficients are equal to zero. So, the degree of the zero polynomial is either undefined, or it is set equal to -1.\n\n### What is zero of a polynomial class 10?\n\nThe zero of a polynomial can be defined as those values of x when substituted in the polynomial, making it equal to zero. In other words, we can say that the zeroes are the roots of the polynomial. We can obtain the zeroes of the polynomial P(x) by equating it to 0.\n\nWhat are real zeroes?\n\nA real zero of a function is a real number that makes the value of the function equal to zero. A real number, r , is a zero of a function f , if f(r)=0 . Example: f(x)=x2−3x+2.\n\n## Does polynomial A⁴ +4a² +5 have real zero?\n\nAs the discriminant (D) is negative, the given polynomial does not have real roots or zeroes.\n\n## What is a polynomial with no real zeros?\n\nA simple example of a quadratic polynomial with no real zeroes is x^2 + 1 which has roots \\pm i where i represents \\sqrt{-1}. An example of a polynomial with one real root is x^2 which has only 0 as a root. And an example of a polynomial with two real roots is x^2 – 1, which has roots \\pm 1.\n\nWhat is the maximum number of real zeros?\n\nWhat is maximum number of real zeros? Complex roots come in pairs, so if there are roots with imaginary parts, there are either 0 or 2 or 4 or 6 of them. So the maximum number of real roots is either 6 or 4 or 2 or 0, so the maximum , in general would be 6.\n\n### How to find the zeros of a polynomial calculator?\n\nUse the Rational Zero Theorem to list all possible rational zeros of the function.\n\n• Use synthetic division to evaluate a given possible zero by synthetically dividing the candidate into the polynomial.\n• Repeat step two using the quotient found from synthetic division.\n• Find the zeros of the quadratic function.\n• ### How do you find zeros in polynomials?\n\nFinding the Rational Zeros of a Polynomial: 1. Possible Zeros: List all possible rational zeros using the Rational Zeros Theorem. 2. Divide: Use Synthetic division to evaluate the polynomial at each of the candidates for rational zeros that you found in Step 1. When the remainder is 0, note the quotient you have obtained. 3.\n\nHow many zeros are in a polynomial?\n\nThere is no real number x that gives us P ( x) = 0. But if you consider the zeros to be complex number, then a polynomial of degree 6 will have exactly 6 zeros. And this statement holds for every polynomial with n degree that it has exactly n zeros." ]

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[ "# Remove characters from first string which are in second\n\n0\n805", null, "## For the given input strings, remove the characters from the first string which are present in second string. (with case sensitivity)\n\n### Example\n\na) Input string1 : computer\nInput string2 : cat\n\nOutput : ompuer\nAfter removing characters from string2 (c, a, t) from string1 we get ompuer\n\nb) Input string1 : occurrence\nInput string2 : car\nOutput : ouene\n\nAfter removing characters from string2 (c, a, r) from string1 we get ouene\n\nTime complexity : O(m + n), m and n are length of strings\n\n## Algorithm\n\na. Get count array from the second string which stores the count of chars from the second string.\n\nb. Check in the input string if it contains characters from count array with frequency > 0 if yes skip it and copy remaining char into input string.\n\nc. After getting this add \\0 to remove any extra characters after output string\n(Null termination).\n\nNote : \\0 means Null ASCII value 0)\n\n### Algorithm working", null, "## C++ Program\n\n``````#include <bits/stdc++.h>\n\nusing namespace std;\n#define ASCII_SIZE 256\n\n//Remove characters from string1 which are in string2\nchar *RemoveChars(char *string1, char *string2)\n{\n//Count array stores the count of chars from string2\nint *count = (int *)calloc(sizeof(int), ASCII_SIZE);\nfor(int i = 0; *(string2+i); i++)\n{\ncount[*(string2+i)]++;\n}\nint i = 0, j = 0;\nwhile(*(string1 + i))\n{\nchar temp = *(string1 + i);\n//If count of charcter is zero add to output\nif(count[temp] == 0)\n{\n*(string1 + j) = *(string1 + i);\nj++;\n}\ni++;\n}\n//Null termination\n//removing extra characters\n*(string1+j) = '\\0';\n\nreturn string1;\n}\n\n//Main function to test above function\nint main()\n{\n//string1\nchar string1[] = \"computer\";\n//string2\nchar string2[] = \"programming\";\ncout<<\"Input strings:\\n\";\ncout<<\"string1: \";\nfor (int i = 0; i < strlen(string1); ++i)\n{\ncout<<string1[i];\n}\ncout<<\"\\nstring2: \";\nfor (int i = 0; i < strlen(string2); ++i)\n{\ncout<<string2[i];\n}\n//print output string\ncout<<\"\\nOutput: \";\ncout<<RemoveChars(string1, string2);\nreturn 0;\n}``````" ]

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[ "# Scaling structure of the velocity statistics in atmospheric boundary layers\n\nSusan Kurien, Victor S. L'vov, Itamar Procaccia, K. R. Sreenivasan\n\nResearch output: Contribution to journalArticlepeer-review\n\n## Abstract\n\nThe statistical objects characterizing turbulence in real turbulent flows differ from those of the ideal homogeneous isotropic model. They contain contributions from various two- and three-dimensional aspects, and from the superposition of inhomogeneous and anisotropic contributions. We employ the recently introduced decomposition of statistical tensor objects into irreducible representations of the SO(3) symmetry group (characterized by j and m indices, where [Formula Presented] to disentangle some of these contributions, separating the universal and the asymptotic from the specific aspects of the flow. The different j contributions transform differently under rotations, and so form a complete basis in which to represent the tensor objects under study. The experimental data are recorded with hot-wire probes placed at various heights in the atmospheric surface layer. Time series data from single probes and from pairs of probes are analyzed to compute the amplitudes and exponents of different contributions to the second order statistical objects characterized by [Formula Presented] 1, and 2. The analysis shows the need to make a careful distinction between long-lived quasi-two-dimensional turbulent motions (close to the ground) and relatively short-lived three-dimensional motions. We demonstrate that the leading scaling exponents in the three leading sectors [Formula Presented] 1, and 2) appear to be different but universal, independent of the positions of the probe, the tensorial component considered, and the large scale properties. The measured values of the scaling exponent are [Formula Presented] [Formula Presented] and [Formula Presented] We present theoretical arguments for the values of these exponents using the Clebsch representation of the Euler equations; neglecting anomalous corrections, the values obtained are 2/3, 1, and 4/3, respectively. Some enigmas and questions for the future are sketched.\n\nOriginal language English (US) 407-421 15 Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 61 1 https://doi.org/10.1103/PhysRevE.61.407 Published - 2000\n\n## ASJC Scopus subject areas\n\n• Statistical and Nonlinear Physics\n• Statistics and Probability\n• Condensed Matter Physics\n\n## Fingerprint\n\nDive into the research topics of 'Scaling structure of the velocity statistics in atmospheric boundary layers'. Together they form a unique fingerprint." ]

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[ "The math used by Plack and Einstein for the discovery of Quantum Physics, the foundations of thermodynamics, and more.\n\n## Requirements\n\n• Calculus (especially derivatives, integrals, limits)\n• Multivariable Calculus\n• Basics of Fourier Analysis\n• For the second part of the course: Lagrange equations, phase space variables (position and momentum)\n\n## Description\n\nFirst part of the course:\n\nThe first part of the course showcases the beautiful mathematics that, in the late 19th century/ early 20th century, led to the discovery of a revolutionary branch in physics: Quantum Mechanics.\n\nPlanck postulated that the energy of oscillators in a black body is quantized. This postulate was introduced by Max Planck in his derivation of his law of black body radiation in 1900. This assumption allowed Planck to derive a formula for the entire spectrum of the radiation emitted by a black body (we will also derive this spectrum in this course). Planck was unable to justify this assumption based on classical physics; he considered quantization as being purely a mathematical trick, rather than (as is now known) a fundamental change in the understanding of the world.\n\nIn 1905, Albert Einstein adapted the Planck postulate to explain the photoelectric effect, but Einstein proposed that the energy of photons themselves was quantized (with photon energy given by the Planck–Einstein relation), and that quantization was not merely a “mathematical trick”. Planck’s postulate was further applied to understanding the Compton effect, and was applied by Niels Bohr to explain the emission spectrum of the hydrogen atom and derive the correct value of the Rydberg constant.\n\nIn addition to the very useful mathematical tools that will be presented and discussed thoroughly, the students have the opportunity to learn about the historical aspects of how Planck tackled the blackbody problem.\n\nCalculus and multivariable Calculus are a prerequisite to the course; other important mathematical tools (such as: Fourier Series, Perseval’s theorem, binomial coefficients, etc.) will be recalled, with emphasis being put on mathematical and physical insights rather than abstract rigor.\n\nSecond part of the course\n\nBy the end of June 1902, just after being accepted as Technical Assistant at the Federal Patent Office in Bern, Albert Einstein, 23, sent to the renowned journal Annalen der Physik a manuscript with the bold title “Kinetic Theory of Thermal Equilibrium and of the Second Law of Thermodynamics”. In the introduction, he explains that he wishes to fill a gap in the foundations of the general theory of heat, “for one has not yet succeeded in deriving the laws of thermal equilibrium and the second law of thermodynamics using only the equations of mechanics and the probability calculus”. He also announces “an extension of the second law that is of importance for the application of thermodynamics”. Finally, he will provide “the mathematical expression of the entropy from the standpoint of mechanics”.\n\nIn particular, in the second part of the course we will see the mathematics Einstein used in his paper from 1902.\n\nBesides, other concepts from Classical mechanics are explained, such as Liouville’s theorem (this theorem is used by Einstein in his article), as well as Hamilton equations and more.\n\nFor the second part, the student should already be familiar with phase space and other concepts from classical physics (such as Lagrange equations).\n\nThird part of the course\n\nIn the third part of the course some of the articles of Einstein’s Annus Mirabilis are explained. In particular, the article on the photoelectric effect and that on the Brownian motion.\n\n## Who this course is for:\n\n• Students interested in the historical fascinating origin of Quantum Physics\n• Students who are interested in the mathematics used by Einstein in 1902 to deal with thermodynamics\n• Students who want to have an in-depth understanding of entropy\n• Students who would like to improve their reasoning and insights in solving physical problems\n• Students interested in explanations given through the lens of mathematics", null, "" ]

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[ "", null, "LAPACK  3.6.1 LAPACK: Linear Algebra PACKage\n subroutine dggbak ( character JOB, character SIDE, integer N, integer ILO, integer IHI, double precision, dimension( * ) LSCALE, double precision, dimension( * ) RSCALE, integer M, double precision, dimension( ldv, * ) V, integer LDV, integer INFO )\n\nDGGBAK\n\nDownload DGGBAK + dependencies [TGZ] [ZIP] [TXT]\n\nPurpose:\n``` DGGBAK forms the right or left eigenvectors of a real generalized\neigenvalue problem A*x = lambda*B*x, by backward transformation on\nthe computed eigenvectors of the balanced pair of matrices output by\nDGGBAL.```\nParameters\n [in] JOB ``` JOB is CHARACTER*1 Specifies the type of backward transformation required: = 'N': do nothing, return immediately; = 'P': do backward transformation for permutation only; = 'S': do backward transformation for scaling only; = 'B': do backward transformations for both permutation and scaling. JOB must be the same as the argument JOB supplied to DGGBAL.``` [in] SIDE ``` SIDE is CHARACTER*1 = 'R': V contains right eigenvectors; = 'L': V contains left eigenvectors.``` [in] N ``` N is INTEGER The number of rows of the matrix V. N >= 0.``` [in] ILO ` ILO is INTEGER` [in] IHI ``` IHI is INTEGER The integers ILO and IHI determined by DGGBAL. 1 <= ILO <= IHI <= N, if N > 0; ILO=1 and IHI=0, if N=0.``` [in] LSCALE ``` LSCALE is DOUBLE PRECISION array, dimension (N) Details of the permutations and/or scaling factors applied to the left side of A and B, as returned by DGGBAL.``` [in] RSCALE ``` RSCALE is DOUBLE PRECISION array, dimension (N) Details of the permutations and/or scaling factors applied to the right side of A and B, as returned by DGGBAL.``` [in] M ``` M is INTEGER The number of columns of the matrix V. M >= 0.``` [in,out] V ``` V is DOUBLE PRECISION array, dimension (LDV,M) On entry, the matrix of right or left eigenvectors to be transformed, as returned by DTGEVC. On exit, V is overwritten by the transformed eigenvectors.``` [in] LDV ``` LDV is INTEGER The leading dimension of the matrix V. LDV >= max(1,N).``` [out] INFO ``` INFO is INTEGER = 0: successful exit. < 0: if INFO = -i, the i-th argument had an illegal value.```\nDate\nNovember 2015\nFurther Details:\n``` See R.C. Ward, Balancing the generalized eigenvalue problem,\nSIAM J. Sci. Stat. Comp. 2 (1981), 141-152.```\n\nDefinition at line 149 of file dggbak.f.\n\n149 *\n150 * -- LAPACK computational routine (version 3.6.0) --\n151 * -- LAPACK is a software package provided by Univ. of Tennessee, --\n152 * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--\n153 * November 2015\n154 *\n155 * .. Scalar Arguments ..\n156  CHARACTER job, side\n157  INTEGER ihi, ilo, info, ldv, m, n\n158 * ..\n159 * .. Array Arguments ..\n160  DOUBLE PRECISION lscale( * ), rscale( * ), v( ldv, * )\n161 * ..\n162 *\n163 * =====================================================================\n164 *\n165 * .. Local Scalars ..\n166  LOGICAL leftv, rightv\n167  INTEGER i, k\n168 * ..\n169 * .. External Functions ..\n170  LOGICAL lsame\n171  EXTERNAL lsame\n172 * ..\n173 * .. External Subroutines ..\n174  EXTERNAL dscal, dswap, xerbla\n175 * ..\n176 * .. Intrinsic Functions ..\n177  INTRINSIC max, int\n178 * ..\n179 * .. Executable Statements ..\n180 *\n181 * Test the input parameters\n182 *\n183  rightv = lsame( side, 'R' )\n184  leftv = lsame( side, 'L' )\n185 *\n186  info = 0\n187  IF( .NOT.lsame( job, 'N' ) .AND. .NOT.lsame( job, 'P' ) .AND.\n188  \\$ .NOT.lsame( job, 'S' ) .AND. .NOT.lsame( job, 'B' ) ) THEN\n189  info = -1\n190  ELSE IF( .NOT.rightv .AND. .NOT.leftv ) THEN\n191  info = -2\n192  ELSE IF( n.LT.0 ) THEN\n193  info = -3\n194  ELSE IF( ilo.LT.1 ) THEN\n195  info = -4\n196  ELSE IF( n.EQ.0 .AND. ihi.EQ.0 .AND. ilo.NE.1 ) THEN\n197  info = -4\n198  ELSE IF( n.GT.0 .AND. ( ihi.LT.ilo .OR. ihi.GT.max( 1, n ) ) )\n199  \\$ THEN\n200  info = -5\n201  ELSE IF( n.EQ.0 .AND. ilo.EQ.1 .AND. ihi.NE.0 ) THEN\n202  info = -5\n203  ELSE IF( m.LT.0 ) THEN\n204  info = -8\n205  ELSE IF( ldv.LT.max( 1, n ) ) THEN\n206  info = -10\n207  END IF\n208  IF( info.NE.0 ) THEN\n209  CALL xerbla( 'DGGBAK', -info )\n210  RETURN\n211  END IF\n212 *\n213 * Quick return if possible\n214 *\n215  IF( n.EQ.0 )\n216  \\$ RETURN\n217  IF( m.EQ.0 )\n218  \\$ RETURN\n219  IF( lsame( job, 'N' ) )\n220  \\$ RETURN\n221 *\n222  IF( ilo.EQ.ihi )\n223  \\$ GO TO 30\n224 *\n225 * Backward balance\n226 *\n227  IF( lsame( job, 'S' ) .OR. lsame( job, 'B' ) ) THEN\n228 *\n229 * Backward transformation on right eigenvectors\n230 *\n231  IF( rightv ) THEN\n232  DO 10 i = ilo, ihi\n233  CALL dscal( m, rscale( i ), v( i, 1 ), ldv )\n234  10 CONTINUE\n235  END IF\n236 *\n237 * Backward transformation on left eigenvectors\n238 *\n239  IF( leftv ) THEN\n240  DO 20 i = ilo, ihi\n241  CALL dscal( m, lscale( i ), v( i, 1 ), ldv )\n242  20 CONTINUE\n243  END IF\n244  END IF\n245 *\n246 * Backward permutation\n247 *\n248  30 CONTINUE\n249  IF( lsame( job, 'P' ) .OR. lsame( job, 'B' ) ) THEN\n250 *\n251 * Backward permutation on right eigenvectors\n252 *\n253  IF( rightv ) THEN\n254  IF( ilo.EQ.1 )\n255  \\$ GO TO 50\n256 *\n257  DO 40 i = ilo - 1, 1, -1\n258  k = int(rscale( i ))\n259  IF( k.EQ.i )\n260  \\$ GO TO 40\n261  CALL dswap( m, v( i, 1 ), ldv, v( k, 1 ), ldv )\n262  40 CONTINUE\n263 *\n264  50 CONTINUE\n265  IF( ihi.EQ.n )\n266  \\$ GO TO 70\n267  DO 60 i = ihi + 1, n\n268  k = int(rscale( i ))\n269  IF( k.EQ.i )\n270  \\$ GO TO 60\n271  CALL dswap( m, v( i, 1 ), ldv, v( k, 1 ), ldv )\n272  60 CONTINUE\n273  END IF\n274 *\n275 * Backward permutation on left eigenvectors\n276 *\n277  70 CONTINUE\n278  IF( leftv ) THEN\n279  IF( ilo.EQ.1 )\n280  \\$ GO TO 90\n281  DO 80 i = ilo - 1, 1, -1\n282  k = int(lscale( i ))\n283  IF( k.EQ.i )\n284  \\$ GO TO 80\n285  CALL dswap( m, v( i, 1 ), ldv, v( k, 1 ), ldv )\n286  80 CONTINUE\n287 *\n288  90 CONTINUE\n289  IF( ihi.EQ.n )\n290  \\$ GO TO 110\n291  DO 100 i = ihi + 1, n\n292  k = int(lscale( i ))\n293  IF( k.EQ.i )\n294  \\$ GO TO 100\n295  CALL dswap( m, v( i, 1 ), ldv, v( k, 1 ), ldv )\n296  100 CONTINUE\n297  END IF\n298  END IF\n299 *\n300  110 CONTINUE\n301 *\n302  RETURN\n303 *\n304 * End of DGGBAK\n305 *\nsubroutine xerbla(SRNAME, INFO)\nXERBLA\nDefinition: xerbla.f:62\nsubroutine dswap(N, DX, INCX, DY, INCY)\nDSWAP\nDefinition: dswap.f:53\nsubroutine dscal(N, DA, DX, INCX)\nDSCAL\nDefinition: dscal.f:55\nlogical function lsame(CA, CB)\nLSAME\nDefinition: lsame.f:55\n\nHere is the call graph for this function:\n\nHere is the caller graph for this function:" ]

[ null, "https://netlib.org/lapack/explore-html-3.6.1/lapack.png", null ]

[ "# Cost-plus Contract\n\n## Definition of \"Cost-plus contract\"\n\nSUN REALTY\nEstate Agents LLC, Real Estate Agent\n.\n\nAgreement in which the contract price to build something is equal to the total costs incurred plus a predetermined profit. The profit may be based on a percentage of cost (e.g., 20% of cost) or a flat profit figure (e.g., \\$60,000). This type of contract is not good for the buyer because the contractor may intentionally overstate the construction costs to obtain a higher profit figure when profit is determined based on a percentage of total costs. If the construction costs are \\$100,000 and the percentage of profit is 25%, the total contract price will be \\$125,000.\n\n#### Have a question or comment?We're here to help.", null, "" ]

[ null, "https://www.realestateagent.com/images/reviews-mockup.png", null ]

