Information Sciences最新文献_第5页

Language-based opacity in modular discrete event systems: Compositional secret-based verification using labeled petri nets 模块化离散事件系统中基于语言的不透明性：使用标记petri网的基于组合秘密的验证

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-27 DOI: 10.1016/j.ins.2025.122701

Salwa Habbachi , Imen Ben Hafaiedh , Zhiwu Li

{"title":"Language-based opacity in modular discrete event systems: Compositional secret-based verification using labeled petri nets","authors":"Salwa Habbachi , Imen Ben Hafaiedh , Zhiwu Li","doi":"10.1016/j.ins.2025.122701","DOIUrl":"10.1016/j.ins.2025.122701","url":null,"abstract":"<div><div>This work focuses on verifying language-based opacity within modular discrete-event systems. We consider a distributed system that is modeled as a composition of multiple interacting modules, each modeled by a labeled Petri net. Ensuring confidentiality in such systems is critical for cyber-physical systems and industrial networks, where unauthorized inference of sensitive data can lead to security breaches. We introduce a new definition of language-based opacity for modular systems and propose three secret-based verification methods that avoid the construction of the monolithic system through parallel composition. Our approach includes three methods: (1) global secret verification via observer synchronization; (2) local, module-level secret verification; and (3) an iterative composition optimization that avoids building the entire modular system, yielding significant computational savings. Experimental results on a benchmark smart manufacturing system demonstrate the practical efficiency of our approach, showing orders-of-magnitude improvement in verification time and memory usage over traditional monolithic approaches.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"724 ","pages":"Article 122701"},"PeriodicalIF":6.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222973","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}

引用次数: 0

Social-semantic enhanced dual-intent hypergraph collaborative filtering 社会语义增强的双意图超图协同过滤

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-27 DOI: 10.1016/j.ins.2025.122714

Xianji Cui , Jinhua Zhang , Yan Lan , Shan Huang

{"title":"Social-semantic enhanced dual-intent hypergraph collaborative filtering","authors":"Xianji Cui , Jinhua Zhang , Yan Lan , Shan Huang","doi":"10.1016/j.ins.2025.122714","DOIUrl":"10.1016/j.ins.2025.122714","url":null,"abstract":"<div><div>Recommender systems provide personalized recommendations by modeling user–item interactions, where disentangling users’ intents is critical for improving recommendation accuracy. While existing intent modeling methods aim to capture fine-grained intent representations, they face two challenges: 1) Neglecting the influence of social semantics on modeling fine-grained intents; 2) Implicit data sparsity and intent redundancy limiting intent characterization. To tackle these challenges, we propose a Social-Semantic Enhanced Dual-Intent Hypergraph Collaborative Filtering (SDIHGCF) model. Specifically, SDIHGCF constructs hypergraph structures to preserve social semantics among users, items, and groups. It encodes features from both social and interest perspectives to achieve user and item representations that integrate individual intent, which signifies private preferences, and collective intent, which denotes overall awareness. To mitigate data sparsity and intent redundancy, where one intent can be represented by others, we use graph contrastive regularization to enforce consistency among users, items, intents, and interactions. Additionally, a bidirectional contrastive learning loss is proposed to enhance intent alignment. Experiments on four datasets demonstrate that SDIHGCF outperforms existing methods, offering novel insights into fine-grained intent modeling.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122714"},"PeriodicalIF":6.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270812","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}

引用次数: 0

Adversarial attacks on industrial soft sensors: Multi-target attacks based on diffusion models 工业软传感器的对抗性攻击：基于扩散模型的多目标攻击

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-27 DOI: 10.1016/j.ins.2025.122732

Qingchao Jiang , Shihao Fan , Zhiying Zhu , Zhenxuan Hou , Weimin Zhong , Lei Tan , Zhenxing Qian , Xinpeng Zhang

