Applied Soft Computing最新文献

{"title":"Intuitionistic fuzzy-valued Choquet operator: theoretical foundations and application to aggregation tasks","authors":"Michał Boczek ,&nbsp;Marek Kaluszka ,&nbsp;Paweł Karczmarek ,&nbsp;Alicja Żmudzińska ,&nbsp;Albert Rachwał ,&nbsp;Michał Dolecki","doi":"10.1016/j.asoc.2026.114751","DOIUrl":"10.1016/j.asoc.2026.114751","url":null,"abstract":"<div><div>Aggregation function plays a fundamental role in decision-making problems involving incomplete information. In particular, the intuitionistic fuzzy-valued Choquet operator (IFC - operator), based on an Archimedean <span><math><mi>t</mi></math></span>-norm, provides a flexible and powerful framework for modeling interactions among criteria in intuitionistic fuzzy environments. Previous studies have mainly focused on examining whether the IFC - operator satisfies the axioms of an aggregation function for selected Archimedean <span><math><mi>t</mi></math></span>-norms. In the paper, we present a complete characterization that allows one to determine whether the IFC - operator qualifies as an aggregation function under a fixed total order. To achieve this, we establish a general theoretical framework that characterizes the conditions under which a generalized Choquet-like operator of the form <span><math><mrow><mtext>F</mtext></mrow><mo>(</mo><msub><mrow><mi>x</mi></mrow><mrow><mi>σ</mi><mo>(</mo><mn>1</mn><mo>)</mo></mrow></msub><mo>,</mo><mo>…</mo><mo>,</mo><msub><mrow><mi>x</mi></mrow><mrow><mi>σ</mi><mo>(</mo><mi>n</mi><mo>)</mo></mrow></msub><mo>,</mo><mi>μ</mi><mo>(</mo><msub><mi>B</mi><mrow><mi>σ</mi><mo>(</mo><mn>1</mn><mo>)</mo></mrow></msub><mo>)</mo><mo>,</mo><mo>…</mo><mo>,</mo><mi>μ</mi><mo>(</mo><msub><mi>B</mi><mrow><mi>σ</mi><mo>(</mo><mi>n</mi><mo>)</mo></mrow></msub><mo>)</mo><mo>)</mo></math></span> satisfies the axioms of aggregation functions given a fixed total order, where the values <span><math><msub><mrow><mi>x</mi></mrow><mrow><mi>σ</mi><mo>(</mo><mi>i</mi><mo>)</mo></mrow></msub></math></span> are taken from an arbitrary linearly ordered set and <span><math><mo>(</mo><msub><mi>B</mi><mrow><mi>σ</mi><mo>(</mo><mi>i</mi><mo>)</mo></mrow></msub><mo>)</mo></math></span> forms a monotonicity family of subsets of <span><math><mo>{</mo><mn>1</mn><mo>,</mo><mo>…</mo><mo>,</mo><mi>n</mi><mo>}</mo></math></span>. We also present some applications of the IFC - operator to combine the results of individual classifiers in the multi-class problems. We show that the IFC - operator can be effectively applied to the multi-class problems as the aggregation function to combine the results of individual classifiers. Extensive numerical experiments confirmed the effectiveness of the proposed method, which achieved high accuracy and consistently outperformed the Choquet integral as well as individual classifiers.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114751"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191837","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 rate-distortion optimal evolutionary algorithm under the fixed bit-rate constraints for the JPEG quantization table optimization","authors":"Qijun Wang ,&nbsp;Hongyan Wei ,&nbsp;Ke Xu ,&nbsp;Bo Du ,&nbsp;Xingyi Zhang","doi":"10.1016/j.asoc.2026.114697","DOIUrl":"10.1016/j.asoc.2026.114697","url":null,"abstract":"<div><div>JPEG has been widely utilized across diverse applications and devices for approximately 30 years, and it will continue to dominate the image ecosystem for an extended duration. Optimizing the quantization table in JPEG compression not only maintains compatibility with the JPEG standard but also enhances compression efficiency, which is crucial for image storage and transmission. Existing Evolutionary Algorithm (EA) based optimization of JPEG quantization tables primarily focuses on optimizing the luminance quantization tables, but the optimization cannot be tailored for specified bit-rates. To address this problem, this paper proposes a rate-distortion optimal evolutionary algorithm with fixed bit-rates for the JPEG quantization table optimization, which optimizes the quantization tables through a co-evolutionary algorithm with two populations: one with bit-rate constraints and the other without any constraints, adopting the rate-distortion optimal principle based population update and different environmental selection strategies for each population. The two populations are evolved simultaneously and cooperate with each other to achieve better optimization performance. Experimental results on classical benchmark datasets demonstrate that our method can achieve the better quantization tables than current state-of-the-art algorithms in both compression efficiency and image quality while more accurate target bit-rate for JPEG compression can also be achieved through our method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114697"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191835","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":"ODPL: An objective-driven decomposed policy learning framework for urban autonomous driving control","authors":"Lizhi Bi ,&nbsp;Bin Shuai ,&nbsp;Zhanru Liu ,&nbsp;Yichen Sun ,&nbsp;Feiyu Yang","doi":"10.1016/j.asoc.2026.114781","DOIUrl":"10.1016/j.asoc.2026.114781","url":null,"abstract":"<div><div>In urban driving (UD) environments, deep reinforcement learning (DRL) demonstrates superior adaptability for autonomous driving control. However, fundamental challenges