Partitioning There is no obvious data structure that could be used to perform a decomposition of this problem’s domain into components that could be mapped to separate processors. Chip Chip Size: Size: 25 25 Chip Chip Size: Size: 54 54 Chip Chip Size: Size: 55 55 Chip Chip Size: Size: 64 64 Chip Chip Size: Size: 85 85 Chip Chip Size: Size: 65 65 Chip Chip Size: Size: 84 84 Chip Chip Size: Size: 114 114 Chip Chip Size: Size: 144 144 Chip Chip Size: Size: 200 200 Chip Chip Size: Size: 174 174 Chip Chip Size: Size: 130 130 Chip Chip Size: Size: 140 140 Chip Chip Size: Size: 143 143 Chip Chip Size: Size: 112 112 Chip Chip Size: Size: 220 220 Chip Chip Size: Size: 150 150 Chip Chip Size: Size: 234 234 Chip Chip Size: Size: 102 102 A fine-grained functional decomposition is therefore needed, where the exploration of each search tree node is handled by a separate task. CS 340 This means that new tasks will be created in a wavefront as the search progresses down the search tree, which will be explored in a breadthfirst fashion. Notice that only tasks on the wavefront will be able to execute concurrently. Page 5
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Communication In a parallel implementation of simple search, tasks can execute independently and need communicate only to report solutions. Chip Chip Size: Size: 25 25 Chip Chip Size: Size: 54 54 Chip Chip Size: Size: 55 55 Chip Chip Size: Size: 64 64 Chip Chip Size: Size: 144 144 Chip Chip Size: Size: 174 174 CS 340 Chip Chip Size: Size: 84 84 Chip Chip Size: Size: 130 130 Chip Chip Size: Size: 140 140 Chip Chip Size: Size: 143 143 Chip Chip Size: Size: 85 85 Chip Chip Size: Size: 65 65 Chip Chip Size: Size: 114 114 Chip Chip Size: Size: 200 200 The parallel algorithm for this problem will also need to keep track of the bounding value (i.e., the smallest chip area found so far), which must be accessed by every task. One possibility would be to encapsulate the bounding value maintenance in a single centralized task with which the other tasks will communicate. This approach is inherently unscalable, since the processor handling the centralized task can only service requests from the other tasks at a particular rate, thus bounding the number of tasks that can execute concurrently. Chip Chip Size: Size: 112 112 Chip Chip Size: Size: 220 220 Chip Chip Size: Size: 150 150 Chip Chip Size: Size: 234 234 Chip Chip Size: Size: 102 102 Page 6
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// Overloaded Parentheses Operator: Returns the matrix value at the slot corresponding // to the parameterized indices. (Returns zero if those indices are invalid.) double SquareMatrix::operator () (int row, int column) { if ( (row >= 0) && (row < size) && (column >= 0) && (column < size) ) return matrix[row][column]; else return 0.0; } // Overloaded Addition Operator: Returns a SquareMatrix that is the sum of the two SquareMatrix // parameter values (using the smaller of their two sizes if they differ in size). SquareMatrix operator + (const SquareMatrix &smA, const SquareMatrix &smB) { SquareMatrix newMatrix; int row, column; newMatrix.size = (smA.size < smB.size) ? smA.size : smB.size; for (row = 0; row < newMatrix.size; row++) for (column = 0; column < newMatrix.size; column++) newMatrix.matrix[row][column] = smA.matrix[row][column] + smB.matrix[row][column]; return newMatrix; } // Overloaded Subtraction Operator: Returns a SquareMatrix thatis the difference of the two // SquareMatrix parameter values (using the smaller of their two sizes if they differ in size). SquareMatrix operator - (const SquareMatrix &smA, const SquareMatrix &smB) { SquareMatrix newMatrix; int row, column; newMatrix.size = (smA.size < smB.size) ? smA.size : smB.size; for (row = 0; row < newMatrix.size; row++) for (column = 0; column < newMatrix.size; column++) newMatrix.matrix[row][column] = smA.matrix[row][column] - smB.matrix[row][column]; return newMatrix; } CHAPTER 8 – Multidimensional Arrays 13
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  EVALUATION RUBRIC DEVELOPED Below Expectations (1-2 Pts) Meets Expectations (3-4 Pts) Exceeds Expectations (5-6 Pts) POINTS Aspects of background or relevant external environment variables not discussed. Research is not thorough or is missing completely. Identified target market(s) do not follow clearly from any research presented. Provides analysis of all relevant background including competition and external environment. Some research undertaken to support analysis. Target market(s) identified. Could be clearer how research led to target market. Background is comprehensively examined and assessed. Competition, external environment, and any other relevant issues thoroughly researched and discussed. Research clearly supports target market(s) choice.   