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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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On Pareto Optimality for the Multinomial Logistic Bandit

arXiv

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arXiv 2025年

作者： Zuo, Jierui Qin, Hanzhang School of Economics and Management Tsinghua University China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

We provide a new online learning algorithm for tackling the Multinomial Logit Bandit (MNL-Bandit) problem. Despite the challenges posed by the combinatorial nature of the MNL model, we develop a novel Upper Confidence Bound (UCB)-based method that achieves Pareto optimality by balancing regret minimization and estimation error of the assortment revenues and the MNL parameters. We develop theoretical guarantees characterizing the tradeoff between regret and estimation error for the MNL-Bandit problem through information-theoretic bounds, and propose a modified UCB algorithm that incorporates forced exploration to improve parameter estimation accuracy while maintaining low regret. Our analysis sheds critical insights into how to optimally balance the collected revenues and the treatment estimation in dynamic assortment optimization. Copyright © 2025, The Authors. All rights reserved.

关键词： Learning algorithms

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Fragility-aware Classification for Understanding Risk and Improving Generalization

arXiv

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arXiv 2025年

作者： Yang, Chen Cui, Zheng Long, Daniel Zhuoyu Qi, Jin Zhan, Ruohan Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology Hong Kong School of Management Zhejiang University China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Classification models play a critical role in data-driven decision-making applications such as medical diagnosis, user profiling, recommendation systems, and default detection. Traditional performance metrics, such as accuracy, focus on overall error rates but fail to account for the confidence of incorrect predictions, thereby overlooking the risk of confident misjudgments. This risk is particularly significant in cost-sensitive and safety-critical domains like medical diagnosis and autonomous driving, where overconfident false predictions may cause severe consequences. To address this issue, we introduce the Fragility Index (FI), a novel metric that evaluates classification performance from a risk-averse perspective by explicitly capturing the tail risk of confident misjudgments. To enhance generalizability, we define FI within the robust satisficing (RS) framework, incorporating data uncertainty. We further develop a model training approach that optimizes FI while maintaining tractability for common loss functions. Specifically, we derive exact reformulations for cross-entropy loss, hinge-type loss, and Lipschitz loss, and extend the approach to deep learning models. Through synthetic experiments and real-world medical diagnosis tasks, we demonstrate that FI effectively identifies misjudgment risk and FI-based training improves model robustness and generalizability. Finally, we extend our framework to deep neural network training, further validating its effectiveness in enhancing deep learning models. © 2025, CC BY.

关键词： Risk management

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The Centrifugal Nuclear Thermal Rocket: Foundations, Challenges, and Progress

The Centrifugal Nuclear Thermal Rocket: Foundations, Challen...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Thomas, Dale Houts, Michael Wang, Dean Hollingsworth, Keith Frederick, Robert Cassibry, Jason Department of Industrial & Systems Engineering and Engineering Management The University of Alabama in Huntsville HuntsvilleAL35899 United States Space Nuclear Propulsion Program Office NASA Marshall Space Flight Center HuntsvilleAL35812 United States Department of Mechanical & Aerospace Engineering The Ohio State University ColumbusOH43210 United States Department of Mechanical & Aerospace Engineering and Interim Dean of the Honors College The University of Alabama in Huntsville HuntsvilleAL35899 United States Department of Mechanical & Aerospace Engineering Propulsion Research Center The University of Alabama in Huntsville HuntsvilleAL35899 United States Department of Mechanical & Aerospace Engineering The University of Alabama in Huntsville HuntsvilleAL35899 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The Centrifugal Nuclear Thermal Rocket (CNTR) is a Nuclear Thermal Propulsion (NTP) concept designed to heat propellant directly by the reactor fuel. The primary difference between the CNTR concept and traditional NTP systems is that rather than using traditional solid fuel elements, the CNTR uses liquid fuel with the liquid contained in rotating cylinders by centrifugal force. If the concept can be successfully realized, the CNTR would have a high specific impulse (up to 1800 s) at high thrust, which may enable (i) viable near-term human Mars exploration by reducing round-trip times to 420 days and (ii) direct injection orbits for scientific rendezvous missions to the Solar System outer planets and Kuiper Belt objects. Research is presently underway to determine resolutions for the significant engineering challenges that the CNTR concept presents. These challenges, the study plans to address them, and progress have been described previously;this paper provides an update on progress to date for the overall research effort, including strategies and key results on revision of a reference configuration, definition of key parameters which allows integration of the results of various analyses, and reviews strategies to mitigate the problem of Uranium vapor saturating the propellant exhaust and significantly impacting the CNTR’s specific impulse. Analyses and experiments, both underway and planned, to update estimates of engine key performance parameters including specific impulse, thrust and thrust to weight ratio along with the identification of selected design margins within the engine itself are described – all toward the goal of enabling plans for a laboratory demonstration of a single Centrifugal Fuel Element. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Rockets

