This book crosses disciplinary boundaries to investigate how the benefits of green spaces can be further incorporated in public health. In this regard, the book highlights how ecosystem services provided by green spaces affect multiple aspects of human health and well-being, offering a strategic way to conceptualize the topic.
For centuries, scholars have observed the range of health benefits associated with exposure to nature. As people continue to move to urban areas, it is essential to include green spaces in cities to ensure sustained human health and well-being. Such insights can not only advance the science but also spark interdisciplinary research and help researchers creatively translate their findings into benefits for the public. The book explores this topic in the context of ‘big picture’ frameworks that enhance communication between the environmental, public health, and social sciences.
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The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing.
Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field.
This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
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The 10 full papers presented were carefully reviewed and selected from 32 submissions. The papers aim at contributing to the understanding of relevant trends of current research on ICT for Ageing Well and eHealth including the ambient assisted living.
The third book in Young’s unique trilogy on causality and development continues to locate and define the central role of causality in biopsychosocial and network/systems development, and as a unifying concept of psychology itself. As a way of discussing causality, in general, initially, the book focuses on the acquisition of handedness and hemispheric specialization in infancy and childhood, and their relations to the development of cognition, language, and emotion, in particular. The second part of the book elaborates an innovative 25-step Neo-Eriksonian model of development across the life course based on a Neo-Piagetian model covered in the previous books, completing a step-by-step account of development over the lifespan cognitively and socio-emotionally. It builds on the concept of neo-stage, which is network-based. From this conceptual synthesis, the author’s robust theory of development and causality identifies potential areas for psychological problems and pathology at each developmental step as well as science-based possibilities for their treatment.
This elegant volume:
Presents a clear picture of the development of handedness and laterality in more depth than has been attempted in the literature to date.
Traces the causal concepts of activation-inhibition coordination and networking in the context of development.
Describes in depth a novel 25-step Neo-Eriksonian lifespan model of development.
Reviews relevant research on Piagetian and Eriksonian theories in development.
Emphasizes the clinical utility of the described 25-step Neo-Eriksonian approach to lifespan development.
A significant step in understanding this highly nuanced subject and synthesizing a broad knowledge base, Causality and Development will find an interested audience among developmental psychologists, mental health practitioners, academics, and researchers.chers.
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Bioinformatics is an integrative field of computer science, genetics, genomics, proteomics, and statistics, which has undoubtedly revolutionized the study of biology and medicine in past decades. It mainly assists in modeling, predicting and interpreting large multidimensional biological data by utilizing advanced computational methods. Despite its enormous potential, bioinformatics is not widely integrated into the academic curriculum as most life science students and researchers are still not equipped with the necessary knowledge to take advantage of this powerful tool. Hence, the primary purpose of our book is to supplement this unmet need by providing an easily accessible platform for students and researchers starting their career in life sciences. This book aims to avoid sophisticated computational algorithms and programming. Instead, it mostly focuses on simple DIY analysis and interpretation of biological data with personal computers. Our belief is that once the beginners acquire these basic skillsets, they will be able to handle most of the bioinformatics tools for their research work and to better understand their experimental outcomes.
Unlike other bioinformatics books which are mostly theoretical, this book provides practical examples for the readers on state-of-the-art open source tools to solve biological problems. Flow charts of experiments, graphical illustrations, and mock data are included for quick reference. Volume I is therefore an ideal companion for students and early stage professionals wishing to master this blooming field.
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