IF 4.5

Array Pub Date : 2025-07-19 DOI: 10.1016/j.array.2025.100450

Jinlong Li , Guojie Ma , Weihong Yang , Ruonan Li , Hongye Wang , Zhaoquan Gu

{"title":"FedDDPG: A reinforcement learning method for federated learning-based vehicle trajectory prediction","authors":"Jinlong Li , Guojie Ma , Weihong Yang , Ruonan Li , Hongye Wang , Zhaoquan Gu","doi":"10.1016/j.array.2025.100450","DOIUrl":"10.1016/j.array.2025.100450","url":null,"abstract":"<div><div>Vehicle Trajectory Prediction (VTP) plays of critical interest in Internet of Vehicles (IoV) as it greatly benefits motion planning and accident prevention for intelligent transportation. Despite its importance, VTP still faces substantial challenges, particularly in collecting distributed data and protecting trajectory privacy. Federated Learning (FL) emerges as a promising approach to address these problems. However, trajectory data collected from roadside units often contains varying levels of noise, which poses unique challenges for traditional FL methods. To address these challenges, this paper proposes a personalized optimization solution called FedDDPG (Federated Learning with Deep Deterministic Policy Gradient) for VTP with FL paradigm. Specifically, FedDDPG exploits the interactive and self-learning characteristics of reinforcement learning to generate optimized weights through agent-based learning during the FL process. By adapting highly noisy trajectory data, the FedDDPG effectively enhances the robustness and personalization of trajectory prediction. Experimental results demonstrate that our FedDDPG significantly improves prediction accuracy, convergence speed, and fairness for VTP under noisy conditions, while maintaining computational and communication overhead at a relatively low level. These findings highlight FedDDPG as a practical and efficient solution for privacy-preserving and distributed trajectory prediction in IoV applications.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100450"},"PeriodicalIF":4.5,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766642","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

Vehicle detection and recognition approach in smart surveillance system: A comparative analysis 智能监控系统中车辆检测与识别方法的比较分析

IF 4.5

Array Pub Date : 2025-07-19 DOI: 10.1016/j.array.2025.100473

Stephanie Ness

{"title":"Vehicle detection and recognition approach in smart surveillance system: A comparative analysis","authors":"Stephanie Ness","doi":"10.1016/j.array.2025.100473","DOIUrl":"10.1016/j.array.2025.100473","url":null,"abstract":"<div><div>Vehicle detection and recognition are one of the main research areas of computer vision and image processing. In recent years, the recognition of vehicles from video clips have been a critical component of intelligent transportation systems (IRS's). It is used for the purpose of detecting and monitoring vehicles, capturing their violations, and regulating traffic. This paper addresses the key issue of vehicle detection and recognition, for these two complex datasets, VRiV and UCSD are considered to segregate between vehicle and non-vehicle objects using video analysis. For data analysis we apply basic preprocessing, which includes frame conversion, background subtraction, noise reduction, and resizing. After that, we implement region-of-interest (ROI) extraction and change detection techniques to optimize the ROI. Next step is extraction of main attributes, such as motion direction and motion angle and apply data normalization to improve the equity of the model training. The study assess the Long Short-Term Memory (LSTM) model's efficacy on the data using benchmark metrics such as confusion matrix, precision, recall, accuracy, and F1-score. The proposed system attain an accuracy of 81 % on the VRiV dataset and 83 % on the UCSD datasets which shows the robust performance in terms of recognition rate.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100473"},"PeriodicalIF":4.5,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771395","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 framework for post-prognosis decision-making utilizing deep reinforcement learning considering imperfect maintenance decisions and Value of Information 考虑不完善维护决策和信息价值的深度强化学习后预后决策框架

