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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;

Synthesis Lectures on Operations Research and Applications

This book discusses how analytics can handle uncertainty and fairness issues in scheduling chemotherapy treatments. Specifically, the authors explore the complexities of chemotherapy scheduling, showing how analytics can be used to create fairer and more efficient chemotherapy schedules. By addressing uncertainty in infusion times and balancing the needs of both patient and staff, the book offers novel insights, practical models, and actionable solutions for healthcare professionals aiming to improve patient care in chemotherapy clinics. Readers will gain a deep understanding of the unique challenges in scheduling chemotherapy treatments, with a focus on handling uncertainty in treatment durations, coordinating clinic resources, and ensuring fairness among patients. This book uses real data from chemotherapy clinics to develop models and generate solutions representing fair and efficient schedules. Alongside methodological knowledge, readers will acquire managerial insights that can directly enhance the scheduling processes in real-world oncology settings.

This book discusses how analytics can handle uncertainty and fairness issues in scheduling chemotherapy treatments. &nbsp;Specifically, the authors explore the complexities of chemotherapy scheduling, showing how analytics can be used to create fairer and more efficient chemotherapy schedules. By addressing uncertainty in infusion times and balancing the needs of both patient and staff, the book offers novel insights, practical models, and actionable solutions for healthcare professionals aiming to improve patient care in chemotherapy clinics. &nbsp;Readers will gain a deep understanding of the unique challenges in scheduling chemotherapy treatments, with a focus on handling uncertainty in treatment durations, coordinating clinic resources, and ensuring fairness among patients. This book uses real data from chemotherapy clinics to develop models and generate solutions representing fair and efficient schedules. Alongside methodological knowledge, readers will acquire managerial insights that can directly enhance the scheduling processes in real-world oncology settings.

Addressing Uncertainty and Fairness

Chemotherapy Appointment Scheduling

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Berichte aus der Elektrotechnik

Methods of Machine Learning for Analysis and Decoupling of Power Delivery Networks on Printed Circuit Boards

Recursions

Artificial intelligence is often framed as a quest to replicate the human brain, promising frictionless cognition and a future of seamless automation. But what if this pervasive narrative obscures a deeper, more "errant" truth?Errant Intelligence challenges the prevailing biological and individualistic interpretations of machine learning, arguing instead for a radical understanding of machine intelligence. The book embraces the deviations, inconsistencies, and "errant behaviour" as fundamental to the discovery of new knowledge, moving beyond the illusion of mere optimisation. Drawing on media theory, cybernetics, and a unique psychoanalytic lens, it explores the "technological unconscious" of machine learning. It traces the historical roots of AI, from early automatons and the Turing machine to natural language processing and contemporary machine learning systems. Challenging the idea of an autonomous, self-generating AI, the book exposes the hidden labour, assumed logics, and inherent biases that animate its operation. It re-evaluates computational thinking, insisting on its inherently social, collective, and symbolic character, and revealing how language and logical paradoxes are not obstacles but constitutive forces that shape intelligent machines.Errant Intelligence offers a vital new framework for understanding the profound co-evolution of human and machine learning. It's time to "unlearn" our assumptions and embrace the productive ambiguity and fallibility at the core of machine intelligence.

A Media Theory of Machine Learning

Errant Intelligence

Dynamic Modeling and Econometrics in Economics and Finance

Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data.
Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts progressively, making it suitable for both intermediate and advanced readers. Each chapter is written in clear, approachable language, ensuring that even those with limited prior experience in econometrics can grasp and apply the material effectively.
The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis.

A Practical Approach

Non-Linearity in Econometric Modeling, Vol. 1

With its groundbreaking approach to financial management under dynamic conditions, this book equips readers with a new perspective on financial systems, moving beyond classical models to embrace the inherent uncertainties of markets.

The CFO’s Survival Kit for an Uncertain World

Quantum Finance

Explore how Artificial Intelligence (AI) is driving the evolution of product management through a unified approach to data. Written as a practical guide, this book equips modern product managers with the tools to harness AI and data to design, build, and scale innovative products. 
Through actionable frameworks and real-world examples, you'll learn to define impactful AI problem statements and evaluate financial ROI and feasibility. Beyond strategy, this book addresses data operating model challenges such as data quality, system integration, adoption barriers, ethics, and governance. You’ll be introduced to a practical framework for Trustworthy AI, one that combines sound data practices, ethical principles, and responsible governance with real-world product management tools.
Whether you are at a startup experimenting with AI or leading transformation in a global enterprise, AI Meets Strategy is your roadmap to building AI-enabled products that people believe in.
What You Will Learn
<ul>
<li>Adopt an AI-first mindset, where AI is central to product strategy.

</li><li>Design sustainable systems that balance innovation with societal impact.

