NTET for AYUSH Teachers Question Bank Book 1500+ MCQ With Detail Explanation As Per Exam Pattern Highlight of Book Covered all 8 Units MCQ As Per Prescribe Exam Level Explanation of all mcq in Detail Design by Expert Faculties As Per New Exam Pattern

The timeless and practical advice in The Magic of Thinking Big clearly demonstrates how you can: Sell more Manage better Lead fearlessly Earn more Enjoy a happier, more fulfilling life With applicable and easy-to-implement insights, you’ll discover: Why believing you can succeed is essential How to quit making excuses The means to overcoming fear and finding confidence How to develop and use creative thinking and dreaming Why making (and getting) the most of your attitudes is critical How to think right towards others The best ways to make “action” a habit How to find victory in defeat Goals for growth, and How to think like a leader "Believe Big,” says Schwartz. “The size of your success is determined by the size of your belief. Think little goals and expect little achievements. Think big goals and win big success. Remember this, too! Big ideas and big plans are often easier -- certainly no more difficult - than small ideas and small plans."

In all parts of Asia, households devote considerable expenditures to private supplementary tutoring. This tutoring may contribute to students' achievement, but it also maintains and exacerbates social inequalities, diverts resources from other uses, and can contribute to inefficiencies in education systems. Such tutoring is widely called shadow education, because it mimics school systems. As the curriculum in the school system changes, so does the shadow. This study documents the scale and nature of shadow education in different parts of the region. Shadow education has been a major phenomenon in East Asia and it has far-reaching economic and social implications.

This Book attempts to make a comprehensive and critical exposition of all the facets of teaching. It evaluates the comparative soundness of the Principles, Methods, Techniques and Devices of Teaching. The chief accent of the book is on helping teachers to teach better. The objective is strictly utilitarian and is designed to serve as a reliable guide to the work in the classroom. The book also offers practical suggestions for making the teaching-learning process effective, inspirational & interesting. It incorporates the approaches recommended by eminent educational philosophers and practitioners. A detailed survey of the valuable teaching practices followed in India and abroad also find an important place in the book.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.