IF 2.3

Array Pub Date : 2025-06-16 DOI: 10.1016/j.array.2025.100423

Lingfeng Yuan, Minghong Xie

{"title":"MHS-Net: A multi-scale heterogeneous synergistic network for single image deraining","authors":"Lingfeng Yuan, Minghong Xie","doi":"10.1016/j.array.2025.100423","DOIUrl":"10.1016/j.array.2025.100423","url":null,"abstract":"<div><div>Single-image rain removal plays a crucial role in improving downstream visual tasks under adverse weather conditions. However, existing methods often fail to effectively balance global–local feature interactions and adaptive feature fusion in complex rain-streak scenarios. To address these challenges, we propose a Multi-scale Heterogeneous Fusion Network (MHF-Net) that integrates three core innovations for enhanced rain removal. The first innovation is the Heterogeneous Synergistic Enhancement (HSE) module, which combines Vision Mamba and convolutional branches to jointly model long-range dependencies and restore fine-grained textures. The second is the Dynamic Perception Adaptive Fusion (DPAF) strategy, which utilizes learnable masks to spatially separate features, reducing fusion artifacts and improving color consistency. Lastly, the Hierarchical Multi-scale Integration Mechanism (HMIM) refines cross-scale features using a pyramid encoder–decoder architecture. On the Rain100L dataset, our approach achieves a notable PSNR improvement of 0.42 dB and elevates SSIM to 0.991, surpassing the state-of-the-art methods. For the more challenging Rain100H dataset, all evaluation metrics show consistent improvements. Visual and residual analyses confirm superior rain removal and detail preservation, while downstream applications, such as semantic segmentation, further demonstrate the practical benefits of the proposed method. Ablation studies validate the contribution of each module in enhancing the overall performance.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100423"},"PeriodicalIF":2.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471082","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

VDTformer: A transformer-based framework for accurate deformation risk detection in cable tunnels VDTformer：一种基于变压器的框架，用于电缆隧道的精确变形风险检测

IF 2.3

Array Pub Date : 2025-06-15 DOI: 10.1016/j.array.2025.100422

Ruipeng Liu , Jieyan Zhang, Pengfei Chen, Yunxun Liu, Wanlin Quan, Junliang Su

引用次数: 0

Utilizing machine learning for transformer relative aging rate prediction based on aging parameters 基于老化参数的变压器相对老化率预测

IF 2.3

Array Pub Date : 2025-06-14 DOI: 10.1016/j.array.2025.100433

Mohsen Hosseinkhanloo , Navid Taghizadegan Kalantari , Vahid Behjat , Sajad Najafi Ravadanegh

{"title":"Utilizing machine learning for transformer relative aging rate prediction based on aging parameters","authors":"Mohsen Hosseinkhanloo , Navid Taghizadegan Kalantari , Vahid Behjat , Sajad Najafi Ravadanegh","doi":"10.1016/j.array.2025.100433","DOIUrl":"10.1016/j.array.2025.100433","url":null,"abstract":"<div><div>This paper presents a novel quantitative approach utilizing machine learning (ML) techniques to predict the aging rate of power transformers based on key aging factors, including loading level, temperature, moisture, and oxygen. Traditional methods for calculating aging rates are limited to discrete values of aging factors, which restricts their applicability in real-world scenarios. Proposed method extends the calculation of transformer aging rate using ML by training data which is achieved by the discrete and limited values obtained in experimental works in laboratories. For this purpose, ML models including Gaussian Process Regression (GPR), Support Vector Machines (SVM), Neural Networks (NN), Fine Tree (FT), Linear Regression (LR), Kernel and Ensemble are utilized to expand aging rate calculation. Values of metrics indicate that GPR had the best accuracy (RMSE = 0.055, R<sup>2</sup> = 1, MSE = 0.003, MAE = 0.028 and MAPE = 7 %). On the contrary, LR had the worst accuracy considering the values RMSE = 3.462, R<sup>2</sup> = 0.57, MSE = 11.986, MAE = 2.36 and MAPE = 2196 %. Taking prediction speed into account, NN had the higher value of 5800 (obs/sec), while GPR had the value of 2600 (obs/sec). Moreover, training time was lower in FT and LR (30.3 s and 34.8 s respectively) compared to other models with higher accuracy.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100433"},"PeriodicalIF":2.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313577","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

Diffusion-based low-light image enhancement with Kolmogorov-Arnold Networks (KANs) 基于扩散的Kolmogorov-Arnold网络（KANs）弱光图像增强

