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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Martin, Xabier A. Panadero, Javier Juan, Angel A. Research Center on Production Management and Engineering Universitat Politècnica de València Plaza Ferrandiz-Salvador Alcoy03801 Spain Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Carrer de les Sitges Bellaterra08193 Spain

ISBN: (纸本)9783031782404

In recent years, management of multiple complex projects has become increasingly common among large-scale companies, posing significant challenges for company managers. These projects are often identified by numerous tasks that have precedence relations and share a set of limited resources. The main goal of project scheduling is to minimize the total time required to complete these projects. However, this requires careful consideration of resource allocation, careful scheduling choices, and exact sequencing to maximize efficiency while managing the complex interactions between task dependencies. A novel discrete-event heuristic is presented to solve this project scheduling problem, which is later extended into a probabilistic algorithm using biased-randomization techniques. In addition, an adaptive mechanism was introduced to tune the parameters of the algorithm for optimal scheduling in different problem instances. Computational results demonstrate the effectiveness of our approach, finding high-quality solutions for difficult problem sets in short computational times. This intuitive and efficient approach can empower company managers to efficiently improve operational efficiency, reduce project costs, and ultimately increase their business’s success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Scheduling algorithms

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Energy management of Electric Vehicles: AI-Driven Strategies for Smart Grid-Connected Charging Hubs 2nd

Energy Management of Electric Vehicles: AI-Driven Strategie...

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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Peyman, Mohammad Martin, Xabier A. Panadero, Javier Research Center on Production Management and Engineering Universitat Politècnica de València Plaza Ferrandiz-Salvador Alcoy03801 Spain Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Carrer de les Sitges Bellaterra08193 Spain

ISBN: (纸本)9783031782404

This paper presents an innovative approach to improving electric vehicle (EV) routing in smart cities by combining heuristics and discrete-event simulation, specifically addressing the team orienteering problem. Initially, a biased-randomized heuristic approach is used to discover the most efficient EV routes, but this does not take into account the limitation imposed by the interconnected nature of the power grid. While these routes appear efficient when assessed independently, a more comprehensive evaluation is performed using a simulation model that includes the complex network of interconnected charging stations. This crucial second phase evaluates the practicality of the heuristic solutions in real-world scenarios by investigating how node interconnection affects route efficiency and charging station service. The simulation results provide concrete insights into optimizing EV mobility, enabling route adaptability to the dynamic urban landscape. This approach efficiently bridges the gap between theoretical optimization and actual implementation, emphasizing the need for simulation in verifying and refining routing strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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Hybrid IT Project management Methodology Based on Best Practices of Project management and Critical Success Factors Influencing IT Project management 8th

Hybrid IT Project Management Methodology Based on Best Pract...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Hayashida-Marchinares, Augusto Herrera, Jose Rivera, Luis Rodriguez, Ciro Rodriguez Krupa, Miroslav Ph. D. Program of the Faculty of Systems Engineering and Informatics Universidad Nacional Mayor de San Marcos Lima Peru Laboratory of Mathematical Sciences Center of Sciences and Technologies Universidade Estadual Do Norte Fluminense-UENF Rio de Janeiro Brazil Department of Management Faculty of Business Administration Prague University of Economics and Business Prague Czech Republic

ISBN: (纸本)9789819738588

Information Technology (IT) project management is important for the growth of organizations. It involves planning and executing projects to achieve strategic objectives. This paper reviews IT project management methodologies and critical success factors to identify best practices for project success. This paper uses a literature review and analysis of the current state of the art of IT project management methodologies. A total of 39 articles were selected and reviewed. The results of the review include a comparison of the main IT project management methodologies and the critical success factors for project success. The conclusion of the paper is that Hybrid Project management Models are a promising approach for IT project management. This approach combines the foundations and best practices of traditional and agile project management methodologies, making it well-suited for the complex and dynamic nature of IT projects. Additionally, the paper identifies the following critical success factors for IT project success: Clear and well-defined project objectives, Effective communication and collaboration between stakeholders, Appropriate risk management and contingency planning, Strong leadership and project management skills and Adequate resources and support. By understanding and addressing these critical success factors, organizations can increase their chances of success in IT projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Project management

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An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels

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Computers in Biology and Medicine 2025年 189卷 109974-109974页

