Qiang Ren – författare

The true measure of any society is how it treats its children, who are in turn that society’s future. Making use of data from the longitudinal Chinese Family Panel Studies survey, the authors of this timely study provide a multi-faceted description and analysis of China’s younger generations. They assess the economic, physical, and social-emotional well-being as well as the cognitive performance and educational attainment of China's children and youth. They pay special attention to the significance of family and community contexts, including the impact of parental absence on millions of left-behind children. Throughout the volume, the authors delineate various forms of disparities, especially the structural inequalities maintained by the Chinese Party-state and the vulnerabilities of children and youth in fragile families and communities. They also analyze the social attitudes and values of Chinese youth. Having grown up in a period of sustained prosperity and greater individual choice, the younger Chinese cohorts are more independent in spirit, more open-minded socially, and significantly less deferential to authority than older cohorts. There is growing recognition in China of the importance of investing in children’s future and of helping the less advantaged. Substantial improvements in child and youth well-being have been achieved in a time of growing economic prosperity. Strong political commitment is needed to sustain existing efforts and to overcome the many obstacles that remain. This book will be of considerable interest to researchers of Chinese society and development.

The true measure of any society is how it treats its children, who are in turn that society’s future. Making use of data from the longitudinal Chinese Family Panel Studies survey, the authors of this timely study provide a multi-faceted description and analysis of China’s younger generations. They assess the economic, physical, and social-emotional well-being as well as the cognitive performance and educational attainment of China's children and youth. They pay special attention to the significance of family and community contexts, including the impact of parental absence on millions of left-behind children. Throughout the volume, the authors delineate various forms of disparities, especially the structural inequalities maintained by the Chinese Party-state and the vulnerabilities of children and youth in fragile families and communities. They also analyze the social attitudes and values of Chinese youth. Having grown up in a period of sustained prosperity and greater individual choice, the younger Chinese cohorts are more independent in spirit, more open-minded socially, and significantly less deferential to authority than older cohorts. There is growing recognition in China of the importance of investing in children’s future and of helping the less advantaged. Substantial improvements in child and youth well-being have been achieved in a time of growing economic prosperity. Strong political commitment is needed to sustain existing efforts and to overcome the many obstacles that remain. This book will be of considerable interest to researchers of Chinese society and development.

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.

This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.

Advances in Time-Domain Computational Electromagnetic Methods Discover state-of-the-art time domain electromagnetic modeling and simulation algorithms Advances in Time-Domain Computational Electromagnetic Methods delivers a thorough exploration of recent developments in time domain computational methods for solving complex electromagnetic problems. The book discusses the main time domain computational electromagnetics techniques, including finite-difference time domain (FDTD), finite-element time domain (FETD), discontinuous Galerkin time domain (DGTD), time domain integral equation (TDIE), and other methods in electromagnetic, multiphysics modeling and simulation, and antenna designs. The book bridges the gap between academic research and real engineering applications by comprehensively surveying the full picture of current state-of-the-art time domain electromagnetic simulation techniques. Among other topics, it offers readers discussions of automatic load balancing schemes for DG-FETD/SETD methods and convolution quadrature time domain integral equation methods for electromagnetic scattering. Advances in Time-Domain Computational Electromagnetic Methods also includes: Introductions to cylindrical, spherical, and symplectic FDTD, as well as FDTD for metasurfaces with GSTC and FDTD for nonlinear metasurfacesExplorations of FETD for dispersive and nonlinear media and SETD-DDM for periodic/ quasi-periodic arraysDiscussions of TDIE, including explicit marching-on-in-time solvers for second-kind time domain integral equations, TD-SIE DDM, and convolution quadrature time domain integral equation methods for electromagnetic scatteringTreatments of deep learning, including time domain electromagnetic forward and inverse modeling using a differentiable programming platformIdeal for undergraduate and graduate students studying the design and development of various kinds of communication systems, as well as professionals working in these fields, Advances in Time-Domain Computational Electromagnetic Methods is also an invaluable resource for those taking advanced graduate courses in computational electromagnetic methods and simulation techniques.

