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Springer Nature Proceedings excluding Computer Science

Dieky Adzkiya

Kistosil Fahim

Applied and Computational Mathematics

ICoMPAC 2023, Sukolilo, Indonesia, September 30

&lt;p&gt;Part 1: Applied Mathematics (Control Systems, Mathematical Simulation, and Modeling).- H. Margaretha, N. Satyahadi and F. V. Ferdinand, Simulation of Investment Behaviors using Risk-Influenced Utility Models of Health and Wealth.- R. K. Dewi, I. Magdalena and I. Marcela, Wave Attenuation by a Combination of Mangroves and Reefball.- R. M. Valerio and I. Magdalena, Resonance Detection in a Semi-Closed Basin with Constant Height and Triangular Width.- R. Rahmawati, L. Harsyiah and Z. W. Baskara, Comparison of Ridge Regression and Principal Component Regression in Overcoming Multicolinierity of Factors Affecting Poverty in Indonesia.- Ahmadin, J. Naiborhu, Fatmawati and Windarto, Identification on Class Nonlinear Control System with Relative Degree Two.- A. F. Khariri, P. K. Intan, Moh. Hafiyusholeh, A. H. Asyhar and A. Fanani, Implementation of K-Means Particle Swarm Optimisation for Clustering Football Players in The Top Five European Football Leagues.- M. Widianto, C. Imron and B. Widodo, A short review of numerical modelling for photocatalytic degradation in dye systems.- S. Subchan, H. Purnawan, T. Asfihani and L. R. Amalia, Reference Tracking of Quadrotor using Modified Nonlinear Model Predictive Control based on Nonlinear Disturbance Observer.- D. Savitri, M. Jakfar and L. Putri, Numerical Simulation and Dynamics of Sedimentation Rate with Seagrass Growth Considering Dredging Factors.- H. Nuha, N. Siswanto and E. Widodo, A Mixed Integer Nonlinear Programming for Allocation Polyethylene Terephthalate (PET) Waste Management in Reverse Logistic Network.- A. Zaki Al Muntazhar, D. Ratna Sulistyaningrum and Subiono, Quantum K-Nearest Neighbors For Object Recognition.- T. Asfihani, F. N. Sihan Novita Lutfiani, A. Maulana Syafi'I, Subchan Subchan and A. Hasan, Adaptive Kalman Filter for Automated Actuator Fault Diagnosis in Unmanned Surface Vehicle.- E. R M Putri, A. Wahyu Widianto, A. Hakam, V. Ryan Tjahjono and H. Susanto, A moving average genetic algorithm (MA-GA)for estimating the COVID-19 dynamic based on a stochastic SIRD model.- A. Hakam, E. R M Putri and L. Mardiyanto, A general solution of Black-Scholes equations on some Rainbow options.- F. Ardhiansyah, R. W. Prastianto, M. Murdjito, K. Suastika and S. Nugroho, Dynamic Response of Floating Crane During Lifting Operation: A Parametric Study.- Part 2: Computational Mathematics (Formal Methods and Data Science): M. T. Ammar, M. Arzaki and G. S. Wulandari, Efficient SAT-Based Approach for Solving Juosan Puzzles.- J. E. Sakti, M. Arzaki and G. S. Wulandari, Modeling Path Puzzles as SAT Problems and How to Solve Them.- H. Tanabe and M. Kimura, A trustworthiness scoring model for reviews using a graph structure.- M. Iqbal, A. Afandy and N. Hidayat, Non-Linear Reward Deep Q Networks for Smooth Action in A Car Game.- Arthur de Clairval, L. A. Erwin Schuler, M. F. Rellier, M. Isa Irawan, I. Mukhlash and M. Iqbal, College Student Activity Recognition from Smartwatch Dataset.- A. Taryana, D. Adzkiya, M. Syifa'ul Mufid, I. Mukhlash and A. Abate, LTL Model Checking for Verification of Electronic Medical Record (EMR) Design.- G. I. Permata Sari, Hsing-Kuo Pao, R. C. Hario Pribadi and M. Iqbal, Multi-Feature Subgraph Fusion with Text Knowledge on Citation Link Prediction.- I Komang Pande Prajadhita Wibawa Putra, I. Irhamah, Nur Iriawan and K. Fithriasari, Predicting Poverty Percentage Based on Satellite Imagery and Point of Interest Using Support Vector Regression and Random Forest Regression (Case Study of Central Java Province).- M. Hutapea, G. Wulandari and M. Arzaki, Preliminary Analysis of Isabelle Proof Assistant in Graph Programming Verification with GP2.- Part 3: Pure Mathematics (Graph Theory and Analysis): T. Irvan Haryadi, R. Rinurwati, D. Winda Setyawati, S. Soleha, I. Herisman, K. Baihaqi and S. Sadjidon, Nonlocal Edge Metric Dimension on Corona Multiproduct Graphs.- N. Ain Supu, I. M. Alamsyah and E. Suwastika, The Vertex Degree of Relative g-noncommuting Graph of the Dihedral Groups.- S. Wibowo and C. R. Indrati, On Conformable Fractional Riesz Bounded p-Variation of Order- α.- H. K. Ningrum, E. Y. Asmarani, I Gede Adhitya Wisnu Wardhana, S. Salwa and Z. Y. Awanis, The Wiener Index, The Harmonic Index, and The Graph Representation of the Prime Ideal Graph for the Integers Modulo Rings with Prime Power Order.- R. Rinurwati, M. Chalid Fansury, A. Hevita G.K. Saefulloh and T. I. Haryadi, Multiplicative Sum Nano Zagreb Index of Various 2-Power Corona Graphs.&lt;br&gt;&lt;/p&gt;