IF 2.3

Array Pub Date : 2025-06-12 DOI: 10.1016/j.array.2025.100431

Chia-Hung Yeh , Cheng-Yue Liou

{"title":"Diffusion-based low-light image enhancement with Kolmogorov-Arnold Networks (KANs)","authors":"Chia-Hung Yeh , Cheng-Yue Liou","doi":"10.1016/j.array.2025.100431","DOIUrl":"10.1016/j.array.2025.100431","url":null,"abstract":"<div><div>Low-light image enhancement is a fundamental task in computer vision, playing a critical role in applications such as autonomous driving, surveillance, and aerial imaging. However, low-light images often suffer from severe noise, loss of detail, and poor contrast, which degrade visual quality and hinder downstream tasks. Traditional stable diffusion-based enhancement methods apply noise uniformly across the entire image during the denoising process, leading to unnecessary detail degradation in texture-rich areas. To address this limitation, we propose an adaptive noise modulation framework that integrates Kolmogorov-Arnold Networks (KANs) into the diffusion process. Unlike conventional approaches, our method leverages KANs to analyze local image structures and selectively control noise distribution, ensuring that critical details are preserved while effectively enhancing darker regions. By iteratively injecting and removing noise through a structure-aware diffusion mechanism, our model progressively refines image features, achieving stable and high-fidelity restoration. Extensive experiments on multiple low-light datasets demonstrate that our method achieves 20.31 dB PSNR and 0.137 LPIPS on the LOL-v2 dataset, outperforming state-of-the-art methods such as EnlightenGAN and PairLIE. Moreover, our model maintains high efficiency with only 0.08M parameters and 13.72G FLOPs, making it well-suited for real-world deployment.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100431"},"PeriodicalIF":2.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312575","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

Context-aware decision making in autonomous vehicles: Integrating social behavior modeling with large language models 自动驾驶汽车中的情境感知决策：将社会行为建模与大型语言模型集成

IF 2.3

Array Pub Date : 2025-06-11 DOI: 10.1016/j.array.2025.100420

Badri Raj Lamichhane, Aueaphum Aueawatthanaphisut, Gun Srijuntongsiri, Teerayut Horanont

{"title":"Context-aware decision making in autonomous vehicles: Integrating social behavior modeling with large language models","authors":"Badri Raj Lamichhane, Aueaphum Aueawatthanaphisut, Gun Srijuntongsiri, Teerayut Horanont","doi":"10.1016/j.array.2025.100420","DOIUrl":"10.1016/j.array.2025.100420","url":null,"abstract":"<div><div>Integrating context-aware decision-making in autonomous vehicles (AVs) is critical for advancing operational efficiency, safety, and user experience. However, existing frameworks struggle to incorporate social context into real-time navigation, relying on deterministic algorithms or reinforcement learning models that overlook implicit social norms and face challenges in translating LLM-derived reasoning into safety-compliant control policies. This paper investigates the application of social behavior modeling fused with large language models (LLMs) to establish a comprehensive framework for context-aware understanding and decision-making processes by AVs. Through understanding of the scene and the deployment of LLMs, this framework enables AVs to interpret and respond to complex social interactions and contextual cues, enhancing adaptability in dynamic environments. We propose concepts and approaches to foster context-aware and socially responsible decision-making processes, including test cases for validation to some level. The results demonstrate substantial improvements in decision accuracy adopting virtual simulation, providing a foundation for addressing complex ethical dilemmas and real-time decision-making challenges that AVs encounter in diverse and dynamic settings.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100420"},"PeriodicalIF":2.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279899","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

DaNet: Domain-adaptive white blood cell classification through synthetic augmentation and cross-domain feature alignment DaNet：基于合成增强和跨域特征对齐的域适应白细胞分类

IF 2.3

Array Pub Date : 2025-06-10 DOI: 10.1016/j.array.2025.100416

Wenpeng Gao , Liantao Lan , Xiaomao Fan

{"title":"DaNet: Domain-adaptive white blood cell classification through synthetic augmentation and cross-domain feature alignment","authors":"Wenpeng Gao , Liantao Lan , Xiaomao Fan","doi":"10.1016/j.array.2025.100416","DOIUrl":"10.1016/j.array.2025.100416","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Automated classification of white blood cells (WBCs) plays a vital role in improving clinical diagnostics and disease monitoring. However, current methods frequently face challenges with generalization, as they depend on training and testing data drawn from the same distribution. This limitation hinders their effectiveness in real-world clinical settings.</div></div><div><h3>Methods:</h3><div>We introduce DaNet, an innovative domain-adaptive method for classifying WBCs that leverages domain generalization techniques. DaNet comprises two key components: Balanced Multi-Domain Mixup (BMDM) and Data Distribution Alignment (DDA). BMDM serves as a data augmentation technique specifically designed to address the high similarity and class imbalance inherent in WBC datasets. By generating synthetic data that captures more distinctive and discriminative features, BMDM enhances the model’s ability to learn robust representations. DDA further aligns these features across different domains, enabling the model to learn domain-invariant characteristics.</div></div><div><h3>Novelty:</h3><div>The proposed BMDM facilitates a more continuous latent space for domain-invariant features across multiple source domains, which effectively alleviates the alignment challenges commonly encountered by DDA-based methods in WBC image classification tasks, particularly in the presence of subtle inter-class differences and class imbalance.</div></div><div><h3>Results:</h3><div>Extensive experiments demonstrate that DaNet performs reliably in WBCs classification tasks, particularly excelling in cross-domain generalization. The method shows solid performance across various blood cell classification tasks, indicating its effectiveness. Its innovative approach to data augmentation and domain alignment enhances the model’s robustness and generalizability.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100416"},"PeriodicalIF":2.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262657","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

Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis 基于自适应CBAM的注意力增强CNN-LSTM混合网络新冠肺炎诊断

IF 2.3

Array Pub Date : 2025-06-05 DOI: 10.1016/j.array.2025.100424

Fatin Nabilah Shaari , Aimi Salihah Abdul Nasir , Wan Azani Mustafa , Wan Aireene Wan Ahmad , Abdul Syafiq Abdull Sukor

{"title":"Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis","authors":"Fatin Nabilah Shaari , Aimi Salihah Abdul Nasir , Wan Azani Mustafa , Wan Aireene Wan Ahmad , Abdul Syafiq Abdull Sukor","doi":"10.1016/j.array.2025.100424","DOIUrl":"10.1016/j.array.2025.100424","url":null,"abstract":"<div><div>Accurate identification of COVID-19 still presents difficulties due to the limitations of RT-PCR testing, such as reduced sensitivity and restricted availability. Chest X-Ray (CXR) imaging modalities, combined with deep learning models, offer a non-invasive solution. However, baseline Convolutional Neural Network (CNN) commonly faced obstacles to fully capture the temporal dependencies present in sequential medical imaging data, limiting their diagnostic performance. To address this, we propose Dual-Attention CNN-LSTM, an innovative hybrid deep learning model designed to enhance COVID-19 detection from CXR images. This model synergizes CNN's spatial feature extraction capabilities with the sequential learning strengths of Long Short-Term Memory (LSTM), further enhanced by the proposed Self-Adaptive Convolutional Block Attention Module (SA-CBAM) and Multi-Head Attention (MHA). SA-CBAM enables CNN to selectively focus on critical lung abnormalities, while MHA empowers LSTM to capture temporal dependencies and dynamic variations in imaging sequences. By fusing these attention-optimized features, Dual-Attention CNN-LSTM delivers an unprecedented level of robustness in CXR classification. Additionally, this study introduces five pre-trained-LSTM models, leveraging transfer learning to enhance CXR pattern recognition and serving as comparative models for the proposed Dual-Attention CNN-LSTM. Our comprehensive evaluation across multiple baseline models for three-class classification (normal, pneumonia, COVID-19) demonstrates that Dual-Attention CNN-LSTM surpasses state-of-the-art performance, achieving a remarkable weighted accuracy of 99.97 %, with precision, recall, specificity, F1-score, and MCC all exceeding 99.95 %. These findings highlight the potential of our approach as a transformative tool for accurate and early disease diagnosis, ultimately improving clinical decision-making and patient outcomes.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100424"},"PeriodicalIF":2.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230852","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

Enhancing mental well-being: An artificial intelligence model for predicting mental disorders 增强心理健康：预测精神障碍的人工智能模型

IF 2.3

Array Pub Date : 2025-06-05 DOI: 10.1016/j.array.2025.100417

Jahanur Biswas , Md. Nahid Hasan , Md. Shakil Rahman Gazi , Md. Mahbubur Rahman

{"title":"Enhancing mental well-being: An artificial intelligence model for predicting mental disorders","authors":"Jahanur Biswas , Md. Nahid Hasan , Md. Shakil Rahman Gazi , Md. Mahbubur Rahman","doi":"10.1016/j.array.2025.100417","DOIUrl":"10.1016/j.array.2025.100417","url":null,"abstract":"<div><div>Pervasive mental health conditions like depression, stress, and anxiety significantly affect individual well-being. Early identification and understanding are critical to minimize their negative effects. This study explores how AI models can be leveraged for improved mental health assessment. We have introduced some machine learning classifiers along with a deep learning model. Among the applied ML classifiers, the ”Ensemble” approach aims to outperform individual models by harnessing their collective strengths. While data acquisition for mental health research can be challenging, we utilized a publicly available dataset from the Kaggle site, acknowledging potential limitations like data imbalances and missing values. This imbalanced dataset is balanced by the Random Oversampling model. In our study, we introduced a state-of-the-art approach to predicting mental conditions such as depression, anxiety, and stress. For every condition, we utilized three machine learning models: the K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), combining these models into an ensemble model where a Voting classifier is used in the ensemble model. We also applied the Artificial Neural network (ANN) as a deep learning model for each disorder. Though the ensemble model performed an excellent outcome, the ANN model found outstanding outcomes from all the models. The ANN model achieved the highest accuracy of 99.73% for depression, 99.89% for anxiety, and 99.39% for stress.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100417"},"PeriodicalIF":2.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262635","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