Objectives Communication objectives do not flow clearly from situation analysis. One or more objective may be difficult to measure, vague, and/or not clearly distinct from Marketing objectives. Complete communication objectives presented and follow reasonably well from situation analysis. Comm objectives are generally measurable and are distinguished from Marketing objectives. Communication objectives are clearly stated and flow fully and naturally from results of situation analysis. Objectives are specific, distinct from Marketing objectives, and measurable.   Message Strategy Basis of positioning is either missing or not presented clearly. If positioning is discussed, not clear what the connection between it and message strategy are. Message strategy is presented and positioning discussed but relationship between positioning platform and message strategy may not be totally clear. Message strategy is clearly presented and positions the product effectively. Positioning platform well-thought through and relationship between positioning and message are clear.   Media Strategy Important elements of media strategy may be missing. No clear connection between media & message strategies. Media strategy is presented and explained. Media strategy is reasonably consistent with message strategy. Media strategy is clearly presented. Media strategy supports and enhances message.   Other Plan Elements IMC plan omits one or more additional element that would contribute effectively. Appropriate public relations, direct marketing, Internet, sales promotion or support media are missing. IMC plan includes some additional elements that are appropriate. May include public relations, direct marketing, Internet, sales promotion or support media. IMC plan includes all additional elements that are appropriate (public relations, direct marketing, Internet, sales promotion, support media). Additional elements are clearly blended into positioning/message strategy.   Integration Lack of consistent message across two or more elements causes understanding of IMC to be questioned. Elements of IMC plan illustrate reasonable consistency and demonstrate understanding of the concept of IMC. The concept of IMC is clearly promoted and demonstrated through the consistent message woven throughout plan elements.   Budget Budget fails to clearly account for all plan items, does not support objectives, or is missing altogether. Full budget is presented and appears to support the plan’s objectives. All plan items accounted for in budget. Budget carefully and fully details each plan element. Supports stated objectives and is reasonable given any existing constraints.   Effectiveness Plan for measuring effectiveness of IMC plan is weak. Method choice questionable or plan is missing altogether. Plan for measuring effectiveness is presented. Choice of methods is reasonable. Measurement of all elements of IMC plan is clearly accounted for. Measurement methods are chosen/designed to produce clear results.   Situation Analysis
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Connections Between AER Strategy and BOR Equity Grids AER Strategy Grid Acquisition Acquisition Strategy Strategy Expansion Expansion Strategy Strategy Retention Retention Strategy Strategy Persona Persona #1 #1 Most Most effective effective acquisition acquisition strategies strategies for for Persona Persona 1 1 Most Most effective effective expansion expansion strategies strategies for for Persona Persona 1 1 Most Most effective effective retention retention strategies strategies for for Persona Persona 1 1 Persona Persona #2 #2 Most Most effective effective acquisition acquisition strategies strategies for for Persona Persona 2 2 Most Most effective effective expansion expansion strategies strategies for for Persona Persona 2 2 Most Most effective effective retention retention strategies strategies for for Persona Persona 2 2 Persona Persona #3 #3 Most Most effective effective acquisition acquisition strategies strategies for for Persona Persona 3 3 Most Most effective effective expansion expansion strategies strategies for for Persona Persona 3 3 Most Most effective effective retention retention strategies strategies for for Persona Persona 3 3 Personas account for customer heterogeneity AER stages account for customer dynamics BOR Equity Grid Marketing Marketing Objectives Objectives Relative Relative Advantages Advantages Sources Sources of of Sustainability Sustainability Brand Brand Strategy Strategy (Chapter (Chapter 5) 5) Brand Brand marketing marketing objectives objectives Relative Relative advantages advantages of of the the firm’s firm’s brand brand vs. vs. competitors’ competitors’ brands brands Brand’s Brand’s sources sources of of sustainability sustainability Offering