The Sunday Times and #1 New York Times Bestseller 'The Technological Republic is far too important to ignore' Times Literary Supplement Silicon Valley's brightest minds once collaborated with governments to advance technologies, securing the West's dominance on the global stage. But that relationship has corroded - with perilous repercussions for us all. Lifting the veil on tech giant Palantir and its broader political project, visionary co-founder and CEO Alexander C. Karp and his long-time deputy Nicholas W. Zamiska offer a searing critique of our collective culture of complacency. To regain geopolitical power and preserve our freedoms, we must tackle the world's most pressing challenges - including the artificial intelligence arms race - together and head-on. Now is the time for the West to wake up. 'Bold and ambitious ... Essential reading' Eric Schmidt 'Fascinating' Financial Times 'A stirring manifesto' Niall Ferguson 'Provocative and insightful' Jamie Dimon

Hard Power, Soft Belief, and the Future of the West

The Technological Republic

Künstliche Intelligenz (KI) prägt viele Bereiche von Wirtschaft, Verwaltung und Gesellschaft. Dabei wirft sie tiefgreifende rechtliche, technische und ethische Fragen auf. Die Regulierung nimmt zwar zunehmend Gestalt an, bleibt aber Stückwerk - und steht der rasanten Entwicklung der Technologie und ihren immer neuen Herausforderungen gegenüber.

Maßgeschneiderter Zugang zum KI-Recht
Der neue StichwortKommentar KI von Dornis/Eichelberger/Seckelmann ermöglicht einen strukturierten Zugang zu einem der dynamischsten Rechtsgebiete unserer Zeit. 

Das Werk
<li>erschließt die zentralen Fragen und Problemfelder rund um KI nach Stichwörtern
</li><li>bietet einen schnellen und strukturierten Zugriff auf alle relevanten Inhalte und
</li><li>verbindet die systematische Tiefe eines Kommentars mit der Benutzerfreundlichkeit eines Lexikons. 

Die alphabetische Ordnung ermöglicht ein gezieltes Nachschlagen, während die Beiträge fundierte juristische und interdisziplinäre Analysen zu jedem Stichwort liefern. 

