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| examples of systems in science: Principles of Systems Science George E. Mobus, Michael C. Kalton, 2014-11-10 This pioneering text provides a comprehensive introduction to systems structure, function, and modeling as applied in all fields of science and engineering. Systems understanding is increasingly recognized as a key to a more holistic education and greater problem solving skills, and is also reflected in the trend toward interdisciplinary approaches to research on complex phenomena. While the concepts and components of systems science will continue to be distributed throughout the various disciplines, undergraduate degree programs in systems science are also being developed, including at the authors’ own institutions. However, the subject is approached, systems science as a basis for understanding the components and drivers of phenomena at all scales should be viewed with the same importance as a traditional liberal arts education. Principles of Systems Science contains many graphs, illustrations, side bars, examples, and problems to enhance understanding. From basic principles of organization, complexity, abstract representations, and behavior (dynamics) to deeper aspects such as the relations between information, knowledge, computation, and system control, to higher order aspects such as auto-organization, emergence and evolution, the book provides an integrated perspective on the comprehensive nature of systems. It ends with practical aspects such as systems analysis, computer modeling, and systems engineering that demonstrate how the knowledge of systems can be used to solve problems in the real world. Each chapter is broken into parts beginning with qualitative descriptions that stand alone for students who have taken intermediate algebra. The second part presents quantitative descriptions that are based on pre-calculus and advanced algebra, providing a more formal treatment for students who have the necessary mathematical background. Numerous examples of systems from every realm of life, including the physical and biological sciences, humanities, social sciences, engineering, pre-med and pre-law, are based on the fundamental systems concepts of boundaries, components as subsystems, processes as flows of materials, energy, and messages, work accomplished, functions performed, hierarchical structures, and more. Understanding these basics enables further understanding both of how systems endure and how they may become increasingly complex and exhibit new properties or characteristics. Serves as a textbook for teaching systems fundamentals in any discipline or for use in an introductory course in systems science degree programs Addresses a wide range of audiences with different levels of mathematical sophistication Includes open-ended questions in special boxes intended to stimulate integrated thinking and class discussion Describes numerous examples of systems in science and society Captures the trend towards interdisciplinary research and problem solving |
| examples of systems in science: Environmental Systems Science Daniel A. Vallero, 2021-05-27 Environmental Systems Science: Theory and Practical Applications looks at pollution and environmental quality from a systems perspective. Credible human and ecological risk estimation and prediction methods are described, including life cycle assessment, feasibility studies, pollution control decision tools, and approaches to determine adverse outcome pathways, fate and transport, sampling and analysis, and cost-effectiveness. The book brings translational science to environmental quality, applying groundbreaking methodologies like informatics, data mining, and applications of secondary data systems. Multiple human and ecological variables are introduced and integrated to support calculations that aid environmental and public health decision making. The book bridges the perspectives of scientists, engineers, and other professionals working in numerous environmental and public health fields addressing problems like toxic substances, deforestation, climate change, and loss of biological diversity, recommending sustainable solutions to these and other seemingly intractable environmental problems. The causal agents discussed include physical, chemical, and biological agents, such as per- and polyfluoroalkyl substances (PFAS), SARS-CoV-2 (the COVID-19 virus), and other emerging contaminants. - Provides an optimistic and interdisciplinary approach, underpinned by scientific first principles and theory to evaluate pollutant sources and sinks, applying biochemodynamic methods, measurements and models - Deconstructs prior initiatives in environmental assessment and management using an interdisciplinary approach to evaluate what has worked and why - Lays out a holistic understanding of the real impact of human activities on the current state of pollution, linking the physical sciences and engineering with socioeconomic, cultural perspectives, and environmental justice - Takes a life cycle view of human and ecological systems, from the molecular to the planetary scale, integrating theories and tools from various disciplines to assess the current and projected states of environmental quality - Explains the elements of risk, reliability and resilience of built and natural systems, including discussions of toxicology, sustainability, and human-pollutant interactions based on spatial, biological, and human activity information, i.e. the exposome |
