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1. |
Record Nr. |
UNINA9910962422903321 |
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Autore |
Kurtz Lisa A |
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Titolo |
Understanding controversial therapies for children with autism, attention deficit disorder, and other learning disabilities : a guide to complementary and alternative medicine / / Lisa A. Kurtz |
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Pubbl/distr/stampa |
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London ; ; Philadelphia, : Jessica Kingsley Publishers, 2008 |
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ISBN |
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9786611781958 |
9781281781956 |
1281781959 |
9781846427619 |
1846427614 |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (212 pages) |
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Collana |
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Classificazione |
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Disciplina |
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Soggetti |
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Learning disabilities - Alternative treatment |
Autism in children - Alternative treatment |
Attention-deficit hyperactivity disorder - Alternative treatment |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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front cover; Understanding Controversial Therapies for Children with Autism, Attention Deficit Disorder, and Other Learning Disabilities: A Guide to Complementary and Alternative Medicine; CONTENTS; Part 1: Introduction; 1. Introduction; 2. Thinking Out of the Box: An Overview of Complementary and Alternative Medicine Approaches; Part 2: Selected Interventions; 3. Alternative Medical Systems; 4. Mind-body Interventions; 5. Biologically-based Interventions; 6. manipulative and Body-based Methods; 7. Energy Therapies |
Part 3: Resources for Children with Autism, Attention Deficit Disorders, and Other Learning Disabilities8. Recommended Reading about Complementary and Alternative Medicine; 9. Agencies, Organizations and Websites; SUBJECT INDEX; AUTHOR INDEX; back cover; |
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Sommario/riassunto |
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Offering a balanced overview of complementary and alternative therapies, this book will be useful for parents of children with autism, ADD or other learning disabilities. The book covers a wide variety of mind-body interventions and manipulative techniques, as well as |
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energy therapies, biologically based methods, and alternative medical systems. For each approach, the author provides a detailed description of what the treatment involves, which professionals will be working with the child, and an explanation of the rationale behind the therapy. She also offers advice on who to approach for trea |
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2. |
Record Nr. |
UNINA9910983346303321 |
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Autore |
Pham Hoang |
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Titolo |
Analytics Modeling in Reliability and Machine Learning and Its Applications / / edited by Hoang Pham |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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9783031726361 |
9783031726354 |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (480 pages) |
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Collana |
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Springer Series in Reliability Engineering, , 2196-999X |
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Disciplina |
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Soggetti |
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Machine learning |
Computers |
Medical care |
Industrial engineering |
Production engineering |
Mathematical optimization |
Aerospace engineering |
Astronautics |
Machine Learning |
Hardware Performance and Reliability |
Health Care |
Industrial and Production Engineering |
Optimization |
Aerospace Technology and Astronautics |
Aprenentatge automàtic |
Ordinadors |
Assistència sanitària |
Enginyeria industrial |
Optimització matemàtica |
Astronàutica |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Preface -- 1. Reliability Analysis For Inventory Management For Repair Parts Based on Imperfect Data.-2. Improved Industrial Risk Analysis via a Human Factor-driven Bayesian Network Approach -- 3. Unsupervised Representation Learning Approach for Intrusion Detection in the Industrial Internet of Things Network Environment -- 4. Aero-engine Life Prediction Based on ARIMA and LSTM with Multi-Head Attention Mechanism -- 5. Human-Machine Integration to Strengthen Risk Management in the Winemaking Industry -- 6. One-Class Classification for Credit Card Fraud Detection: A Detailed Study with Comparative Insights from Binary Classification -- 7. Performance Analysis of Big Transfer Models on Biomedical Image Classification -- 8. Machine Learning Approach for Testing the Efficiency of Software Reliability Estimators of Weibull Class Models -- 9. Holistic Perishable Pharmaceutical Inventory Management System -- 10. Optimum Switch Self-Check Interval for Safety-Critical Device Mission Reliability -- 11. Accurate Estimation of Cargo Power Using Machine Learning Algorithms -- 12. Digital Transformation in Software Quality Assurance -- 13. Stress Studies: A Review -- 14. Higher Order Dynamic Mode Decomposition-based Timeseries Forecasting for Covid-19 -- 15. System Trustability: New Concept and Applications -- 16. Digital Twin Implementation in Small and Medium Size Enterprises: A Case Study -- 17. Software Reliability Modeling: A Review. |
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Sommario/riassunto |
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This book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning. |
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