Edward Chang – författare

This project is unique in the field for a number of reasons, both in structure and in content. Specifically, it will have leading experts on specific age groups (Childhood to Adolescence, Young Adulthood to Middle Age, and The Elderly) within the cultural groups of interest (European-Americans, African-Americans, Asian-Americans, Hispanic-Americans, and Native Americans) contribute a chapter covering current research on both positive and negative functioning for each population. Each chapter will present basic demographic information, strengths that contribute to resilience, and three significant challenges each group faces to maintaining mental health. Each chapter will then include an integrative section, where ideas are advanced about how the strengths of each group can be harnessed to address the challenges that group faces. To conclude, each chapter will propose future directions for research which addresses integrative approaches to mental health for each group, and the implications that such approaches could have for future treatment. The main points of each section of each chapter will be visually summarized in a concluding table.

This project is unique in the field for a number of reasons, both in structure and in content. Specifically, it will have leading experts on specific age groups (Childhood to Adolescence, Young Adulthood to Middle Age, and The Elderly) within the cultural groups of interest (European-Americans, African-Americans, Asian-Americans, Hispanic-Americans, and Native Americans) contribute a chapter covering current research on both positive and negative functioning for each population. Each chapter will present basic demographic information, strengths that contribute to resilience, and three significant challenges each group faces to maintaining mental health. Each chapter will then include an integrative section, where ideas are advanced about how the strengths of each group can be harnessed to address the challenges that group faces. To conclude, each chapter will propose future directions for research which addresses integrative approaches to mental health for each group, and the implications that such approaches could have for future treatment. The main points of each section of each chapter will be visually summarized in a concluding table.

A comprehensive, problem-based guide to modern reconstructive surgery from renowned expertsReconstructive Surgery: A Problem-Based Approach edited by Matthew M. Hanasono, Samir Mardini, Steven L. Moran, Michel H. Saint-Cyr, and Edward I. Chang fills a gap among plastic surgery textbooks. The book focuses on reconstructive surgery by region and is problem based, rather than a flap atlas or a superficial survey of general plastic surgery. The text covers the entire spectrum of reconstructive surgery, including reconstruction of the head and neck; breast, trunk, and pelvis; upper and lower extremities; and the lymphatic system. Systemically organized by five regions, the book includes 52 chapters designed to be referenced prior to a consultation or surgical case. Each chapter is succinct, yet comprehensive, and written by world-renowned experts in the field. The authors discuss clinical decision-making using the best available evidence. This unique resource defines the current state of the art and reflects the camaraderie and mutual support that runs strongly within the field of reconstructive plastic surgery.Key HighlightsContributions from the most prominent, accomplished reconstructive surgeons in the worldDecades of accumulated experience distilled into pertinent anatomic knowledge, thoughtfully selected case examples, insightful pearls and pitfalls, and evidence-based treatment algorithmsAmple operative color images and illustrations enhance acquisition of anatomic knowledge and approachesThis comprehensive, practical, and authoritative textbook provides an essential portable companion for all trainee and seasoned surgeons to consult in the clinic, operating room, or during late-night call.This print book includes a scratch off code to access a complimentary digital copy on MedOne.Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.

