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Book description

The best-selling Programming and Problem Solving with C++, now in it's Sixth Edition, remains the clearest introduction to C++, object-oriented programming, and software development available. Renowned author team Nell Dale and Chip Weems are careful to include all topics and guidelines put forth by the ACM/IEEE to make this text ideal for the one- or two-term CS1 course. Their philosophy centers on making the difficult concepts of computer science programming accessible to all students, while maintaining the breadth of detail and topics covered.

Key Features: -The coverage of advanced object-oriented design and data structures has been moved to later in the text. -Provides the highly successful concise and student-friendly writing style that is a trademark for the Dale/Weems textbook series in computer science. -Introduces C++ language constructs in parallel with the appropriate theory so students see and understand its practical application. -Strong pedagogical elements, a hallmark feature of Dale/Weems' successful hands-on teaching approach, include Software Maintenance case studies, Problem-Solving case studies, Testing & Debugging exercises, Exam Preparation exercises, Programming Warm-up exercises, Programming Problems, Demonstration Projects, and Quick Check exercises. -A complete package of student and instructor resources include a student companion website containing all the source code for the programs and exercises in the text, additional appendices with C++ reference material and further discussion of topics from the text, and a complete digital lab manual in C++. Instructors are provided all the solutions to the exercises in the text, the source code, a Test Bank, and PowerPoint Lecture Outlines organized by chapter.

