Engineering Applications of Artificial Intelligence最新文献

{"title":"Patent technology knowledge recommendation by integrating large language models and knowledge graphs","authors":"Peiyi Yang,&nbsp;Xuewei Wu,&nbsp;Peihan Wen","doi":"10.1016/j.engappai.2026.114176","DOIUrl":"10.1016/j.engappai.2026.114176","url":null,"abstract":"<div><div>With the increasing complexity of technology and the interdisciplinary nature of research and development (R&amp;D) activities, how to quickly acquire highly relevant and interpretable patent knowledge has become an important issue in engineering innovation. In view of the high information density, abundance of specialized terminology, and complex structure inherent in patent texts, as well as the stringent requirements for accuracy and interpretability in knowledge recommendation within R&amp;D scenarios, a technology knowledge recommendation framework based on patent texts was proposed, which integrates knowledge ontology, graph representation learning, and Retrieval-Augmented Generation (RAG). Firstly, a hierarchical fine-grained ontology model for patent technology knowledge was constructed. On this basis, efficient knowledge extraction and knowledge graph (KG) construction were realized through two-stage prompt engineering. Secondly, a KG representation learning method combining semantic information and structural information is proposed, which integrates semantic enhanced representation and relational graph convolutional networks to realize technology knowledge mining. Finally, the ontology-driven meta-path generation strategy is introduced and integrated with RAG, the reasoning path is generated through large language model (LLM), and the pruning mechanism based on LLM score is introduced to augment the relevance and interpretability of the recommended content. Case-based experiments demonstrate that the proposed method outperforms baseline approaches and provides technical support for the reuse and innovation of patent knowledge in R&amp;D scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114176"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Dual-channel heterogeneous graph framework with multi-view contrastive learning for drug–drug interaction prediction","authors":"Shilong Wang,&nbsp;Hai Cui,&nbsp;Yanchen Qu,&nbsp;Xiaobo Li,&nbsp;Yijia Zhang","doi":"10.1016/j.engappai.2026.114141","DOIUrl":"10.1016/j.engappai.2026.114141","url":null,"abstract":"<div><div>Drug-drug interactions can result in severe adverse reactions and pose substantial threats to public health. Therefore, accurately predicting potential interactions between drugs has become a critical research direction. Recently, network-based approaches have created new opportunities in this field. However, existing methods still struggle to generalize to unseen drugs and are susceptible to structural perturbations, leading to limited robustness. Moreover, most methods rely on shallow or static fusion of heterogeneous drug representations, lacking mechanisms to adaptively capture complementary structural and semantic information. To address these issues, we propose a dual-channel heterogeneous graph framework that performs gated feature fusion between molecular graph representations and biomedical knowledge graph representations, while incorporating multi-view contrastive learning to enhance prediction performance. The proposed framework leverages a pretrained heterogeneous graph neural network to jointly model structural and semantic dependencies, thereby improving representation quality and model generalization. In addition, a multi-view contrastive learning strategy is introduced to further strengthen the discriminative power and robustness of drug representations. Experimental results demonstrate that our method consistently outperforms state-of-the-art models across all benchmark datasets. Further case studies confirm its effectiveness in predicting drug interaction relationships, highlighting its potential to provide reliable computational support for clinical decision-making and drug discovery.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114141"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-axial vibration fatigue optimization strategy based on the artificial intelligence algorithm","authors":"Xiaopeng Zhang ,&nbsp;Boqiang Zhang ,&nbsp;Dengfeng Wang ,&nbsp;Zihao Meng ,&nbsp;Fengmin Lian ,&nbsp;Jialin Dong ,&nbsp;Shang Zhang ,&nbsp;Hongli Chen ,&nbsp;Haijun Ruan","doi":"10.1016/j.engappai.2026.114084","DOIUrl":"10.1016/j.engappai.2026.114084","url":null,"abstract":"<div><div>To reduce the computational cost in the multi-criteria optimization process based on multi-axial vibration fatigue life and improve the fairness of multi-criteria decision-making, this paper proposes a multi-axial vibration fatigue optimization strategy based on artificial intelligence. This strategy combines a hybrid surrogate model using Random Forest (RF) and Radial Basis Function Neural Network (RBFNN), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Modified Preference Selection Index (MPSI). The multiaxial vibration fatigue formula is derived for calculating the fatigue life of structures. The RF-RBFNN hybrid surrogate model is used to fit the relationship between variables and responses. The NSGA-II is employed to mine the Approximation set from the hybrid surrogate model, and the Modified Preference Selection Index method is used to determine the best compromise solution. The research results indicate that, while satisfying all constraint indicators, the weight reduction rate of the vehicle frame is 4.9%, and the prediction accuracy of the fatigue life surrogate model is 97.015%. The main contribution of this paper is to extend the artificial intelligence algorithm to the field of multi-axis vibration fatigue optimization and apply it to the solution process of the fatigue life of the tractor frame.