Large language models can write poetry, pass bar exams, and generate fluent code. Yet they continue to fail in the domains where intelligence must be accountable: distinguishing causation from correlation, recognizing when evidence is insufficient, preserving commitments over time,and correcting their own reasoning failures. This book argues that the transition from pattern matching to genuine reasoning requires a System-2 layer grounded in coordinated diagnosis, audit, causal validation, memory, and meta-cognitive control.The volume develops this architecture from first principles through operational protocols. Semantic Anchoring (UCCT) demonstrates how contextual constraints can bind latent model representations into governed reasoning processes rather than prior-driven completion. Regulated Causal Anchoring (RCA) and RAudit diagnose sycophancy, pathological skepticism, and trace-output inconsistency without relying exclusively on ground-truth supervision. The Causal Abstraction Bridge and the CausalTSK benchmark reveal where models collapse from interventional and counterfactual reasoning back into associative prediction.Epistemic Regret Minimization (ERM) identifies causal shortcuts and failures of warranted inference, while Reinforcement Learning from Epistemic Regret (RLER) transforms those reasoning failures into a structured learning signal. Trivium introduces temporal accountability through a Causal Transaction Log, and Quadrivium integrates contextual, causal, temporal, and meta-cognitive regulation into a unified System-2 MACI architecture.The author's central thesis is that Artificial General Intelligence (AGI) will not emerge from scaling monolithic pattern-completion systems alone. AGI will require architectures capable of explaining why an answer is warranted, refusing conclusions when evidence remains indeterminate, and improving through epistemic failure.Written for researchers, advanced students, and practitioners, this book presents a framework for AI systems that are not merely impressive, but auditable, corrigible, and trustworthy. It is suitable for graduate-level courses in artificial intelligence, multi-agent systems, causal reasoning, and trustworthy AI.

Large language models can write poetry, pass bar exams, and generate fluent code. Yet they continue to fail in the domains where intelligence must be accountable: distinguishing causation from correlation, recognizing when evidence is insufficient, preserving commitments over time,and correcting their own reasoning failures. This book argues that the transition from pattern matching to genuine reasoning requires a System-2 layer grounded in coordinated diagnosis, audit, causal validation, memory, and meta-cognitive control.The volume develops this architecture from first principles through operational protocols. Semantic Anchoring (UCCT) demonstrates how contextual constraints can bind latent model representations into governed reasoning processes rather than prior-driven completion. Regulated Causal Anchoring (RCA) and RAudit diagnose sycophancy, pathological skepticism, and trace-output inconsistency without relying exclusively on ground-truth supervision. The Causal Abstraction Bridge and the CausalTSK benchmark reveal where models collapse from interventional and counterfactual reasoning back into associative prediction.Epistemic Regret Minimization (ERM) identifies causal shortcuts and failures of warranted inference, while Reinforcement Learning from Epistemic Regret (RLER) transforms those reasoning failures into a structured learning signal. Trivium introduces temporal accountability through a Causal Transaction Log, and Quadrivium integrates contextual, causal, temporal, and meta-cognitive regulation into a unified System-2 MACI architecture.The author's central thesis is that Artificial General Intelligence (AGI) will not emerge from scaling monolithic pattern-completion systems alone. AGI will require architectures capable of explaining why an answer is warranted, refusing conclusions when evidence remains indeterminate, and improving through epistemic failure.Written for researchers, advanced students, and practitioners, this book presents a framework for AI systems that are not merely impressive, but auditable, corrigible, and trustworthy. It is suitable for graduate-level courses in artificial intelligence, multi-agent systems, causal reasoning, and trustworthy AI.

Today's large language models excel at pattern recall yet falter on long-range planning, self-critique, context loss, and the tendency of maximum-likelihood training to reward popularity over quality. MACI offers a promising route to AGI by orchestrating specialized LLM agents through explicit protocols rather than enlarging a single model. Several modules remedy complementary weaknesses: adversarial-collaborative debate surfaces hidden assumptions; critical-reading rubrics filter incoherent arguments; information-theoretic signals steer dialogue quantitatively; transactional memory enables reliable long-horizon execution; and a dual-agent ethical court adjudicates outputs. Crucially, MACI also modulates linguistic behavior, tuning each agent's contentiousness and emotional tone, so the collective explores ideas from contrasting, affect-aware perspectives before converging.Fourteen aphorisms distill the framework's philosophy, including "Intelligence emerges from regulated collaboration, not isolated brilliance" and "Exploration must remain in tension with exploitation." Across healthcare diagnosis, investment support, scheduling, supply-chain management, and news-bias mitigation, MACI ensembles deliver significant improvements in reasoning depth, planning horizon, and reliability compared with similar-sized single models. By uniting structured debate, information-theoretic coordination, persistent memory, affect-aware discourse, and deliberative ethics, MACI demonstrates that rigorously validated multi-agent collaboration provides a practical, interpretable path toward robust general intelligence.