| examples of systems in science: Philosophy of Complex Systems , 2011-05-23 The domain of nonlinear dynamical systems and its mathematical underpinnings has been developing exponentially for a century, the last 35 years seeing an outpouring of new ideas and applications and a concomitant confluence with ideas of complex systems and their applications from irreversible thermodynamics. A few examples are in meteorology, ecological dynamics, and social and economic dynamics. These new ideas have profound implications for our understanding and practice in domains involving complexity, predictability and determinism, equilibrium, control, planning, individuality, responsibility and so on.Our intention is to draw together in this volume, we believe for the first time, a comprehensive picture of the manifold philosophically interesting impacts of recent developments in understanding nonlinear systems and the unique aspects of their complexity. The book will focus specifically on the philosophical concepts, principles, judgments and problems distinctly raised by work in the domain of complex nonlinear dynamical systems, especially in recent years.-Comprehensive coverage of all main theories in the philosophy of Complex Systems -Clearly written expositions of fundamental ideas and concepts -Definitive discussions by leading researchers in the field -Summaries of leading-edge research in related fields are also included |
| examples of systems in science: A Framework for K-12 Science Education National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on a Conceptual Framework for New K-12 Science Education Standards, 2012-02-28 Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments. |
| examples of systems in science: Facets of Systems Science George J. Klir, 2012-12-06 This book has a rather strange history. It began in spring 1989, thirteen years after our Systems Science Department at SUNY-Binghamton was established, when I was asked by a group of students in our doctoral program to have a meeting with them. The spokesman of the group, Cliff Joslyn, opened our meeting by stating its purpose. I can closely paraphrase what he said: We called this meeting to discuss with you, as Chairman of the Department, a fundamental problem with our systems science curriculum. In general, we consider it a good curriculum: we learn a lot of concepts, principles, and methodological tools, mathematical, computational, heu ristic, which are fundamental to understanding and dealing with systems. And, yet, we learn virtually nothing about systems science itself. What is systems science? What are its historical roots? What are its aims? Where does it stand and where is it likely to go? These are pressing questions to us. After all, aren't we supposed to carry the systems science flag after we graduate from this program? We feel that a broad introductory course to systems science is urgently needed in the curriculum. Do you agree with this assessment? The answer was obvious and, yet, not easy to give: I agree, of course, but I do not see how the situation could be alleviated in the foreseeable future. |
| examples of systems in science: Feedback Systems Karl Johan Åström, Richard M. Murray, 2021-02-02 The essential introduction to the principles and applications of feedback systems—now fully revised and expanded This textbook covers the mathematics needed to model, analyze, and design feedback systems. Now more user-friendly than ever, this revised and expanded edition of Feedback Systems is a one-volume resource for students and researchers in mathematics and engineering. It has applications across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. Features a new chapter on design principles and tools, illustrating the types of problems that can be solved using feedback Includes a new chapter on fundamental limits and new material on the Routh-Hurwitz criterion and root locus plots Provides exercises at the end of every chapter Comes with an electronic solutions manual An ideal textbook for undergraduate and graduate students Indispensable for researchers seeking a self-contained resource on control theory |
| examples of systems in science: What Is a Complex System? James Ladyman, K. Wiesner, 2020-08-05 A clear, concise introduction to the quickly growing field of complexity science that explains its conceptual and mathematical foundations What is a complex system? Although complexity science is used to understand phenomena as diverse as the behavior of honeybees, the economic markets, the human brain, and the climate, there is no agreement about its foundations. In this introduction for students, academics, and general readers, philosopher of science James Ladyman and physicist Karoline Wiesner develop an account of complexity that brings the different concepts and mathematical measures applied to complex systems into a single framework. They introduce the different features of complex systems, discuss different conceptions of complexity, and develop their own account. They explain why complexity science is so important in today's world. |
| examples of systems in science: An Integrated Framework for Assessing the Value of Community-Based Prevention Institute of Medicine, Board on Population Health and Public Health Practice, Committee on Valuing Community-Based, Non-Clinical Prevention Policies and Wellness Strategies, 2012-11-29 During the past century the major causes of morbidity and mortality in the United States have shifted from those related to communicable diseases to those due to chronic diseases. Just as the major causes of morbidity and mortality have changed, so too has the understanding of health and what makes people healthy or ill. Research has documented the importance of the social determinants of health (for example, socioeconomic status and education) that affect health directly as well as through their impact on other health determinants such as risk factors. Targeting interventions toward the conditions associated with today's challenges to living a healthy life requires an increased emphasis on the