China's breathtaking diversity of natural habitats--from mountains and deserts to grasslands and lush tropical forests--is home to more than 10 percent of the world's mammal species. This one-of-a-kind pocket guide describes the characteristics, geographic distribution, natural history, and conservation status of all 558 species of mammals found in China. An up-to-date distribution map accompanies each species account, and beautiful color illustrations by wildlife artist Federico Gemma depict a majority of the species. The definitive text is written by leading specialists and follows the most current global standards for mammalian systematics. This field-ready pocket edition of A Guide to the Mammals of China makes the rich mammal fauna of China accessible to ecotravelers and naturalists like never before. The comprehensive pocket guide to all of China's 558 mammal species Describes the physical characteristics, geographic distribution, natural history, and conservation status of every species Features up-to-date distribution maps and stunning color illustrations throughout Written by a team of leading specialists

China's stunning diversity of natural habitats--from parched deserts to lush tropical forests--is home to more than 10 percent of the world's mammal species. A Guide to the Mammals of China is the most comprehensive guide to all 556 species of mammals found in China. It is the only single-volume reference of its kind to fully describe the physical characteristics, geographic distribution, natural history, and conservation status of every species. An up-to-date distribution map accompanies each species account, and color plates illustrate a majority of species. Written by a team of leading specialists, including Professor Wang Sung who provides a history of Chinese mammalogy, A Guide to the Mammals of China is the ideal reference for researchers and a delight for anyone interested in China's rich mammal fauna. The definitive, comprehensive, up-to-date guide to all of China's 556 mammal species High-quality color plates accompany the detailed text Each species account comes with a distribution map Organized taxonomically for easy reference Includes an extensive bibliography

The diversity of US institutions of higher education is manifested in many ways. This study looks at that diversity from the economic perspective by studying the subsidy structure through the distribution of institutional price-cost ratio (PCR), defined as the sum of net tuition price divided by total supplier cost and equals to one minus subsidy-cost ratio (SCR). IPEDS Finance, Enrollment, and Institutional Characteristics survey data for academic year 2006-2007 are used. Significant between-sector differences are found in terms of both central locations and ranges of PCR. Public two-year institutions have the lowest average PCR (0.12) and smallest within-group variation while for-profit four-year institutions have the highest average PCR (0.93). The within-group variations are quite large for both private nonprofit and for-profit sectors. Nine types of subsidy structure are constructed and used to categorize institutions, which reveal considerable overlapping between public and private nonprofit sectors and between private nonprofit and for-profit sectors. Private nonprofit sector is consistently shown as the "hybrid" sector with more similarities to the public sector. This study highlights price-cost ratio as an important metric for economics of higher education because it integrates targeted price adjustments (list price - net price) and general subsidy (supplier cost - list price), allows for negative subsidy, and accounts for cost variations. It succinctly provides a holistic view of the subsidy-profit spectrum and serves the purpose to rectify the currently skewed perspective that predominantly focuses on "student aid" (redefined as "targeted price adjustments") and for the most part excludes the for-profit sector. A byproduct of this study is a detailed account of how to adjust new GASB/FASB-based IPEDS Finance data to derive meaningful price and cost measures to support cross-sector comparison.

Dornyei (2005) proposed the L2 motivational self system in response to the need to develop the socioeducational model. This study further tests the validation of the L2 motivational self system by investigating beginning Chinese language learners at the college level in the United States. A questionnaire combining two published questionnaires was administered to 197 subjects, including heritage language learners and nonheritage language learners, and compared the motivational representations of the two types of learners. This is the first study to test the L2 motivational self system by investigating learners of a language other than English. Through a correlation analysis, the study found significant correlations between (a) integrativeness and the ideal L2 Self; (b) ideal L2 self and motivational strength; (c) ideal L2 self, ought-to L2 self, instrumentality-promotion, and instrumentality-prevention; and (d) ideal L2 self, international posture, and willingness to communicate. Through a MANOVA analysis, the heritage and nonheritage language learners were found different in six variables: motivational strength, ought-to L2 self, family influence, cultural interest, prevention, and international posture. The study supports previous studies on the theoretical legitimacy of the L2 motivational self system and suggests that applying the L2 motivational self system can be extended to a language other than English and to second-language settings.

Every day large and increasing amounts of unstructured information are created, putting ever more demands on retrieval methods, classification, automatic data analysis and management. Clustering is an important and efficient way for organizing and analyzing information and data. One of the most widely used dynamic clustering algorithms is k-means clustering. This dissertation presents our k-centers Min-Max dynamic clustering algorithm (KCMM) and k-centers mean-shift reverse mean-shift dynamic clustering algorithm (KCMRM). These algorithms are designed to modify k-means in order to achieve improved performance and help with specific goals in certain domains. These two algorithms can be applied to many fields such as wireless sensor networks, server or facility location optimization, and molecular networks. Their application in wireless sensor networks are described in this dissertation. The k-centers Min-Max clustering algorithm uses a smallest enclosing disk/sphere algorithm to attain a minimum of the maximum distance between a cluster node and data nodes. Our approach results in fewer iterations, and shorter maximum intra-cluster distances than the standard k-meansclustering algorithm with either uniform distribution or normal distribution. Most notably, it can achieve much better performance when the size of clusters is large, or when the clusters includes large numbers of member nodes in normal distribution. The k-centers mean-shift reverse mean-shift clustering algorithm is proposed to solve the "empty cluster" problem which is caused by random deployment. It employs a Gaussian function as a kernel function, discovers the relationship between mean shift and gradient ascent on the estimated density surface, and iteratively moves cluster nodes away from their weighted means. This results in cluster nodes which better accommodate the distribution of data nodes. The k-centers mean-shift reverse mean-shift algorithm can not only reduce the number of empty clusters, but can also make the sizes of clusters are more evenly balanced compared to k-means and k-centers Min-Max clustering algorithms. In wireless sensor networks, addressing energy dissipation is a key issue. For heterogeneous wireless sensor networks, energy consumption to transmit data is proportional to the distance between sensor nodes and cluster heads or to a base station. Clustering is one of the best methods to reduce energy dissipation and extend network lifetimes. The k-centersMin-Max and k-centers mean-shift reverse mean-shift clustering algorithms are applied to two proposed protocols, KCMM and KCMRM, for wireless sensor networks. Desirable features of the proposed clustering protocols KCMM and KCMRM include: energy efficiency; distributed and localized data aggregation; adaptation to changes in sensor distribution; robustness to partial damage; and self-recovery. Besides the above features, KCMRM protocol can make use of cluster heads efficiently and can reduce empty clusters.