Table of contents

  • What Is Programming?
  • How Do We Write a Program?
  • What Is an Algorithm?
  • What Is a Programming Language?
  • What Kinds of Instructions Can Be Written in a Programming Language?
  • What Is Software Maintenance?
  • Software Maintenance Case Study: An Introduction to Software Maintenance
  • 1.3 What’s Inside the Computer?
  • Software Piracy
  • Privacy of Data
  • Use of Computer Resources
  • Software Engineering
  • Ask Questions
  • Look for Things That Are Familiar
  • Solve by Analogy
  • Means-Ends Analysis
  • Divide and Conquer
  • The Building-Block Approach
  • Merging Solutions
  • Mental Blocks: The Fear of Starting
  • Algorithmic Problem Solving
  • Problem-Solving Case Study: Leap Year Algorithm
  • Quick Check Answers
  • Exam Preparation Exercises
  • Programming Warm-Up Exercises
  • Case Study Follow-Up
  • Line Number
  • C++ Program Structure
  • Syntax and Semantics
  • Syntax Templates
  • Naming Program Elements: Identifiers
  • Data and Data Types
  • Naming Elements: Declarations
  • Taking Action: Executable Statements
  • Beyond Minimalism: Adding Comments to a Program
  • Blocks (Compound Statements)
  • The C++ Preprocessor
  • Software Maintenance Case Study: Adding Titles to Names
  • Creating Blank Lines
  • Inserting Blanks Within a Line
  • Special Characters
  • Entering a Program
  • Compiling and Running a Program
  • Problem-Solving Case Study: Printing a Chessboard
  • Testing and Debugging
  • Programming Problems
  • 3.1 Overview of C++ Data Types
  • Integral Types
  • Floating-Point Types
  • Named Constant Declarations
  • Variable Declarations
  • Arithmetic Operators
  • Increment and Decrement Operators
  • Precedence Rules
  • Type Coercion and Type Casting
  • Software Maintenance Case Study: Precedence Error
  • Value-Returning Functions
  • Library Functions
  • Void Functions
  • Integers and Strings
  • Floating-Point Numbers
  • The length and size Functions
  • The find Function
  • The substr Function
  • Accessing Characters Within a String: The at Function
  • Converting to Lowercase and Uppercase
  • Problem-Solving Case Study: Mortgage Payment Calculator
  • Input Streams and the Extraction Operator (>>)
  • The Reading Marker and the Newline Character
  • Reading Character Data with the get Function
  • Skipping Characters with the ignore Function
  • Reading String Data
  • 4.2 Interactive Input/Output
  • 4.3 Noninteractive Input/Output
  • Using Files
  • Run-Time Input of File Names
  • 4.5 Input Failure
  • 4.6 Software Design Methodologies
  • Implementing the Design
  • A Perspective on Design
  • Problem-Solving Case Study: Displaying a Name in Multiple Formats
  • The bool Data Type
  • Logical Expressions
  • The If-Then-Else Form
  • The If-Then Form
  • A Common Mistake
  • Software Maintenance Case Study: Incorrect Output
  • The Dangling else
  • Precedence of Operators
  • Relational Operators with Floating-Point Types
  • 5.6 Testing the State of an I/O Stream
  • Problem-Solving Case Study: BMI Calculator
  • Testing in the Problem-Solving Phase: The Algorithm Walk-Through
  • Testing in the Implementation Phase
  • The Test Plan
  • Tests Performed Automatically During Compilation and Execution
  • 6.1 The While Statement
  • 6.2 Phases of Loop Execution
  • Count-Controlled Loops
  • Event-Controlled Loops
  • Looping Subtasks
  • Software Maintenance Case Study: Make a Program General
  • Designing the Flow of Control
  • Designing the Process Within the Loop
  • The Loop Exit
  • Designing Nested Loops
  • Problem-Solving Case Study: Recording Studio Design
  • Loop-Testing Strategy
  • Test Plans Involving Loops
  • 7.1 The Switch Statement
  • 7.2 The Do-While Statement
  • 7.3 The For Statement
  • Software Maintenance Case Study: Changing a Loop Implementation
  • 7.4 The Break and Continue Statements
  • 7.5 Guidelines for Choosing a Looping Statement
  • Assignment Operators and Assignment Expressions
  • Bitwise Operators
  • The Cast Operation
  • The sizeof Operator
  • The ?: Operator
  • Operator Precedence
  • Type Coercion in Arithmetic and Relational Expressions
  • Problem-Solving Case Study: The Rich Uncle
  • When to Use Functions
  • Why Do Modules Need an Interface Design?
  • Designing Interfaces
  • Writing Modules as Void Functions
  • Flow of Control in Function Calls
  • Function Parameters
  • Function Call (Invocation)
  • Function Declarations and Definitions
  • Local Variables
  • The Return Statement
  • Value Parameters
  • Reference Parameters
  • Using Expressions with Parameters
  • A Last Word of Caution About Argument and Parameter Lists
  • Writing Assertions as Function Documentation
  • Problem-Solving Case Study: Lawn Care Company Billing
  • The assert Library Function
  • Scope Rules
  • Variable Declarations and Definitions
  • Initializations in Declarations
  • Software Maintenance Case Study: Debug a Simple Program
  • Side Effects
  • Global Constants
  • Complete Example
  • Boolean Functions
  • Interface Design and Side Effects
  • When to Use Value-Returning Functions
  • 9.5 Type Coercion in Assignments, Argument Passing, and Return of a Function Value
  • Problem-Solving Case Study: Health Profile
  • Stubs and Drivers
  • Numeric Types
  • The Typedef Statement
  • Enumeration Types
  • Named and Anonymous Data Types
  • 10.3 Simple Versus Structured Data Types
