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Human Centred Intelligent Systems: Proceedings of KES-HCIS 2021 Conference (Smart Innovation, Systems and Technologies #244)
by Alfred Zimmermann Robert J. Howlett Lakhmi C. Jain Rainer SchmidtThis book highlights new trends and challenges in intelligent systems, which play an essential part in the digital transformation of many areas of science and practice. It includes papers offering a deeper understanding of the human-centred perspective on artificial intelligence, of intelligent value co-creation, ethics, value-oriented digital models, transparency, and intelligent digital architectures and engineering to support digital services and intelligent systems, the transformation of structures in digital business and intelligent systems based on human practices, as well as the study of interaction and co-adaptation of humans and systems. All papers were originally presented at the International KES Conference on Human Centred Intelligent Systems 2021 (KES HCIS 2021) held on June 14–16, 2021 in the KES Virtual Conference Centre.
Architecting the Digital Transformation: Digital Business, Technology, Decision Support, Management (Intelligent Systems Reference Library #188)
by Alfred Zimmermann Rainer Schmidt Lakhmi C. JainThis research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters: · Digital Transformation · Digital Business · Digital Architecture · Decision Support · Digital Applications Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.
Architektur der digitalen Transformation: Digital Business, Technologie, Entscheidungsunterstützung, Management
by Alfred Zimmermann Rainer Schmidt Lakhmi C. JainDieses forschungsorientierte Buch enthält wichtige Beiträge zur Gestaltung der digitalen Transformation. Es umfasst die folgenden Hauptabschnitte in 20 Kapiteln:- Digitale Transformation- Digitales Geschäft- Digitale Architektur- Entscheidungshilfe- Digitale Anwendungen Es konzentriert sich auf digitale Architekturen für intelligente digitale Produkte und Dienstleistungen und ist eine wertvolle Ressource für Forscher, Doktoranden, Postgraduierte, Absolventen, Studenten, Akademiker und Praktiker, die sich für die digitale Transformation interessieren.
Das Hidden-Markov-Modell: Zufallsprozesse mit verborgenen Zuständen und ihre wahrscheinlichkeitstheoretischen Grundlagen (essentials)
by Karl-Heinz ZimmermannIm Mittelpunkt dieses essentials steht eine Einführung in ein bekanntes statistisches Modell, das Hidden-Markov-Modell.Damit können Probleme bewältigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustandsspezifische Beschreibung geschlossen werden soll.Die Anwendungen des Hidden-Markov-Modells liegen hauptsächlich in den Bereichen Bioinformatik, Computerlinguistik, maschinelles Lernen und Signalverarbeitung.In diesem Büchlein werden die beiden zentralen Problemstellungen in HMMs behandelt.Das Problem der Inferenz wird mit dem berühmten Viterbi-Algorithmus gelöst, und das Problem der Parameterschätzung wird mit zwei bekannten Methoden angegangen (Erwartungsmaximierung und Baum-Welch).
Documentary Across Platforms: Reverse Engineering Media, Place, and Politics
by Patricia R. ZimmermannEssays “capturing media ecologies as varied as museum installations, film festival showings, photography, and multiple varieties of internet sharing.” —Jump CutIn Documentary Across Platforms, noted scholar of film and experimental media Patricia R. Zimmermann offers a glimpse into the ever-evolving constellation of practices known as “documentary” and the way in which they investigate, engage with, and interrogate the world.Collected here for the first time are her celebrated essays and speculations about documentary, experimental, and new media published outside of traditional scholarly venues. These essays envision documentary as a complex ecology composed of different technologies, sets of practices, and specific relationships to communities, engagement, politics, and social struggles. Through the lens of reverse engineering—the concept that ideas, just like objects, can be disassembled to learn how they work and then rebuilt into something new and better—Zimmermann explores how numerous small-scale documentary works present strategies of intervention into existing power structures. Adaptive to their context, modular, and unfixed, the documentary practices she explores exploit both sophisticated high-end professional and consumer-grade amateur technologies, moving through different political terrains, different platforms, and different exhibition contexts.Together these essays demonstrate documentary’s role as a conceptual practice to think through how the world is organized and to imagine ways that it might be reorganized with actions, communities, and ideas.
Rethinking Productivity in Software Engineering
by Thomas Zimmermann Caitlin SadowskiGet the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity.The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering.Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions.What You'll LearnReview the definitions and dimensions of software productivitySee how time management is having the opposite of the intended effectDevelop valuable dashboardsUnderstand the impact of sensors on productivityAvoid software development wasteWork with human-centered methods to measure productivityLook at the intersection of neuroscience and productivityManage interruptions and context-switchingWho Book Is ForIndustry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology.