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It's All in the Mix: Technology choice between driverless and human-driven vehicles in sharing systems

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European Journal of Operational Research 2025年第3期324卷 969-980页

作者： Martin, Layla Minner, Stefan Pavone, Marco Schiffer, Maximilian School of Industrial Engineering Eindhoven University of Technology Eindhoven Netherlands Eindhoven AI Systems Institute Eindhoven University of Technology Eindhoven Netherlands School of Management Technical University of Munich Munich80333 Germany Technical University of Munich Garching85478 Germany Department of Aeronautics and Astronautics Stanford University StanfordCA94305 United States

Operators of vehicle-sharing systems such as carsharing or ride-hailing can benefit from integrating driverless vehicles into their fleet. In this context, we study the impact of optimal fleet size and composition on an operator's profitability, which entails a non-trivial tradeoff between operational benefits and higher upfront investment for driverless vehicles. We analyze a strategic fleet sizing and composition problem, integrating a rebalancing problem, which we formalize as a Markov decision process. We incorporate the rebalancing problem with a time-dependent fluid approximation to devise a scalable linear programming solution approach, which we improve by state-dependent emergency rebalancing. We present a numerical study on artificial and real-world instances that reveals significant profit improvement potential of driverless and mixed fleets compared to human-driven fleets. For real-world instances, the profit improvement amounts up to 20.4% over exclusively human-driven fleets. If both vehicle types incur equal operational costs, operators optimally mix a small number of driverless vehicles with a large number of human-driven vehicles. Mixed fleets are particularly beneficial if demand varies over time, and operators consequently shift rebalancing to lower-demand periods. © 2025

关键词： Markov processes

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Technical Note—Online Matching with Bayesian Rewards

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Operations Research 2025年第1期73卷 278-289页

作者： Simchi-Levi, David Sun, Rui Wang, Xinshang Institute for Data Systems and Society Department of Civil & Environmental Engineering Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Alibaba Group US San MateoCA94402 United States Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200240 China

We study in this paper an online matching problem where a central platform needs to match a number of limited resources to different groups of users that arrive sequentially over time. The reward of each matching option depends on both the type of resource and the time period the user arrives. The matching rewards are assumed to be unknown but drawn from probability distributions that are known a priori. The platform then needs to learn the true rewards online based on real-time observations of the matching results. The goal of the central platform is to maximize the total reward from all of the matchings without violating the resource capacity constraints. We formulate this matching problem with Bayesian rewards as a Markovian multiarmed bandit problem with budget constraints, where each arm corresponds to a pair of a resources and a time period. We devise our algorithm by first finding policies for each single arm separately via a relaxed linear program and then "assembling" these policies together through judicious selection criteria and well-designed pulling orders. We prove that the expected reward of our algorithm is at least 12 (√ffiffiffi 2 - 1) of the expected reward of an optimal algorithm. Copyright: © 2023 INFORMS.

