Qiang Xu

Cancer continues to be a growing problem as it is the foremost cause of death worldwide, killing millions of people each year. The number of people battling cancer continues to increase, owing to different reasons, such as lifestyle choices. Clinically, determining the cause of cancer is very challenging and often inaccurate. Incorporating efficient and accurate algorithms to detect cancer cases is becoming increasingly beneficial for scientists in computer science and healthcare, as well as a long-term benefit for doctors, patients, clinic practitioners, and more. Specifically, an automation of computation in machine learning could be a solution in the next generation of big data science technology. Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis presents algorithms that have been developed to evaluate big data approaches and cancer research. The chapters include artificial intelligence and machine learning approaches, as well as case studies to solve the predictive issues in colon cancer research. This book includes concepts and techniques used to run tasks in an automated manner with the intent to improve better accuracy in comparison with previous studies and methods. This book also covers the processes of research design, development, and outcome analytics in this field. Doctors, IT consultants, IT specialists, medical software professionals, data scientists, researchers, computer scientists, healthcare practitioners, academicians, and students can benefit from this critical resource.

This book first provides a comprehensive coverage of state-of-the-art validation solutions based on real-time signal tracing to guarantee the correctness of VLSI circuits. The authors discuss several key challenges in post-silicon validation and provide automated solutions that are systematic and cost-effective. A series of automatic tracing solutions and innovative design for debug (DfD) techniques are described, including techniques for trace signal selection for enhancing visibility of functional errors, a multiplexed signal tracing strategy for improving functional error detection, a tracing solution for debugging electrical errors, an interconnection fabric for increasing data bandwidth and supporting multi-core debug, an interconnection fabric design and optimization technique to increase transfer flexibility and a DfD design and associated tracing solution for improving debug efficiency and expanding tracing window. The solutions presented in this book improve the validation quality of VLSI circuits, and ultimately enable the design and fabrication of reliable electronic devices.

This book first provides a comprehensive coverage of state-of-the-art validation solutions based on real-time signal tracing to guarantee the correctness of VLSI circuits. The authors discuss several key challenges in post-silicon validation and provide automated solutions that are systematic and cost-effective. A series of automatic tracing solutions and innovative design for debug (DfD) techniques are described, including techniques for trace signal selection for enhancing visibility of functional errors, a multiplexed signal tracing strategy for improving functional error detection, a tracing solution for debugging electrical errors, an interconnection fabric for increasing data bandwidth and supporting multi-core debug, an interconnection fabric design and optimization technique to increase transfer flexibility and a DfD design and associated tracing solution for improving debug efficiency and expanding tracing window. The solutions presented in this book improve the validation quality of VLSI circuits, and ultimately enable the design and fabrication of reliable electronic devices.

Revision with unchanged content. Studies have identified both stability and change in criminal behavior across time. While most youth mature out of delinquency in their late teens and early adulthood, some juvenile delinquents persist in their criminal behavior with increased frequency and severity. Researchers are often confronted with this question: "Why will some youth continue their criminal behavior over time, when the majority of their peers have made the transition to confor-mity'' Based on a longitudinal sample of previously institutionalized youth, this work examines and tests alternative perspectives on life course persistent criminal behavior. Findings of this work highlight several important factors that affect persistent criminal behavior. Demographic differences in pathways to persistent criminal behavior, such as gender and race variations are also discussed. The book is addressed to researchers and students in Juvenile Delinquency, Criminology and Sociology. It is also directed towards profes-sionals in Criminal Justice, Corrections, and Social Work.