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This book features state-of-the-art contributions from two well-established conferences: Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2020) and Mass Customization and Personalization Conference (MCPC2020). Together, they focus on the joint design, development, and management of products, production systems, and business for sustainable customization and personalization. The book covers a large range of topics within this domain, ranging from industrial success factors to original contributions within the field.
This book constitutes the refereed proceedings of the 5th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2016, held in Da Nang, Vietnam, in November/December 2016. The IUKM symposia aim to provide a forum for exchanges of research results and ideas, and experience of application among researchers and practitioners involved with all aspects of uncertainty modelling and management.
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Nano-Networks, Nano-Net, held in Boston, MS, USA, in September 2008. The 17 revised full papers presented together with 5 invited presentations were carefully reviewed and selected. The papers address the whole spectrum of Nano-Networks and spans topis like modeling, simulation, statdards, architectural aspects, novel information and graph theory aspects, device physics and interconnects, nanorobotics as well as nano-biological systems.
This book explores recent progress in RNA secondary, tertiary structure prediction, and its application from an expansive point of view. Because of advancements in experimental protocols and devices, the integration of new types of data as well as new analysis techniques is necessary, and this volume discusses additional topics that are closely related to RNA structure prediction, such as the detection of structure-disrupting mutations, high-throughput structure analysis, and 3D structure design. Written for the highly successful Methods in Molecular Biology series, chapters feature the kind of detailed implementation advice that leads to quality research results. Authoritative and practical, RNA Structure Prediction serves as a valuable guide for both experimental and computational RNA researchers.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
This book covers available approaches to improving the performance and impact of long-term projections of the national energy sector development. In turn, it introduces an original multi-stage approach to narrowing down the uncertainty range of the input data and resulting projections. Its unique contribution is that it limits the scope for each of the projection timeframe segments step-by-step. This is done in the course of iterative calculations, which employ dedicated methods and other tools to elucidate and solve top-priority problems specific to each time segment. In closing, the book provides a detailed treatment of two essential research problems: 1) long-term forecasting for regional energy markets, and 2) the quantitative assessment of a) the barriers that are likely to hinder energy sector development and b) strategic-level energy security threats.
This book is a printed edition of the Special Issue Optimisation Models and Methods in Energy Systems that was published in Energies
This book discusses the optimal design and operation of multi-carrier energy systems, providing a comprehensive review of existing systems as well as proposing new models. Chapters cover the theoretical background and application examples of interconnecting energy technologies such as combined heat and power plants, natural gas-fired power plants, power to gas technology, hydropower plants, and water desalination systems, taking into account the operational and technical constraints of each interconnecting element and the network constraint of each energy system. This book will be a valuable reference for power network and mechanical system professionals and engineers, electrical power engineering researchers and developers, and professionals from affiliated power system planning communities. Provides insight on the design and operation of multi-carrier energy systems; Covers both theoretical aspects and technical applications; Includes case studies to help apply concepts to real engineering situations.
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Special edition compiled in partnership with Frontiers sponsored by the Clean Air Task Force. The realisation of Net Zero by 2050 will require the ability for strategy developers, operational planners and decision makers to better manage uncertainty, complexity and emergence. The application of the orthodox set of decision support tools and processes that have been used to explore deep decarbonisation options to 2050 have blinded decision makers to uncertainty, complexity and emergence. Tools have often been used which are inappropriate to the types of decisions being made – a competency which has been glaringly revealed during the C-19 Pandemic. This Frontiers Research Topic will highlight the need for an interdisciplinary, mixed methods approach bringing together insights from modelling, decision science, psychology, anthropology, and sociology to form a compendium of current best practice for decision making for the net zero transformation and new research frontiers. Develop greater awareness amongst policymakers, practitioners and academics as to the importance of: • Understanding the nature of uncertainty when dealing with problems associated with the Net Zero Energy System Transformation; • Increasing importance of deliberative processes to map different value sets beyond least cost; • Acknowledging that decision making under uncertainty requires competency-based training leading to a full appreciation of the tasks at hand. Suggested areas within scope are listed in points 1-12 below. Authors are free to choose specific areas of interest, and to combine these where useful. In general, it will be useful to consider practical application of [ideas], e.g • development of `Use Cases’ and `Decision Making Contexts’ may be useful, e.g. National Govt establishing its Carbon Budget; Institution setting up its investment portfolio. • understanding of how decisions are being made within different jurisdictions, political cultures, and types of organizations (public/private). What is the role of `Decision Context' i.e. organisational decision-making structures, cultures, the role of zeitgeist and dominant narratives, or the relation between academic expertise and policy-makers. 1. Decision making from an end-to-end perspective and the need to take a holistic and interdisciplinary perspective [Editorial Cover Article]. 2. Gap between what policy makers and decision makers around net zero climate policy seek to address and what decision support tools can actually do. Why that gap is increasing (if it is)? 3. Understanding the nature of uncertainty when applying the relevant decision support tool and processes. Not all uncertainty can be addressed within the decision support tool itself. Role of optimism bias; potential role of least worst regret approaches etc 4. What different decision support tools can inform decision makers around net zero climate policy and need for a basket of tools. 5. Why parametric decision support tools and models are pre-eminent - the role of consolidative modelling and exploratory modelling. The inertia of modelling approaches: why it is so hard to break modelling paradigms? 6. What decision science informs us about how decisions are actually made - the importance of process, the role of transparency and deliberation with analysis. 7. Processes that address the biases identified in decision science and impact of identity politics on deliberative decision making. 8. Why decision making under deep uncertainty requires competency-based training, deep subject matter expertise and systemic knowledge. 9. Ministerial and policy making and the decision support requirements: US, EU, UK & China 10. The role of narratives and how uncertainty can be communicated to societal audiences. Storylines and other narrative approaches 11. How to develop participatory approaches allow multiple values, diversity of stakeholders in which climate communication and decision making exists in an iterative exchange with policy. We have started the journey e.g. the role of climate assemblies… what next? 12. Decision making under deep (climate) uncertainty by the financial sector We acknowledge the funding of the manuscripts published in this Research Topic by the Clean Air Task Force. We hereby state publicly that the Clean Air Task Force has had no editorial input in articles included in this Research Topic, thus ensuring that all aspects of this Research Topic are evaluated objectively, unbiased by any specific policy or opinion of the Clean Air Task Force.