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Optimizing life cycle sustainability based on municipal solid waste streams and treatment potentials

Environment systems and Decisions 2024年第4期44卷 887-905页

作者： Aleisa, Esra Al-Jarallah, Rawa Industrial and Management Systems Engineering Department College of Engineering and Petroleum Kuwait University Safat 13060 Kuwait Civil Engineering Department College of Engineering and Petroleum Kuwait University Safat 13060 Kuwait

This study applies multicriteria mathematical modeling to optimize municipal solid waste (MSW) management across a three bottom-line (BL) framework: environmental, social and economic. The interrelationships and the ripple secondary impacts among the three BLs are examined systematically using an augmented simplex lattice mixture (ASLM) method. Detailed waste and waste treatments, including pyrolysis (PY), anaerobic digestion (AD), animal feed (AF), composting (CP), recycling (RE), incineration (INC) and landfilling (LF), are constructed based on waste stream fractions and treatment allowable limits. The environmental BL is assessed using life cycle assessment (LCA). The economic BL is determined by calculating the per ton capital recovery with return, and the social bottom line is assessed using the analytic hierarchy process (AHP). The three bottom lines are optimized through a mathematical model using CPLEX solver. The results indicate that CP abates 973 kgCO₂eq/t compared to 61.8 kgCO₂eq/t from AD and 28.3 kgCO₂eq/t from AF. CP generates $23.5/t despite its low social desirability. Plastic waste PY credits ethylene by 364 kg/t, however, it costs $226.7/t despite the subtraction of credited energy and recovered byproducts. Metal RE carbon and water footprints are −236 kgCO₂eq/t and 268 m³/t, respectively. AF is the second-best economic scenario after metal RE as it generates up to $122.6/t in profit. AF production scores are second highest within the social BL after plastic PY;however, the supporting legislation sub-indicator is low. The ASLM support policy that assigns 66% to the environmental BL and 16.7% to the economic and social BL to achieve carbon neutrality within the MSW sector. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Economic assessment Kuwait Life cycle assessment (LCA) Mathematical modeling Municipal solid waste (MSW) Sensitivity Social assessment

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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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A Study on Customer Utility Based on Differences in Stage Configuration and Ticket Sales Methods

A Study on Customer Utility Based on Differences in Stage Co...

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2024 IEEE International Conference on Industrial engineering and engineering management, IEEM 2024

作者： Mototaka, Katsuki Hasuike, Takashi Department of Industrial and Management Systems Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798350386097

Stage configuration and ticket sales methods are crucial elements that influence the customer experience of live entertainment. This study uses social simulations considering both stage configuration and ticket sales methods for live entertainment and assesses their impact on customer utility. Five stage configurations are examined (rectangular, convex, two-stage, three-stage, and circular configurations), with each seat’s value defined and calculated as the seat utility value. Based on these values, simulations are conducted for three ticket sales methods (uniform pricing, multi-seat pricing, and auction sales). The results show that the circular stage configuration is the optimal layout for maximizing customer utility across all three sales methods, while the frequently used rectangular stage configuration has the lowest provided value. Additionally, the combination of the convex stage configuration with ticket sales considering agents’ winning history and the two-stage configuration with auction sales are both effective and suitable. © 2024 IEEE.

关键词： Sales

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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Tube-based coalitional model predictive control for tracking 63

Tube-based coalitional model predictive control for tracking

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sanchez-Amores, A. Ferramosca, A. Maestre, J.M. Camacho, E.F. University of Seville Department of Systems and Automation Engineering Spain University of Bergamo Information and Production Engineering Department of Management Italy

ISBN: (纸本)9798350316339

This work explores a coalitional control approach for input-coupled multi-agent systems to robustly track changing setpoints. In this partially cooperative framework, agents can share a public portion of their input with neighboring agents while keeping the remainder private. Negotiations over the bounds defining the public and private input sets can be triggered on demand. Additionally, coupling disturbances are addressed using a tube-based approach, guiding the system towards desired target points. The effectiveness of the proposed approach is demonstrated through its application to a simulated eight-input coupled tank benchmark. © 2024 IEEE.

