تحسين النتائح
ترتيب حسب
من
الى
الصفحة 1
الصفحة 1
Computational and Statistical Approaches to Genomics
Computational and Statistical Approaches to Genomics, 2nd Edition, aims to help researchers deal with current genomic challenges. During the three years after the publication of the first edition of this book, the computational and statistical research in genomics have become increasingly more important and indispensable for understanding cellular behavior under a variety of environmental conditions and for tackling challenging clinical problems. In the first edition, the organizational structure was: data à analysis à synthesis à application. In the second edition, the same structure remains, but the chapters that primarily focused on applications have been deleted.
Choices in Palliative Care : Issues in Health Care Delivery
Keeping up with these statistics means improving on traditional concepts of palliative care to meet growing demands. Choices in Palliative Care brings together 31 leading experts to spotlight core issues in the field, identify ways PC can fill gaps in current care systems, and demonstrate state-of-the-art care that is both cost-effective and clinically appropriate. This far-sighted volume redefines palliative care as interdisciplinary and integrative, providing liaisons between patients, families, and doctors; minimizing loved ones’ care burdens; bridging acute and long-term care to respond to clients’ evolving needs; adaptable to non-fatal (and possibly curable) chronic illnesses.
Cancer in the Arab World
Over the past few decades, the Arab world has witnessed a swift evolution in healthcare provision. Nonetheless, Arab countries have considerable variability in economic capabilities, resource allocation, and intellectual talent that inevitably reflect on access to modern cancer care and prevention. This book is authored by experts from the Arab world who provide vital information on cancer statistics and risk factors, available clinical care pathways and infrastructure, and prevention programs in their individual countries. The chapters also address specific challenges in each country and insights into future directions to achieve optimal care with conventional and novel diagnostics and therapies to keep up with the era of precision medicine.
Brown and Mulholland’s drug calculations
A realistic practice problems and unique "proof" step in the answer key that allows you to double-check your answers to avoid medication errors. This edition continues to promote critical thinking, clinical judgment, and patient safety with respect to accurate drug dosages. Helpful worksheets, assessment tests, and Clinical Alerts call attention to situations in actual practice that have resulted in drug errors — giving you extensive hands-on practice for the Next Generation NCLEX® and beyond.
Biostatistica in radiologia : Progettare, realizzare e scrivere un lavoro scientifico radiologico = Biostatistics in Radiology: Designing, creating and writing a radiological scientific work
The progressive affirmation of evidence-based medicine requires radiology to make a qualitative leap: from demonstrating the ability to see more and better to demonstrating a significant improvement in the health or quality of life of patients.
Basic & Clinical Biostatistics
Provides medical students, researchers, and practitioners with the knowledge needed to develop sound judgment about data applicable to clinical care. This fifth edition has been updated throughout to deliver a comprehensive, timely introduction to biostatistics and epidemiology as applied to medicine, clinical practice, and research. Particular emphasis is on study design and interpretation of results of research.
Assessment of cancer screening : A primer
Provides an in-depth look at the many aspects of cancer screening and its assessment, including screening phenomena, performance measures, population-level outcomes, research designs, and other important and timely topics.
Applied Spatial Data Analysis with R
Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data, The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.
Analytical scientists in pharmaceutical product development : Task management and practical knowledge
Explains task management concepts and outlines practical knowledge to help pharmaceutical analytical scientists become productive and enhance their career. •Presents broad topics such as product development process, regulatory requirement, task and project management, innovation mindset, molecular recognition, separation science, degradation chemistry, and statistics. •Provokes thinking through figures, tables, and case studies to help understand how the various functions integrate and how analytical development can work efficiently and effectively by applying science and creativity in their work. •Discusses how to efficiently develop a fit-for-purpose HPLC method without screening dozens of columns, gradients, or mobile phase combinations each time, since the extra effort may not provide enough of a benefit to justify the cost and time in a fast-paced product development environment.
Analysing Ecological Data
This book provides a practical introduction to analysing ecological data using real data sets collected as part of postgraduate ecological studies or research projects. The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modelling techniques), multivariate analysis, time series analysis (e.g. common trends) and spatial statistics. The second part provides 17 case studies, mainly written together with biologists who attended courses given by the first authors. The case studies include topics ranging from terrestrial ecology to marine biology.
An introduction to clinical trials
An Introduction to clinical trials is a concise step-by-step guide to the principles and practices of clinical trials for those studying clinical trials or new to working on one. Clinical trials are critical to the progress of medicine and improving healthcare / as they evaluate whether new treatments and interventions work. They are also complex / multidisciplinary projects that integrate science / ethics / and legal requirements in the conduct of medical research.
A Changing World : Challenges for Landscape Research
Written primarily for researchers and advanced students in environmental and social sciences, this latest book in Springer’s Landscape Series looks at some of the emerging fields and new challenges in landscape research. These include: the role of value systems in perceiving, appreciating, and managing landscapes the ‘space’ and ‘place’ concept in landscape research GIS and remote sensing techniques for gathering and processing spatially and temporally explicit land cover, vegetation, and land use data methods of landscape history landscape genetics and genetic methods to test landscape connectivity and dispersal of plant and animal species
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Machine learning and big data : Concepts, algorithms, tools and applications
Showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Location, Transport and Land-Use : Modelling Spatial-Temporal Information
Shows the use of statistical tools for forecasting and analyzing implications of land-use decisions. The idea is that la- use on a map is necessarily a consequence of individual, and often conflicting, siting decisions over time.
Linear Systems, Signal Processing and Hypercomplex Analysis ; Chapman University, November 2017
includes contributions originating from a conference held at Chapman University during November 14-19, 2017. It presents original research by experts in signal processing, linear systems, operator theory, complex and hypercomplex analysis and related topics.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: Includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. / The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. / Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. / Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. / Considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