{"title":"Adversarial attacks on industrial soft sensors: Multi-target attacks based on diffusion models","authors":"Qingchao Jiang , Shihao Fan , Zhiying Zhu , Zhenxuan Hou , Weimin Zhong , Lei Tan , Zhenxing Qian , Xinpeng Zhang","doi":"10.1016/j.ins.2025.122732","DOIUrl":"10.1016/j.ins.2025.122732","url":null,"abstract":"<div><div>Industrial soft sensors serve as critical instruments for real-time monitoring and quality prediction in complex industrial systems, including chemical processing and energy production. While adversarial attacks on these sensors have garnered extensive attention, a critical gap persists: existing methods are fundamentally limited to single-target objectives. They fail to address inherent multi-variable couplings in industrial processes, limiting applicability in real-world scenarios requiring coordinated control of interdependent variables. To bridge this gap, this paper introduces a multi-target adversarial example attack framework based on diffusion models (DMAA) for the first time, which integrates noise scheduling and inverse denoising processes to generate adversarial examples that are more reasonable and invisible. The framework incorporates a multi-target attack optimization module, which facilitates targeted bias control for several key variables after the noise is added. Subsequently, it leverages a multilayer perceptron to effectively predict noise and generate adversarial examples, thereby driving the multi-target prediction outcomes to diverge from the actual ground truth. In case study of the sulfur recovery unit dataset (SRU), compared to existing methods, the proposed method shows significant advantages in attack effectiveness and stealth, providing new insights for the security evaluation and defense mechanism design of industrial soft sensors.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122732"},"PeriodicalIF":6.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271320","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}

引用次数: 0

NLSC: A noise-robust label shift correction framework via three-head training and class-adaptive cleaning 通过三头训练和类别自适应清洗的噪声鲁棒标签移位校正框架

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-27 DOI: 10.1016/j.ins.2025.122706

Xiaowen Wu, Ruidong Fan, Tingjin Luo, Chenping Hou

{"title":"NLSC: A noise-robust label shift correction framework via three-head training and class-adaptive cleaning","authors":"Xiaowen Wu, Ruidong Fan, Tingjin Luo, Chenping Hou","doi":"10.1016/j.ins.2025.122706","DOIUrl":"10.1016/j.ins.2025.122706","url":null,"abstract":"<div><div>Label shift occurs when the conditional distributions remain consistent between source and target domains, but the marginal label distributions differ. For instance, during the early stage of the COVID-19 outbreak, the proportion of pneumonia cases compared to common cold cases in hospitals may have been relatively low. This ratio could shift dramatically in later stages of the pandemic, with pneumonia cases becoming predominant, even though the symptomatic presentation of each disease remained consistent. Existing label shift methods typically aim to adapt a classifier’s output to match the target domain’s label distribution, assuming the source domain has clean labels. However, real-world scenarios often involve label noise in the source domain. For example, during COVID-19’s early phase, mild and confusable symptoms frequently led to misdiagnoses of COVID-19 as the common cold, introducing label noise. Such noise compromises the effectiveness of traditional methods, necessitating novel approaches. To address this, we analyze classifier error bounds under label shift correction using noisy source data. Based on this analysis, we propose a Noise-robust Label Shift Correction (NLSC) framework. NLSC employs a Three-Head Architecture Training (THAT) strategy for robust feature learning and a Class-Adaptive Threshold Cleaning (CATC) strategy for source data purification. Extensive experiments confirm that our method outperforms existing state-of-the-art techniques, particularly in real-world scenarios with high source domain noise rates.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122706"},"PeriodicalIF":6.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270810","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}

引用次数: 0

FGNet: Robust lane detection for autonomous driving via frequency-guided feature enhancement FGNet：通过频率引导特征增强实现自动驾驶的鲁棒车道检测

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-26 DOI: 10.1016/j.ins.2025.122694

Zilong Zhou , Xuyang Lu , Ping Liu , Haibo Huang

{"title":"FGNet: Robust lane detection for autonomous driving via frequency-guided feature enhancement","authors":"Zilong Zhou , Xuyang Lu , Ping Liu , Haibo Huang","doi":"10.1016/j.ins.2025.122694","DOIUrl":"10.1016/j.ins.2025.122694","url":null,"abstract":"<div><div>Lane detection is a critical component in autonomous driving perception systems. Complex road scenarios featuring varying lane appearances, challenging lighting conditions, and vehicle occlusions pose significant challenges for accurate lane detection. To address these problems, we propose FGNet, a robust lane detection framework that enhances feature representation through frequency-domain analysis and adaptive global-local fusion. We first introduce a Wavelet-enhanced Feature Pyramid Network (WLFPN) that leverages discrete wavelet decomposition and directional convolutions to capture high-frequency geometric features critical for lane structure modeling. Subsequently, a Global-Aware Feature Refinement (GAFR) module is designed to overcome insufficient global context integration in existing anchor-based methods, enabling adaptive feature enhancement through spatially-aware attention and selective fusion mechanisms. Finally, a Dynamic Loss Harmonizer (DLH) employs momentum-based dynamic weight adjustment to optimize multi-loss learning, improving training stability and convergence. Extensive experiments demonstrate that FGNet achieves state-of-the-art performance with F1 scores of 80.64 % and 97.89 % on the challenging CULane and TuSimple datasets, respectively, outperforming existing methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"724 ","pages":"Article 122694"},"PeriodicalIF":6.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223112","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}