[ "# Tweetable Mathematical Art [closed]\n\nInteger math can generate amazing patterns when laid out over a grid. Even the most basic functions can yield stunningly elaborate designs!\n\nWrite 3 Tweetable (meaning 140 characters or less) function bodies for the red, green, and blue values for a 1024x1024 image.\n\nThe input to the functions will be two integers i (column number for the given pixel) and j (row number for the given pixel) and the output will be an unsigned short between 0 and 1023, inclusive, which represents the amount of the given color present in the pixel (i,j).\n\nFor example, the following three functions produce the picture below:\n\n/* RED */\nreturn (unsigned short)sqrt((double)(_sq(i-DIM/2)*_sq(j-DIM/2))*2.0);\n/* GREEN */\nreturn (unsigned short)sqrt((double)(\n(_sq(i-DIM/2)|_sq(j-DIM/2))*\n(_sq(i-DIM/2)&_sq(j-DIM/2))\n));\n/* BLUE */\nreturn (unsigned short)sqrt((double)(_sq(i-DIM/2)&_sq(j-DIM/2))*2.0);", null, "/* RED */\nreturn i&&j?(i%j)&(j%i):0;\n/* GREEN */\nreturn i&&j?(i%j)+(j%i):0;\n/* BLUE */\nreturn i&&j?(i%j)|(j%i):0;", null, "# The Rules\n\n• Given this C++ code, substitute in your functions. I have provided a few macros and have included the library, and you may include complex.h. You may use any functions from these libraries and/or my macros. Please do not use any external resources beyond this.\n• If that version isn't working for you, make sure you're compiling with:\n\ng++ filename.cpp -std=c++11\n\n\nIf that doesn't work, please use the alternate version using unsigned chars instead of unsigned shorts.\n\nMichaelangelo has provided a cleaned up 24-bit or 48-bit color output version.\n\n• You may implement your own version in another language, but it must behave in the same way as the provided C++ version, and only functions from C++'s built-ins, the library, or the provided macros may be used to make it fair.\n• Please include either a smaller version or an embedded copy of your image. They are made into a ppm format and may need to be converted to another for proper viewing on stackexchange.\n• Function bodies (not including signature) must be 140 characters or less.\n• This is a popularity contest - most votes wins\n• Added C++ tag because the nature of the rules excludes other languages. We generally prefer language-agnostic challenges unless they have a good reason to require a specific set. – algorithmshark Aug 2 '14 at 4:48\n• To the close voters calling this too broad, please try writing an answer to this first. It's surprisingly restrictive... – trichoplax Aug 3 '14 at 22:24\n• This is my favorite thing I've seen on here in, like, ever! – David Conrad Aug 4 '14 at 16:25\n• I love that this question feels like an old-school demo scene. – mskfisher Aug 5 '14 at 13:31\n• This type of question encourages participation in code golf. I'm generally disinclined to answer a straight golf question as I'm not confident of doing well. With this type of question the byte limit makes me try a simple answer, learn golfing techniques along the way, and then use them to make more complex answers. This is like a stepping stone into answering straight golf questions. I think it could be key in bringing more people in. – trichoplax Aug 7 '14 at 23:43\n\n## Mandelbrot 3 x 133 chars\n\nThe first thing that popped into my mind was \"Mandelbrot!\".\n\nYes, I know there already is a mandelbrot submission. After confirming that I'm able to get it below 140 characters myself, I have taken the tricks and optimizations from that solution into mine (thanks Martin and Todd). That left space to choose an interesting location and zoom, as well as a nice color theme:", null, "unsigned char RD(int i,int j){\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880)\n{b=2*a*b+j*8e-9-.645411;a=c-d+i*8e-9+.356888;}\nreturn 255*pow((n-80)/800,3.);\n}\nunsigned char GR(int i,int j){\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880)\n{b=2*a*b+j*8e-9-.645411;a=c-d+i*8e-9+.356888;}\nreturn 255*pow((n-80)/800,.7);\n}\nunsigned char BL(int i,int j){\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880)\n{b=2*a*b+j*8e-9-.645411;a=c-d+i*8e-9+.356888;}\nreturn 255*pow((n-80)/800,.5);\n}\n\n\n## 132 chars total\n\nI tried to get it down to 140 for all 3 channels. There is a bit of color noise near the edge, and the location is not as interesting as the first one, but: 132 chars", null, "unsigned char RD(int i,int j){\ndouble a=0,b=0,d,n=0;\nfor(;a*a+(d=b*b)<4&&n++<8192;b=2*a*b+j/5e4+.06,a=a*a-d+i/5e4+.34);\nreturn n/4;\n}\nunsigned char GR(int i,int j){\nreturn 2*RD(i,j);\n}\nunsigned char BL(int i,int j){\nreturn 4*RD(i,j);\n}\n\n• Those colours are gorgeous! – Martin Ender Aug 4 '14 at 21:12\n• I love this one, best looking image yet! – Roy van Rijn Aug 5 '14 at 8:56\n• This is my wallpaper now. – Cipher Aug 6 '14 at 9:20\n\n# Flat\n\nI started out putting a plaid/gingham pattern into perspective like a boundless table cloth:\n\nunsigned char RD(int i,int j){\nfloat s=3./(j+99);\nreturn (int((i+DIM)*s+j*s)%2+int((DIM*2-i)*s+j*s)%2)*127;\n}\nunsigned char GR(int i,int j){\nfloat s=3./(j+99);\nreturn (int((i+DIM)*s+j*s)%2+int((DIM*2-i)*s+j*s)%2)*127;\n}\nunsigned char BL(int i,int j){\nfloat s=3./(j+99);\nreturn (int((i+DIM)*s+j*s)%2+int((DIM*2-i)*s+j*s)%2)*127;\n}", null, "# Ripple\n\nThen I introduced a ripple (not strictly correct perspective, but still in 140 characters):\n\nunsigned char RD(int i,int j){\nfloat s=3./(j+99);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM)*35)*s;\nreturn (int((i+DIM)*s+y)%2+int((DIM*2-i)*s+y)%2)*127;\n}\nunsigned char GR(int i,int j){\nfloat s=3./(j+99);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM)*35)*s;\nreturn (int((i+DIM)*s+y)%2+int((DIM*2-i)*s+y)%2)*127;\n}\nunsigned char BL(int i,int j){\nfloat s=3./(j+99);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM)*35)*s;\nreturn (int((i+DIM)*s+y)%2+int((DIM*2-i)*s+y)%2)*127;\n}", null, "# Colour\n\nThen I made some of the colours more fine grained to give detail on a wider range of scales, and to make the picture more colourful...\n\nunsigned char RD(int i,int j){\nfloat s=3./(j+99);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM)*35)*s;\nreturn (int((i+DIM)*s+y)%2+int((DIM*2-i)*s+y)%2)*127;\n}\nunsigned char GR(int i,int j){\nfloat s=3./(j+99);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM)*35)*s;\nreturn (int(5*((i+DIM)*s+y))%2+int(5*((DIM*2-i)*s+y))%2)*127;\n}\nunsigned char BL(int i,int j){\nfloat s=3./(j+99);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM)*35)*s;\nreturn (int(29*((i+DIM)*s+y))%2+int(29*((DIM*2-i)*s+y))%2)*127;\n}", null, "# In motion\n\nReducing the code just slightly more allows for defining a wave phase P with 2 decimal places, which is just enough for frames close enough for smooth animation. I've reduced the amplitude at this stage to avoid inducing sea sickness, and shifted the whole image up a further 151 pixels (at the cost of 1 extra character) to push the aliasing off the top of the image. Animated aliasing is mesmerising.\n\nunsigned char RD(int i,int j){\n#define P 6.03\nfloat s=3./(j+250),y=(j+sin((i*i+_sq(j-700)*5)/100./DIM+P)*15)*s;return (int((i+DIM)*s+y)%2+int((DIM*2-i)*s+y)%2)*127;}\n\nunsigned char GR(int i,int j){\nfloat s=3./(j+250);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM+P)*15)*s;\nreturn (int(5*((i+DIM)*s+y))%2+int(5*((DIM*2-i)*s+y))%2)*127;}\n\nunsigned char BL(int i,int j){\nfloat s=3./(j+250);\nfloat y=(j+sin((i*i+_sq(j-700)*5)/100./DIM+P)*15)*s;\nreturn (int(29*((i+DIM)*s+y))%2+int(29*((DIM*2-i)*s+y))%2)*127;}", null, "• This is legendary. (Y) Keep it up. :P – Mohammad Areeb Siddiqui Aug 8 '14 at 16:15\n• But how exactly motion is implemented? In original framework there's no frame changing logic, is there? – esteewhy Aug 12 '14 at 15:34\n• @esteewhy only still images can be produced. The GIF shows a sequence of still frames, each of which was produced by changing the value after #define P. It required golfing down to allow the additional characters for #define P 6.03. – trichoplax Aug 12 '14 at 16:03\n• STOP! Do you really want to upvote the top answer? There are some far more interesting ones if you scroll down through the next two pages. – trichoplax Jan 30 '15 at 8:23\n• I recommend sorting the answers by \"oldest\" and then you can see how new approaches developed as new answers came in. – trichoplax Jul 10 '15 at 20:59\n\n## Random painter", null, "char red_fn(int i,int j){\n#define r(n)(rand()%n)\nstatic char c;return!c[i][j]?c[i][j]=!r(999)?r(256):red_fn((i+r(2))%1024,(j+r(2))%1024):c[i][j];\n}\nchar green_fn(int i,int j){\nstatic char c;return!c[i][j]?c[i][j]=!r(999)?r(256):green_fn((i+r(2))%1024,(j+r(2))%1024):c[i][j];\n}\nchar blue_fn(int i,int j){\nstatic char c;return!c[i][j]?c[i][j]=!r(999)?r(256):blue_fn((i+r(2))%1024,(j+r(2))%1024):c[i][j];\n}\n\n\nHere is a randomness-based entry. For about 0.1% of the pixels it chooses a random colour, for the others it uses the same colour as a random adjacent pixel. Note that each colour does this independently, so this is actually just an overlay of a random green, blue and red picture. To get different results on different runs, you'll need to add srand(time(NULL)) to the main function.\n\nNow for some variations.\n\nBy skipping pixels we can make it a bit more blurry.", null, "And then we can slowly change the colours, where the overflows result in abrupt changes which make this look even more like brush strokes", null, "Things I need to figure out:\n\n• For some reason I can't put srand within those functions without getting a segfault.\n• If I could make the random walks the same across all three colours it might look a bit more orderly.\n\nYou can also make the random walk isotropic, like\n\nstatic char c;return!c[i][j]?c[i][j]=r(999)?red_fn((i+r(5)+1022)%1024,(j+r(5)+1022)%1024):r(256):c[i][j];\n\n\nto give you", null, "## More random paintings\n\nI've played around with this a bit more and created some other random paintings. Not all of these are possible within the limitations of this challenge, so I don't want to include them here. But you can see them in this imgur gallery along with some descriptions of how I produced them.\n\nI'm tempted to develop all these possibilities into a framework and put it on GitHub. (Not that stuff like this doesn't already exist, but it's fun anyway!)\n\n• I love these. I hadn't realised it would be possible to take into account adjacent pixels without having access to the pixel data - smooth work! – trichoplax Aug 2 '14 at 17:11\n• Reminds me very much of this old contest where the rules were to put a pixel of every color in the image. – teh internets is made of catz Aug 3 '14 at 10:20\n• Wow! These pictures are absolutely beautiful! – raptortech97 Aug 6 '14 at 20:03\n• Cool gallery, the radial ones are neat. – teh internets is made of catz Aug 7 '14 at 12:26\n• I see Reptar: last image in the post (the isotropic one), top-right quadrant. – Tim Pederick Aug 11 '14 at 12:39\n\n# Some swirly pointy things\n\nYes, I knew exactly what to name it.", null, "unsigned short RD(int i,int j){\nreturn(sqrt(_sq(73.-i)+_sq(609-j))+1)/(sqrt(abs(sin((sqrt(_sq(860.-i)+_sq(162-j)))/115.0)))+1)/200;\n}\nunsigned short GR(int i,int j){\nreturn(sqrt(_sq(160.-i)+_sq(60-j))+1)/(sqrt(abs(sin((sqrt(_sq(86.-i)+_sq(860-j)))/115.0)))+1)/200;\n}\nunsigned short BL(int i,int j){\nreturn(sqrt(_sq(844.-i)+_sq(200-j))+1)/(sqrt(abs(sin((sqrt(_sq(250.-i)+_sq(20-j)))/115.0)))+1)/200;\n}\n\n\nEDIT: No longer uses pow. EDIT 2: @PhiNotPi pointed out that I don't need to use abs as much.\n\nYou can change the reference points pretty easily to get a different picture:", null, "unsigned short RD(int i,int j){\nreturn(sqrt(_sq(148.-i)+_sq(1000-j))+1)/(sqrt(abs(sin((sqrt(_sq(500.-i)+_sq(400-j)))/115.0)))+1)/200;\n}\nunsigned short GR(int i,int j){\nreturn(sqrt(_sq(610.-i)+_sq(60-j))+1)/(sqrt(abs(sin((sqrt(_sq(864.-i)+_sq(860-j)))/115.0)))+1)/200;\n}\nunsigned short BL(int i,int j){\nreturn(sqrt(_sq(180.-i)+_sq(100-j))+1)/(sqrt(abs(sin((sqrt(_sq(503.-i)+_sq(103-j)))/115.0)))+1)/200;\n}\n\n\n@EricTressler pointed out that my pictures have Batman in them.", null, "Of course, there has to be a Mandelbrot submission.", null, "char red_fn(int i,int j){\nfloat x=0,y=0;int k;for(k=0;k++<256;){float a=x*x-y*y+(i-768.0)/512;y=2*x*y+(j-512.0)/512;x=a;if(x*x+y*y>4)break;}return k>31?256:k*8;\n}\nchar green_fn(int i,int j){\nfloat x=0,y=0;int k;for(k=0;k++<256;){float a=x*x-y*y+(i-768.0)/512;y=2*x*y+(j-512.0)/512;x=a;if(x*x+y*y>4)break;}return k>63?256:k*4;\n}\nchar blue_fn(int i,int j){\nfloat x=0,y=0;int k;for(k=0;k++<256;){float a=x*x-y*y+(i-768.0)/512;y=2*x*y+(j-512.0)/512;x=a;if(x*x+y*y>4)break;}return k;\n}\n\n\nTrying to improve the colour scheme now. Is it cheating if I define the computation as a macro is red_fn and use that macro in the other two so I have more characters for fancy colour selection in green and blue?\n\nEdit: It's really hard to come up with decent colour schemes with these few remaining bytes. Here is one other version:\n\n/* RED */ return log(k)*47;\n/* GREEN */ return log(k)*47;\n/* BLUE */ return 128-log(k)*23;", null, "And as per githubphagocyte's suggestion and with Todd Lehman's improvements, we can easily pick smaller sections:\n\nE.g.\n\nchar red_fn(int i,int j){\nfloat x=0,y=0,k=0,X,Y;while(k++<256e2&&(X=x*x)+(Y=y*y)<4)y=2*x*y+(j-89500)/102400.,x=X-Y+(i-14680)/102400.;return log(k)/10.15*256;\n}\nchar green_fn(int i,int j){\nfloat x=0,y=0,k=0,X,Y;while(k++<256e2&&(X=x*x)+(Y=y*y)<4)y=2*x*y+(j-89500)/102400.,x=X-Y+(i-14680)/102400.;return log(k)/10.15*256;\n}\nchar blue_fn(int i,int j){\nfloat x=0,y=0,k=0,X,Y;while(k++<256e2&&(X=x*x)+(Y=y*y)<4)y=2*x*y+(j-89500)/102400.,x=X-Y+(i-14680)/102400.;return 128-k/200;\n}\n\n\ngives", null, "• @tomsmeding I have to confess, this is the first time I ever implemented the Mandelbrot set. – Martin Ender Aug 2 '14 at 11:12\n• As iconic as the full Mandelbrot set is (+1, by the way!), it looks like you've left yourself just enough room to adjust the parameters and post an answer with some of the stunningly twisted detail of a deep zoom. – trichoplax Aug 2 '14 at 12:28\n• @githubphagocyte I already thought about that, but couldn't yet be bothered to recompile and rerun and convert every time until I've figured out decent parameters ;). Might do so later. First I've got to try a completely different function though. ;) – Martin Ender Aug 2 '14 at 12:30\n• @githubphagocyte finally got around to adding that. thanks for the suggestion! – Martin Ender Aug 2 '14 at 20:00\n• Thanks @Todd, I've updated the final picture with that. I used 25600 iterations, that too long enough. ;) – Martin Ender Aug 2 '14 at 21:56\n\n# Julia sets\n\nIf there's a Mandelbrot, there should be a Julia set too.", null, "You can spend hours tweaking the parameters and functions, so this is just a quick one that looks decent.\n\nInspired from Martin's participation.\n\nunsigned short red_fn(int i, int j){\n#define D(x) (x-DIM/2.)/(DIM/2.)\nfloat x=D(i),y=D(j),X,Y,n=0;while(n++<200&&(X=x*x)+(Y=y*y)<4){x=X-Y+.36237;y=2*x*y+.32;}return log(n)*256;}\n\nunsigned short green_fn(int i, int j){\nfloat x=D(i),y=D(j),X,Y,n=0;while(n++<200&&(x*x+y*y)<4){X=x;Y=y;x=X*X-Y*Y+-.7;y=2*X*Y+.27015;}return log(n)*128;}\n\nunsigned short blue_fn(int i, int j){\nfloat x=D(i),y=D(j),X,Y,n=0;while(n++<600&&(x*x+y*y)<4){X=x;Y=y;x=X*X-Y*Y+.36237;y=2*X*Y+.32;}return log(n)*128;}\n\n\n# Would you like some RNG?\n\nOK, Sparr's comment put me on the track to randomize the parameters of these little Julias. I first tried to do bit-level hacking with the result of time(0) but C++ doesn't allow hexadecimal floating point litterals so this was a dead-end (with my limited knowledge at least). I could have used some heavy casting to achieve it, but that wouldn't have fit into the 140 bytes.\n\nI didn't have much room left anyway, so I had to drop the red Julia to put my macros and have a more conventional RNG (timed seed and real rand(), woohoo!).", null, "Whoops, something is missing. Obviously, these parameters have to be static or else you have some weird results (but funny, maybe I'll investigate a bit later if I find something interesting).\n\nSo here we are, with only green and blue channels:", null, "", null, "", null, "Now let's add a simple red pattern to fill the void. Not really imaginative, but I'm not a graphic programer ... yet :-)", null, "", null, "And finally the new code with random parameters:\n\nunsigned short red_fn(int i, int j){\nstatic int n=1;if(n){--n;srand(time(0));}\n#define R rand()/16384.-1\n#define S static float r=R,k=R;float\nreturn _cb(i^j);}\n\nunsigned short green_fn(int i, int j){\n#define D(x) (x-DIM/2.)/(DIM/2.),\nS x=D(i)y=D(j)X,Y;int n=0;while(n++<200&&(X=x)*x+(Y=y)*y<4){x=X*X-Y*Y+r;y=2*X*Y+k;}return log(n)*512;}\n\nunsigned short blue_fn(int i, int j){\nS x=D(i)y=D(j)X,Y;int n=0;while(n++<200&&(X=x)*x+(Y=y)*y<4){x=X*X-Y*Y+r;y=2*X*Y+k;}return log(n)*512;}\n\n\nThere's still room left now ...\n\n• do you have room to randomize the parameters each run with srand(time(0) and rand()? or just time(0)? – Sparr Aug 3 '14 at 17:56\n• That last one is going on my wall. – cjfaure Aug 3 '14 at 21:27\n• @Sparr updated with your suggestion. Had some fun :-). – teh internets is made of catz Aug 3 '14 at 21:28\n\nThis one is interesting because it doesn't use the i, j parameters at all. Instead it remembers state in a static variable.\n\nunsigned char RD(int i,int j){\nstatic double k;k+=rand()/1./RAND_MAX;int l=k;l%=512;return l>255?511-l:l;\n}\nunsigned char GR(int i,int j){\nstatic double k;k+=rand()/1./RAND_MAX;int l=k;l%=512;return l>255?511-l:l;\n}\nunsigned char BL(int i,int j){\nstatic double k;k+=rand()/1./RAND_MAX;int l=k;l%=512;return l>255?511-l:l;\n}", null, "• It would be interesting to see the results of this code on different platforms/compilers. The value of RAND_MAX varies widely and could give completely different images... – trichoplax Aug 5 '14 at 0:40\n• It shouldn't change much. (double)rand()/RAND_MAX should always be in the range [0.0, 1.0]. – Manuel Kasten Aug 5 '14 at 6:16\n• This is one of my favorites! – Calvin's Hobbies Aug 6 '14 at 0:23\n• It is not only interesting - it is beautiful! – Martin Thoma Aug 8 '14 at 4:48", null, "/* RED */\nint a=(j?i%j:i)*4;int b=i-32;int c=j-32;return _sq(abs(i-512))+_sq(abs(j-512))>_sq(384)?a:int(sqrt((b+c)/2))^_cb((b-c)*2);\n/* GREEN */\nint a=(j?i%j:i)*4;return _sq(abs(i-512))+_sq(abs(j-512))>_sq(384)?a:int(sqrt((i+j)/2))^_cb((i-j)*2);\n/* BLUE */\nint a=(j?i%j:i)*4;int b=i+32;int c=j+32;return _sq(abs(i-512))+_sq(abs(j-512))>_sq(384)?a:int(sqrt((b+c)/2))^_cb((b-c)*2);\n\n• That is really beautiful, +1. – Milo Aug 3 '14 at 5:09\n• This is my favorite. It looks like a professionally made piece of graphic design. :D – cjfaure Aug 3 '14 at 20:58\n• It looks like a wafer of microprocessors. macrophotographer.net/images/ss_rvsi_5.jpg – s0rce Aug 8 '14 at 15:03\n• It looks like a minimalist wallpaper. – A.L Aug 11 '14 at 1:24\n• It looks similar to the rainbow Apple logo. – LegionMammal978 Dec 22 '15 at 16:03\n\n## Buddhabrot (+ Antibuddhabrot)\n\nEdit: It's a proper Buddhabrot now!\n\nEdit: I managed to cap the colour intensity within the byte limit, so there are no more falsely black pixels due to overflow.\n\nI really wanted to stop after four... but...", null, "This gets slightly compressed during upload (and shrunk upon embedding) so if you want to admire all the detail, here is the interesting 512x512 cropped out (which doesn't get compressed and is displayed in its full size):", null, "Thanks to githubphagocyte for the idea. This required some rather complicated abuse of all three colour functions:\n\nunsigned short RD(int i,int j){\n#define f(a,b)for(a=0;++a<b;)\n#define D float x=0,y=0\nstatic int z,m,n;if(!z){z=1;f(m,4096)f(n,4096)BL(m-4096,n-4096);};return GR(i,j);\n}\nunsigned short GR(int i,int j){\n#define R a=x*x-y*y+i/1024.+2;y=2*x*y+j/1024.+2\nstatic float c[DIM][DIM],p;if(i>=0)return(p=c[i][j])>DM1?DM1:p;c[j+DIM][i/2+DIM]+=i%2*2+1;\n}\nunsigned short BL(int i,int j){\nD,a,k,p=0;if(i<0)f(k,5e5){R;x=a;if(x*x>4||y*y>4)break;GR(int((x-2)*256)*2-p,(y-2)*256);if(!p&&k==5e5-1){x=y=k=0;p=1;}}else{return GR(i,j);}\n}\n\n\nThere are some bytes left for a better colour scheme, but so far I haven't found anything that beats the grey-scale image.\n\nThe code as given uses 4096x4096 starting points and does up to 500,000 iterations on each of them to determine if the trajectories escape or not. That took between 6 and 7 hours on my machine. You can get decent results with a 2k by 2k grid and 10k iterations, which takes two minutes, and even just a 1k by 1k grid with 1k iterations looks quite nice (that takes like 3 seconds). If you want to fiddle around with those parameters, there are a few places that need to change:\n\n• To change the Mandelbrot recursion depth, adjust both instances of 5e5 in BL to your iteration count.\n• To change the grid resolution, change all four 4096 in RD to your desired resolution and the 1024. in GR by the same factor to maintain the correct scaling.\n• You will probably also need to scale the return c[i][j] in GR since that only contains the absolute number of visits of each pixel. The maximum colour seems to be mostly independent of the iteration count and scales linearly with the total number of starting points. So if you want to use a 1k by 1k grid, you might want to return c[i][j]*16; or similar, but that factor sometimes needs some fiddling.\n\nFor those not familiar with the Buddhabrot (like myself a couple of days ago), it's based on the Mandelbrot computation, but each pixel's intensity is how often that pixel was visited in the iterations of the escaping trajectories. If we're counting the visits during non-escaping trajectories, it's an Antibuddhabrot. There is an even more sophisticated version called Nebulabrot where you use a different recursion depth for each colour channel. But I'll leave that to someone else. For more information, as always, Wikipedia.\n\nOriginally, I didn't distinguish between escaping and non-escaping trajectories. That generated a plot which is the union of a Buddhabrot and an Antibuddhabrot (as pointed out by githubphagocyte).\n\nunsigned short RD(int i,int j){\n#define f(a)for(a=0;++a<DIM;)\nstatic int z;float x=0,y=0,m,n,k;if(!z){z=1;f(m)f(n)GR(m-DIM,n-DIM);};return BL(i,j);\n}\nunsigned short GR(int i,int j){\nfloat x=0,y=0,a,k;if(i<0)f(k){a=x*x-y*y+(i+256.0)/512;y=2*x*y+(j+512.0)/512;x=a;if(x*x+y*y>4)break;BL((x-.6)*512,(y-1)*512);}return BL(i,j);\n}\nunsigned short BL(int i,int j){\nstatic float c[DIM][DIM];if(i<0&&i>-DIM-1&&j<0&&j>-DIM-1)c[j+DIM][i+DIM]++;else if(i>0&&i<DIM&&j>0&&j<DIM)return log(c[i][j])*110;\n}", null, "This one looks a bit like a faded photograph... I like that.\n\n• I shall make this into a hat. – cjfaure Aug 7 '14 at 21:31\n• I am truly astonished that you got this down to 3 lots of 140 bytes. The new buddabrot image is beautiful. – trichoplax Aug 9 '14 at 12:26\n• This is really impressive. – copumpkin Aug 10 '14 at 18:19\n• The first one is really artful. Reminds me of jellyfish. +1 – Igby Largeman Aug 12 '14 at 2:19\n• This one is my favorite submission. Nice work! – thomallen Aug 19 '14 at 22:40\n\n# Sierpinski Pentagon\n\nYou may have seen the chaos game method of approximating Sierpinski's Triangle by plotting points half way to a randomly chosen vertex. Here I have taken the same approach using 5 vertices. The shortest code I could settle on included hard coding the 5 vertices, and there was no way I was going to fit it all into 140 characters. So I've delegated the red component to a simple backdrop, and used the spare space in the red function to define a macro to bring the other two functions under 140 too. So everything is valid at the cost of having no red component in the pentagon.\n\nunsigned char RD(int i,int j){\n#define A int x=0,y=0,p={512,9,0,381,196,981,827,981,DM1,381}\nauto s=99./(j+99);return GR(i,j)?0:abs(53-int((3e3-i)*s+j*s)%107);}\n\nunsigned char GR(int i,int j){static int c[DIM][DIM];if(i+j<1){A;for(int n=0;n<2e7;n++){int v=(rand()%11+1)%5*2;x+=p[v];x/=2;y+=p[v+1];y/=2;c[x][y]++;}}return c[i][j];}\n\nunsigned char BL(int i,int j){static int c[DIM][DIM];if(i+j<1){A;for(int n=0;n<3e7;n++){int v=(rand()%11+4)%5*2;x+=p[v];x/=2;y+=p[v+1];y/=2;c[x][y]++;}}return c[i][j];}\n\n\nThanks to Martin Büttner for the idea mentioned in the question's comments about defining a macro in one function to then use in another, and also for using memoisation to fill the pixels in an arbitrary order rather than being restricted to the raster order of the main function.", null, "The image is over 500KB so it gets automatically converted to jpg by stack exchange. This blurs some of the finer detail, so I've also included just the top right quarter as a png to show the original look:", null, "# Sheet Music\n\nSierpinski music. :D The guys on chat say it looks more like the punched paper for music boxes.", null, "unsigned short RD(int i,int j){\nreturn ((int)(100*sin((i+400)*(j+100)/11115)))&i;\n}\nunsigned short GR(int i,int j){\nreturn RD(i,j);\n}\nunsigned short BL(int i,int j){\nreturn RD(i,j);\n}\n\n\nSome details on how this works...um, it's actually just a zoom-in on a render of some wavy Sierpinski triangles. The sheet-music look (and also the blockiness) is the result of integer truncation. If I change the red function to, say,\n\nreturn ((int)(100*sin((i+400)*(j+100)/11115.0)));\n\n\nthe truncation is removed and we get the full resolution render:", null, "So yeah, that's interesting.\n\n• It's like Squarepusher transcribed into neumes – r3mainer Aug 4 '14 at 14:37\n• @squeamishossifrage What did I just watch...? – cjfaure Aug 4 '14 at 14:46\n• :-) Chris Cunningham's videos are a bit odd, aren't they? – r3mainer Aug 4 '14 at 18:31\n• the second one looks like it's moving when I scroll the page – user13267 Aug 5 '14 at 22:22\n• In scrolling down the site, the last one really seemed to be moving. Nice optical illusion. – Kyle Kanos Aug 6 '14 at 16:02\n\n# Random Voronoi diagram generator anyone?\n\nOK, this one gave me a hard time. I think it's pretty nice though, even if the results are not so arty as some others. That's the deal with randomness. Maybe some intermediate images look better, but I really wanted to have a fully working algorithm with voronoi diagrams.", null, "# Edit:", null, "This is one example of the final algorithm. The image is basically the superposition of three voronoi diagram, one for each color component (red, green, blue).\n\n## Code\n\nungolfed, commented version at the end\n\nunsigned short red_fn(int i, int j){\nint t,k=0,l,e,d=2e7;srand(time(0));while(k<64){t[k]=rand()%DIM;if((e=_sq(i-t[k])+_sq(j-t[42&k++]))<d)d=e,l=k;}return t[l];\n}\n\nunsigned short green_fn(int i, int j){\nstatic int t;int k=0,l,e,d=2e7;while(k<64){if(!t[k])t[k]=rand()%DIM;if((e=_sq(i-t[k])+_sq(j-t[42&k++]))<d)d=e,l=k;}return t[l];\n}\n\nunsigned short blue_fn(int i, int j){\nstatic int t;int k=0,l,e,d=2e7;while(k<64){if(!t[k])t[k]=rand()%DIM;if((e=_sq(i-t[k])+_sq(j-t[42&k++]))<d)d=e,l=k;}return t[l];\n}\n\n\nIt took me a lot of efforts, so I feel like sharing the results at different stages, and there are nice (incorrect) ones to show.\n\n## First step: have some points placed randomly, with x=y", null, "I have converted it to jpeg because the original png was too heavy for upload (>2MB), I bet that's way more than 50 shades of grey!\n\n## Second: have a better y coordinate\n\nI couldn't afford to have another table of coordinates randomly generated for the y axis, so I needed a simple way to get \" random \" ones in as few characters as possible. I went for using the x coordinate of another point in the table, by doing a bitwise AND on the index of the point.", null, "## 3rd: I don't remember but it's getting nice\n\nBut at this time I was way over 140 chars, so I needed to golf it down quite a bit.", null, "## 4th: scanlines\n\nJust kidding, this is not wanted but kind of cool, methinks.", null, "", null, "Still working on reducing the size of the algorithm, I am proud to present:\n\n### StarFox edition", null, "### Voronoi instagram", null, "## 5th: increase the number of points\n\nI have now a working piece of code, so let's go from 25 to 60 points.", null, "That's hard to see from only one image, but the points are nearly all located in the same y range. Of course, I didn't change the bitwise operation, &42 is much better:", null, "And here we are, at the same point as the very first image from this post. Let's now explain the code for the rare ones that would be interested.\n\n## Ungolfed and explained code\n\nunsigned short red_fn(int i, int j)\n{\nint t, // table of 64 points's x coordinate\nk = 0, // used for loops\nl, // retains the index of the nearest point\ne, // for intermediary results\nd = 2e7; // d is the minimum distance to the (i,j) pixel encoutnered so far\n// it is initially set to 2e7=2'000'000 to be greater than the maximum distance 1024²\n\nsrand(time(0)); // seed for random based on time of run\n// if the run overlaps two seconds, a split will be observed on the red diagram but that is\n// the better compromise I found\n\nwhile(k < 64) // for every point\n{\nt[k] = rand() % DIM; // assign it a random x coordinate in [0, 1023] range\n// this is done at each call unfortunately because static keyword and srand(...)\n// were mutually exclusive, lenght-wise\n\nif (\n(e= // assign the distance between pixel (i,j) and point of index k\n_sq(i - t[k]) // first part of the euclidian distance\n+\n_sq(j - t[42 & k++]) // second part, but this is the trick to have \"\" random \"\" y coordinates\n// instead of having another table to generate and look at, this uses the x coordinate of another point\n// 42 is 101010 in binary, which is a better pattern to apply a & on; it doesn't use all the table\n// I could have used 42^k to have a bijection k <-> 42^k but this creates a very visible pattern splitting the image at the diagonal\n// this also post-increments k for the while loop\n) < d // chekcs if the distance we just calculated is lower than the minimal one we knew\n)\n// { // if that is the case\nd=e, // update the minimal distance\nl=k; // retain the index of the point for this distance\n// the comma ',' here is a trick to have multiple expressions in a single statement\n// and therefore avoiding the curly braces for the if\n// }\n}\n\nreturn t[l]; // finally, return the x coordinate of the nearest point\n// wait, what ? well, the different areas around points need to have a\n// \"\" random \"\" color too, and this does the trick without adding any variables\n}\n\n// The general idea is the same so I will only comment the differences from green_fn\nunsigned short green_fn(int i, int j)\n{\nstatic int t; // we don't need to bother a srand() call, so we can have these points\n// static and generate their coordinates only once without adding too much characters\n// in C++, objects with static storage are initialized to 0\n// the table is therefore filled with 60 zeros\n// see http://stackoverflow.com/a/201116/1119972\n\nint k = 0, l, e, d = 2e7;\n\nwhile(k<64)\n{\nif( !t[k] ) // this checks if the value at index k is equal to 0 or not\n// the negation of 0 will cast to true, and any other number to false\nt[k] = rand() % DIM; // assign it a random x coordinate\n\n// the following is identical to red_fn\nif((e=_sq(i-t[k])+_sq(j-t[42&k++]))<d)\nd=e,l=k;\n}\n\nreturn t[l];\n}\n\n\n• I love Voronoi diagrams. +1 for fitting it in 3 tweets! – Martin Ender Aug 5 '14 at 19:37\n• This is one of my personal favorites. The scan line variants are very aesthetically pleasing. – Fraxtil Aug 7 '14 at 1:37\n• Love how you explained the code – Andrea Aug 10 '14 at 22:19\n• Do a barrel roll! – Starson Hochschild Aug 23 '14 at 2:27\n• the second pic in 4th:scanlines is beautiful. – Khaled.K Dec 27 '15 at 9:10\n\nThe Lyapunov Fractal", null, "The string used to generate this was AABAB and the parameter space was [2,4]x[2,4]. (explanation of string and parameter space here)\n\nWith limited code space I thought this colouring was pretty cool.\n\n //RED\nfloat r,s=0,x=.5;for(int k=0;k++<50;)r=k%5==2||k%5==4?(2.*j)/DIM+2:(2.*i)/DIM+2,x*=r*(1-x),s+=log(fabs(r-r*2*x));return abs(s);\n//GREEN\nfloat r,s=0,x=.5;for(int k=0;k++<50;)r=k%5==2||k%5==4?(2.*j)/DIM+2:(2.*i)/DIM+2,x*=r*(1-x),s+=log(fabs(r-r*2*x));return s>0?s:0;\n//BLUE\nfloat r,s=0,x=.5;for(int k=0;k++<50;)r=k%5==2||k%5==4?(2.*j)/DIM+2:(2.*i)/DIM+2,x*=r*(1-x),s+=log(fabs(r-r*2*x));return abs(s*x);\n\n\nI also made a variation of the Mandelbrot set. It uses a map similar to the Mandelbrot set map. Say M(x,y) is the Mandelbrot map. Then M(sin(x),cos(y)) is the map I use, and instead of checking for escaping values I use x, and y since they are always bounded.\n\n//RED\nfloat x=0,y=0;for(int k=0;k++<15;){float t=_sq(sin(x))-_sq(cos(y))+(i-512.)/512;y=2*sin(x)*cos(y)+(j-512.0)/512;x=t;}return 2.5*(x*x+y*y);\n//GREEN\nfloat x=0,y=0;for(int k=0;k++<15;){float t=_sq(sin(x))-_sq(cos(y))+(i-512.)/512;y=2*sin(x)*cos(y)+(j-512.0)/512;x=t;}return 15*fabs(x);\n//BLUE\nfloat x=0,y=0;for(int k=0;k++<15;){float t=_sq(sin(x))-_sq(cos(y))+(i-512.)/512;y=2*sin(x)*cos(y)+(j-512.0)/512;x=t;}return 15*fabs(y);", null, "EDIT\n\nAfter much pain I finally got around to creating a gif of the second image morphing. Here it is:", null, "• Nice psychedelic look for the second. – teh internets is made of catz Aug 5 '14 at 9:17\n• These are insane! +1 – cjfaure Aug 5 '14 at 9:45\n• Scary fractal is scary ༼ ༎ຶ ෴ ༎ຶ༽ – Tobia Aug 7 '14 at 18:50\n• Holy shit that second is scary. Amaximg how much you can get out of the simple z=z^2+c. – tomsmeding Aug 8 '14 at 15:37\n• If Edward Munch used to paint fractals, this would have been what The Scream looked like. – teh internets is made of catz Aug 9 '14 at 19:03\n\nBecause unicorns.", null, "I couldn't get the OPs version with unsigned short and colour values up to 1023 working, so until that is fixed, here is a version using char and maximum colour value of 255.\n\nchar red_fn(int i,int j){\nreturn (char)(_sq(cos(atan2(j-512,i-512)/2))*255);\n}\nchar green_fn(int i,int j){\nreturn (char)(_sq(cos(atan2(j-512,i-512)/2-2*acos(-1)/3))*255);\n}\nchar blue_fn(int i,int j){\nreturn (char)(_sq(cos(atan2(j-512,i-512)/2+2*acos(-1)/3))*255);\n}\n\n• I'd like to see the color channels individually. That would be cool. – clapp Aug 28 '15 at 6:27\n\n## Logistic Hills", null, "## The functions\n\nunsigned char RD(int i,int j){\n#define A float a=0,b,k,r,x\n#define B int e,o\n#define C(x) x>255?255:x\n#define R return\n#define D DIM\nR BL(i,j)*(D-i)/D;\n}\nunsigned char GR(int i,int j){\n#define E DM1\n#define F static float\n#define G for(\n#define H r=a*1.6/D+2.4;x=1.0001*b/D\nR BL(i,j)*(D-j/2)/D;\n}\nunsigned char BL(int i,int j){\nF c[D][D];if(i+j<1){A;B;G;a<D;a+=0.1){G b=0;b<D;b++){H;G k=0;k<D;k++){x=r*x*(1-x);if(k>D/2){e=a;o=(E*x);c[e][o]+=0.01;}}}}}R C(c[j][i])*i/D;\n}\n\n\n## Ungolfed\n\nAll of the #defines are to fit BL under 140 chars. Here is the ungolfed version of the blue algorithm, slightly modified:\n\nfor(double a=0;a<DIM;a+=0.1){ // Incrementing a by 1 will miss points\nfor(int b=0;b<DIM;b++){ // 1024 here is arbitrary, but convenient\ndouble r = a*(1.6/DIM)+2.4; // This is the r in the logistic bifurcation diagram (x axis)\ndouble x = 1.0001*b/DIM; // This is x in the logistic bifurcation diagram (y axis). The 1.0001 is because nice fractions can lead to pathological behavior.\nfor(int k=0;k<DIM;k++){\nx = r*x*(1-x); // Apply the logistic map to x\n// We do this DIM/2 times without recording anything, just to get x out of unstable values\nif(k>DIM/2){\nif(c[(int)a][(int)(DM1*x)]<255){\nc[(int)a][(int)(DM1*x)]+=0.01; // x makes a mark in c[][]\n} // In the golfed code, I just always add 0.01 here, and clip c to 255\n}\n}\n}\n}\n\n\nWhere the values of x fall the most often for a given r (j value), the plot becomes lighter (usually depicted as darker).\n\n• Aww, I was thinking about how to do this one yesterday. +1 for figuring it out. I actually thing the palette is really nice as it is! :) – Martin Ender Aug 7 '14 at 7:14\n• I stole the dirty tricks from you and githubphagocyte, though I take responsibility for the ugly #defines. Especially \"#define G for(\". – Eric Tressler Aug 7 '14 at 7:24\n• looks more like a tournament bracket visualizer – Kevin L Aug 7 '14 at 16:50\n• Not pictured at top: winner dies – Eric Tressler Aug 7 '14 at 17:48\n• Can I get a poster-sized print of this? With 3 faded tweets in the background. :-) – Andrew Cheong Aug 9 '14 at 17:26\n\n# Diffusion Limited Aggregation\n\nI've always been fascinated by diffusion limited aggregation and the number of different ways it appears in the real world.\n\nI found it difficult to write this in just 140 characters per function so I've had to make the code horrible (or beautiful, if you like things like ++d%=4 and for(n=1;n;n++)). The three colour functions call each other and define macros for each other to use, so it doesn't read well, but each function is just under 140 characters.\n\nunsigned char RD(int i,int j){\n#define D DIM\n#define M m[(x+D+(d==0)-(d==2))%D][(y+D+(d==1)-(d==3))%D]\n#define R rand()%D\n#define B m[x][y]\nreturn(i+j)?256-(BL(i,j))/2:0;}\n\nunsigned char GR(int i,int j){\n#define A static int m[D][D],e,x,y,d,c,f,n;if(i+j<1){for(d=D*D;d;d--){m[d%D][d/D]=d%6?0:rand()%2000?1:255;}for(n=1\nreturn RD(i,j);}\n\nunsigned char BL(int i,int j){A;n;n++){x=R;y=R;if(B==1){f=1;for(d=0;d<4;d++){c[d]=M;f=f<c[d]?c[d]:f;}if(f>2){B=f-1;}else{++e%=4;d=e;if(!c[e]){B=0;M=1;}}}}}return m[i][j];}", null, "To visualise how the particles gradually aggregate, I produced snapshots at regular intervals. Each frame was produced by replacing the 1 in for(n=1;n;n++) with 0, -1<<29, -2<<29, -3<<29, 4<<29, 3<<29, 2<<29, 1<<29, 1. This kept it just under the 140 character limit for each run.", null, "You can see that aggregates growing close to each other deprive each other of particles and grow more slowly.\n\nBy making a slight change to the code you can see the remaining particles that haven't become attached to the aggregates yet. This shows the denser regions where growth will happen more quickly and the very sparse regions between aggregates where no more growth can occur due to all the particles having been used up.\n\nunsigned char RD(int i,int j){\n#define D DIM\n#define M m[(x+D+(d==0)-(d==2))%D][(y+D+(d==1)-(d==3))%D]\n#define R rand()%D\n#define B m[x][y]\nreturn(i+j)?256-BL(i,j):0;}\n\nunsigned char GR(int i,int j){\n#define A static int m[D][D],e,x,y,d,c,f,n;if(i+j<1){for(d=D*D;d;d--){m[d%D][d/D]=d%6?0:rand()%2000?1:255;}for(n=1\nreturn RD(i,j);}\n\nunsigned char BL(int i,int j){A;n;n++){x=R;y=R;if(B==1){f=1;for(d=0;d<4;d++){c[d]=M;f=f<c[d]?c[d]:f;}if(f>2){B=f-1;}else{++e%=4;d=e;if(!c[e]){B=0;M=1;}}}}}return m[i][j];}", null, "This can be animated in the same way as before:", null, "# Spiral (140 exactly)", null, "This is 140 characters exactly if you don't include the function headers and brackets. It's as much spiral complexity I could fit in the character limit.\n\nunsigned char RD(int i,int j){\nreturn DIM-BL(2*i,2*j);\n}\nunsigned char GR(int i,int j){\nreturn BL(j,i)+128;\n}\nunsigned char BL(int i,int j){\ni-=512;j-=512;int d=sqrt(i*i+j*j);return d+atan2(j,i)*82+sin(_cr(d*d))*32+sin(atan2(j,i)*10)*64;\n}\n\n\nI gradually built on a simple spiral, adding patterns to the spiral edges and experimenting with how different spirals could be combined to look cool. Here is an ungolfed version with comments explaining what each piece does. Messing with parameters can produce some interesting results.\n\nunsigned char RD(int i,int j){\n// *2 expand the spiral\nreturn DIM - BL(2*i, 2*j);\n}\nunsigned char GR(int i,int j){\n// notice swapped parameters\n// 128 changes phase of the spiral\nreturn BL(j,i)+128;\n}\nunsigned char BL(int i,int j){\n// center it\ni -= DIM / 2;\nj -= DIM / 2;\n\ndouble theta = atan2(j,i); //angle that point is from center\ndouble prc = theta / 3.14f / 2.0f; // percent around the circle\n\nint dist = sqrt(i*i + j*j); // distance from center\n\n// EDIT: if you change this to something like \"prc * n * 256\" where n\n// is an integer, the spirals will line up for any arbitrarily sized\n// DIM value, or if you make separate DIMX and DIMY values!\nint makeSpiral = prc * DIM / 2;\n\n// makes pattern on edge of the spiral\nint waves = sin(_cr(dist * dist)) * 32 + sin(theta * 10) * 64;\n\nreturn dist + makeSpiral + waves;\n}\n\n\nMessing with parameters:\n\nHere, the spirals are lined up but have different edge patterns. Instead of the blocky edges in the main example, this has edges entirely comprised of sin waves.", null, "Here, the gradient has been removed:", null, "An animation (which for some reason doesn't appear to be looping after I uploaded it, sorry. Also, I had to shrink it. Just open it in a new tab if you missed the animation):", null, "And, here's the imgur album with all images in it. I'd love to see if anyone can find other cool spiral patterns. Also, I must say, this is by far one of the coolest challenges on here I have ever seen. Enjoy!\n\nEDIT: Here are some backgrounds made from these spirals with altered parameters.\n\nAlso, by combining my spiral edge patterns with some of the fractals I've seen on here through the use of xor/and/or operations, here is a final spiral:", null, "• These are fantastic! If you look around the other answers you might be able to find ideas to golf this down even further if you wanted more room. A few of the answers use #define in one function to define a macro that all 3 can use, so you can offload the bulk of the calculation into other colour functions. Martin Büttner introduced me to that trick. – trichoplax Aug 8 '14 at 22:49\n• Thank you! In my case, as far as I can find, my code lacks the kind of duplicate logic patterns which would benefit from pound defines. However, if you see any, I would appreciate if you would identify them to me, especially seeing I haven't used C/C++ extensively in years. – xleviator Aug 9 '14 at 0:36\n• Finding duplicate sections would indeed help even more, but even without any duplication you can simply move code from BL to RD or GN by defining it as a macro in RD or GN and then using it in BL. That should give you twice as much room for extra code. – trichoplax Aug 9 '14 at 1:44\n• Ah! I see. I didn't even realize that each function body itself had the 140 character limit. I suppose next time I should read the prompt more carefully. Thank you for pointing that out! – xleviator Aug 9 '14 at 1:46\n• As was being discussed in the chat, your non-looping GIF should be easily fixable. I think it's worth doing as the brief bit of animation it currently shows looks great. – trichoplax Aug 9 '14 at 12:35\n\n## Tribute to a classic\n\nV1: Inspired by DreamWarrior's \"Be happy\", this straightforward submission embeds a small pixel-art image in each colour channel. I didn't even have to golf the code!\nV2: now with considerably shorter code & a thick black border isolating only the \"game screen\".\nV3: spaceship, bullet, damaged aliens and blue border, oh my! Trying to aim for this, roughly.\n\n// RED\n#define g(I,S,W,M)j/128%8==I&W>>(j/32%4*16+i/64)%M&S[abs(i/4%16-8)-(I%2&i%64<32)]>>j/4%8&1\nreturn g(1,\"_\\xB6\\\\\\x98\\0\\0\\0\",255L<<36,64)?j:0;\n\n// GREEN\n#define S g(6,\"\\xFF\\xFE\\xF8\\xF8\\xF8\\xF8\\xF0\\x0\",1L<<22,64)|i/4==104&j/24==30\nreturn g(2,\"<\\xBC\\xB6}\\30p\\0\\0\",4080,32)|S?j:0;\n\n// BLUE\nreturn g(3,\"_7\\xB6\\xFE\\x5E\\34\\0\",0x70000000FD0,64)|S|abs(i/4-128)==80&abs(j/4-128)<96|abs(j/4-128)==96&abs(i/4-128)<80?j:0;", null, "I happened to stumble upon an edit by Umber Ferrule whose avatar inspired me to add another pixel-art-based entry. Since the core idea of the code is largely similar to the Space Invaders one, I'm appending it to this entry, though the two definitely had different challenging points. For this one, getting pink right (at the expense of white) and the fact that it's a rather big sprite proved nice challenges. The hexadecimal escapes (\\xFF etc) in the red channel represent their corresponding characters in the source file (that is, the red channel in the source file contains binary data), whereas the octal escapes are literal (i.e. present in the source file).\n\n// RED\n#define g(S)(S[i/29%18*2+j/29/8%2]>>j/29%8&1)*DM1*(abs(i-512)<247&abs(j-464)<232)\nreturn g(\"\\xF3\\xF2\\xF2\\x10\\xF4\\0\\xF2\\x10\\xE1\\xE0\\x81\\0\\x80\\0\\x80\\0\\0\\0\\0\\0@\\0! \\x03d8,=\\x2C\\x99\\x84\\xC3\\x82\\xE1\\xE3\");\n\n// GREEN\nreturn g(\";\\376z\\34\\377\\374\\372\\30k\\360\\3\\200\\0\\0\\0\\0\\0\\0\\200\\0\\300\\0\\341 \\373d\\307\\354\\303\\374e\\374;\\376;\\377\")? DM1 : BL(i,j)? DM1/2 : 0;\n\n// BLUE\nreturn g(\"\\363\\360\\362\\20\\364\\0\\362\\20\\341\\340\\200\\0\\200\\0\\200\\0\\0\\0\\0\\0\\0\\0\\0\\0\\0\\08\\0<\\0\\230\\0\\300\\0\\341\\340\") / 2;", null, "• I love this. Plenty of room to add extra features too... – trichoplax Aug 10 '14 at 23:45\n• Yup, there's plenty of tricks to pull to reduce the size. I might give a go at extending it tomorrow. – FireFly Aug 10 '14 at 23:49\n• This is incredibly short now. Could you fit one of these bit patterns into the texture in your raycasting answer...? – trichoplax Aug 11 '14 at 1:37\n• @MartinBüttner oops, you're correct. I fixed it and made another update to the functions. – FireFly Aug 11 '14 at 8:44\n• Neat, I like how you took the 8x8 pixel art and \"resized\" it on the fly. However, I had to make a few changes and I'm still not getting exactly your image. I changed the 1L and 255L to 1LL and 255LL. Since that made it better, I am assuming you're probably compiling in 64bit mode and there's some bit width issues making the rest of my image come out wrong. But, still, nice job! – DreamWarrior Aug 11 '14 at 17:47\n\n# Action Painting\n\nI wanted to try recreating something similar to the work of Jackson Pollock - dripping and pouring paint over a horizontal canvas. Although I liked the results, the code was much too long to post to this question and my best efforts still only reduced it to about 600 bytes. So the code posted here (which has functions of 139 bytes, 140 bytes, and 140 bytes respectively) was produced with an enormous amount of help from some of the geniuses in chat. Huge thanks to:\n\nfor a relentless group golfing session.\n\nunsigned char RD(int i,int j){\n#define E(q)return i+j?T-((T-BL(i,j))*q):T;\n#define T 255\n#define R .1*(rand()%11)\n#define M(v)(v>0&v<DIM)*int(v)\n#define J [j]*250;\nE(21)}\n\nunsigned char GR(int i,int j){\n#define S DIM][DIM],n=1e3,r,a,s,c,x,y,d=.1,e,f;for(;i+j<1&&n--;x=R*DM1,y=R*DM1,s=R*R*R*R,a=R*7,r=s*T)for(c=R;r>1;x+=s*cos(a),y+=s*sin\nE(21)}\n\nunsigned char BL(int i,int j){static float m[S(a),d=rand()%39?d:-d,a+=d*R,s*=1+R/99,r*=.998)for(e=-r;e++<r;)for(f=-r;f++<r;)m[M(x+e)*(e*e+f*f<r)][M(y+f)]=c;return T-m[i]J}", null, "The E(q) macro is used in the RD and GR functions. Changing the value of the argument changes the way the red and green components of the colours change. The J macro ends with a number which is used to determine how much the blue component changes, which in turn affects the red and green components because they are calculated from it. I've include some images with the red and green arguments of E varied to show the variety of colour combinations possible. Hover over the images for the red and green values if you want to run these yourself.", null, "", null, "", null, "", null, "All of these images can be viewed at full size if you download them. The file size is small as the flat colour suits the PNG compression algorithm, so no lossy compression was required in order to upload to the site.\n\nIf you'd like to see images from various stages in the golfing process as we tried out different things, you can look in the action painting chat.\n\n• I've been following this question and all the answers for a couple weeks now, and I have to say, this is the first one that made my jaw actually drop. HOLY AMAZINGNESS. I mean, all of the answers here are great — but this one is something I never would have expected to be possible. – Todd Lehman Aug 14 '14 at 18:47\n• @ToddLehman thank you! This certainly isn't something I would be capable of alone - I know because I tried... – trichoplax Aug 14 '14 at 19:40\n• AWESOME! One of the best in this question and for me the only one(maybe except winner) which looks like drawed by human :) – cyriel Aug 18 '14 at 19:24\n• @cyriel thanks very much. You could say this one was drawn by 5 humans... – trichoplax Aug 18 '14 at 21:31\n\nFigured I'd play with this code's parameters... All credit goes to @Manuel Kasten. These are just so cool that I couldn't resist posting.", null, "/* RED */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+(j)*9e-9-.645411;a=c-d+(i)*9e-9+.356888;}\nreturn 1000*pow((n)/800,.5);\n/* GREEN */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+(j)*9e-9-.645411;a=c-d+(i)*9e-9+.356888;}\nreturn 8000*pow((n)/800,.5);\n/* BLUE */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+(j)*9e-9-.645411;a=c-d+(i)*9e-9+.356888;}\nreturn 8000*pow((n)/800,.5);\n\n\nBubbleGumRupture http://i57.tinypic.com/3150eqa.png\n\n/* RED */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+(j)*9e-9-.645411;a=c-d+(i)*9e-9+.356888;}\nreturn 8000*pow((n)/800,.5);\n/* GREEN */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+(j)*9e-9-.645411;a=c-d+(i)*9e-9+.356888;}\nreturn 40*pow((n)/800,.5);\n/* BLUE */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+(j)*9e-9-.645411;a=c-d+(i)*9e-9+.356888;}\nreturn 10*pow((n)/800,.5);\n\n\nSeussZoom http://i59.tinypic.com/am3ypi.png\n\n/* RED */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+j*8e-8-.645411;a=c-d+i*8e-8+.356888;}\nreturn 2000*pow((n)/800,.5);\n/* GREEN */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+j*8e-8-.645411;a=c-d+i*8e-8+.356888;}\nreturn 1000*pow((n)/800,.5);\n/* BLUE */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+j*8e-8-.645411;a=c-d+i*8e-8+.356888;}\nreturn 4000*pow((n)/800,.5);\n\n\nSeussEternalForest http://i61.tinypic.com/35akv91.png\n\n/* RED */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+j*8e-9-.645411;a=c-d+i*8e-9+.356888;}\nreturn 2000*pow((n)/800,.5);\n/* GREEN */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+j*8e-9-.645411;a=c-d+i*8e-9+.356888;}\nreturn 1000*pow((n)/800,.5);\n/* BLUE */\ndouble a=0,b=0,c,d,n=0;\nwhile((c=a*a)+(d=b*b)<4&&n++<880){b=2*a*b+j*8e-9-.645411;a=c-d+i*8e-9+.356888;}\nreturn 4000*pow((n)/800,.5);\n\n• Looks like Dr. Seuss to me. Very cool! – DLosc Aug 7 '14 at 21:36\n• Haha, I actually named the bottom two files Seuss1 and Sueuss2 – Kyle McCormick Aug 8 '14 at 13:17\n\nEdit: This is now a valid answer, thanks to the forward declarations of GR and BL.\n\nHaving fun with Hofstadter's Q-sequence! If we're using the radial distance from some point as the input and the output as the inverse colour, we get something which looks like coloured vinyl.", null, "The sequence is very similar to the Fibonacci sequence, but instead of going 1 and 2 steps back in the sequence, you take the two previous values to determine how far to go back before taking the sum. It grows roughly linear, but every now and then there's a burst of chaos (at increasing intervals) which then settles down to an almost linear sequence again before the next burst:", null, "You can see these ripples in the image after regions which look very \"flat\" in colour.\n\nOf course, using only one colour is boring.", null, "Now for the code. I need the recursive function to compute the sequence. To do that I use RD whenever j is negative. Unfortunately, that does not leave enough characters to compute the red channel value itself, so RD in turn calls GR with an offset to produce the red channel.\n\nunsigned short RD(int i,int j){\nstatic int h;return j<0?h[i]?h[i]:h[i]=i<2?1:RD(i-RD(i-1,j),j)+RD(i-RD(i-2,j),j):GR(i+256,j+512);\n}\nunsigned short GR(int i,int j){\nreturn DIM-4*RD(sqrt((i-512)*(i-512)+(j-768)*(j-768))/2.9,-1);\n}\nunsigned short BL(int i,int j){\nreturn DIM-4*RD(sqrt((i-768)*(i-768)+(j-256)*(j-256))/2.9,-1);\n}\n\n\nOf course, this is pretty much the simplest possible usage of the sequence, and there are loads of characters left. Feel free to borrow it and do other crazy things with it!\n\nHere is another version where the boundary and the colours are determined by the Q-sequence. In this case, there was enough room in RD so that I didn't even need the forward declaration:\n\nunsigned short RD(int i,int j){\nstatic int h;return j<0?h[i]?h[i]:h[i]=i<2?1:RD(i-RD(i-1,j),j)+RD(i-RD(i-2,j),j):RD(2*RD(i,-1)-i+512>1023-j?i:1023-i,-1)/0.6;\n}\nunsigned short GR(int i,int j){\nreturn RD(i, j);\n}\nunsigned short BL(int i,int j){\nreturn RD(i, j);\n}", null, "• That second grey-ish image is stunning! – tomsmeding Aug 2 '14 at 14:19\n• Can you compactify this enough to use the r/g/b functions themselves recursively, with invalid coordinates for the recursive calls? – Sparr Aug 2 '14 at 23:00\n• I loved the multi-colour image. Nice answer! – Alex Aug 8 '14 at 18:59\n\nThis calculates the Joukowsky transform of a set of concentric circles centred on a point slightly offset from the origin. I slightly modified the intensities in the blue channel to give a bit of colour variation.\n\nunsigned short RD(int i,int j){\ndouble r=i/256.-2,s=j/256.-2,q=r*r+s*s,n=hypot(r+(.866-r/2)/q,s+(r*.866+s/2)/q),\nd=.5/log(n);if(d<0||d>1)d=1;return d*(sin(n*10)*511+512);\n}\nunsigned short GR(int i,int j){\nreturn 0;\n}\nunsigned short BL(int i,int j){\ndouble r=i/256.-2,s=j/256.-2,q=r*r+s*s;return RD(i,j)*sqrt(q/40);\n}", null, "# Objective-C\n\nRewrote the C++ code in Objective-C cos I couldn't get it to compile... It gave the same results as other answer when running on my iPad, so that's all good.\n\nHere's my submission:", null, "The code behind it is fairly simple:\n\nunsigned short red_fn(int i,int j)\n{\nreturn j^j-i^i;\n}\nunsigned short green_fn(int i,int j)\n{\nreturn (i-DIM)^2+(j-DIM)^2;\n}\nunsigned short blue_fn(int i,int j)\n{\nreturn i^i-j^j;\n}\n\n\nYou can zoom in on squares by multiplying i and j by 0.5, 0.25 etc. before they are processed.\n\n• Are you sure that is the same code you used? The ^ look kind of odd, because (i^i) is always 0 (the XOR), and the ^2 looks more like a square than a XOR bit. – Manuel Ferreria Aug 28 '14 at 18:01\n• @ManuelFerreria With the XOR, the code is actually compiled like this: x^(x-y)^y (this threw me off the first time too). If you have iOS capabilities, here's my code: gist.github.com/Jugale/28df46f87037d81d2a8f – Max Chuquimia Aug 29 '14 at 23:37\n\n# Sierpinski Paint Splash\n\nI wanted to play more with colors so I kept changing my other answer (the swirly one) and eventually ended up with this.", null, "unsigned short RD(int i,int j){\nreturn(sqrt(_sq(abs(73.-i))+_sq(abs(609.-j)))+1.)/abs(sin((sqrt(_sq(abs(860.-i))+_sq(abs(162.-j))))/115.)+2)/(115^i&j);\n}\nunsigned short GR(int i,int j){\nreturn(sqrt(_sq(abs(160.-i))+_sq(abs(60.-j)))+1.)/abs(sin((sqrt(_sq(abs(73.-i))+_sq(abs(609.-j))))/115.)+2)/(115^i&j);\n}\nunsigned short BL(int i,int j){\nreturn(sqrt(_sq(abs(600.-i))+_sq(abs(259.-j)))+1.)/abs(sin((sqrt(_sq(abs(250.-i))+_sq(abs(20.-j))))/115.)+2)/(115^i&j);\n}\n\n\nIt's my avatar now. :P\n\n• Good job. sir, good job. – EaterOfCode Aug 6 '14 at 9:50\n\nI feel compelled to submit this entry that I will call \"undefined behavior\", which will illustrate what your compiler does with functions that are supposed to return a value but don't:\n\nunsigned short red_fn(int i,int j){}\nunsigned short green_fn(int i,int j){}\nunsigned short blue_fn(int i,int j){}\n\n\nAll black pixels:", null, "Pseudo-random pixels:", null, "And, of course, a host of other possible results depending on your compiler, computer, memory manager, etc.\n\n• Which did you get? – tomsmeding Aug 2 '14 at 21:21\n• I got solid black and solid color that changed between different runs of the program, with different compilers. – Sparr Aug 2 '14 at 21:39\n• My compiler just errors and yells at me for not returning a value. – Pharap Aug 6 '14 at 16:04\n• @Pharap that's not a bad thing :) – Sparr Aug 9 '14 at 22:19\n• I doubt you would ever get such nice randomness as your second picture suggests. A constant value, the index of the loop etc. are much more likely (whatever is stored inside EAX when the function is called). – example Aug 11 '14 at 12:55\n\n# groovy", null, "Just some trigonometry and weird macro tricks.\n\nRD:\n\n#define I (i-512)\n#define J (j-512)\n#define A (sin((i+j)/64.)*cos((i-j)/64.))\nreturn atan2(I*cos A-J*sin A,I*sin A+J*cos A)/M_PI*1024+1024;\n\n\nGR:\n\n#undef A\n#define A (M_PI/3+sin((i+j)/64.)*cos((i-j)/64.))\nreturn atan2(I*cos A-J*sin A,I*sin A+J*cos A)/M_PI*1024+1024;\n\n\nBL:\n\n#undef A\n#define A (2*M_PI/3+sin((i+j)/64.)*cos((i-j)/64.))\nreturn atan2(I*cos A-J*sin A,I*sin A+J*cos A)/M_PI*1024+1024;\n\n\nEDIT: if M_PI isn't allowed due to only being present on POSIX-compatible systems, it can be replaced with the literal 3.14.\n\n• I you've got spare characters, acos(-1) is a good replacement for M_PI. – Martin Ender Aug 6 '14 at 12:23\n\nI'm not good at math. I was always poor student at math class. So I made simple one.", null, "I used modified user1455003's Javascript code. And this is my full code.\n\nfunction red(x, y) {\nreturn (x + y) & y;\n}\n\nfunction green(x, y) {\nreturn (255 + x - y) & x;\n}\n\nfunction blue(x, y) {\n// looks like blue channel is useless\nreturn Math.pow(x, y) & y;\n}\n\n\nIt's very short so all three functions fits in one tweet.", null, "function red(x, y) {\nreturn Math.cos(x & y) << 16;\n}\n\nfunction green(x, y) {\nreturn red(DIM - x, DIM - y);\n}\n\nfunction blue(x, y) {\nreturn Math.tan(x ^ y) << 8;\n}\n\n\nAnother very short functions. I found this sierpinski pattern (and some tangent pattern) while messing around with various math functions. This is full code\n\n• Just i&j renders the Sierpinski triangle actually. Which is awesome. – cjfaure Aug 7 '14 at 20:00\n• That last one is profile picture-worthy. – mbomb007 Jul 28 '15 at 20:49\n\n# JavaScript\n\nvar can = document.createElement('canvas');\ncan.width=1024;\ncan.height=1024;\ncan.style.position='fixed';\ncan.style.left='0px';\ncan.style.top='0px';\ncan.onclick=function(){\ndocument.body.removeChild(can);\n};\n\ndocument.body.appendChild(can);\n\nvar ctx = can.getContext('2d');\nvar imageData = ctx.getImageData(0,0,1024,1024);\nvar data = imageData.data;\nvar x = 0, y = 0;\nfor (var i = 0, len = data.length; i < len;) {\ndata[i++] = red(x, y) >> 2;\ndata[i++] = green(x, y) >> 2;\ndata[i++] = blue(x, y) >> 2;\ndata[i++] = 255;\nif (++x === 1024) x=0, y++;\n}\nctx.putImageData(imageData,0,0);\n\nfunction red(x,y){\nif(x>600||y>560) return 1024\nx+=35,y+=41\nreturn y%124<20&&x%108<20?1024:(y+62)%124<20&&(x+54)%108<20?1024:0\n}\n\nfunction green(x,y){\nif(x>600||y>560) return y%160<80?0:1024\nx+=35,y+=41\nreturn y%124<20&&x%108<20?1024:(y+62)%124<20&&(x+54)%108<20?1024:0\n}\n\nfunction blue(x,y) {\nreturn ((x>600||y>560)&&y%160<80)?0:1024;\n}", null, "Another version. function bodies are tweetable.\n\nfunction red(x,y){\nc=x*y%1024\nif(x>600||y>560) return c\nx+=35,y+=41\nreturn y%124<20&&x%108<20?c:(y+62)%124<20&&(x+54)%108<20?c:0\n}\n\nfunction green(x,y){\nc=x*y%1024\nif(x>600||y>560) return y%160<80?0:c\nx+=35,y+=41\nreturn y%124<20&&x%108<20?c:(y+62)%124<20&&(x+54)%108<20?c:0\n}\n\nfunction blue(x,y) {\nreturn ((x>600||y>560)&&y%160<80)?0:x*y%1024;\n}", null, "Revised image render function. draw(rgbFunctions, setCloseEvent);\n\nfunction draw(F,e){\nvar D=document\nvar c,id,d,x,y,i,L,s=1024,b=D.getElementsByTagName('body')\nc=D.createElement('canvas').getContext('2d')\nif(e)c.canvas.onclick=function(){b.removeChild(c.canvas)}\nb.appendChild(c.canvas)\nc.canvas.width=c.canvas.height=s\nG=c.getImageData(0,0,s,s)\nd=G.data\nx=y=i=0;\nfor (L=d.length;i<L;){\nd[i++]=F.r(x,y)>>2\nd[i++]=F.g(x,y)>>2\nd[i++]=F.b(x,y)>>2\nd[i++]=255;\nif(++x===s)x=0,y++\n}\nc.putImageData(G,0,0)\n}\n\n\n# Purple\n\nvar purple = {\nr: function(i,j) {\nif (j < 512) j=1024-j\nreturn (i % j) | i\n},\ng: function(i,j){\nif (j < 512) j = 1024 -j\nreturn (1024-i ^ (i %j)) % j\n},\nb: function(i,j){\nif (j < 512) j = 1024 -j\nreturn 1024-i | i+j %512\n}\n};\n\ndraw(purple,true);", null, "• CHEATER! CHEATER! ;D (it's a valid answer, just too clever :P Nice one!) – tomsmeding Aug 2 '14 at 9:17\n• Hahah.. yeah I know so I submitted one that's more in the spirit of the question. I actually tried to make patterns and thought I wonder if could actually draw something. – wolfhammer Aug 2 '14 at 9:34\n• You can make the shallow colour images look a slightly richer by adding some film-grain noise in the lower-bits by applying a \"| Math.random() * 256\" to the end of them. Makes the darker shades more randomly perturbed without altering highlights. ( and increase the number depending on the darkness threshold ) – Kent Fredric Jan 30 '15 at 8:20\n• rgb randomness @ [10,728,728] i.imgur.com/ms4Cuzo.png – Kent Fredric Jan 30 '15 at 8:29\n\n# Planetary Painter\n\n//red\nstatic int r[DIM];int p=rand()%9-4;r[i]=i&r[i]?(r[i]+r[i-1])/2:i?r[i-1]:512;r[i]+=r[i]+p>0?p:0;return r[i]?r[i]<DIM?r[i]:DM1:0;\n//green\nstatic int r[DIM];int p=rand()%7-3;r[i]=i&r[i]?(r[i]+r[i-1])/2:i?r[i-1]:512;r[i]+=r[i]+p>0?p:0;return r[i]?r[i]<DIM?r[i]:DM1:0;\n//blue\nstatic int r[DIM];int p=rand()%15-7;r[i]=i&r[i]?(r[i]+r[i-1])/2:i?r[i-1]:512;r[i]+=r[i]+p>0?p:0;return r[i]?r[i]<DIM?r[i]:DM1:0;\n\n\nInspired by Martin's obviously awesome entry, this is a different take on it. Instead of randomly seeding a portion of the pixels, I start with the top left corner as RGB(512,512,512), and take random walks on each color from there. The result looks like something from a telescope (imo).\n\nEach pixel takes the average of the pixels above/left of it and adds a bit o' random. You can play with the variability by changing the p variable, but I think what I'm using is a good balance (mainly because I like blue, so more blur volatility gives good results).\n\nThere's a slight negative bias from integer division when averaging. I think it works out, though, and give a nice darkening effect to the bottom corner.\n\nOf course, to get more than just a single result, you'll need to add an srand() line to your main function.", null, "• If the image were a bit larger, it'd look like rays of light. o: – cjfaure Aug 7 '14 at 8:02\n• @cjfaure if you view the image full size (download/right click and view image/whatever works on your system) then it looks even more beautiful with the extra detail. – trichoplax Feb 10 '15 at 12:46\n• make it a circle surrounded by black, and that'll make it a planet! – Khaled.K Dec 27 '15 at 9:39\n• I attempted to wrap this around a sphere in blender, and I rendered an animation. See ti here: gfycat.com/SameAnotherDinosaur – starbeamrainbowlabs Jan 6 '16 at 18:40\n\n# Reflected waves\n\nunsigned char RD(int i,int j){\n#define A static double w=8000,l,k,r,d,p,q,a,b,x,y;x=i;y=j;for(a=8;a+9;a--){for(b=8;b+9;b--){l=i-a*DIM-(int(a)%2?227:796);\nreturn 0;}\n\nunsigned char GR(int i,int j){\n#define B k=j-b*DIM-(int(b)%2?417:606);r=sqrt(l*l+k*k);d=16*cos((r-w)/7)*exp(-_sq(r-w)/120);p=d*l/r;q=d*k/r;x-=p;y-=q;}}\nreturn 0;}\n\nunsigned char BL(int i,int j){AB\nreturn (int(x/64)+int(y/64))%2*255;}\n\n\nA basic chess board pattern distorted according to the position of a wave expanding from a point like a stone dropped in a pond (very far from physically accurate!). The variable w is the number of pixels from that point that the wave has moved. If w is large enough, the wave reflects from the sides of the image.\n\n## w = 225", null, "## w = 360", null, "## w = 5390", null, "Here is a GIF showing a succession of images as the wave expands. I've provided a number of different sizes, each showing as many frames as the 500KB file size limit will allow.", null, "", null, "", null, "If I can find a way to fit it in, I'd ideally like to have wave interference modelled so that the waves look more realistic when they cross. I'm pleased with the reflection though.\n\nNote that I haven't really modelled wave reflection in 3 lots of 140 bytes. There's not really any reflection going on, it just happens to look like it. I've hidden the explanation in case anyone wants to guess first:\n\nThe first reflected wave is identical to a wave originating from the other side of the image edge, the same distance away as the original point. So the code calculates the correct position for the 4 points required to give the effect of reflection from each of the 4 edges. Further levels of reflected wave are all identical to a wave originating in a further away tile, if you imagine the image as one tile in a plane. The code gives the illusion of 8 levels of reflection by displaying 189 separate expanding circles, each placed in the correct point in a 17 by 17 grid, so that they pass through the central square of the grid (that is, the image square) at just the right times to give the impression of the required current level of reflection. This is simple (and short!) to code, but runs quite slowly...\n\n• Love the GIFs and the explanation. – DLosc Aug 7 '14 at 21:45\n• Neato! But man, entries like these make me think I need a faster computer (or more patience, lol). Either your computer is a lot faster, or I don't want to think how long it took you to generate all those GIF frames. – DreamWarrior Aug 7 '14 at 22:34\n• @DreamWarrior It's not me that's patient. It's my laptop that doesn't mind running overnight while I sleep... – trichoplax Aug 7 '14 at 22:38\n• I see Pacman in the second image. – A.L Aug 11 '14 at 1:29" ]