factors that affect the current cause of morbidity and mortality, factors such as the social determinants of health. Many community-based prevention interventions target such conditions. Community-based prevention interventions offer three distinct strengths. First, because the intervention is implemented population-wide it is inclusive and not dependent on access to a health care system. Second, by directing strategies at an entire population an intervention can reach individuals at all levels of risk. And finally, some lifestyle and behavioral risk factors are shaped by conditions not under an individual's control. For example, encouraging an individual to eat healthy food when none is accessible undermines the potential for successful behavioral change. Community-based prevention interventions can be designed to affect environmental and social conditions that are out of the reach of clinical services. Four foundations - the California Endowment, the de Beaumont Foundation, the W.K. Kellogg Foundation, and the Robert Wood Johnson Foundation - asked the Institute of Medicine to convene an expert committee to develop a framework for assessing the value of community-based, non-clinical prevention policies and wellness strategies, especially those targeting the prevention of long-term, chronic diseases. The charge to the committee was to define community-based, non-clinical prevention policy and wellness strategies; define the value for community-based, non-clinical prevention policies and wellness strategies; and analyze current frameworks used to assess the value of community-based, non-clinical prevention policies and wellness strategies, including the methodologies and measures used and the short- and long-term impacts of such prevention policy and wellness strategies on health care spending and public health. An Integrated Framework for Assessing the Value of Community-Based Prevention summarizes the committee's findings. |
| examples of systems in science: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| examples of systems in science: Systems Approaches and Their Application Mats-Olov Olsson, Gunnar Sjöstedt, 2006-01-16 This book describes the application of systems thinking across a broad field of cases representing research, teaching, decision support and construction. All cases are presented by experts who have actually been involved in the activities they describe. The broad selection of cases captures the great variation of systems thinking, and how it is integrated into models and theories and solid knowledge pertaining to different substantive areas. |
| examples of systems in science: Thinking in Systems Donella Meadows, 2008-12-03 The classic book on systems thinking—with more than half a million copies sold worldwide! This is a fabulous book... This book opened my mind and reshaped the way I think about investing.—Forbes Thinking in Systems is required reading for anyone hoping to run a successful company, community, or country. Learning how to think in systems is now part of change-agent literacy. And this is the best book of its kind.—Hunter Lovins In the years following her role as the lead author of the international bestseller, Limits to Growth—the first book to show the consequences of unchecked growth on a finite planet—Donella Meadows remained a pioneer of environmental and social analysis until her untimely death in 2001. Thinking in Systems is a concise and crucial book offering insight for problem solving on scales ranging from the personal to the global. Edited by the Sustainability Institute’s Diana Wright, this essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life. Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking. While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner. In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions. |
| examples of systems in science: Systems Analysis and Modeling Donald W. Boyd, 2000-10-19 Systems Analysis and Modeling presents a fresh, new approach to systems analysis and modeling with a systems science flavor that stimulates systems thinking. After introducing systems modeling principles, the ensuing wide selection of examples aptly illustrate that anything which changes over time can be modeled as a system. Each example begins with a knowledge base that displays relevant information obtained from systems analysis. The diversity of examples clearly establishes a new protocol for synthesizing systems models. - Macro-to-micro, top-down approach - Multidisciplinary examples - Incorporation of human knowledge to synthesise a systems model - Clear and concise systems delimitation - Complex systems using simple mathematics - Exact reproduction of historical data plus model generated secondary data - Systems simulation via systems models |
| examples of systems in science: EarthComm , 2018 |
| examples of systems in science: Next Generation Earth Systems Science at the National Science Foundation National Academies of Sciences Engineering and Medicine, Policy and Global Affairs, Division on Engineering and Physical Sciences, Division on Earth and Life Studies, Division of Behavioral and Social Sciences and Education, Committee on Advancing a Systems Approach to Studying the Earth a Strategy for the National Science Foundation, 2022-06-22 The National Science Foundation (NSF) has played a key role over the past several decades in advancing understanding of Earth's systems by funding research on atmospheric, ocean, hydrologic, geologic, polar, ecosystem, social, and engineering-related processes. Today, however, those systems are being driven like never before by human technologies and activities. Our understanding has struggled to keep pace with the rapidity and magnitude of human-driven changes, their impacts on human and ecosystem sustainability and resilience, and the effectiveness of different pathways to address those challenges. Given the urgency of understanding human-driven changes, NSF will need to sustain and expand its efforts to achieve greater impact. The time is ripe to create a next-generation Earth systems science initiative that emphasizes research on complex interconnections and feedbacks between natural and social processes. This will require NSF to place an increased emphasis on research inspired by real-world problems while maintaining their strong legacy of curiosity driven research across many disciplines ? as well as enhance the participation of social, engineering, and data scientists, and strengthen efforts to include diverse perspectives in research. |
