Applied Soft Computing最新文献

{"title":"An enhanced multi-criteria decision making framework for evaluating LLM-integrated smart product-service systems using spherical fuzzy rough numbers","authors":"Zhongkui Liu ,&nbsp;Danhui Zhang ,&nbsp;Qinghua Liu","doi":"10.1016/j.asoc.2026.114797","DOIUrl":"10.1016/j.asoc.2026.114797","url":null,"abstract":"<div><div>Integrating Large Language Model (LLM) into Smart Product-Service Systems (SPSS) introduces challenges in design evaluation due to inherent uncertainties and complex decision criteria. To address these challenges, we propose SFRN-LOPCOW-RAFSI, a novel multi-criteria decision making (MCDM) framework that combines spherical fuzzy rough numbers (SFRN), logarithmic percentage change-driven objective weighting (LOPCOW), and ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI). This study applies the proposed framework to a conceptual design evaluation case for a refrigerator service system, where four LLM-integrated service solutions are assessed based on 15 evaluation criteria. The methodology consists of three stages: (1) defining the problem structure, (2) computing the weight coefficients of the criteria using the SFRN-based LOPCOW method, and (3) ranking the design alternatives through the SFRN-based RAFSI approach. SFRN can represent a more extensive range of uncertainty and effectively handle uncertainty within the decision-making group, enhancing the reliability of the evaluation process. Sensitivity analysis and comparative analysis were conducted to validate the proposed method, confirming its robustness and effectiveness. This study's findings provide valuable insights for DMs in optimizing the integration of LLMs into SPSS design processes.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"193 ","pages":"Article 114797"},"PeriodicalIF":6.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196903","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":"Multi-objective decision-making control of cutter suction dredger based on multi-scale graph representation with Bayesian optimization","authors":"Yong Chen ,&nbsp;Qiubing Ren ,&nbsp;Mingchao Li ,&nbsp;Bolong Gao ,&nbsp;Yonghuang Xiang ,&nbsp;Zhongzhi Fu","doi":"10.1016/j.asoc.2026.114811","DOIUrl":"10.1016/j.asoc.2026.114811","url":null,"abstract":"<div><div>The operational parameters of cutter suction dredger (CSD) are frequently set based on empirical experience, leading to low productivity and high energy consumption. To address this issue, the present study proposes a Bayesian multi-objective optimization (MOO) framework integrating a multi-scale adaptive graph convolutional network (MAGCN) model and a multi-objective tree-structured Parzen estimator (MOTPE) algorithm. This framework aims to predict and optimize construction productivity and energy consumption performance while providing well-informed support for intelligent decision-making control. First, a hybrid feature selection method combining maximum information coefficient and Pearson correlation coefficient is applied to identify 6 key control parameters from 256 operational parameters. Second, an MAGCN model with multi-scale graph representation is proposed to establish the nonlinear mapping among geological conditions, operational parameters, construction productivity, and energy consumption. Third, the MOO framework with its mathematical formulation is constructed, and the Pareto optimal front for productivity and energy consumption is derived using the MOTPE-based Bayesian optimization algorithm. The optimal trade-off solution and corresponding operational parameters are determined by a weighted sum method. The proposed MOO framework is validated using operational data from the Tian Jing Hao CSD in the Pinglu Canal dredging project in China. The results show that the MAGCN model achieves high prediction accuracy for real-time productivity and energy consumption, with R² values of 0.942 and 0.979, respectively. The MOTPE-driven optimization reduces standard deviations of operational parameters by 21.24 % - 97.90 %, enhancing operational stability. The framework improves productivity by 3.79 % and reduces energy consumption by 3.53 %. This work provides real-time decision-making support for optimizing CSD operations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"193 ","pages":"Article 114811"},"PeriodicalIF":6.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196907","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":"MFU-Depth: Modeling and fusing uncertainty for self-supervised multi-frame monocular depth estimation","authors":"Fuji Fu ,&nbsp;Jinfu Yang ,&nbsp;Jiaqi Ma","doi":"10.1016/j.asoc.2026.114705","DOIUrl":"10.1016/j.asoc.2026.114705","url":null,"abstract":"<div><div>Self-supervised monocular depth estimation, valued for its simplified configuration and excellent geometric perception, is gaining attention in autonomous driving and robotics. Mainstream self-supervised multi-frame methods, due to the lack of a unified multi-cue uncertainty representation mechanism, often struggle to effectively reflect depth prediction errors. To address this issue, we propose a self-supervised multi-frame monocular depth estimation method by Modeling and Fusing Uncertainty, termed MFU-Depth. (1) At the probability distribution level, we design an Uncertainty Estimation-based Single-frame Probability Prediction (UE-SPP) module and a Depth-Aware Multi-frame Probability Transformation (DA-MPT) module, achieving unified uncertainty modeling of single-frame and multi-frame cues in the form of probability distributions. Building on these, we establish a Relation Modeling-guided Probability Fusion (RM-PF) mechanism, which adaptively adjusts fusion weights through pixel-wise difference relation modeling, enabling the complementary integration of these two distributions. (2) At the probabilistic self-supervision level, an Information Entropy Uncertainty Loss (IEUL) is devised to further model the uncertainty of both single-frame and fused depth probability distributions, thereby mitigating supervision distortion caused by high-uncertainty pixels. Experimental results show that MFU-Depth achieves exceptional performance on multiple public datasets. Compared to baseline methods, MFU-Depth reduces the depth error by 15.3% and the uncertainty metric by 29.0%.