Gamedesign und Spieleentwicklung für Dummies (Für Dummies)
by Thorsten ZimprichSie wollten schon immer Ihre eigene Spielidee umsetzen? Gamedesign ist Ihr Traumberuf? Dieses Buch zeigt Ihnen, wie Sie eigenen Spielcharakteren Leben einhauchen und Spieler mit originellen Spielregeln lange begeistern. Das ganze Buch ist als Lernkampagne mit zahlreichen Questen und Boss Challenges organisiert: Sie lesen, lernen und üben spielend in der Charakterklasse "Gamedesigner" und erhalten Erfahrungspunkte und Belohnungen. Nutzen Sie die Liste der zu erlernenden Fähigkeiten und Entwicklungsmöglichkeiten sowie zahlreiche Übungen, um selbstbestimmt mit Spaß zu lernen.
Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning (Advances in Intelligent Systems and Computing #744)
by Thi Thi Zin Jerry Chun-Wei LinThis book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.
Foundations and Practice of Security: 11th International Symposium, FPS 2018, Montreal, QC, Canada, November 13–15, 2018, Revised Selected Papers (Lecture Notes in Computer Science #11358)
by Nur Zincir-Heywood Guillaume Bonfante Mourad Debbabi Joaquin Garcia-AlfaroThis book constitutes the revised selected papers of the 11th International Symposium on Foundations and Practice of Security, FPS 2018, held in Montreal, QC, Canada, in March 2018. The 16 full papers, 1 short paper, 1 position paper and 2 invited papers presented in this book, were carefully reviewed and selected from 51 submissions. They cover a range of topics including mobile security; cloud security and big data; IoT security; software security, malware analysis, and vulnerability detection; cryptography; cyber physical security and hardware security; and access control.
Geopedology: An Integration of Geomorphology and Pedology for Soil and Landscape Studies
by Joseph Alfred Zinck Graciela Metternicht Héctor Francisco del Valle Marcos AngeliniThis updated and revised second edition brings geopedology issues into the current context. This new edition extends the work on popular topics such as digital soil mapping, GIS and landscape mapping, and it also gives valuable insight with up-to-date theoretical discussions and new application with relevant case studies. This textbook offers a proven approach for reliable mapping of soil-landscape relationships to derive information for policy, planning and management at scales ranging from local to regional. Filled with didactic elements such as case studies, visual aids (maps, charts and figures), questions and answers, the book is of interest to geohazard studies, land use conflict analysis, land use planning, land degradation assessment, and land suitability analysis. Soil is a vital resource for society at large and an important determinant of the economic status of nations. The intensification of natural disasters and the increased land use competition for food and energy have raised awareness of the relevant role the pedosphere plays in natural and anthropogenic environments. Recent papers and global initiatives show a renewed interest in soil research and its applications for improved planning and management of this fragile and finite resource.
Apprenez à programmer par vous-même
by Zach ZinfadelCe livre ne vous apprendra pas seulement la programmation, car il couvre aussi des sujets que d’autres cours ou livres n’enseignent pas. Il vous fournira des instructions spécifiques ainsi que des bribes de code faciles à comprendre qui vous montreront comment programmer correctement. Il comprend notamment : — des tutoriels de programmation en HTML et en Javascript ; — une introduction à la programmation et au codage ; — une explication sur les variables et la façon de les utiliser ; — tout ce qui concerne les tableaux et les structures logiques ; — comment écrire votre premier programme.
Aprenda programar códigos como um Profissional: Criar jogos, Aplicativos & Programas
by Zach ZinfadelAprenda programar códigos como um Profissional: Criar jogos, Aplicativos & Programas por Zach Zinfadel Com este livro você não aprende só programação, mas também aborda tópicos que as aulas e outros livros não ensinam. Fornece também instruções específicas e fáceis de seguir, passos de como criar códigos de forma correta. Itens inclusos: - Tutoriais de programação em HTML e JavaScript. - Introdução à programação e códigos. - O que são variáveis e como usá-las. - Tudo sobre arrays e declarações lógicas. - Tudo sobre funções e como elas funcionam. - Como criar seu primeiro programa. >>> Clique acima para comprar instantaneamente! Gênero: COMPUTADORES / Programação / Geral Gênero Secundário: COMPUTADORES / Programação Web / Web Língua: Português BR
Algorithmic Thinking: A Problem-Based Introduction
by Daniel ZingaroA hands-on, problem-based introduction to building algorithms and data structures to solve problems with a computer.Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems.Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: • The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book • Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations • The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies • The heap data structure to determine the amount of money given away in a promotion • The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionaryNOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check?
