M.N.M.M. (musings of the Not so Mellow Man)is the thoughts and observations of the author that grew up in a family that applauded diversity and self deprecation. These are the collection of thoughts and comments he wrote down to remember moments in time and to get them out of his head and out to the world.

The book is a compilation of selected papers from 2020 International Conference on Electrical and Electronics Engineering (ICEEE 2020) held in National Power Training Institute HQ (Govt. of India) on February 21 – 22, 2020. The work focuses on the current development in the fields of electrical and electronics engineering like power generation, transmission and distribution, renewable energy sources and technology, power electronics and applications, robotics, artificial intelligence and IoT, control, and automation and instrumentation, electronics devices, circuits and systems, wireless and optical communication, RF and microwaves, VLSI, and signal processing. The book is beneficial for readers from both academia and industry.

Typically, landscape ecologists use empirical observations to conduct research and devise solutions for applied problems in conservation and management. In some instances, they rely on advice and input of experienced professionals in both developing and applying knowledge. Given the wealth of expert knowledge and the risks of its informal and implicit applications in landscape ecology, it is necessary to formally recognize and characterize expert knowledge and bring rigor to methods for its applications. In this context, the broad goal of this book is to introduce the concept of expert knowledge and examine its role in landscape ecological applications. We plan to do so in three steps: First we introduce the topic to landscape ecologists, explore salient characteristics of experts and expert knowledge, and describe methods used in capturing and formalizing that knowledge. Second, we present examples of research in landscape ecology from a variety of ecosystems and geographic locations that formally incorporate expert knowledge. These case studies address a range of topics that will interest landscape ecologists and other resource management and conservation professionals including the specific roles of expert knowledge in developing, testing, parameterizing, and applying models; estimating the uncertainty in expert knowledge; developing methods of formalizing and incorporating expert knowledge; and using expert knowledge as competing models and a source of alternate hypotheses. Third, we synthesize the state of knowledge on this topic and critically examine the advantages and disadvantages of incorporating expert knowledge in landscape ecological applications. The disciplinary subject areas we address are broad and cover much of the scope of contemporary landscape ecology, including broad-scale forest management and conservation, quantifying forest disturbances and succession, conservation of habitats for a range of avian and mammal species, vulnerability and conservation of marine ecosystems, and the spread and impacts of invasive plants. This text incorporates the collective experience and knowledge of over 35 researchers in landscape ecology representing a diverse range of disciplinary subject areas and geographic locations. Through this text, we will catalyze further thought and investigations on expert knowledge among the target readership of researchers, practitioners, and graduate students in landscape ecology.

This book constitutes the refereed proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, held in Montréal, QC, Canada, in May 2014. The 22 regular papers and 18 short papers presented together with 3 invited talks were carefully reviewed and selected from 94 submissions. The papers cover a variety of topics within AI, such as: agent systems; AI applications; automated reasoning; bioinformatics and BioNLP; case-based reasoning; cognitive models; constraint satisfaction; data mining; E-commerce; evolutionary computation; games; information retrieval; knowledge representation; machine learning; multi-media processing; natural language processing; neural nets; planning; privacy-preserving data mining; robotics; search; smart graphics; uncertainty; user modeling; web applications.

This textbook offers a complete one-semester course in probability, covering the essential topics necessary for further study in the areas of probability and statistics. The book begins with a review of the fundamentals of measure theory and integration. Probability measures, random variables, and their laws are introduced next, along with the main analytic tools for their investigation, accompanied by some applications to statistics. Questions of convergence lead to classical results such as the law of large numbers and the central limit theorem with their applications also to statistical analysis and more. Conditioning is the next main topic, followed by a thorough introduction to discrete time martingales. Some attention is given to computer simulation. Through the text, over 150 exercises with full solutions not only reinforce the concepts presented, but also provide students with opportunities to develop their problem-solving skills, and make this textbook suitable for guided self-study. Based on years of teaching experience, the author's expertise will be evident in the clear presentation of material and the carefully chosen exercises. Assuming familiarity with measure and integration theory as well as elementary notions of probability, the book is specifically designed for teaching in parallel with a first course in measure theory. An invaluable resource for both instructors and students alike, it offers ideal preparation for further courses in statistics or probability, such as stochastic calculus, as covered in the author's book on the topic.

The 1998 edition of Education at a Glance provides comparable information on the human and financial resources invested in education, on how education and learning systems operate and evolve, and on the returns to educational investments.

Observing computational chemistry's proven value to the introduction of new medicines, Computational Medicinal Chemistry for Drug Discovery offers the techniques most frequently utilized by industry and academia for ligand design. Featuring contributions from more than 50 preeminent scientists, this book surveys molecular structure computation, intermolecular behavior, ligand-receptor interaction, and modeling. It also examines molecular mechanics, semi-empirical methods, wave function-based quantum chemistry, density functional theory, 3-D structure generation, and hybrid methods.