Offering Strategy Strategy (Chapter (Chapter 6) 6) Offering Offering and and innovation innovation objectives objectives Relative Relative advantages advantages of of the the firm’s firm’s offering offering vs. vs. competitors’ competitors’ offerings offerings Offering’s Offering’s sources sources of of sustainability sustainability Relationship Relationship marketing marketing objectives objectives Relative Relative advantages advantages of of the the firm’s firm’s relationships relationships vs. vs. competitors’ competitors’ relationships relationships Relationship Relationship marketing’s marketing’s sources sources of of sustainability sustainability Environmental Trends Technology Technology trends trends Regulatory Regulatory trends trends Relationship Relationship Strategy Strategy (Chapter (Chapter 7) 7) Socioeconomic Socioeconomic trends trends © Palmatier 38
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What do I learn while at GDC? • Data structures, data structures, data structures, shading, data structures, data structures, data structures, physics, data structures, data structures, graphics, data structures, data structures, data structures, memory management, data structures, data structures, & data structures
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Which Skillsets Does Your Organization Need? • Accounting • Advertising & Graphic Design • Agric ultural Systems Technology • Architectural Systems Technology • Air Conditioning, Heating, & Refrigeration Technology • Automotive Systems Technology • Baking and Pastry Arts • Biopharmaceutical Technology • Business Analytic s • Business Administration • Business Administration: Global Business Management • Business Administration: Human Resources Management • Business Administration: Marketing • Civil Engineer ing Technology • Collision Repair and Refinishing Technology • Construction Equipment Systems Tec hnology • Construction Management Technology • Criminal Justice Tec hnology • Criminal Justice Tec hnology : Forensic Science • Criminal Justice Tec hnology: Latent Evidenc e • Culinar y Arts • Diesel and Heav y Equipment Technology • Elec trical Systems Technology • Elec tronic s Engineering Technology • Facility Maintenance Technology • Geometrics Technology • Health and Fitness Scienc e • Heav y Equipment Operator • Hospitality Management • Human Servic es Technology • Human Servic es Technology: Mental Health • Human Servic es Technology: Substance Abuse • Human Servic es Technology: Substance Abuse Intervention • IT- Computer Engineering • IT- Computer Progr amming & Development • IT- Cyber Sec urity • IT- Data Science & Programming Support Systems • IT- Healthcare Business Informatics • IT- Mobile Applications Developer • IT- Network Management • IT- Stor age And Virtualization • IT- Technical Support • IT- Web Designer • IT- Web Developer • Interior Design • Mec hanical Engineering Tec hnology • Medical Offic e Administration • Office Administration • Plumbing • Simulation & Game Development • Supply Chain Management/Distribution Management • Supply Chain Management/ Global Logistics Technology • Welding Technology  ©201 8
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IUC’s scope will focus on two functions (Finance and IT) Adherence to process definitions within the Hackett taxonomy is key to comparability; processes are defined end to end Selling and General Administrative Scope Finance Finance Human Human Resources Resources Sales* Sales* Executive Executive and and Corporate Corporate Services Services**  Total  Sales  General Cash Cash Disbursements Disbursements Total Rewards Rewards Administration Administration Sales Execution Execution General Administration Administration Management Management    Revenue Cycle Payroll Services Sales Operations   Travel and  Revenue Cycle Payroll Services Sales Operations Travel and Transportation Transportation Services Services  Planning  Real Accounting Accounting and and External External Reporting Reporting  Data Data Mgmt., Mgmt., Reporting Reporting & & Compliance Compliance Planning and and Strategy Strategy Real Estate Estate & & Facilities Facilities Management Management  Staffing  Function  Government Tax Tax Management Management Staffing Services Services Function Management Management Government Affairs Affairs  Labor  Legal Treasury Treasury Management Management Labor Relations Relations Legal  Workforce  Quality Compliance Compliance Management Management Workforce Development Development Services Services Service* Quality Management Management Service*  Organisational  Planning Risk  Order  Planning & & Performance Performance Management Management Organisational Effectiveness Effectiveness Risk and and Security Security Management Management Order and