  • Accessing Individual Components
  • Aggregate Operations on Structs
  • More About Struct Declarations
  • Binding Like Items
  • 10.5 Hierarchical Records
  • 10.6 Unions
  • Pointer Variables
  • Pointers Expressions
  • 10.8 Reference Types
  • Problem-Solving Case Study: Stylistical Analysis of Text
  • Coping with Input Errors
  • Debugging with Pointers
  • Declaring Arrays
  • Out-of-Bounds Array Indexes
  • Initializing Arrays in Declarations
  • (Lack of) Aggregate Array Operations
  • Examples of Declaring and Accessing Arrays
  • Passing Arrays as Arguments
  • Commenting Arrays
  • Using Typedef with Arrays
  • Pointer Expressions and Arrays
  • C-Style Strings
  • Strings as Arrays
  • C String Operations
  • Converting C Strings to C++ Strings
  • Which String Representation to Use
  • Arrays of Records
  • Subarray Processing
  • Indexes with Semantic Content
  • 11.4 Two-Dimensional Arrays
  • 11.5 Passing Two-Dimensional Arrays as Arguments
  • Sum the Rows
  • Sum the Columns Revised
  • Sum the Columns
  • Initialize the Array
  • Print the Array
  • 11.7 Another Way of Defining Two-Dimensional Arrays
  • 11.8 Multidimensional Arrays
  • Problem-Solving Case Study: Calculating Exam Statistics
  • Problem-Solving Case Study: Favorite Rock Group
  • One-Dimensional Arrays
  • Complex Structures
  • Multidimensional Arrays
  • 12.1 Abstract Data Types
  • Implementing the Member Functions
  • Classes, Objects, and Members
  • Built-in Operations on Objects
  • Class Scope
  • User-Written Header Files
  • Specification and Implementation Files
  • Compiling and Linking a Multifile Program
  • 12.4 What Is an Object?
  • Encapsulation
  • Abstraction
  • Designing for Modifiability and Reuse
  • Software Maintenance Case Study: Comparing Two TimeOfDay Objects
  • Specification of the ADT
  • Implementation File
  • Design of an Entry Class
  • Diagramming a Class
  • Diagramming Composition of Classes
  • Problem-Solving Case Study: Create an Array of Name Objects
  • 13.1 What Is a List?
  • Refining Responsibilities
  • Data Representation
  • Example Program
  • Basic Operations
  • Insertion and Deletion
  • Sequential Search
  • Software Maintenance Case Study: Enhancing Class List with a Sort
  • Binary Search
  • Insert and Delete
  • 13.6 More on UML Diagrams
  • Problem-Solving Case Study: Calculating Exam Statistics Revisited
  • Allocating Dynamic Data
  • Deleting Dynamic Data
  • Constants and Dynamic Data
  • 14.2 Sequential Versus Linked Structures
  • 14.3 Creating a Dynamic Linked List: A Walk-Through Example
  • Creating an Empty Linked List
  • Inserting into a Linked List
  • Traversals of a Linked List
  • Deleting from a Linked List
  • Resetting the List
  • Getting the Next Item
  • Testing for the Full Linked List
  • Searching the List
  • Shallow Versus Deep Copying
  • Copy-Constructor
  • Insert(60) (pick up with loop)
  • Insert(100)
  • Problem-Solving Case Study: Creating a Sorted List of Entry Objects
  • 15.1 Object-Oriented Programming
  • Inheritance and the Object-Oriented Design Process
  • Deriving One Class from Another Class
  • Specification of the ExpandedEntry Class
  • Implementation of the ExpandedEntry Class
  • Constructor Execution Order
  • Software Maintenance Case Study: Extending TimeOfDay with Support for a Time Zone
  • The Slicing Problem
  • Virtual Functions
  • Brainstorming
  • Scenario Exploration
  • Responsibility Algorithms
  • A Final Word
  • 15.5 Implementing a Design
  • Problem-Solving Case Study: Creating an Appointment Calendar
  • Defining a Class Template
  • Instantiating a Class Template
  • Another Way of Implementing Incoming Parameters: const References
  • Organization of Program Code
  • A Word of Caution
  • Function Overloading
  • Defining a Function Template Outside a Class
  • Instantiating a Function Template
  • Using *this
  • The throw Statement
  • The try-catch Statement
  • Nonlocal Exception Handlers
  • Rethrowing an Exception
  • Standard Exceptions
  • Software Maintenance Case Study: Adding Exceptions to the Date Class
  • Problem-Solving Case Study: Starship Weight and Balance
  • 17.1 Abstract Data Structures versus Implementations
  • Priority Queues
  • Bidirectional Lists
  • The vector Template
  • The list Template
  • The stack Template
  • The queue Template
  • The priority_queue Template
  • The deque Template
  • Software Maintenance Case Study: Appointment Calendar Using STL List
  • Binary Trees
  • Hash Tables
  • The set Template
  • The map Template
  • Problem-Solving Case Study: Creating a Deck of Cards
  • 18.1 What Is Recursion?
  • 18.2 Recursive Algorithms with Simple Variables
  • 18.3 Towers of Hanoi
  • 18.4 Recursive Algorithms with Structured Variables
  • Software Maintenance Case Study: Substituting Binary Search for Linear Search
  • Printing a Dynamic Linked List in Reverse Order
  • Copying a Dynamic Linked List
  • 18.6 Recursion or Iteration?
  • Problem-Solving Case Study: Quicksort
  • Appendix A: Reserved Words
  • Appendix B: Operator Precedence
  • Appendix C: A Selection of Standard Library Routines
  • Appendix D: Using This Book with a Prestandard Version of C++
  • Appendix E: Character Sets
  • Appendix F: Program Style, Formatting, and Documentation
  • Appendix G: More on Floating-Point Numbers
  • Appendix H: Using C Strings
  • Appendix I: C++ char Constants