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[ "FutureStarr\n\n5 Calculator\n\n## 5 Calculator", null, "## 5 Calculator\n\nOn a computer, a calculator is a program that assists in performing mathematical operations. Calculators are often capable of performing other functions, such as calculations involving square roots and exponents. In modern calculators, most features are adjusted using knobs, switches and buttons. In some relatively simple calculators, mechanical parts were used (the most common type being stepping motors)\n\n### Calculator\n\nvia GIPHY\n\nThis percentage calculator is a tool that lets you do a simple calculation: what percent of X is Y? The tool is pretty straightforward. All you need to do is fill in two fields, and the third one will be calculated for you automatically. This method will allow you to answer the question of how to find a percentage of two numbers. Furthermore, our percentage calculator also allows you to perform calculations in the opposite way, i.e., how to find a percentage of a number. Try entering various values into the different fields and see how quick and easy-to-use this handy tool is. Is only knowing how to get a percentage of a number is not enough for you? If you are looking for more extensive calculations, hit the advanced mode button under the calculator.\n\nPercentage is one of many ways to express a dimensionless relation of two numbers (the other methods being ratios, described in our ratio calculator, and fractions). Percentages are very popular since they can describe situations that involve large numbers (e.g., estimating chances for winning the lottery), average (e.g., determining final grade of your course) as well as very small ones (like volumetric proportion of NOâ‚‚ in the air, also frequently expressed by PPM - parts per million). (Source: www.omnicalculator.com)\n\n### Use\n\nThis percentage calculator is a tool that lets you do a simple calculation: what percent of X is Y? The tool is pretty straightforward. All you need to do is fill in two fields, and the third one will be calculated for you automatically. This method will allow you to answer the question of how to find a percentage of two numbers. Furthermore, our percentage calculator also allows you to perform calculations in the opposite way, i.e., how to find a percentage of a number. Try entering various values into the different fields and see how quick and easy-to-use this handy tool is. Is only knowing how to get a percentage of a number is not enough for you? If you are looking for more extensive calculations, hit the advanced mode button under the calculator.\n\nThis is all nice, but we usually do not use percents just by themselves. Mostly, we want to answer how big is one number in relation to another number?. To try to visualize it, imagine that we have something everyone likes, for example, a large packet of cookies (or donuts or chocolates, whatever you prefer 😉 - we will stick to cookies). Let's try to find an answer to the question of what is 40% of 20? It is 40 hundredths of 20, so if we divided 20 cookies into 100 even parts (good luck with that!), 40 of those parts would be 40% of 20 cookies. Let's do the math: (Source: www.omnicalculator.com)\n\n### Want\n\nPercentage increase is useful when you want to analyse how a value has changed with time. Although percentage increase is very similar to absolute increase, the former is more useful when comparing multiple data sets. For example, a change from 1 to 51 and from 50 to 100 both have an absolute change of 50, but the percentage increase for the first is 5000%, while for the second it is 100%, so the first change grew a lot more. This is why percentage increase is the most common way of measuring growth.\n\nPercentage increase is useful when you want to analyse how a value has changed with time. Although percentage increase is very similar to absolute increase, the former is more useful when comparing multiple data sets. For example, a change from 1 to 51 and from 50 to 100 both have an absolute change of 50, but the percentage increase for the first is 5000%, while for the second it is 100%, so the first change grew a lot more. This is why percentage increase is the most common way of measuring growth. (Source: www.omnicalculator.com)\n\n### Time\n\nPercentage increase is useful when you want to analyse how a value has changed with time. Although percentage increase is very similar to absolute increase, the former is more useful when comparing multiple data sets. For example, a change from 1 to 51 and from 50 to 100 both have an absolute change of 50, but the percentage increase for the first is 5000%, while for the second it is 100%, so the first change grew a lot more. This is why percentage increase is the most common way of measuring growth.\n\nThe tube technology of the ANITA was superseded in June 1963 by the U.S. manufactured Friden EC-130, which had an all-transistor design, a stack of four 13-digit numbers displayed on a 5-inch (13 cm) cathode ray tube (CRT), and introduced Reverse Polish Notation (RPN) to the calculator market for a price of \\$2200, which was about three times the cost of an electromechanical calculator of the time. Like Bell Punch, Friden was a manufacturer of mechanical calculators that had decided that the future lay in electronics. In 1964 more all-transistor electronic calculators were introduced: Sharp introduced the CS-10A, which weighed 25 kilograms (55 lb) and cost 500,000 yen (\\$4586.75), and Industria Macchine Elettroniche of Italy introduced the IME 84, to which several extra keyboard and display units could be connected so that several people could make use of it (but apparently not at the same time). The Victor 3900 was the first to use integrated circuits in place of individual transistors, but production problems delayed sales until 1966. (Source: en.wikipedia.org)\n\n## Related Articles\n\n•", null, "#### Simplify Sqrt 32", null, "May 29, 2022     |     Faisal Arman\n•", null, "#### A 4 Calculator", null, "May 29, 2022     |     Muhammad Umair\n•", null, "#### 3 Fraction Calculator Online", null, "May 29, 2022     |     Muhammad Umair\n•", null, "#### 17 18 As a Percent", null, "May 29, 2022     |     sheraz naseer\n•", null, "#### 27 Out of 30 As a Percentage:", null, "May 29, 2022     |     Abid Ali\n•", null, "#### What Percent Is 33 Out of 40", null, "May 29, 2022     |     Muhammad Waseem\n•", null, "#### Fraction Form Calculator OR", null, "May 29, 2022     |     Jamshaid Aslam\n•", null, "#### 2 Out of 9 Percentage", null, "May 29, 2022     |     Muhammad Waseem\n•", null, "#### A Pv of Bond", null, "May 29, 2022     |     Muhammad Waseem\n•", null, "#### A Cacu Loan Calculator", null, "May 29, 2022     |     Shaveez Haider\n•", null, "#### A 5kg to Lbs", null, "May 29, 2022     |     Amir jameel\n•", null, "#### 4 Out of 14 Percentage", null, "May 29, 2022     |     hammad hussain\n•", null, "", null, "May 29, 2022     |     Muhammad Umair\n•", null, "#### 626 Area Code:", null, "May 29, 2022     |     mohammad umair\n•", null, "#### A Catolator", null, "May 29, 2022     |     Abid Ali" ]