and Contract Contract Management Management (OTC) (OTC)  Total  Corporate Fiscal Communications  Service Fiscal Analysis Analysis Total Rewards Rewards Planning Planning Execution Corporate Communications Service Execution  Strategic  Planning Function  Service Function Management Management Strategic Workforce Workforce Planning Planning Planning and and Strategy Strategy Service Operations Operations  Function  Executive Office  Planning Function Management Management and Strategy Executive Office Planning and Strategy  Function Function Management Management Information Technology          Information Technology            Infrastructure Infrastructure Management Management End End User User Support Support Infrastructure Infrastructure Development Development Application Application Maintenance Maintenance Application Application Development Development & & Implement. Implement. Quality Quality Assurance Assurance Risk Risk Management Management IT IT Business Business Planning Planning Enterprise Enterprise Architecture Architecture Planning Planning Emerging Emerging Technologies Technologies Function Function Management Management Procurement Procurement            Supply Supply Data Data Management Management Requisition Requisition and and PO PO Processing Processing Supplier Scheduling Supplier Scheduling Receipt Receipt Processing Processing Compliance Compliance Management Management Customer Customer Management Management Sourcing Sourcing Execution Execution Supplier Supplier Management Management and and Development Development Sourcing Sourcing & & Supply Supply Base Base Strategy Strategy Function Function Strategy Strategy and and Performance Performance Management Management Function Function Management Management Marketing* Marketing*      Marketing Marketing Communication Communication Brand Brand and and Product Product Management Management Planning and Planning and Strategy Strategy Market Market Research Research and and Analytics Analytics Function Management Function Management Capture FTEs and Costs as defined regardless of where they are organizationally located
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VLSI Floorplan: Algorithm Outline The common sequential approach for this problem is a tree-based, depth-first technique called branchand-bound, in which subtrees are pruned whenever it becomes clear that they become too costly. Component Component A A Placement Placement Chip Size: 25 Component Component B B Placement: Placement: Six Six Possibilities Possibilities Chip Size: 54 Chip Size: 55 Chip Size: 64 Chip Size: 85 Chip Size: 65 Chip Size: 84 Component Component C C Placement Placement (B1 (B1 Version): Version): Twelve Twelve Possibilities Possibilities Chip Size: 114 Chip Size: 144 Chip Size: 200 CS 340 Chip Size: 174 Chip Size: 143 Chip Size: 140 Chip Size: 112 Chip Size: 130 Chip Size: 220 Chip Size: 150 Chip Size: 234 Chip Size: 102 Page 4
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NeMoFinder adapts SPIN [27] to extract frequent trees and expands them into non-isomorphic graphs.[8] NeMoFinder utilizes frequent size-n trees to partition the input network into a collection of size-n graphs, afterward finding frequent size-n sub-graphs by expansion of frequent trees edge-by-edge until getting a complete size-n graph Kn. The algorithm finds NMs in undirected networks and is not limited to extracting only induced sub-graphs. Furthermore, NeMoFinder is an exact enumeration algorithm and is not based on a sampling method. As Chen et al. claim, NeMoFinder is applicable for detecting relatively large NMs, for instance, finding NMs up to size-12 from the whole S. cerevisiae (yeast) PPI network as the authors claimed.[28] NeMoFinder consists of three main steps. First, finding frequent size-n trees, then utilizing repeated size-n trees to divide the entire network into a collection of size-n graphs, finally, performing sub-graph join operations to find frequent size-n sub-graphs.[26] In the first step, the algorithm detects all non-isomorphic size-n trees and mappings from a tree to the network. In the second step, the ranges of these mappings are employed to partition the network into size-n graphs. Up to this step, there is no distinction between NeMoFinder and an exact enumeration method. However, a large portion of non-isomorphic size-n graphs still remain. NeMoFinder exploits a heuristic to enumerate non-tree size-n graphs by the obtained information from preceding steps. The main advantage is in third step, which generates candidate sub-graphs from previously enumerated sub-graphs. This generation of new size-n sub-graphs is done by joining each previous sub-graph with derivative sub-graphs from itself called cousin sub-graphs. These new sub-graphs contain