The most challenging, most invigorating holes a golfer can tackle. In this beautiful book, Peper and Campbell, two writers who know golf inside and out, provide a concise and entertaining tour of the world's best links courses. Full color.

Software and Systems Traceability provides a comprehensive description of the practices and theories of software traceability across all phases of the software development lifecycle. The term software traceability is derived from the concept of requirements traceability. Requirements traceability is the ability to track a requirement all the way from its origins to the downstream work products that implement that requirement in a software system. Software traceability is defined as the ability to relate the various types of software artefacts created during the development of software systems. Traceability relations can improve the quality of a product being developed, and reduce the time and cost of development. More specifically, traceability relations can support evolution of software systems, reuse of parts of a system by comparing components of new and existing systems, validation that a system meets its requirements, understanding of the rationale for certain design and implementation decisions, and analysis of the implications of changes in the system.

Health services are often fragmented along organizational lines with limited communication among the public health–related programs or organizations, such as mental health, social services, and public health services. This can result in disjointed decision making without necessary data and knowledge, organizational fragmentation, and disparate knowledge development across the full array of public health needs. When new questions or challenges arise that require collaboration, individual public health practitioners (e.g., surveillance specialists and epidemiologists) often do not have the time and energy to spend on them. Smart Use of State Public Health Data for Health Disparity Assessment promotes data integration to aid crosscutting program collaboration. It explains how to maximize the use of various datasets from state health departments for assessing health disparity and for disease prevention. The authors offer practical advice on state public health data use, their strengths and weaknesses, data management insight, and lessons learned. They propose a bottom-up approach for building an integrated public health data warehouse that includes localized public health data. The book is divided into three sections: Section I has seven chapters devoted to knowledge and skill preparations for recognizing disparity issues and integrating and analyzing local public health data. Section II provides a systematic surveillance effort by linking census tract poverty to other health disparity dimensions. Section III provides in-depth studies related to Sections I and II. All data used in the book have been geocoded to the census tract level, making it possible to go more local, even down to the neighborhood level.

This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.

Arthur Milnes Marshall was a 19th-century scientist who gave lectures addressing the biological debates of his time. They covered topics including evolution, embryology, development and inheritance, with Charles Darwin’s name and those of other important biologists distributed liberally throughout. Marshall was a zoologist, embryologist, anatomist and Darwin enthusiast, as well as an accomplished mountaineer and sportsman. He was a humanist, an admired academic teacher and brilliant public educator. The lectures reveal his passion for communicating his subject, to his students and to the working men and women of Manchester, and they provide a remarkable snapshot of the state of biological science at the close of the 19th century. His death in 1893 aged only 41, on a climbing expedition in the Lake District, left a fascinating time capsule in the form of lectures from a critical transitional period in the history of biology. Evolution by natural selection was the established doctrine but genes were undefined, with Mendel’s work yet to be recognised. Embryology was suggesting recapitulation but ancestry, genetics and missing links awaited liberation from theoreticians and the stones of palaeontology. Microscopy was flourishing and cell science was finding its feet, but DNA and molecular science were far in the future. Had Marshall lived and worked into the 20th century, these lectures would undoubtedly have been superseded and forgotten. Instead, they reveal biology’s transformation from a descriptive exercise to an experimental science, its rejection of purpose and design in evolution, and the shift of its axis from continental Europe to Britain and the United States. Professor Martin Luck discovered these lectures (published by CF Marshall in two volumes shortly after his brother’s death) languishing in a university corridor. His careful curation, introductions to each lecture and copious annotations on the organisms, theories and scientists discussed, illuminate their significance as prequels to modern biology. Marshall’s own story brings the lectures and their social context into sharp relief. Biology in Transition will interest anyone curious about the history of science, especially biology, evolution, genetics and its 19th-century pioneers.

The exponentially increasing gap between processors and off-chip memory, as measured in processor cycles, is rapidly turning memory latency into a major processor performance bottleneck. Traditional solutions, such as employing multiple levels of caches, are expensive and do not work well with some applications. We evaluate a technique, called runahead pre-processing, that can significantly improve processor performance. The instruction and data stream prefetches generated during runahead episodes led to a significant performance improvement for all of the benchmarks we examined. We found that runahead typically led to about a 30% reduction in CPI for the four Spec95 integer benchmarks that we simulated, while runahead was able to reduce CPI by 77% for the STREAM benchmark. This is for a five stage pipeline with two levels of split instruction and data caches: 8KB each of L1, and 1MB each of L2. A significant result is that when the latency to off-chip memory increases, or if the caching performance for a particular benchmark is poor, runahead is especially effective as the processor has more opportunities in which to pre-process instructions. Finally, runahead appears particularly well suited for use with high clock-rate in-order processors that employ relatively inexpensive memory hierarchies.