Decision Analytics Journal最新文献

An analytics-based framework for optimizing resource allocation and preemptive scheduling in manufacturing 基于分析的制造业资源分配和优先调度优化框架

Decision Analytics Journal Pub Date : 2025-06-18 DOI: 10.1016/j.dajour.2025.100596

Urtzi Otamendi , Iñigo Martinez , Xabier Belaunzaran , Arkaitz Artetxe , Javier Franco , Alejandro Uribe , Igor G. Olaizola , Basilio Sierra

{"title":"An analytics-based framework for optimizing resource allocation and preemptive scheduling in manufacturing","authors":"Urtzi Otamendi , Iñigo Martinez , Xabier Belaunzaran , Arkaitz Artetxe , Javier Franco , Alejandro Uribe , Igor G. Olaizola , Basilio Sierra","doi":"10.1016/j.dajour.2025.100596","DOIUrl":"10.1016/j.dajour.2025.100596","url":null,"abstract":"<div><div>Production scheduling is critical in manufacturing operations, requiring the optimal assignment of limited resources. This paper introduces a novel generalization of the Unrelated Parallel Machine (UPM) problem, addressing key real-world complexities: sequence- and machine-dependent setup times, resource assignment constraints, and preemptive scheduling. These extensions, particularly workforce assignments where specific qualifications and availability schedules determine employee eligibility, represent a significant step forward in industrial scheduling research. A Mixed Integer Linear Programming (MILP) model and three constraint-specific variations were developed to evaluate performance and scalability and isolate preemption and resource constraints. Extensive computational experiments demonstrated a trade-off between model applicability and computational efficiency. The proposed model achieved realistic job distribution across machines but encountered scalability challenges due to the combinatorial complexity introduced by what we term dense eligibility matrices, representing a high proportion of potential employee-machine assignments. The preemption-only model optimized makespan effectively, while the resource-focused model provided more practical solutions at the cost of higher processing times. The baseline UPM with sequence-dependent setup times (UPMS) model exhibited computational efficiency but lacked applicability to dynamic industrial environments. This study highlights the impact of preemption and resource assignment on scheduling optimization and underscores the importance of sparsity reduction techniques to enhance scalability. By bridging gaps in workforce management and operational flexibility, the proposed framework provides a robust foundation for addressing complex industrial scheduling challenges.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100596"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust support vector machine approach for Raman data classification 拉曼数据分类的鲁棒支持向量机方法

Decision Analytics Journal Pub Date : 2025-06-18 DOI: 10.1016/j.dajour.2025.100595

Marco Piazza , Andrea Spinelli , Francesca Maggioni , Marzia Bedoni , Enza Messina

{"title":"A robust support vector machine approach for Raman data classification","authors":"Marco Piazza , Andrea Spinelli , Francesca Maggioni , Marzia Bedoni , Enza Messina","doi":"10.1016/j.dajour.2025.100595","DOIUrl":"10.1016/j.dajour.2025.100595","url":null,"abstract":"<div><div>Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, <em>Raman spectroscopy</em> analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra’s inherent complexity and variability make the manual analysis challenging, even for domain experts. For the same reason, the use of traditional <em>Statistical Learning</em> and <em>Machine Learning</em> techniques could not guarantee for accurate and reliable results. Machine learning models, combined with robust optimization techniques, offer the possibility to improve the classification accuracy and enhance the resilience of predictive models under data uncertainty. In this paper, we investigate the performance of a novel robust formulation for <em>Support Vector Machine</em> (SVM) in classifying COVID-19 samples obtained from Raman spectroscopy. Given the noisy and perturbed nature of biological samples, we protect the classification process against uncertainty through the application of robust optimization techniques. Specifically, we consider the robust counterparts of deterministic SVM formulations using bounded-by-norm uncertainty sets. We explore the cases of both linear and kernel-induced classifiers, addressing binary and multiclass classification tasks. The effectiveness of our approach is evaluated on real-world COVID-19 Raman saliva samples provided by Italian hospitals. We assess the performance of the proposed method by comparing the results of our numerical experiments with those of a state-of-the-art classifier, showing the potential of robust classifiers in handling uncertain Raman data.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100595"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid approach using deep clustering and Lagrangian relaxation for sustainable waste logistics 基于深度聚类和拉格朗日松弛的可持续废物物流混合方法

Decision Analytics Journal Pub Date : 2025-06-10 DOI: 10.1016/j.dajour.2025.100590