IF 2.3

Array Pub Date : 2025-07-19 DOI: 10.1016/j.array.2025.100454

P. Komninos, D. Zarouchas

{"title":"A framework for post-prognosis decision-making utilizing deep reinforcement learning considering imperfect maintenance decisions and Value of Information","authors":"P. Komninos, D. Zarouchas","doi":"10.1016/j.array.2025.100454","DOIUrl":"10.1016/j.array.2025.100454","url":null,"abstract":"<div><div>The digitalization era has introduced an abundance of data that can be harnessed to monitor and predict the health of structures. This paper presents a comprehensive framework for post-prognosis decision-making that utilizes deep reinforcement learning (DRL) to manage maintenance decisions on multi-component systems subject to imperfect repairs. The proposed framework integrates raw sensory data acquisition, feature extraction, prognostics, imperfect repair modeling, and decision-making. This integration considers all these tasks independent, promoting flexibility and paving the way for more advanced and adaptable maintenance solutions in real-world applications. The framework’s effectiveness is demonstrated through a case study involving tension-tension fatigue experiments on open-hole aluminum coupons representing multiple dependent components, where the ability to make stochastic RUL estimations and schedule maintenance actions is evaluated. The results demonstrate that the framework can effectively extend the lifecycle of the system while accommodating uncertainties in maintenance actions. This work utilizes the Value of Information to choose the optimal times to acquire new data, resulting in computational efficiency and significant resource savings. Finally, it emphasizes the importance of decomposing uncertainty into epistemic and aleatoric to convert the total uncertainty into decision probabilities over the chosen actions, ensuring reliability and enhancing the interpretability of the DRL model.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100454"},"PeriodicalIF":2.3,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678953","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

Taxonomy, challenges, and future directions for AI-driven industrial cooling systems 人工智能驱动的工业冷却系统的分类、挑战和未来方向

IF 2.3

Array Pub Date : 2025-07-17 DOI: 10.1016/j.array.2025.100448

Md Mohsin Kabir, Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed

{"title":"Taxonomy, challenges, and future directions for AI-driven industrial cooling systems","authors":"Md Mohsin Kabir, Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed","doi":"10.1016/j.array.2025.100448","DOIUrl":"10.1016/j.array.2025.100448","url":null,"abstract":"<div><div>The efficiency and reliability of industrial cooling systems are critical for sectors such as energy systems, electronics manufacturing, and data centers. Traditional cooling systems rely on reactive maintenance, leading to increased downtime, energy consumption, and operating costs. Recent advances in artificial intelligence (AI), including machine learning (ML), deep learning (DL), and physics-informed neural networks (PINNs), have enabled proactive fault diagnosis and predictive maintenance in industrial cooling systems, significantly reducing energy use and improving operational reliability. However, current AI applications face challenges, such as limited access to quality datasets, computational complexity, integration with legacy systems, and model scalability. This paper systematically addresses these gaps by providing a detailed taxonomy of AI-driven cooling system diagnostics, categorizing state-of-the-art methods, and identifying critical research challenges. Our main contribution is a structured taxonomy that integrates ML, DL, and PINNs, offering a clear framework for analyzing current practices and potential improvements. The paper highlights critical insights across 138 reviewed studies, emphasizing the transformative role of hybrid AI frameworks in diagnostics, including use cases in HVAC, data centers, and thermal imaging. Notably, the integration of ML, DL, and PINNs has been shown to improve fault detection accuracy, energy efficiency, and model interpretability, paving the way for scalable, real-time deployments.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100448"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686413","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

Cobb angle prediction for adolescent idiopathic scoliosis via an explainable machine learning model 通过可解释的机器学习模型预测青少年特发性脊柱侧凸的Cobb角

IF 2.3

Array Pub Date : 2025-07-17 DOI: 10.1016/j.array.2025.100455

Yu Ding , Bin Li , Xiaoyong Guo

{"title":"Cobb angle prediction for adolescent idiopathic scoliosis via an explainable machine learning model","authors":"Yu Ding , Bin Li , Xiaoyong Guo","doi":"10.1016/j.array.2025.100455","DOIUrl":"10.1016/j.array.2025.100455","url":null,"abstract":"<div><div>This study aims to build an accurate and interpretable machine learning model capable of adolescent idiopathic scoliosis prognostication. A tree-based gradient boosting machine is incorporated with a recently proposed Shapley-value-based explanation method-TreeExplainer. Anthropometric training data are collected from a public orthopedics clinic, and each instance is characterized by nine features with a prediction target. We adopt a transfer-learning strategy that takes advantage of the additive property of tree-based gradient boosting, allowing a gradient boosting machine regressor to be trained with limited labeled examples. Cross-validation estimation shows a satisfactory performance for predicting future spine curvature (Cobb angle). The root mean square error (<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>), the mean absolute percentage error (<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>), and the Pearson correlation coefficient are 3.69 ± 1.23, 2.81 ± 1.69, and 0.92 ± 0.01, respectively. Moreover, the overfitting has been largely removed, and the model may be generalized well to new patients. A well-trained model is taken as the input to the TreeExplainer. The output of the TreeExplainer provides us a richer understanding that demonstrates how a feature’s value impacts the model’s prediction for every instance. The patterns identified can substantially improve the human-artificial intelligence collaboration in the clinical management of patients with adolescent idiopathic scoliosis by preventing serious scoliosis progression and reducing healthcare costs.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100455"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679060","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

Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach 银行业贷款预测、递延利率和客户细分：一种计算智能方法

IF 2.3

Array Pub Date : 2025-07-17 DOI: 10.1016/j.array.2025.100460

Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

{"title":"Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach","authors":"Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah","doi":"10.1016/j.array.2025.100460","DOIUrl":"10.1016/j.array.2025.100460","url":null,"abstract":"<div><div>Accurate loan default prediction and customer segmentation are critical challenges in the banking industry. This study proposes a novel hybrid model integrating Multi-Layer Perceptron (MLP) neural networks with Self-Adaptive Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) frameworks. GA handles feature selection, while PSO optimizes MLP hyperparameters (e.g., learning rate, neurons, activation functions). The model dynamically enhances classification accuracy and resilience, particularly for imbalanced datasets in loan default prediction. Using real-world data from Sina Bank, the system outperforms Logistic Regression, Decision Trees, and Random Forests. The GA-PSO optimization process, which integrates both PSO and GA to optimize the MLP model's parameters, plays a crucial role in enhancing the accuracy and scalability of the system. Specifically, the GA-PSO-MLP model achieves a 15 % higher classification accuracy than Logistic Regression, a 12 % improvement over Decision Trees, and an 8 % gain over Random Forests. Additionally, false positive rates are reduced by 20 %, and mean squared error (MSE) is lowered by 18 %. The F1-score of the proposed model is 92.3 %, compared to 79.8 % (Logistic Regression), 81.5 % (Decision Trees), and 85.2 % (Random Forests), further highlighting its advantage in handling imbalanced datasets. Extensive numerical validation and sensitivity analysis further highlight the model's effectiveness in delivering actionable insights that enhance customer management strategies and mitigate financial risks. This research makes a substantial contribution to the application of machine learning in banking, facilitating more accurate data-driven decision-making and more robust risk management practices.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100460"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678951","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

The future of libraries: Integrating pepper and computer vision for smart assistance 图书馆的未来：整合胡椒和计算机视觉的智能辅助

IF 2.3

Array Pub Date : 2025-07-17 DOI: 10.1016/j.array.2025.100469

Claire Trinquet, Deepti Mishra , Akshara Pande

{"title":"The future of libraries: Integrating pepper and computer vision for smart assistance","authors":"Claire Trinquet, Deepti Mishra , Akshara Pande","doi":"10.1016/j.array.2025.100469","DOIUrl":"10.1016/j.array.2025.100469","url":null,"abstract":"<div><div>In recent decades, the utilization of social robots in our daily lives has increased, but they are different from robots designed for libraries. On the one hand, library robots cannot establish social interactions, while social robots lack the necessary sensors to identify books. The present study aims to integrate the social robot Pepper's camera with computer vision techniques to enable Pepper to read the titles of books in front of it. This involves two main steps. The first step is to detect objects, i.e. books, from the scene. Thereafter, the titles of the books need to be read from the previous step. To achieve the first objective, two object detection models, YOLOv4 and YOLOv9, were employed. To accomplish the second goal, three OCR models —EasyOCR, Pytesseract, and Keras-OCR — were used. The results indicate that with the YOLOv9 model, all books were detected, whereas with the YOLOv4 model, 94 % books were identified. The findings of the present study suggest that when the YOLOv4 model and YOLOv9 were applied, EasyOCR performed well at a distance of 50 cm with a resolution of 3. Although the results of the OCR do not match perfectly with the written text on the books, the error rate is quite low for recognition by humans and computers. Therefore, there is a need to employ more advanced object detection and OCR technologies in future work.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100469"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663694","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

Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making 使用集成学习进行严重程度分类和倾向预测，以家庭为基础的患者管理和适当的决策

