The purpose of this book is to gain a better understanding of the multitude of factors that determine longer life and improved quality of life in the years a person is alive. While the emphasis is primarily on the social and behavioral determinants that have an effect on the health and well-being of individuals, this publication also addresses quality of life factors and determinants more broadly. Each chapter in this book considers an area of investigation and ends with suggestions for future research and implications of current research for policy and practice. The introductory chapter summarizes the state of Americans’ health and well-being in comparison to our international peers and presents background information concerning the limitations of current approaches to improving health and well-being. Following the introduction, there are 21 chapters that examine the effects of various behavioral risk factors on population health, identify trends in life expectancy and quality of life, and suggest avenues for research in the behavioral and social science arenas to address problems affecting the U.S. population and populations in other developed and developing countries around the world. Undergraduate and graduate students pursuing coursework in health statistics, health population demographics, behavioral and social science, and heatlh policy may be interested in this content. Additionally, policymakers, legislators, heatlh educators, and scientific organizations around the world may also have an interest in this resource.

This issue of Dermatologic Clinics, guest edited by Dr. John J. DiGiovanna, is devoted to Skin Science Contributions in Clinical Dermatology. Articles in this issue include Mosaic mTORopathies; CYLD: Cause and Therapeutic Target for Cylindromas; Current Status of Dedicator of Cytokinesis (DOCK), DOCK8, DOCK2; IL-22 relation to Tumor Aggression and Role of Cyclosporine; Dual Lineage of Dermal Fibroblasts and Potential for Scar Correction, Reformation of Adnexal Structures; Inflammasome Instigated Autoinflammatory Disorders; Pigment Cell Biology; Antimicrobial Peptides; and Choosing Friend or Foe? Commensal-specific tolerance.

This book includes studies that represent the state of the art in science education research and convey a sense of the variation in educational traditions around the world. The papers are organized into six main sections: science teaching processes, conceptual understanding, reasoning strategies, early years science education, and affective and social aspects of science teaching and learning. The volume features 18 papers, selected from the most outstanding papers presented during the 10th European Science Education Research Association (ESERA) Conference, held in Nicosia, Cyprus, in September 2013. The theme of the conference was “Science Education Research for Evidence-based Teaching and Coherence in Learning”. The studies presented underline aspects of great relevance in contemporary science education: the need to reflect on different approaches to enhance our knowledge of learning processes and the role of context, designed or circumstantial, formal or non-formal, in learning and instruction. These studies are innovative in the issues they explore, the methods they use, or the ways in which emergent knowledge in the field is represented. The book is of interest to science educators and science education researchers with a commitment to evidence informed teaching and learning.

This book examines the strategic principles that define the customer experience. Building on the recent findings in the domains of behavioral economics and social psychology, Customer Science discusses the customer experience from three different perspectives: what customers do—how they identify a problem, seek a solution, and interact with the offering; what they think and feel during this process—how they evaluate different market offerings; and what motivates their behavior—why they act the way they do. In this context, it examines all components of the customer experience—from activating a need to buying a company’s offerings, to becoming a loyal customer and advocate for the company. The different stages of customer interaction with the company and its offerings are presented in the form of a customer experience map, which functions as the organizing principle for this book. The customer experience map is the blueprint for understanding the different stages of the customer experience and facilitating managerial decision making at each stage. The customer experience map is also the foundation of the customer experience canvas, a practical tool to identify the key questions managers should ask as they strive to create impactful customer experiences.

Spiritual Insights from the New Science is a guide to the deep spiritual wisdom drawn from one of the newest areas of science — the study of complex systems. The author, a former research scientist with over three decades of experience in the field of complexity science, tells her story of being attracted, as a young student, to the study of self-organizing systems where she encountered the strange and beautiful topics of chaos, fractals and other concepts that comprise complexity science. Using the events of her life, she describes lessons drawn from this science that provide insights into not only her own life, but all our lives. These insights show us how to weather the often disruptive events we all experience when growing and changing.The book goes on to explore, through the unfolding story of the author's life as a practicing scientist, other key concepts from the science of complex systems: cycles and rhythms, attractors and bifurcations, chaos, fractals, self-organization, and emergence. Examples drawn from religious rituals, dance, philosophical teachings, mysticism, native American spirituality, and other sources are used to illustrate how these scientific insights apply to all aspects of life, especially the spiritual. Spiritual Insights from the New Science shows the links between this new science and our human spirituality and presents, in engaging, accessible language, the argument that the study of nature can lead to a better understanding of the deepest meaning of our lives.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

Data Science and Data Analytics explores the foundational concepts, methodologies, and tools that drive data-driven decision-making in various industries. This book provides a comprehensive overview of data collection, processing, analysis, and visualization techniques, emphasizing practical applications and real-world case studies. Readers will gain insights into statistical methods, machine learning algorithms, and the importance of data ethics, equipping them with the knowledge to harness the power of data for informed decision-making and strategic planning in an increasingly data-centric world.

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.