360 Grad-Blick mit dem SWK-KI
Die kommentierten Begriffe decken alle wesentlichen rechtlichen, technischen, wirtschaftlichen und ethischen Aspekte der KI ab. Das Spektrum umfasst sowohl spezifische Rechtsprobleme als auch übergeordnete Themen. Ein Schwerpunkt liegt auf der Interaktion von KI und Gesellschaft. Behandelt werden unter anderem Algorithmentransparenz, autonomes Fahren, Bias und Diskriminierung. Weitere Themen sind Hochrisiko-KI-Systeme, Datenschutz, Produktsicherheit und Regulierung.

Fazit
Mit seinem praxisorientierten Ansatz, seiner klaren Struktur und seiner interdisziplinären Perspektive setzt der SWK-KI Maßstäbe. Das Werk ist ein unverzichtbarer Begleiter für alle, die sich mit KI beschäftigen - sei es in der Entwicklung, Anwendung oder Regulierung. Es richtet sich daher gleichermaßen an Rechtsanwält:innen, Unternehmensjurist:innen, Compliance und AI Officer, Entwickler:innen, Behörden, Versicherungen, Interessenverbände und Wissenschaftler:innen. 

Die Autor:innen des von Prof. Dr. Tim W. Dornis, J.S.M.; Prof. Dr. Jan Eichelberger, LL.M. oec. und Prof. Dr. Margrit Seckelmann, M.A. herausgegebenen Werkes setzen sich seit Jahren intensiv mit den rechtlichen Fragestellungen und Herausforderungen der KI auseinander. Sie verfügen über umfassende Fachkenntnisse in diesem sich stetig weiterentwickelnden Fachgebiet: 
Katharina Baehr | Prof. Dr. Susanne Beck, LL.M. | Prof. Dr. Nadja Braun Binder | Prof. Dr. Petra Buck-Heeb | Prof. Dr. Tim W. Dornis, J.S.M. | Prof. Dr. Jan Eichelberger, LL.M. oec. | Prof. Dr. Jörg Fritzsche | Birte-Gesa Früh, LL.M. | Prof. Dr. Malte Gruber | Prof. Dr. Axel Halfmeier, LL.M. | RiAG Mario Hilzinger | StA Richard Hu | Prof. Dr. Dietmar Hübner | RAin Dr. Lisa Käde | RA Prof. Dr. Thomas Klindt | Jari Kohne | Roman Konertz, LL.M., B.Sc. | Asst.-Prof. Dr. Pascal König | Niklas Krause, LL.M. | Dr. Wolfgang Kreißig | Dr. Simone Kuhlmann | Nils Lilienthal | Finja Maasjost | Jann Maatz | Lina Marquard, LL.B. | RAin Julia Meggyes | Hendrik Meier | Prof. Dr. Juliane Mendelsohn | Prof. Dr. Catrin Misselhorn | Inga Müller | RiAG Dr. Jan Mysegades | Dr. Ioannis Revolidis | Nelli Schlee | Prof. Dr. Hans-Patrick Schroeder | Prof. Dr. Margrit Seckelmann, M.A. | Dr. Sikandar Siddiqui | Dr. Pascal T. Sierek | Prof. Dr. Christoph Sorge | Prof. Dr. Felipe Temming, LL.M. | Prof. Dr. Antje von Ungern-Sternberg | Dorothea Utermöhlen, LL.M. | PD Dr. Tobias Voigt | Dr. Mike Weber | Prof. Dr. Christoph Wendelstein | Jannik Zeiser, M.Sc.
</li>

Künstliche Intelligenz (KI) prägt viele Bereiche von Wirtschaft, Verwaltung und Gesellschaft. Dabei wirft sie tiefgreifende rechtliche, technische und ethische Fragen auf. Die Regulierung nimmt zwar zunehmend Gestalt an, bleibt aber Stückwerk – und steht der rasanten Entwicklung der Technologie und ihren immer neuen Herausforderungen gegenüber.

Maßgeschneiderter Zugang zum KI-Recht
Der neue StichwortKommentar KI von Dornis/Eichelberger/Seckelmann ermöglicht einen strukturierten Zugang zu einem der dynamischsten Rechtsgebiete unserer Zeit. 