Algorithmic Thinking, 2nd Edition: Learn Algorithms to Level Up Your Coding Skills
by Daniel ZingaroGet in the game and learn essential computer algorithms by solving competitive programming problems, in the fully revised second edition of the bestselling original. (Still no math required!)Are you hitting a wall with data structures and algorithms? Whether you&’re a student prepping for coding interviews or an independent learner, this book is your essential guide to efficient problem-solving in programming.UNLOCK THE POWER OF DATA STRUCTURES & ALGORITHMS:Learn the intricacies of hash tables, recursion, dynamic programming, trees, graphs, and heaps. Become proficient in choosing and implementing the best solutions for any coding challenge.REAL-WORLD, COMPETITION-PROVEN CODE EXAMPLES:The programs and challenges in this book aren&’t just theoretical—they&’re drawn from real programming competitions. Train with problems that have tested and honed the skills of coders around the world.GET INTERVIEW-READY:Prepare yourself for coding interviews with practice exercises that help you think algorithmically, weigh different solutions, and implement the best choices efficiently.WRITTEN IN C, USEFUL ACROSS LANGUAGES:The code examples are written in C and designed for clarity and accessibility to those familiar with languages like C++, Java, or Python. If you need help with the C code, no problem: We&’ve got recommended reading, too.Algorithmic Thinking is the complete package, providing the solid foundation you need to elevate your coding skills to the next level.
Learn to Code by Solving Problems: A Python Programming Primer
by Daniel ZingaroLearn to Code by Solving Problems is a practical introduction to programming using Python. It uses coding-competition challenges to teach you the mechanics of coding and how to think like a savvy programmer.Computers are capable of solving almost any problem when given the right instructions. That&’s where programming comes in. This beginner&’s book will have you writing Python programs right away. You&’ll solve interesting problems drawn from real coding competitions and build your programming skills as you go. Every chapter presents problems from coding challenge websites, where online judges test your solutions and provide targeted feedback. As you practice using core Python features, functions, and techniques, you&’ll develop a clear understanding of data structures, algorithms, and other programming basics. Bonus exercises invite you to explore new concepts on your own, and multiple-choice questions encourage you to think about how each piece of code works. You&’ll learn how to: • Run Python code, work with strings, and use variables • Write programs that make decisions • Make code more efficient with while and for loops • Use Python sets, lists, and dictionaries to organize, sort, and search data • Design programs using functions and top-down design • Create complete-search algorithms and use Big O notation to design more efficient code By the end of the book, you&’ll not only be proficient in Python, but you&’ll also understand how to think through problems and tackle them with code. Programming languages come and go, but this book gives you the lasting foundation you need to start thinking like a programmer.
Service Engineering: Von Dienstleistungen Zu Digitalen Service-systemen
by Christian Zinke Stephan Klingner Kyrill MeyerDas Buch beleuchtet aktuelle Herausforderungen des Service Engineerings und zeigt dessen Entwicklung im Kontext digitaler Service-Systeme sowie Chancen und Möglichkeiten, die digitale Dienstleistungsangebote und vernetzte Lösungen bieten. Wissenschaftliche Ausführungen werden durch praxisorientierte Beispiele, welche die Anwendung der Lösungsansätze und Methoden demonstrieren, ergänzt. Damit betrachten die Autoren dieses Sammelbands einerseits die Evolution der systematischen Dienstleistungsentwicklung in Theorie und Praxis und stellen andererseits aktuelle Forschungsthemen und Entwicklungstendenzen vor. Dazu ziehen die Herausgeber Arbeiten der Partner des Forschernetzwerkes FOKUS Service Engineering heran. Die Beiträge sprechen sowohl Wissenschaftler als auch Vertreter der Praxis an.
Social Business Transformation: Werkzeuge für erfolgreiche digitale Zusammenarbeit in Unternehmen
by Christian Zinke-Wehlmann Julia FriedrichDie Zukunft der Arbeit verlagert sich zunehmend in den digitalen Raum. Deutsche Unternehmen positionieren sich in der Gestaltung dieses Raumes bislang häufig in der Rolle der Nachzügler, weil sie die Bedeutung des Einsatzes sozialer Technologien (z.B. Enterprise Social Networks) für das Unternehmen unterschätzen. Das resultierende Risiko im globalen Wettbewerb unterzugehen, ist gerade für den Mittelstand erheblich.Um zukunftsfähige Wege einzuschlagen, braucht es neue Gestaltungsansätze. Einer davon ist Social Business. Social Business wird im vorliegenden Werk als ganzheitliches Konzept beschrieben, welches durch den systematischen Einsatz digitaler und kollaborativer Werkzeuge neue Möglichkeiten der Innovation und Wertschöpfung schafft. Soziale Technologien ermöglichen eine proaktive Gestaltung unternehmensinterner und -übergreifender Prozesse und bieten insbesondere im Wissensmanagement, in der Kommunikation sowie der Zusammenarbeit produktionssteigernde Potenziale.Im Rahmen dieses Werkes werden praktische Anwendungen, Konzepte zur Umsetzung von Social Business und Leitlinien für den Transformationsprozess wissenschaftlich fundiert und praxisnah präsentiert.