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[ "# Problem: Calculate ΔHrxn for the following reaction2 NOCl (g) → N2 (g) + 02 (g) + Cl2 (g)Given the following set of reactions;1/2 N2 (g) + 1/2 02 (g) → NO (g)          ΔH= 90.3KJNO (g) + 1/2 Cl2 (g) → NOCl (g)          ΔH = - 38.6 KJ\n\n###### FREE Expert Solution\n\nWe’re being asked to determine the enthalpy change (ΔHrxn) for the chemical reaction:\n\n2 NOCl (g) → N(g) + O(g) + Cl(g)\n\nWe can use Hess’s Law to determine the enthalpy change of the overall reaction from the given reactions:\n\n1. 1/2 N(g) + 1/2 O(g) → NO (g)          ΔH = 90.3KJ\n2. NO (g) + 1/2 Cl(g) → NOCl (g)           ΔH = - 38.6 KJ\n\nWe now need to find a combination of reactions that when added up, gives us the overall reaction.\n\n98% (100 ratings)", null, "###### Problem Details\n\nCalculate ΔHrxn for the following reaction\n\n2 NOCl (g) → N(g) + 0(g) + Cl(g)\n\nGiven the following set of reactions;\n\n1/2 N(g) + 1/2 0(g) → NO (g)          ΔH= 90.3KJ\nNO (g) + 1/2 Cl(g) → NOCl (g)          ΔH = - 38.6 KJ\n\nFrequently Asked Questions\n\nWhat scientific concept do you need to know in order to solve this problem?\n\nOur tutors have indicated that to solve this problem you will need to apply the Hess's Law concept. You can view video lessons to learn Hess's Law. Or if you need more Hess's Law practice, you can also practice Hess's Law practice problems.\n\nWhat professor is this problem relevant for?\n\nBased on our data, we think this problem is relevant for Professor Waddell's class at UC." ]

[ null, "https://cdn.clutchprep.com/assets/button-view-text-solution.png", null ]

[ "University Physics Volume 1\n\n# G | The Greek Alphabet\n\nUniversity Physics Volume 1G | The Greek Alphabet\nName Capital Lowercase Name Capital Lowercase\nAlpha A $αα$ Nu N $νν$\nBeta B $ββ$ Xi $ΞΞ$ $ξξ$\nGamma $ΓΓ$ $γγ$ Omicron O $οο$\nDelta $ΔΔ$ $δδ$ Pi $ΠΠ$ $ππ$\nEpsilon E $εε$ Rho P $ρρ$\nZeta Z $ζζ$ Sigma $ΣΣ$ $σσ$\nEta H $ηη$ Tau T $ττ$\nTheta $ΘΘ$ $θθ$ Upsilon $ϒϒ$ $υυ$\nlota I $ιι$ Phi $ΦΦ$ $ϕϕ$\nKappa K $κκ$ Chi X $χχ$\nLambda $ΛΛ$ $λλ$ Psi $ψψ$ $ψψ$\nMu M $μμ$ Omega $ΩΩ$ $ωω$\nTable G1 The Greek Alphabet" ]

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[ "# Indexing and Selecting Data with Pandas\n\nIndexing in Pandas :\nIndexing in pandas means simply selecting particular rows and columns of data from a DataFrame. Indexing could mean selecting all the rows and some of the columns, some of the rows and all of the columns, or some of each of the rows and columns. Indexing can also be known as Subset Selection.", null, "Let’s see some example of indexing in Pandas. In this article, we are using “`nba.csv`” file to download the CSV, click here.\n\n#### Selecting some rows and some columns\n\nLet’s take a DataFrame with some fake data, now we perform indexing on this DataFrame. In this, we are selecting some rows and some columns from a DataFrame. Dataframe with dataset.", null, "Suppose we want to select columns `Age`, `College` and `Salary` for only rows with a labels `Amir Johnson` and `Terry Rozier`", null, "Our final DataFrame would look like this:", null, "#### Selecting some rows and all columns\n\nLet’s say we want to select row `Amir Jhonson`, `Terry Rozier` and `John Holland` with all columns in a dataframe.", null, "Our final DataFrame would look like this:", null, "#### Selecting some columns and all rows\n\nLet’s say we want to select columns Age, Height and Salary with all rows in a dataframe.", null, "Our final DataFrame would look like this:", null, "### Pandas Indexing using `[ ]`, ` .loc[]`, `.iloc[ ]`, `.ix[ ]`\n\nThere are a lot of ways to pull the elements, rows, and columns from a DataFrame. There are some indexing method in Pandas which help in getting an element from a DataFrame. These indexing methods appear very similar but behave very differently. Pandas support four types of Multi-axes indexing they are:\n\n• Dataframe.[ ] ; This function also known as indexing operator\n• Dataframe.loc[ ] : This function is used for labels.\n• Dataframe.iloc[ ] : This function is used for positions or integer based\n• Dataframe.ix[] : This function is used for both label and integer based\n\nCollectively, they are called the indexers. These are by far the most common ways to index data. These are four function which help in getting the elements, rows, and columns from a DataFrame.\n\nIndexing a Dataframe using indexing operator `[]` :\nIndexing operator is used to refer to the square brackets following an object. The `.loc` and `.iloc` indexers also use the indexing operator to make selections. In this indexing operator to refer to df[].\n\n#### Selecting a single columns\n\nIn order to select a single column, we simply put the name of the column in-between the brackets\n\n `# importing pandas package ` `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  `# retrieving columns by indexing operator ` `first ``=` `data[``\"Age\"``] ` ` `  ` `  ` `  `print``(first) `\n\nOutput:", null, "#### Selecting multiple columns\n\nIn order to select multiple columns, we have to pass a list of columns in an indexing operator.\n\n `# importing pandas package ` `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  `# retrieving multiple columns by indexing operator ` `first ``=` `data[[``\"Age\"``, ``\"College\"``, ``\"Salary\"``]] ` ` `  ` `  ` `  `first `\n\nOutput:", null, "Indexing a DataFrame using `.loc[ ]` :\nThis function selects data by the label of the rows and columns. The `df.loc` indexer selects data in a different way than just the indexing operator. It can select subsets of rows or columns. It can also simultaneously select subsets of rows and columns.\n\n#### Selecting a single row\n\nIn order to select a single row using `.loc[]`, we put a single row label in a `.loc` function.\n\n `# importing pandas package ` `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  `# retrieving row by loc method ` `first ``=` `data.loc[``\"Avery Bradley\"``] ` `second ``=` `data.loc[``\"R.J. Hunter\"``] ` ` `  ` `  `print``(first, ``\"\\n\\n\\n\"``, second) `\n\nOutput:\nAs shown in the output image, two series were returned since there was only one parameter both of the times.", null, "#### Selecting multiple rows\n\nIn order to select multiple rows, we put all the row labels in a list and pass that to `.loc` function.\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  `# retrieving multiple rows by loc method ` `first ``=` `data.loc[[``\"Avery Bradley\"``, ``\"R.J. Hunter\"``]] ` ` `  ` `  ` `  `print``(first) `\n\nOutput:", null, "#### Selecting two rows and three columns\n\nIn order to select two rows and three columns, we select a two rows which we want to select and three columns and put it in a separate list like this:\n\n```Dataframe.loc[[\"row1\", \"row2\"], [\"column1\", \"column2\", \"column3\"]]\n```\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  `# retrieving two rows and three columns by loc method ` `first ``=` `data.loc[[``\"Avery Bradley\"``, ``\"R.J. Hunter\"``], ` `                   ``[``\"Team\"``, ``\"Number\"``, ``\"Position\"``]] ` ` `  ` `  ` `  `print``(first) `\n\nOutput:", null, "#### Selecting all of the rows and some columns\n\nIn order to select all of the rows and some columns, we use single colon [:] to select all of rows and list of some columns which we want to select like this:\n\n```Dataframe.loc[:, [\"column1\", \"column2\", \"column3\"]]\n```\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  `# retrieving all rows and some columns by loc method ` `first ``=` `data.loc[:, [``\"Team\"``, ``\"Number\"``, ``\"Position\"``]] ` ` `  ` `  ` `  `print``(first) `\n\nOutput:", null, "Indexing a DataFrame using `.iloc[ ]` :\nThis function allows us to retrieve rows and columns by position. In order to do that, we’ll need to specify the positions of the rows that we want, and the positions of the columns that we want as well. The `df.iloc `indexer is very similar to `df.loc `but only uses integer locations to make its selections.\n\n#### Selecting a single row\n\nIn order to select a single row using `.iloc[]`, we can pass a single integer to `.iloc[]` function.\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  ` `  `# retrieving rows by iloc method  ` `row2 ``=` `data.iloc[``3``]  ` ` `  ` `  ` `  `print``(row2) `\n\nOutput:", null, "#### Selecting multiple rows\n\nIn order to select multiple rows, we can pass a list of integer to `.iloc[]` function.\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  ` `  `# retrieving multiple rows by iloc method  ` `row2 ``=` `data.iloc [[``3``, ``5``, ``7``]] ` ` `  ` `  ` `  `row2 `\n\nOutput:", null, "#### Selecting two rows and two columns\n\nIn order to select two rows and two columns, we create a list of 2 integer for rows and list of 2 integer for columns then pass to a `.iloc[]` function.\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  ` `  `# retrieving two rows and two columns by iloc method  ` `row2 ``=` `data.iloc [[``3``, ``4``], [``1``, ``2``]] ` ` `  ` `  ` `  `print``(row2) `\n\nOutput:", null, "#### Selecting all the rows and a some columns\n\nIn order to select all rows and some columns, we use single colon [:] to select all of rows and for columns we make a list of integer then pass to a `.iloc[]` function.\n\n `import` `pandas as pd ` ` `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` ` `  ` `  `# retrieving all rows and some columns by iloc method  ` `row2 ``=` `data.iloc [:, [``1``, ``2``]] ` ` `  ` `  ` `  `print``(row2) `\n\nOutput:", null, "Indexing a using Dataframe.ix[ ] :\nEarly in the development of pandas, there existed another indexer, `ix`. This indexer was capable of selecting both by label and by integer location. While it was versatile, it caused lots of confusion because it’s not explicit. Sometimes integers can also be labels for rows or columns. Thus there were instances where it was ambiguous. Generally, `ix` is label based and acts just as the .loc indexer. However, `.ix` also supports integer type selections (as in .iloc) where passed an integer. This only works where the index of the DataFrame is not integer based `.ix` will accept any of the inputs of `.loc` and `.iloc`.\nNote: The .ix indexer has been deprecated in recent versions of Pandas.\n\n#### Selecting a single row using `.ix[]` as `.loc[]`\n\nIn order to select a single row, we put a single row label in a `.ix` function. This function act similar as .loc[] if we pass a row label as a argument of a function.\n\n `# importing pandas package ` `import` `pandas as pd ` `  `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` `  `  `# retrieving row by ix method ` `first ``=` `data.ix[``\"Avery Bradley\"``] ` ` `  `  `  `  `  `print``(first) ` ` `\n\nOutput:", null, "#### Selecting a single row using `.ix[]` as `.iloc[]`\n\nIn order to select a single row, we can pass a single integer to `.ix[]` function. This function similar as a iloc[] function if we pass an integer in a `.ix[]` function.\n\n `# importing pandas package ` `import` `pandas as pd ` `  `  `# making data frame from csv file ` `data ``=` `pd.read_csv(``\"nba.csv\"``, index_col ``=``\"Name\"``) ` `  `  `# retrieving row by ix method ` `first ``=` `data.ix[``1``] ` ` `  `  `  `  `  `print``(first) `\n\nOutput:", null, "#### Methods for indexing in DataFrame\n\nFunction Description\nDataframe.head() Return top` n` rows of a data frame.\nDataframe.tail() Return bottom `n` rows of a data frame.\nDataframe.at[] Access a single value for a row/column label pair.\nDataframe.iat[] Access a single value for a row/column pair by integer position.\nDataframe.tail() Purely integer-location based indexing for selection by position.\nDataFrame.lookup() Label-based “fancy indexing” function for DataFrame.\nDataFrame.pop() Return item and drop from frame.\nDataFrame.xs() Returns a cross-section (row(s) or column(s)) from the DataFrame.\nDataFrame.get() Get item from object for given key (DataFrame column, Panel slice, etc.).\nDataFrame.isin() Return boolean DataFrame showing whether each element in the DataFrame is contained in values.\nDataFrame.where() Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.\nDataFrame.mask() Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.\nDataFrame.query() Query the columns of a frame with a boolean expression.\nDataFrame.insert() Insert column into DataFrame at specified location.\n\nWhether you're preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, GeeksforGeeks Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we've already empowered, and we're here to do the same for you. Don't miss out - check it out now!" ]

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[ "Search a number\nBaseRepresentation\nbin11000101000100100011011\n3110011002012101\n4120220210123\n53123121002\n6350224231\n7105613621\noct30504433\n913132171\n106457627\n113710790\n1221b5077\n1314513a7\n14c01511\n15878587\nhex62891b\n\n6457627 has 4 divisors (see below), whose sum is σ = 7044696. Its totient is φ = 5870560.\n\nThe previous prime is 6457621. The next prime is 6457631. The reversal of 6457627 is 7267546.\n\nIt is a semiprime because it is the product of two primes.\n\nIt is a cyclic number.\n\nIt is a de Polignac number, because none of the positive numbers 2k-6457627 is a prime.\n\nIt is a super-2 number, since 2×64576272 = 83401892942258, which contains 22 as substring.\n\nIt is a d-powerful number, because it can be written as 65 + 411 + 57 + 75 + 66 + 221 + 75 .\n\nIt is a Duffinian number.\n\nIt is not an unprimeable number, because it can be changed into a prime (6457621) by changing a digit.\n\nIt is a polite number, since it can be written in 3 ways as a sum of consecutive naturals, for example, 293518 + ... + 293539.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (1761174).\n\nAlmost surely, 26457627 is an apocalyptic number.\n\n6457627 is a deficient number, since it is larger than the sum of its proper divisors (587069).\n\n6457627 is a wasteful number, since it uses less digits than its factorization.\n\n6457627 is an evil number, because the sum of its binary digits is even.\n\nThe sum of its prime factors is 587068.\n\nThe product of its digits is 70560, while the sum is 37.\n\nThe square root of 6457627 is about 2541.1861403683. The cubic root of 6457627 is about 186.2191408718.\n\nThe spelling of 6457627 in words is \"six million, four hundred fifty-seven thousand, six hundred twenty-seven\".\n\nDivisors: 1 11 587057 6457627" ]