| examples of systems in science: Systems Thinking for Health Systems Strengthening World Health Organization, 2009 Makes the case for systems thinking in an easily accessible form for a broad interdisciplinary audience, including health system stewards, programme implementers, researchers, evaluators, and funding partners. |
| examples of systems in science: General System Theory Ludwig von Bertalanffy, 2015 The classic book on a major modern theory |
| examples of systems in science: Thinking, Fast and Slow Daniel Kahneman, 2011-10-25 *Major New York Times Bestseller *More than 2.6 million copies sold *One of The New York Times Book Review's ten best books of the year *Selected by The Wall Street Journal as one of the best nonfiction books of the year *Presidential Medal of Freedom Recipient *Daniel Kahneman's work with Amos Tversky is the subject of Michael Lewis's best-selling The Undoing Project: A Friendship That Changed Our Minds In his mega bestseller, Thinking, Fast and Slow, Daniel Kahneman, world-famous psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive biases on everything from playing the stock market to planning our next vacation—each of these can be understood only by knowing how the two systems shape our judgments and decisions. Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives—and how we can use different techniques to guard against the mental glitches that often get us into trouble. Topping bestseller lists for almost ten years, Thinking, Fast and Slow is a contemporary classic, an essential book that has changed the lives of millions of readers. |
| examples of systems in science: An Introduction to Hybrid Dynamical Systems Arjan J. van der Schaft, Hans Schumacher, 2007-10-03 This book is about dynamical systems that are hybrid in the sense that they contain both continuous and discrete state variables. Recently there has been increased research interest in the study of the interaction between discrete and continuous dynamics. The present volume provides a first attempt in book form to bring together concepts and methods dealing with hybrid systems from various areas, and to look at these from a unified perspective. The authors have chosen a mode of exposition that is largely based on illustrative examples rather than on the abstract theorem-proof format because the systematic study of hybrid systems is still in its infancy. The examples are taken from many different application areas, ranging from power converters to communication protocols and from chaos to mathematical finance. Subjects covered include the following: definition of hybrid systems; description formats; existence and uniqueness of solutions; special subclasses (variable-structure systems, complementarity systems); reachability and verification; stability and stabilizability; control design methods. The book will be of interest to scientists from a wide range of disciplines including: computer science, control theory, dynamical system theory, systems modeling and simulation, and operations research. |
| examples of systems in science: Concepts of Biology Samantha Fowler, Rebecca Roush, James Wise, 2023-05-12 Black & white print. Concepts of Biology is designed for the typical introductory biology course for nonmajors, covering standard scope and sequence requirements. The text includes interesting applications and conveys the major themes of biology, with content that is meaningful and easy to understand. The book is designed to demonstrate biology concepts and to promote scientific literacy. |
| examples of systems in science: 21 CFR Part 11 Orlando López, 2004-01-15 Covering regulatory requirements stipulated by the FDA, this book delineates the organization, planning, verification, and documentation activities and procedural controls required for compliance with worldwide computer systems validation regulations. The author introduces supporting technologies such as encryption and digital signatures and places |
| examples of systems in science: Systems Brian Wilson, 1991-01-08 Systems: Concepts, Methodologies and Applications Second Edition Brian Wilson Department of Systems and Information Management Lancaster University, UK The result of many years' experience, this book, now extensively revised and updated, emphasizes the application of systems concepts and methodologies that have been developed at Lancaster University. In particular the book is about problem solving and the relationship between theory and practice. Complementary to Systems Thinking, Systems Practice by Peter Checkland (Wiley, 1981), which has become a classic in the field, this book shows how systems ideas can be used to cope with real-life problems. Reviews of the first edition-- ... an excellent book which provides a synthesis of the action-research undertaken by the well-known Department of Systems, University of Lancaster ... Wilson's lucid style of writing and the historical perspective of the Lancaster learning experience provide a strong contextural case for the concept of a human activity system to investigate 'badly-defined' [Checkland's 'soft'] systems. Chris Beaumont, Journal of the Operational Research Society, January 1985 ... This volume, expertly compiled by Brian Wilson, is the latest and probably the clearest statement in book form of the philosophy of that department [Department of Systems, University of Lancaster] ... a volume which deserves to be read ... E. R. Carson, Kybernetes, 12, 1985 ... Systems: Concepts, Methodologies and Applications is Wilson's account of his professional life at Lancaster since then (1966). His careful reflection on the work of so many years deserves attention. Trevor Williams, Futures, December 1985 |
