WINNING INSIGHTS INTO THE DSO BUSINESS MODEL, WHETHER YOU'RE SELLING YOUR PRACTICE, PARTNERING WITH A DSO, COMPETING AGAINST THEM, OR LAUNCHING YOUR OWN DSO. The history of DSOs can be traced back to 1939, when Robert F. Beauchamp maneuvered against an impending California regulation that prohibited dentists from owning more than two practices. Before the law took effect, Dr. Beauchamp acquired seven more practices-a creative move that marked the beginning of corporate dentistry. Today, DSOs are the fastest-growing segment of dentistry, and their impact on the profession has been profound. If you're considering selling to a DSO, partnering with one, competing with one, or even starting a DSO yourself, The DSO Decision gives you the clarity, knowledge and tools you need to succeed.

This book is significant in that it offers an in-depth historical analysis of educational leadership and educational policy in the United States and around the globe. The book focuses on how leadership preparation and practices as well as policy and procedures have affected and continues to effect all stakeholders including school leaders, teachers, and students. The aim of the book is to examine both the positive and negative implications (nationally and internationally) of: (1) trends in educational leadership preparation; (2) trends in educational leadership practices; (3) educational policy; and (4) the procedures and the intended/unintended consequences associated with such policies. ENDORSEMENTS: "Equity & Access: An analysis of Educational Leadership Preparation, Policy, & Practice provides an excellent lens into some of the field's most relevant and timely topics. This book provides practical resources on multiple levels. Specifically, it can help educators incorporate various concepts into practice. Additionally, readers will strengthen their knowledge base in leadership preparation. This book is a must-read and should be adopted in Educational Leadership programs across the country." — David Buckman, Augusta University "Centering equity and access at the heart of the educational leadership conversation makes this book volume a standout in the array of educational leadership texts. In a single-source, each author challenges learners to think through their motivations, and the consequences of their actions and decision-making in the real-world, as they also acquire practical skills and concrete knowledge. Never has the time been more appropriate than now, for a focused book like this one – a needed and welcome addition for those invested in the preparation and on-going development of educational leaders." — Stephanie James, Jacksonville University "Equity & Access serves as a stark reminder that, as practicing school leaders in the 21st Century, everything we do and every decision we make should be done so through the lens of what is equitable for all stakeholders including students, staff, parents, community members, and business owners." — James Wrede, Southern California Principal "Connecting theories of effective leadership to practical examples is not an easy task. It demands a rigorous interdisciplinary approach, thoughtful theory of change, and must be grounded to the lives of real people. This new edited volume, On Leadership, is alive with lessons! It provides a roadmap where educational equity is not merely the destination, but the journey." — Vajra M. Watson, Sacramento State

This book will be bought by researchers and graduates students in Artificial Intelligence and management as well as practising managers and consultants interested in the application of IT and information systems in real business environment.

7. 1. 1 Background Uncertainty can be considered as the lack of adequate information to make a decision. It is important to quantify uncertainties in mathematical models used for design and optimization of nondeterministic engineering systems. In general, - certainty can be broadly classi?ed into three types (Bae et al. 2004; Ha-Rok 2004; Klir and Wierman 1998; Oberkampf and Helton 2002; Sentz 2002). The ?rst one is aleatory uncertainty (also referred to as stochastic uncertainty or inherent - certainty) – it results from the fact that a system can behave in random ways. For example, the failure of an engine can be modeled as an aleatory uncertaintybecause the failure can occur at a random time. One cannot predict exactly when the engine will fail even if a large quantity of failure data is gathered (available). The second one is epistemic uncertainty (also known as subjective uncertainty or reducible - certainty) – it is the uncertainty of the outcome of some random event due to lack of knowledge or information in any phase or activity of the modeling process. By gaining information about the system or environmental factors, one can reduce the epistemic uncertainty. For example, a lack of experimental data to characterize new materials and processes leads to epistemic uncertainty.

The evidence discovered at underwater crime scenes must be handled with the same attention to proper chain of custody procedures as with any other type of investigation. Improper handling of these scenes can lead to evidence being lost, unrecognizable, destroyed, contaminated, or rendered inadmissible at the time of trial. Updated and expanded, Und

This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control. Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers. Provides a history of AI in power grid operation and planning; Introduces deep learning algorithms and applications in power systems; Includes several representative case studies.

This book is about marketing models and the process of model building. Our primary focus is on models that can be used by managers to support marketing decisions. It has long been known that simple models usually outperform judgments in predicting outcomes in a wide variety of contexts. For example, models of judgments tend to provide better forecasts of the outcomes than the judgments themselves (because the model eliminates the noise in judgments). And since judgments never fully reflect the complexities of the many forces that influence outcomes, it is easy to see why models of actual outcomes should be very attractive to (marketing) decision makers. Thus, appropriately constructed models can provide insights about structural relations between marketing variables. Since models explicate the relations, both the process of model building and the model that ultimately results can improve the quality of marketing decisions. Managers often use rules of thumb for decisions. For example, a brand manager will have defined a specific set of alternative brands as the competitive set within a product category. Usually this set is based on perceived similarities in brand characteristics, advertising messages, etc. If a new marketing initiative occurs for one of the other brands, the brand manager will have a strong inclination to react. The reaction is partly based on the manager's desire to maintain some competitive parity in the mar keting variables.