Bone substitute biomaterials are fundamental to the biomedical sector, and have recently benefitted from extensive research and technological advances aimed at minimizing failure rates and reducing the need for further surgery. This book reviews these developments, with a particular focus on the desirable properties for bone substitute materials and their potential to encourage bone repair and regeneration.

Part I covers the principles of bone substitute biomaterials for medical applications. One chapter reviews the quantification of bone mechanics at the whole-bone, micro-scale, and non-scale levels, while others discuss biomineralization, osteoductivization, materials to fill bone defects, and bioresorbable materials. Part II focuses on biomaterials as scaffolds and implants, including multi-functional scaffolds, bioceramics, and titanium-based foams. Finally, Part III reviews further materials with the potential to encourage bone repair and regeneration, including cartilage grafts, chitosan, inorganic polymer composites, and marine organisms.
Provides a detailed and accurate overview of the bone substitute biomaterials, a fundamental part of the biomaterials and biomedical sectorProvides readers with the principles of bone substitute biomaterialsReviews biomaterials for bone regeneration

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Solar Photovoltaic Technology Production: Potential Environmental Impacts and Implications for Governance provides an overview of the emerging industrial PV sector, its technologies, and the regulatory frameworks supporting them. This new book reviews and categorizes the potential environmental impacts of several main PV technologies, examining the extent to which current EU governance frameworks regulate such impacts. By identifying the gaps or regulatory mismatches and creating a basis for normative recommendations on governance change, this book analyzes potential governance implications and their impacts in relation to manufacturers upscaling PV production techniques.

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis. The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping. Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis.

The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping.

Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.

First published in 1999, this influential volume explores Macroeconomic Adjustment with a particular focus on India. Its inspiration originated from the introduction of stabilisation and structural adjustment policies in India in 1991. Mallick examines the application of this policy package by the International Monetary Fund and the World Bank to Developing Economies. First looking at the initial conditions and generators of imbalances, the appropriate policy framework for India's initial conditions and structural characteristics is considered. While the effectiveness of the IMF had been strongly criticised, Mallick explains how it could be used more effectively. He argues that the programs applied are often contradictory and, using India as an example, examines the effects of policy reform on its trade sector, the repercussions on the direct economy and the costs associated with such policies in restoring stability and future economic growth, with particular support for the Vector Autoregression (VAR) framework. Mallick forwards a new structural model for policy purposes, evaluated for overall performance and optimal control.

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

First published in 1999, this influential volume explores Macroeconomic Adjustment with a particular focus on India. Its inspiration originated from the introduction of stabilisation and structural adjustment policies in India in 1991. Mallick examines the application of this policy package by the International Monetary Fund and the World Bank to Developing Economies. First looking at the initial conditions and generators of imbalances, the appropriate policy framework for India's initial conditions and structural characteristics is considered. While the effectiveness of the IMF had been strongly criticised, Mallick explains how it could be used more effectively. He argues that the programs applied are often contradictory and, using India as an example, examines the effects of policy reform on its trade sector, the repercussions on the direct economy and the costs associated with such policies in restoring stability and future economic growth, with particular support for the Vector Autoregression (VAR) framework. Mallick forwards a new structural model for policy purposes, evaluated for overall performance and optimal control.

Written by a multidisciplinary team of experts involved in the development of standards and guidelines for its management in the USA, UK, Europe and Asia, the book contains succinct and knowledgeable summaries of the management of thyroid cancer. Every chapter describes a different aspect of care, and provides clear and detailed information about caring for patients with this group of tumors.

This is an invaluable reference to health care professionals, from primary to tertiary care, involved in the management of thyroid cancer such as clinical nurse specialists, clinical psychologists, family medicine practitioners, specialists in palliative care (especially for anaplastic thyroid cancers), geneticists and surgeons, endocrinologists, oncologists, pathologists, and radiologists.

Electromagnetic waves and their role in probing the earth are important for the exploration of the earth's deep crust. This book is not only for scientists in geophysics a useful source of information, but also for professionals in oil and gas exploration, geophysicists and engineers alike.