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Simple and Complex Fractures of the Humerus
The treatment of humeral fractures is a complex issue and the source of considerable controversy. In the case of fractures of the proximal humerus, early range of motion is the main aim of treatment. If a fracture modifies the anatomy or function of the glenohumeral and scapulothoracic joints, the surgeon must adhere meticulously to treatment principles in order to ensure a satisfactory outcome. Humeral shaft fractures are frequent, accounting for 1% to 3% of all fractures in adults; while excellent functional results have been reported with nonoperative management, open reduction and internal fixation is preferred in specific clinical settings. In contrast, intra-articular fractures of the ...
Orthopedic Sports Medicine
Aim of this book is to give an update on the main issues in sports traumatology and orthopedics, involving different body sections. Exploring the most important aspects of sports medicine - from anatomy to normal movements description, from diagnosis to sports injuries treatment, from conservative to surgical treatments - it gives a global overview of the field, describing also diagnostic tools recently introduced in this field, such as hip arthroscopy, and taking into consideration related areas like nutrition, prevention, training and rehabilitation. The single chapters deal with relevant problems such as emergencies on the field, pathologies of the upper and lower extremity, spine problems in athletes. Each injury is approached on the basis of both the specific body area and of the different sports/activities. Written by a multidisciplinary team of experts, this volume will be a fundamental book for orthopedic surgeons, physiotherapists, general practitioners, personal and athletic trainers, offering them a useful tool for the management of most frequent injuries in sports medicine.
Marco Spada
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Handbook on Neural Information Processing
This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.