</li><li>Craft enterprise-wide AI strategies that align with business goals, governance standards, and ethical principles 




Who This Book is For

Aspiring to senior Product Managers
</li></ul>

Explore how Artificial Intelligence (AI) is driving the evolution of product management through a unified approach to data. Written as a practical guide, this book equips modern product managers with the tools to harness AI and data to design, build, and scale innovative products. Through actionable frameworks and real-world examples, you'll learn to define impactful AI problem statements and evaluate financial ROI and feasibility. Beyond strategy, this book addresses data operating model challenges such as data quality, system integration, adoption barriers, ethics, and governance. You’ll be introduced to a practical framework for Trustworthy AI, one that combines sound data practices, ethical principles, and responsible governance with real-world product management tools.
 Whether you are at a startup experimenting with AI or leading transformation in a global enterprise, 
 AI Meets Strategy
 is your roadmap to building AI-enabled products that people believe in.
 What You Will Learn<ul><li>Adopt an AI-first mindset, where AI is central to product strategy.</li><li>Design sustainable systems that balance innovation with societal impact.</li><li>Craft enterprise-wide AI strategies that align with business goals, governance standards, and ethical principles </li></ul>Who This Book is ForAspiring to senior Product Managers

A Product Manager’s Guide to Leading Innovation

AI Meets Strategy

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems. <ul><li>Integrates a rich array of machine learning methods into causal modeling frameworks</li><li>Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis</li><li>Features a breadth of real-world examples from recent papers</li><li>Includes coding implementation in Python, R, and Stata</li></ul>

Applied Microeconometrics

Platform Supply Chains in the AI Era

Risk Management

Platform Supply Chains 5

IPP Studies in the Frontiers of China’s Public Policy

Guangdong, located in South China, and its core area, the Pearl River Delta, have become the most vibrant hub of innovation in China. This book traces Guangdong's journey from its successful rise as the "world factory" at the end of the 20th century to its current status as China's center for innovative industries, revealing the internal mechanisms of the "black box" of how the Chinese economy gained the innovation advantage. The book summarizes Guangdong's development experience as a "bottom-up" innovation model, where in the stages of factor-driven, investment-driven, and innovation-driven development, companies’ strategies of technology and innovation strategies align with and mutually reinforce local governments’ industrial and development policies, collectively creating an upward-spreading innovation network and system that forms a unique regional economic growth engine. The book's discussion on technology learning strategies, absorptive capacity, dynamic capabilities, R&amp;D investment, innovation platforms, innovation ecosystems, and regional innovation policies holds valuable reference for government officials, corporate executives, business scholars, economists, and journalists.

Guangdong, located in South China, and its core area, the Pearl River Delta, have become the most vibrant hub of innovation in China. This book traces Guangdong's journey from its successful rise as the "world factory" at the end of the 20th century to its current status as China's center for innovative industries, revealing the internal mechanisms of the "black box" of how the Chinese economy gained the innovation advantage. The book summarizes Guangdong's development experience as a "bottom-up" innovation model, where in the stages of factor-driven, investment-driven, and innovation-driven development, companies’ strategies of technology and innovation strategies align with and mutually reinforce local governments’ industrial and development policies, collectively creating an upward-spreading innovation network and system that forms a unique regional economic growth engine. The book's discussion on technology learning strategies, absorptive capacity, dynamic capabilities, R&amp;D investment, innovation platforms, innovation ecosystems, and regional innovation policies holds valuable reference for government officials, corporate executives, business scholars, economists, and journalists.

Bottom Up Innovation in Guangdong

Remade in South China

Studies in Economic History

This book reveals how the formation of the Chinese socialist system in the 1950s dramatically transformed the nation’s economy, and charts how the newly established economic system functioned during the planned economy period (1949-1978), through focusing on Shanghai, which had served as the center of China’s modern economy since the latter half of the nineteenth century. Drawing on the rich historical primary sources of the Shanghai Municipal Archives, this study examines the impact of the institutional change wrought by the introduction of the socialist system on Shanghai’s economy, through a careful comparison with the economic structure before 1949. Moreover, it also explores the distinctive features of the Chinese socialist system, first and foremost the relationship between local governments and local businesses that came about through the socialist transformation of private enterprise, by analyzing Shanghai’s fiscal structure and a series of key industries, including the textile, machinery, rubber processing, cement, and power industries. This historical study proffers a new understanding of the long-term economic history of modern China across the pivotal 1949 and 1978 divides and sheds light on the origin of what has come to be known as "socialism with Chinese characteristics" today.

This book reveals how the formation of the Chinese socialist system in the 1950s dramatically transformed the nation’s economy, and charts how the newly established economic system functioned during the planned economy period (1949-1978), through focusing on Shanghai, which had served as the center of China’s modern economy since the latter half of the nineteenth century. Drawing on the rich historical primary sources of the Shanghai Municipal Archives, this study examines the impact of the institutional change wrought by the introduction of the socialist system on Shanghai’s economy, through a careful comparison with the economic structure before 1949. Moreover, it also explores the distinctive features of the Chinese socialist system, first and foremost the relationship between local governments and local businesses that came about through the socialist transformation of private enterprise, by analyzing Shanghai’s fiscal structure and a series of key industries, including the textile, machinery, rubber processing, cement, and power industries. This historical study proffers a new understanding of the long-term economic history of modern China across the pivotal 1949 and 1978 divides and sheds light on the origin of what has come to be known as “socialism with Chinese characteristics” today.

The Impact of the Planned Economy and the Socialist Transformation

Shanghai under the Socialist System

Innovations, Evidence and Pathways Toward Equitable Development

Financial Inclusion for Viksit Bharat

Applied and Computational Mathematics

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