| examples of systems in science: Grey Systems Sifeng Liu, Jeffrey Yi Lin Forrest, 2010-12-09 Due to inherent limitations in human sensing organs, most data collected for various purposes contain uncertainties. Even at the rare occasions when accurate data are available, the truthful predictions derived on the data tend to create chaotic consequences. So, to effectively process and make sense out of available data, we need methods to deal with uncertainty inherently existing inside the data. The intent of this monograph is to explore the fundamental theory, methods, and techniques of practical application of grey systems theory, initiated by Professor Deng Julong in 1982. This volume presents most of the recent advances of the theory accomplished by scholars from around the world. From studying this book, the reader will not only acquire an overall knowledge of this new theory but also be able to follow the most current research activities. All examples presented are based on practical applications of the theory when urgent real-life problems had to be addressed. Last but not the least, this book concludes with three appendices. The first one compares grey systems theory and interval analysis while revealing the fact that interval analysis is a part of grey mathematics. The second appendix presents an array of different approaches of studying uncertainties. And, the last appendix shows how uncertainties appear using general systems approach. |
| examples of systems in science: International Encyclopedia of Housing and Home , 2012-10-09 Available online via SciVerse ScienceDirect, or in print for a limited time only, The International Encyclopedia of Housing and Home, Seven Volume Set is the first international reference work for housing scholars and professionals, that uses studies in economics and finance, psychology, social policy, sociology, anthropology, geography, architecture, law, and other disciplines to create an international portrait of housing in all its facets: from meanings of home at the microscale, to impacts on macro-economy. This comprehensive work is edited by distinguished housing expert Susan J. Smith, together with Marja Elsinga, Ong Seow Eng, Lorna Fox O'Mahony and Susan Wachter, and a multi-disciplinary editorial team of 20 world-class scholars in all. Working at the cutting edge of their subject, liaising with an expert editorial advisory board, and engaging with policy-makers and professionals, the editors have worked for almost five years to secure the quality, reach, relevance and coherence of this work. A broad and inclusive table of contents signals (or tesitifes to) detailed investigation of historical and theoretical material as well as in-depth analysis of current issues. This seven-volume set contains over 500 entries, listed alphabetically, but grouped into seven thematic sections including methods and approaches; economics and finance; environments; home and homelessness; institutions; policy; and welfare and well-being. Housing professionals, both academics and practitioners, will find The International Encyclopedia of Housing and Home useful for teaching, discovery, and research needs. International in scope, engaging with trends in every world region The editorial board and contributors are drawn from a wide constituency, collating expertise from academics, policy makers, professionals and practitioners, and from every key center for housing research Every entry stands alone on its merits and is accessed alphabetically, yet each is fully cross-referenced, and attached to one of seven thematic categories whose ‘wholes' far exceed the sum of their parts |
| examples of systems in science: Policy Practice and Digital Science Marijn Janssen, Maria A. Wimmer, Ameneh Deljoo, 2015-06-03 The explosive growth in data, computational power, and social media creates new opportunities for innovating the processes and solutions of Information and communications technology (ICT) based policy-making and research. To take advantage of these developments in the digital world, new approaches, concepts, instruments and methods are needed to navigate the societal and computational complexity. This requires extensive interdisciplinary knowledge of public administration, policy analyses, information systems, complex systems and computer science. This book provides the foundation for this new interdisciplinary field, in which various traditional disciplines are blending. Both policy makers, executors and those in charge of policy implementations acknowledge that ICT is becoming more important and is changing the policy-making process, resulting in a next generation policy-making based on ICT support. Web 2.0 and even Web 3.0 point to the specific applications of social networks, semantically enriched and linked data, whereas policy-making has also to do with the use of the vast amount of data, predictions and forecasts, and improving the outcomes of policy-making, which is confronted with an increasing complexity and uncertainty of the outcomes. The field of policy-making is changing and driven by developments like open data, computational methods for processing data, opining mining, simulation and visualization of rich data sets, all combined with public engagement, social media and participatory tools. |