Das Werk
erschließt die zentralen Fragen und Problemfelder rund um KI nach Stichwörtern
bietet einen schnellen und strukturierten Zugriff auf alle relevanten Inhalte und
verbindet die systematische Tiefe eines Kommentars mit der Benutzerfreundlichkeit eines Lexikons. 

Die alphabetische Ordnung ermöglicht ein gezieltes Nachschlagen, während die Beiträge fundierte juristische und interdisziplinäre Analysen zu jedem Stichwort liefern. 

360 Grad-Blick mit dem SWK-KI
Die kommentierten Begriffe decken alle wesentlichen rechtlichen, technischen, wirtschaftlichen und ethischen Aspekte der KI ab. Das Spektrum umfasst sowohl spezifische Rechtsprobleme als auch übergeordnete Themen. Ein Schwerpunkt liegt auf der Interaktion von KI und Gesellschaft. Behandelt werden unter anderem Algorithmentransparenz, autonomes Fahren, Bias und Diskriminierung. Weitere Themen sind Hochrisiko-KI-Systeme, Datenschutz, Produktsicherheit und Regulierung.

Fazit
Mit seinem praxisorientierten Ansatz, seiner klaren Struktur und seiner interdisziplinären Perspektive setzt der SWK-KI Maßstäbe. Das Werk ist ein unverzichtbarer Begleiter für alle, die sich mit KI beschäftigen – sei es in der Entwicklung, Anwendung oder Regulierung. Es richtet sich daher gleichermaßen an Rechtsanwält:innen, Unternehmensjurist:innen, Compliance und AI Officer, Entwickler:innen, Behörden, Versicherungen, Interessenverbände und Wissenschaftler:innen. 

Die Autor:innen des von Prof. Dr. Tim W. Dornis, J.S.M.; Prof. Dr. Jan Eichelberger, LL.M. oec. und Prof. Dr. Margrit Seckelmann, M.A. herausgegebenen Werkes setzen sich seit Jahren intensiv mit den rechtlichen Fragestellungen und Herausforderungen der KI auseinander. Sie verfügen über umfassende Fachkenntnisse in diesem sich stetig weiterentwickelnden Fachgebiet: 
Katharina Baehr | Prof. Dr. Susanne Beck, LL.M. | Prof. Dr. Nadja Braun Binder | Prof. Dr. Petra Buck-Heeb | Prof. Dr. Tim W. Dornis, J.S.M. | Prof. Dr. Jan Eichelberger, LL.M. oec. | Prof. Dr. Jörg Fritzsche | Birte-Gesa Früh, LL.M. | Prof. Dr. Malte Gruber | Prof. Dr. Axel Halfmeier, LL.M. | RiAG Mario Hilzinger | StA Richard Hu | Prof. Dr. Dietmar Hübner | RAin Dr. Lisa Käde | RA Prof. Dr. Thomas Klindt | Jari Kohne | Roman Konertz, LL.M., B.Sc. |  Asst.-Prof. Dr. Pascal König | Niklas Krause, LL.M. | Dr. Wolfgang Kreißig | Dr. Simone Kuhlmann | Nils Lilienthal | Finja Maasjost | Jann Maatz | Lina Marquard, LL.B. | RAin Julia Meggyes | Hendrik Meier | Prof. Dr. Juliane Mendelsohn | Prof. Dr. Catrin Misselhorn | Inga Müller | RiAG Dr. Jan Mysegades | Dr. Ioannis Revolidis | Nelli Schlee | Prof. Dr. Hans-Patrick Schroeder | Prof. Dr. Margrit Seckelmann, M.A. | Dr. Sikandar Siddiqui | Dr. Pascal T. Sierek | Prof. Dr. Christoph Sorge | Prof. Dr. Felipe Temming, LL.M. | Prof. Dr. Antje von Ungern-Sternberg | Dorothea Utermöhlen, LL.M. | PD Dr. Tobias Voigt | Dr. Mike Weber | Prof. Dr. Christoph Wendelstein | Jannik Zeiser, M.Sc.