作者： Mohammed, Areej Alshraideh, Hussam Abu-Helalah, Munir Shamayleh, Abdulrahim Department of Industrial Engineering Engineering Systems Management Program American University of Sharjah SharjahP.O. Box 26666 United Arab Emirates Department of Industrial Engineering American University of Sharjah SharjahP.O. Box 26666 United Arab Emirates Industrial Engineering Department Jordan University of Science and Technology Irbid Jordan Department of Family and Community Medicine School of Medicine University of Jordan Public Health Institute Amman Jordan

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potential to enhance disease diagnosis through data-driven approaches relying on non-invasive techniques. However, no studies have explored the application of fully non-invasive methods for predicting thyroid-stimulating hormone (TSH) levels. Consequently, this study introduces a novel, fully non-invasive framework for predicting TSH levels by developing an innovative hybrid machine learning model that balances performance, complexity, and interpretability. Seven ML models were evaluated, and the best-performing models were integrated into a hybrid approach to balance performance, complexity, and interpretability. A dataset of 6190 instances from Jordan was used for model development. Four-dimensional non-invasive factors, including demographics, symptoms, family history, and newly engineered symptom scores, were incorporated into the model. The hybrid model achieved an R2 of 94.2 % and RMSE of 0.015, demonstrating superior predictive performance. Model interpretability was ensured using LIME and SHAP explainers, confirming aggregated symptom scores' critical role in enhancing prediction accuracy. A robust feature selection technique was implemented, reducing model complexity and enhancing performance. Among the top ten features for predicting TSH levels were hypothyroidism and hyperthyroidism symptom scores, family history, cold intolerance, itchy-dry skin, sweating, hand tremors, and palpitations. The model can be employed to develop cost-effective diagnostic tools for thyroid disorders. It also offers a robust framework that can be generalized to predict other biomarkers and applied in diverse contexts. © 2025 Elsevier Ltd

关键词： Prediction models

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Thévenin Network Approach for Modeling Mutual Coupling in Narrowband Uniform Linear Arrays

Thévenin Network Approach for Modeling Mutual Coupling in N...

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IEEE Latin American Symposium on Circuits and systems (LASCAS)

作者： José A. Apolinário Cláudio A. B. Saunders Filho Antonio L. L. Ramos Program of Electrical Engineering Military Institute of Engineering (IME) Rio de Janeiro Brazil Department of Science and Industry Systems (IRI) TNM University of South-Eastern Norway (USN) Kongsberg Norway

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper investigates mutual coupling in narrow-band uniform linear antenna arrays through Thévenin network modeling. After an in-depth analysis, we derive a Thévenin network representing a given mutual coupling matrix and develop a method to achieve an equivalent decoupled model that significantly reduces computational and memory requirements in practical applications.

关键词： Mutual coupling Analytical models Circuits and systems Array signal processing Computational modeling Memory management Linear antenna arrays Integrated circuit modeling Narrowband

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Image segmentation via two-step deep variational priors

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Pattern Recognition Letters 2025年 195卷 44-50页

作者： Tan, Lu Tai, Xue-Cheng Li, Ling Liu, Wan-Quan Chan, Raymond H. Hong, Dan-Feng School of Computer Science and Information Engineering Bengbu University Bengbu Anhui China Norwegian Research Center (NORCE) Bergen Norway School of Elec Eng Comp and Math Sci (EECMS) Curtin University Perth Australia School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Operations and Risk Management and School of Data Science Lingnan University Tuen Mun Hong Kong Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

This paper proposes an iterative deep variational approach for image segmentation in a fusion manner: it is not only able to realize selective segmentation, but can also alleviate the issue of parameter/initialization dependency. Moreover, it possesses a refinement process designed to handle challenging scenarios, such as images containing obscured, damaged, or absent objects, or those with complex backgrounds. Our proposed approach consists of two main procedures, i.e., selective segmentation and shape transformation. The first procedure works as a stem in a totally unsupervised way. A convolutional neural network (CNN) based architecture is properly incorporated into the selective weighting constrained variational segmentation model. The second procedure is to further refine the outputs. This part can be achieved in two ways: one direction is to establish a joint model with the semantic shape constraint. The other technical direction is to make the shape descriptor separated from the joint model and work as an individual unit. In the proposed approach, the minimization problem is transformed from iterative minimization for each variable to automatically minimizing the loss function by learning the generator network parameters. This also leads to a good inductive bias associated with classic variational methods. Extensive experiments have demonstrated the significant advantages. © 2025 Elsevier B.V.