Product information

  • Title: Programming and Problem Solving with C++: Comprehensive, 6th Edition
  • Author(s): Nell Dale, Chip Weems
  • Release date: March 2013
  • Publisher(s): Jones & Bartlett Learning
  • ISBN: 9781449694272

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programming and problem solving with c comprehensive 6th edition

Programming and Problem Solving with C++: Comprehensive, Sixth Edition

  • Chip Weems, Nell Dale
  • Jones and Bartlett Learning

programming and problem solving with c comprehensive 6th edition

In this Book

  • Overview of Programming and Problem Solving
  • C++ Syntax and Semantics, and the Program Development Process
  • Numeric Types, Expressions, and Output
  • Program Input and the Software Design Process
  • Conditions, Logical Expressions, and Selection Control Structures
  • Additional Control Structures
  • Scope, Lifetime, and More on Functions
  • User-Defined Data Types
  • Classes and Abstraction
  • Array-Based Lists
  • Dynamic Data and Linked Lists
  • Inheritance, Polymorphism, and Object-Oriented Design
  • Templates, Operator Overloading, and Exceptions
  • Introduction to Data Structures Using the Standard Template Library

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Prioritization Methods and Techniques – Part 2: MoSCoW Method

In my previous article, Prioritization Methods and Techniques - Part 1: Why Prioritize and the Kano Model , I talked about the need to prioritize and the Kano model as a prioritization method. In this second article in the series on prioritization methods and techniques, I will discuss the MoSCoW method.

The MoSCoW method is a highly widespread prioritization method which was popularized by Dynamic Systems Development Method (DSDM). The term MoSCoW has nothing to do with the capital of Russia. It is an acronym derived from the first letter of each of four prioritization categories – Must have, Should have, Could have and Won’t have.[1] The two “O” are added to make the word pronounceable.

Must have: This category contains requirements or features that are absolutely mandatory. Those are fundamental to the system (being a product or a service). If any of them are neglected, the system will certainly not work or will have no value for the customer.

Should have: These features are important, ideally, we should have them for the system to work correctly. If they are not there, a workaround may be possible, but it can be costly or cumbersome. Yet, they are not mandatory and therefore do not have the highest priority. Simply put, they don’t have much impact on delivery success right now, though they must be implemented soon enough (after the “must-haves”).

Could have: These are useful additions (often small-scale improvements) that add tangible value. These are “nice-to-have” requests. In general, they do not take considerable resources, but they are not essential to implement either. Their absence won’t affect almost anything, or at least wouldn’t impact the release negatively.

Won’t have (sometimes also known as “would like to have, but not this time”): These items are not worth the investment (of time, money, energy) and are unlikely to make the cut (at least not in the near future). These requirements are of the lowest importance and can be easily omitted (definitely considered out of scope for the first release) or rescheduled for future releases.

