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The field of muscular dystrophies has expanded significantly with the discovery of the genetic defects and protein products underlying these disorders. New treatments such as antisense oligonucleotides and siRNAs and gene therapies are now in clinical application and in trials. As clinical trials increase, outcome measurement becomes very important, and more validated tools are being developed. This book provides a comprehensive review of these disorders. Clinical features, diagnostic testing including genetic testing of these disorders, the basis and utility of genetic testing, and the basis of genetic therapies are all covered in detail. This book will provide neuromuscular neurologists, general neurologists, and neuromuscular fellows with a much needed update in the field.
In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent “complexity” and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it “smart” is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization? 2.) How can we achieve a more optimal optimization? 3.) Is it possible to demand “more optimal machine learning,” exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture?
This handbook addresses the question of how best to manage quality in architecture for the mutual benefit of design practices and their clients. Based on research from the last two decades, it explores the general principles, tools and techniques that can be adapted to the unique culture of any design practice.The book addresses all aspects of quality in creating the built environment, with international contributions representing some of the best thinking that exists in design practice management. It is aimed at the entire design team - those who have a role in design inputs, design processes and design execution; including project managers, contractors, suppliers and clients. An accompanying website also provides commentary and updates on the text.Topics are linked to relevant sections of the current quality standard, and the standard is interpreted as to its application to design practice. Practices interested in establishing an ISO 9001-compliant quality system will find all the tools they need.
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