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114084"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Seismic fragility assessment of curved girder bridges under vehicle-induced risks: A specialized deep learning-based neural network approach","authors":"Wei-zuo Guo ,&nbsp;Wei-you Guo ,&nbsp;Yan Gong ,&nbsp;Shu-mao Qiu","doi":"10.1016/j.engappai.2026.114066","DOIUrl":"10.1016/j.engappai.2026.114066","url":null,"abstract":"<div><div>The special horizontal alignment of curved girder bridges often leads to higher seismic demands than those of straight bridges, resulting in greater seismic fragility. With the continuing growth in transportation and logistics demand, the likelihood of heavy vehicles being stranded on bridges during earthquakes further increases, amplifying the seismic risk of curved girder bridges. However, existing data-driven seismic fragility assessment methods generally neglect the additional risks introduced by vehicle loads. Therefore, this study develops a specialized deep learning model—the seismic fragility embedding neural network under vehicle-induced risks for curved girder bridges (SFENR)—to assess their seismic fragility under combined vehicle–earthquake effects. An automated parametric finite element (APFE) program is developed to efficiently simulate the vehicle–curved girder bridge system and batch-produce nonlinear dynamic responses, thereby providing essential data support for training the SFENR model. A case study is then conducted on a typical three-span continuous curved box girder bridge to systematically investigate how vehicles with different weights and positions affect the seismic fragility of bridge components. The results demonstrate that the proposed SFENR model substantially outperforms conventional neural networks in terms of both memory efficiency and prediction accuracy. Specifically, the SFENR achieves a nearly 50% reduction in memory usage while improving Accuracy by 2–10%, with both Precision and Recall consistently maintained above 70%. Furthermore, the fragility curves of structural components exhibit greater sensitivity to variations in the tangential rather than radial positions of vehicles on the bridge deck. The presence of vehicles induces a non-monotonic effect on the seismic fragility of curved girder bridges—meaning that vehicles increase fragility at lower ground motion intensities but reduce it at higher intensities. This highlights the importance of considering vehicle effects in seismic risk evaluation and advances the development of more reliable fragility assessment for highway bridges.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114066"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Dimos: Diffusion model with unified sequential state space for session-based recommendation","authors":"Weiyue Li ,&nbsp;Ming Gao ,&nbsp;Bowei Chen ,&nbsp;Jingmin An ,&nbsp;Hao Dong ,&nbsp;Wei Jiang ,&nbsp;Jiafu Tang","doi":"10.1016/j.engappai.2026.114131","DOIUrl":"10.1016/j.engappai.2026.114131","url":null,"abstract":"<div><div>Session-based recommendation aims to predict the next item based on the user–item interactions within the current session. Many existing methods adopt discriminative approaches to learn specific preference representations, while few methods introduce generative approaches to learn underlying preference distributions, failing to handle limited and noisy interactions effectively. Moreover, naive implementations of generative models face a trade-off between effectiveness and efficiency, limiting their practical utility. To address these challenges, we propose Dimos, a dual-branch framework comprising an exploring branch and an exploiting branch, which leverage diffusion models and attention networks to capture implicit and explicit preferences, respectively. At the core of Dimos is Bi-MaKAN, a novel backbone architecture featuring a pair of parameter-sharing bidirectional Mamba blocks and a Kolmogorov–Arnold network-based feature fusion layer, designed to enhance both performance and efficiency. To further improve generalization and reduce overfitting, we unify the sequential state spaces of both branches. Additionally, we introduce a linearly weighted fusion mechanism that integrates preference representations from both branches, enabling flexible adjustment of implicit and explicit preference contributions during training and inference. Extensive experiments on three real-world benchmark datasets demonstrate the superiority of Dimos, achieving up to 2.79% improvement in Recall, 3.09% in Mean Reciprocal Rank (MRR), and 3.00% in Normalized Discounted Cumulative Gain (NDCG) over state-of-the-art baselines. Efficiency evaluations show substantial gains, with reductions of 94.32% in Graphics Processing Unit (GPU) memory usage, 66.81% in training time, and 98.80% in inference time. In-depth analyses reveal a collaborative effect between the two branches during both training and inference, with dataset scale modulating their relative importance.