When prioritizing requirements in a project, DSDM recommends no more than 60% effort for “must-haves” requirements and a sensible pool of “could-haves”, usually around 20% effort (see Figure 1 below). Anything that is higher than 60% effort for the “must-haves” poses a risk to the success and predictability of the project, unless the environment and the used technology are well understood, there are minimal external risks/dependencies and the team is experienced and well established. Note that we are talking about a balance based on estimated effort of requirements (i.e. the expected time it takes to implement the prioritized features) and not total number of requirements. When calculating effort for a specific timeframe (e.g. first release), “won’t haves” are excluded, as they are considered out of scope for this timeframe.[2]

MoSCoW technique.png

Figure 1: Balancing priorities using the MoSCoW prioritization technique (recommendation by DSDM[2])

Practical example

Let’s take a simple practical example. How can you categorize the features required to manufacture a child’s bicycle?

Must have: two wheels ; a frame

Should have: brakes for safe stopping; pedals; ability to adjust the saddle to accommodate growth; safety cover for the chain; stabilizers or the ability to fit them when needed (the last two features can also be classified as “could-haves” depending how essential they are for the child/parents)

Could have: bell or horn to alert others in proximity; attractive color of the bike; front suspension; Presta valves for inflating tires

Won’t have: valve caps to cover the tires valve; Bluetooth bike speaker

Even though it may seem strange not to have the pedals and the brakes in the “must have” category, in reality they are not mandatory for a child’s bike. By definition a bike is two-wheeled transportation device, so it must certainly have two wheels and a frame to link the wheels together, but everything else is subject to discussion and negotiation. For example, small kids can learn to ride a bike by simply using their feet, so no pedals and brakes are really needed. This simple example also shows that there is often a disconnect between expectations and requirements. People often have high level of expectations, but high expectations are different from must-have requirements which are mandatory and non-negotiable.

Let’s now look at the advantages and disadvantages of the MoSCoW method.

Pros of MoSCoW

  • Simplicity. The MoSCoW method is one of the simplest prioritization techniques. It does not require searching for detailed data or making complicated calculations. So, it is easy to master and use because it is based on simple principles. Using this prioritization scheme in a product management context promotes mutual understanding between product people (product managers and product owners) and stakeholders. It is also a great method to resolve conflicts and to bring stakeholders to consensus. Prioritizing work using MoSCoW is fast and transparent.
  • Agility for flexible scheduling and implementation. Since this prioritization method has no strict time limits for the implementation, except for the “must-have” category (items there should always go first and be implemented as soon as possible), it allows for flexible implementation timeframes per feature. Therefore, a team can easily adjust feature deliveries or releases on favorable terms based on agreement with customers/stakeholders.

Cons of MoSCoW

The MoSCoW technique is very simple, but such simplicity comes with some pitfalls.

  • The technique lacks a clear consistency of implementation and lacks specific planning per feature. Even though priorities can be easily and quickly set, the MoSCoW method prioritizes the backlog items in four categories (in a similar fashion to the Kano model, covered in my previous article, which also prioritizes features in different categories), so it does not introduce any sequencing of features/backlog items and lacks specific planning. This makes it quite challenging for product people to decide on the exact priority of a feature compared to another one within the same category. At the end of the day, this drawback might put the entire release at risk.
  • MoSCoW classification rules can be subjective and this creates imbalance between the absolutely required (must have or mandatory) and slightly desirable. Often, the blurred lines between categories make it hard to decide in which category a feature should go into, specifically when we talk about “must-have” and “should-have” lists. But it is sometimes also the case between “should-haves” and could-haves”. This happens due to the subjectivity of requirements. Therefore, features or stories allocated to the different categories should be approached with great thought and care and the chosen categorization should be agreed with (or well explained to) all stakeholders.

When to use the MoSCoW method

The MoSCoW method is probably the simplest and most widespread prioritization scheme for new product development, and more specifically for small products. But as we saw above, this technique also has its disadvantages and is not always effective. For instance, if you have a complicated backlog with many time-sensitive releases, consider choosing other prioritization method or complementing MoSCoW with another more accurate or comprehensive technique.