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[ "# how much coal is required to generate 1 mw electricity\n\nHow much coal is required to generate 1 MWH of electricity How much coal is required to generate 1 MWH of electricity Alright,so once we know the heat required to generate 600 MW of electricity,then we can divide it by How much steam is required to generate 1MW of electricity The amount of steam required to produce 1 MW of How much coal is required to generate 1 MWH of electricity? How much coal is required to generate 1 How much coal, natural gas, or petroleum is used to How much coal, natural gas, or or petroleum is used to generate a kilowatthour of electricity? Amount of fuel used to generate 1 kWh: Coal = 0.00052 short How much coal, natural gas, or petroleum is used to How much coal, natural gas, or The amount of fuel used to generate electricity depends on the efficiency or Amount of fuel used to generate 1 kWh: Coal = 0\n\nHow much fuel is required to produce electricity which is the actual amount of fuel required to produce 1 kWh. For coal to electricity. much electricity How to Calculate the Coal Quantity Used in a Power Plant How to make quick estimate of the coal required We take the example of a 100 MW Coal Heat rate is the heat input required to produce one unit of electricity How much coal is required to run a 100-watt light bulb 24 Where electricity is produced from a coal fired power station, how much coal is required to run a 100-watt light bulb 24 hours a That gives 0.1 kW x 8,760 hours Coal Required for 1 MW Power Plant,Coal power plant Coal Required for 1 MW Power Plant. how much coal is required to generate 1MW The amount of fuel used to generate electricity depends on the efficiency or How much coal does a 500 mw power plant use daily – How much coal does a 500 mw power plant use produce 500 MW of electricity is 1429 MW (500/0.35). 1 watt of power much coal is required to generate 1 MW\n\nHow Much Water Does It Take to Make Electricity? – IEEE How Much Water Does It Take to Make Electricity? energy required to power 1 home in the enough natural gas to generate 1000 kWh of electricity. How much steam is required to generate 1MW of electricity The amount of steam required to produce 1 MW of How much coal is required to generate 1 MWH of electricity? How much coal is required to generate 1 How much coal, natural gas, or petroleum is used to How much coal, natural gas, or or petroleum is used to generate a kilowatthour of electricity? Amount of fuel used to generate 1 kWh: Coal = 0.00052 short How much coal, natural gas, or petroleum is used to How much coal, natural gas, or The amount of fuel used to generate electricity depends on the efficiency or Amount of fuel used to generate 1 kWh: Coal = 0 How much fuel is required to produce electricity which is the actual amount of fuel required to produce 1 kWh. For coal to electricity. much electricity\n\nHow to Calculate the Coal Quantity Used in a Power Plant How to make quick estimate of the coal required We take the example of a 100 MW Coal Heat rate is the heat input required to produce one unit of electricity How much coal is required to run a 100-watt light bulb 24 Where electricity is produced from a coal fired power station, how much coal is required to run a 100-watt light bulb 24 hours a That gives 0.1 kW x 8,760 hours Coal Required for 1 MW Power Plant,Coal power plant Coal Required for 1 MW Power Plant. how much coal is required to generate 1MW The amount of fuel used to generate electricity depends on the efficiency or How much coal does a 500 mw power plant use daily – How much coal does a 500 mw power plant use produce 500 MW of electricity is 1429 MW (500/0.35). 1 watt of power much coal is required to generate 1 MW How Much Water Does It Take to Make Electricity? – IEEE How Much Water Does It Take to Make Electricity? energy required to power 1 home in the enough natural gas to generate 1000 kWh of electricity.\n\nWhat is 1 MW of electricity? | Yahoo Answers 2010-2-15 · What is 1 MW of electricity? How much time is required to generate 1 MW What does the thumbrule 1 ton of garbage equals 1 MW of electricity in\n\nKeywords: FBC Boiler Manufacturer, FBC Boiler for Power Generation , how much coal is required to generate 1 mw electricity" ]

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[ "# Fully nonlinear propagation of waves in a uniform current using NURBS numerical wave tank\n\nArash Abbasnia, C. Guedes Soares\n\nResearch output: Contribution to journalArticlepeer-review\n\n3 Citations (Scopus)\n\n## Abstract\n\nA two-dimensional Numerical Wave Tank (NWT) is developed to simulate in the time domain fully nonlinear propagation of ocean waves in uniform currents. Fully nonlinear free surface motion is modeled using Mixed Eulerian-Lagrangian (MEL) scheme and Non-Uniform Rational B-Spline (NURBS) formulation. The potential theory and Laplace equation are solved in the Eulerian frame using a high-order formulation of Boundary Elements Method (BEM) at each time step. The distribution of boundary values is defined based on NURBS to discretize the boundary integral equation. The spatial derivatives of the boundary values are computed precisely based on NURBS. Material node approach and fourth-order Runge-Kutta time integration are employed to update the free surface boundary conditions. Propagation of robust nonlinear wave is simulated to examine the accuracy and convergence of the present fully nonlinear numerical procedure. Propagation the nonlinear regular waves in the uniform flows are also studied and the solutions are compared with the theoretical results. Propagation of the irregular nonlinear waves is studied to compare with the analytical solutions. Furthermore, fully nonlinear irregular wave in the uniform currents is simulated.\nOriginal language English 115-125 11 Ocean Engineering 163 06 Jun 2018 https://doi.org/10.1016/j.oceaneng.2018.05.039 Published - 01 Sep 2018 Yes\n\n## Keywords\n\n• Irregular wave\n• Non-Uniform Rational B-Spline\n• Numerical wave tank\n• Uniform current\n\n## Fingerprint\n\nDive into the research topics of 'Fully nonlinear propagation of waves in a uniform current using NURBS numerical wave tank'. Together they form a unique fingerprint." ]

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[ "```int(43)\nbool(false)\nint(0)\n```\n```int(1306)\nbool(false)\nint(43)\n```\n```int(2309)\nbool(false)\nint(43)\n```\n```int(55)\nbool(false)\nint(43)\n```\n```int(1307)\nbool(false)\nint(43)\n```\n```int(41)\nbool(false)\nint(0)\n```\n```int(60)\nbool(false)\nint(41)\n```\n```int(1311)\nbool(false)\nint(41)\n```\n```int(1371)\nbool(false)\nint(41)\n```\n```int(2576)\nbool(false)\nint(41)\n```\n```int(406)\nbool(false)\nint(41)\n```\n```int(42)\nbool(false)\nint(0)\n```\n```int(1304)\nbool(false)\nint(42)\n```\n```int(1303)\nbool(false)\nint(42)\n```\n```int(407)\nbool(false)\nint(42)\n```\n```int(62)\nbool(false)\nint(42)\n```\n```int(51)\nbool(false)\nint(42)\n```\n```int(2587)\nbool(false)\nint(42)\n```\n```int(3161)\nbool(false)\nint(42)\n```\n```int(3163)\nbool(false)\nint(42)\n```\n```int(3164)\nbool(false)\nint(42)\n```\n```int(3165)\nbool(false)\nint(42)\n```\n```int(3166)\nbool(false)\nint(42)\n```\n```int(3167)\nbool(false)\nint(42)\n```\n```int(3168)\nbool(false)\nint(42)\n```\n```int(3169)\nbool(false)\nint(42)\n```\n```int(3170)\nbool(false)\nint(42)\n```\n```int(3171)\nbool(false)\nint(42)\n```\n```int(3172)\nbool(false)\nint(42)\n```\n```int(3173)\nbool(false)\nint(42)\n```\n```int(3174)\nbool(false)\nint(42)\n```\n```int(2951)\nbool(false)\nint(0)\n```\n```int(2952)\nbool(false)\nint(2951)\n```\n```int(2953)\nbool(false)\nint(2951)\n```\n```int(2954)\nbool(false)\nint(2951)\n```\n```int(2955)\nbool(false)\nint(2951)\n```" ]

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[ "Enter the accuracy percentage and the full scale pressure of the gauge into the calculator to determine the full scale accuracy.\n\n## Full Scale Accuracy Formula\n\nThe following equation is used to calculate the Full Scale Accuracy.\n\nFSA = (A%/100) * FSP\n\n• Where FSA is the full scale accuracy\n• A% is the percentage accuracy\n• FSP is the full scale pressure.\n\nTo calculate a full scale accuracy, multiply the percentage accuracy by the full scale pressure.\n\n## What is a Full Scale Accuracy?\n\nDefinition:\n\nA full scale accuracy is a type of accuracy that is dependent on the reading of a gauge. For example, for a flow rate device, the accuracy would be measured as a percentage of the maximum flow rate.\n\n## How to Calculate Full Scale Accuracy?\n\nExample Problem:\n\nThe following example outlines the steps and information needed to calculate Full Scale Accuracy.\n\nFirst, determine the accuracy percentage. For this example, the accuracy percentage is 2%.\n\nNext, determine the full scale pressure. In this example, the full scale pressure is 500 N/m^2.\n\nFinally, calculate the full scale accuracy using the formula above:\n\nFSA = (A%/100) * FSP\n\nFSA = (2/100) * 500\n\nFSA = 10 N/m^2" ]

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[ "# Discount Worksheet 7Th Grade Pdf\n\nDiscount Worksheet 7Th Grade Pdf. Use a sales tax rate of 5%. Tax tip discount worksheet december 1 2019 impact.", null, "Percent Discount, Taxes And Tips Coloring Worksheet | Consumer Math, Money Math Worksheets, Seventh Grade Math from www.pinterest.com\n\nEffortlessmath.com answers discount, tax and tip 1) \\$530.00 2) \\$378.00 3) \\$856.00 4) \\$20,072.50 5) \\$262.50 6) \\$266,250 7) \\$320.00 8) \\$127.50 9) \\$37.50 10) \\$450.00 11) \\$28.80 12) \\$69.00 13) \\$38.40 14) \\$22.50 15) \\$60.00 16) \\$36.00 17) \\$33.00 Your parents wanted to give the waiter a 15% tip. This is set of 2 worksheets on percents and calculating discounts and final price.worksheet 1:\n\n### To Find The Discount, Multiply The Rate (As A Decimal) By The Original Price.\n\nSales tax and discount worksheet 7th grade answer key 3. Discount was 20%, what was the original price? 10 is what percent of 50.\n\n### Grade 6 Ns 1.4 And Grade 7 Ns 1.7 Example 1:\n\nDisplaying top 8 worksheets found for 7th grade discount and markup. Tax, tip, and discount word problems solve each word problem using either method. Percentage worksheets for grade 7 pdf.\n\n### What Was The Original Price Of The Item Mike.\n\nBar models help students build upon their prior understanding of percentages and apply that knowledge to solving word problems. 7th grade tax tip discount worksheet. Percent problems free math worksheets for 7th grade math blaster 332747.\n\n### 2) In A Grocery Store, A \\$12 Case Of Soda Is Labeled, Get A 20% Discount. What Is The Discount?\n\nDiscount, tax, and tip worksheet name: Students will calculate tax and total price.worksheet 2: 7.mike bought an item for \\$14,000 after a discount of 30%.\n\n### \\$4.00 At 25% Off Basketball:\n\nStudents will calculate discount and final sale price.worksheets are copyright material and are intended for use in the classroom only. Discount, sale price, and markup. Tax tip discount worksheet december 1 2019 impact." ]

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[ "# Difference between IRR and effective rate of return", null, "The internal rate of return and the effective rate of return on an investment differ in that the former does not take into account the reinvestment of internal cash flows and the latter does.\n\nIn other words, the effective rate of return is the annual percentage of return resulting from reinvesting the internal cash flows of an investment at a given rate.\n\n## Effective rate of return (ERR)\n\nThe effective rate of return is the return that an investor receives for reinvesting the cash flows generated by an investment at a certain rate.\n\nAn example of internal cash flows are the coupons that a bond pays or the dividends that a company pays for having its shares in its portfolio. They are called internal cash flows because the main investment, in the case of a bond, is to obtain a positive return on that bond and the coupons that the investor receives are money inflows that are inside the main investment (internal).\n\nThe coupons we receive are money that we can leave in the bank or reinvest. The action of reinvesting these coupons implies that when we want to calculate their rate of return together with the return of the main investment we have to use the effective rate of return.\n\n## Difference between IRR and TRE\n\nThe difference between the IRR and the ERR is that the IRR only takes into account the capital returns of an investment. Those returns can be left in a bank account or they can be invested in another asset of greater or lesser risk, being the stock market or deposits, respectively.\n\nFor this reason, we speak of reinvestment of capital flows, because from an investment another investment can be derived that is made from the money earned from the first one. So, if we have in mind to make two simultaneous investments and we want to know what our effective profitability is, we will have to calculate the ERR since it takes into account the reinvestment rate.\n\nHere is a diagram that describes the difference between the IRR and the ERR:", null, "TIR and TRE scheme\n\n## Formula effective rate of return (ERR)\n\nEffective Return Rate (ERR)\n\nWhere:\n\n• Cn: capitalization of internal flows.\n• C0: initial capital or initial price in the case of a bond.\n• x%: reinvestment rate.\n• n: number of periods that the investment lasts.\n\nTRE is expressed which depends on a certain percentage x because we need that percentage to calculate the rate. Without this percentage, we do not know at what rate we can reinvest the internal flows of the investment or coupons in the case of a bond.\n\n## Formula for the internal rate of return (IRR)\n\nInternal Rate of Return (IRR)\n\nThe IRR is the rate of return that makes future updated capital flows equal to the initial capital or price in the case of a bond.\n\n## Example of IRR and TRE\n\nIn this example we will assume that we have bought a bond at a price of 97.25%, which offers annual coupons of 3.5%, which is amortized over the face value and that its maturity is within 3 years.\n\nAs good investors that we are, we know that each year until the maturity of the bond we are going to deposit 3.5 monetary units in our bank account, which are the coupons that the issuer pays us for having bought the bond from them.\n\nFirst, we calculate what the return on our investment will be. To do this, we can use the formula for the internal rate of return (IRR).\n\n## IRR formula\n\nIRR formula\n\nWhere:\n\n• C0: Initial capital or Initial price.\n• Cn: Final Capital or Final Price.\n• n: number of periods that the investment lasts.\n• IRR: interest rate that makes the future updated capital flows equal to the initial capital or initial price.\n\nOnce we have known the formula, we can substitute the variables for the values ​​that we already know:\n\nIRR calculation\n\nSo, if each year until maturity we have 3.5 monetary units, we can decide whether to leave them there or invest them. Depending on our risk profile, we will choose an investment with lower or higher risk. Taking into account that we have bought a bond, our profile is that of a conservative investor and, therefore, we are more likely to choose a deposit to reinvest the coupons.\n\nTherefore, if we choose to reinvest the coupons, it means that each year until the bond matures, we will invest the 3.5 monetary units in a deposit that gives us a return. We will call the return on the deposit financed with capital from another investment the reinvestment rate. And it will be this rate that we will take into account when calculating the effective profitability.\n\n## Formula of the Effective Rate of Return (ERR)\n\nEffective Return Rate\n\nWhere:\n\n• Cn: capitalization of internal flows.\n• C0: initial capital or initial price in the case of a bond.\n• x%: reinvestment rate.\n• n: number of periods that the investment lasts.\n\nTRE is expressed which depends on a certain percentage x because we need that percentage to calculate the rate. Without this percentage, we do not know at what rate we can reinvest the internal flows of the investment or coupons in the case of the bond.\n\nWe have to bear in mind that we have to capitalize the first coupon using compound capitalization because it exceeds one year. Then the capitalization of the second coupon does not need to be made compound because it is only one year.\n\nCapitalization of flows\n\nOnce we know C3, we can calculate the ERR:\n\nCalculation of the ERR\n\nThen, it is concluded that the profitability of a bond of these characteristics is 4.5% and that if we reinvest its coupons at a rate of 2%, the effective profitability, that is, that of the bond and that of the reinvestment, would be of 4.41%.\n\nTags:  USA derivatives economic-analysis\n\nclose\n\n### Popular Posts\n\neconomic-dictionary\n\ncomparisons\n\n### Asset types\n\neconomic-dictionary\n\npresent\n\n### Popular Categories", null, "" ]

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[ "Coarea formula\n\n2010 Mathematics Subject Classification: Primary: 49Q15 [MSN][ZBL]\n\nThe coarea formula is a far-reaching generalization of Fubini's theorem in the Euclidean space using \"curvilinear coordinates\" or \"distorted foliations\". In what follows we denote by $\\mathcal{H}^n$ the $n$-dimensional Hausdorff measure in $\\mathbb R^m$ and by $\\lambda$ the Lebesgue measure in any $\\mathbb R^k$.\n\nLipschitz maps\n\nConsider a Lipschitz map $f: \\mathbb R^m \\to \\mathbb R^n$, where $m\\geq n$. Recall that, by Rademacher's theorem, $f$ is differentiable $\\lambda$-a.e.. At any point $y\\in \\mathbb R^n$ of differentiability we denote by $J f (y)$ the Jacobian of $f$ in $y$, that is the square root of the determinant of $Df|_y \\cdot Df|_y^t$ (which, by the Cauchy Binet formula, equals the sum of the squares of the determinants of all $n\\times n$ minors of the Jacobian matrix $Df|_y$, see Jacobian).\n\nTheorem 1 Let $f: \\mathbb R^m \\to \\mathbb R^n$ be a Lipschitz function. Then for each $\\lambda$-measurable $A\\subset \\mathbb R^m$, we have\n\n(a) The map $y\\mapsto J f (y)$ is Lebesgue measurable;\n\n(b) The set $A\\cap f^{-1} (z)$ is an $m-n$-dimensional rectifiable set for $\\lambda$-a.e. $z\\in \\mathbb R^n$;\n\n(c) The map $z\\mapsto \\mathcal{H}^{m-n} (A\\cap f^{-1} (\\{z\\}))$ is Lebesgue measurable;\n\n(d) The following formula holds \\begin{equation}\\label{e:coarea} \\int_A Jf (y)\\, dy = \\int_{\\mathbb R^n} \\mathcal{H}^{m-n} (A\\cap f^{-1} (\\{z\\})\\, dz\\, . \\end{equation}\n\nCp. with Theorem 1 of Section 3.4.2 of [EG] concerning the points (a), (c) and (d). For point (b) see Theorem 2.93 of [AFP]. The statement of Theorem 1 can be considerably generalized: in particular one can consider Lipschitz maps $f: A \\to E$ where $A$ is an $m$-dimensional rectifiable subset of $\\mathbb R^M$ and $E$ is an $n$-dimensional rectifiable subset of $\\mathbb R^N$. In this case the Jacobian must be suitably defined using an appropriate concept of tangential differentiation. We refer the reader to Theorem 3.2.22 of [Fe].\n\nFubini type statement\n\nA relatively simple corollary of Theorem 1 is given by the following more general statement, which is also often referred to as Coarea formula.\n\nTheorem 2 Let $f$ be as in Theorem 1 and $g: \\mathbb R^m\\to \\mathbb R$ a $\\lambda$-summable function. Then the map $g|_{f^{-1} \\{z\\}}$ is $\\mathcal{H}^{m-n}$ summable for $\\lambda$-a.e. $z\\in \\mathbb R^n$ and the following formula holds \\begin{equation}\\label{e:coarea2} \\int_{\\mathbb R^m} g (y)\\, Jf (y)\\, dy = \\int_{\\mathbb R^n} \\int_{f^{-1} (\\{z\\})} g(w)\\, d\\mathcal{H}^{m-n} (w)\\, dz\\, . \\end{equation}\n\nCp. with Theorem 2 of Section 3.4.3 of [EG]. Observe that when $f$ is the projection onto the first $n$ coordinates, \\eqref{e:coarea2} reduces to the classical Fubini theorem.\n\nRelation to Sard's theorem\n\nAssume $f$ in Theorem 1 is of class $C^r$ for some $r> m-n$. Then by Sard's theorem we conclude that $\\lambda$-a.e. $z\\in \\mathbb R^n$ is a regular value of $f$ and hence that $f^{-1} (z)$ is a $C^r$ $m-n$-dimensional submanifold of $\\mathbb R^m$. Since $C^1$ submanifolds are rectifiable sets, this implies conclusion (b) in Theorem 1. Thus Theorem 1 can also be considered as an appropriate generalization of Sard's theorem.\n\nBV functions\n\nIn case $n=1$ the Lipschitz regularity of $f$ can be considerably relaxed leading to the Fleming-Rishel coarea formula for functions of bounded variation: compare to the Section Coarea formula therein." ]

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[ "```\t%0 Journal Article\n%A J. Sulaiman and M. Othman and M. K. Hasan\n%D 2010\n%J International Journal of Mathematical and Computational Sciences\n%B World Academy of Science, Engineering and Technology\n%I Open Science Index 38, 2010\n%T MEGSOR Iterative Scheme for the Solution of 2D Elliptic PDE's\n%U https://publications.waset.org/pdf/7862\n%V 38\n%X Recently, the findings on the MEG iterative scheme has demonstrated to accelerate the convergence rate in solving any system of linear equations generated by using approximation equations of boundary value problems. Based on the same scheme, the aim of this paper is to investigate the capability of a family of four-point block iterative methods with a weighted parameter, &omega; such as the 4 Point-EGSOR, 4 Point-EDGSOR, and 4 Point-MEGSOR in solving two-dimensional elliptic partial differential equations by using the second-order finite difference approximation. In fact, the formulation and implementation of three four-point block iterative methods are also presented. Finally, the experimental results show that the Four Point MEGSOR iterative scheme is superior as compared with the existing four point block schemes.\n\n%P 264 - 270\n```" ]

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[ "# The Azimuth Project Blog - hierarchical organization and biological evolution (part 1)\n\nThis page is a blog article in progress, written by Cameron Smith. To discuss this article while it’s being written, visit the Azimuth Forum.\n\nAn attempt to review some of the literature on major transitions in evolution and multi-level selection, sketch a few connections to concepts in category theory, and discuss the potential for using experimental evolution to investigate and strengthen those connections.\n\nEdit :: Source :: Part 2 :: Part 3\n\n#### Hierarchical Organization and Biological Evolution (Part 1)\n\nMy thesis has been that one path to the construction of a non-trivial theory of complex systems is by way of a theory of hierarchy. Empirically, a large proportion of the complex systems we observe in nature exhibit hierarchic structure. On theoretical grounds we could expect complex systems to be hierarchies in a world in which complexity had to evolve from simplicity. - Herbert Simon, 1962\n(Many of the concepts that) have dominated scientific thinking for three hundred years, are based upon the understanding that at smaller and smaller scales—both in space and in time—physical systems become simple, smooth and without detail. A more careful articulation of these ideas would note that the fine scale structure of planets, materials and atoms is not without detail. However, for many problems, such detail becomes irrelevant at the larger scale. Since the details (become) irrelevant (at such larger scales), formulating theories in a way that assumes that the detail does not exist yields the same results as (theories that do not make this assumption). - Yaneer Bar-Yam\n\nThoughts like these lead me to believe that, as a whole, we humans need to reassess some of our approaches to understanding. I’m not opposed to reductionism, but I think it would be useful to try to characterize those situations that might require something more than an exclusively reductionist approach. One way to do that is to break down some barriers that we’ve constructed between disciplines. So I’m here on Azimuth trying to help out this process.\n\nIndeed, Azimuth is just one of many endeavors people are beginning to work on that might just lead to the unification of humanity into a superorganism. Regardless of the external reality, a fear of climate change could have a unifying effect. And, if we humans are simply a set of constituents of the superorganism that is Earth’s biosphere, it appears we are its only candidate germ line. So, assuming we’d like our descendants to have a chance at existence in the universe, we need to figure out either how to keep this superorganism alive or help it reproduce.\n\nWe each have to recognize our own individual limitations of time, commitment, and brainpower. So, I’m trying to limit my work to the study of biological evolution rather than conjuring up a ‘pet theory of everything’. However, I’m also trying not to let those disciplinary and institutional barriers limit the tools I find valuable, or the people I interact with. So, the more I’ve thought about the complexity (let’s just let ‘complexity’ = ‘anything humans don’t yet understand’ for now) of evolution, the more I’ve been driven to search for new languages. And in that search, I’ve been driven toward pure mathematics, where there are many exciting languages lurking around. Perhaps one of these languages has already obviated the need to invent new ideas to understand biological evolution… or perhaps an altogether new language needs to be constructed.\n\nThe prospects of a general theory of evolution point to the same intellectual challenge that we see in the quote above from Bar-Yam: assuming we’d like to be able to consistently manipulate the universe, when can we neglect details and when can’t we?\n\nConsider the level of organization concept. Since different details of a system can be effectively ignored at different scales, our scientific theories have themselves become ‘stratified’:\n\n• G. L. Farre, The energetic structure of observation: a philosophical disquisition, American Behavioral Scientist 40 (May 1997), 717-728.\n\nIn other words, science tends to be organized in ‘layers’. These layers have come to be conceived of as levels of organization, and each scientific theory tends to address only one of these levels (click the image to see the flash animation that ascends through many scales or levels):\n\nIt might be useful to work explicitly on connecting theories that tell us about particular levels of organization in order to attempt to develop some theories that transcend levels of organization. One type of insight that could be gained from this approach is an understanding of the mutual development of bottom-up ostensibly mechanistic models of simple systems and top-down initially phenomenological models of complex ones.\n\nSimon has written an interesting discussion of the quasi-continuum that ranges from simple systems to complex ones:\n\n• H. A. Simon, The architecture of complexity, Proceedings of the American Philosophical Society 106 (1962), 467–482.\n\nBut if we take an ideological perspective on science that says “let’s unify everything!” (scientific monism), a significant challenge is the development of a language able to unify our descriptions of simple and complex systems. Such a language might help communication among scientists who work with complex systems that apparently involve multiple levels of organization. Something like category theory may provide the nucleus of the framework necessary to formally address this challenge. But, in order to head in that direction, I’ll try out a few examples in a series of posts, albeit from the somewhat limited perspective of a biologist, from which some patterns might begin to surface.\n\nIn this introductory post, I’ll try to set a basis for thinking about this tension between simple and complex systems without wading through any treatises on ‘complexity’. It will be remarkably imprecise, but I’ll try to describe the ways in which I think it provides a useful metaphor for thinking about how we humans have dealt with this simple ↔ complex tension in science. Another tack that I think could accomplish a similar goal, perhaps in a clearer way, would be to discuss fractals, power laws and maybe even renormalization. I might try that out in a later post if I get a little help from my new Azimuth friends, but I don’t think I’m qualified yet to do it alone.\n\n#### Simple and complex systems\n\nWhat is the organizational structure of the products of evolutionary processes? Herbert Simon provides a perspective that I find intuitive in his parable of two watchmakers.\n\nHe argues that the systems containing modules that don’t instantaneously fall apart (‘stable intermediates’) and can be assembled hierarchically take less time to evolve complexity than systems that lack stable intermediates. Given a particular set of internal and environmental constraints that can only be satisfied by some relatively complex system, a hierarchically organized one will be capable of meeting those constraints with the fewest resources and in the least time (i.e. most efficiently). The constraints any system is subject to determine the types of structures that can form. If hierarchical organization is an unavoidable outcome of evolutionary processes, it should be possible to characterize the causes that lead to its emergence.\n\nSimon describes a property that some complex systems have in common, which he refers to as ‘near decomposability’:\n\n• H. A. Simon, Near decomposability and the speed of evolution, Industrial and Corporate Change 11 (June 2002), 587-599.\n\nA system is nearly decomposable if it’s made of parts that interact rather weakly with each other; these parts in turn being made of smaller parts with the same property.\n\nFor example, suppose we have a system modelled by a first-order linear differential equation. To be concrete, consider the fictitious building imagined by Simon: the Mellon Institute, with 12 rooms. Suppose the temperature of the $i$th room at time $t$ is $T_i(t)$. Of course most real systems seem to be nonlinear, but for the sake of this metaphor we can imagine that the temperatures of these rooms interact in a linear way, like this:\n\n$\\frac{d}{d t}T_i(t) = \\sum_{j}a_{ij}\\left(T_{j}(t)-T_{i}(t)\\right),$\n\nwhere $a_{ij}$ are some numbers. Suppose also that the matrix $a_{ij}$ looks like this:\n\nFor the sake of the metaphor I’m trudging through here, let’s also assume\n\n$a\\gg\\epsilon_l\\gg\\epsilon_2$\n\nThen our system is nearly decomposable. Why? It has three ‘layers’, with two cells at the top level, each divided into two subcells, and each of these subdivided into three sub-subcells. The numbers of the rows and columns designate the cells, cells 1–6 and 7–12 constitute the two top-level subsystems, cells 1–3, 4–6, 7–9 and 10–12 the four second-level sub- systems. The interactions within the latter subsystems have intensity $a$, those within the former two subsystems, intensity $\\epsilon_l$, and those between components of the largest subsystems, intensity $\\epsilon_2$ (Simon, 2002). This is why Simon states that this matrix is in near-diagonal form. Another, probably more common, terminology for this would be near block diagonal form. This terminology is a bit sloppy, but it basically means that we have a square matrix whose diagonal entries are square matrices and all other elements are approximately zero. That ‘approximately’ there is what differentiates near block diagonal matrices from honest block diagonal matrices whose off diagonal matrix elements are precisely zero.\n\nThis is a trivial system, but it illustrates that the near decomposability of the coefficient matrix allows these equations to be solved in a near hierarchical fashion. As an approximation, rather than simulating all the equations at once (e.g. all twelve in this example) one can take a recursive approach and solve the four systems of three equations (each of the blocks containing $a$s), and average the results to produce initial conditions for two systems of two equations with coefficients:\n\n$\\begin{bmatrix} \\epsilon_1 & \\epsilon_1 & \\epsilon_2 & \\epsilon_2 \\\\ \\epsilon_1 & \\epsilon_1 & \\epsilon_2 & \\epsilon_2\\\\ \\epsilon_2 & \\epsilon_2 & \\epsilon_1 & \\epsilon_1 \\\\ \\epsilon_2 & \\epsilon_2 & \\epsilon_1 & \\epsilon_1 \\end{bmatrix},$\n\nand then average those results to produce initial conditions for a single system of two equations with coefficients:\n\n$\\begin{bmatrix} \\epsilon_2 & \\epsilon_2 \\\\ \\epsilon_2 & \\epsilon_2 \\end{bmatrix}.$\n\nThis example of simplification indicates that the study of a nearly decomposable systems system can be reduced to a series of smaller modules, which can be simulated in less computational time, if the error introduced in this approximation is tolerable. The degree to which this method saves time depends on the relationship between the size of the whole system and the size and number of hierarchical levels. However, as an example, given that the time complexity for matrix inversion (i.e. solving a system of linear equations) is $O(n^2)$, then the hierarchical decomposition would lead to an algorithm with time complexity $O\\left(\\left(\\frac{n}{L}\\right)^2\\right)$, where $L$ is the number of levels in the decomposition. (For example, $L=4$ in the Mellon Institute, assuming the individual rooms are the lowest level).\n\nAll of this deserves to be made much more precise. However, there are some potential metaphorical consequences for the evolution of complex systems:\n\nIf we begin with a population of systems of comparable complexity, some of which are nearly decomposable and some of which are not, the nearly decomposable systems will, on average, increase their fitness through evolutionary processes much faster than the remaining systems, and will soon come to dominate the entire population. Notice that the claim is not that more complex systems will evolve more rapidly than less complex systems, but that, at any level of complexity, nearly decomposable systems will evolve much faster than systems of comparable complexity that are not nearly decomposable. ([Simon, 2002](#Simon2002))\n\nThe point I’d like to make is that in this system, the idea of switching back and forth between simple and complex perspectives is made explicit: we get a sort of conceptual parallax:\n\nIn this simple case, the approximation that Simon suggests works well; however, for some other systems, it wouldn’t work at all. If we aren’t careful, we might even become victims of the Dunning-Kruger effect. In other words: if we don’t understand a system well from the start, we may overestimate how well we understand the limitations inherent to the simplifications we employ in studying it.\n\nBut if we at least recognize the potential of falling victim to the Dunning-Kruger effect, we can vigilantly guard against it in trying to understand, for example, the currently paradoxical tension between ‘groups’ and ‘individuals’ that lies at the heart of evolutionary theory… and probably also the caricatures of evolution that breed social controversy.\n\nKeeping this in mind, my starting point in the next post in this series will be to provide some examples of hierarchical organization in biological systems. I’ll also set the stage for a discussion of evolution viewed as a dynamic process involving structural and functional transitions in hierarchical organization—or for the physicists out there, something like phase transitions!\n\n#### References not cited in this blog article\n\nT. F. H. Allen and T. B. Starr, Hierarchy: Perspectives for Ecological Complexity. Chicago: University of Chicago Press, 1982, p. 326. $\\hookleftarrow$\n\nA. J. Arnold and K. Fristrup, The theory of evolution by natural selection: a hierarchical expansion, Paleobiology, vol. 8, no. 2, pp. 113–129, 1982. $\\hookleftarrow$\n\nA. C. Ehresmann and J. P. Vanbremeersch, Memory Evolutive Systems; Hierarchy, Emergence, Cognition, Volume 4 (Studies in Multidisciplinarity). Elsevier Science, 2007, p. 402. $\\hookleftarrow$\n\nS. A. Frank, George Price’s contributions to evolutionary genetics., Journal of theoretical biology, vol. 175, no. 3, pp. 373-88, Aug. 1995. $\\hookleftarrow^1$ $\\hookleftarrow^2$\n\nS. Okasha, Evolution and the levels of selection. New York: Oxford University Press, USA, 2006. $\\hookleftarrow$\n\nG. R. Price, Selection and Covariance, Nature, vol. 227, no. 5257, pp. 520-521, Aug. 1970. $\\hookleftarrow$\n\nG. R. Price, Extension of covariance selection mathematics, Annals of Human Genetics, vol. 35, no. 4, pp. 485-490, Apr. 1972. $\\hookleftarrow$\n\nG. R. Price, The nature of selection, Journal of Theoretical Biology, vol. 175, no. 3, pp. 389-396, Aug. 1995. (written ca. 1971 and published posthumously) $\\hookleftarrow$\n\nJ. Maynard Smith and E. Szathmáry, The major transitions in evolution. New York: Oxford University Press, USA, 1995. $\\hookleftarrow$\n\ncategory: blog, biology" ]