引用次数: 0

A novel distributionally robust uncertain optimization method with application to bus bridging service under rail disruptions 一种新的分布鲁棒不确定优化方法，并应用于铁路中断下的公交线桥服务

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-26 DOI: 10.1016/j.ins.2025.122683

Shize Ning, Hongguang Ma

{"title":"A novel distributionally robust uncertain optimization method with application to bus bridging service under rail disruptions","authors":"Shize Ning, Hongguang Ma","doi":"10.1016/j.ins.2025.122683","DOIUrl":"10.1016/j.ins.2025.122683","url":null,"abstract":"<div><div>Bus bridging service (BBS), as an effective means of evacuating passengers during rail disruptions, has received significant attention. However, the BBS network under rail disruptions involves complex uncertainty. In view of this, this paper innovatively defines an uncertainty distribution set to describe this uncertainty. Based on the defined uncertainty distribution set and the best-case scenario, this paper proposes a novel distributionally robust uncertain optimization method for the BBS network under rail disruptions, and constructs the corresponding model. To overcome the computational challenges of the model, this paper clarifies the specific structural characteristics of the uncertainty distribution set. By using uncertainty theory and dual techniques, the proposed model is equivalently transformed into either a mixed-integer linear programming formulation or a mixed-integer second-order cone programming formulation. The proposed method not only extends uncertainty theory under the ambiguity of the uncertainty distribution but also provides a theoretically derived computational formulation for the model. Finally, a real-world case validates the model, while sensitivity analysis and comparative experiments demonstrate the validity and advantages of the proposed method and model.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122683"},"PeriodicalIF":6.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271317","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}

引用次数: 0

A cognitive dual-attention network with feature specificity for automated CRC polyp detection 一种具有特征特异性的认知双注意网络用于CRC息肉自动检测

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-26 DOI: 10.1016/j.ins.2025.122727

T.P. Raseena , S.R. Balasundaram , Jitendra Kumar

{"title":"A cognitive dual-attention network with feature specificity for automated CRC polyp detection","authors":"T.P. Raseena , S.R. Balasundaram , Jitendra Kumar","doi":"10.1016/j.ins.2025.122727","DOIUrl":"10.1016/j.ins.2025.122727","url":null,"abstract":"<div><div>Colorectal cancer is the foremost cause of cancer-related deaths worldwide. Thus, early diagnosis of precancerous polyps is crucial for more effective treatment outcomes. To address the persistent issues in colorectal cancer diagnosis, this study proposes a novel classification model, E-D<sup>2</sup>AN (Efficiently Dilated Dual Attention Network), capable of accurately distinguishing colorectal abnormalities from normal colonoscopy images. The proposed model features dual attention mechanisms of EDCAM (Efficiently Dilated Channel Attention Mechanism) and ESAM (Efficient Spatial Attention Mechanism) strategies, utilizing an extended ResNet50 as the backbone. This network prioritizes efficient attention mechanisms, enriching feature extraction through an expanded receptive field and significantly driving attention-focused learning to achieve improved precision in the classification model. This combination improves the model’s ability to locate and focus on crucial regions in images, resulting in higher diagnostic precision. Dropblock regularization is also used strategically to reduce overfitting and improve generalization to unseen images. The proposed E-D<sup>2</sup>AN model excels across all three benchmark datasets, demonstrating superior performance over existing approaches. Prominently, it achieves an accuracy of 83.69 % on PolypsSet, 99.74 % on CKC, and 98.75 % on Kvasir-2 datasets. These findings reveal the model’s ability to improve the accuracy and reliability of early polyp detection.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122727"},"PeriodicalIF":6.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271323","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}

引用次数: 0

A survey of folding-based zero-knowledge proofs 基于折叠的零知识证明综述

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-26 DOI: 10.1016/j.ins.2025.122698