On the other hand, it is quite reasonable to use MoSCoW when prioritizing work for small (and not too complex) products, which does not have many technical limitations. The MoSCoW requirements help product people and teams take a strategic, orderly approach to prioritization. This method is great for avoiding wasted time, arguments and misdirection.

In my next article I will talk about the Eisenhower matrix. Meanwhile, if you want to know more about prioritizing using the MoSCoW method, please feel free to contact me.

About this article or just curious about working at BlinkLane? Contact Martin or take a look at our open vacancy .

References:

[1] Griffiths, M. (2012). PMI-ACP Exam Prep (2nd ed.). RMC Publications Inc.

[2] Agile Business Consoritum (n.d.). Chapter 10: MoSCoW Prioritisation. Retrieved from https://www.agilebusiness.org/page/ProjectFramework_10_MoSCoWPrioritisation

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19th Edition of Global Conference on Catalysis, Chemical Engineering & Technology

Victor Mukhin

  • Scientific Program

Victor Mukhin, Speaker at Chemical Engineering Conferences

Title : Active carbons as nanoporous materials for solving of environmental problems

However, up to now, the main carriers of catalytic additives have been mineral sorbents: silica gels, alumogels. This is obviously due to the fact that they consist of pure homogeneous components SiO2 and Al2O3, respectively. It is generally known that impurities, especially the ash elements, are catalytic poisons that reduce the effectiveness of the catalyst. Therefore, carbon sorbents with 5-15% by weight of ash elements in their composition are not used in the above mentioned technologies. However, in such an important field as a gas-mask technique, carbon sorbents (active carbons) are carriers of catalytic additives, providing effective protection of a person against any types of potent poisonous substances (PPS). In ESPE “JSC "Neorganika" there has been developed the technology of unique ashless spherical carbon carrier-catalysts by the method of liquid forming of furfural copolymers with subsequent gas-vapor activation, brand PAC. Active carbons PAC have 100% qualitative characteristics of the three main properties of carbon sorbents: strength - 100%, the proportion of sorbing pores in the pore space – 100%, purity - 100% (ash content is close to zero). A particularly outstanding feature of active PAC carbons is their uniquely high mechanical compressive strength of 740 ± 40 MPa, which is 3-7 times larger than that of  such materials as granite, quartzite, electric coal, and is comparable to the value for cast iron - 400-1000 MPa. This allows the PAC to operate under severe conditions in moving and fluidized beds.  Obviously, it is time to actively develop catalysts based on PAC sorbents for oil refining, petrochemicals, gas processing and various technologies of organic synthesis.

Victor M. Mukhin was born in 1946 in the town of Orsk, Russia. In 1970 he graduated the Technological Institute in Leningrad. Victor M. Mukhin was directed to work to the scientific-industrial organization "Neorganika" (Elektrostal, Moscow region) where he is working during 47 years, at present as the head of the laboratory of carbon sorbents.     Victor M. Mukhin defended a Ph. D. thesis and a doctoral thesis at the Mendeleev University of Chemical Technology of Russia (in 1979 and 1997 accordingly). Professor of Mendeleev University of Chemical Technology of Russia. Scientific interests: production, investigation and application of active carbons, technological and ecological carbon-adsorptive processes, environmental protection, production of ecologically clean food.   

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Organization of Continuous Process Simulation via E-Network

In this article, the proposed approaches to continuous process simulation via E-network are considered in order to construct analytical and simulation models using unified methodology. Principals of continuous process simulation via E-networks using Block Function Diagram programming technology are presented. The construction of continuous process dynamic models is based on the state space method and is implemented using E-network algorithmic simulation scheme.

Mechanical Engineering, Automation and Control Systems

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https://doi.org/10.4028/www.scientific.net/AMM.756.500

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[1] Questions of Complex System Algorithm Simulation, Collection of Research Papers, Publ. Editor V.V. Ivanischev. – Leningrad: LIIAN, 1989. 235 p.