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[ "# Todo List with Date column in Database table\n\nCheckbuttons Part I & II Task list Task with Database Part III", null, "This script uses SQLite database to store tasks ( todo list ) with status ( True or False ). We can use MySQL database also to run this script by changing the connection part.\n\nTkinter task list with Calendar for user selection of date and storing in Database tables\n\n## Connection to Database and Creating table\n\nUse the code below to connect to SQLite database ( or create the database ) and then create the table my_tasks.\nHere we have added one extra column dt to store Date.\nSome sample data is added while creating the table.\nUpdate the path of the sqlite database file in this code.\nTo use MySQL database the connection string needs to be changed and the create table Query is also different due to the auto-increment column.\n\nOR Use the SQL dump for MySQL given at end of this page.\n\n## Connection to Database in main script.\n\nFirst we will connect to our SQLite or MySQL database. The variable `my_conn` we will use in our main script. We will call the connection object from main script.\n``from tk_checkbutton4_connect import my_conn # database connection``\nInside the file tk_checkbutton4_connect.py we will keep this code.\n``````from sqlalchemy import create_engine\nfrom sqlalchemy.exc import SQLAlchemyError\ntry: # connection to database\n#my_path=\"D:\\\\testing\\\\sqlite\\\\my_db.db\" #Change the path\n#my_conn = create_engine(\"sqlite:///\" + my_path)\n### for MySQL database , use the below line and remove the above line\nmy_conn =create_engine(\"mysql+mysqldb://root:pw@localhost/my_tutorial\")\nexcept SQLAlchemyError as e:\nerror = str(e.__dict__['orig'])\nprint(error)``````\n\n## Updating table with task status\n\n.\nHere while updating the font option of the checkbuttons we will also update the database table my_tasks by making status column True or False. Based on the Checkbutton status the Query is prepared and status column is updated.\n``````def my_upd(k):\nif(my_ref[k].get()==True):\nmy_ref[k].config(font=f_done,fg='green')\nq='UPDATE my_tasks SET status=True WHERE id='+str(k)\nelse:\nmy_ref[k].config(font=f_normal,fg='blue')\nq='UPDATE my_tasks SET status=False WHERE id='+str(k)\nr_set=my_conn.execute(q)\nmsg=\"No. Updated:\"+str(r_set.rowcount) # Number of rows updated\nmy_msg(msg) # show message for 3 seconds ``````\n\n## Displaying the tasks with status\n\nWe will keep all the steps required to display the tasks inside a function. This function we will call after every addition or deletion of tasks from the database table. Here we have kept the code inside the function my_show().\nInside the function first we will we will clear all the tasks ( previously displayed) by using grid_slaves(). Note that we have used one frame ( task_frame ) as container to hold all the rows of tasks.\n``````for w in task_frame.grid_slaves(): #Loop through each row\nw.grid_forget() # remove the row ``````\nWe will read the tasks and the status of the task from the my_tasks table to display. In our table my_tasks, we have four columns, id, tasks , status and dt. We will create one dictionary by using id as Key with tasks, status , dt list as values.\n\n## Collecting date and displaying with task\n\nWe are collecting date column ( dt ) value and then displaying the along with the checkbutton.\n``````q='SELECT * FROM my_tasks '\nmy_cursor=my_conn.execute(q)\nr_set=my_cursor.fetchall()\nmy_dict = {row: [row,row,row] for row in r_set}``````\nWe are using the date column value and converting to string by using strftime(). Here we are using the list of Date formats to show date.\n``````#dt=datetime.strptime(my_dict[k],'%Y-%m-%d').strftime('%d-%b-%Y') # sqlite\ndt=datetime.strftime(my_dict[k],'%d-%b-%Y') # MySQL\nHere is the full code for the function my_disp()\n``````my_ref={} # to store references to checkboxes\ndef my_show():\nfor w in task_frame.grid_slaves(): #Loop through each row\nw.grid_forget() # remove the row\nmy_cursor=my_conn.execute(q)\nr_set=my_cursor.fetchall()\nmy_dict = {row: [row,row,row] for row in r_set}\n\ni=2 # row number\nfor k in my_dict.keys(): # Number of checkbuttons\nvar=tk.BooleanVar() # variable\nvar.set(my_dict[k]) # set to value of status column\nif my_dict[k]==True: # set font based on status column\nfont,fg=f_done,'green' # if True\nelse:\nfont,fg=f_normal,'blue'\nvariable=var,onvalue=True,offvalue=False,font=font,fg=fg,\ncommand=lambda k=k: my_upd(k))\n#dt=datetime.strptime(my_dict[k],'%Y-%m-%d').strftime('%d-%b-%Y') # sqlite\ndt=datetime.strftime(my_dict[k],'%d-%b-%Y') # MySQL\nmy_ref[k]=[ck,var] # to hold the references\ni=i+1 # increase the row number ``````\n\nOn click of the button b1 the function add_task() will be called.\n``````b1=tk.Button(my_w,text='+',font=18,command=lambda:add_task())\nb1.grid(row=0,column=2)``````\nInside the function add_task() we will fist read the data entered by user in the Entry box e1 by using get() method.\nAs we are using Calendar library , we have imported the same at the top of the page.\n``from tkcalendar import DateEntry``\nTo display the calendar with other components\n``````cal=DateEntry(my_w,selectmode='day',font=18)\ncal.grid(row=1,column=1)``````\nTo display and collect user selected calendar date use this\n``dt=cal.get_date()``\n\nWe will remove the user entered data in Entry box by using delete() method. Using the id value of just added task we will prepare a message string msg. This message will be passed to the function my_msg(msg) to display the id for 3 seconds.\n``````def add_task():\ndt=cal.get_date()\n#dt = date.today() # todays date\nmy_data=(e1.get(),False,dt) # data to pass using query\n### for SQLite use the below line and remove MySQL line\n# VALUES(?,?,?)\",my_data)\n### for MySQL use the below line and remove the above line\nVALUES(%s,%s,%s)\",my_data)\n\ne1.delete(0,'end') # remove the task from entry box\nmy_msg(msg) # show message for 3 seconds\nmy_show() # refresh the view ``````\n\nOnce the chckbutton is checked then the status of the Task is updated to True( check the code above ). We can remove all the records or tasks for which the status is already changed True. After deleting we will display number of completed tasks deleted by using rowcount.\n``````def delete_task(): # remove all completed tasks\nmsg=\"No Deleted:\"+str(r_set.rowcount) # Number of rows deleted\nmy_msg(msg) # show message for 3 seconds\nmy_show() # refresh the view ``````\n\n## Showing messages\n\nTo communicate with user we will be displaying different message for some time period. Here we have kept the display duration as 3000 milliseconds ( 3 seconds ). This function my_msg() is called by different operations and pass the message to display as parameter. After 3000 milliseconds ( 3 seconds ) the message is removed by updating the text option of label l2 to blank string by using config() method.\n``````def my_msg(msg):\nl2.config(text=msg) # show message\nmy_w.after(3000,lambda:l2.config(text='')) # remove after 3 seconds``````\nFull code is here ( Change the path to your database or connection string.\ntk_checkbutton4_connect.py\n\ntk_checkbutton4_main.py\n\n## SQL Dump for my_tasks table ( for MySQL )\n\n``````CREATE TABLE IF NOT EXISTS `my_tasks` (\n`id` int(11) NOT NULL AUTO_INCREMENT,\n`status` tinyint(1) DEFAULT NULL,\n`dt` date NOT NULL,\nPRIMARY KEY (`id`)\n) ENGINE=InnoDB DEFAULT CHARSET=latin1 AUTO_INCREMENT=10 ;\n\n--\n-- Dumping data for table `my_tasks`\n--\n\n(1, 'My tasks 1 ', True,'2023-09-26'),\n\n## Difference between SQLite and MySQL\n\n1. While creating the table take care of Auto-increment task id column.\n2. To Pass the parameters the placeholders are different. For MySQL it is %s and for SQLite it is ?.\n\nSubscribe to our YouTube Channel here\n\n## Subscribe\n\n* indicates required\nSubscribe to plus2net", null, "plus2net.com" ]

[ null, "https://www.plus2net.com/python/images/tk-task-date.jpg", null, "https://www.plus2net.com/images/top2.jpg", null ]

[ ">\n\n## NCERT Solutions for Class 8 Maths Chapter 2 Linear Equations in One Variable Exercise 2.5\n\nYou will find Chapter 2 Linear Equations in One Variable Exercise 2.5 Class 8 Maths NCERT Solutions which will help you in getting more marks in the class test and exams. NCERT Solutions for Class 8 Maths are prepared by Studyrankers experts who have provided accurate and detailed solutions of every questions chapterwise so you don't have to waste your precious time in finding. These NCERT questions and answers are updated as per the latest syllabus of CBSE.\n\nExercise 2.5 has only two questions which are about simplfying and solving the given linear equations.", null, "", null, "", null, "X" ]

[ null, "https://1.bp.blogspot.com/-48FkoNtwAVM/XnyBFExp0SI/AAAAAAAAMQI/SrDrvGOctNwHgnBOH2HveNyLfgzsocNiwCLcBGAsYHQ/s1600/Exercise-2.5-linear-equations-in-one-variables-NCERT-Solutions-for-Class-8-Maths-1.jpg", null, "https://1.bp.blogspot.com/-eZbymUs-D-8/XnyBFBNPrSI/AAAAAAAAMQM/wdS7l0H7-wwGAFKzumvvLQ9YikW6YqYEACLcBGAsYHQ/s1600/Exercise-2.5-linear-equations-in-one-variables-NCERT-Solutions-for-Class-8-Maths-2.jpg", null, "https://1.bp.blogspot.com/-SoZ1Ye_JhCM/XnyBFIXGt-I/AAAAAAAAMQE/eFCMVRvaGXAhD3bXXojsuSxTQ-cXdXqbACLcBGAsYHQ/s1600/Exercise-2.5-linear-equations-in-one-variables-NCERT-Solutions-for-Class-8-Maths-3.jpg", null ]

[ "# Does the VBA “And” operator evaluate the second argument when the first is false ?\n\nDoes the VBA “And” operator evaluate the second argument when the first is false ?\n\n• 3 Answer(s)\n\nTry this best solution :\n\nVBA “And” operator evaluate the second argument when the first is false is called “short-circuit evaluation”.\n\nBut VBA have not it.\n\nThis way that was chosen there complicated alternative a Select Case for the If. And this is also an example of using nested Ifs.\n\nVBA does not have short-circuit evaluation.\n\nAlternatively using the AND operator, because It is technically able to use 2 If-then statements. If doing it a lot of times for unnecessary, so did not notice the savings, so go for whatever is more readable. VBA handles multiple If-then statements faster than Select Case, if need to have really technical,\n\nIt create a big anomaly in a situation like this:\n\n```If i <= UBound(Arr, 1) And j <= UBound(Arr, 2) And Arr(i, 1) <= UBound(Arr2, 1) Then\nArr2(Arr(i, 1), j) = Arr(i, j)\nEnd If\n```\nwhich is wrong, more suitably :\n\n```If i <= UBound(Arr, 1) And j <= UBound(Arr, 2) Then\nIf Arr(i, 1) <= UBound(Arr2, 1) Then\nArr2(Arr(i, 1), j) = Arr(i, j)\nEnd If\nEnd If\n```\nIn case having dislike to nested ifs:\n\n```If i > UBound(Arr, 1) Or j > UBound(Arr, 2) Then\n*// Do Nothing\nElseIf Arr(i, 1) > UBound(Arr2, 1) Then\n*// Do Nothing\nElse\nArr2(Arr(i, 1), j) = Arr(i, j)\nEnd If\n```\n\n• ### Your Answer\n\nBy posting your answer, you agree to the privacy policy and terms of service." ]

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[ "# Find the function f whose derivative is $\\sin^2 x$ [closed]\n\nFind the function $f$ whose derivative is\n\n$$f'(x)=\\sin^2(x)$$\n\nand where $f(\\pi)=\\pi$.\n\n$$f(x)=\\frac12(x-\\sin x\\cos x)$$ What do I do from here?\n\n• Make sure that letting $x=\\pi$ makes $f(x)=\\pi$. Note that the antiderivative of $f'(x)$ is $f(x)+C$ for some constant $C$. – abiessu Sep 10 '13 at 15:12\n\nYou label this as differential equations, is this supposed to be integral calculus?\n\nYou have solved the indefinite integral and forgot to add +c.\n\n$$\\int \\sin^2(x)dx=\\int \\frac{1-\\cos(2x)}{2}dx=\\frac{x}{2}-\\frac{\\sin(2x)}{4}+c.$$\n\nNow evaluate at $f(\\pi)=\\pi$ to see $\\pi=\\frac{\\pi}{2}+c$ and solve.\n\n$$f'(x)=\\sin^2x\\implies f(x)=\\frac12(x-\\sin x\\cos x) \\color{red}{+ C}\\;,\\;\\;C=\\;\\text{a constant} .$$\n$$\\pi=f(\\pi)=\\frac{\\pi}2+C\\implies C=\\frac{\\pi}2\\implies \\color{blue}{f(x)=\\frac12(x-\\sin x\\cos x)+\\frac{\\pi}2}$$\n$$f'(x) = \\sin^2(x) \\Rightarrow f(x) = \\int \\sin^2 x \\ dx = \\int \\frac{1}{2} \\left(1 - \\cos 2x \\right) \\ dx = \\frac{x}{2} - \\frac{\\sin 2x}{4} + C$$\n$$f(\\pi) = \\pi \\Rightarrow \\frac{\\pi}{2}+ C = \\pi\\Rightarrow C = \\frac{\\pi}{2}$$\n$$\\Rightarrow f(x) = \\frac{x}{2} - \\frac{\\sin 2x}{4} + \\frac{\\pi }{2}$$" ]

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[ "# Simplify 604/1446 to lowest terms\n\n/\n\n#### Solution for what is 604/1446 in simplest fraction\n\n604/1446 =\n\n604:2/1446:2 = 302/723\n\nNow we have: what is 604/1446 in simplest fraction = 302/723\n\nQuestion: How to reduce 604/1446 to its lowest terms?\n\nStep by step simplifying fractions:\n\nStep 1: Find GCD(604,1446) = 2.\n\nStep 2: Divide both numerator & denominator by 2\n= 604/2/1446/2 = 302/723\n\nTherefore, 302/723 is simplified fraction for 604/1446" ]

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[ "Percentage Calculator /\n\n# What is 8 percent of 51\n\n%\nAnswer: 8% of 51 is 4.08\n\n### How to calculate 8 percent of 51\n\nFormula: p% of x = (p/100) * x = (8/100) * 51 = 0.08 * 51 = 4.08\n\n100% = 51\n8% = 4.08" ]

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[ "Multiplying and Dividing Fractions Worksheets This is a fantastic bundle which includes everything you need to know about the multiplying and dividing fractions across 29 in-depth pages. 2×1 3 = 2 7 ×4= 4 5 ×10 11 = Jamil is working out 1 2 of 6 11 For each pair of sentences tick the one that is true. The worksheets cover multiplying and dividing fractions with equal denominators, different denominators, by integers and by mixed fractions. This colorful worksheet shows your child how to multiply a mixed number with a fraction, and gives him practice with the method. Multiply & Divide Fractions Year 8 1 Name Match each representation to the correct calculation. You do . Preview. These ready-to-use printable worksheets help assess student learning! There are over 100 free fraction worksheets in PDFs below to support the many concepts encountered with fractions. Created: Aug 23, 2011 | Updated: Oct 14, 2014. Read more. Multiplying and Dividing Fractions Multiplying and Dividing Fractions – Example 1: Multiply fractions. Division fraction worksheet generator. Free. Multiplying and dividing decimals worksheets pdf. Practice dividing fractions and mixed numbers with these printable pages. Type keywords and hit enter. Algebraic expressions pdf printable worksheets with integers, decimals and fractions. 5th grade. Author: Created by bcooper87. Divide Decimals worksheet by dh2119 - Teaching Resources - Tes #205089. not. Bicycle Math Word Problems . 2 3 marks 3 Complete the calculations. When starting with fractions, begin by focusing on 1/2 and then a 1/4 before moving to equivalent fractions and using the 4 operations with fractions (adding, subtracting, multiplying and dividing) 10 Worksheets focusing on 1/2. Read more . Dividing fractions: Keep, Change, Flip: Keep the first fraction, change the division sign to multiplication, and flip the numerator and denominator of the second fraction. Our Multiplying and Dividing Fractions and Mixed Numbers worksheets are designed to supplement our Multiplying and Dividing Fractions and Mixed Numbers lessons. Multiplying and Dividing Fractions Test/Worksheet. You are here: Home → Worksheets → Fraction multiplication Worksheets for fraction multiplication. Reduce each answer to its lowest terms. Multiplyingctions Worksheets Pdf 6th Grade And Dividing Word ... #407325. Many worksheets include illustrations and models, as well as word problems. Multiplying and Dividing Fractions worksheet. Secret Code Riddles . Steal a march on your peers in multiplying two fractions with these pdf worksheets! Decimals Worksheets #205086 . Adding Fractions Worksheets 3 5 ÷3= 4 9 ÷1 9 = 8 3 ÷2 3 = 3 marks 4 Complete the calculations. Dividing Fractions. Multiply or divide each set of fractions. D. Russell. Worksheet Illustrating Whole Number and Fraction Multiplication. Dividing Fractions Puzzle Worksheet: File Size: 808 kb: File Type: pdf: Download File. Which means to make it more difficult for the students. Knowing whether to multiply or divide fractions in word problems can be tricky for students. 9/23 Dividing Fractions Puzzle Worksheet. Fraction Simplification Worksheet - Sanfranciscolife #407328. Alien Math Maze . Multiplying and Dividing Fractions. pdf: Download File. Dec 24, 2018 - There are 3 multiplying and dividing worksheets. Decimals Worksheets #205088. $$\\frac{2}{5} \\times \\frac{3}{4}=$$ Solution: Finally convert it into the lowest trem and then simplify the fraction. Worksheet. These worksheets are pdf documents that may be reproduced for your own use and for your student's use. Some of the worksheets for this concept are Exercise work, Fractions packet, Fractions work multiplying and dividing fractions, Adding or subtracting fractions with different denominators, Addsubtracting fractions and mixed numbers, Fractions work, Fraction review, S learning c entre rules for fractions. Fraction denominators reach as high as forty as students multiply or divide by a fraction or whole number to determine the answer. Tape … Starter has questions such as, how many halves in 3 wholes and some mixed number / top heavy fraction conversion. The circle and number line images on the following worksheets were made with the Fraction Designer pages that can be found on this web site. Our engaging DMAS pdf worksheets contain 10 exercises each where children are required to work through the multiplication and division operations first, followed by the addition and subtraction and evaluate the numerical expressions. Students will complete 28 math problems. ID: 486195 Language: English School subject: dividing fractions, multiplying and dividing decimals Grade/level: 6th grade Age: 11-12 Main content: Dividing fractions, multiplying and dividing decimals Other contents: Add to my workbooks (3) Download file pdf Embed in … Tens and Ones Place Value Worksheet . Multiplying and dividing worksheet 1 explores multiplying and dividing fractions as well as whole numbers and by . Worksheet by Kuta Software LLC Course 1 Multiplying and Dividing Fractions Name_____ Date_____ Period____ ©k x2o0E1h4B hKeuTtNaa zSbogfqtQwOaArJe[ aLcLPCi.j n WAiltlD rrJiIgYhqtAsW Xrjeosne^rCvgeddG.-1-Find each product. Displaying top 8 worksheets found for - Add Subtract Multiply And Divide Fractions Pdf. Live worksheets > English > Math > Fractions > Multiply and Divide fractions Multiply and Divide fractions Checking understanding on multiplication and division with fractions. Worksheets for teaching basic fraction recognition skills and fraction concepts, as well as operations with fractions. No login required. URL: Click Here. Decimals Worksheets #205087. When it comes to fraction multiplication of two fractions multiply the numerators and denominators and express it as a fraction again. Working with algebraic expressions is a fundamental skill in algebra. Adding Subtracting Multiplying And Dividing Fractions Word ... #407327. Dividing fraction worksheets. Multiplication And Division Of Algebraic Fractions Worksheet ... #407329 . Completing Multiplication Equations | Two Fractions. Multiplying-and-dividing-fractions. Preview and details Files included (2) docx, 600 KB. Popular Worksheets . math worksheets multiplying dividing fractions them and add large size of adding subtracting multiply divide work subtract free wor via :aletra.info adding fractions with unlike denominators worksheets fresh and g different like grade subtracting 5th pdf via :finalit.info 10/31 One-Step ... Multiplying and Dividing Rational Fractions Puzzle Worksheet: File Size: 621 kb: File Type: pdf: Download File. Multiplying and dividing fractions is the focus of this festive Christmas holiday color by number math practice sheet. Loading... Save for later. Be sure to check out the fun interactive fraction activities and additional worksheets below! 2 Math lus Motion, ® at: am: Multiplying Fractions - Set 2. Multiplying and dividing decimals worksheets pdf Collection. Title: Multiplying and Dividing Fractions Worksheet Author: USER Last modified by: Haddie Dowson Created Date: 4/7/2014 3:58:00 PM Company: LHPS Other titles Count and Trace Shapes . This p Fractions MUL 1. Kindergarten Multiplication And Division With Decimals Worksheets ... #205090. In a fraction the numerator can be divided by the denominator. The worksheets can be made in html or PDF format — both are … You can also type 5-9, that means value 5,6,7,8 or 9 will be used for the denominator. Worksheet with starter main and extention. Math. The ‘dot’ multiplication symbol is used in some problems. Illustrating Whole Number and Fraction Multiplication. Multiplying Algebraic Fractions Worksheets For ~ Clubdetirologrono #407326. The extension activity challenges pupils to identity the calculation required in wordy problems. Free. Use the procedure you learned in the video to multiply these fractions together. need to simplify your answers. Draw on the profound practice in our printable add, subtract, multiply, and divide worksheets that herald the dawn of a new arithmetic life! Instructions: Use the procedure you learned in the video to multiply these fractions. 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Multiplying and Dividing Fractions Worksheets Understanding the properties of rotations, reflections, and translations of 2D figures Worksheets Solving word problems associated with fractions Worksheets Loading... Save for later. Worksheet 1. And details Files included ( 2 ) docx, 600 kb pdf documents that may reproduced! Fraction denominators reach as high as forty as students multiply or divide by a fraction, and him... Basic fraction recognition skills and fraction concepts high as forty as students multiply or divide fractions in word can! 1 Name Match each representation to the correct calculation fractions with these printable.! Fraction denominators reach as high as forty as students multiply or divide by a again. The extension activity challenges pupils to identity the calculation required in wordy problems required in problems... Steal a march on your peers in Multiplying two fractions with these pdf worksheets of two with! 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[ "# Module Coupling Slice – based Test case Prioritization\n\nModule-coupling slice-based test case prioritization is a technique that uses the module-coupling value in order to identify the critical modules in the software and hence prioritize the test cases in the test suite based on these critical modules.\n\nSince most of the test case prioritization techniques do not take into account the fact that change in one module could also be propagated in other modules of the software. So, instead of prioritizing test cases on a simple criterion like coverage, risk, requirements, etc., the critical modules could be identified based on which test case prioritization could be performed. Below is the algorithm used for performing module-coupling slice-based test case prioritization:\n\n#### Algorithm for the Module-coupling slice-based test case prioritization\n\n1. Firstly, we create a module coupling matrix C which indicates the coupling among all the modules in the program. Construct an m X m module coupling matrix, where m is the number of modules in the program. Fill the matrix using the module-coupling values, i.e. Cij represents the coupling between module i and module j. The module coupling matrix is symmetric in nature, i.e. Cij = Cji, and all the diagonal elements i.e. Cii for all is equal to 1.\n2. Construct a module cohesion matrix S by using the cohesion type of each module and the corresponding value for each cohesion type. This is a 1 X m matrix, where m is the number of modules in the program.\n3. The final step is to create a module dependency matrix D, which is an m X m matrix, where m is the number of modules in the program. It is constructed using the following equation:\n```Dij = 0.15 * ( Si + Sj ) + 0.7 * Cij\n\nwhere, Cij is not equal to 0\nAlso,\nDij = 0 where Cij = 0\nDii = 1 for all i\n\n```\n\nAs we can see, the module coupling matrix C and module cohesion matrix S is used to construct the module dependence matrix D. The link having the highest value in the module dependence matrix are given the highest priority and the test cases are prioritized for the same modules that are associated with those links.\n\n#### Solved Example\n\nQuestion: Using the following call graph and tables, construct the Module Coupling, Module Cohesion, and Module Dependency Matrices:", null, "", null, "", null, "Solution\n\nUsing the Algorithm stated above, we can compute the matrices as follows:\n\n1. Module Coupling Matrix (C)\n\nCreate an m x m  matrix, where m is the number of modules. Here, from the given call graph we can see that there are eight modules and hence the value of m = 8. So, create a 8 x 8 matrix and start filling it on the basis of the value that is associated with the coupling type between modules i and j\n\n```Example 1:\n\nFor the link 1-2 in call graph,\nthe coupling type = Data Coupling (Refer given table in the Question)\ni = 1 and j = 2\nC12 = 0.50 since value associated with Data Coupling Type is 0.50\n\nExample 2:\n\nFor the link 3-7 in call graph,\nthe coupling type = Stamp Coupling (Refer given table in the Question)\ni = 3 and j = 7\nC37 = 0.60 since value associated with Stamp Coupling Type is 0.60\n```\n\nFill the entire matrix in a similar manner and the complete Module Coupling Matrix will be as shown below:", null, "2. Module Cohesion Matrix (S)\n\nIt is a 1 x m matrix which is simple to create using the tables provided in the question. Use the module cohesion type and the values associated with the cohesion types to fill this matrix.\n\n```Example 1:\n\nFor the module 1 in call graph,\nthe cohesion type = Coincidental (Refer given table in the Question)\nS1 = 0.80 since value associated with Coincidental Cohesion Type is 0.80\n\nExample 2:\n\nFor the module 4 in call graph,\nthe cohesion type = Temporal (Refer given table in the Question)\nS4 = 0.20 since value associated with Temporal Cohesion Type is 0.20\n```\n\nFill the entire cohesion matrix as described above and the complete Module Cohesion Matrix will be:", null, "3. Module Dependency Matrix (D)\n\nIt is an m X m matrix that is constructed using both Module Coupling and Module Cohesion Matrix. Use the formula stated in the algorithm.\n\n```Example 1:\n\nFor the link 1-2 in call graph,\nD12 = 0.15 * ( S1 + S2 ) + 0.7 * C12\nSince S1 = 0.80, S2 = 0.30 and C12 = 0.50\nTherefore,\nD12 = 0.15 * ( 0.80 + 0.30 ) + 0.7 * 0.50\n= 0.515 = 0.52 (round off to 2 decimal places)\n\nExample 2:\n\nFor the link 4-6 in call graph,\nD46 = 0.15 * ( S4 + S6 ) + 0.7 * C46\nSince S4 = 0.20, S6 = 0.40 and C46 = 0.70\nTherefore,\nD46 = 0.15 * ( 0.20 + 0.40 ) + 0.7 * 0.70\n= 0.58\n```\n\nAfter filling the entire matrix, the module dependence matrix will be:", null, "From the Module Dependency Matrix shown above, we can see that modules 4, 5, 6, and 8 have the highest value in the dependence matrix obtained, and hence test cases for these modules should be given higher priority as compared to other modules.\n\nAttention reader! Don’t stop learning now. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready.\n\nMy Personal Notes arrow_drop_up", null, "Check out this Author's contributed articles.\n\nIf you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks.\n\nPlease Improve this article if you find anything incorrect by clicking on the \"Improve Article\" button below." ]