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114705"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080105","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":"MACSENet: A novel lightweight CNN with multi-scale atrous convolutions and attention mechanism for accurate lung cancer detection","authors":"Fazal Hadi ,&nbsp;Omair Bilal ,&nbsp;Sohaib Asif","doi":"10.1016/j.asoc.2026.114698","DOIUrl":"10.1016/j.asoc.2026.114698","url":null,"abstract":"<div><div>Lung cancer represents a critical global health challenge, characterized by high mortality rates and complex diagnostic processes. Identifying tumors in CT images manually is a time-intensive and complex task. While deep learning models can aid in the process, their complexity often leads to overfitting, and their single-branch, linear structures struggle to capture multi-scale features, ultimately reducing detection accuracy. This paper presents MACSENet, an innovative lightweight CNN developed to improve early detection and diagnostic accuracy using advanced deep learning techniques. The proposed model combines three key components: a standard CNN for basic feature extraction, a multiscale atrous convolutional module for capturing spatial features at different resolutions, and a squeeze-and-excitation (SE) block to refine channel-wise features through adaptive attention mechanisms. We designed the Multiscale Atrous Feature Extraction Block (MAFEB), which incorporates a multiscale atrous convolution module with varying dilation rates. This approach expands the receptive field without adding extra parameters, capturing intricate tumor details and providing a comprehensive contextual understanding to address the complex morphological variations in lung cancer. The SE block is applied after extracting features from the multiscale atrous convolution module, dynamically refining the feature maps by amplifying key diagnostic information and suppressing irrelevant details. To assess the broader applicability and generalizability of MACSENet, we evaluated it on the publicly available IQ-OTH/NCCD lung cancer dataset, which consists of 1097 CT images from 110 patients, encompassing benign, malignant, and normal cases. Additional experiments were carried out to evaluate the robustness of MACSENet using two other datasets: the Chest CT-Scan dataset, which consists of 1000 images across four classes, and the Lung histopathological images dataset, containing 15,000 images across three classes. The experimental results demonstrate that MACSENet outperforms both traditional deep CNNs and state-of-the-art methods in terms of accuracy and efficiency. Our approach achieved an outstanding accuracy of 99.55 % on the IQ-OTH/NCCD dataset, and 92 % and 99.97 % on the additional datasets while maintaining the smallest parameter size of 0.27 million among all competitors. Additionally, the model exhibited the smallest model size and FLOPS compared to other deep CNNs. Our proposed model is also compared with well-established baseline models including ResNet, DenseNet, and MobileNet series to demonstrate its superior performance in lung cancer detection. The proposed approach provides critical insights to support clinical decision-making for advancing patient care and treatment precision in lung cancer diagnostics.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114698"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080113","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 two-stage solution incorporating large neighborhood search for the priority-aware 2D bin packing problem in furniture manufacturing","authors":"Jianming Wang ,&nbsp;Yang Xu ,&nbsp;Zhouwang Yang","doi":"10.1016/j.asoc.2026.114659","DOIUrl":"10.1016/j.asoc.2026.114659","url":null,"abstract":"<div><div>This paper explores the two-dimensional bin packing problem involving both rectangular and irregular shapes, with a focus on part priority, a critical factor in the furniture manufacturing and woodworking industries. Part priority is essential due to specific processing requirements and customer urgency. We propose a two-stage methodology aimed at minimizing both the number of bins containing priority parts and the total number of bins utilized. In the first stage, multiple parts are iteratively paired and approximated as rectangles to maximize the overall benefit of all pairings, diverging from traditional methods that focus on pairing two specific parts. The second stage introduces a rectangular packing module that incorporates two large-neighborhood search (LNS) algorithms. This module employs efficient operators that respect the priority objective, addressing the difficulty of large-scale priority problems. We evaluate the strengths and limitations of the rectangularization approach through experiments on irregular bin packing benchmarks and assess its applicability. Experiments on rectangular benchmark instances demonstrate the superiority of our approach in large-scale scenarios. Furthermore, tests on industrial data reveal that our method increases material utilization by 0.86% and reduces the number of priority bins by 1.71%, surpassing leading commercial software in both objectives. These results suggest that the proposed approach can be integrated into cutting software to provide practical and efficient solutions, thereby advancing intelligent manufacturing.