| examples of systems in science: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| examples of systems in science: Complexity Melanie Mitchell, 2009-04-01 What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of neurons produce something as extraordinarily complex as consciousness? In this remarkably clear and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa Book Award in Science--offers a wide-ranging overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for its contribution to solving some of the most important scientific questions of our time. |
| examples of systems in science: Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices Christina V. Schwarz, Cynthia Passmore, Brian J. Reiser , 2017-01-31 When it’s time for a game change, you need a guide to the new rules. Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices provides a play-by-play understanding of the practices strand of A Framework for K–12 Science Education (Framework) and the Next Generation Science Standards (NGSS). Written in clear, nontechnical language, this book provides a wealth of real-world examples to show you what’s different about practice-centered teaching and learning at all grade levels. The book addresses three important questions: 1. How will engaging students in science and engineering practices help improve science education? 2. What do the eight practices look like in the classroom? 3. How can educators engage students in practices to bring the NGSS to life? Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices was developed for K–12 science teachers, curriculum developers, teacher educators, and administrators. Many of its authors contributed to the Framework’s initial vision and tested their ideas in actual science classrooms. If you want a fresh game plan to help students work together to generate and revise knowledge—not just receive and repeat information—this book is for you. |
| examples of systems in science: Science Without Laws Angela N. H. Creager, Elizabeth Lunbeck, M. Norton Wise, 2007-09-03 A comparison of the use of model systems and exemplary cases across fields in the natural and social sciences. |
| examples of systems in science: A New Kind of Science Stephen Wolfram, 2002 This work presents a series of dramatic discoveries never before made public. Starting from a collection of simple computer experiments---illustrated in the book by striking computer graphics---Wolfram shows how their unexpected results force a whole new way of looking at the operation of our universe. Wolfram uses his approach to tackle a remarkable array of fundamental problems in science: from the origin of the Second Law of thermodynamics, to the development of complexity in biology, the computational limitations of mathematics, the possibility of a truly fundamental theory of physics, and the interplay between free will and determinism. |
| examples of systems in science: The Science of Effective Mentorship in STEMM National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Effective Mentoring in STEMM, 2020-01-24 Mentorship is a catalyst capable of unleashing one's potential for discovery, curiosity, and participation in STEMM and subsequently improving the training environment in which that STEMM potential is fostered. Mentoring relationships provide developmental spaces in which students' STEMM skills are honed and pathways into STEMM fields can be discovered. Because mentorship can be so influential in shaping the future STEMM workforce, its occurrence should not be left to chance or idiosyncratic implementation. There is a gap between what we know about effective mentoring and how it is practiced in higher education. The Science of Effective Mentorship in STEMM studies mentoring programs and practices at the undergraduate and graduate levels. It explores the importance of mentorship, the science of mentoring relationships, mentorship of underrepresented students in STEMM, mentorship structures and behaviors, and institutional cultures that support mentorship. This report and its complementary interactive guide present insights on effective programs and practices that can be adopted and adapted by institutions, departments, and individual faculty members. |
| examples of systems in science: Science Literacy National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on Science Literacy and Public Perception of Science, 2016-11-14 Science is a way of knowing about the world. At once a process, a product, and an institution, science enables people to both engage in the construction of new knowledge as well as use information to achieve desired ends. Access to scienceâ€whether using knowledge or creating itâ€necessitates some level of familiarity with the enterprise and practice of science: we refer to this as science literacy. Science literacy is desirable not only for individuals, but also for the health and well- being of communities and society. More than just basic knowledge of science facts, contemporary definitions of science literacy have expanded to include understandings of scientific processes and practices, familiarity with how science and scientists work, a capacity to weigh and evaluate the products of science, and an ability to engage in civic decisions about the value of science. Although science literacy has traditionally been seen as the responsibility of individuals, individuals are nested within communities that are nested within societiesâ€and, as a result, individual science literacy is limited or enhanced by the circumstances of that nesting. Science Literacy studies the role of science literacy in public support of science. This report synthesizes the available research literature on science literacy, makes recommendations on the need to improve the understanding of science and scientific research in the United States, and considers the relationship between scientific literacy and support for and use of science and research. |