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[ "the zeros are 77, 1 + i\n\nThere has to be another complex zero to balance the given one. This zero is 1-i. The factors of the polynomial are (x-7)²(x-1+i)(x-1-i)=(x²-14x+49)(x²-2x+1+1)=\n\n(x²-14x+49)(x²-2x+2)=\n\nx⁴-2x³+2x²\n\n-14x³+28x²-28x\n\n+49x²-98x+98=\n\nx⁴-16x³+79x²-126x+98. This is the lowest degree polynomial with the given factors. Other polynomials of the same degree can be found by multiplying this one by a constant. Higher degree polynomials can be found by multiplying by another factor of the type x+c where c is a constant.\n\nby Top Rated User (695k points)" ]

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[ "# Find expected value using CDF\n\nI'm going to start out by saying this is a homework problem straight out of the book. I have spent a couple hours looking up how to find expected values, and have determined I understand nothing.\n\nLet $$X$$ have the CDF $$F(x) = 1 - x^{-\\alpha}, x\\ge1$$.\nFind $$E(X)$$ for those values of $$\\alpha$$ for which $$E(X)$$ exists.\n\nI have no idea how to even start this. How can I determine which values of $$\\alpha$$ exist? I also don't know what to do with the CDF (I'm assuming this means Cumulative Distribution Function). There are formulas for finding the expected value when you have a frequency function or density function. Wikipedia says the CDF of $$X$$ can be defined in terms of the probability density function $$f$$ as follows:\n\n$$F(x) = \\int_{-\\infty}^x f(t)\\,dt$$\n\nThis is as far as I got. Where do I go from here?\n\nEDIT: I meant to put $$x\\ge1$$.\n\nEdited for the comment from probabilityislogic\n\nNote that $F(1)=0$ in this case so the distribution has probability $0$ of being less than $1$, so $x \\ge 1$, and you will also need $\\alpha > 0$ for an increasing cdf.\n\nIf you have the cdf then you want the anti-integral or derivative which with a continuous distribution like this\n\n$$f(x) = \\frac{dF(x)}{dx}$$\n\nand in reverse $F(x) = \\int_{1}^x f(t)\\,dt$ for $x \\ge 1$.\n\nThen to find the expectation you need to find\n\n$$E[X] = \\int_{1}^{\\infty} x f(x)\\,dx$$\n\nproviding that this exists. I will leave the calculus to you.\n\n• @henry - $F(1)=1-1^{-\\alpha}=1-1=0$, so support can't be below 1 (as CDF is a non-decreasing function) – probabilityislogic Apr 30 '11 at 6:59\n• @probabilityislogic: You may be correct in terms of the book. I will change my response. – Henry Apr 30 '11 at 9:49\n• Thanks for the response. What does f(x) represent? The probability density function? Is the derivative of the cdf always f(x)? – styfle Apr 30 '11 at 20:23\n• $f(x)$ is indeed supposed to be the probability density function. If the cdf has a derivative then it is the density, though there are distributions (for example discrete) where the cdf does not have a derivative everywhere – Henry Apr 30 '11 at 20:24\n• @styfle: If it exists then $E[X^2] = \\int_{1}^{\\infty} x^2 f(x)\\,dx$, and similarly for the expectations of other functions of $x$. – Henry Apr 30 '11 at 22:08\n\n# Usage of the density function is not necessary\n\n## Integrate 1 minus the CDF\n\nWhen you have a random variable $X$ that has a support that is non-negative (that is, the variable has nonzero density/probability for only positive values), you can use the following property:\n\n$$E(X) = \\int_0^\\infty \\left( 1 - F_X(x) \\right) \\,\\mathrm{d}x$$\n\nA similar property applies in the case of a discrete random variable.\n\n## Proof\n\nSince $1 - F_X(x) = P(X\\geq x) = \\int_x^\\infty f_X(t) \\,\\mathrm{d}t$,\n\n$$\\int_0^\\infty \\left( 1 - F_X(x) \\right) \\,\\mathrm{d}x = \\int_0^\\infty P(X\\geq x) \\,\\mathrm{d}x = \\int_0^\\infty \\int_x^\\infty f_X(t) \\,\\mathrm{d}t \\mathrm{d}x$$\n\nThen change the order of integration:\n\n$$= \\int_0^\\infty \\int_0^t f_X(t) \\,\\mathrm{d}x \\mathrm{d}t = \\int_0^\\infty \\left[xf_X(t)\\right]_0^t \\,\\mathrm{d}t = \\int_0^\\infty t f_X(t) \\,\\mathrm{d}t$$\n\nRecognizing that $t$ is a dummy variable, or taking the simple substitution $t=x$ and $\\mathrm{d}t = \\mathrm{d}x$,\n\n$$= \\int_0^\\infty x f_X(x) \\,\\mathrm{d}x = \\mathrm{E}(X)$$\n\nI used the Formulas for special cases section of the Expected value article on Wikipedia to refresh my memory on the proof. That section also contains proofs for the discrete random variable case and also for the case that no density function exists.\n\n• +1 great result: the integral of the cdf is really simple, moreover, it is wise to avoid derivatives, whenever we can (they are not as well behaved as integrals ;)). Additional: using the cdf to calculate the variance see here math.stackexchange.com/questions/1415366/… – loved.by.Jesus May 30 '17 at 21:46\n• When you change the order of integration, how do you get the integration limits? – Zaz Oct 1 '17 at 16:40\n• The standard proof does not assume that $X$ has a density. – ae0709 Sep 27 '18 at 23:19\n• @Zaz we set the integration limits so that the same part of (t, x) space is covered. The original constraints are x >0 and t > x. We can't have the outer limits depend on the inner variable, but we can define the same region as t > 0 and 0 < x < t. Good examples of this process here: mathinsight.org/… – fredcallaway Oct 7 '19 at 21:16\n\nThe result extends to the $k$th moment of $X$ as well. Here is a graphical representation:", null, "I think you actually mean $x\\geq 1$, otherwise the CDF is vacuous, as $F(1)=1-1^{-\\alpha}=1-1=0$.\n\nWhat you \"know\" about CDFs is that they eventually approach zero as the argument $x$ decreases without bound and eventually approach one as $x \\to \\infty$. They are also non-decreasing, so this means $0\\leq F(y)\\leq F(x)\\leq 1$ for all $y\\leq x$.\n\nSo if we plug in the CDF we get:\n\n$$0\\leq 1-x^{-\\alpha}\\leq 1\\implies 1\\geq \\frac{1}{x^{\\alpha}}\\geq 0\\implies x^{\\alpha}\\geq 1 > 0\\implies x\\geq 1 \\>.$$\n\nFrom this we conclude that the support for $x$ is $x\\geq 1$. Now we also require $\\lim_{x\\to\\infty} F(x)=1$ which implies that $\\alpha>0$\n\nTo work out what values the expectation exists, we require:\n\n$$\\newcommand{\\rd}{\\mathrm{d}}E(X)=\\int_{1}^{\\infty}x\\frac{\\rd F(x)}{\\rd x}\\rd x=\\alpha\\int_{1}^{\\infty}x^{-\\alpha} \\rd x$$\n\nAnd this last expression shows that for $E(X)$ to exist, we must have $-\\alpha<-1$, which in turn implies $\\alpha>1$. This can easily be extended to determine the values of $\\alpha$ for which the $r$'th raw moment $E(X^{r})$ exists.\n\n• (+1) Particularly for the sharp-eyed recognition that the given support was incorrect. – cardinal Apr 30 '11 at 14:00\n• Thanks for the response. I fixed the question. I meant to put x>=1. How did you know to first differentiate the cdf to get the density function? – styfle Apr 30 '11 at 20:37\n• @styfle - because that's what a PDF is, whenever the CDF is continuous and differentiable. You can see this by looking at how you have defined your CDF. Differentiating an integral just gives you the integrand when the upper limit is the subject of the differentiation. – probabilityislogic May 1 '11 at 1:00\n• @styfle - the PDF can also be seen as the probability that a RV lies in an infinitesimal interval. $Pr(x<X<x+dx)=F(x+dx)-F(x)\\to \\frac{dF(x)}{dx}dx=f(x)dx$ as $dx\\to 0$. This way holds more generally, even for discrete RV and RV without a density (the limit is just something other than a derivative) – probabilityislogic May 1 '11 at 1:04\n\nThe Answer requiring change of order is unnecessarily ugly. Here's a more elegant 2 line proof.\n\n$$\\int udv = uv - \\int vdu$$\n\nNow take $$du = dx$$ and $$v = 1- F(x)$$\n\n$$\\int_{0}^{\\infty} [ 1- F(x)] dx = [x(1-F(x)) ]_{0}^{\\infty} + \\int_{0}^{\\infty} x f(x)dx$$\n\n$$= 0 + \\int_{0}^{\\infty} x f(x)dx$$\n\n$$= \\mathbb{E}[X] \\qquad \\blacksquare$$\n\n• I think you mean to let du-dx so that u=x. – Michael R. Chernick Aug 30 '19 at 19:20" ]

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[ "# Equation - Exponential Function\n\n#### Alvy\n\nHello guys.\n\nI know this must be pretty basic. But, I don't get it.\n\nHow do I solve 0,6 = 0,95^x ?\n\nThis is from an equation. I got up to this point, but I can't finish it. Any help would be appreciated.\n\n#### earboth\n\nMHF Hall of Honor\nHello guys.\n\nI know this must be pretty basic. But, I don't get it.\n\nHow do I solve 0,6 = 0,95^x ?\n\nThis is from an equation. I got up to this point, but I can't finish it. Any help would be appreciated.\n1. Use logarithms:\n\n$$\\displaystyle 0,6 = 0,95^x~\\implies~x=\\log_{0.95}(0.6)$$\n\n2. Now use the base-change-formula:\n\n$$\\displaystyle x=\\log_{0.95}(0.6)~\\implies~x=\\frac{\\ln(0.6)}{\\ln(0.95)}$$\n\n3. Hint: Better use a decimal point to separate the integer part from the fraction part of a number.\n\n•", null, "Alvy\n\n#### e^(i*pi)\n\nMHF Hall of Honor\nHello guys.\n\nI know this must be pretty basic. But, I don't get it.\n\nHow do I solve 0,6 = 0,95^x ?\n\nThis is from an equation. I got up to this point, but I can't finish it. Any help would be appreciated.\n$$\\displaystyle \\ln(0.6) = x\\ln(0.95)$$\n\n$$\\displaystyle x = \\frac{\\ln(0.6)}{\\ln(0.95)}$$\n\n•", null, "Alvy" ]

[ null, "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7", null, "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7", null ]

[ "# For a given reaction, ΔH = 35.5 kJ mol-1 and ΔS = 83.6 J K-1 mol-1. The reaction is spontaneous at\n\nmore_vert\nFor a given reaction, $\\Delta$H = 35.5 kJ mol-1 and $\\Delta$S = 83.6 J K-1 mol-1. The reaction is spontaneous at (Assume that $\\Delta$H and $\\Delta$S do not vary with temperature.)\n\n(a) T > 425 K\n\n(b) all temperatures\n\n(c) T > 298 K\n\n(d) T < 425 K\n\nmore_vert\n\nverified\n$ΔG < 0 \\text{ } i.e., ΔH - TΔS < 0$\n$T> {ΔH \\over ΔS}$\n$T> \\left({{35.5 \\times1000 \\over 83.6}=424.6\\approx425 K}\\right)$" ]

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[ "", null, "LFD Book Forum", null, "Perceptron Learning Algorithm", null, "#11\n tcristo", null, "Member Join Date: Apr 2012 Posts: 23", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by GraceLAX", null, "If an algorithm always converges would the Pr(f(x) ne g(x)) = 0?\n\nI believe you will need to generate a separate set of test data to process and determine the error rate. Don't update the weights when processing the test data, just process each data point and determine whether it was correct or not. Aggregate those results and you should have your classifier error rate.\n\nBTW: This is how I did it which I guess shouldn't necessarily be confused with the correct way of doing it", null, "#12\n cool8137", null, "Junior Member Join Date: Apr 2012 Location: Lalitpur, Nepal Posts: 5", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by GraceLAX", null, "Thanks for the clarification. That helped quite a bit. If an algorithm always converges would the Pr(f(x) ne g(x)) = 0?\nI think, the probability depends on the number of data. Just as our Hypothesis function h converses towards the Target Function f, once all of the training data (x) agrees with the training values (y), the iteration stops, before it actually reaches f (simply because our sample training data satisfies to the final h, which is g)\n\nFor example: if the target function was a 45degree st. line (x2=x1), and there was only one training data say ((x1(1),x2(1)),y(1))=((1,2),+1), and if our first hypothesis function was the horizontal axis itself, the Perceptron Algorithm would stop in its first iteration, and conclude the the horizontal-axis to be close to the Target function (which was actually 45degree line through origin). But if we increase the number of data, the hypothesis function is forced to converge towards the target function, with more iterations.\n\nBut there will always be some discrepancy i guess, unless you are very lucky.\n#13", null, "htlin", null, "NTU Join Date: Aug 2009 Location: Taipei, Taiwan Posts: 601", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by tcristo", null, "I believe you will need to generate a separate set of test data to process and determine the error rate. Don't update the weights when processing the test data, just process each data point and determine whether it was correct or not. Aggregate those results and you should have your classifier error rate. BTW: This is how I did it which I guess shouldn't necessarily be confused with the correct way of doing it", null, "This is what I'd do, too.", null, "__________________\nWhen one teaches, two learn.\n#14", null, "htlin", null, "NTU Join Date: Aug 2009 Location: Taipei, Taiwan Posts: 601", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by cool8137", null, "I think, the probability depends on the number of data. Just as our Hypothesis function h converses towards the Target Function f, once all of the training data (x) agrees with the training values (y), the iteration stops, before it actually reaches f (simply because our sample training data satisfies to the final h, which is g) For example: if the target function was a 45degree st. line (x2=x1), and there was only one training data say ((x1(1),x2(1)),y(1))=((1,2),+1), and if our first hypothesis function was the horizontal axis itself, the Perceptron Algorithm would stop in its first iteration, and conclude the the horizontal-axis to be close to the Target function (which was actually 45degree line through origin). But if we increase the number of data, the hypothesis function is forced to converge towards the target function, with more iterations. But there will always be some discrepancy i guess, unless you are very lucky.\nIndeed. I've made a similar comment here:\n\nhttp://book.caltech.edu/bookforum/sh...31&postcount=4\n__________________\nWhen one teaches, two learn.\n#15", null, "htlin", null, "NTU Join Date: Aug 2009 Location: Taipei, Taiwan Posts: 601", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by GraceLAX", null, "I think it would be interesting if we can all input our actual numbers and you later show a histogram of what people entered on their homework solutions. ;-)\nI cannot speak for the Caltech instructor but in my NTU class I ask each student to do so on there own (repeating the procedure for, say, 1000 times) and plot the histogram. That is an interesting first assignment.\n\nQuote:\n Originally Posted by GraceLAX", null, "I'm having a hard time deciding how to answer the multiple choice Q 7-10. The answer depends upon if I use log or linear scaling. Aren't CS algorithm efficiencies usually classified in log scaling? Or am I over-thinking this?\nI personally don't see any need to consider scaling.\n\nQuote:\n Originally Posted by GraceLAX", null, "If an algorithm always converges would the Pr(f(x) ne g(x)) = 0?\nYou can see my replies here:\n\nhttp://book.caltech.edu/bookforum/sh...31&postcount=4\n\nHope this helps.\n__________________\nWhen one teaches, two learn.\n#16\n IamMrBB", null, "Invited Guest Join Date: Apr 2012 Posts: 107", null, "Re: Perceptron Learning Algorithm\n\nFor answering Q7 and Q9: (just as an example) is 25 closer to 5 or closer to 50?\n\nIn other words is \"closest\" in q7/q9 defined using the absolute or relative difference.\n\n25/5 = 5 and 50/25=2 so 25 is closer to 50 than to 5 using relative difference, while using absolute distance, 25 is closer to 5 than to 50.\n\nHope the course staff can confirm which definition to use in answering the question.\n\nMany thanks!", null, "#17", null, "yaser", null, "Caltech Join Date: Aug 2009 Location: Pasadena, California, USA Posts: 1,477", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by IamMrBB", null, "For answering Q7 and Q9: (just as an example) is 25 closer to 5 of closer to 50? In other words is \"closest\" in q7/q9 defined using the absolute or relative difference. 25/5 = 5 and 50/25=2 so 25 is closer to 50 than to 5 using relative difference, while using absolute distance, 25 is closer to 5 than to 50. Hope the course staff can confirm which definition to use in answering the question. Many thanks!", null, "Please use absolute distance, not ratio.\n__________________\nWhere everyone thinks alike, no one thinks very much\n#18\n [email protected]", null, "Junior Member Join Date: Apr 2012 Posts: 7", null, "Re: Perceptron Learning Algorithm\n\nI understand how to write the code to generate a random line and random points, which are assigned +/- based on their location relative to the line. (I'm assuming that [-1,1]x[-1,1] means the x-y plane (the typical axis that I've been seeing since middle school... correct me if I'm wrong and that notation means something like binary space...)\n\nI understand setting the initial weights to 0.\n\nHere is where I'm getting confused:\n\nWhen you say \"sign(w0+w1x1+w2x2)\", where if the function is positive, the outcome is +1 and visa versa, does the function itself actually generate a negative number? If so, how do you get it to generate a negative number when your learning algorithm takes steps of positive 1?\n\nLet's say that my f function is something simple like y=2x. Let's say that my random points lie on each side of the line such that I end up with the following points:\n(1,1,3), (1,3,7), (1,2,3), and (1,4,7). These map ++ and - -, since they are on opposite sides of the line.\n\nDuring the initial step, setting the weights equal to zero yields zero on each of these functions. So, we iterate once by setting the weights equal to 1. Plugging the weights into the first two points yields a positive value. (1+1+3) and (1+3+7) Yet, the bottom two points are still positive. As long as my iterative step is a positive 1, I can't get a negative number in the bottom rows. How does that work? Is that even how the learning is supposed to function?\n#19\n jsarrett", null, "Member Join Date: Apr 2012 Location: Sunland, CA Posts: 13", null, "Re: Perceptron Learning Algorithm\n\nQuote:\n Originally Posted by [email protected]", null, "Let's say that my f function is something simple like y=2x. Let's say that my random points lie on each side of the line such that I end up with the following points: (1,1,3), (1,3,7), (1,2,3), and (1,4,7). These map ++ and - -, since they are on opposite sides of the line. During the initial step, setting the weights equal to zero yields zero on each of these functions.\nNot quite. The \"guess\" made at each iteration puts these points at zero, but then you compare those zeros with your data, and you see that they should have been -1 or +1 depending on the point. Therefore your current hypothesis is bad, and should be improved, a la w += y*x[n]\n\nQuote:\n Originally Posted by [email protected]", null, "So, we iterate once by setting the weights equal to 1.\nThe update step is to move your classifier line by considering *only* 1 mis-classified point. And importantly, to update the line in the specific way that makes sure that point will then be classified correctly.\n\nQuote:\n Originally Posted by [email protected]", null, "Plugging the weights into the first two points yields a positive value. (1+1+3) and (1+3+7) Yet, the bottom two points are still positive. As long as my iterative step is a positive 1, I can't get a negative number in the bottom rows. How does that work? Is that even how the learning is supposed to function?\nTake a closer look at the interpretation of w. w is a 3-vector that describes a line in 2-d. The augmentation we do to make PLA convenient to implement has a specific interpretation of w;\nlet k =<w, w>. k is like a direction of a gradient, where it goes linearly from 0 to 1 in the length of k. w is a distance along k we draw our line at.\n\nThis means that the dot product of x[n] and w can be thought of as a measure of \"agreement\". By the 2-d vector analogy from class(which holds in 3d), you can see that in one simple step we can improve this \"agreement\" from bad to good because we know which way in the 3-space must be better (y[n]*x[n]). The miracle of the Perceptron is that just by doing this several times, we *will always* arrive at an acceptable answer.\n#20\n fattie", null, "Junior Member Join Date: Apr 2012 Posts: 1", null, "Re: Perceptron Learning Algorithm\n\nAnother question: My PLA converges, there's no problem with that. But given sample sizes (N = 10 and N = 100), I have to choose the same answer for both iterations questions. Is that OK?", null, "Thread Tools", null, "Show Printable Version", null, "Email this Page Display Modes", null, "Linear Mode", null, "Switch to Hybrid Mode", null, "Switch to Threaded Mode", null, "Posting Rules You may not post new threads You may not post replies You may not post attachments You may not edit your posts BB code is On Smilies are On [IMG] code is On HTML code is Off Forum Rules\n Forum Jump User Control Panel Private Messages Subscriptions Who's Online Search Forums Forums Home General     General Discussion of Machine Learning     Free Additional Material         Dynamic e-Chapters         Dynamic e-Appendices Course Discussions     Online LFD course         General comments on the course         Homework 1         Homework 2         Homework 3         Homework 4         Homework 5         Homework 6         Homework 7         Homework 8         The Final         Create New Homework Problems Book Feedback - Learning From Data     General comments on the book     Chapter 1 - The Learning Problem     Chapter 2 - Training versus Testing     Chapter 3 - The Linear Model     Chapter 4 - Overfitting     Chapter 5 - Three Learning Principles     e-Chapter 6 - Similarity Based Methods     e-Chapter 7 - Neural Networks     e-Chapter 8 - Support Vector Machines     e-Chapter 9 - Learning Aides     Appendix and Notation     e-Appendices\n\nAll times are GMT -7. The time now is 02:56 AM.", null, "The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity." ]