Teena Thomas , Chandrasekharan Rajendran , Hans Ziegler , Sumit Saxena

{"title":"A hybrid approach using deep clustering and Lagrangian relaxation for sustainable waste logistics","authors":"Teena Thomas , Chandrasekharan Rajendran , Hans Ziegler , Sumit Saxena","doi":"10.1016/j.dajour.2025.100590","DOIUrl":"10.1016/j.dajour.2025.100590","url":null,"abstract":"<div><div>Optimizing solid waste management (SWM) is essential for ensuring a sustainable and healthy environment in a city. This study considers a two-echelon solid waste logistics system (2E-SWLS) in a metropolitan city with a fleet of capacitated heterogeneous vehicles. The problem consists of waste collection sites, transfer stations acting as intermediate facilities and dumping yards. The objective is to identify the best locations for transfer stations and optimize the logistics system by minimizing total cost. The problem is formulated as a Mixed Integer Linear Programming (MILP) model. To address large-scale city network complexities, we propose a Cluster-Fix-Optimize Matheuristic (C-F-OM), as the MILP model fails to provide a solution within the given CPU time. This method involves a deep learning-based clustering of sites, determining the transfer station location within each cluster and optimizing the associated operational and logistic decisions while serving as a benchmark solution to the problem. Additionally, we introduce a Lagrangian Relaxation-Fix-Optimize Matheuristic (LR-F-OM) to determine a lower bound for 2E-SWLS. The effectiveness of this lower bound is compared with that of the conventional subgradient method. The upper bound derived from LR-F-OM outperforms the C-F-OM solution and promises significant savings of approximately 50%, when compared to the existing solution approaches in a case study in India by providing insights on facility and logistical configurations for improving the operational efficiency. The study also provides managerial insights on factors such as vehicle fleet heterogeneity, transfer station capacity, demand variations at waste collection sites, and vehicle operational costs on total cost.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"16 ","pages":"Article 100590"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A structured review of large language models in metaheuristic optimisation 元启发式优化中大型语言模型的结构化回顾

Decision Analytics Journal Pub Date : 2025-06-01 DOI: 10.1016/j.dajour.2025.100587

Reza Ghanbarzadeh , Seyedali Mirjalili

{"title":"A structured review of large language models in metaheuristic optimisation","authors":"Reza Ghanbarzadeh , Seyedali Mirjalili","doi":"10.1016/j.dajour.2025.100587","DOIUrl":"10.1016/j.dajour.2025.100587","url":null,"abstract":"<div><div>Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have emerged as powerful tools capable of enhancing, automating, and adapting various stages of the optimisation process. This systematic literature review investigates the evolving role of large language models in metaheuristic optimisation, with a focus on algorithm generation, parameter tuning, hybridisation, constraint handling, and multi-objective optimisation. Following PRISMA guidelines, 25 studies from nine major scientific databases were selected and analysed. Through thematic analysis, a novel role-based taxonomy was developed that categorises large language model contributions into four functional roles: Advisor, Refiner, Enhancer, and Innovator. The findings demonstrate that large language models support the automation of metaheuristic workflows, enable dynamic adaptation, and contribute to the creation of novel heuristic strategies. Despite these advantages, the review also identifies persistent limitations, including prompt sensitivity, computational overhead, and scalability challenges. These issues highlight the need for more robust evaluation frameworks and benchmarking practices. This review offers a comprehensive synthesis of the current landscape, highlights research gaps, and provides actionable insights for researchers and practitioners aiming to integrate large language models into advanced optimisation systems across domains such as engineering, logistics, and computational design.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100587"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel fractional order model for analyzing counterterrorism operations and mitigating extremism 一种分析反恐行动和减轻极端主义的新型分数阶模型

Decision Analytics Journal Pub Date : 2025-06-01 DOI: 10.1016/j.dajour.2025.100589

