This book highlights recent advances in natural computing, including biology and its theory, bio-inspired computing, computational aesthetics, computational models and theories, computing with natural media, philosophy of natural computing and educational technology. It presents extended versions of the best papers selected from the symposium "7th International Workshop on Natural Computing" (IWNC7), held in Tokyo, Japan, in 2013. The target audience is not limited to researchers working in natural computing but also those active in biological engineering, fine/media art design, aesthetics and philosophy.

Economists can use computer algebra systems to manipulate symbolic models, derive numerical computations, and analyze empirical relationships among variables. Maxima is an open-source multi-platform computer algebra system that rivals proprietary software. Maxima's symbolic and computational capabilities enable economists and financial analysts to develop a deeper understanding of models by allowing them to explore the implications of differences in parameter values, providing numerical solutions to problems that would be otherwise intractable, and by providing graphical representations that can guide analysis. This book provides a step-by-step tutorial for using this program to examine the economic relationships that form the core of microeconomics in a way that complements traditional modeling techniques. Readers learn how to phrase the relevant analysis and how symbolic expressions, numerical computations, and graphical representations can be used to learn from microeconomic models. In particular, comparative statics analysis is facilitated. Little has been published on Maxima and its applications in economics and finance, and this volume will appeal to advanced undergraduates, graduate-level students studying microeconomics, academic researchers in economics and finance, economists, and financial analysts.

The book opens with a short introduction to Indian music, in particular classical Hindustani music, followed by a chapter on the role of statistics in computational musicology. The authors then show how to analyze musical structure using Rubato, the music software package for statistical analysis, in particular addressing modeling, melodic similarity and lengths, and entropy analysis; they then show how to analyze musical performance. Finally, they explain how the concept of seminatural composition can help a music composer to obtain the opening line of a raga-based song using Monte Carlo simulation. The book will be of interest to musicians and musicologists, particularly those engaged with Indian music.

Candlesticks have become a key component of platforms and charting programs for financial trading. With these charts, traders can learn underlying patterns for interpreting price action history and forecasts. This A-Z guide shows portfolio managers, quants, strategists, and analysts how to use Python to recognize, scan, trade, and backtest the profitability of candlestick patterns. Financial author, trading consultant, and institutional market strategist Sofien Kaabar shows you how to create a candlestick scanner and indicator so you can compare the profitability of these patterns. With this hands-on guide, you'll also explore a new type of charting system similar to candlesticks, as well as new patterns that have never been presented before. With this book, you will: Create and understand the conditions required for classic and modern candlestick patterns Learn the market psychology behind them Use a framework to learn how backtesting trading strategies are conducted Explore different charting systems and understand their limitations Import OHLC historical FX data in Python in different time frames Use algorithms to scan for and reproduce patterns Learn a pattern's potential by evaluating its profitability and predictability

Among the various multi-level formulations of mathematical models in decision making processes, this book focuses on the bi-level model. Being the most frequently used, the bi-level model addresses conflicts which exist in multi-level decision making processes. From the perspective of bi-level structure and uncertainty, this book takes real-life problems as the background, focuses on the so-called random-like uncertainty, and develops the general framework of random-like bi-level decision making problems. The random-like uncertainty considered in this book includes random phenomenon, random-overlapped random (Ra-Ra) phenomenon and fuzzy-overlapped random (Ra-Fu) phenomenon. Basic theory, models, algorithms and practical applications for different types of random-like bi-level decision making problems are also presented in this book.

This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics.

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.

This book gathers a selection of invited and contributed lectures from the European Conference on Numerical Mathematics and Advanced Applications (ENUMATH) held in Lausanne, Switzerland, August 26-30, 2013. It provides an overview of recent developments in numerical analysis, computational mathematics and applications from leading experts in the field. New results on finite element methods, multiscale methods, numerical linear algebra and discretization techniques for fluid mechanics and optics are presented. As such, the book offers a valuable resource for a wide range of readers looking for a state-of-the-art overview of advanced techniques, algorithms and results in numerical mathematics and scientific computing.

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein's famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.

This book presents four mathematical essays which explore the foundations of mathematics and related topics ranging from philosophy and logic to modern computer mathematics. While connected to the historical evolution of these concepts, the essays place strong emphasis on developments still to come. The book originated in a 2002 symposium celebrating the work of Bruno Buchberger, Professor of Computer Mathematics at Johannes Kepler University, Linz, Austria, on the occasion of his 60th birthday. Among many other accomplishments, Professor Buchberger in 1985 was the founding editor of the Journal of Symbolic Computation; the founder of the Research Institute for Symbolic Computation (RISC) and its chairman from 1987-2000; the founder in 1990 of the Softwarepark Hagenberg, Austria, and since then its director. More than a decade in the making, Mathematics, Computer Science and Logic - A Never Ending Story includes essays by leading authorities, on such topics as mathematical foundations from the perspective of computer verification; a symbolic-computational philosophy and methodology for mathematics; the role of logic and algebra in software engineering; and new directions in the foundations of mathematics. These inspiring essays invite general, mathematically interested readers to share state-of-the-art ideas which advance the never ending story of mathematics, computer science and logic. Mathematics, Computer Science and Logic - A Never Ending Story is edited by Professor Peter Paule, Bruno Buchberger's successor as director of the Research Institute for Symbolic Computation.

