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Аннотация
Questions and Answers for Dental Nurses An essential study aid for dental nursing students preparing for the NEBDN exam The newly revised Fourth Edition of Questions and Answers for Dental Nurses delivers a comprehensive and invaluable revision guide that covers the full curriculum of the National Examining Board for Dental Nurses (NEBDN) National Diploma in Dental Nursing. It is fully updated and incorporates recent developments in dentistry and changes to relevant legislation and regulation. The included questions mimic the style of questions used in the NEBDN examination and the accompanying answers and explanations discuss why a given answer is the best one. All four General Dental Council development outcomes—formerly called “domains”—are covered in the book, allowing students to gauge their progress and understanding on all of the areas they’ll be tested on. The book also includes: A thorough introduction to communication in dental nursing, including obtaining consents and record keeping, handling complaints, raising concerns and oral health instruction Comprehensive explorations of management and leadership, including chairside support, practice management, and health and safety Practical discussions of clinical considerations, including infection prevention and control, oral anatomy and physiology, dental pathology and microbiology, and assessment and diagnosis In-depth examinations of professionalism in the dental nursing context, including GDC standards, legal and ethical issues, and equality and diversity Questions and Answers for Dental Nurses 4th Edition is an essential resource for dental nurse students enrolled in the National Examining Board for Dental Nurses National Diploma training course, as well as dental tutors, trainers, and educators preparing candidates for this qualification.
Аннотация
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
Аннотация
ADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound—that can be really helpful to rapid diagnosis. Audience This book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector.
Аннотация
Der immer tiefgreifendere Einzug der Digitalisierung in allen Phasen des Bauens und die detaillierte Zusammenstellung von Instandsetzungsstrategien für den Hoch- und Ingenieurbau sind die bestimmenden Themen des Beton-Kalender 2022. <br> In drei eigenständigen Beiträgen erhalten Sie einen umfassenden Überblick zum derzeitigen Regelwerk für den Schutz und die Instandhaltung von Betonbauwerken in Deutschland, Österreich und der Schweiz. In weiteren Beiträgen wird über neue Erhaltungsstrategien für Brücken und Bundesfernstraßen in Deutschland berichtet. Abgerundet wird dieser erste Themenkomplex mit einer kritischen und wegweisenden Diskussion um die Nachhaltigkeit im Betonbau. <br> Unter dem Schwerpunkt «Digitalisierung» finden Sie einen umfassenden Überblick zum aktuellen Stand von digitaler Fertigung im Betonbau und den Herausforderungen, welche das digitale Bauen und Planen für Ingenieure bereithalten. In weiteren Beiträgen wird über die Möglichkeiten des Einsatzes schwacher Künstlicher Intelligenz für ingenieurtechnische Anwendungen und den aktuellen Stand der additiven Fertigung im Betonbau berichtet. <br> Weitere Beiträge befassen sich mit den Besonderheiten der Tragwerksplanung im Bestand, speziell in Österreich, sowie mit den Möglichkeiten zur Verstärkung von Tragwerken mit Carbonbeton. Den Abschluss des diesjährigen Kalenders bildet ein Hintergrundbeitrag zur Notwendigkeit und den Zielen der Neufassung der DAfStb-Richtlinie «Belastungsversuche an Betonbauwerken» sowie der vollständige Abdruck der Richtlinie in der Ausgabe von Juli 2020 im Kapitel «Normen und Regelwerke». <br> <br>
