Applied Soft Computing最新文献

{"title":"Semantic enhancement in graph contrastive learning by combining topology and features","authors":"Bowen Mao ,&nbsp;Wenjun Wang ,&nbsp;Wei Yu ,&nbsp;Qiang Tian ,&nbsp;Wenkai Wang ,&nbsp;Jiye Liu ,&nbsp;Han Bao ,&nbsp;Nannan Wu","doi":"10.1016/j.asoc.2026.114773","DOIUrl":"10.1016/j.asoc.2026.114773","url":null,"abstract":"<div><div>Graph contrastive learning (GCL), which learns node representations by contrasting two augmented graphs, has attracted significant research interest. The core of GCL is to obtain high-quality positive and negative samples as contrastive pairs, thereby learning the latent semantic information of the original graph. Existing GCL methods typically use random augmentation strategies to generate augmented views. In existing methods, the embeddings of the same node across two augmented graph views are treated as positive pairs, while embeddings of different nodes are regarded as negative pairs. Nevertheless, random graph augmentations may inadvertently distort the inherent structural and semantic properties of the graph, thereby inducing semantic drift for the same node between the two augmented views. Additionally, two different nodes of the same class may have similar semantics after augmentation. Therefore, we argue that the previous method of defining positive and negative pairs faces a bottleneck due to potential inauthenticity between the pairs. To address this issue, we propose a method called Semantic-Aware Graph Contrastive Learning with Positive Sampling (GCLPS), which mines latent semantic information in the graph, incorporating truly semantically similar node pairs as positive pairs. This approach reduces the impact of false positive pairs and helps the GNN learn the semantic relationships between nodes. Specifically, in the topology aspect, we select one-hop neighbors of each node in the original graph as positive pairs. In the feature aspect, we represent nodes on augmented views using subgraph-level graph kernels and choose positive pairs based on their similarity. Finally, we combine these two aspects to form the final set of positive pairs for contrastive loss calculation. Extensive experiments on six benchmark datasets demonstrate that GCLPS achieves superior performance in various downstream tasks compared to state-of-the-art methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114773"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid NSGA-II and delayed local search approach for home healthcare scheduling and routing optimization","authors":"Radhia Zaghdoud ,&nbsp;Issam Zidi ,&nbsp;Olfa Ben Rhaiem ,&nbsp;Salim El Khediri ,&nbsp;Khaled Mesghouni","doi":"10.1016/j.asoc.2026.114617","DOIUrl":"10.1016/j.asoc.2026.114617","url":null,"abstract":"<div><div>The home healthcare sector has emerged as a vital component of modern healthcare systems, addressing the needs of aging populations and individuals with disabilities. However, efficiently managing caregiver schedules and routing while balancing service time windows, skill compatibility, and operational costs remains a critical challenge. This study tackles a multi-objective Home Healthcare Scheduling and Routing Problem (HHCSRP) by optimizing three competing objectives: minimizing total travel time, ensuring equitable workload distribution among caregivers, and reducing waiting time between patient visits. We propose a novel two-stage hybrid algorithm that strategically integrates the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with a tailored local search method. In the first stage, NSGA-II generates a diverse initial population over multiple generations, while the second stage embeds local search at mid-generations to refine solutions without premature convergence. Computational experiments using adapted Solomon’s Vehicle Routing Problem with Time Windows (VRPTW) benchmarks demonstrate the effectiveness of the algorithm. Our approach achieves superior hypervolume (0.57–0.86) and Pareto front diversity (<span><math><mo>|</mo><mi>F</mi><mi>P</mi><mo>|</mo></math></span> = 94.27 on average), outperforming existing methods in balancing efficiency, fairness, and scalability. The results highlight the robustness of the method in handling large-scale instances (100 patients), offering a scalable tool for real-world home healthcare logistics. This work advances multi-objective optimization in healthcare operations, providing actionable insights for administrators to harmonize patient satisfaction and operational efficiency.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114617"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circular intuitionistic fuzzy Dombi Bonferroni mean aggregation operators and MEREC-RAFSI approach for optimizing vehicle routing software","authors":"Freeha Qamar ,&nbsp;Iqra Zareef ,&nbsp;Muhammad Riaz ,&nbsp;Muhammad Aslam ,&nbsp;Vladimir Simic ,&nbsp;Dragan Pamucar","doi":"10.1016/j.asoc.2026.114642","DOIUrl":"10.1016/j.asoc.2026.114642","url":null,"abstract":"<div><div>For the purpose of optimizing their routes, last mile delivery (LMD) organizations use vehicle routing software (VRS). The topic of VRS selection is of the utmost significance for corporations that deal with managing deliveries at the last mile. This study presents an innovative VRS selection methodology specifically designed