关键词： Budget control

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A Novel Approach to Smart Grid Energy management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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Simulation to predict the behaviour of a new seaport gate: An application in the Sines container terminal

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Procedia Computer Science 2025年 253卷 2409-2418页

作者： Raquel Gil Pereira Rui Borges Lopes Leonor Teixeira Center for Research & Development in Mathematics and Applications (CIDMA) Department of Economics Management Industrial Engineering and Tourism (DEGEIT) University of Aveiro Aveiro Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Intelligent Systems Associate Laboratory (LASI) Department of Economics Management Industrial Engineering and Tourism (DEGEIT)University of Aveiro Aveiro Portugal

With rapid technological changes and globalized supply chains, intelligent and adaptable logistics systems are crucial, particularly at seaports. Container terminals face increasing volumes and demands, leading to potential truck congestion and long queues. Automation offers a solution by optimizing port performance and adapting to dynamic conditions, though port digitalization is complex due to diverse stakeholders and information flows. This work is part of the digital transformation of the Port of Sines - Portugal, specifically in the Smart Gate concept context. For this purpose, the Port of Sines Administration, together with other partners, has established the prerequisite of a pre-gate, which is currently being analysed from the point of view of its processes, the technologies to be included and the information models to be supported. This work aims to dynamically analyse the pre-gate model by testing different scenarios to propose improvements to the inherent processes using simulation. The study culminated in a discrete-event simulation model with Anylogic software to predict pre-gate behaviour and foresee potential issues. By testing various scenarios and evaluating their effects on key performance indicators, this model supports the continuous improvement of pre-gate processes.

关键词： port logistics container terminal smart gate truck congestion simulation Anylogic

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Incremental Machine Learning-Integrated Blockchain for Real-Time Security Protection in Cyber-Enabled Manufacturing systems

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Journal of Computing and Information Science in engineering 2025年第4期25卷 041004页

作者： Oskolkov, Boris Kan, Chen Tian, Wenmeng Law, Andrew Chung Chee Liu, Chenang The School of Industrial Engineering & Management https://***/01g9vbr38Oklahoma State University Stillwater OK 74078 Department of Industrial Manufacturing and Systems Engineering The University of Texas at Arlington Arlington TX 76019 Department of Industrial and Systems Engineering Mississippi State University Mississippi State MS 39762 IoTeX Palo Alto CA 94306 The School of Industrial Engineering & Management Oklahoma State University Stillwater OK 74078

Cyber-enabled manufacturing systems are becoming increasingly data-rich, generating vast amounts of real-time sensor data for quality control and process optimization. However, this proliferation of data also exposes these systems to significant cyber-physical security threats. For instance, malicious attackers may delete, change, or replace original data, leading to defective products, damaged equipment, or operational safety hazards. False data injection attacks can compromise machine learning models, resulting in erroneous predictions and decisions. To mitigate these risks, it is crucial to employ robust data processing techniques that can adapt to varying process conditions and detect anomalies in real-time. In this context, the incremental machine learning (IML) approaches can be valuable, allowing models to be updated incrementally with newly collected data without retraining from scratch. Moreover, although recent studies have demonstrated the potential of blockchain in enhancing data security within manufacturing systems, most existing security frameworks are primarily based on cryptography, which does not sufficiently address data quality issues. Thus, this study proposes a gatekeeper mechanism to integrate IML with blockchain and discusses how this integration could potentially increase the data integrity of cyber-enabled manufacturing systems. The proposed IML-integrated blockchain can address the data security concerns from both intentional alterations (e.g., malicious tampering) and unintentional alterations (e.g., process anomalies and outliers). The real-world case study results show that the proposed gatekeeper integration algorithm can successfully filter out over 80% of malicious data entries while maintaining comparable classification performance to standard IML models. Furthermore, the integration of blockchain enables effective detection of tampering attempts, ensuring the trustworthiness of the stored information.

关键词： Security security protection industrial internet of things Manufacturing engineering informatics blockchain incremental learning cyber manufacturing Blockchain technology Manufacturing systems machine learning for engineering applications Machine learning

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of Computer Applications and management 2025.

关键词： control strategies liquid transportation modeling of system

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