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114131"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Adaptive critical speed prediction for straddle-type monorail operational safety: A meta-learning framework with few-shot deployment","authors":"Junchao Zhou ,&nbsp;Ao Chen ,&nbsp;Shangwu Huang ,&nbsp;Jianjie Gao ,&nbsp;Haiping Du","doi":"10.1016/j.engappai.2026.114063","DOIUrl":"10.1016/j.engappai.2026.114063","url":null,"abstract":"<div><div>Accelerating global urbanization has intensified the demand for efficient and sustainable transportation solutions in high-density areas. Traditional ground-based transit systems face congestion and pollution challenges in spatially constrained regions. Against this backdrop, the Straddle-type Monorail System (SMS), distinguished by its lightweight structure, lower infrastructure costs, and unique elevated spatial efficiency, emerges as a critical option for optimizing urban commuting networks. However, a fundamental challenge for Straddle-type Monorail Vehicle (SMV) operational safety is lateral shimmy vibration instability. Conventional dynamic modelling approaches struggle to predict shimmy bifurcation boundaries effectively due to computational inefficiency and poor parametric generalization. To address these limitations, this research proposes a novel meta-learning framework named MAML-CNN-LSTM-Attention (M-CLA) for few-shot critical speed prediction, which integrates Model-Agnostic Meta-Learning (MAML), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Attention mechanism. Trained on a 7-DOF vehicle-track coupling model, the M-CLA framework processes lateral displacement and velocity time-series data to achieve 99.67% prediction accuracy for the critical speed under few-shot conditions. It demonstrates rapid adaptation and superior generalization across scenarios with minimal data, offering a practical AI tool for enhancing SMS safety, reducing maintenance costs, and preventing derailments. The framework rapidly adapts to new operational scenarios with minimal data, outperforming traditional deep learning methods in both prediction accuracy and cross-condition generalization. It provides infrastructure managers with an Artificial Intelligence (AI)-driven tool for dynamic optimization and safety evaluation of SMS, effectively contributing to derailment prevention, maintenance cost reduction, and enhanced operational safety across diverse urban rail transit environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114063"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhanced anomaly interpretation in intelligent vehicles: A causal constraint graph attention network for root cause diagnosis","authors":"Shixiang Chen ,&nbsp;Haigen Min ,&nbsp;Xia Wu ,&nbsp;Xiangmo Zhao","doi":"10.1016/j.engappai.2026.114030","DOIUrl":"10.1016/j.engappai.2026.114030","url":null,"abstract":"<div><div>Intelligent vehicles increasingly rely on multi-sensor data for real-time environmental perception and decision-making, yet vehicular sensors are vulnerable to various disturbances, generating anomalies that threaten safety. Traditional anomaly diagnosis methods, such as graph attention networks, suffer from spurious correlations and fixed thresholds, leading to unreliable diagnoses under distribution shifts. To overcome these limitations, this paper proposes a causal constraint graph attention network for multi-sensor data anomaly detection and root cause diagnosis in intelligent vehicles. First, to address spurious correlations and parameter estimation bias in traditional graph attention networks, a sparse connection attention mechanism is designed that transforms fully-connected attention computation into one guided by node causal relationships. Second, to address the false positives and false negatives issues caused by a fixed threshold under environmental changes, a sliding adaptive threshold calculation method based on data anomaly degree weighting is proposed. Third, to achieve interpretable anomaly analysis, a hierarchical anomaly diagnosis strategy is implemented, which integrates feature reconstruction errors, variable causal relationships, and graph attention network weights for root cause localization and identification in anomaly propagation paths. Experiments with real vehicle data and public datasets demonstrate that the proposed method achieves excellent anomaly detection performance and enables an interpretable root cause diagnosis for six types of anomalies, benefiting from the causal constraint attention mechanism and adaptive threshold. With real-time inference capability, the proposed method provides significant opportunities for practical deployment of anomaly diagnosis in intelligent vehicle applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114030"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 framework integrating data-driven and computational fluid dynamics simulation for continuous blast furnace monitoring","authors":"Xin Wang,&nbsp;Xiao-Yu Tang,&nbsp;Zheng Hao ,&nbsp;Kunwei Lin,&nbsp;Chunjie Yang,&nbsp;Wenhai