Big Data in Bioeconomy: Results from the European DataBio Project
by Christian Zinke-Wehlmann Caj Södergård Tomas Mildorf Ephrem Habyarimana Arne J. Berre Jose A. FernandesThis edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen.With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future.With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources.
Complex Network Analysis in Python: Recognize - Construct - Visualize - Analyze - Interpret
by Dmitry ZinovievConstruct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value
by Dmitry ZinovievGo from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.What You Need:You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
Pythonic Programming
by Dmitry ZinovievMake your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break. Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented. This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers. Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips. What You Need: You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
Resourceful Code Reuse
by Dmitry ZinovievReusing well-written, well-debugged, and well-tested code improves productivity, code quality, and software configurability and relieves pressure on software developers. When you organize your code into self-contained modular units, you can use them as building blocks for your future projects and share them with other programmers, if needed. Understand the benefits and downsides of seven code reuse models so you can confidently reuse code at any development stage. Create static and dynamic libraries in C and Python, two of the most popular modern programming languages. Adapt your code for the real world: deploy shared functions remotely and build software that accesses them using remote procedure calls. Avoid the drawbacks and harness the benefits associated with seven code reuse models. Create static and dynamic libraries in C and Python, deploy shared functions remotely, and build software that makes intelligent use of remote procedure calls. In no time at all, you'll develop the confidence to reuse code at any stage of real-world development. This one-stop solution covers the complete build cycle: editing, compiling, linking, and running a ready program. Apply Linux/macOS power software development tools, such as ld, ldd, ranlib, and nm, to construct and explore state-of-the-art function libraries in C that could be linked with application-specific code either permanently or for the duration of execution. Learn why Python has modules for reuse and how they differ from C object files and libraries. Understand the risks and other negative implications of sharing and reuse. As a bonus, distill the dependencies between your project's components and automate and optimize your build process with the "make" utility. Whether you are an amateur coder or an experienced developer, become a more productive and resourceful programmer by reusing previously written code. What You Need: To compile and run the C examples mentioned in the book, you need a decent C compiler (GCC is the best, but Intel and Microsoft would probably work, too) and a set of C development tools: maker (make), linker (ld), file, strip, ldd, and ranlib. Again, the GNU development toolset works marvels; other toolsets may or may not work. All examples in the book have been tested on a Linux computer but will most likely work on macOS. For the Python examples, a Python-3.x interpreter is all you want. No third-party modules are required.
STEM in the Technopolis: The Power of STEM Education in Regional Technology Policy
by Cliff Zintgraff Sang C. Suh Bruce Kellison Paul E. RestaThis book addresses how forward-thinking local communities are integrating pre-college STEM education, STEM pedagogy, industry clusters, college programs, and local, state and national policies to improve educational experiences, drive local development, gain competitive advantage for the communities, and lead students to rewarding careers. This book consists of three sections: foundational principles, city/regional case studies from across the globe, and state and national context. The authors explore the hypothesis that when pre-college STEM education is integrated with city and regional development, regions can drive a virtuous cycle of education, economic development, and quality of life.Why should pre-college STEM education be included in regional technology policy? When local leaders talk about regional policy, they usually talk about how government, universities and industry should work together. This relationship is important, but what about the hundreds of millions of pre-college students, taught by tens of millions of teachers, supported by hundreds of thousands of volunteers, who deliver STEM education around the world? Leaders in the communities featured in STEM in the Technopolis have recognized the need to prepare students at an early age, and the power of real-world connections in the process. The authors advocate for this approach to be expanded. They describe how STEM pedagogy, priority industry clusters, cross-sector collaboration, and the local incarnations of global development challenges can be made to work together for the good of all citizens in local communities. This book will be of interest to government policymakers, school administrators, industry executives, and non-profit executives. The book will be useful as a reference to teachers, professors, industry professional volunteers, non-profit staff, and program leaders who are developing, running, or teaching in STEM programs or working to improve quality of life in their communities.
The Monte Carlo Simulation Method for System Reliability and Risk Analysis
by Enrico ZioMonte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergraduate and graduate students as well as researchers and practitioners. It provides a powerful tool for all those involved in system analysis for reliability, maintenance and risk evaluations.
Liferay 6.x Portal Enterprise Intranets Cookbook
by Katarzyna Ziolkowska Piotr FilipowiczIf you are a Java developer or administrator with a technical background and want to install and configure Liferay Portal as an enterprise intranet, this is the book for you. In short, reusable recipes help you realize business goals as working features in Liferay. This book will also give you useful hints on how to easily improve the default functionality of the system and its performance.