for LMD businesses. We regulate the issue within the confines of multi-criteria decision-making (MCDM). Criteria for assessment are based on solid literature, mathematical formulation, and professional judgments. The utilization of circular intuitionistic fuzzy set (CIFS) for this model offers a more adaptable and evocative method for expressing unclear and conflicting data. A novel operator termed as a CIFDBM operator is presented to serve as an aggregation operator to boost the effectiveness of aggregation, influenced by the fundamental Bonferroni mean (BM) operator and centered on Dombi t-norm and t-conorm. Our research sets out a novel hybrid structure that integrates CIFS-based decision-making along with MEREC (method based on the removal effects of criteria) as a weighting technique and RAFSI (ranking of alternatives through functional mapping of criterion subintervals into a single interval) as a ranking approach. A robust MCDM hybrid approach named CIFS-MEREC-RAFSI is designed, which provides a reliable, competent, and quality decision for VRS selection problems containing inconsistent, uncertain, and vague data. The system outperforms state-of-the-art CIF-based MCDM approaches by 17%–22% in terms of ranking stability, resulting in more consistent and dependable rankings for all options.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114642"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method for large-scale group decision-making with application to e-commerce software system evaluation","authors":"Chuan Yue","doi":"10.1016/j.asoc.2026.114595","DOIUrl":"10.1016/j.asoc.2026.114595","url":null,"abstract":"<div><h3>BACKGROUND</h3><div>Large-scale group decision-making (LSGDM) in big data environments faces challenges in robust data center construction, objective expert weighting, and efficient information fusion.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to develop a novel LSGDM framework integrating a Golden Ratio-based data center and an inversion-based data quality metric to improve ranking stability and decision reliability.</div></div><div><h3>METHODS</h3><div>A GR-based data center was introduced to replace conventional mean/median centers, alongside an inversion-number-driven quality metric for expert weighting and a scalable aggregation technique for converting crisp data into intuitionistic fuzzy matrices. The framework was validated through dynamic experiments and sensitivity analysis.</div></div><div><h3>RESULTS</h3><div>The GR-based center outperformed mean/median centers in 95% of test scenarios. The inversion-based method achieved perfect ranking consistency (Kendall’s <span><math><mi>τ</mi><mo>=</mo><mn>1</mn></math></span>), showing a 50% improvement over entropy-based methods (<span><math><mi>τ</mi><mo>=</mo><mn>2</mn><mrow><mo>/</mo></mrow><mn>3</mn></math></span>), and maintained 100% ranking stability under parameter variations—20 times higher than entropy-based approaches.</div></div><div><h3>CONCLUSION</h3><div>The proposed framework offers a robust, quantitatively validated solution for LSGDM in data-intensive environments, with significant advantages in consistency and scalability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114595"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small aerial object detection through FPN-DETR integrated with a novel alignment proposal network","authors":"Usman Ahmad ,&nbsp;Tianlei Ma ,&nbsp;Jing Liang ,&nbsp;Ponnuthurai Nagaratnam Suganthan ,&nbsp;Kunjie Yu ,&nbsp;Faisal Mehmood ,&nbsp;Farhad Banoori","doi":"10.1016/j.asoc.2026.114620","DOIUrl":"10.1016/j.asoc.2026.114620","url":null,"abstract":"<div><div>In the domain of aerial imagery analysis, Small Aerial Object Detection (SAOD) presents significant challenges due to extensive scale variations, diverse orientations, and cluttered arrangements. Existing methods rely on anchor-based boxes or dense points, which involve complex manual steps such as anchor generation, transformation, and non-maximum suppression reasoning. This study proposes an innovative model that incorporates a Feature Pyramid Network into a Detection Transformer (FPN-DETR) to effectively address these challenges. The proposed approach employs the strengths of detection Transformers in modeling long-range dependencies, enabling a more comprehensive understanding of the aerial scene context, extraction of rich and scale-invariant features, and enhancement of detection accuracy for small aerial objects across varied scales and orientations. Additionally, this study develops a Novel Alignment Proposal Network (APN) with a novel loss function to further enhance FPN-DETR, resulting in the creation of the FPN-DETR+APN model. This network eliminates the time-consuming process of creating hand-crafted rotational anchors, which leads to computational complexity in existing conventional oriented detectors. Furthermore, FPN-DETR+APN generates oriented proposals that adeptly capture the diverse orientations of small objects in aerial scenes and provides improved positional priors for feature pooling, thereby enhancing cross-attention modulation in the Transformer decoder. The proposed method demonstrates superior performance in detecting small aerial objects, surpassing state-of-the-art detection frameworks in terms of precision, recall, and overall