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Rough Multiple Objective Decision Making

Rough Multiple Objective Decision Making

Under intense scrutiny for the last few decades Multiple Objective Decision Making (MODM) has been useful for dealing with the multiple-criteria decisions and planning problems associated with many important applications in fields including management science engineering design and transportation. Rough set theory has also proved to be an effective mathematical tool to counter the vague description of objects in fields such as artificial intelligence expert systems civil engineering medical data analysis data mining pattern recognition and decision theory. Rough Multiple Objective Decision Making is perhaps the first book to combine state-of-the-art application of rough set theory rough approximation techniques and MODM. It illustrates traditional techniques—and some that employ simulation-based intelligent algorithms—to solve a wide range of realistic problems. Application of rough theory can remedy two types of uncertainty (randomness and fuzziness) which present significant drawbacks to existing decision-making methods so the authors illustrate the use of rough sets to approximate the feasible set and they explore use of rough intervals to demonstrate relative coefficients and parameters involved in bi-level MODM. The book reviews relevant literature and introduces models for both random and fuzzy rough MODM applying proposed models and algorithms to problem solutions. Given the broad range of uses for decision making the authors offer background and guidance for rough approximation to real-world problems with case studies that focus on engineering applications including construction site layout planning water resource allocation and resource-constrained project scheduling. The text presents a general framework of rough MODM including basic theory models and algorithms as well as a proposed methodological system and discussion of future research.

GBP 74.99
1

Programming for Hybrid Multi/Manycore MPP Systems

Programming for Hybrid Multi/Manycore MPP Systems

Ask not what your compiler can do for you ask what you can do for your compiler. John Levesque Director of Cray’s Supercomputing Centers of ExcellenceThe next decade of computationally intense computing lies with more powerful multi/manycore nodes where processors share a large memory space. These nodes will be the building block for systems that range from a single node workstation up to systems approaching the exaflop regime. The node itself will consist of 10’s to 100’s of MIMD (multiple instruction multiple data) processing units with SIMD (single instruction multiple data) parallel instructions. Since a standard affordable memory architecture will not be able to supply the bandwidth required by these cores new memory organizations will be introduced. These new node architectures will represent a significant challenge to application developers. Programming for Hybrid Multi/Manycore MPP Systems attempts to briefly describe the current state-of-the-art in programming these systems and proposes an approach for developing a performance-portable application that can effectively utilize all of these systems from a single application. The book starts with a strategy for optimizing an application for multi/manycore architectures. It then looks at the three typical architectures covering their advantages and disadvantages. The next section of the book explores the other important component of the target—the compiler. The compiler will ultimately convert the input language to executable code on the target and the book explores how to make the compiler do what we want. The book then talks about gathering runtime statistics from running the application on the important problem sets previously discussed. How best to utilize available memory bandwidth and virtualization is covered next along with hybridization of a program. The last part of the book includes several major applications and examines future hardware advancements and how the application developer may prepare for those advancements.

GBP 44.99
1

Innovative Strategies Statistical Solutions and Simulations for Modern Clinical Trials

Innovative Strategies Statistical Solutions and Simulations for Modern Clinical Trials

This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development…. Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously. Jay Herson Johns Hopkins UniversityThe pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost time or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs precision medicine survival-endpoints dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons this book incorporates both statistical and clinical/medical perspectives. | Innovative Strategies Statistical Solutions and Simulations for Modern Clinical Trials

GBP 44.99
1