remain unsolved in existing DRL methods: error accumulation in Actor-Critic architectures, inconsistent learning signals from hand-crafted reward functions, and signal confusion in multi-dimensional control tasks. This paper proposes an Objective-Driven Decomposed Policy Learning (ODPL) framework for reliable urban autonomous driving control. The framework deconstructs the traditional reinforcement learning paradigm by eliminating Critic networks and directly utilizing driving-specific objectives as learning signals, thereby improving learning efficiency and accelerating convergence. ODPL employs a task decomposition strategy that decouples steering and throttle control into independent subtasks, resolving the signal confusion problem inherent in unified network architectures. Furthermore, to address credit assignment issues caused by physical delays, ODPL integrates a delay-aware state transition model that establishes precise causal relationships between current states and historical actions through a delay buffering mechanism. Simulation results in the CARLA environment demonstrate that the proposed ODPL framework achieves 70%–110% performance improvements over advanced baseline methods across various driving scenarios. The framework exhibits exceptional performance in complex curve scenarios while maintaining stable learning convergence under cold-start conditions without pre-training data.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114781"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191833","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":"MTCCM: A multi-task conflict control model for Web API recommendation with semantic enhancement","authors":"Kezhou Chen ,&nbsp;Hongbin Zhang ,&nbsp;Zhengming Chen ,&nbsp;Lianglun Cheng ,&nbsp;Xu Lu","doi":"10.1016/j.asoc.2026.114749","DOIUrl":"10.1016/j.asoc.2026.114749","url":null,"abstract":"<div><div>With the rapid growth of web application programming interfaces (Web APIs), mashup development has emerged as a key paradigm that integrates existing APIs to meet the complex needs of various applications. The increasing number of Web APIs complicates selection and reduces development efficiency, while deep learning-based recommendation methods struggle with data sparsity and unstructured inputs. Multi-task learning (MTL) offers a promising solution by leveraging related tasks; however, many existing approaches fail to account for task conflicts, thereby limiting their effectiveness. We propose a multi-task conflict control model (MTCCM) for Web API recommendation to address these issues, integrating three synergistic modules. The semantic enhancement module (SEM) leverages a large language model with prompt-based inputs to structure unorganized mashups and Web API descriptions, enhancing semantic representation and mitigating issues with unstructured data. The conflict control module (CCM) operates in two stages: task conflict analysis identifies optimal auxiliary tasks, and gradient conflict control refines training by adjusting the direction of conflicting gradients. The neural network module (NNM) adopts a shared architecture with two task-specific outputs: one for Web API prediction and the other for Web API classification, which serves as an auxiliary task to improve the primary prediction performance. Extensive experiments demonstrate that MTCCM significantly outperforms state-of-the-art baselines, establishing a new benchmark in multi-task Web API recommendation.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114749"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191832","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":"MUL-UNet: A lightweight multi-weather image restoration network with enhanced edge preservation","authors":"Rujia Li ,&nbsp;Shuai Zhang ,&nbsp;Xiaoling Huang ,&nbsp;Jiahao Nie ,&nbsp;Mengfei Liu","doi":"10.1016/j.asoc.2026.114756","DOIUrl":"10.1016/j.asoc.2026.114756","url":null,"abstract":"<div><div>Image restoration aims to reconstruct degraded images under adverse weather conditions into high-quality images, providing reliable inputs for computer vision tasks. However, current deep learning image restoration algorithms face three major challenges: First, existing approaches primarily focus on single-weather scenarios, struggling to adapt to complex and varying weather conditions; Second, insufficient integration of edge priors during image processing leads to loss of edge details in reconstructed images; Third, existing weather removal methods rely on deep network architectures, limiting deployment in resource-constrained scenarios. To address these challenges, this study proposes MUL-UNet, a novel multi-task image restoration network with a single-backbone, multi-branch U-shaped architecture. First, a Multi-task Encoder (MTE) with a representation encoder head and dual auxiliary branches is proposed to achieve efficient multi-weather image restoration through adaptive weight adjustment for different weather scenarios; Second, a Gradient Feature Fusion Module (GFFM) is proposed to receive multi-scale edge feature maps extracted by the representation encoder head in MTE and fuse them with the multi-scale image feature maps extracted by the U-Net encoder in single-backbone through skip connections, enhancing edge detail preservation; Finally, Multi-scale Convolution Block (MConv Block) is proposed to replace traditional large kernel convolutions, reducing network parameters while