R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download. The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections.

Thirty years ago mathematical, as opposed to applied numerical, computation was difficult to perform and so relatively little used. Three threads changed that: the emergence of the personal computer; the discovery of fiber-optics and the consequent development of the modern internet; and the building of the Three "M's" Maple, Mathematica and Matlab. We intend to persuade that Mathematica and other similar tools are worth knowing, assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better. We also hope to explain how to become an "experimental mathematician" while learning to be better at proving things. To accomplish this our material is divided into three main chapters followed by a postscript. These cover elementary number theory, calculus of one and several variables, introductory linear algebra, and visualization and interactive geometric computation.

This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.

Learn how to configure, implement, enhance, and customize SAP OEE to address manufacturing performance management. Manufacturing Performance Management using SAP OEE will show you how to connect your business processes with your plant systems and how to integrate SAP OEE with ERP through standard workflows and shop floor systems for automated data collection. Manufacturing Performance Management using SAP OEE is a must-have comprehensive guide to implementing SAP OEE. It will ensure that SAP consultants and users understand how SAP OEE can offer solutions for manufacturing performance management in process industries. With this book in hand, managing shop floor execution effectively will become easier than ever. Authors Dipankar Saha and Mahalakshmi Symsunder, both SAP manufacturing solution experts, and Sumanta Chakraborty, product owner of SAP OEE, will explain execution and processing related concepts, manual and automatic data collection through the OEE Worker UI, and how to enhance and customize interfaces and dashboards for your specific purposes. You'll learn how to capture and categorize production and loss data and use it effectively for root-cause analysis. In addition, this book will show you: Various down-time handling scenarios. How to monitor, calculate, and define standard as well as industry-specific KPIs. How to carry out standard operational analytics for continuous improvement on the shop floor, at local plant level using MII and SAP Lumira, and also global consolidated analytics at corporation level using SAP HANA. Steps to benchmark manufacturing performance to compare similar manufacturing plants' performance, leading to a more efficient and effective shop floor. Manufacturing Performance Management using SAP OEE will provide you with in-depth coverage of SAP OEE and how to effectively leverage its features. This will allow you to efficiently manage the manufacturing process and to enhance the shop floor's overall performance, making you the sought-after SAP OEE expert in the organization. What You Will Learn Configure your ERP OEE add-on to build your plant and global hierarchy and relevant master data and KPIs Use the SAP OEE standard integration (SAP OEEINT) to integrate your ECC and OEE system to establish bi-directional integration between the enterprise and the shop floor Enable your shop floor operator on the OEE Worker UI to handle shop floor production execution Use SAP OEE as a tool for measuring manufacturing performance Enhance and customize SAP OEE to suit your specific requirements Create local plant-based reporting using SAP Lumira and MII Use standard SAP OEE HANA analytics Who This Book Is For SAP MII, ME, and OEE consultants and users who will implement and use the solution.

This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

Learn how to develop powerful data analytics applications quickly for SQL Server database administrators and developers. Organizations will be able to sift data and derive the business intelligence needed to drive business decisions and profit. The addition of R to SQL Server 2016 places a powerful analytical processor into an environment most developers are already comfortable with - Visual Studio. This book walks even the newest of users through the creation process of a powerful R-language tool set for use in analyzing and reporting on your data. As a SQL Server database administrator or developer, it is sometimes difficult to stay on the bleeding edge of technology. Microsoft's addition of R to SQL Server 2016 is sure to be a game-changer, and the language will certainly become an integral part of future releases. R is in fact widely used today in statistical and related applications, and its use is only growing. Beginning SQL Server R Services helps you jump on board this important trend by providing good examples with detailed explanations of the WHY and not just the HOW. Walks you through setup and installation of SQL Server R Services. Explains the basics of working with R Tools for Visual Studio. Provides a road map to successfully creating custom R code. What You Will Learn Discover R's role in the SQL Server 2016 hierarchy. Manage the components needed to run SQL Server R Services code. Run R-language analytics and queries inside the database. Create analytic solutions that run across multiple datasets. Gain in-depth knowledge of the R language itself. Implement custom SQL Server R Services solutions. Who This Book Is For Any level of database administrator or developer, but specifically it's for those developers with the need to develop powerful data analytics applications quickly. Seasoned R developers will appreciate the book for its robust learning pattern, using visual aids in combination with properties explanations and scenarios. Beginning SQL Server R Services is the perfect "new hire" gift for new database developers in any organization.

This volume contains pioneering contributions to both the theory and practice of optimal experimental design. Topics include the optimality of designs in linear and nonlinear models, as well as designs for correlated observations and for sequential experimentation. There is an emphasis on applications to medicine, in particular, to the design of clinical trials. Scientists from Europe, the US, Asia, Australia and Africa contributed to this volume of papers from the 11th Workshop on Model Oriented Design and Analysis.