one additional edge in comparison to the previous sub-graphs. However, there exist some problems in generating new sub-graphs: There is no clear method to derive cousins from a graph, joining a sub-graph with its cousins leads to redundancy in generating particular sub-graph more than once, and cousin determination is done by a canonical representation of the adjacency matrix which is not closed under join operation. NeMoFinder is an efficient network motif finding algorithm for motifs up to size 12 only for protein-protein interaction networks, which are presented as undirected graphs. And it is not able to work on directed networks which are so important in the field of complex and biological networks. The pseudocode of NeMoFinder is shown here: NeMoFinder Input: G - PPI network; N - Number of randomized networks; K - Maximal network motif size; F - Frequency threshold; S - Uniqueness threshold; Output: U - Repeated and unique network motif set; D ← ∅; for motif-size k from 3 to K do T ← FindRepeatedTrees(k); GDk ← GraphPartition(G, T) D ← D ∪ T; D′ ← T; i ← k; while D″ = ∅ and i ≤ k × (k - 1) / 2 do D′ ← FindRepeatedGraphs(k, i, D′); D ← D ∪ D′; i ← i + 1; end while end for for counter i from 1 to N do Grand ← RandomizedNetworkGeneration(); for each g ∈ D do GetRandFrequency(g, Grand); end for end for U ← ∅; for each g ∈ D do s ← GetUniqunessValue(g); if s ≥ S then U ← U ∪ {g}; end if end for return U Grochow and Kellis [29] proposed an exact alg for enumerating sub-graph appearances, which is based on a motif-centric approach, which means that the frequency of a given sub-graph,called the query graph, is exhaustively determined by searching for all possible mappings from the query graph into the larger network. It is claimed [29] that a motif-centric method in comparison to network-centric methods has some beneficial features. First of all it avoids the increased complexity of sub-graph enumeration. Also, by using mapping instead of enumerating, it enables an improvement in the isomorphism test. To improve the performance of the alg, since it is an inefficient exact enumeration alg, the authors introduced a fast method which is called symmetry-breaking conditions. During straightforward sub-graph isomorphism tests, a sub-graph may be mapped to the same sub-graph of the query graph multiple times. In Grochow-Kellis alg symmetrybreaking is used to avoid such multiple mappings. GK alg and symmetry-breaking condition which eliminates redundant isomorphism tests. (a) graph G, (b) illustration of all automorphisms of G that is showed in (a). From set AutG we can obtain a set of symmetrybreaking conditions of G given by SymG in (c). Only the first mapping in AutG satisfies the SynG conditions; so, by applying SymG in Isomorphism Extension module alg only enumerate each match-able sub-graph to G once. Note that SynG is not a unique set for an arbitrary graph G. The GK alg discovers the whole set of mappings of a given query graph to the network in two major steps. It starts with the computation of symmetry-breaking conditions of the query graph. Next, by means of a branch-and-bound method, alg tries to find every possible mapping from the query graph to the network that meets the associated symmetry-breaking conditions. Computing symmetry-breaking conditions requires finding all automorphisms of a given query graph. Even though, there is no efficient (or polynomial time) algorithm for the graph automorphism problem, this problem can be tackled efficiently in practice by McKay’s tools.[24][25] As it is claimed, using symmetry-breaking conditions in NM detection lead to save a great deal of running time. Moreover, it can be inferred from the results in [29][30] that using (a) graph G, (b) illustration of all automorphisms of G that is showed in (a). From set AutG we can obtain a set the symmetry-breaking conditions results in high efficiency particularly for directed networks in comparison to undirected of symmetry-breaking conditions of G given by SymG networks. The symmetry-breaking conditions used in the GK algorithm are similar to the restriction which ESU algorithm in (c). Only the first mapping in AutG satisfies the applies to the labels in EXT and SUB sets. In conclusion, the GK algorithm computes the exact number of appearance of a SynG conditions; as a result, by applying SymG in the given query graph in a large complex network and exploiting symmetry-breaking conditions improves the algorithm Isomorphism Extension module the algorithm only performance. Also, GK alg is 1 of the known algorithms having no limitation for motif size in implementation and potentially enumerate each match-able sub-graph in network to G once. SynG is not a unique set for an arbitrary graph G. it can find motifs of any size.