Introduction to current modeling methods for electromagnetic metasurfaces using the generalized sheet transition condition Numerical Methods for Modeling Metasurface with GSTC provides a systemic and deep monograph of numerical modeling approaches for metasurfaces and thoroughly explores various numerical methods, encompassing not only those in the frequency domain, but also those in the time domain that incorporate generalized sheet transition conditions (GSTCs). It then introduces the mainstream full-wave computational electromagnetic method, including finite-difference frequency-domain method (FDFD), method of moments (MoM), finite element method (FEM), spectral element method (SEM), finite-difference time-domain method (FDTD), and discontinue Galerkin time-domain method (DGTD). And the incorporation of GSTC with these numerical methods. Well-designed examples show that the GSTC equivalence is a quick and accurate tool for metasurface analysis. Written by a team of highly qualified authors, Numerical Methods for Modeling Metasurface with GSTC also includes information on: The basics of metasurfaces, the GSTC technique, and computational electromagnetics methodsMetasurfaces modeling, covering media homogenization techniques, GSTC theory, and the susceptibility synthesis methodMethods to simulate steady or transient responses of both planar and curved metasurfacesNumerical Methods for Modeling Metasurface with GSTC is an essential up-to-date reference on the subject for academic researchers and engineers in electromagnetics, optics, and applied physics and graduate and senior undergraduate students in related programs of study.

China’s concentrated HIV epidemic is on the brink of becoming a generalized one and syphilis infection has become a major public health threat. A better understanding of the proximate social determinants of HIV related to gender will be crucial to effectively curbing HIV and other STIs in China.

This monograph offers a detailed analysis of the creation, pilot implementation, and possible wide adoption of the real property tax at the local level in China. Starting in 2003, as China’s economy gradually recovered from the Asian financial crisis that started in 1998, the real property market entered a period of rapid expansion, followed immediately by rampant speculation, rising housing costs, and official corruption. Over the last ten years, the price of real property in most cities has more than tripled, especially in metropolitan areas. In an effort to curb this, the government has instituted a number of property-market controls, including property tax pilot programs in Shanghai and Chongqing. While this is the latest of a number of fiscal reforms, it is a very important one that carries with it the ability to change the landscape of public finance, intergovernmental relations, and local governance in China. It represents a fundamental change in the provision of public services, the relationship between local governments and tax payers, and the status of localities in the government structure. Taking a public choice perspective, the authors argue that the local property tax should be used not solely as a means of controlling housing prices but should be fully employed as a fiscal and budgetary institution that will contribute to mitigating multifarious socio-economic problems resulting from economic growth, rapid urbanization, and widening income disparity. As this program is the first of its kind, so this book is the first detailed study of property tax in China; as such, it will appeal to researchers of public finance and public policy. It will also be of great interest to policymakers in China and in other countries that are considering adopting or reforming their versions of the local property tax. It fills the gap in a growing body of literature about the inner workings of Chinese economics and policy.

This monograph offers a detailed analysis of the creation, pilot implementation, and possible wide adoption of the real property tax at the local level in China. Starting in 2003, as China’s economy gradually recovered from the Asian financial crisis that started in 1998, the real property market entered a period of rapid expansion, followed immediately by rampant speculation, rising housing costs, and official corruption. Over the last ten years, the price of real property in most cities has more than tripled, especially in metropolitan areas. In an effort to curb this, the government has instituted a number of property-market controls, including property tax pilot programs in Shanghai and Chongqing. While this is the latest of a number of fiscal reforms, it is a very important one that carries with it the ability to change the landscape of public finance, intergovernmental relations, and local governance in China. It represents a fundamental change in the provision of public services, the relationship between local governments and tax payers, and the status of localities in the government structure. Taking a public choice perspective, the authors argue that the local property tax should be used not solely as a means of controlling housing prices but should be fully employed as a fiscal and budgetary institution that will contribute to mitigating multifarious socio-economic problems resulting from economic growth, rapid urbanization, and widening income disparity. As this program is the first of its kind, so this book is the first detailed study of property tax in China; as such, it will appeal to researchers of public finance and public policy. It will also be of great interest to policymakers in China and in other countries that are considering adopting or reforming their versions of the local property tax. It fills the gap in a growing body of literature about the inner workings of Chinese economics and policy.

China’s concentrated HIV epidemic is on the brink of becoming a generalized one and syphilis infection has become a major public health threat. A better understanding of the proximate social determinants of HIV related to gender will be crucial to effectively curbing HIV and other STIs in China.

This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios.

This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios.