As an area of research that continues to develop, the study of linguistics worldwide presents the opportunity for the improvement of cross-cultural communication through education and research. Language educators are charged with the task of instructing students to effectively communicate across cultures in a multi-lingual word. The Handbook of Research on Teaching Methods in Language Translation and Interpretation presents an interdisciplinary approach to educational contexts across cultures for the study of verbal and written linguistics in order to broaden students' communicative and problem solving abilities. This book is an essential reference source for academicians, researchers, professionals, and students in the fields of education and linguistics, interested in the assessment and evaluation of pedagogical research to enhance learning methodologies and practices.

The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well.  The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields. 

This book is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all non-redundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor image-guided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple- template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.