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114659"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080197","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 differential evolutionary algorithm improvement framework based on artificial potential fields","authors":"Zhe Yang ,&nbsp;Libao Deng ,&nbsp;Xianxin Mao ,&nbsp;Lili Zhang","doi":"10.1016/j.asoc.2026.114714","DOIUrl":"10.1016/j.asoc.2026.114714","url":null,"abstract":"<div><div>The Artificial Potential Field (APF) method, widely utilized in path planning, uses virtual environmental forces derived from potential field gradients for guidance. While Differential Evolution (DE) algorithms are recognized for their simplicity and robust global optimization capabilities, they often suffer from premature convergence and reduced search efficiency in complex scenarios. Motivated by the similarity between APF pathfinding and DE optimization, this study proposes an APF-inspired framework that utilizes virtual forces to guide the population. Attractive forces guide individuals toward potential optima, while repulsive forces generated by treating inferior solutions as virtual obstacles mitigate the risk of stagnation. This mechanism effectively steers the population toward promising areas, thereby enhancing search efficiency. Furthermore, an integrated adaptive strategy enhances performance by dynamically adjusting key parameters based on problem characteristics. Comprehensive experiments on the CEC2020 benchmark suite and 36 real-world engineering problems validate the effectiveness of the framework. The results indicate performance improvements over baseline algorithms and competitiveness in comparative studies.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114714"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096146","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":"Development of federated learning neural networks with combined horizontal and vertical data partitioning","authors":"Amir Anees ,&nbsp;Matthew Field ,&nbsp;Lois Holloway","doi":"10.1016/j.asoc.2026.114734","DOIUrl":"10.1016/j.asoc.2026.114734","url":null,"abstract":"<div><div>Federated learning trains a global machine learning model without moving data that is distributed geographically, thereby preserving privacy. Most of the existing works focus on either horizontal- or vertical-federated learning. This paper proposes two new federated learning frameworks, addressing different dataset setups, for combined horizontal and vertical data-partitioned scenarios based on neural networks. The data is horizontally partitioned for a subset of clients, referred to as horizontal clients, and it is further vertically partitioned with another client, labelled as a vertical client. In the first proposed framework, referred to as ‘Horizontal-OutputFed’, the output feature is stored at clients with horizontal partitioning, while in the second proposed framework, referred to as ‘Vertical-OutputFed’, the output feature is stored at the vertical client. The accuracy, loss and convergence rate of the Horizontal-OutputFed are the same as those achieved with a horizontally partitioned federated learning framework. Furthermore, the impact of non-identically and independently distributed data is the same in both the Horizontal-OutputFed and purely horizontal federated learning setups for combined features. Compared to a centrally trained model, the globally trained model in the Vertical-OutputFed has the same performance metrics such as accuracy and convergence rate. Moreover, there is no effect of imbalanced data on the performance of the Vertical-OutputFed, contrary to some of the previous works on individual horizontal- or vertical-federated learning. Privacy attack analysis were conducted for active, passive, white-box, and black-box attacks demonstrating no risk of input and output feature leakage, nor any data inference from the resulting models or while sharing hidden layers’ outputs for either of the combined federated approaches.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114734"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191516","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":"Optimal scheduling of integrated energy systems with power-to-hydrogen for flexible supply-demand balance via Q-learning-enhanced grey wolf optimizer","authors":"Kuiyuan Pan ,&nbsp;Tianhe Sun ,&nbsp;Xinfu Pang ,&nbsp;Zedong Zheng ,&nbsp;Haibo Li","doi":"10.1016/j.asoc.2026.114795","DOIUrl":"10.1016/j.asoc.2026.114795","url":null,"abstract":"<div><div>The integrated energy system (IES) has garnered increasing attention as an effective means of optimizing the utilization of multiple energy sources. With the growing share of renewable energy, enhancing the flexibility of system operation has become a critical issue. This paper proposes an optimal scheduling model for IES that incorporates flexible supply-demand balance constraints. Firstly, a quantitative model for flexible resources is introduced, followed by the formulation of a flexible supply-demand balance constraint model. Secondly, focusing on minimizing operating costs, an optimal scheduling