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E-raamat: Concise Guide to Computing Foundations: Core Concepts and Select Scientific Applications

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Sep-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319299549
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Concise Guide to Computing Foundations: Core Concepts and Select Scientific ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Sep-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319299549
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This book will help future scientists to become more intelligent users of computing technology in their practice of science. The content is suitable for introductory courses on the foundations of computing and the specific application of computers in different areas of science. The text presents a set of modules for use in existing science courses in order to integrate individual aspects of computational thinking, as well as a set of modules introducing the computer science concepts needed to understand the computing involved. These modules guide science students in their independent learning. The book covers computing applications in such diverse areas as bioinformatics, chemical kinetics, hydrogeological modeling, and mechanics of materials, geographic information systems, flow analysis, the solving of equations, curve fitting, optimization, and scientific data acquisition. The computing topics covered include simulations, errors, data representation, algorithms, XMS, compressio

n, databases, performance, and complexity.

Introduction to Computational ScienceTypes of Visualization and ModelingData Types: Representation, Abstraction, LimitationsScientific Data AcquisitionProcedures: Algorithms and AbstractionSolving EquationsIterative SolutionsSolving Sets of EquationsProcedures: Performance and ComplexitySelf-Defining Data: Compression, XML and DatabasesSearchingCurve FittingOptimizationData Organization and AnalysisAppendix A: NetLogoAppendix B: LabQuestAppendix C: GIS
1 Introduction to Computational Science
1(8)
1.1 Objectives
1(1)
1.2 Definitions
1(1)
1.3 Introductory Example
2(2)
1.4 Another Example
4(2)
1.5 What Is Computational Science?
6(2)
1.6 Related Modules
8(1)
References
8(1)
2 Types of Visualization and Modeling
9(12)
2.1 Objectives
9(1)
2.2 Definitions
9(1)
2.3 Motivation
9(1)
2.4 Introduction
10(1)
2.5 Agent-Based Chemical Kinetics
11(4)
2.6 Systems Dynamics Chemical Kinetics
15(4)
2.6.1 Simple First Order Reaction
15(3)
2.6.2 Reversible First Order Reactions
18(1)
2.7 Computing Questions
19(1)
2.8 Related Modules
19(2)
References
20(1)
3 Data Types: Representation, Abstraction, Limitations
21(18)
3.1 Objectives
21(1)
3.2 Definitions
21(1)
3.3 Motivation
22(1)
3.4 Abstraction
22(2)
3.5 Limitations and Space Issues
24(2)
3.6 Limits and Errors
26(2)
3.7 Order of Operation
28(1)
3.8 Accuracy and Speed
29(3)
3.9 Collecting Groups of Similar Data
32(3)
3.10 Adding Structure to the Homogeneous: Trees
35(1)
3.11 Adding Structure to the Homogeneous Collections: Graphs
36(1)
3.12 Adding Structure to Homogeneous Collections: Stacks and Queues
37(1)
3.13 Related Modules
38(1)
References
38(1)
4 Scientific Data Acquisition
39(6)
4.1 Objectives
39(1)
4.2 List of Terms
39(1)
4.3 Motivation
39(1)
4.4 A First Problem --- Introduction
40(1)
4.5 Sensor Considerations
41(1)
4.6 Computing Issues
42(1)
4.7 A Second Problem --- Design
43(1)
4.8 A Third Problem --- Bonus
44(1)
4.9 Computing Questions
44(1)
4.10 Related Modules
44(1)
5 Procedures: Algorithms and Abstraction
45(14)
5.1 Objectives
45(1)
5.2 Definitions
45(1)
5.3 Motivation
45(1)
5.4 Procedures
46(1)
5.5 Control Structure Example
47(1)
5.6 Procedural Abstraction
48(1)
5.7 Theater Lights Part 1
48(3)
5.8 Theater Lights Part 2
51(1)