关键词： Multi agent systems

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Factorial design analytics on effects of material parameter uncertainties in multiphysics modeling of additive manufacturing

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npj Computational Materials 2023年第1期9卷 1848-1865页

作者： Amanda Giam Fan Chen Jiaxiang Cai Wentao Yan Integrative Sciences and Engineering Programme NUS Graduate SchoolNational University of SingaporeSingapore 119077Singapore Department of Mechanical Engineering National University of SingaporeSingapore 117575Singapore Department of Industrial Systems Engineering and Management National University of SingaporeSingapore 117576Singapore

A bottleneck in Laser Powder Bed Fusion(L-PBF)metal additive manufacturing(AM)is the quality inconsistency of its *** address this issue without costly experimentation,computational multi-physics modeling has been used,but the effectiveness is limited by parameter uncertainties and their *** propose a full factorial design and variable selection approach for the analytics of main and interaction effects arising from material parameter uncertainties in multi-physics *** is collected from high-fidelity thermal-fluid simulations based on a 2-level full factorial design for 5 selected material *** physical phenomena of the L-PBF process are analyzed to extract physics-based domain knowledge,which are used to establish a validation checkpoint for our *** data visualization with half-normal probability plots,interaction plots and standard deviation plots,is used to assess if the checkpoint is being *** then apply the combination of best subset selection and the LASSO method on multiple linear regression models for comprehensive variable *** yield statistically and phyiscally validated findings with practical implications,emphasizing the importance of parameter interactions under uncertainty,and their relation to the underlying physics of L-PBF.

关键词： parameter additive analytic

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Simulation of Discovering Market Opportunities Using AI Methods 25

Simulation of Discovering Market Opportunities Using AI Meth...

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25th European Conference on Knowledge management, ECKM 2024

作者： Trzcielinski, Stefan Pawlowski, Grzegorz Department od Management Systems Faculty of Engineering Management Poznan University of Technology Poznan Poland

ISBN: (纸本)9781917204101

Due to the strong turbulence that exists in the business environment, the view is sometimes articulated that the time horizon of decisions is no longer a hallmark of strategic management. Opposing views argue that a turbulent environment changes the trajectory of reaching future objectives determined by strategic decisions. Such trajectories require the company to be agile, which is manifested by seizing opportunities. Opportunities arise because the environment is volatile. An example of market opportunity is the unsatisfied demand for products. Buyers are driven by different motives when choosing between products that are alternative to them. From the producer's point of view, the purchase is therefore random. As a consequence, the alignment of the offered product portfolio with the actual purchasing decisions of customers is undetermined. In this article, we address the problem of determining the product portfolio structure expected by customers, i.e. one that is consistent with their future purchasing decisions. We hypothesise that this problem can be represented by a model of flows between alternative resources. Problems of this type are solved using Markov Chain Monte Carlo (MCMC) methods. In order to discover the expected demand (opportunity), we simulated the flow model we developed using the Metropolis-Hastings algorithm, which is a special case of the MCMC method used in AI. Due to operating on numerical data, such simulations fall under quantitative research. In the article, we present a model of customer flows between alternative products and then the simulation result, which is the expected value of these flows (stationary point). We convert this value into the structure of the product portfolio using a model of customer purchasing decisions that we have developed. The obtained results confirm the effectiveness of our method and have significant practical value, especially for SMEs. It also enables the assessment of the company's product strategy based

关键词： Markov chains

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A Machine Learning Approach to Detect Lung Nodules Using Reinforcement Learning Based on Imbalanced Classification

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SN Computer Science 2024年第4期5卷 370页

作者： Rezaei, Seyed Reza Ahmadi, Abbas Department of Industrial Engineering and Management Systems Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

Lung cancer is one of the fatal diseases affecting millions of people globally. Importantly, accuracy and rapid lung nodules detection on CT images are significant challenges. Doctors and radiologists' manual detection of lung nodules have low efficiency due to the variety of size, shape, and nodule’s location. Approaches based on machine learning play a fundamental and important role in the integration and analysis of these large and complex data sets. Considering the different viewpoints of learning methods and diverse lung cancer data, machine learning is an attractive and interesting method for researchers. This paper proposes a novel machine-learning approach to detect lung nodules using reinforcement learning based on imbalanced classification. Due to many healthy data, the ANN classifier tends towards this class rather than the suspicious one. To avoid this problem, we apply reinforcement learning to learn ANN parameters. The results obtained from our proposed model on the LIDC-IDRI data set showed better results than state of the arts on standard performance metrics such as IOU and HD. Also, according to accuracy metrics proposed method have the highest value of 94.91, compared to the previous best methods. Compared with advanced approaches, experimental results on the LIDC dataset indicated that the proposed model could detect various nodules and achieve the highest accuracy at the lowest time. Due to the nature of imbalanced data on most cancers, the proposed method can be adapted to other cancers. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： CT images Imbalanced classification Lung cancer Machine learning approach Reinforcement learning

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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

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Computer Modeling in engineering & Sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of Management Science and Engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China Department of Industrial Engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing *** DFJSP research only considers machine constraints and ignores worker *** one critical factor of production,effective utilization of worker resources can increase ***,energy consumption is a growing concern due to the increasingly serious environmental ***,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this *** solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling *** further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are *** strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto *** effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 *** results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning

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