Cyprian Omukhwaya Sakwa , Andrew Omala Anyembe , Fagen Li

{"title":"A survey of folding-based zero-knowledge proofs","authors":"Cyprian Omukhwaya Sakwa , Andrew Omala Anyembe , Fagen Li","doi":"10.1016/j.ins.2025.122698","DOIUrl":"10.1016/j.ins.2025.122698","url":null,"abstract":"<div><div>This survey uniquely approaches zero-knowledge proofs (ZKPs) through the lens of folding schemes, offering a fresh framework to analyze efficiency, scalability, and post-quantum resilience. By focusing on folding, we unify diverse protocols, clarify trade-offs, and identify practical engineering constraints, providing both researchers and practitioners with actionable insights. Folding schemes have emerged as the simplest and fastest approach to incrementally verifiable computation (IVC), enabling recursive zero-knowledge arguments with constant recursion overhead. We present a unifying model of folding-based ZKPs across R1CS, Plonkish/CCS, and AIR; synthesize the state of the art from Nova, SuperNova, HyperNova, and cycle-of-curves instantiations to recent post-quantum lattice-based foldings; provide a rigorous comparison of prover time, verifier work, proof size, setup assumptions, and recursion overhead; and map real deployments—including Lurk/Nova, Sonobe-based light clients, and VIMz-style media proofs—to practical constraints. Finally, we highlight open problems such as hybrid elliptic-curve–lattice designs and engineering targets for memory-bounded provers, showing how this folding-centric view advances both theoretical understanding and real-world deployment of ZKPs.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"724 ","pages":"Article 122698"},"PeriodicalIF":6.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222974","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}

引用次数: 0

Optimizing the number of floorplanning layers for stacked integrated circuits based on spiking variational graph auto-encoders 基于尖峰变分图自编码器的堆叠集成电路布局层数优化

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-26 DOI: 10.1016/j.ins.2025.122681

Kaikai Qiao , Ai Chen , Lidan Wang , Shukai Duan

{"title":"Optimizing the number of floorplanning layers for stacked integrated circuits based on spiking variational graph auto-encoders","authors":"Kaikai Qiao , Ai Chen , Lidan Wang , Shukai Duan","doi":"10.1016/j.ins.2025.122681","DOIUrl":"10.1016/j.ins.2025.122681","url":null,"abstract":"<div><div>As the complexity of chip design continues to increase, the stacking of multiple device layers in a three-dimensional (3D) architecture has emerged as a promising approach to improve performance, power efficiency and area (PPA). The optimization of macro-module arrangement and inter-tier connections in 3D stacked chip layout is significantly influenced by the selection of the number of layers, which affects both the feasibility of the layout optimization and the final performance of the chips. In this paper, we creatively propose the Spiking Variational Graph Auto-Encoders (S-VGAE), which aim to be applied in several varieties of stacked clustering to partition the data set comprising 22 integrated circuits. By converting graph topology into spatiotemporal pulse patterns, the Spiking Graph Convolution fundamentally enhances the representational capacity of subsequent Graph Auto-Encoders. In the layout stage for all dies, we propose the Memristive-Inspired Bottom-up Left Justified Learning (MBLJL) Strategy to determine the better performance of bi-level or tri-level stacked floorplanning layout.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"724 ","pages":"Article 122681"},"PeriodicalIF":6.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222975","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}

引用次数: 0

A lightweight real-time detection transformer model for surface defect detection systems 一种用于表面缺陷检测系统的轻量级实时检测变压器模型

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2025-09-25 DOI: 10.1016/j.ins.2025.122685

Yingqiang Hou, Xindong Zhang

{"title":"A lightweight real-time detection transformer model for surface defect detection systems","authors":"Yingqiang Hou, Xindong Zhang","doi":"10.1016/j.ins.2025.122685","DOIUrl":"10.1016/j.ins.2025.122685","url":null,"abstract":"<div><div>Accurate and efficient detection of surface defects is essential for production and infrastructure monitoring. A lightweight real-time surface defect detector is proposed, the Surface Defect Detection Real-Time DEtection TRansformer (SDD-RTDETR). It builds upon the Real-Time DEtection TRansformer v2 (RT-DETRv2). Key innovations include Re-Parameterized Partial Convolution (RPConv) within the BasicBlock_RPConv, which minimizes computational workload and memory requirements while boosting performance. The model also proposes the Efficient Multi-Scale Attention-based Feature Interaction (EMSAFI) module to strengthen feature extraction capabilities and employs the lightweight fusion architecture LiteScaleNeck to optimize feature fusion. Additionally, the Inner-Minimum Point Distance Intersection over Union (Inner-MPDIoU) loss refines bounding box regression, further improving model performance. The experimental findings reveal that SDD-RTDETR excels across multiple surface defect datasets. In contrast to the benchmark model, this approach improves detection accuracy while decreasing parameters by 34.6 % and computational complexity by 23.0 %, validating its adaptability and generalization ability in surface defect testing. With its lightweight structure and superior capability, SDD-RTDETR provides an effective approach for large-scale, immediate inspection, driving automation in quality control and infrastructure monitoring.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122685"},"PeriodicalIF":6.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271322","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}

引用次数: 0