5.9 Leaves on the River Part 1
52(3)
5.10 Leaves on the River Part 2
55(2)
5.11 Related Modules
57(2)
References
57(2)
6 Solving Equations
59(6)
6.1 Objectives
59(1)
6.2 List of Terms
59(1)
6.3 Motivation
59(1)
6.4 Discussion
60(2)
6.5 Computing Questions
62(1)
6.6 Related Modules
63(2)
Web Resources
63(2)
7 Iterative Solutions
65(18)
7.1 Objectives
65(1)
7.2 List of Terms
65(1)
7.3 Motivation
65(1)
7.4 An Example: Have a Hang-Up
66(4)
7.5 Another Design: Out on a Limb
70(4)
7.6 The Solution Is at Hand... with Solver
74(4)
7.7 But Wait, There's More!
78(2)
7.8 Integers Are Not Real...Numbers
80(1)
7.9 Computing Questions
80(1)
7.10 Related Modules
81(2)
References
81(2)
8 Solving Sets of Equations
83(14)
8.1 Objectives
83(1)
8.2 Definitions
83(1)
8.3 Motivation
84(1)
8.4 Problem Definition
84(1)
8.5 Boundary Conditions
85(1)
8.6 Solution Methods
86(1)
8.7 Numerical Aspects
86(3)
8.8 An Excel Solution
89(1)
8.9 Setting Up Excel for Iterative Calculation
90(2)
8.10 A Matrix Solution
92(2)
8.11 The Modeling Process
94(1)
8.12 Computing Questions
95(1)
8.13 Related Modules
95(2)
Further Study
95(2)
9 Procedures: Performance and Complexity
97(12)
9.1 Objectives
97(1)
9.2 Definitions
97(1)
9.3 Motivation
97(1)
9.4 Simulation Model Performance
98(1)
9.5 Example of Computational Complexity: Tick Marks
99(3)
9.6 Another Example of Computational Complexity: Color a Square of Patches in NetLogo
102(1)
9.7 Example of Computational Complexity: Merge Sort
103(2)
9.8 Standard Big-Oh Function Classifications for Comparing Algorithms
105(2)
9.9 Related Modules
107(2)
References
107(2)
10 Self-Defining Data: Compression, XML and Databases
109(10)
10.1 Objectives
109(1)
10.2 Definitions
109(1)
10.3 Motivation
109(1)
10.4 Self-Defining Type 1: Compression
110(2)
10.5 Self-Defining Type 2: XML
112(1)
10.6 Self-Defining Type 3: Databases
112(3)
10.7 Self-Defining Type 3 Part 2: Data Warehouses
115(2)
10.8 Self-Defining Type 3 Part 3: Other Database Types
117(1)
10.9 Related Modules
118(1)
Reference
118(1)
11 Searching
119(10)
11.1 Objectives
119(1)
11.2 Definitions
119(1)
11.3 Motivation
120(1)
11.4 Searching Amino Acids
120(3)
11.5 BLASTP Algorithm
123(2)
11.6 Computing Questions
125(1)
11.7 Related Modules
126(3)
Ten Protein Sequences of 99 Amino Acids
126(1)
References
127(2)
12 Curve Fitting
129(6)
12.1 Objectives
129(1)
12.2 Definitions
129(1)
12.3 Motivation
129(1)
12.4 Fitting "By Hand"
130(1)
12.5 Fitting By Hand with Graphing Aid
131(1)
12.6 Fitting via Numerical Analysis (Regression)
132(1)
12.7 Fitting via Excel
133(1)
12.8 Computing Questions
134(1)
12.9 Related Modules
134(1)
13 Optimization
135(16)
13.1 Objectives
135(1)
13.2 Definitions
135(1)
13.3 Motivation
135(1)
13.4 What Makes Up an Optimization Problem?
136(1)
13.5 What Is the "Language" of an Optimization Problem?
136(1)
13.6 Working Through the Setup of an Optimization Problem
137(2)
13.7 Solving an Optimization Problem
139(1)
13.8 Simulated Annealing
139(3)
13.9 Genetic Algorithm
142(3)
13.10 Linear Programming
145(1)
13.11 Simplex Method
146(2)
13.12 Computing Questions
148(1)
13.13 Related Modules
149(2)
References
149(2)
14 Data Organization and Analysis
151(8)
14.1 Objectives
151(1)
14.2 Definitions
151(1)
14.3 Motivation
152(1)
14.4 Spatial Data Representation
152(1)
14.5 Joining Data
152(4)
14.6 Spatial Joining
156(1)
14.7 Computing Questions
156(1)
14.8 Related Modules
157(2)
Reference
157(2)
Appendix: NetLogo Tutorial 159(6)
Appendix: LabQuest Tutorial 165(8)
Appendix: GIS Tutorial 173(8)
Definitions 181(8)
Index 189
Dr. Kevin Brewer is Co-Chair and Professor in the Department of Engineering in the Walker School of Engineering at Olivet Nazarene University, Bourbonnais, IL, USA.

Dr. Cathy Bareiss is a Professor of Computer Science at the same institution.
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