model for IES is developed, incorporating power-to-hydrogen (P2H) technology and considering the flexibility of supply-demand balance constraints. Finally, a grey wolf optimizer (GWO) based on Q-learning (Q-GWO) is designed to solve the optimal scheduling model. The Q-learning algorithm is used to dynamically adjust the search factor of the Q-GWO, thereby enhancing the algorithm's search capability. Simulation results under various scenarios demonstrate that the proposed optimization model significantly improves the flexibility and economy of system operation while reducing the wind curtailment rate. The superiority of the Q-GWO algorithm in solving this problem is confirmed by comparing its convergence performance with that of other algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114795"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191850","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":"Soft-label guided multi-granularity prompts learning for human-object interaction detection","authors":"Xiaoqian Han ,&nbsp;Xiaowei Zhang ,&nbsp;Guanglin Niu ,&nbsp;Mingliang Zhou ,&nbsp;Zhenkuan Pan","doi":"10.1016/j.asoc.2026.114765","DOIUrl":"10.1016/j.asoc.2026.114765","url":null,"abstract":"<div><div>Vision-language models (VLMs) have driven substantial progress in human-object interaction (HOI) detection. However, existing VLM-based HOI detectors typically rely on coarse multimodal prompts for knowledge transfer, which makes it difficult to comprehensively capture interaction-relevant contextual cues and consequently weakens generalization to HOI detection. Meanwhile, hard-label supervised learning fundamentally ignores semantic correlations among interaction categories, which tends to suppress knowledge transfer due to misalignment with the continuous semantic similarity structure encoded by VLM representations in the embedding space. To address these challenges, we propose SMPL, a <strong>S</strong>oft-label guided <strong>M</strong>ulti-granularity <strong>P</strong>rompt <strong>L</strong>earning model for HOI detection, which facilitates prompt learning by jointly capturing multi-level interaction cues and providing semantically calibrated supervision aligned with VLM embeddings. Specifically, we design multi-granularity visual and textual prompts to capture interaction cues at different levels of detail, thereby improving generalization to interaction categories. Moreover, we introduce soft-label learning to jointly optimize interaction classification with the hard-labels and soft-label supervision, which naturally reflects interaction-level semantic similarity, enabling the model to learn implicit interaction relations without additional annotations. Extensive experiments demonstrate that SMPL achieves 38.97 mAP on the HICO-DET dataset and improves performance by 2.64 mAP over the current state of the art on the challenging Rare split. SMPL also performs strongly under multiple zero-shot HOI settings, demonstrating excellent generalization to unseen interactions. The code and models are available at <span><span>https://github.com/hxqstree/SMPL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114765"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191780","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":"An intelligent over-sampling framework with physics-driven guidance for noise robust imbalanced rolling bearing fault diagnosis","authors":"Weidong Jiao ,&nbsp;Daxuan Lin ,&nbsp;Zhilin Dong ,&nbsp;Jianfeng Sun ,&nbsp;Wanxiu Xu ,&nbsp;Xiaohao Chen ,&nbsp;Siyu Liu ,&nbsp;Yonghua Jiang","doi":"10.1016/j.asoc.2026.114791","DOIUrl":"10.1016/j.asoc.2026.114791","url":null,"abstract":"<div><div>Driven by advances in deep learning, intelligent fault diagnosis is widely deployed in predictive maintenance and reliability assurance for rotating machinery. However, these methodologies hinge on noiseless, balanced data—an ideal that rarely aligns with real-world engineering scenarios. To address this issue, a Physics-driven Feature Space Synthetic Minority Over-Sampling Technique (PFS-SMOTE) is introduced that augments minority-class samples in the feature space by incorporating physical prior knowledge, thereby improving the performance of intelligent fault-diagnosis models. Initially, a Neural Blind Deconvolution Mechanism (NBDM) is proposed, which utilizes adaptive fuzzy kernels optimized via complex frequency domain vectors to effectively extract fault impulse features. Next, we construct the Multi-Resolution Time-Frequency Convolution (MRTFConv) layer, utilizing Short-Time Fourier Transform-inspired kernels with adaptive Gaussian temporal envelopes and learnable oscillatory components for physically interpretable time-frequency representations. Building upon these innovations, a deep Siamese Network, integrating NBDM and MRTFConv, maps original data into a physically separable feature space, guided by a physics-informed loss function encompassing contrastive loss, time-frequency consistency loss, and fuzzy kernel consistency loss. Ultimately, PFS-SMOTE generates synthetic fault samples in this well-separated space, ensuring a balanced dataset. Experimental validation across two bearing datasets demonstrates that PFS-SMOTE achieves 100 % accuracy under an imbalance ratio (IR) of 1:10 and maintains 93 % accuracy at SNR= 0 dB. Even under extreme conditions with IR= 1:40 and SNR= 4 dB, the method still achieves an accuracy rate of 89.5 %.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114791"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191781","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}