Developing a novel approach for passive damped LCL filter and controller parameter design using PSO algorithm in VSC-based islanded microgrids 在基于vsc的孤岛微电网中，提出了一种基于粒子群算法的被动阻尼LCL滤波器和控制器参数设计新方法

IF 2.3

Array Pub Date : 2025-05-31 DOI: 10.1016/j.array.2025.100414

Yared Bekele Beyene , Getachew Biru Worku , Lina Bertling Tjernberg

{"title":"Developing a novel approach for passive damped LCL filter and controller parameter design using PSO algorithm in VSC-based islanded microgrids","authors":"Yared Bekele Beyene , Getachew Biru Worku , Lina Bertling Tjernberg","doi":"10.1016/j.array.2025.100414","DOIUrl":"10.1016/j.array.2025.100414","url":null,"abstract":"<div><div>In this research, a new approach called Particle Swarm Optimization–Proportional–Integral (PSO-PI) is proposed for optimizing the gains of current and voltage controllers as well as the <span><math><mrow><mi>L</mi><mi>C</mi><mi>L</mi></mrow></math></span> filter parameters in voltage source converter (VSC)-based islanded microgrids. The control problem is framed as an optimization task, where PSO optimally tunes parameters. Unlike conventional offline methods, this study employs a simulation-based online optimization framework, integrating PSO within SIMULINK environment for dynamic, iterative adjustments, enhancing adaptability and efficiency. Well-founded mathematical models define parameter bounds, ensuring a unity ratio between converter-side and coupling inductances, and setting the switching-to-resonance frequency ratio by considering converter-side and output ripple currents. The objective function is formulated to improve the tracking performance of the outer voltage and inner current control loops with respect to their reference signals while minimizing total harmonic distortion (THD) and maintaining an optimal balance between filtering effectiveness and system performance. The PSO-PI approach achieves a more compact <span><math><mrow><mi>L</mi><mi>C</mi><mi>L</mi></mrow></math></span> filter while complying with IEEE-519 standards and outperforms the conventional method (CM). Simulations validate its effectiveness under various disturbances, including load changes, faults, and parameter variations, demonstrating improved damping and robustness in VSC-based islanded microgrids. Notably, improved transient response is achieved, with a 61.80% reduction in settling time and a 51.34% decrease in overshoot. Integrating PSO within the SIMULINK framework enables dynamic fine-tuning of VSC parameters through simulation-driven optimization, highlighting its potential as a robust microgrid control strategy.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100414"},"PeriodicalIF":2.3,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194458","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

Beyond raw data: AI-driven biosensor fusion for enhancing athletic performance 原始数据之外：人工智能驱动的生物传感器融合，以提高运动员的表现

IF 2.3

Array Pub Date : 2025-05-24 DOI: 10.1016/j.array.2025.100418

Peng Luo, Jun Song

{"title":"Beyond raw data: AI-driven biosensor fusion for enhancing athletic performance","authors":"Peng Luo, Jun Song","doi":"10.1016/j.array.2025.100418","DOIUrl":"10.1016/j.array.2025.100418","url":null,"abstract":"<div><div>We have all heard a lot about the potential of data enabled by artificial intelligence (AI) to improve performance. This progress has seen the steady advancements of wearable biosensors capable of generating live data and feedback on an athlete's physiological state, allowing for smarter training and enabling consistent performance improvement. However, wearable biosensors often struggle with noise and signal interference caused by various factors, including muscle movements, sweat, and environmental conditions. This study proposes the Smart Performance Analysis and Real-time Tracking Algorithm (SPARTA) for enhancing athletic performance using AI techniques. SPARTA leverages AI algorithms to analyze real-time physiological data - including heart rate, oxygen saturation, skin conductance, and cortisol levels - enabling dynamic adjustments to training loads and recovery protocols. Experimental evaluations using the Biosensor-Student Health Fitness Dataset (n = 500 input samples) demonstrated SPARTA’ s capability to achieve 91.34 % accuracy in SpO<sub>2</sub> monitoring, 88.72 % precision in skin conductance detection, 82.64 % correlation with laboratory assays for sweat electrolyte analysis, and 78.65 % accuracy in non-invasive cortisol level tracking. With more advances in artificial intelligence, wearable biosensors will greatly help boost athletic performance, further dominating the sports & fitness globe.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100418"},"PeriodicalIF":2.3,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189911","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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