IF 2.3

Array Pub Date : 2025-07-16 DOI: 10.1016/j.array.2025.100453

Amjad El Khatib , Chaima Ben Abdallah , Ahmed Nait Sidi Moh

{"title":"Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making","authors":"Amjad El Khatib , Chaima Ben Abdallah , Ahmed Nait Sidi Moh","doi":"10.1016/j.array.2025.100453","DOIUrl":"10.1016/j.array.2025.100453","url":null,"abstract":"<div><div>Home-based patient evaluation is crucial for determining whether patients require immediate transfer to emergency departments (ED) or can safely remain at home. Traditional methods relying on subjective clinical judgments can lead to inconsistencies, ED overcrowding, and increased healthcare costs. This study proposes a dual-phase support model integrating severity classification using the Emergency Severity Index and patient disposition prediction through a stacking ensemble learning (EL) approach. Our model combines Multilayer Perceptron (MLP), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) as base learners, with RF as the meta-learner. Additionally, we developed and compared a voting EL model using the same base learners. To further assess performance, we evaluated the individual models alongside deep learning approaches, including Long Short-Term Memory and Deep Convolutional Neural Networks. Model performance was assessed using a real-world dataset with accuracy and the area under the receiver operating characteristic curve (AUROC) as evaluation metrics. The proposed stacking ensemble model achieved 91.68% accuracy and an AUROC of 97.04%, outperforming individual models and the voting EL. Integrating severity classification with disposition prediction improved accuracy by 1.28%. The findings highlight the effectiveness of stacking ensemble learning in enhancing patient evaluation accuracy, optimizing healthcare resource allocation, and reducing unnecessary ED visits. Additionally, the combination of severity classification and disposition prediction contributed to improved model performance, demonstrating the advantage of integrating both tasks in a unified framework. Comparative analysis of different models provided valuable insights into performance tradeoffs, reinforcing the potential of ensemble learning for home-based patient assessment.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100453"},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678952","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

CLORG: A contrastive learning-based framework for morphological representation and classification of organoids CLORG：基于对比学习的类器官形态表征和分类框架

IF 2.3

Array Pub Date : 2025-07-15 DOI: 10.1016/j.array.2025.100446

Yafang Wei , Pengwei Hu , Xun Deng , Feng Tan , Thomas Herget , Mei Gao , Lun Hu , Xin Luo

{"title":"CLORG: A contrastive learning-based framework for morphological representation and classification of organoids","authors":"Yafang Wei , Pengwei Hu , Xun Deng , Feng Tan , Thomas Herget , Mei Gao , Lun Hu , Xin Luo","doi":"10.1016/j.array.2025.100446","DOIUrl":"10.1016/j.array.2025.100446","url":null,"abstract":"<div><div>Organoids are three-dimensional structures derived from stem cells or primary cells, widely used in disease research and regenerative medicine. However, the presence of significant noise and morphological heterogeneity in their bright-field images makes it challenging to distinguish between different categories of organoids. This study is the first to propose a deep learning framework, CLORG, based on supervised contrastive learning. By narrowing the distance between samples of the same class through contrastive learning and incorporating Fourier transform to enhance the representation of frequency-domain information, the framework efficiently performs multi-class classification of organoids. This, in turn, facilitates the analysis of organoid developmental trends and supports drug screening and evaluation. Experiments on colon and intestinal organoid datasets demonstrate that CLORG achieves accuracies of 91.68% and 86.93%, respectively, with improvements of 3.35% and 1.89% over baseline models. The findings validate the effectiveness of CLORG in organoid image multi-class classification tasks and highlight its significant implications for organoid analysis and research.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100446"},"PeriodicalIF":2.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655499","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

RelayGAN: Sequential knowledge propagation for sustainable multi-generation RelayGAN：可持续多代的顺序知识传播

IF 2.3

Array Pub Date : 2025-07-14 DOI: 10.1016/j.array.2025.100444

Namkyung Yoon, Hwangnam Kim

{"title":"RelayGAN: Sequential knowledge propagation for sustainable multi-generation","authors":"Namkyung Yoon, Hwangnam Kim","doi":"10.1016/j.array.2025.100444","DOIUrl":"10.1016/j.array.2025.100444","url":null,"abstract":"<div><div>With the development of artificial intelligence technology, the need for a large amount of high-quality learning data is increasing to be used in various fields. This paper proposes RelayGAN, a new generative model that integrates knowledge inherent in multiple energy data based on the Generative Adversarial Network(GAN) sequentially, similar to relay running. To evaluate the effectiveness of RelayGAN, we conducted extensive experiments using quantitative methods. We employ three statistical metrics, including the Pearson correlation coefficient, the Mann–Whitney U test, and the Kolmogorov–Smirnov test, to validate the quality of the generated data. This shows that RelayGAN improves the performance of conventional multitasking learning-based GAN under the same conditions. Through this, we demonstrate that RelayGAN consistently outperforms state-of-the-art generative models in terms of data quality and pattern preservation. Furthermore, we verify that RelayGAN leverages sequential knowledge transfer to reduce redundant learning processes in accordance with the principles of sustainable AI development, increasing computational efficiency and contributing to eco-friendly AI. Beyond energy data, RelayGAN is a promising approach for multi-source data generation in various edge intelligence applications, ultimately contributing to data-driven innovation.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100444"},"PeriodicalIF":2.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633154","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

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