Metal Oxides for Next Generation Optoelectronic, Photonic and Photovoltaic Applications focuses on the optoelectronic, photonic and photovoltaic behaviors of metallic oxides and closely related phenomena, from elementary principles to the latest findings. Each chapter includes a comprehensive evaluation of the synthesis and characterization of the most relevant metal oxides nanostructures for each application. In addition, there is a focus on methods to tune the materials’ properties in order to improve devices performance.  This book is suitable for researchers and practitioners in academia and industry working in the disciplines of materials science and engineering, chemistry and physics. Metal oxides are widely used in various optoelectronic devices, photonics, display devices, smart windows, sensors, optical components, energy-saving, and harvesting devices. Each application requires materials with their own specific properties. By controlling the particle size, shape, crystal structure, one can tune various properties of metal oxides viz. bandgap, absorption properties, conductivity, which alter the material for the specific application.

Learn How to Properly Use the Latest Analytics Approaches in Your Organization Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies. The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagrams Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.

This self-contained book provides three fundamental and generic approaches (logical, probabilistic, and modal) to representing and reasoning with agent epistemic states, specifically in the context of decision making. Each of these approaches can be applied to the construction of intelligent software agents for making decisions, thereby creating computational foundations for decision-making agents. In addition, the book introduces a formal integration of the three approaches into a single unified approach that combines the advantages of all the approaches. Finally, the symbolic argumentation approach to decision making developed in this book, combining logic and probability, offers several advantages over the traditional approach to decision making which is based on simple rule-based expert systems or expected utility theory.