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A quantitative assessment of policy-driven impacts on circular economy of electric vehicle batteries in the United States

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Environment systems and Decisions 2025年第1期45卷 1-21页

作者： Ren, Guannan Eveleigh, Timothy Department of Engineering Management and Systems Engineering The George Washington University Washington DC United States

Electric vehicle battery recycling has emerged as a critical process in transitioning to sustainable transportation. As the demand for electric vehicles continues to rise, so does the need to address the end-of-life management of their batteries. Current battery recycling rates need to be increased to meet the growing demands for raw materials. This work incorporates system dynamics to model crucial factors, such as regulatory frameworks, economic incentives, and technological processes that influence the cost-effectiveness and efficiency of battery recycling systems. A composite model that integrates stock-and-flow structures with delays to capture the interconnectedness of variables such as battery production rates, demand and price fluctuations, recycling infrastructure capacity, and the effectiveness of recycling processes is developed to study the overall supply chain efficiency. By simulating scenarios like stricter regulations on battery disposal, incentives for recycling, or investments in research and development for battery designs and advanced recycling technologies, the aggregate model offers policymakers, industry stakeholders, and researchers insights into the effects of policies or process updates on electric vehicle battery recycling rates. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Circular economy Closed-loop supply chain Electric vehicle Policy Recycling Sustainability

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A Symbiosis of Multi-Criteria Decision Making and Electroencephalography: A Review of Techniques, Applications, and Future Directions

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IEEE Access 2025年 13卷 75071-75088页

作者： Adem, Aylin Cakit, Erman Dagdeviren, Metin Szopa, Anna Karwowski, Waldemar University of Central Florida Department of Industrial Engineering and Management Systems OrlandoFL32816-2993 United States Gazi University Department of Industrial Engineering Ankara06570 Turkey

Multi-criteria decision-making (MCDM) problems are generally oriented toward strategic decisions and have a significant economic aspect, holding a special place in decision-making processes. To address these problems, specialized techniques have been developed and utilized. Electroencephalography (EEG) is a technique that measures brain activity, and the results can be used both to diagnose neurological disorders and to create more productive and healthier working conditions in a wide variety of areas. This paper discusses the collaborative nature between MCDM and EEG, focusing on how EEG and MCDM processes can enhance each other. Based on the database search results, thirty-five out of 149 papers were selected for the review process. By examining the literature on both macro and micro scales, the following were mainly retrieved: a) the MCDM techniques applied and their use in analyzing EEG data, b) EEG devices and wave types used in the MCDM process, c) the attributes of MCDM or EEG that were focused on, and d) current trends in knowledge and research opportunities. The results of this study will help identify potential future research areas, as well as provide a comprehensive overview of the existing literature. To summarize the findings, it can be concluded that EEG measurements of decision-makers’ cognitive states during the application of MCDM techniques improve the MCDM process and the presentation of these techniques, or they enhance the results obtained by MCDM based on the cognitive states of decision-makers when evaluating alternatives. Additionally, MCDM techniques contribute to improvements in the classification or feature extraction stages of data obtained through EEG. © 2013 IEEE.

关键词： Neurons

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Innovative Mechanical Ventilation Control for Enhanced Indoor Air Quality and Energy Efficiency

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Energy engineering 2025年第3期122卷 861-883页

作者： Giovanni Miracco Francesco Nicoletti Vittorio Ferraro Dimitrios Kaliakatsos Department of Mechanical Energy and Management Engineering(DIMEG)University of CalabriaCosenza87036Italy Department of Computer ModellingElectronics and Systems Engineering(DIMES)University of CalabriaCosenza87036Italy

Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational *** study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy *** classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely *** each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity *** proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and *** addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy *** demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air *** findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as ***,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor *** study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.