Mutaz Mohammad , Isa Abdullahi Baba , Evren Hincal , Fathalla A. Rihan

{"title":"A novel fractional order model for analyzing counterterrorism operations and mitigating extremism","authors":"Mutaz Mohammad , Isa Abdullahi Baba , Evren Hincal , Fathalla A. Rihan","doi":"10.1016/j.dajour.2025.100589","DOIUrl":"10.1016/j.dajour.2025.100589","url":null,"abstract":"<div><div>This study examines the profound impact of terrorism on individuals and society by developing a fractional-order mathematical model to analyze and enhance counterterrorism efforts. The model accounts for the persistent and complex nature of extremist behavior, particularly emphasizing the importance of preventing violent extremism before it escalates into terrorism. Real-world data on terrorist activities in Nigeria – specifically from the Boko Haram insurgency – was used to calibrate and validate the model, ensuring its relevance and accuracy. The model reveals that the basic reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) plays a decisive role in determining the long-term success of counterterrorism strategies. Numerical simulations show that terrorist activities decline when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo><</mo><mn>1</mn></mrow></math></span>, while they persist or escalate when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>></mo><mn>1</mn></mrow></math></span>. A comprehensive sensitivity analysis further identifies the most influential parameters affecting <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, providing actionable insights into where interventions can be most effective. Parameters related to recruitment, ideological spread, and counter-radicalization efforts were found to have the highest impact. The study concludes by offering strategic recommendations informed by the simulation and sensitivity results, aiming to support the design of more targeted and sustainable counterterrorism policies.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100589"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A shadow-based framework for label noise detection and data quality enhancement 基于阴影的标签噪声检测和数据质量增强框架

Decision Analytics Journal Pub Date : 2025-06-01 DOI: 10.1016/j.dajour.2025.100588

Wanwan Zheng

{"title":"A shadow-based framework for label noise detection and data quality enhancement","authors":"Wanwan Zheng","doi":"10.1016/j.dajour.2025.100588","DOIUrl":"10.1016/j.dajour.2025.100588","url":null,"abstract":"<div><div>Machine learning algorithms are typically evaluated using benchmark datasets under the assumption that these datasets are clean. However, recent studies have revealed the presence of label noise in many benchmark datasets, indicating a biased evaluation to date. Confident learning (CL), an emerging method for noise detection, has been regarded a higher priority than the development of new learning algorithms. Although CL is promoted as applicable to various types of data, existing research has largely concentrated on its application to large-scale datasets. Given that many domains handle datasets of more modest size, this study proposed a shadow-based framework for label noise detection called ShadowN, and conducted a comprehensive comparison with CL using six smaller datasets. Four key aspects were examined: the number of detected noises, the distribution of assigned noise scores, the improvement in classification accuracy, and the accuracy of noise detection with artificial noise injection. The results indicated that ShadowN achieved the highest overall classification accuracy and demonstrated superior precision and F-score across all noise levels. While the current implementation of ShadowN is limited to binary classification, our findings underscore its practical value and demonstrate its potential for enhancing data quality in real-world machine learning workflows.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100588"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytics-based framework for military technology adoption and combat strategy 军事技术采用和作战战略的基于分析的框架

Decision Analytics Journal Pub Date : 2025-06-01 DOI: 10.1016/j.dajour.2025.100586

Peter C. Schuur

{"title":"An analytics-based framework for military technology adoption and combat strategy","authors":"Peter C. Schuur","doi":"10.1016/j.dajour.2025.100586","DOIUrl":"10.1016/j.dajour.2025.100586","url":null,"abstract":"<div><div>Introducing new technology into a military force during an ongoing conflict presents significant challenges, extending beyond logistics to include the uncertainty of how effectively soldiers can adapt to and deploy the new capabilities. This study examines how the mastery of new technology shapes the dynamics of warfare and informs effective decision-making strategies. Our methodology is grounded in a model-based approach. We begin with Lanchester’s square law model, which provides a framework for analyzing modern combat scenarios involving long-range weapons. To extend this framework, we incorporate the Bass diffusion model, enabling the simultaneous examination of the progression of the conflict and the learning curve associated with the new technology. Subsequently, we utilize insights from studying the adoption of a single technology to analyze the introduction of multiple new technologies provided by different suppliers. In this context, considerations of technological effectiveness and supplier reliability become critical in making balanced procurement decisions. To support this process, we propose a Market Share Attraction model to guide decision-making effectively.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100586"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A resource-constrained optimization model for parallel machine scheduling with constraint programming 基于约束规划的并行机器调度资源约束优化模型

Decision Analytics Journal Pub Date : 2025-05-22 DOI: 10.1016/j.dajour.2025.100585

Mohamed Amine Abdeljaouad , Zied Bahroun , Nour El Houda Saadani , Rahaf Sheiko , Karam Al-Assaf

