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  • Technology Innovation for the Circular Economy : Recycling, Remanufacturing, Design, System Analysis and Logistics
    Technology Innovation for the Circular Economy : Recycling, Remanufacturing, Design, System Analysis and Logistics

    TECHNOLOGY INNOVATION FOR THE CIRCULAR ECONOMY The book comprises 56 peer-reviewed chapters comprehensively covering in-depth areas of circular economy design, planning, business models, and enabling technologies. Some of the greatest opportunities for innovation in the circular economy are in remanufacturing, refurbishment, reuse, and recycling.Critical to its growth, however, are developments in product design approaches and the manufacturing business model that are often met with challenges in the current, largely linear economies of today’s global manufacturing chains. The conference hosted by the REMADE Institute in Rochester, NY, brought together U.S. and international researchers, industry engineers, technologists, and policymakers, to discuss the myriad intertwining issues relating to the circular economy. This book consists of 56 chapters in 10 distinct parts covering broad areas of research and applications in the circular economy area.The first four parts explore the system level work related to circular economy approaches, models and advancements including the use of artificial intelligence (AI) and machine learning to guide implementation, as well as design for circularity approaches.Mechanical and chemical recycling technologies follow, highlighting some of the most advanced research in those areas.Next, innovation in remanufacturing is addressed with descriptions of some of the most advanced work in this field.This is followed by tire remanufacturing and recycling, highlighting innovative technologies in addressing the volume of end-of-use tires.Pathways to net-zero emissions in manufacturing of materials concludes the book, with a focus on industrial decarbonization. Audience This book has a wide audience in academic institutes, business professionals and engineers in a variety of manufacturing industries.It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.

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  • Analytical Technology in Nutrition Analysis
    Analytical Technology in Nutrition Analysis

    Due to increasing global food needs as a result of population growth, the use of new food sources has gained interest in the last decade. However, the inclusion of new foods in our diet, as well as the increased interest of the population in consuming foods with better nutritional properties, has increased the need for adequate food analytical methods. This monographic issue presents innovative methods of chemical analysis of foods, as well as the nutritional and chemical characterization of foods whose consumption is expected to increase worldwide in the coming years.

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  • Business Model Innovation : Concepts, Analysis, and Cases
    Business Model Innovation : Concepts, Analysis, and Cases

    Rooted in strategic management research, Business Model Innovation explores the concepts, tools, and techniques that enable organizations to gain and/or maintain a competitive advantage in the face of technological innovation, globalization, and an increasingly knowledge-intensive economy.Updated with all-new cases, this second edition of the must-have for those looking to grasp the fundamentals of business model innovation, explores the novel ways in which an organization can generate, deliver, and monetize benefits to customers.

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  • Pioneering Progress : American Science, Technology, and Innovation Policy
    Pioneering Progress : American Science, Technology, and Innovation Policy


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  • How are calculations for circuit analysis in digital technology performed?

    Calculations for circuit analysis in digital technology are performed using Boolean algebra and logic gates. Boolean algebra is used to represent the logical relationships between inputs and outputs in a digital circuit. Logic gates, such as AND, OR, and NOT gates, are then used to implement these logical relationships in the circuit. By applying Boolean algebra and using logic gates, engineers can analyze and design digital circuits to ensure proper functionality and performance.

  • Is source analysis the same as text analysis?

    Source analysis and text analysis are related but not the same. Source analysis involves examining the origin, context, and credibility of a source, while text analysis focuses on interpreting and understanding the content of a text itself. Source analysis helps determine the reliability and bias of a source, while text analysis delves into the meaning, structure, and language used in a text. Both are important in research and critical thinking, but they serve different purposes in analyzing information.

  • Is dialog analysis the same as scene analysis?

    Dialog analysis and scene analysis are not the same, although they are related. Dialog analysis focuses specifically on the spoken interactions between characters, examining the content, tone, and subtext of the conversations. On the other hand, scene analysis encompasses a broader view of the entire scene, including the setting, actions, and non-verbal communication in addition to the dialog. While dialog analysis is a part of scene analysis, scene analysis includes a more comprehensive examination of all elements within a particular scene.

  • What is the analysis of the semen analysis?

    The analysis of a semen analysis typically includes evaluating the volume of semen, sperm count, sperm motility, and sperm morphology. The volume of semen provides information about the amount of fluid produced by the male reproductive system. Sperm count measures the number of sperm present in the semen, while sperm motility assesses the movement and speed of the sperm. Sperm morphology examines the shape and structure of the sperm, looking for abnormalities that may affect fertility. Overall, the analysis provides important information about a man's fertility potential and can help identify any potential issues with sperm quality or quantity.

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  • The Idea of Progress in Forensic Authorship Analysis
    The Idea of Progress in Forensic Authorship Analysis

    This Element examines progress in research and practice in forensic authorship analysis.It describes the existing research base and examines what makes an authorship analysis more or less reliable.Further to this, the author describes the recent history of forensic science and the scientific revolution brought about by the invention of DNA evidence.They chart the rise of three major changes in forensic science – the recognition of contextual bias in analysts, the need for validation studies and shift in logic of providing identification evidence.This Element addresses the idea of progress in forensic authorship analysis in terms of these three issues with regard to new knowledge about the nature of authorship and methods in stylistics and stylometry.The author proposes that the focus needs to shift to validation of protocols for approaching case questions, rather than on validation of systems or general approaches.This title is also available as Open Access on Cambridge Core.