关键词： Indoor air quality mechanical ventilation system innovative control system energy efficiency Energy-Plus simulation

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Change-point detection with deep learning:A review

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Frontiers of engineering management 2025年第1期12卷 154-176页

作者： Ruiyu XU Zheren SONG Jianguo WU Chao WANG Shiyu ZHOU Department of Industrial Engineering and Management Peking UniversityBeijing 100871China Department of Industrial and Systems Engineering University of IowaIowa CityIA 52242USA Department of Industrial and Systems Engineering University of Wisconsin-MadisonMadisonWI 53706USA

Recent advances in deep learning have led to the creation of various methods for change-point detection(CPD).These methods enhance the ability of CPD tech-niques to handle complex,high-dimensional data,making them more adaptable and less dependent on strict assump-tions about data *** methods have also demonstrated high accuracy and have been applied across various fields,including manufacturing,healthcare,activity monitoring,finance,and environmental *** review provides an overview of how these methods are applied,the data sets they use,and how their performance is *** also organizes techniques into supervised and unsupervised categories,citing key ***,we explore ongoing challenges and suggest directions for future research to improve interpretability,generalizability,and real-world implementation.

关键词： change-point detection deep learning supervised learning unsupervised learning time-series analysis

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing 6

Industry 5.0 in Healthcare: An Integrated Framework for Huma...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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Improving lung cancer detection via MobileNetV2 and stacked-GRU with explainable AI

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1189-1196页

作者： Bagheri Tofighi, Abolfazl Ahmadi, Abbas Mosadegh, Hadi Department of Industrial Engineering and Management Systems Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

Accurate and timely detection of lung cancer is crucial for effective treatment planning. This study introduces MobileNetV2-SGRU, a novel transfer learning-based predictor for lung cancer classification. It utilizes MobileNetV2 to automatically extract meaningful features from lung CT images. These extracted features processed through stacked gated recurrent unit layers, enabling the model to capture the sequential and temporal information present in the images. Additionally, the model incorporates Grad-CAM, an explainable artificial intelligence technique, to enhance interpretability and transparency of the model. An evaluation of MobileNetV2-SGRU on the IQ-OTH/NCCD dataset demonstrated its outstanding performance in lung cancer classification. The model achieves impressive accuracy, precision, recall, and F1-score of 96.83%, 96.78%, 96.83%, and 96.78%, respectively, demonstrating its superior predictive capabilities compared to existing methods. Furthermore, the results indicate reduced computation time and enhanced accuracy in diagnosing lung cancer. With advanced performance metrics and the incorporation of Grad-CAM visualization for interpretability, the model offers valuable insights for clinicians in validating and understanding its predictions, ultimately contributing to more effective treatment planning. © Bharati Vidyapeeth's Institute of Computer Applications and management 2024.

关键词： Explainable AI Grad-CAM Lung cancer Medical imaging MobileNetV2-SGRU Transfer learning

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Integrating Potential Pronunciations for Enhanced Mispronunciation Detection and Diagnosis Ability in LLMs

Integrating Potential Pronunciations for Enhanced Mispronunc...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Minglin Xu, Jing Chen, Xueyuan Meng, Helen Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Large Language Models (LLMs) have exhibited significant potentials across various tasks. However, how to leverage the power of LLMs in the mispronunciation detection and diagnosis (MDD) task is still under-explored. In this paper, we propose a PP-ATP model, which integrates potential pronunciations covering common mispronunciations into the prompt part of LLMs, to enhance the MDD ability of LLMs in second language (L2) English. Specifically, the proposed PP-ATP model is composed of an audio encoder, an LLM decoder, and an adapter. Taking speech representations from the audio encoder as the audio prompt and reference sentence with canonical and potential pronunciations as text prompt, the LLM decoder is adapted to predict the actual pronunciation in the given L2 speech. Experiments show that our PP-ATP model achieves new state-of-the-art (SOTA) performance in MDD on CU-CHLOE corpus, confirming the effectiveness of potential pronunciation integration. © 2025 IEEE.