| examples of systems in science: Systems Engineering Derek K. Hitchins, 2008-03-11 This book conceives, presents and exemplifies a contemporary, general systems methodology that is straightforward and accessible, providing guidance in practical application, as well as explaining concept and theory. The book is presented both as a text for students, with topic assignments, and as a reference for practitioners, through case studies. Utilizing recent research and developments in systems science, methods and tools, Hitchins has developed a unified systems methodology, employable when tackling virtually any problem, from the small technological, to the global socioeconomic. Founded in the powerful ‘systems approach’, Hitchins’ systems methodology brings together both soft and hard system scientific methods into one methodological framework. This can be applied when addressing complex problems, issues and situations, and for creating robust, provable solutions, resolutions and dissolutions to those problems – supposing such to exist. This book details and explores: the systems approach, using theory and method to reveal systems engineering as applied systems science, bridging the gulf between Problem and Solution Spaces; a ‘universal’ Systems Methodology (including an extensive view of systems engineering, embracing both soft and hard systems) which encompasses all five stages of Hitchins’ 5-layer Systems Engineering Model (artifact, project, enterprise, industry and socio-economy); case studies illustrating how the systems methodology may be used to address a diverse range of situations and issues, including conceiving a new defense capability, proposing a feasible way to tackle global warming, tackling enterprise interventions, how and why things can go wrong, and many more. Systems Engineering will give an immeasurable advantage to managers, practitioners and consultants in a wide range of organizations and fields including police, defense, procurement, communications, transport, management, electrical, electronic, aerospace, requirements, software and computer engineering. It is an essential reference for researchers seeking ‘systems enlightenment’, including graduate students who require a comprehensive reference text on the subject, and also government departments and systems engineering institutions |
| examples of systems in science: Fundamentals of Linear State Space Systems John S. Bay, 1999 Spans a broad range of linear system theory concepts, but does so in a complete and sequential style. It is suitable for a first-year graduate or advanced undergraduate course in any field of engineering. State space methods are derived from first principles while drawing on the students' previous understanding of physical and mathematical concepts. The text requires only a knowledge of basic signals and systems theory, but takes the student, in a single semester, all the way through state feedback, observers, Kalman filters, and elementary I.Q.G. control. |
| examples of systems in science: The Art of Systems Architecting Mark W. Maier, 2009-01-06 If engineering is the art and science of technical problem solving, systems architecting happens when you don't yet know what the problem is. The third edition of a highly respected bestseller, The Art of Systems Architecting provides in-depth coverage of the least understood part of systems design: moving from a vague concept and limited resources |
| examples of systems in science: Complexity M. Mitchell Waldrop, 2019-10-01 “If you liked Chaos, you’ll love Complexity. Waldrop creates the most exciting intellectual adventure story of the year” (The Washington Post). In a rarified world of scientific research, a revolution has been brewing. Its activists are not anarchists, but rather Nobel Laureates in physics and economics and pony-tailed graduates, mathematicians, and computer scientists from all over the world. They have formed an iconoclastic think-tank and their radical idea is to create a new science: complexity. They want to know how a primordial soup of simple molecules managed to turn itself into the first living cell—and what the origin of life some four billion years ago can tell us about the process of technological innovation today. This book is their story—the story of how they have tried to forge what they like to call the science of the twenty-first century. “Lucidly shows physicists, biologists, computer scientists and economists swapping metaphors and reveling in the sense that epochal discoveries are just around the corner . . . [Waldrop] has a special talent for relaying the exhilaration of moments of intellectual insight.” —The New York Times Book Review “Where I enjoyed the book was when it dove into the actual question of complexity, talking about complex systems in economics, biology, genetics, computer modeling, and so on. Snippets of rare beauty here and there almost took your breath away.” —Medium “[Waldrop] provides a good grounding of what may indeed be the first flowering of a new science.” —Publishers Weekly |
| examples of systems in science: Anatomy and Physiology J. Gordon Betts, Peter DeSaix, Jody E. Johnson, Oksana Korol, Dean H. Kruse, Brandon Poe, James A. Wise, Mark Womble, Kelly A. Young, 2013-04-25 |