Wang","doi":"10.1016/j.engappai.2026.114092","DOIUrl":"10.1016/j.engappai.2026.114092","url":null,"abstract":"<div><div>Due to the complex and nonlinear characteristics of blast furnace (BF) systems, conventional data/mechanism-driven modeling methods have been facing challenges in continuously on-field BF internal state monitoring. Mechanism-driven computational fluid dynamics (CFD) simulations, while interpretable, have high computational costs that prevent real-time application. Conversely, data-driven methods often ignore the coupling relationships between variables and fail to provide a comprehensive understanding of the BF's internal state, which hinders precise control. In response to the aforementioned issues, this paper proposes a novel state continuous monitoring framework that incorporates data-driven with offline pre-calculated CFD simulations. It decouples the intricate and nonlinear BF operation process into a set of interpretable sub-modes, modeling each sub-mode via numerical simulations, then reconstructs the real-time BF transient by properly selecting and fusing some sub-modes. In the offline stage, the particle swarm optimization (PSO) algorithm is employed to obtain the sub-modes that best represent BF operational states. The CFD simulation is then conducted for multiple physical field states corresponding to each sub-mode. For online monitoring, a multi-mode fusion strategy (MMFS) is designed to achieve the real-time BF transient modeling. Application in a BF in South China validates the effectiveness of the proposed framework.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114092"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Robust classification method for printed circuit board defects based on virtual and real space cooperative diffusion model","authors":"Xinyi Yu ,&nbsp;Haotian Sun ,&nbsp;Yuanfu He ,&nbsp;Jinmin Peng ,&nbsp;Xianping Zeng ,&nbsp;Liangshen Chen","doi":"10.1016/j.engappai.2026.114126","DOIUrl":"10.1016/j.engappai.2026.114126","url":null,"abstract":"<div><div>Printed circuit board defect detection based on deep learning is a key technology in intelligent manufacturing. However, datasets collected in industrial environments often suffer from label noise due to inconsistent expert knowledge and ambiguous defect features, which severely degrades the performance of conventional models. To address this challenge, this paper proposes a robust classification framework based on a virtual-real space collaborative diffusion model. The proposed method introduces a distributed guidance mechanism to construct a latent semantic space (virtual space), which provides probabilistic priors for the inverse diffusion process in the real image space. Furthermore, a parameterized label encoding module is designed to mitigate the loss of fine-grained semantics during forward diffusion, and an attribute interaction attention mechanism is proposed to enhance the modeling of key defect attributes. Extensive experiments on the publicly available dataset with varying levels of label noise demonstrate that our method outperforms state-of-the-art approaches in terms of classification accuracy, robustness, and generated image quality, especially under strong noise conditions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114126"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"LLM4CGDS: Large language model-based agents for Chinese graded document simplification","authors":"Dengzhao Fang ,&nbsp;Jipeng Qiang ,&nbsp;Wenjie Hou ,&nbsp;Yi Zhu ,&nbsp;Jingtong Gao ,&nbsp;Xiangyu Zhao","doi":"10.1016/j.engappai.2026.113905","DOIUrl":"10.1016/j.engappai.2026.113905","url":null,"abstract":"<div><div>Graded reading tailors text difficulty to learners’ proficiency by producing multiple versions of the same content—an approach long embraced in language education but still dependent on labor-intensive, expert-driven adaptation. In this paper, we introduce the task of <strong>C</strong>hinese <strong>G</strong>raded <strong>D</strong>ocument <strong>S</strong>implification (CGDS) for non-native learners, which seeks to automate the creation of multi-level reading materials in accordance with established proficiency standards. Guided by the three stages of the Hanyu Shuiping Kaoshi (HSK) 3.0 framework (Levels 1–3 for Advanced, Levels 4–6 for Intermediate, and Levels 7–9 for Beginner learners), we propose Large Language Model for Chinese Graded Document Simplification (LLM4CGDS), a rule-guided, large language model (LLM)-based framework that integrates HSK-level readability constraints and external knowledge retrieval to control document-level simplification without requiring supervised fine-tuning. To foster further research, we construct two complementary datasets: <strong>J</strong>ourney to the <strong>W</strong>est <strong>D</strong>ocument <strong>S</strong>implification (JWDS) and <strong>M</strong>ulti-<strong>D</strong>omain <strong>D</strong>ocument <strong>S</strong>implification (MDDS) that covering diverse genres and difficulty levels. Experimental evaluation on two datasets demonstrates that LLM4CGDS substantially outperforms direct prompting of state-of-the-art LLMs in both readability control and meaning preservation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113905"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}