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In 1986, the FFC requested that the NRC appoint a committee to examine the field and propose ways by which the POE process could be improved to better serve public and private sector organizations. The resulting report, Post-Occupancy Evaluation Practices in the Building Process: Opportunities for Improvement, proposed a broader view of POEs-from being simply the end phase of a building project to being an integral part of the entire building process. The authoring committee recommended a series of actions related to policy, procedures, and innovative technologies and techniques to achieve that broader view. In 2000, the FFC funded a second study to look at the state of the practice of POEs and lessons-learned programs among federal agencies and in private, public, and academic organizations both here and abroad. The sponsor agencies specifically wanted to determine whether and how information gathered during POE processes could be used to help inform decisions made in the programming, budgeting, design, construction, and operation phases of facility acquisition in a useful and timely way. To complete this study, the FFC commissioned a set of papers by recognized experts in this field, conducted a survey of selected federal agencies with POE programs, and held a forum at the National Academy of Sciences on March 13, 2001, to address these issues. This report is the result of those efforts.
Coastal and estuarine environments at the interface of terrestrial and marine areas are among the most productive in the world. However, since the beginning of the industrial era, these ecosystems have been subjected to strong anthropogenic pressures intensified from the second half of the 20th century, when there was a marked acceleration in the warming (climate change) of the continents, particularly at high latitudes. Coastal ecosystems are highly vulnerable to alteration of their physical, chemical and biological characteristics (marine intrusion, acidification of marine environments, changes in ecosystems, evolution and artificialization of the coastline, etc.).In contact with heavily populated areas, these environments are often the receptacle of a lot of chemical and biological pollution sources that significantly diminish their resilience. In this context of accelerated evolution and degradation of these areas important for food security of many populations around the world, it is necessary to better identify the factors of pressure and understand, at different scales of observation, their effects and impacts on the biodiversity and on the socio-eco-systems, in order to determine the degree of vulnerability of these coastal ecosystems and the risks they face. A transdisciplinary and integrated approach is required to prevent risks. Within this framework, operational coastal oceanography occupies an important place but also the implementation of a true socio-eco-system approach in order to set up an environmentally friendly development.
The aim of this collective work is to give an account of the topicality and dynamics of new research in the didactics of evolution, by articulating francophone and international work. The various contributions pursue a reflection on the challenges of teaching and learning about evolution, based on historical, epistemological and societal approaches. The themes addressed illustrate the vitality and diversity of research issues in educational sciences, from primary school to university. Structured around different theoretical fields (problematization, didactics of the curriculum, nature of science, etc.), this book explores the content, teaching and learning processes and approaches, teaching practices, as well as pre-service and in-service teacher training, with a view to both intelligibility and feasibility.
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
On cover: IEA statistics.
Thoroughly updated and reorganized, Strickberger's Evolution, Fourth Edition, presents biology students with a basic introduction to prevailing knowledge and ideas about evolution, discussing how, why, and where the world and its organisms changed throughout history. Keeping consistent with Strickberger's engaging writing style, the authors carefully unfold a broad range of philosophical and historical topics that frame the theories of today including cosmological and geological evolution and its impact on life, the origins of life on earth, the development of molecular pathways from genetic systems to organismic morphology and function, the evolutionary history of organisms from microbes to animals, and the numerous molecular and populational concepts that explain the earth's dynamic evolution. Important Notice: The digital edition of this book is missing some of the images or content found in the physical edition.
This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.
Comparison of objects, events, and situations is integral to judgment; comparisons of the self with other people comprise one of the building blocks of human conduct and experience. After four decades of research, the topic of social comparison is more popular than ever. In this timely handbook a distinguished roster of researchers and theoreticians describe where the field has been since its development in the early 1950s and where it is likely to go next.