detection robustness. This study achieves 83.25 % mAP on DOTA-v1.0, 63.07 % mAP on DOTA-v2.0, 97.53 % on HRSC2016, 95.98 % on SSDD, 60.78 % on VisDrone2021, and 94.80 % on HRSID datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114620"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A structure-guided framework for multimodal optimization leveraging chaotic search and persistence-based clustering","authors":"Xiang Meng ,&nbsp;Yan Pei","doi":"10.1016/j.asoc.2026.114687","DOIUrl":"10.1016/j.asoc.2026.114687","url":null,"abstract":"<div><div>Multimodal optimization remains a fundamental challenge due to the difficulty of preserving population diversity while efficiently locating multiple optima. To address this challenge, we propose the structure-guided multimodal optimization (SRS-GMO) framework, which explicitly models the evolutionary process as a structured search over topological regions in the decision space, characterized by a novel structural relevance score (SRS). This score integrates persistence, spatial distinctiveness, and recent evolutionary activity to adaptively allocate computational resources to promising niches while maintaining overall structural diversity. SRS-GMO employs chaotic evolution based on logistic maps to enhance global exploration and facilitate escaping from local optima. We instantiate the framework into two variants: SRS-GMO-SO for single objective multimodal optimization problems and SRS-GMO-MO for multi-objective multimodal optimization problems, respectively. Extensive experiments on the CEC2013 and MMF benchmark suites demonstrate that SRS-GMO achieves superior performance in peak coverage, convergence stability, and structural diversity preservation. We also provide a per-module computational complexity analysis and discuss practical accelerations for scalability. These results validate the effectiveness of the proposed framework in addressing the challenges of both single- and multi-objective multimodal optimization.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114687"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The attention mechanism for intelligent text generation of traditional cultural symbols in artistic design","authors":"Xi Xu ,&nbsp;Da Guo","doi":"10.1016/j.asoc.2026.114733","DOIUrl":"10.1016/j.asoc.2026.114733","url":null,"abstract":"<div><div>Amid the digital wave, how to leverage artificial intelligence technologies to accurately interpret and reconstruct traditional cultural symbols has become a key issue in the field of artistic design. Existing image caption generation methods can identify the visual features of objects. But they often fail to capture the inherent cultural spirit and symbolic meanings when dealing with cultural symbols with profound historical connotations and abstract metaphors. This results in generated texts that remain at the level of superficial visual descriptions, lacking cultural depth. In response to this semantic gap, this study proposes an intelligent text generation model integrated with a cross-modal attention mechanism. The model constructs a dual-stream encoding architecture based on the Visual Geometry Group 19 (VGG19) and Bidirectional Encoder Representations from Transformers (BERT). By introducing multi-head self-attention and cross-modal attention modules, it achieves dynamic alignment between visual regions and cultural semantics. Different from traditional methods, the model can actively retrieve key visual clues from images according to the generated textual context. It accurately describes the physical forms of symbols and effectively interprets the cultural implications behind them. Experimental results show that this model achieves scores of 0.42 and 1.31 on the Bilingual Evaluation Understudy-4 (BLEU-4) and Consensus-based Image Description Evaluation (CIDEr) metrics, respectively, significantly outperforming benchmark models. It provides a new paradigm with both accuracy and cultural expressiveness for the digital inheritance and innovative design of traditional cultural symbols.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114733"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical attentive feature refinement network with cross-resolution detail preservation for medical image segmentation","authors":"Xin Shu ,&nbsp;Anqi Shi ,&nbsp;Xiaofang Guo ,&nbsp;Yan Fan ,&nbsp;Xin Zhang","doi":"10.1016/j.asoc.2026.114760","DOIUrl":"10.1016/j.asoc.2026.114760","url":null,"abstract":"<div><div>Deep learning technology, particularly hierarchical encoder-decoder architectures, is developing rapidly in medical image segmentation, which aids in precise identification, localization, and quantification of anatomical structures and diseased areas in clinical diagnosis. However, most of the existing models face two critical challenges: (1) hierarchical semantic gaps caused by inconsistent receptive adaptation across different network depths; (2) gradual loss of fine details in traditional multi-scale fusion schemes, which disproportionately affects small-scale pathological features. Therefore, this paper proposes a Hierarchical Attentive Feature Refinement Network with Cross-Resolution Detail Preservation (HAFRN-CDP) to tackle these problems. Firstly, a Dual-Branch Pooling Fusion Encoder (DPFE) structure is designed to capture features of