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[ "### the optimal rotation speed for ball mill\n\nBall Mill Critical Speed - 911 Metallurgist. A Ball Mill Critical Speed (actually ball, rod, AG or SAG) is the speed at which the centrifugal forces equal gravitational forces at the mill shell's inside surface and no balls will fall from its position onto the shell.\n\n### theory about mill critical speed\n\nMill Speed Critical Speed erzherzog-albrecht.de. derivation of critical speed in a ball mill pdf. importance of critical speed of the mill critical speed of mill Axial transport in dry ball mills CFD critical speed of mill 12 Dec 2003 in the front to allow the mill internals to be seen The fill level was 30 by volume and the mill speed was 75 ...\n\n### calculate critical speed of ball mill\n\nHow To Calculate Critical Speed Of A Ball Mill. Critical speed of the ball mill nc in powder metallurgy critical rotational speed nc of ball mill crusher usa about critical rotational speed nc of ball millrelated informationball mill is the most common used grinding mill for stone powder making powder grinding production line materials are conveyed to the storage bin by the bucket .\n\n### critical rotating speed of a mill\n\ncritical rotating speed of a mill [randpic] Critical Rotating Speed Of A Mill riedel-zeller.de The Effect of Ball Size Diameter on Milling Performance,.- formula for critical speed of a rotating mill,The critical speed is the speed at which the mill load sticks to the walls of the\n\n### critical speed of the ball mill nc in powder metallurgy\n\nSimulation of a ball mill operating with a low ball charge . The batch mill used has a diameter and length equal 254mm and eight charge lifters equally spaced It was operated with a ball charge equal to 20 J=0.2 powder filling voids between the balls equal 50 U= 0.5 and ran at 70 of the critical speed The breakage parameters were determined by Austin BII method Austin et al 1984 Five\n\n### rotational speed and critical speed of a ball mi\n\ncritical rotational speed for ball milli. 2.2 Rotation Speed Calculation of Ball Mill Critical Speed_ When the ball mill cylinder is rotated, there is no relative slip between the grinding medium and the cylinder wall, and it just starts to run in a state of rotation with the cylinder of the mill.\n\n### Ball Mill Critical Speed - Mineral Processing & Metallurgy\n\nA Ball Mill Critical Speed (actually ball, rod, AG or SAG) is the speed at which the centrifugal forces equal gravitational forces at the mill shell's inside surface and no balls will fall from its position onto the shell. The imagery below helps explain what goes on inside a mill as speed varies. Use our online formula. The mill speed is typically defined as the percent of the Theoretical ...\n\n### Tumbler Ball Mills - Powder Metallurgy - Beyond Discovery\n\nIf the slip of the charge against the mill chamber wall is assumed to be negligible, critical rotational speed of the mill may be calculated: Fig. 19 Tumbler mill used for milling metal powders where D is the mill diameter, in feet; and Nc is the critical speed of the mill, in revolutions per minute.\n\n### Rotation of ball mill at critical speed - Material Science\n\nAt critical speed of rotation, ball mill rotates at . Discussion; Sravanthi -Posted on 07 Oct 15 The material to be disintegrated is tumbled in a container along with wear resistant solid balls in ball milling method.\n\n### formula for critical speed for a ball mill\n\nBall Mill Critical Speed. Formula For Critical Speed Of A Ball Mill Ball Mills Mine The point where the mill becomes a centrifuge is called the critical speed and ball mills usually operate at 65 to 75 of the critical speed ball mills are generally used to grind material 14 inch and finer down to the particle size of 20 to 75 microns.\n\n### critical rotational speed nc of ball mill\n\ncritical rotational speed nc of ball mill Theory and Practice forTheory and Practice forJar Ball andreduces, critical rotational speed ball mill how to find rpm. Ball Mills - Mine Engineer.Com. This formula calculates the critical speed of any ball mill. Most ball mills operate most efficiently between 65% and 75% of their critical speed.\n\n### critical speed ball mill calculation\n\nCalculation Of Ball Mill Critical Speed Crushers. Critical speed grinding mill equation n mill speed, rmin g total grinding medium, t mechanical efficiency, when the center drive, when the edge drive, rotation speed calculation of ball mill critical speed when the ball mill cylinder is rotated, there is no relative slip between the grinding medium and\n\n### critical speed calculation ball mill\n\nBall Mill Critical Speed - Mineral Processing Metallurgy. A Ball Mill Critical Speed (actually ball, rod, AG or SAG) is the speed at which the centrifugal forces equal gravitational forces at the mill shell's inside surface and no balls will fall from its position onto the shell.\n\n### ball mill design critical speed formula\n\ncritical rotational speed ball mill formula Caesar Heavy. Ball mill critical speed mineral processing amp metallurgy a ball mill critical speed actually ball, rod, ag or sag is the speed at which the centrifugal forces equal gravitational forces at the mill shells inside surface and no balls will fall from its position onto the shell\n\n### What is the critical rotation speed in revolutions per ...\n\nWhat is the critical rotation speed in revolutions per second, for a ball mill of 1.2 m diameter charged with 70 mm dia balls? a) 0.5 b) 1.0 c) 2.76 d) 0.66\n\n### Mill Speed - an overview | ScienceDirect Topics\n\nSAG mills of comparable size but containing say 10% ball charge (in addition to the rocks), normally, operate between 70 and 75% of the critical speed. Dry Aerofall mills are run at about 85% of the critical speed. The breakage of particles depends on the speed of rotation.\n\n### Critical rotation speed for ball-milling - ScienceDirect\n\nCritical rotation speed of dry ball-mill was studied by experiments and by numerical simulation using Discrete Element Method (DEM). The results carried out by both methods showed good agreement. It has been commonly accepted that the critical rotation speed is a function of a ball radius and a jar diameter.\n\n### derivation of the critical speed of ball mill\n\nRotation of ball mill at critical speed . 2015 8 6 If speed of rotation is low it results in insignificant milling action as balls only move in lower part of the drum If speed of rotation is very high the balls cling to the drum walls due to centrifugal force Therefore intense grinding action is obtained only when drum rotates at critical speed ...\n\n### critical speed formula for ball mill\n\nThe Influence of Ball Mill Critical Speed on Production Efficiency The critical speed of various ball mills is in direct proportion with its diameter, ... Nov. 10-14, 1980) dB * = 4.5 ... Nc = Rotational Mill Speed, as a percentage of the Mill Critical Speed. Mechanical …\n\n### Products Center\n\nmobile ball mill cu zn pb equipment manufacturer; Silica Powder Processing Equipment; critical rotational speed nc of ball mill; zenith crusher products …\n\n### Critical Speed Calculation Formula Of Ball Mill\n\nstrength of system and coal load at a constant rotational speed of ball mill. However, ... Critical speed nC should be considered before other parameters are analyzed. .... According to the arc length computational formula, we can infer that. Read more\n\n### critical speed of a ball mill to grind ores\n\ncritical rotational speed nc of ball mill; recommended granite milling speed; critical rpm ball mill; wsg low speed crusher trade in thailand company; Get In Touch. Address: No.416 Jianye Road, South Jinqiao Area, Pudong, Shanghai, China. Call Now: 386256. 386258. Solutions.\n\n### formula for critical speed of a ball mill\n\nThe formula to calculate critical speed is given below. N c = 42.305 /sqt (D-d) N c = critical speed of the mill. D = mill diameter specified in meters. d = diameter of the ball. In pract Ball Mills are driven at a speed of 50-90% of the critical speed, the factor being influenced by …\n\n### Recommended Ball Mill Speed & Liner Configuration\n\nMill critical speed is defined as that rotational speed at which an infinitely small particle will centrifuge, assuming no slippage between the particle and the shell. The critical speed, Nc, in revolutions per minute, is a function of the mill diameter, D, …\n\n### critical speed sag millcritical success grinding\n\nformula for critical speed of rotation of a ball mill. Critical Speed Of Ball Mill Formula Derivation. 3 Apr 2018 SemiAutogenous Grinding SAG mill and a ball mill. apply Bonds equation to industrial mills, which differ from the standard, For each mill there is a critical speed that creates centrifuging Figure 37c of the With the help of Figure 38, the concepts used in derivation of tumbling mills\n\n### SAGMILLING.COM .:. Mill Critical Speed Determination\n\nThe \"Critical Speed\" for a grinding mill is defined as the rotational speed where centrifugal forces equal gravitational forces at the mill shell's inside surface. This is the rotational speed where balls will not fall away from the mill's shell. Result #1: This mill would need to spin at RPM to be at critical speed. Result #2: This mill's ...\n\n### Derivation Of Critical Speed Of Ball Mill – Samac\n\nBall Mill Critical Speed. Jun 19, 2015 A Ball Mill Critical Speed (actually ball, rod, AG or SAG) is the speed at which the centrifugal forces equal gravitational forces at the mill shell's inside surface and no balls will fall from its position onto the shell The imagery below helps explain what goes on inside a mill as speed varies Use our online formula The mill speed is typically defined ...\n\n### Optimum revolution and rotational directions and their ...\n\nMio et al. (2004) determined the critical rotation-to-revolution speed of vials and sun using the planetary ball mill for the effective grinding of the gibbsite powder sample. Sharma (2012 ...\n\n### what is critical speed in a ball mill\n\nthe mill The common range of mill speeds is 65 to 80 of critical depending on mill type size and the application The critical speed of a ball mill is calculated as 5419 divided by the square root of the radius in feet The rotational speed is defined as a percentage of the critical speed Smaller diameter mills...\n\n### The ball mill - Chemical Engineering - Beyond Discovery\n\nThe corresponding critical rotational speed, Nc in revolutions per unit time, is given by: In this equation, r is the radius of the mill less that of the particle. It is found that the optimum speed is between one-half and three-quarters of the critical speed. Figure 2.28 illustrates conditions in a ball mill operating at the correct rate.\n\n### Critical Speed Formula For Ball Mill\n\ncritical speed Nickzom Blog. Jul 20 2021 The image above represents shaft power ball mill length To compute for shaft power ball mill length six essential parameters are needed and these parameters are Value of C Volume Load Percentage J Critical Speed V cr Bulk Density s.g Mill Length L and Mill Internal Diameter D .\n\n### Mechanical Milling: a Top Down Approach for the Synthesis ...\n\nThe tumbler ball mill is operated closed to the critical speed beyond which the balls are pinned to . 24 Thakur Prasad Yadav . et al.: Mechanical Milling: a Top Down Approach for ... either of these kinds of mills also depends on the speed of rotation of the tumbler millcylinder or …\n\n### Kecepatan Ccritical Grinding Mill\n\nBall Mill Kecepatan Kritis. Formula Kecepatan Kritis Ball Mill apa kecepatan kritis ball mill formula untuk kecepatan kritis ball mill mill grinding jan a ball mill critical speed actually ball rod ag or sag is the speed at which the formula derivation ends up as follow critical speed is nc nbsp ball milling a ball mill is a type of grinder used to grind and blend materials for use in mineral\n\n# Do you have any questions ?\n\nWhether you want to work with us or are interested in learning more about our products,we’d love to hear from you." ]

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[ "# Triangle Congruence Worksheet\n\nTriangle Congruence Worksheet triangle congruence worksheet triangle congruence proofs worksheet shanepaulneil free. triangle congruence worksheet math teacher mambo puzzle sheet printable. triangle congruence worksheet triangle congruence worksheet pdf free download printable free. Triangle Congruence Worksheet triangle congruence worksheet triangle congruence worksheet answers pdf livinghealthybulletin templates. triangle congruence worksheet proving triangles congruent worksheet anything that lets the kids template. triangle congruence worksheet practice 4 2 triangle congruence sss and sas 9th 11th grade templates. Triangle Congruence Worksheet triangle congruence worksheet math teacher mambo puzzle sheet templates.", null, "Triangle Congruence Worksheet Triangle Congruence Proofs Worksheet Shanepaulneil Free", null, "Triangle Congruence Worksheet Math Teacher Mambo Puzzle Sheet Printable", null, "Triangle Congruence Worksheet Triangle Congruence Worksheet Pdf Free Download Printable Free", null, "Triangle Congruence Worksheet Triangle Congruence Worksheet Answers Pdf Livinghealthybulletin Templates", null, "Triangle Congruence Worksheet Proving Triangles Congruent Worksheet Anything That Lets The Kids Template", null, "Triangle Congruence Worksheet Practice 4 2 Triangle Congruence Sss And Sas 9th 11th Grade Templates", null, "Triangle Congruence Worksheet Math Teacher Mambo Puzzle Sheet Templates\n\nTriangle Congruence Worksheet triangle congruence worksheet math teacher mambo puzzle sheet printable. triangle congruence worksheet triangle congruence worksheet pdf free download printable free. triangle congruence worksheet triangle congruence worksheet answers pdf livinghealthybulletin templates. triangle congruence worksheet proving triangles congruent worksheet anything that lets the kids template. triangle congruence worksheet practice 4 2 triangle congruence sss and sas 9th 11th grade templates.\n\n### Related Post to Triangle Congruence Worksheet\n\nThis site uses Akismet to reduce spam. Learn how your comment data is processed." ]

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[ "# 周末AI课堂：卷积之上的新操作 代码篇\n\nAI课堂开讲，就差你了！\n\nfrom keras.layers import Conv2D\n\nConv2D(filters=32,kernel_size=(3,3),strides=(1,1),\\\n\nfrom keras.layers import\n\nfrom keras.layers import Input\n\nfrom keras.models import Model\n\nfrom keras.layers import Dense,Activation,Flatten\n\nfrom keras.layers import BatchNormalization as BN\n\ndef Dense_bn(x,units):\n\nx = Dense(units)(x)\n\nx =BN()(x)\n\nx =Activation('relu')(x)\n\nreturn x\n\nfrom keras.layers import Conv2D,MaxPooling2D\n\ndef normal_model():\n\nx= Input(shape=(28*28,))\n\nx2= Dense_bn(x,1000)\n\nx3=Dense_bn(x2,512)\n\nx4=Dense_bn(x3,256)\n\ny=Dense(10,activation='softmax')(x4)\n\nmodel = Model(inputs=x, outputs=y)\n\nmodel.compile(optimizer='SGD',\\\n\nloss='categorical_crossentropy',\\\n\nmetrics=['accuracy'])\n\nreturn(model)\n\nTotal params: 1,438,482\n\nTrainable params: 1,434,946\n\nNon-trainable params: 3,536\n\ndef Conv2D_bn(x,filters,length,width,\\\n\nx = Conv2D(filters, (length, width),\\\n\nx = BN()(x)\n\nx = Activation('relu')(x)\n\nreturn x\n\ndef conv_model():\n\nx= Input(shape=(28,28,1))\n\nx2= Conv2D_bn(x,64,3,3)\n\nx3= Conv2D_bn(x2,32,3,3)\n\nx4=Flatten()(x3)\n\nx5=Dense_bn(x4,32)\n\ny=Dense(10,activation='softmax')(x5)\n\nmodel = Model(inputs=x, outputs=y）\n\nmodel.compile(optimizer='SGD',\\\n\nloss='categorical_crossentropy',\\\n\nmetrics=['accuracy'])\n\nreturn(model)\n\nTotal params: 822,794\n\nTrainable params: 822,538\n\nNon-trainable params: 256\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\nmodel_1=normal_model()\n\nhis1=model_1.fit(X_train_normal,train_labels,\\\n\nbatch_size=128,validation_split=0.3,\\\n\nverbose=1,epochs=10)\n\nmodel_2=conv_model()\n\nhis2=model_2.fit(X_train_conv,train_labels,batch_size=128,\\ validation_split=0.3,verbose=1,\\\n\nepochs=10)\n\nw2=his2.history\n\nw1=his1.history\n\nsns.set(style='whitegrid')\n\nplt.plot(range(10),w1['acc'],'b-.',label='Normal train')\n\nplt.plot(range(10),w2['acc'],'r-.',label='CNN train')\n\nplt.plot(range(10),w1['val_acc'],'b-',label='Normal test')\n\nplt.plot(range(10),w2['val_acc'],'r-',label='CNN test')\n\nplt.xlabel('epochs')\n\nplt.ylabel('accuracy')\n\nplt.legend()plt.show()\n\ndef conv_model_pooling():\n\nx= Input(shape=(28,28,1))\n\nx2= Conv2D_bn(x,64,3,3)\n\nx3=MaxPooling2D(2)(x2)\n\nx4= Conv2D_bn(x3,32,3,3)\n\nx5=Flatten()(x4)\n\nx6=Dense_bn(x5,32)\n\ny=Dense(10,activation='softmax')(x6)\n\nmodel = Model(inputs=x, outputs=y)\n\nmodel.compile(optimizer='SGD',loss='categorical_crossentropy',\\\n\nmetrics=['accuracy'])\n\nreturn(model)\n\nTotal params: 220,682\n\nTrainable params: 220,426\n\nNon-trainable params: 256\n\n• 发表于:\n• 原文链接https://kuaibao.qq.com/s/20181209A13WBP00?refer=cp_1026\n• 腾讯「云+社区」是腾讯内容开放平台帐号（企鹅号）传播渠道之一，根据《腾讯内容开放平台服务协议》转载发布内容。\n• 如有侵权，请联系 [email protected] 删除。\n\n2021-11-29\n\n2021-11-29\n\n2021-11-29\n\n2021-11-29\n\n2021-11-29\n\n2021-11-29\n\n## 相关快讯\n\n2018-06-14\n\n2018-05-11\n\n2021-11-29\n\n2021-11-29\n\n2021-11-29\n• ### OpenCV4.0 Mask RCNN 实例分割示例 C＋/Python实现\n\n2021-11-29", null, "" ]

[ null, "https://imgcache.qq.com/open_proj/proj_qcloud_v2/community/portal/css/img/wechat-qr.jpg", null ]

[ "# #00aab2 Color Information\n\nIn a RGB color space, hex #00aab2 is composed of 0% red, 66.7% green and 69.8% blue. Whereas in a CMYK color space, it is composed of 100% cyan, 4.5% magenta, 0% yellow and 30.2% black. It has a hue angle of 182.7 degrees, a saturation of 100% and a lightness of 34.9%. #00aab2 color hex could be obtained by blending #00ffff with #005565. Closest websafe color is: #009999.\n\n• R 0\n• G 67\n• B 70\nRGB color chart\n• C 100\n• M 4\n• Y 0\n• K 30\nCMYK color chart\n\n#00aab2 color description : Dark cyan.\n\n# #00aab2 Color Conversion\n\nThe hexadecimal color #00aab2 has RGB values of R:0, G:170, B:178 and CMYK values of C:1, M:0.04, Y:0, K:0.3. Its decimal value is 43698.\n\nHex triplet RGB Decimal 00aab2 `#00aab2` 0, 170, 178 `rgb(0,170,178)` 0, 66.7, 69.8 `rgb(0%,66.7%,69.8%)` 100, 4, 0, 30 182.7°, 100, 34.9 `hsl(182.7,100%,34.9%)` 182.7°, 100, 69.8 009999 `#009999`\nCIE-LAB 63.311, -32.976, -14.52 22.408, 31.961, 47.105 0.221, 0.315, 31.961 63.311, 36.031, 203.765 63.311, -48.127, -17.347 56.534, -28.185, -9.827 00000000, 10101010, 10110010\n\n# Color Schemes with #00aab2\n\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #b20800\n``#b20800` `rgb(178,8,0)``\nComplementary Color\n• #00b261\n``#00b261` `rgb(0,178,97)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #0051b2\n``#0051b2` `rgb(0,81,178)``\nAnalogous Color\n• #b26100\n``#b26100` `rgb(178,97,0)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #b20051\n``#b20051` `rgb(178,0,81)``\nSplit Complementary Color\n• #aab200\n``#aab200` `rgb(170,178,0)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #b200aa\n``#b200aa` `rgb(178,0,170)``\n• #00b208\n``#00b208` `rgb(0,178,8)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #b200aa\n``#b200aa` `rgb(178,0,170)``\n• #b20800\n``#b20800` `rgb(178,8,0)``\n• #006166\n``#006166` `rgb(0,97,102)``\n• #00797f\n``#00797f` `rgb(0,121,127)``\n• #009299\n``#009299` `rgb(0,146,153)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #00c2cc\n``#00c2cc` `rgb(0,194,204)``\n• #00dbe5\n``#00dbe5` `rgb(0,219,229)``\n• #00f3ff\n``#00f3ff` `rgb(0,243,255)``\nMonochromatic Color\n\n# Alternatives to #00aab2\n\nBelow, you can see some colors close to #00aab2. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #00b28e\n``#00b28e` `rgb(0,178,142)``\n• #00b29c\n``#00b29c` `rgb(0,178,156)``\n• #00b2ab\n``#00b2ab` `rgb(0,178,171)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #009bb2\n``#009bb2` `rgb(0,155,178)``\n• #008cb2\n``#008cb2` `rgb(0,140,178)``\n• #007eb2\n``#007eb2` `rgb(0,126,178)``\nSimilar Colors\n\n# #00aab2 Preview\n\nThis text has a font color of #00aab2.\n\n``<span style=\"color:#00aab2;\">Text here</span>``\n#00aab2 background color\n\nThis paragraph has a background color of #00aab2.\n\n``<p style=\"background-color:#00aab2;\">Content here</p>``\n#00aab2 border color\n\nThis element has a border color of #00aab2.\n\n``<div style=\"border:1px solid #00aab2;\">Content here</div>``\nCSS codes\n``.text {color:#00aab2;}``\n``.background {background-color:#00aab2;}``\n``.border {border:1px solid #00aab2;}``\n\n# Shades and Tints of #00aab2\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #000101 is the darkest color, while #edfeff is the lightest one.\n\n• #000101\n``#000101` `rgb(0,1,1)``\n• #001415\n``#001415` `rgb(0,20,21)``\n• #002729\n``#002729` `rgb(0,39,41)``\n• #003a3c\n``#003a3c` `rgb(0,58,60)``\n• #004c50\n``#004c50` `rgb(0,76,80)``\n• #005f64\n``#005f64` `rgb(0,95,100)``\n• #007277\n``#007277` `rgb(0,114,119)``\n• #00858b\n``#00858b` `rgb(0,133,139)``\n• #00979e\n``#00979e` `rgb(0,151,158)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\n• #00bdc6\n``#00bdc6` `rgb(0,189,198)``\n• #00cfd9\n``#00cfd9` `rgb(0,207,217)``\n• #00e2ed\n``#00e2ed` `rgb(0,226,237)``\n• #01f4ff\n``#01f4ff` `rgb(1,244,255)``\n• #15f4ff\n``#15f4ff` `rgb(21,244,255)``\n• #29f5ff\n``#29f5ff` `rgb(41,245,255)``\n• #3cf6ff\n``#3cf6ff` `rgb(60,246,255)``\n• #50f7ff\n``#50f7ff` `rgb(80,247,255)``\n• #64f8ff\n``#64f8ff` `rgb(100,248,255)``\n• #77f9ff\n``#77f9ff` `rgb(119,249,255)``\n• #8bfaff\n``#8bfaff` `rgb(139,250,255)``\n• #9efbff\n``#9efbff` `rgb(158,251,255)``\n• #b2fcff\n``#b2fcff` `rgb(178,252,255)``\n• #c6fcff\n``#c6fcff` `rgb(198,252,255)``\n• #d9fdff\n``#d9fdff` `rgb(217,253,255)``\n• #edfeff\n``#edfeff` `rgb(237,254,255)``\nTint Color Variation\n\n# Tones of #00aab2\n\nA tone is produced by adding gray to any pure hue. In this case, #525f60 is the less saturated color, while #00aab2 is the most saturated one.\n\n• #525f60\n``#525f60` `rgb(82,95,96)``\n• #4b6567\n``#4b6567` `rgb(75,101,103)``\n• #446c6e\n``#446c6e` `rgb(68,108,110)``\n• #3e7274\n``#3e7274` `rgb(62,114,116)``\n• #37787b\n``#37787b` `rgb(55,120,123)``\n• #307e82\n``#307e82` `rgb(48,126,130)``\n• #298589\n``#298589` `rgb(41,133,137)``\n• #228b90\n``#228b90` `rgb(34,139,144)``\n• #1b9197\n``#1b9197` `rgb(27,145,151)``\n• #15979d\n``#15979d` `rgb(21,151,157)``\n• #0e9ea4\n``#0e9ea4` `rgb(14,158,164)``\n• #07a4ab\n``#07a4ab` `rgb(7,164,171)``\n• #00aab2\n``#00aab2` `rgb(0,170,178)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #00aab2 is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]

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[ "# Photoelectic effect and units\n\n## Homework Statement\n\nHI, I have this photoelectric effect problem, it is not so difficult but when i got to the units and red about it , i got confused.\nThe Problem : Photo-electrons are completely hold up by electric potential with potential of 3V. The work function is given. I have to calculate the incident light frequency.\n\n## The Attempt at a Solution\n\nSo the equation is\nEmax=hfinc\n\nAnd the units are\n[V]=[J]-[J]\nThis does not work. So I thought lets convert them to eV which is possible for [J].\nBut when I start to think about the V -> eV its gets confusing because V are not measurement of energy but eV are.\n\nSo what do you think guys, can I just do the conversion from [v] to [ev] by multiplying V by e or it is wrong?" ]

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[ "## News\n\nIn 1888, Heinrich Hertz, in Karlsruhe, published his experimental validations of Maxwell's equations and forced a conceptual revolution in European theoretical physicists by showing that electromagnetic effects propagate at a finite speed. He also discovered the existence of radio waves.\n\nBeginning in the middle of the nineteenth century, Scottish physicist James Clerk Maxwell developed twenty equations that described how electromagnetism worked, which have since been simplified into four basic formulas. The first equation illustrates the electric force field surrounding the distribution of electrical charge. The second shows how magnetic field lines curl to form closed loops. The third and four equations show how electric and magnetic fields interact with one another to produce an electromagnetic wave.\n\nHeinrich Hertz, born to a wealthy Hamburg family, earned a doctorate in physics at the unusually young age of 23. Only a few years later, in 1886, he began to research electromagnetic waves, testing Maxwell's theories, which had gained little traction in continental Europe.\n\nHertz's experiments showed that the electromagnetic effects that Maxwell had identified propagated at a finite speed. They were not simply instantaneous forces. Hertz developed techniques for measuring the wavelength and velocity of electromagnetic waves and described their reflection and refraction. In the process, he discovered the existence of radio waves and determined that they behaved like light.\n\nHertz's oscillator was composed of two rods to serve as a receiver and a spark gap as a receiving antennae. When the antennae picked up a radio wave, a spark would jump. Hertz found that these signals had the properties of electromagnetic waves. These radio waves had the same velocity as light.\n\nIn 1888, Heinrich Hertz, in Karlsruhe, published his experimental validations of Maxwell's equations and forced a conceptual revolution in European theoretical physicists by showing that electromagnetic effects propagate at a finite speed. He also discovered the existence of radio waves.\n\nBeginning in the middle of the nineteenth century, Scottish physicist James Clerk Maxwell developed twenty equations that described how electromagnetism worked, which have since been simplified into four basic formulas. The first equation illustrates the electric force field surrounding the distribution of electrical charge. The second shows how magnetic field lines curl to form closed loops. The third and four equations show how electric and magnetic fields interact with one another to produce an electromagnetic wave.\n\nHeinrich Hertz, born to a wealthy Hamburg family, earned a doctorate in physics at the unusually young age of 23. Only a few years later, in 1886, he began to research electromagnetic waves, testing Maxwell's theories, which had gained little traction in continental Europe.\n\nHertz's experiments showed that the electromagnetic effects that Maxwell had identified propagated at a finite speed. They were not simply instantaneous forces. Hertz developed techniques for measuring the wavelength and velocity of electromagnetic waves and described their reflection and refraction. In the process, he discovered the existence of radio waves and determined that they behaved like light.\n\nHertz's oscillator was composed of two rods to serve as a receiver and a spark gap as a receiving antennae. When the antennae picked up a radio wave, a spark would jump. Hertz found that these signals had the properties of electromagnetic waves. These radio waves had the same velocity as light.\n\nIn 1888, Heinrich Hertz, in Karlsruhe, published his experimental validations of Maxwell's equations and forced a conceptual revolution in European theoretical physicists by showing that electromagnetic effects propagate at a finite speed. He also discovered the existence of radio waves.\n\nBeginning in the middle of the nineteenth century, Scottish physicist James Clerk Maxwell developed twenty equations that described how electromagnetism worked, which have since been simplified into four basic formulas. The first equation illustrates the electric force field surrounding the distribution of electrical charge. The second shows how magnetic field lines curl to form closed loops. The third and four equations show how electric and magnetic fields interact with one another to produce an electromagnetic wave.\n\nHeinrich Hertz, born to a wealthy Hamburg family, earned a doctorate in physics at the unusually young age of 23. Only a few years later, in 1886, he began to research electromagnetic waves, testing Maxwell's theories, which had gained little traction in continental Europe.\n\nHertz's experiments showed that the electromagnetic effects that Maxwell had identified propagated at a finite speed. They were not simply instantaneous forces. Hertz developed techniques for measuring the wavelength and velocity of electromagnetic waves and described their reflection and refraction. In the process, he discovered the existence of radio waves and determined that they behaved like light.\n\nHertz's oscillator was composed of two rods to serve as a receiver and a spark gap as a receiving antennae. When the antennae picked up a radio wave, a spark would jump. Hertz found that these signals had the properties of electromagnetic waves. These radio waves had the same velocity as light.\n\nIn 1888, Heinrich Hertz, in Karlsruhe, published his experimental validations of Maxwell's equations and forced a conceptual revolution in European theoretical physicists by showing that electromagnetic effects propagate at a finite speed. He also discovered the existence of radio waves.\n\nBeginning in the middle of the nineteenth century, Scottish physicist James Clerk Maxwell developed twenty equations that described how electromagnetism worked, which have since been simplified into four basic formulas. The first equation illustrates the electric force field surrounding the distribution of electrical charge. The second shows how magnetic field lines curl to form closed loops. The third and four equations show how electric and magnetic fields interact with one another to produce an electromagnetic wave.\n\nHeinrich Hertz, born to a wealthy Hamburg family, earned a doctorate in physics at the unusually young age of 23. Only a few years later, in 1886, he began to research electromagnetic waves, testing Maxwell's theories, which had gained little traction in continental Europe.\n\nHertz's experiments showed that the electromagnetic effects that Maxwell had identified propagated at a finite speed. They were not simply instantaneous forces. Hertz developed techniques for measuring the wavelength and velocity of electromagnetic waves and described their reflection and refraction. In the process, he discovered the existence of radio waves and determined that they behaved like light.\n\nHertz's oscillator was composed of two rods to serve as a receiver and a spark gap as a receiving antennae. When the antennae picked up a radio wave, a spark would jump. Hertz found that these signals had the properties of electromagnetic waves. These radio waves had the same velocity as light." ]

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