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[4] E. Best, C. Fernandez, Non–sequential Processes, a Petri Net View, EATCS Monographs on Theoretical Computer Science. Berlin, 1988, No 13.

[5] V.E. Kotov, Petri Networks. Moscow: Nauka, 1984, 160 p.

[6] Yu.T. Fndryushkov, G.I. Pranyavichus, Е–networks as the Means of Formalized Description of Calculating Systems, Abstracts of Russian Scientific Conference «Area Networks of Packet Switching». Riga, 1978, p.170–175.

[7] G.I. Pranyavichus, D. Yu. Shvanite, E-network Application for Creating Imitational Models. Mathematics and Mathematical Simulation. Vilnus, 1980, 4th Edition, pp.68-72.

[8] B. Ya. Sovetov, S.A. Yakovlev, System Simulation: College Textbook for Specialty «Automated systems of Information Processing and Control». 2nd Edition, Moscow: Vysshaya Shkola, 1998, 319 p.

[9] G.P. Tsapko, Е-network Method of Information and Logical Design of Avionic Suites. Tomsk; Tomsk Polytechnic University Publishing. 104 p.

[10] A.S. Urmaev, Simulation Basis for Analogue Computer Systems. Chief Editorial Board of Physics and Mathematics Literature of Nauka, Publishing. Moscow, 1978, 271 p.

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programming and problem solving with c comprehensive 6th edition

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Programming and Problem Solving with C++: Comprehensive: Comprehensive 6th Edition

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  • Publisher Jones & Bartlett Learning
  • Publication date March 20, 2013
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  • Publisher ‏ : ‎ Jones & Bartlett Learning; 6th edition (March 20, 2013)
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  3. Programming and Problem Solving with C++: Comprehensive, 6th Edition

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  4. Programming and Problem Solving with C++: Comprehensive 6th Edition

    Comprehensive and student-friendly, Programming and Problem Solving with C++, Sixth Edition remains the definitive text for introductory computer science programming courses. A Brief Edition (978-1-284-02864-5) is available for the one-term course. Every new printed copy of the text is packaged with Navigate 2 Advantage Acess & full student ...

  5. Programming and Problem Solving with C++

    The best-selling Programming and Problem Solving with C++, now in it's Sixth Edition, remains the clearest introduction to C++, object-oriented programming, and software development available. Renowned author team Nell Dale and Chip Weems are careful to include all topics and guidelines put forth by the ACM/IEEE to make this text ideal for the one- or two-term CS1 course.

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    Comprehensive and student-friendly, Programming and Problem Solving with C++, Sixth Edition remains the definitive text for introductory computer science programming courses. A Brief Edition (978-1-284-02864-5) is available for the one-term course.

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    Comprehensive and student-friendly, Programming and Problem Solving with C++, Sixth Edition remains the definitive text for introductory computer science programming courses. A Reorganized and Updated Edition of the Bestselling, Definitive C++ Text The bestselling Programming and Problem Solving with C++ is the single clearest and most comprehensive introduction to C++, object-oriented ...

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    In this article, the proposed approaches to continuous process simulation via E-network are considered in order to construct analytical and simulation models using unified methodology. Principals of continuous process simulation via E-networks using Block Function Diagram programming technology are presented. The construction of continuous process dynamic models is based on the state space ...

  21. Programming and Problem Solving with C++: Comprehensive: Comprehensive

    The best-selling Programming and Problem Solving with C++, now in it's Sixth Edition, remains the clearest introduction to C++, object-oriented programming, and software development available. Renowned author team Nell Dale and Chip Weems are careful to include all topics and guidelines put forth by the ACM/IEEE to make this text ideal for the ...

  22. Chapter 1 Solutions

    Access Programming And Problem Solving With C++: Comprehensive 6th Edition Chapter 1 solutions now. Our solutions are written by Chegg experts so you can be assured of the highest quality! ... Programming And Problem Solving With C++: Comprehensive | 6th Edition. ISBN-13: 9781449694265 ISBN: 1449694268 Authors: Nell Dale, Chip Weems Rent | Buy ...