This comprehensive database on physical properties of pure ionic liquids (ILs) contains data collected from 269 peer-reviewed papers in the period from 1982 to June 2008. There are more than 9,400 data points on the 29 kinds of physicochemical properties for 1886 available ionic liquids, from which 807 kinds of cations and 185 kinds of anions were extracted. This book includes nearly all known pure ILs and their known physicochemical properties through June 2008. In addition, the authors incorporate the main applications of individual ILs and a large number of references.Nearly 50 tables include typical data, experimental and modelling or simulation comparison, and model parameters, enhancing the application of ILs100 figures--from QSPR, EOS and gE models to quantum and molecular simulations--help readers understand ILs at molecular levelApplications illustrate the role of IL properties in industry, in particular the development of novel clean processes and products

Ionic Liquids: Physicochemical Properties

Science & Math

Zhang, Suojiang

Lu, Xingmei

Zhou, Qing

Li, Xiaohua

Zhang, Xiangping

Li, Shucai

Chemistry

Elsevier Science

Temperament and Child Development in Context (Elements in Child Development)

Children's temperament is a central individual characteristic that has significant implications, directly and indirectly, for their social, emotional, behavioral, cognitive, and health outcomes, through its evocative and moderating effects on other social and contextual influences. Accounting for these contextual influences is critical to articulating the role of temperament in children's development. This Element defines temperament and describes its roots in neurobiological systems as well as its relevance to children's developmental outcomes, with a focus on understanding the influence of temperament in children's social and environmental contexts. It covers key developmental periods, situating the contribution of temperament to children's development in complex and changing processes and contexts from infancy through adolescence. The Element concludes by underscoring the value of integrating contextual, relational, and dynamic systems approaches and pointing to future directions in temperament research and application.

Health, Fitness & Dieting

Lengua, Liliana J.

Gartstein, Maria A.

Colder, Craig R.

Jacques, Debrielle T.

Psychology & Counseling

Cambridge University Press

Adolescence is often thought of as a period during which parent–child interactions can be relatively stressed and conflictual. There are individual differences in this regard, however, with only a modest percent of youth experiencing extremely conflictual relationships with their parents. Relatively little empirical research, however, addresses individual differences in the quality of parent–adolescent interactions concerning potentially conflictual issues. The research reported in this monograph examined dispositional and parenting predictors of the quality of parents’ and their adolescent children’s emotional displays and positive and negative verbalizations when dealing with conflictual issues. Of particular interest were patterns of continuity and discontinuity in the factors related to conflicts. A multimethod, multireporter (mother, teacher, and sometimes adolescent reports) longitudinal approach(over 4 years) was used to assess adolescents’ dispositional characteristics (control/regulation, resiliency, and negative emotionality), youths’ externalizing problems, and parenting variables (warmth, positive expressivity, discussion of emotion, positive and negative family expressivity). Parentadolescent conflicts appear to be influenced by both child characteristics and quality of prior and concurrent parenting, and child effects may be more evident than parent effects in this pattern of relations.

Understanding Mother-Adolescent Conflict Discussions: Concurrent and Across-Time Prediction from Youths' Dispositions andParenting (Monographs of the Society for Research in Child Development)

Eisenberg, Nancy

Maxon, Elizabeth

Gershoff, Elizabeth T.

Hofer, Claire

Spinrad, Tracy L.

Valiente, Carlos

Cumberland, Amanda

Liew, Jeffrey

Reiser, Mark

Wiley/Blackwell

Discusses the relations among the three fundamental life activities: growth, maintenance and reproduction in Paphiopedilum armeniacum (Orchidaceae), with a focus on plant propagation. This book also covers the biological and ecological characteristics of Paphiopedilum armeniacum, and the population performance in different vegetation patterns.

The Reproduction Strategy of Paphiopedilum Armeniacum Orchidacae

Liu, Zhongjian

Chen, Lijun

Biological Sciences

Nova Science Pub Inc

The Chief Financial Officer (Chinese Edition)

Jiangsu Peoples Publishing House

年复一年

Hai Chao She Ying Yi Shu Chu Ban She

Inferring latent structure and causality is crucial for understanding underlying patterns and relationships hidden in the data. This book covers selected models for latent structures and causal networks and inference methods for these models.After an introduction to the EM algorithm on incomplete data, the book provides a detailed coverage of a few widely used latent structure models, including mixture models, hidden Markov models, and stochastic block models. EM and variation EM algorithms are developed for parameter estimation under these models, with comparison to their Bayesian inference counterparts. We make further extensions of these models to related problems, such as clustering, motif discovery, Kalman filtering, and exchangeable random graphs. Conditional independence structures are utilized to infer the latent structures in the above models, which can be represented graphically. This notion generalizes naturally to the second part on graphical models that use graph separation to encode conditional independence. We cover a variety of graphical models, including undirected graphs, directed acyclic graphs (DAGs), chain graphs, and acyclic directed mixed graphs (ADMGs), and various Markov properties for these models. Recent methods that learn the structure of a graphical model from data are reviewed and discussed. In particular, DAGs and Bayesian networks are an important class of mathematical models for causality. After an introduction to causal inference with DAGs and structural equation models, we provide a detailed review of recent research on causal discovery via structure learning of graphs. Finally, we briefly introduce the causal bandit problem with sequential intervention.

Latent Structure and Causality: Inference from Data

Mathematics

World Scientific Pub Co Inc

Ocular Myasthenia Gravis (Experts' Perspectives on Medical Advances)

Medical Books

Chen, Jian

Medicine

Springer

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