{"title":"A resource-constrained optimization model for parallel machine scheduling with constraint programming","authors":"Mohamed Amine Abdeljaouad , Zied Bahroun , Nour El Houda Saadani , Rahaf Sheiko , Karam Al-Assaf","doi":"10.1016/j.dajour.2025.100585","DOIUrl":"10.1016/j.dajour.2025.100585","url":null,"abstract":"<div><div>This study investigates an NP-hard parallel machine scheduling problem, a critical challenge in manufacturing, healthcare, and logistics industries where efficient resource allocation is essential. The issue involves scheduling operations where each task requires an additional resource, with multiple resource types available, each limited to a single copy. The objective is to minimize the makespan, which is defined as the total completion time of all tasks. A novel constraint programming model is designed to solve the problem to optimality. The proposed model is benchmarked against two existing linear mathematical formulations, achieving up to 95% faster computational times while solving instances with up to 20 machines, 40 resources, and 90 operations per resource—scenarios the linear models failed to handle within reasonable computational limits. Furthermore, the model exhibits excellent scalability, effectively solving more extensive and complex instances. These findings underscore the potential of constraint programming as a powerful tool for tackling complex scheduling problems in resource-constrained environments, with applications in industries where resource-sharing is critical.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100585"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimization-based approach to fleet reliability and allocation in open-pit mining 基于优化的露天矿机队可靠性与配置方法

Decision Analytics Journal Pub Date : 2025-05-08 DOI: 10.1016/j.dajour.2025.100583

Sena Senses, Mustafa Kumral

{"title":"An optimization-based approach to fleet reliability and allocation in open-pit mining","authors":"Sena Senses, Mustafa Kumral","doi":"10.1016/j.dajour.2025.100583","DOIUrl":"10.1016/j.dajour.2025.100583","url":null,"abstract":"<div><div>Open-pit mining operations depend heavily on the availability and reliability of complex equipment fleets, where the failure of one component can disrupt overall productivity. This study proposes two complementary optimization models to enhance fleet allocation and reliability in the mining industry. The first model — a Mixed-Integer Nonlinear Programming (MINLP) formulation — supports short-term planning by maximizing the minimum reliability of heterogeneous truck–shovel sub-systems under production and utilization constraints. The second model focuses on medium-term reliability enhancement, allocating targeted reliability improvements to critical components based on equipment degradation and operational history. Both models are validated using real operational data from an open pit mine, which includes failure and repair time datasets from 17 trucks and 2 hydraulic shovels. Reliability curves are estimated using the power law model under a Non-Homogeneous Poisson Process (NHPP) assumption. Results show that optimal allocation can achieve production targets of 4,489 tons per hour with a minimum sub-system reliability of 0.7. Furthermore, reliability improvements tailored to engine-hour-based cost functions can effectively restore operational performance over a one-week horizon. This research bridges the gap between fleet allocation and reliability allocation and introduces a novel framework for integrating reliability into equipment planning. The models provide actionable insights for mining operations to optimize equipment deployment, reduce failure risk, and support more resilient and cost-effective planning.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100583"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of sentiment analytics in banking headlines 对银行业头条新闻中情绪分析的系统回顾

Decision Analytics Journal Pub Date : 2025-05-07 DOI: 10.1016/j.dajour.2025.100584

Muhunthan Jayanthakumaran , Nagesh Shukla , Biswajeet Pradhan , Ghassan Beydoun

{"title":"A systematic review of sentiment analytics in banking headlines","authors":"Muhunthan Jayanthakumaran , Nagesh Shukla , Biswajeet Pradhan , Ghassan Beydoun","doi":"10.1016/j.dajour.2025.100584","DOIUrl":"10.1016/j.dajour.2025.100584","url":null,"abstract":"<div><div>This systematic review investigates sentiment analysis of news headlines in the banking sector, a field susceptible to public sentiment, as demonstrated by phenomena like bank runs leading to rapid deposit withdrawals. We trace the evolution of analytic methods from traditional machine learning to advanced deep learning models, notably Bidirectional Encoder Representations from Transformer (BERT) and Generative Pre-trained Transformer (GPT). Our study highlights their applications including headline generation, sentiment measurement, fake news detection, and analysis of political bias. Despite significant advancements, we uncover research gaps, such as the ineffective use of these methodologies in banking analysis, the underuse of GPT, and a focus on performance rather than practical application. Looking ahead, we note the increasing significance of Large Language Model (LLM), the untapped potential of headline analysis in banking, and the growing interest in this area spurred by rapid technological advancements. Our findings emphasise the pivotal role of sentiment analysis in deciphering market trends and improving decision making in finance, underscoring its strategic importance in the banking industry.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100584"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0