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Simulation Results Department of Electrical and Computer Engineering Strategy of AoC Introduction Development of Mobile Network Future Works Simulation Conclusion • Zero-determinant Strategy • Tit-for-Tat Strategy • Pavlov Strategy • Cooperative Strategy • Non-cooperative Strategy ( ) ( ) ( ) ( ) Strategy of PoC • Any Strategy • Tit-for-Tat Strategy • Pavlov Strategy • Cooperative Strategy • Non-cooperative Strategy ( ) ( ) ( ) ( ) ( )
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strategy connectNewEvents(min_new, computeTEnSnpGuard(min_old, max_new: min_new, interger) max_new : integer; TEnSnpGuardStr : string) { if (min_old << max_new) if(min_new max_new) then then computeTEnSnpGuard(min_old + 1, min_new, max_new, connectOneNewEventToOtherNewEvents(min_new, max_new); TEnSnpGuardStr + "(#S" + intToString(min_old) + " == 0)&&"); else connectNewEvents(min_new+1, max_new); endif; addEventswithGuard(min_new, max_new, TEnSnpGuardStr + "(#S" + intToString(min_old) + "== 0))?1:0"); } endif; } strategy connectOneNewEventToOtherNewEvents(event_num, max_new: integer) {... // several strategies not show here (e.g., addEventswithGuard) strategy if(event_num addEvents(min_new, < max_new) then max_new : integer; TEnSnpGuardStr : string) { connectTwoEvents(event_num, max_new); if connectNewEvents(event_num, (min_new <= max_new) then max_new-1); endif; addNewEvent(min_new, TEnSnpGuardStr); } addEvents(min_new+1, max_new, TEnSnpGuardStr); strategy endif; connectTwoEvents(first_num, second_num : integer) {} strategy declare firstinProg, addNewEvent(event_num secondinProg : :atom; integer; TEnSnpGuardStr : string) { declare secondTProc1, secondTProc2 : atom; declare first_numStr, start, stTran, inProg, second_numStr, endTran :TProcSnp_guard1, atom; TProcSnp_guard2 : string; declare TStSnp_guard : string; first_numStr := intToString(first_num); start := findAtom("StSnpSht"); second_numStr := intToString(second_num); stTran := addAtom("ImmTransition", TProcSnp_guard1 := "((#S" + first_numStr "TStSnp" +"+ ==intToString(event_num)); 0) && (#S" + second_numStr + " == 1))?1 : 0"; TStSnp_guard := "(#S" TProcSnp_guard2 := "((#S" + intToString(event_num) + second_numStr + " +==" == 0) && 1)?1 (#S" : 0";+ first_numStr + " == 1))?1 : 0"; stTran.setAttribute("Guard", firstinProg := findAtom("SnpInProg" TStSnp_guard); + first_numStr); secondinProg := findAtom("SnpInProg" + second_numStr); inProg := addAtom("Place", secondTProc1 := addAtom("ImmTransition", "SnpInProg" + intToString(event_num)); "TProcSnp" + first_numStr + "," + second_numStr); endTran := addAtom("ImmTransition", secondTProc1.setAttribute("Guard", TProcSnp_guard1); "TEnSnp" + intToString(event_num)); endTran.setAttribute("Guard", secondTProc2 := addAtom("ImmTransition", TEnSnpGuardStr); "TProcSnp" + second_numStr + "," + first_numStr); addConnection("InpImmedArc", start,TProcSnp_guard2); secondTProc2.setAttribute("Guard", stTran); addConnection("OutImmedArc", stTran, inProg); addConnection("InpImmedArc", firstinProg, inProg, endTran); secondTProc1); addConnection("OutImmedArc", secondTProc1, endTran, start); secondinProg); } addConnection("InpImmedArc", secondinProg, secondTProc2); addConnection("OutImmedArc", secondTProc2, firstinProg); } Scaling a Base SRN Model strategy computeTEnSnpGuard(min_old, min_new, max_new : integer; TEnSnpGuardStr : string) { if (min_old < max_new) then computeTEnSnpGuard(min_old + 1, min_new, max_new, TEnSnpGuardStr + "(#S" + intToString(min_old) + " == 0)&&"); else addEventswithGuard(min_new, max_new, TEnSnpGuardStr + "(#S" + intToString(min_old) + "== 0))?1:0"); endif; } ... // several strategies not show here (e.g., addEventswithGuard) strategy addEvents(min_new, max_new : integer; TEnSnpGuardStr : string) { if (min_new <= max_new) then addNewEvent(min_new, TEnSnpGuardStr); addEvents(min_new+1, max_new, TEnSnpGuardStr); endif; } strategy addNewEvent(event_num : integer; TEnSnpGuardStr : string) { declare start, stTran, inProg, endTran : atom; declare TStSnp_guard : string; strategy connectNewEvents(min_new, max_new: interger) { if(min_new < max_new) then connectOneNewEventToOtherNewEvents(min_new, max_new); connectNewEvents(min_new+1, max_new); endif; } strategy connectOneNewEventToOtherNewEvents(event_num, max_new: integer) { if(event_num < max_new) then connectTwoEvents(event_num, max_new); connectNewEvents(event_num, max_new-1); endif; } strategy connectTwoEvents(first_num, second_num : integer) { declare firstinProg, secondinProg : atom; declare secondTProc1, secondTProc2 : atom; declare first_numStr, second_numStr, TProcSnp_guard1, TProcSnp_guard2 : string; first_numStr := intToString(first_num); second_numStr := intToString(second_num); TProcSnp_guard1 := "((#S" + first_numStr + " == 0) && (#S" + second_numStr + " == 1))?1 : 0"; TProcSnp_guard2 := "((#S" + second_numStr + " == 0) && (#S" + first_numStr + " == 1))?1 : 0"; firstinProg := findAtom("SnpInProg" + first_numStr); secondinProg := findAtom("SnpInProg" + second_numStr); secondTProc1 := addAtom("ImmTransition", "TProcSnp" + first_numStr + "," + second_numStr); secondTProc1.setAttribute("Guard", TProcSnp_guard1); secondTProc2 := addAtom("ImmTransition", "TProcSnp" + second_numStr + "," + first_numStr); secondTProc2.setAttribute("Guard", TProcSnp_guard2); start := findAtom("StSnpSht"); stTran := addAtom("ImmTransition", "TStSnp" + intToString(event_num)); TStSnp_guard := "(#S" + intToString(event_num) + " == 1)?1 : 0"; stTran.setAttribute("Guard", TStSnp_guard); inProg := addAtom("Place", "SnpInProg" + intToString(event_num)); endTran := addAtom("ImmTransition", "TEnSnp" + intToString(event_num)); endTran.setAttribute("Guard", TEnSnpGuardStr); addConnection("InpImmedArc", start, stTran); addConnection("OutImmedArc", stTran, inProg); addConnection("InpImmedArc", inProg, endTran); addConnection("OutImmedArc", endTran, start); addConnection("InpImmedArc", firstinProg, secondTProc1); addConnection("OutImmedArc", secondTProc1, secondinProg); addConnection("InpImmedArc", secondinProg, secondTProc2); addConnection("OutImmedArc", secondTProc2, firstinProg); } } 28
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Why Why Do Do Structures Structures Differ? Differ? –– Environment Environment Key KeyDimensions: Dimensions: • • Capacity: Capacity:the thedegree degreeto to which whichan anenvironment environment can cansupport supportgrowth. growth. • • Volatility: Volatility:the thedegree degreeof of instability instabilityininthe the environment. environment. • • Complexity: Complexity:the thedegree degree of ofheterogeneity heterogeneityand and concentration concentrationamong among environmental environmental elements. elements.
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// Overloaded Multiplication Operator: Returns a SquareMatrix that // is the product of the two parameterized SquareMatrix values // (using the smaller of their two sizes if they differ in size). SquareMatrix operator * (const SquareMatrix &smA, const SquareMatrix &smB) { SquareMatrix newMatrix; int row, column, index; newMatrix.size = (smA.size < smB.size) ? smA.size : smB.size; for (row = 0; row < newMatrix.size; row++) for (column = 0; column < newMatrix.size; column++) { newMatrix.matrix[row][column] = 0.0; for (index = 0; index < newMatrix.size; index++) newMatrix.matrix[row][column] += smA.matrix[row][index] * smB.matrix[index][column]; } return newMatrix; } // Overloaded Multiplication Operator: Returns a SquareMatrix // that is the product of the parameterized scalar value and // the parameterized SquareMatrix value. SquareMatrix operator * (double coefficient, const SquareMatrix &sm) { SquareMatrix newMatrix; int row, column; newMatrix.size = sm.size; for (row = 0; row < newMatrix.size; row++) for (column = 0; column < newMatrix.size; column++) newMatrix.matrix[row][column] = coefficient * sm.matrix[row][column]; return newMatrix; } CHAPTER 8 – Multidimensional Arrays 14
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Graphics that show progression help the reader grasp the order of events. Long Term Plan (2018 & Forward): Once IBC establishes itself with a large membership presence, the current executive board members must effectively train the younger individuals in the organization the necessary skills to ensure the organization’s long term survival. This includes a deep understanding of the case frameworks and an understanding of the administrative tasks that accompany an organization. From there, IBC should look to establish a deep network of alumni who are professionals in the consulting field. As the organization grows, the alumni base will become more significant, starting a cycle of recruitment opportunities that improve ever year. First Year Plan (2017 – 2018): In August of 2017, IBC executives established the organization on-campus through the Student Organization Business Office (SOBO), providing Iowa Business Consulting with access to important university resources and recruiting opportunities. In September of 2017, the executive board established the organization’s general practices, including organizational case frameworks, training and layout, which are primarily based off of the Harvard Business School framework for solving case problems. In October of 2017, the IBC team