Stichwortkommentar Künstliche Intelligenz

A new era of SQL Server is here, and this latest edition of Grant Fritchey’s best-selling dive into SQL Server query performance can ensure your queries keep up.

The fundamentals are still here: You’ll learn how statistics and indexes impact performance, how to identify poorly performing queries, and how to discover effective solutions. But this new edition also includes advanced features unique to SQL Server 2025, such as AI integration for automatic tuning, insights on using extended events, automatic execution plan correction, and more. It’s a must-have resource designed to empower you to troubleshoot slow-performing queries and make them run faster than ever.

The book is a treasure trove of instruction, insight, and advice. As you dive in, you’ll encounter key fundamentals, from statistics and data distribution to cardinality and parameter sniffing, and learn to analyze and design your indexes and queries using best practices that prevent performance problems before they occur. You’ll also explore advanced features like Query Store for managing and controlling execution plans, automated performance tuning, and memory-optimized OLTP tables and procedures—and learn how SQL Server 2025 makes it all more powerful and automatable than ever.

What You Will Learn

Use Query Store to understand and easily change query performance

Recognize and eliminate bottlenecks leading to slow performance

Tune queries whether on-premises, in containers, or on cloud platform providers

Implement best practices in T-SQL to minimize performance risk

Design in the performance that you need through careful query and index design

Understand how built-in, automatic tuning can assist your performance enhancement efforts

Protect query performance during upgrades to the newer versions of SQL Server




Who This Book Is For

Developers and database administrators with responsibility for query performance in SQL Server environments, and anyone responsible for writing or creating T-SQL queries and in need of insight into bottlenecks—including how to identify them, understand them, and eliminate them

Troubleshoot and Optimize Query Performance

SQL Server 2025 Query Performance Tuning

Rapidly transform your analytics teams to deliver AI-driven insights.

The AI-driven Data Team by Nicholas Kelly is a proven-to-work playbook for data and analytics leaders who want to align analytics capabilities with the demands of an AI-powered business environment. Written for leaders accountable for data, analytics and business intelligence, this book provides tools for diagnosing capability gaps among data analytics teams, modernizing legacy stacks and delivering AI-driven insights to help organizations make better decisions. 

You'll learn how to:
- Diagnose skills gaps and map AI-augmented career paths
- Integrate modern AI tools with existing analytics stacks
- Launch six revenue-driving and cost-focused pilots in 90 days
- Embed governance without slowing innovation
- Apply a ROI framework, governance checklist and 90-Day Charter

Drawing on expert insights and real-world applications, this book helps you upskill your analysts, strengthen AI governance and provide AI-driven insights that drive real results.

Themes include: AI strategy, data governance, analytics leadership, ROI from AI, organizational transformation, executive decision-making

Improve Your Analytics with AI

The Ai-Driven Data Team

In the HCD Measurement Guides, you'll learn how to implement measurement strategies using "compiled indicators"-a ontologically-based system of measuring complex, multidimensional outcomes like human-centered design projects. 

This approach allows measurement of indirectly measurable situations without requiring control over all variables, uses accessible computational methods rather than sophisticated statistical modeling, and has been validated by major institutions including USAID, OECD, and the European Union's Eurostat. 

The Concepts portion explains the "why" behind this measurement methodology and works in tandem with the companion Operations guide that details the "how" of identifying indicators, scoring them, and compiling scores to understand both the overall value of offerings and how they should evolve based on evidence.

Concepts & Operations

HCD Measurement Guides

Revolution of AI and Machine Learning for Design and Automation

A roadmap for business leaders to unlock the full potential of data, machine learning, and artificial intelligenceMore than 90 percent of businesses surveyed say they want to capitalize on data--but two-thirds admit to feeling stuck. What these leaders do not realize is that to succeed at becoming a data-driven business requires developing a nuanced understanding of why data holds such transformative power, what a data-inspired culture looks like, and how to get there.Data Inspired shows that the secret isn't to be more data-driven--it is to become data-inspired. This book reveals the crucial strategic distinction between using data to optimize existing operations and using them as a catalyst for deep transformation and innovation. Wernicke argues that this success hinges less on technology and more on reshaping an organization's culture and decision-making habits. And with the book's proven principles, real-world examples, and comprehensive toolbox of methods for building a culture of evidence-based inquiry, readers will learn that success means far more than just adding more data to a business: it is about creating a thoroughly data-inspired organization with innovative work methods, new structures, and unmatched efficiency.Data Inspired will help business leaders, managers, and data professionals move beyond incremental improvements to achieve lasting, powerful change.