maintaining feature extraction capabilities, thus improving model deployment efficiency. Experimental results show that the proposed model achieves state-of-the-art performance on multiple benchmark datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114756"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191513","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":"Transformer and variational autoencoder-based multi-view fusion intelligent decision network for automated rock breaking","authors":"Shiwei Wang,&nbsp;Yu Liu,&nbsp;Jinguang Lin,&nbsp;Yuanxin You","doi":"10.1016/j.asoc.2026.114695","DOIUrl":"10.1016/j.asoc.2026.114695","url":null,"abstract":"<div><div>The secondary breaking task is a critical step in ensuring the normal operation of crusher jaws, aiming to break rocks exceeding the processing capacity of the crusher into suitable sizes. Existing remote operation modes for controlling breakers face high complexity, low efficiency, and safety risks, which make them challenging to meet practical demands. To address these limitations, this paper proposes T-MVDN, an end-to-end intelligent decision-making algorithm based on imitation learning, designed to optimize the entire process from scene perception to action planning. The T-MVDN integrates the Transformer architecture, CVAE latent space encoding, and action sequence modeling modules, utilizing multi-view RGB-D data fusion and local spatial attention mechanisms to significantly enhance robustness and decision-making efficiency in complex environments. Moreover, this study systematically defines the operational workflow and prioritization for secondary breaking tasks. The algorithm was validated in a high-fidelity simulation environment built using Blender and CoppeliaSim, and further evaluated through real-world experiments to assess its feasibility in actual engineering applications. Experimental results demonstrate that the proposed method enables efficient and safe autonomous breaking operations in real task scenarios. This research represents the first application of imitation learning to autonomous planning in secondary breaking scenarios, offering a novel perspective on automation in the construction and mining sectors.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114695"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191522","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 deep transfer learning approach for road extraction from high-resolution satellite images based on D-LinkNet and fuzzy systems","authors":"Shahnaz Sadat Mortazavi,&nbsp;Yousef Sharafi,&nbsp;Mohammad Teshnehlab","doi":"10.1016/j.asoc.2026.114748","DOIUrl":"10.1016/j.asoc.2026.114748","url":null,"abstract":"<div><div>Road extraction from high-resolution satellite imagery is essential for applications such as urban planning, disaster response, and intelligent transportation systems. This paper proposes an enhanced version of the <span>D</span>-LinkNet model through a hybrid dual-encoder architecture that integrates complementary pre-trained networks, AlexNet or ShuffleNetv2, combined with ResNet34 to capture both fine-grained textures and high-level semantic features. To improve training dynamics and convergence stability, a fuzzy logic-based adaptive learning rate controller is introduced, dynamically adjusting the learning rate based on performance feedback. Additionally, a dynamic loss function combining Binary Cross-Entropy and Dice Loss with adaptive weighting is employed to mitigate class imbalance and preserve road structure continuity. The proposed method is evaluated on two benchmark datasets: DeepGlobe and Massachusetts. Experimental results show that the hybrid-fuzzy model outperforms the baseline <span>D</span>-LinkNet, achieving up to 4.0 % improvement in Intersection over Union (IoU), 6.0 % in F1-score, and 1.94 % in Pixel Accuracy on the Massachusetts dataset, highlighting the practical effectiveness and robustness of the proposed framework.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114748"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191557","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":"Fault sample generation under continuous degradation using CRGAN-MDTR: A conditional recurrent GAN with maximum degenerate trend retention","authors":"Yujie Cheng ,&nbsp;Pengchao Wang ,&nbsp;Haoxin Gu ,&nbsp;Jiyan Zeng ,&nbsp;Jian Ma","doi":"10.1016/j.asoc.2026.114728","DOIUrl":"10.1016/j.asoc.2026.114728","url":null,"abstract":"<div><div>Optimal data conditions are crucial prerequisites for the effective implementation of data-driven fault-diagnosis methods. However, the lack of sufficient training samples, especially under conditions of unknown fault severity levels, significantly hampers their performance in practical engineering applications. A generative adversarial network (GAN) offers a feasible solution, but existing GAN-based models face challenges in generating missing samples with varying fault degrees during continuous degradation. To address this issue, we propose a novel data-generation method, called Conditional Recurrent GAN with Maximum Degenerate Trend Retention (CRGAN-MDTR). The CRGAN integrates long short-term memory (LSTM) units into a Conditional GAN (CGAN) architecture, enhancing its ability to learn temporal dependencies from continuous degradation trends. In addition, a degradation trend reconstruction network (MDTR) is incorporated alongside the CRGAN discriminator, enabling