put new members through case training and a series of sample cases designed to test their problem solving ability. By February of 2017, IBC hopes to initiate client engagements by sending managing directors into the field to scout opportunities and consult on the organization’s first 4 case projects. Once IBC establishes itself with a large membership presence, the current executive board members must effectively train the younger individuals in the organization the necessary skills to ensure the organization’s long term survival. This includes a deep understanding of the case frameworks and an understanding of the administrative tasks that accompany an organization. From there, IBC should look to establish a deep network of alumni who are professionals in the consulting field. As the organization grows, the alumni base will become more significant, starting a cycle of recruitment opportunities that improve ever year. The primary goal of Iowa Business Consulting is to prepare students for interviews and on-job success. August 2018: Establish beginning steps of alumni base in the consulting industry through current member job placement May 2018: Turn the organization over to young leadership team with the hopes of continuing the organization on pre-established framework February 2017: Initiate client engagements, sending managing directors into the field to scout opportunities to consult on 4 case projects October 2017: Initiated new members, putting the team through case training and a series of sample cases designed to test their problem solving ability October 2017: Recruiting initial class of 20 members, selected via applications and intensive interview process including behavioral, market-sizing, and brain-teaser questions September 2017: Established general practices, including organizational case frameworks, training, and layout August 2017: Established IBC as an official on-campus organization
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Selecting Strategies That Capture Opportunities Cost-Based Cost-Based Strategy Strategy Broad-Based Broad-Based Strategy Strategy Choosing Choosing the the Right Right Strategy Strategy for for Performance Performance Differentiati Differentiati on-Based on-Based Strategy Strategy © 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use. Focus Focus Strategy Strategy 3–20
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AER Strategy Grid: Micro-Analysis of Customer Heterogeneity and Dynamics  Output of MP#2 is a microanalysis of customer heterogeneity and dynamics in the firm’s customer portfolio  The insights from MP#2 then can be inserted into the AER strategy grid to reveal highimpact strategies  Each box in this grid describes the most effective strategy for a unique persona at a single point in time AER Strategy Grid Acquisition Acquisition Strategy Strategy Expansion Expansion Strategy Strategy Retention Retention Strategy Strategy Persona Persona #1 #1 Most Most effective effective acquisition acquisition strategies strategies for for Persona Persona 1 1 Most Most effective effective expansion expansion strategies strategies for for Persona Persona 1 1 Most Most effective effective retention retention strategies strategies for for Persona Persona 1 1 Persona Persona #2 #2 Most Most effective effective acquisition acquisition strategies strategies for for Persona Persona 2 2 Most Most effective effective expansion expansion strategies strategies for for Persona Persona 2 2 Most Most effective effective retention retention strategies strategies for for Persona Persona 2 2 Persona Persona #3 #3 Most Most effective effective acquisition acquisition strategies strategies for for Persona Persona 3 3 Most Most effective effective expansion expansion strategies strategies for for Persona Persona 3 3 Most Most effective effective retention retention strategies strategies for for Persona Persona 3 3 © Palmatier 39
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• Some Definitions – Maxi-min (mini-max) strategy: The maxi-min strategy is the path with the highest possible (maximum) worst (minimum) outcome. – Dominant strategy: A strategy is dominant if it does better than all other strategies in at least one circumstance and as well as every other strategy in all circumstances. – Dominated strategy: A strategy is dominated if it does worse than another strategy in at least one circumstance and no better than that strategy in all circumstances. – Nash Equilibrium: An outcome is in Nash equilibrium if, given that outcome, no party regrets its choice of strategy (cannot do better).
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