Building an Organizational Culture of Inquiry for Lasting Transformation

Data Inspired

This book introduces the reader to the complex and multidisciplinary approaches of Process Analytical Technologies (PAT) necessary to meet current biomanufacturing regulatory and quality expectations.Process Analytical Technology and Data Analysis for Manufacturing of Biologics examines pharmaceutical biomanufacturing opportunities and challenges. The book introduces the principles and methodologies of quality by design (QbD) for biomanufacturing and biopharmaceuticals. It also discusses the fundamentals of process monitoring and control, process data management, storage, and security. The authors also present the collection and analysis of data using cutting-edge analytical methods like real-time tracking and control, multivariate data analysis, and deployment of ML algorithms. The book includes examples, case studies, and problems to facilitate the incorporation of PAT, QbD, and data science curricula in teaching environments.This book is ideal for students who need an overview of the state-of-the-art in this expanding field and is invaluable for experienced PAT practitioners in biopharmaceuticals manufacturing. It also makes a useful reference and training tool for regulatory officials.

Process Analytical Technology and Data Analysis for the Manufacturing of Biologics

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Build AI Models from Scratch (No PhD Required) Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch. No experience with deep learning required! Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory. You’ll start from the basics, and using PyTorch with real datasets, you’ll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub. You’ll build and train models to:&nbsp; <ul><li>Classify and analyze images, sequences, and time series</li><li>Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models</li><li>Process natural language with recurrent neural networks and transformers</li><li>Model molecules and physical systems with graph neural networks</li><li>Improve continuously through reinforcement and active learning</li><li>Predict chaotic systems with reservoir computing</li></ul> Whether you’re an engineer, scientist, or professional developer, you’ll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you’ll move from using AI tools to creating them.

Deep Learning Crash Course

An intimate portrait of the world's most brilliant tech visionary and his game-changing company DeepMind, from award-winning financial journalist and historian Sebastian Mallaby Even in a tech world crowded with visionary leaders, Demis Hassabis is recognized as a special case. Born to working class, immigrant parents in North London, a chess prodigy by five and wizard coder in his teens, he turned down a seven-figure job offer from a video-game studio to study science at Cambridge. Long before the current obsession with AI, he founded the path-breaking company DeepMind in order to pursue a single, audacious goal: the dream of artificial superintelligence, which would solve humanity's hardest problems, change life and work as we know it, and perhaps even unlock the deepest mysteries of the Universe. For his scientific achievements, he won a Nobel Prize in 2024, and his company, now Google DeepMind, is considered the tech giant's engine room. For the past three years, Sebastian Mallaby has had unprecedented access to Hassabis and DeepMind, conducting hundreds of hours of interviews with him and his inner circle as well as detractors and rivals at other companies. The result is a revelation-packed portrait of a singular mind and a historic reckoning with the AI revolution, a shift potentially more significant than any since the dawn of complex thought 70,000 years ago. As Mallaby chronicles, DeepMind is locked in an arms race with Silicon Valley competitors to build artificial general intelligence, and thereby become the keeper of humanity's future. Yet this is not a Silicon Valley story. Hassabis has remained in Britain, and unlike his rivals, his aims are not wealth and power but scientific enlightenment. Like them, however, he is haunted by the memory of Robert Oppenheimer, the creator of the atom bomb. He aims to control the technology, but the technology may ultimately control him - and humanity writ large.