| examples of systems in science: Intelligent Systems And Soft Computing For Nuclear Science And Industry - Proceedings Of The 2nd International Flins Workshop Da Ruan, Pierre D'hondt, Etienne E Kerre, Paul Govaerts, 1996-07-29 Following FLINS '94, the 1st International workshop on fuzzy logic and intelligent technologies in nuclear science, FLINS '96 aimed to introduce the principles of intelligent systems and soft computing, such as fuzzy logic, neural networks, genetic algorithms (and any combination of these three), knowledge-based expert systems and complex problem-solving techniques, in nuclear science and industry and in related fields.This volume presents carefully selected papers drawn from more than 20 countries. It covers theoretical aspects of intelligent systems and soft computing, together with their applications in nuclear science and industry. |
| examples of systems in science: Spatio-Temporal Pattern Formation Daniel Walgraef, 1996-12-13 Spatio-temporal patterns appear almost everywhere in nature, and their description and understanding still raise important and basic questions. However, if one looks back 20 or 30 years, definite progress has been made in the modeling of insta bilities, analysis of the dynamics in their vicinity, pattern formation and stability, quantitative experimental and numerical analysis of patterns, and so on. Universal behaviors of complex systems close to instabilities have been determined, leading to the wide interdisciplinarity of a field that is now referred to as nonlinear science or science of complexity, and in which initial concepts of dissipative structures or synergetics are deeply rooted. In pioneering domains related to hydrodynamics or chemical instabilities, the interactions between experimentalists and theoreticians, sometimes on a daily basis, have been a key to progress. Everyone in the field praises the role played by the interactions and permanent feedbacks between ex perimental, numerical, and analytical studies in the achievements obtained during these years. Many aspects of convective patterns in normal fluids, binary mixtures or liquid crystals are now understood and described in this framework. The generic pres ence of defects in extended systems is now well established and has induced new developments in the physics of laser with large Fresnel numbers. Last but not least, almost 40 years after his celebrated paper, Turing structures have finally been ob tained in real-life chemical reactors, triggering anew intense activity in the field of reaction-diffusion systems. |
| examples of systems in science: Research and Networks for Decision Support in the NOAA Sectoral Applications Research Program National Research Council, Division of Behavioral and Social Sciences and Education, Committee on the Human Dimensions of Global Change, Panel on Design Issues for the NOAA Sectoral Applications Research Program, 2007-11-02 This study recommends a definition of decision support that emphasizes communication rather than translation and a strategy by which the small NOAA Sectoral Applications Research program can advance decision support. The book emphasizes that seasonal climate forecasts provide fundamentally new kinds of information and that integrating this information into real-world decisions will require social innovations that are not easily accomplished. It recommends that the program invest in (a) research to identify and foster the innovations needed to make information about climate variability and change more usable in specific sectors, including research on the processes that influence success or failure in the creation of knowledge-action networks for making climate information; (b) workshops to identify, catalyze, and assess the potential of knowledge-action networks in particular resource areas or decision domains; and (c) pilot projects to create or enhance these networks for supporting decisions in climate-affected sectors. It recommends that evaluation of the program be addressed with a monitoring approach. |
| examples of systems in science: Systems Science: Theory, Analysis, Modeling, and Design George E. Mobus, 2022-03-09 This book describes a comprehensive approach to applying systems science formally to the deep analysis of a wide variety of complex systems. Detailed ‘how-to’ examples of the three phases (analysis-modeling-design) of systems science are applied to systems of various types (machines, organic (e.g. ecosystem), and supra-organic (e.g. business organizations and government). The complexity of the global system has reached proportions that seriously challenge our abilities to understand the consequences of our use of technology, modification of natural ecosystems, or even how to govern ourselves. For this reason, complex mathematics is eschewed when simpler structures will suffice, allowing the widest possible audience to apply and benefit from the available tools and concepts of systems science in their own work. The book shows, in detail, how to functionally and structurally deconstruct complex systems using a fundamental language of systems. It shows how to capture the discovered details in a structured knowledge base from which abstract models can be derived for simulation. The knowledge base is also shown to be a basis for generating system design specifications for human-built artifacts, or policy recommendations/policy mechanisms for socio-economic-ecological systems management. The book builds on principles and methods found in the authors’ textbook Principles of Systems Science (co-authored with Michael Kalton), but without prerequisites. It will appeal to a broad audience that deals with complex systems every day, from design engineers to economic and ecological systems managers and policymakers. |
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Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 …
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Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 Apache …
Events - Apache ECharts
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