different scales and types to effectively alleviate information loss in the encoder. Secondly, a Fine Detail Retention (FDR) module is proposed to emphasize the high-resolution features in the encoder head, perceiving features at different scales and semantic levels. Thirdly, a novel Convolution Block (CB), which is composed of a Multi-Scale Residual Aggregation (MSRA) module and a Bi-Branch Global Attention (BGA) module, is presented to reduce the interference of redundant information caused by multi-scale strategy. Finally, in the decoder part, this paper proposes a Cross-Level Feature Fusion (CLFF) module to recover the details and structure by fusing features at different stages and scales. Extensive comparative experiments are conducted on three biomedical image datasets, and the results demonstrate that our HAFRN-CDP exhibits competitive performance compared to the state-of-the-art, with mDice of 92.87 % on JSUAH-Cerebellum and 91.77 % on 2018DSB, while also maintaining high mPrecision of 85.42 % and mRecall of 95.77 % on BUSI (malignant), confirming both its accuracy and robustness across diverse biomedical segmentation tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114760"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Node importance identification method for urban rail transit networks based on improved mutual information considering inter-station cross-dependencies","authors":"Yanhui Yin ,&nbsp;Guanghong Hu ,&nbsp;Zhuo Huang ,&nbsp;Jihui Chen ,&nbsp;Wencheng Huang","doi":"10.1016/j.asoc.2026.114755","DOIUrl":"10.1016/j.asoc.2026.114755","url":null,"abstract":"<div><div>Identifying and protecting the key nodes of urban rail transit networks (URTN) is crucial for the normal operation of rail transit. However, most of the previous studies mainly relied on a single topological structure or passenger flow to identify important nodes, ignoring the inter-station cross-dependencies among the stations. In this paper, an Improved Mutual Information (IMI) is introduced to identify the node importance of URTN considering inter-station cross-dependencies. IMI emphasizes that the influence of a node will spread to the adjacent nodes within the effective range, and the importance of a node is the result of the combined influence of the adjacent nodes within that range. The comparative numerical researches have been conducted, the effectiveness and reliability of the IMI algorithm are validated from three aspects: network connectivity, node importance evaluation, and statistical analysis. Furthermore, under random and intentional attacks, we utilize the relative size of the maximal connected subgraph (RSMCS) and the relative network efficiency (RNE) before and after nodes are attacked to study the invulnerability of undirected unweighted networks of URTN. Additionally, we utilize the weighted RSMCS and the weighted RNE to study the invulnerability of bi-directional weighted URTN networks. Finally, A case study is performed with the Chengdu URTN serving as the context, the results show that, the IMI is suitable for networks with strong inter-station cross-dependencies (especially URTN), and can identify nodes with low degree but crucial due to their high inter-station cross-dependencies. It has higher discrimination ability for nodes ranked lower, and can capture stations with lower node degree but closely related to other important stations or important routes. Finally, suggestions for improving the performance and invulnerability of URTN are proposed from four aspects including expanding the capacity of the station, strengthening the connection between urban bus network and URTN, protecting important stations and routes with strong inter-station cross-dependencies through IMI algorithm, improving operation plans and alleviating peak passenger flow pressure.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114755"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential-game algorithm for resource optimization of wireless sensor network","authors":"Ning Yao ,&nbsp;Dong Liu ,&nbsp;Bai Chen ,&nbsp;Xiaochen Hao ,&nbsp;Rongrong Yin","doi":"10.1016/j.asoc.2026.114641","DOIUrl":"10.1016/j.asoc.2026.114641","url":null,"abstract":"<div><div>In response to the resource competition optimization problem in wireless sensor network, this paper first uses differential evolution algorithm to transform single-object multi-objective optimization into multi-object single-objective optimization, and combines game theory to design a new differential game algorithm. The algorithm conforms to the differential evolution framework on the outer layer and meets the requirements of game theory on the inner layer. And theoretically proved the low complexity and algorithm convergence. Then, the designed differential game algorithm was applied to the wireless sensor network to design the channel allocation algorithm. The convergence and effectiveness of the algorithm were verified through simulation. Finally, the differential game algorithm was discussed, and it was theoretically proved that the algorithm is also applicable to multi-objective optimization of multiple objects, providing a design idea for low complexity convergence multi-objective optimization algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114641"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}