Demis Hassabis, DeepMind and the Quest for Superintelligence

The Infinity Machine

A critical introduction to the psychiatric technologies of the future Artificial intelligence (AI) promises to transform nearly every area of modern life, and psychiatry is no exception. The field faces the mounting crisises in diagnostic inaccuracy, treatment stagnation, and persistent failures in suicide prevention, while traditional approaches struggle to keep pace with patient needs. AI and neuroscience offer a new path forward, pointing toward a future where mental health care is grounded in objective data, individualized predictions, and biologically informed treatments. Decoding Despair provides a cutting-edge look at these emerging scientific possibilities and the long-standing norms they may replace. Beginning with an examination of psychiatry's current challenges, it moves toward a present where AI-powered tools are beginning to reshape diagnostics, treatment selection, and drug development. The book offers readers a deeper understanding of mental health as both a scientific field and a lived human experience, while igniting an essential conversation about the transformative potential of precision psychiatry and technology. Decoding Despair readers will also find: <ul> <li>Exploration of the patient's journey from first symptoms to being prescribed medication</li> <li>Deep dive into the healthcare system and an understanding of the connections between stakeholders</li> <li>Clear explanations of how AI models work and how they can be practically applied to solving issues in mental health</li> <li>Deep introduction into the world of data and the different models of data acquisition necessary for building AI solutions</li> <li>Analysis of the potential innovations that could come first and the conditions necessary for them to occur</li> </ul> &nbsp; Decoding Despair is ideal for professionals and innovators building the future of psychiatry-as well as anyone seeking to understand their own mental health and the technological breakthroughs poised to reshape this critical field of care.

How AI is Reshaping Psychiatry

Decoding Despair

This open access book is a unique resource in computational mathematical optimization. It conveys the entire spectrum from the basic theory to the most advanced tricks in reformulations and decomposition approaches to solving mixed-integer linear programs. The book has useful and partly previously unpublished facts for students, teachers, and practitioners alike. The content covers algorithmic ideas like column generation, cutting planes, and branch-and-price, reformulation techniques like Dantzig-Wolfe decomposition and Lagrangian relaxation, the very important application domains of vehicle routing and crew scheduling, and has some practical advice when it comes to an implementation. Many notes and even more didactic illustrations and examples bring the theory to life, complemented by almost 140 exercises, including solutions. As a side effect, readers learn ways how to formulate integer programming models for a great variety of combinatorial optimization problems. The material draws from more than 400 references and the four authors' own decades of experience in the field. Several photos taken by the authors bring a personal touch to the writing, among other tiny anecdotal elements that the connoisseur will appreciate. The book is not, and cannot be encyclopedic, but it has the ambition to be the standard text and main reference in the field for the years to come.
Students, researchers, and practitioners aiming to solve large, complex discrete and combinatorial optimization problems—whether in industry, science, or technology—will find this book essential for exploring optimal and near-optimal solutions through decomposition methods and column generation.

This open access book is a unique resource in computational mathematical optimization. It conveys the entire spectrum from the basic theory to the most advanced tricks in reformulations and decomposition approaches to solving mixed-integer linear programs. The book has useful and partly previously unpublished facts for students, teachers, and practitioners alike. The content covers algorithmic ideas like column generation, cutting planes, and branch-and-price, reformulation techniques like Dantzig-Wolfe decomposition and Lagrangian relaxation, the very important application domains of vehicle routing and crew scheduling, and has some practical advice when it comes to an implementation. Many notes and even more didactic illustrations and examples bring the theory to life, complemented by almost 140 exercises, including solutions. As a side effect, readers learn ways how to formulate integer programming models for a great variety of combinatorial optimization problems. The material draws from more than 400 references and the four authors' own decades of experience in the field. Several photos taken by the authors bring a personal touch to the writing, among other tiny anecdotal elements that the connoisseur will appreciate. The book is not, and cannot be encyclopedic, but it has the ambition to be the standard text and main reference in the field for the years to come.Students, researchers, and practitioners aiming to solve large, complex discrete and combinatorial optimization problems—whether in industry, science, or technology—will find this book essential for exploring optimal and near-optimal solutions through decomposition methods and column generation.

Branch-and-Price

Applied and Computational Mathematics

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