关键词： L2 English large language model mispronunciation detection and diagnosis potential pronunciations

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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

Single-Loop Variance-Reduced Stochastic Algorithm for Noncon...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Xia Zhu, Linglingzhi Zheng, Taoli So, Anthony Man-Cho Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of O(ϵ−4), surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations. © 2025 IEEE.

关键词： nonconvex-concave minimax optimization single-loop stochastic algorithm variance reduction

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Graph-Based Variation Propagation Network for Modeling and Prediction of Hybrid Multi-Stage Manufacturing systems

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14317-14331页

作者： Alenezi, Dhari F. Shi, Jianjun Li, Jing Georgia Institute of Technology H. Milton Stewart School of Industrial and Systems Engineering AtlantaGA30332 United States Kuwait University Department of Industrial and Management Systems Engineering Sabah Al Salem University City 13060 Kuwait

Multistage Manufacturing systems (MMS) are common in industries involving complex processes with multiple stages, each impacting the final product quality. Traditional product quality modeling approaches struggle with the intricate interdependencies and variable structures within these systems, further complicated by the extensive sensor-generated data. In this paper, we introduce Graph-based Variation Propagation Network (GVPNet), an innovative approach for end-to-end learning of product quality representations and their propagation through MMS stages. GVPNet utilizes a heterogeneous Graph Attention Network (hGAT) architecture that uses a graph representation specifically tailored for MMS, facilitating the effective application of graph neural networks (GNN) in this context. The network's ability to aggregate information and learn node embeddings enables it to predict quality variables several stages ahead, thus offering a proactive tool for quality control. GVPNet was applied in two real world case studies and demonstrated its superiority in predictive accuracy and robustness over benchmarks. Note to Practitioners - This paper introduces the Graph-based Variation Propagation Network (GVPNet), designed to enhance quality prediction in Multistage Manufacturing systems (MMS) where traditional models fall short. Integrating a tailored graph representation with the Graph Attention Network (hGAT), GVPNet excels in capturing the complex dynamics of MMS, including inter-stage dependencies. This approach enables more accurate and proactive quality control in various manufacturing contexts, providing a significant tool for practitioners to optimize processes and improve product quality, even in data-rich environments. © 2025 IEEE.

关键词： Smart manufacturing

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A budget-adaptive allocation rule for optimal computing budget allocation

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European Journal of Operational Research 2025年第2期325卷 247-260页

作者： Cao, Zirui Wang, Haowei Chew, Ek Peng Li, Haobin Tan, Kok Choon Department of Industrial Systems Engineering and Management National University of Singapore 1 Engineering Drive 2 Singapore117576 Singapore Department of Analytics and Operations National University of Singapore 15 Kent Ridge Drive Singapore119245 Singapore

Simulation-based ranking and selection (R&S) is a popular technique for optimizing discrete-event systems (DESs). It evaluates the mean performance of system designs by simulation outputs and aims to identify the best system design from a set of alternatives by intelligently allocating a limited simulation budget. In R&S, the optimal computing budget allocation (OCBA) is an efficient budget allocation rule that asymptotically maximizes the probability of correct selection (PCS). In this paper, we first show the asymptotic OCBA rule can be recovered by considering a large-scale problem with a specific large budget. Considering a sufficiently large budget can greatly simplify computations, but it also causes the asymptotic OCBA rule ignoring the impact of budget. To address this, we then derive a budget-adaptive rule under the setting where budget is not large enough to simplify computations. The proposed budget-adaptive rule determines the ratio of total budget allocated to designs based on the budget size, and its budget-adaptive property highlights the significant impact of budget on allocation strategy. Based on the proposed budget-adaptive rule, two heuristic algorithms are developed. In the numerical experiments, the superior efficiency of our proposed allocation rule is shown. © 2025 Elsevier B.V.

关键词： Budget-adaptive allocation rule Optimal computing budget allocation Ranking and selection Simulation

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