关键词： Flexible module Integration approach Iterative deep variational priors

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Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes

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Journal of Computing and Information Science in engineering 2025年第4期25卷 041002页

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University 915 Partners Way Raleigh NC 27607 Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University 915 Partners Way Raleigh NC 27607

Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.

关键词： Failure mechanisms mixture (log)-location-scale distribution Failure Weibull distribution multiple failure modes Signals Service life (Equipment) prognostics data analytics Deep learning machine learning for engineering applications engineering systems and industry applications industrial Internet of things big data and analytics

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Disambiguation of Chinese Polyphones in an End-to-End Framework with Semantic Features Extracted by Pre-trained BERT

arXiv

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arXiv 2025年

作者： Dai, Dongyang Wu, Zhiyong Kang, Shiyin Wu, Xixin Jia, Jia Su, Dan Yu, Dong Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin N.T. Hong Kong Tencent AI Lab. Tencent Shenzhen China

Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this paper, we propose an end-to-end framework to predict the pronunciation of a polyphonic character, which accepts sentence containing polyphonic character as input in the form of Chinese character sequence without the necessity of any preprocessing. The proposed method consists of a pre-trained bidirectional encoder representations from Transformers (BERT) model and a neural network (NN) based classifier. The pre-trained BERT model extracts semantic features from a raw Chinese character sequence and the NN based classifier predicts the polyphonic character’s pronunciation according to BERT output. In out experiments, we implemented three classifiers, a fully-connected network based classifier, a long short-term memory (LSTM) network based classifier and a Transformer block based classifier. The experimental results compared with the baseline approach based on LSTM demonstrate that, the pre-trained model extracts effective semantic features, which greatly enhances the performance of polyphone disambiguation. In addition, we also explored the impact of contextual information on polyphone disambiguation. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Alignment-Free Estimation of Read to Genome Distances and Its Applications 29th

Alignment-Free Estimation of Read to Genome Distances and...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Şapcı, Ali Osman Berk Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States

ISBN: (纸本)9783031902512

Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matching references could further help downstream analyses such as taxonomic characterization or even placing the read on a reference phylogeny. Such distances can be computed using alignment. Since alignment becomes impractical for ultra-large reference databases, many use k-mer-based tools for downstream tasks, but these methods do not compute distances. We introduce krepp to compute the distance between a read and a genome using a maximum likelihood framework built around k-mers. We further introduce algorithms to place reads on a reference tree. Empirical evaluations show that krepp accurately estimates distances, scales well with large databases, and can place short reads coming from any part of the genome on a reference phylogeny. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Average nucleotide identity Metagenomics Phylogenetic placement

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Optimizing waste pre-collection model in Indonesian communities: comparing drop-off and door-to-door models through social cost–benefit analysis

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Journal of Material Cycles and Waste management 2025年 1-17页

作者： Miftahadi, Machmuddin Fitra Hibino, Kohei Rachman, Indriyani Matsumoto, Toru Graduate Program in Environmental System Graduate School of Environmental Engineering The University of Kitakyushu Kitakyushu808-0135 Japan Kitakyushu Urban Centre Institute for Global Environmental Strategies Kitakyushu805-0062 Japan Department of Natural Science Education School of Postgraduate Studies Universitas Pakuan Kota Bogor16143 Indonesia Research Center for Urban Energy Management Institute of Environmental Science and Technology The University of Kitakyushu Kitakyushu808-0135 Japan

Indonesia lacks an established waste pre-collection system resulting 90% waste ending up in open-dumping site. This study identifies a 35-m radius per drop-off points as the most optimal model. The City of South Tangerang, on 21 residential areas with a population of 63,157, being examine. Utilizing ArcGIS Pro and Python scripting, 12 scenarios were developed, model drop-off radii of 15–45 m and household limits of 4–32 per drop-off point, including combined models. Spatial clustering KD-Tree algorithm and Proximity-based Routing were employed to determine collection placements and walking distances. Heatmap and ANOVA tests evaluated conformity with residential characteristics. Model then organized into eight frequency schemes, utilizing motorcart and handcart systems with 4-day and 2-day pickup schedules for waste bank and in-house compost programs. This optimized model reduces collection time by up to 81% and costs by 50% compared to traditional door-to-door systems, achieving cost reductions of $30–$38 USD per ton and lowering CO₂ emissions. The discussion covers opportunity costs and occupational risk. This approach supports Indonesia’s waste reduction targets and offers policy alignment for urban waste management. © The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature 2025.

关键词： Python

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