controlled generation of fault samples and further improving their quality. The proposed CRGAN-MDTR was comprehensively evaluated through ablation studies and comparisons with commonly used generative techniques. The evaluation employed three categories of metrics: Sample Consistency, Fault Severity Consistency, and Classification Task-Oriented Metrics, to ensure robustness across multiple dimensions. Results demonstrated that CRGAN-MDTR consistently achieved superior performance, including improved consistency and diagnostic accuracy, with the highest accuracy increase reaching 4.7 %. Notably, the model maintained high robustness in noise response experiments and effectively interpolated fault samples at varying degradation levels, addressing a critical gap in fault diagnosis methodologies. These findings validate the robustness and effectiveness of CRGAN-MDTR in addressing fault data scarcity across varying fault severity levels. This study contributes a promising solution to enhancing fault diagnosis performance under extreme data scarcity conditions and provides a foundation for future research on real-world industrial applications.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114728"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191561","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":"Unraveling interdependencies in circular business models for renewable energy: A novel MCDM approach to systemic challenges","authors":"Ismail Erol ,&nbsp;Ahmet Oztel ,&nbsp;Javad Pakdel","doi":"10.1016/j.asoc.2026.114747","DOIUrl":"10.1016/j.asoc.2026.114747","url":null,"abstract":"<div><div>The adoption of Circular Business Models (CBMs) in the Renewable Energy Industry (REI) is pivotal for advancing the United Nations Sustainable Development Goals (SDGs), particularly Goal 7 (affordable and clean energy), Goal 12 (responsible consumption and production), and Goal 13 (climate action). However, it faces complex systemic and economic challenges. This study introduces a novel Interval-Valued Spherical Fuzzy Interpretive Structural Modeling (IVSF-ISM) and MICMAC framework to analyze the interdependencies among challenges, derived from a PRISMA-guided systematic literature review of 42 peer-reviewed articles. The proposed methodology captures uncertainty in expert judgments, structuring challenges into a six-level hierarchy and classifying them into driving, linkage, dependent, and autonomous categories based on their influence and dependence. Key findings highlight regulatory uncertainty, lack of interoperability, and fragmented value chain collaboration as primary driving challenges, amplifying barriers such as high initial investment and customer resistance to non-ownership models. Linkage challenges, including revenue unpredictability and data privacy risks, create feedback loops that exacerbate systemic risks. This research contributes to technological forecasting by offering a systemic perspective on CBM adoption and its societal impact. By addressing root causes, this study advances REI for optimizing resource allocation and fostering equitable, scalable energy transitions, contributing to sustainable energy.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114747"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191563","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":"CUBA: Controlled untargeted backdoor attack against deep neural networks","authors":"Yinghao Wu,&nbsp;Liyan Zhang","doi":"10.1016/j.asoc.2026.114772","DOIUrl":"10.1016/j.asoc.2026.114772","url":null,"abstract":"<div><div>Backdoor attacks have emerged as a critical security threat against deep neural networks in recent years. The majority of existing backdoor attacks focus on targeted backdoor attacks, where trigger is strongly associated with specific malicious behavior. Various backdoor detection methods depend on this inherent property and show effective results in identifying and mitigating such targeted attacks. However, a purely untargeted attack in backdoor scenarios is, in some sense, self-weakening, since the target nature is what makes backdoor attacks so powerful. In light of this, we introduce a novel <u>C</u>onstrained <u>U</u>ntargeted <u>B</u>ackdoor <u>A</u>ttack (CUBA), which combines the flexibility of untargeted attacks with the intentionality of targeted attacks. The compromised model, when presented with backdoor images, will classify them into random classes within a constrained range of target classes selected by the attacker. This range can be all incorrect classes (Full Range Attack) or a predefined subset (Narrow Range Attack). This combination of randomness and determinedness enables the proposed untargeted backdoor attack to natively circumvent existing backdoor defense methods. To implement the untargeted backdoor attack under controlled flexibility, we propose applying logit normalization on cross-entropy loss with flipped one-hot labels. By constraining the logit during training, the compromised model will show a uniform distribution across selected target classes, resulting in controlled untargeted attack. The experimental results demonstrate that the proposed CUBA achieves remarkable attack success rates across multiple datasets, reaching 99.99%, 99.94% and 99.79% on MNIST, GTSRB and CIFAR10 respectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114772"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191849","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}