"A well-written and -illustrated work, recommended for all college libraries. Lower-division undergraduates through faculty."
--CHOICE, December 2002

This book presents recent results on positivity and optimization of polynomials in non-commuting variables. Researchers in non-commutative algebraic geometry, control theory, system engineering, optimization, quantum physics and information science will find the unified notation and mixture of algebraic geometry and mathematical programming useful. Theoretical results are matched with algorithmic considerations; several examples and information on how to use NCSOStools open source package to obtain the results provided. Results are presented on detecting the eigenvalue and trace positivity of polynomials in non-commuting variables using Newton chip method and Newton cyclic chip method, relaxations for constrained and unconstrained optimization problems, semidefinite programming formulations of the relaxations and finite convergence of the hierarchies of these relaxations, and the practical efficiency of algorithms.

Agricultural Internet of Things and Decision Support for Smart Farming reveals how a set of key enabling technologies (KET) related to agronomic management, remote and proximal sensing, data mining, decision-making and automation can be efficiently integrated in one system. Chapters cover how KETs enable real-time monitoring of soil conditions, determine real-time, site-specific requirements of crop systems, help develop a decision support system (DSS) aimed at maximizing the efficient use of resources, and provide planning for agronomic inputs differentiated in time and space. This book is ideal for researchers, academics, post-graduate students and practitioners who want to embrace new agricultural technologies.

Enhancing Enterprise Intelligence: Leveraging ERP, CRM, SCM, PLM, BPM, and BI takes a fresh look at the benefits of enterprise systems (ES), focusing on the fact that ES collectively contribute to enhancing the intelligence quotient of an enterprise. The book provides an overview of the characteristic domains (i.e., business functions, processes, and activities) addressed by the various categories of ES, namely, ERP, CRM, SCM, PLM, BPM, and BI. The book begins with an overview of agile enterprises and dimensions of intelligent enterprises. The middle chapters detail CRM's decisive concept of customer centricity, SCM's differentiating concept of customer responsiveness, and PLM's stupendous transformative potential for renewing the enterprise along with the establishment of a collaborative enterprise with BPM and enterprise BPM methodology. The latter chapters deal with the realization of an informed enterprise with BI coupled with the novel concept of decision patterns. The author highlights the fact that any end-user application's effectiveness and performance can be enhanced by transforming it from a bare transaction to one clothed by a surrounding context formed from an aggregate of all relevant past decision patterns. The final chapter examines various aspects relating to a successful ES implementation project, and the appendix provides an overview of the SAP Business Suite to give you a practical context to the discussions presented in the book.

Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research.What You Will Learn: How to acquire and install R Hot to import and export data and scripts How to analyze data and generate graphics How to program in R to write custom functions Hot to use R for interactive statistical explorations How to conduct bootstrapping and other advanced techniques

In this book, noted expert Andrew Okungbowa explains SAP Asset Accounting (FI-AA) in SAP-ERP, including its associated business benefits, and guides you through the considerable complexities of SAP-ERP configuration. Using FI-AA for fixed asset management enables you to manage assets in multinational companies across a broad range of industries and produce reports to meet various needs in line with legal requirements. Configuring SAP-ERP can be a daunting exercise, however, and there are few resources that address these issues. Asset Accounting Configuration in SAP ERP fills that resource gap by covering the major aspects of SAP FI-AA for anyone with SAP experience and the basic accounting knowledge and bookkeeping skills necessary to apply configuration. It provides configuration explanations in the simplest forms possible and provides step-by-step guidance with illustrations and practical examples. What You'll Learn "li>In-depth coverage of FI-AA syllabus * How to configure FI-AA accounting in SAP * How to integrate FI-AA accounting with other SAP modules * How to explain the functionalities of SAP FI-AA * Knowledge gained from real-world practical examples and case studies Who This Book Is For The key target audience for this book includes SAP consultants, developers, accountants, support organizations and beginners. It is also a resourceful learning manual for universities and institutions whose curricula covers SAP-ERP Asset Accounting.

Agile may be the best-kept management secret on the planet and if you want a quickstart introduction, then Agile NOW is essential reading. Agile is a different way of thinking that’s steeped in common sense and produces immediate results. That’s why there’s a quiet revolution going on.

Agile will help you design better products, get faster results, cut down costs, and keep improving as you go. With a simple system called The Golden Triangle - Prioritising, Time Boxing and Change Management - you can hit the ground running and get started immediately.

Agile NOW is slim, accessible and easy to dip into - yet covers all the essential theory and provides practical advice. Agile is for everyone - from one-person start-ups to multinationals – the promise of quicker, cheaper, better has universal appeal. Agile NOW shows you how to get going fast at minimal cost.

This is the first book to show the capabilities of Microsoft Excel to teach engineering statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical engineering problems. If understanding statistics isn't your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in engineering courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2013 for Engineering Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand engineering problems. Practice problems are provided at the end of each chapter with their solutions in an Appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.