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  • The Future of Pharmaceuticals : A Nonlinear Analysis
    The Future of Pharmaceuticals : A Nonlinear Analysis

    Before now, biological systems could only be expressed in terms of linear relationships, however, as knowledge grows and new techniques of analysis on biological systems is made available, we are realizing the non-linearity of these systems.The concepts and techniques of nonlinear analysis allow for more realistic and accurate models in science.The Future of Pharmaceuticals: A Nonlinear Analysis provides an opportunity to understand the non-linearity of biological systems and its application in various areas of science, primarily pharmaceutical sciences.This book will benefit professionals in pharmaceutical industries, academia, and policy who are interested in an entirely new approach to how we will treat disease in the future. Key Features:Addresses a new approach of nonlinear analysis. Applies a theory of projection to chalk out the future, instead of basing on linear evolution.Provides an opportunity to better understand the non-linearity in biological systems and its applications in various areas of science, primarily pharmaceutical sciences. Helps change the thought process for those looking for answers to their questions which they do not find in the linear relationship approach. Encourages a broader perspective for the creative process of drug development.

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  • Healthcare Finance : Modern Financial Analysis for Accelerating Biomedical Innovation
    Healthcare Finance : Modern Financial Analysis for Accelerating Biomedical Innovation

    An introductory finance textbook for the healthcare industry We are living in a golden age of biomedical innovation, yet entrepreneurs still struggle with the so-called Valley of Death when seeking funding for their biotech start-ups.In Healthcare Finance, Andrew Lo and Shomesh Chaudhuri show that there are better ways to finance breakthrough therapies, and they provide the essential financial tools and concepts for creating the next generation of healthcare technologies. Geared for MBA and life sciences students, as well as biopharma executives and healthcare investment professionals, this textbook covers the theory and application of financial techniques such as diversification, discounted cash flow analysis, real options, Monte Carlo simulation, and securitization, all within the context of managing biomedical assets.The book demonstrates that more efficient funding structures can reduce financial risks, lower the cost of capital, and bring more lifesaving therapies to patients faster.Readers will gain the background, framework, and techniques needed to reshape the healthcare industry in positive ways.Finance doesn’t have to be a zero-sum game, and Healthcare Finance proves that it is possible to do well by doing good. Explores new financing methods for the biopharma industryProvides accessible explanations for making good business decisions in the life sciencesAnalyzes real-world examples, case studies, and practical applicationsIncludes access to videos of lectures and recitations, interactive figures, self-graded problem sets, and other online content

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  • Handbook of Meta-analysis in Ecology and Evolution
    Handbook of Meta-analysis in Ecology and Evolution

    Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies.This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts.The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results.The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used).Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets.This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. * Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation * Brings together experts from a broad range of fields * Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species * Helps you choose the right software * Draws on numerous examples based on real biological datasets

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  • What is the difference between a textual analysis, a scene analysis, and a narrative analysis?

    A textual analysis focuses on the language and structure of a written text, examining the use of literary devices, themes, and symbolism. A scene analysis, on the other hand, zooms in on a specific segment of a film or play, analyzing the visual and auditory elements, character interactions, and the overall impact of the scene on the narrative. Narrative analysis, meanwhile, looks at the broader story structure, character development, and the way the plot unfolds over the course of the entire text. Each type of analysis offers a unique perspective on the text and allows for a deeper understanding of its meaning and impact.

  • What is the difference between Analysis and Higher Analysis?

    Analysis is the branch of mathematics that deals with limits, continuity, derivatives, integrals, and infinite series. It is a fundamental area of mathematics that is essential for understanding calculus and other advanced mathematical concepts. Higher Analysis, on the other hand, refers to more advanced topics within the field of analysis, such as functional analysis, complex analysis, and harmonic analysis. These areas delve deeper into the study of functions, spaces, and structures, and often involve more abstract and complex mathematical techniques.

  • Is source analysis the same as factual text analysis?

    Source analysis and factual text analysis are related but not the same. Source analysis involves evaluating the credibility, bias, and perspective of the source of information, while factual text analysis focuses on examining the content of the text for accuracy and reliability of the information presented. Source analysis helps determine the trustworthiness of the information, while factual text analysis assesses the content itself. Both are important in critically evaluating information, but they involve different aspects of analysis.

  • What is the difference between Analysis and Advanced Analysis?

    Analysis typically involves examining data or information to understand its components, structure, and meaning. Advanced Analysis, on the other hand, involves a more in-depth and complex examination of data using sophisticated techniques and tools. It often requires a higher level of expertise and specialized knowledge to uncover deeper insights, patterns, and trends that may not be easily visible through traditional analysis methods. Advanced Analysis may also involve predictive modeling, machine learning, or other advanced statistical techniques to extract valuable insights from data.

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