Bøker utgitt av Springer Nature Singapore

This book provides an authoritative, inter-disciplinary, and up-to-date survey of relevant concepts, research areas, and applications of intellectual capital. Until now, the literature had lacked a comprehensive analysis of intellectual capital (IC) in regard to sustainability, block chain, and other related technologies and virtual environments. This book shows the importance of intellectual capital for contemporary organizations: how it contributes to theories of the firm, how it affects organizational performance, how is it linked with the organizational ambidexterity, how it connects to the technological developments like block chain and digital technologies, and what would be its association with sustainability. Central to our thesis is the systemic nature of intellectual capital in organizations: how intellectual capital interacts with and complements other organizational resources and developments. This book also shows as to how applying the notion of intellectual capital toorganizations requires us to consider how intangible forms of capital differ from more traditional forms, implying the need for a theory of firm that accommodates a concept of dynamic, heterogeneous intellectual capital. Although a lot has been written on IC, this book proves to be the first with scholastic and action-oriented perspective on as to how a firm can manage its IC to create value. This book also demonstrates as to how the subjective aspects of IC can be measured and what can be their strategic implications. A discussion on IC disclosure also appears in the latter part of the book. In doing so, this book reveals as to how the value creation of today¿s businesses is driven by the IC. This book also introduces the readers to the new application of IC and its association with the contemporary disruptive technologies.This is a book for IC researchers and academicians who want to understand the diverse aspects of IC, for business managers who want to be at the cutting edge, for those early in their careers who seek a challenging new path, and for the top-level managers of the world who have their eye on the future.

This book owes a great deal to the outstanding universal value of the natural heritage of Hubei Shennongjia, which offers an outstanding example of the ongoing ecological processes occurring in the development of intact subtropical mixed broad-leaved evergreen and deciduous forests in the northern hemisphere. The book demonstrates the value from the typical example of mountain altitudinal biological zones in the Oriental Deciduous Forest Biogeographical Province, and the vital origin location for global temperate flora, harboring the highest concentration of global temperate genera. Moreover, the heritage value in exceptional biodiversity and key habitat for numerous relic, rare, endangered, endemic, and type specimen species are presented. The richness of deciduous woody species in Shennongjia is the highest in the world.

Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a "cloud" to conventional complexity-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues. The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the "effective" hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability. We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.

This book summarizes the recent advancements for visualized medicine in terms of fundamental principles, rapidly emerging techniques and developing frontiers. Especially with the combination of artificial intelligence (AI), medical imaging agents and medical robots, smart medical technologies have been innovated and applied to the clinical uses to serve the fatal human diseases diagnosis, treatment, prognosis and data analysis. This philosophy comprehensively revolutionizes the treatment strategy of human healthcare, and will enable precision medicine and precision surgery further intuitively detectable, smartly analyzable and accurately operational. This book will discuss and conclude: 1) state-of-the-art definition of visualized medicine; 2) advanced techniques and clinical applications of visualized medicine in the past decade; 3) novel frontiers and brand-new technologies, e.g. artificial intelligence (AI), surgical robots, etc. 4) revolutionary impacts on disease diagnosis, treatments and prognosis; 5) future challenges and perspectives.