Engineering Applications of Artificial Intelligence最新文献

{"title":"Cross-scale hybrid attention network for enhancing performance prediction of modified asphalt binder preparation","authors":"Jiakang Zhang ,&nbsp;Guoan Gan ,&nbsp;Kun Long ,&nbsp;Allen A. Zhang ,&nbsp;Jing Shang ,&nbsp;Chuanqi Yan ,&nbsp;Changfa Ai","doi":"10.1016/j.engappai.2026.114106","DOIUrl":"10.1016/j.engappai.2026.114106","url":null,"abstract":"<div><div>Asphalt materials form the foundation of pavement durability, with styrene–butadiene–styrene (SBS) copolymers widely used to enhance performance. However, the preparation of SBS-modified asphalt (SBSMA) still relies heavily on inefficient trial-and-error approaches. Although artificial intelligence–based methods have been applied to asphalt performance prediction, most existing models directly map preparation parameters to macro-performance, neglecting cross-scale mechanisms linking preparation parameters, micro-properties, and macroscopic behavior. This limitation reduces their robustness and practical applicability in complex material systems. To address this issue, this study proposes a Cross-Scale Hybrid Attention Network (CSA-Net) that explicitly models hierarchical information transfer from preparation parameters to micro-properties and further to macro-performance. CSA-Net adopts a dual-branch architecture: a micro-branch predicts micro-properties using attention-enhanced preparation features, while a macro-branch integrates attention-refined preparation features and predicted micro-features through a second attention module. Joint optimization of micro- and macro-level tasks is achieved via a composite loss function. A comprehensive experimental dataset comprising 864 SBSMA samples was established. Results show that CSA-Net achieves high accuracy in macro-performance prediction, with coefficients of determination (R²) consistently exceeding 0.982, mean absolute percentage errors below 5%, and root mean square errors within experimental uncertainty ranges. Compared with single-scale, multi-scale, and non-attention benchmark models, CSA-Net exhibits improved robustness, as demonstrated by Monte Carlo simulations, with the interquartile range of R² reduced by more than 25%. Shapley additive explanations analysis further reveals meaningful cross-scale relationships between preparation parameters, microstructural evolution, and macroscopic performance. Overall, CSA-Net provides a robust and interpretable framework for intelligent design and performance prediction of modified asphalt binders.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114106"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174975","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":"Optimizing potential-based reward automata in partially observable reinforcement learning using genetic local search","authors":"Zhengwei Zhu ,&nbsp;Zhixuan Chen ,&nbsp;Chenyang Zhu ,&nbsp;Wen Si ,&nbsp;Fang Wang","doi":"10.1016/j.engappai.2026.114054","DOIUrl":"10.1016/j.engappai.2026.114054","url":null,"abstract":"<div><div>Partially observable reinforcement learning extends the reinforcement learning framework to environments in which agents have limited visibility of the state space, making it particularly relevant for applications in robotics and autonomous vehicle navigation. However, a primary challenge in partially observable reinforcement learning is defining effective reward functions that can guide the learning process despite partial observability. To address this challenge, this paper introduces a novel approach for constructing potential-based reward automata by employing genetic local search methods. Specifically, our method constructs these automata from compressed representations of exploration trajectories, which succinctly capture critical decision points and essential state transitions while eliminating redundant steps. By optimizing trajectory samples and shortening agent trajectories to their crucial transitions, our technique significantly reduces computational overhead. Formally, we define the learning objective as an optimization problem aimed at maximizing the log-likelihood of future observations while simultaneously minimizing the structural complexity of the learned reward automata. Furthermore, by incorporating value-based strategies to estimate potential values within the reward automata, our approach improves learning efficiency and facilitates the identification of optimal reward structures. We empirically evaluate our proposed method on seven partially observable grid-world benchmarks. Experimental results demonstrate that our method achieves superior performance relative to state-of-the-art reward automata-based techniques, exhibiting both accelerated learning speeds and higher accumulated rewards. Additionally, our genetic local search algorithm consistently outperforms comparative heuristic methods in terms of learning curves and reward accumulation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114054"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175212","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":"Bayesian optimization interval type-3 fuzzy broad compensated intelligent control for flue gas oxygen content","authors":"Weiwei Yang ,&nbsp;Jian Tang ,&nbsp;Wen Yu ,&nbsp;Junfei Qiao","doi":"10.1016/j.engappai.2026.114044","DOIUrl":"10.1016/j.engappai.2026.114044","url":null,"abstract":"<div><div>In industrial sites of municipal solid waste incineration (MSWI) processes in developing countries such as China, manual control modes based on domain experts' embodied intelligence are commonly used for stable operation. Flue gas oxygen content is a crucial controlled variable in the MSWI process, where traditional control methods often lack adaptability and robustness under nonlinear uncertainties. To achieve high-precision and robust oxygen content control, this study aims to develop a novel intelligent control strategy. We propose a Bayesian optimization (BO)-based interval type-3 fuzzy broad compensated control method. The core of this approach is a parallel control architecture, which integrates an interval type-3 fuzzy broad learning system (IT3FBLS) constructed from prior knowledge with a conventional proportion integration differentiation (PID) controller. Furthermore, the BO algorithm is introduced to automatically tune the numerous hyperparameters of the hybrid IT3FBLS-PID controller, ensuring optimal performance. Experimental validation using data from an actual MSWI power plant demonstrates that, compared to conventional PID and fuzzy PID controllers, the proposed method achieves smaller steady-state error, faster response speed, and exhibits superior disturbance rejection capability. This work introduces a novel parallel control paradigm that effectively combines the interpretability and adaptability of advanced fuzzy broad learning systems with the stability of classical control. It also offers a practical BO-driven solution for parameter optimization, aimed at enhancing intelligent applications in complex industrial control systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114044"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175307","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":"Rephrasing detection in machine generated content using deep learning transformers and feature engineering with local agnostic interpretability","authors":"Syeda Hira Amjad ,&nbsp;Hikmat Ullah Khan ,&nbsp;Ali Daud ,&nbsp;Anam Naz ,&nbsp;Aseel Smerat","doi":"10.1016/j.engappai.2026.114056","DOIUrl":"10.1016/j.engappai.2026.114056","url":null,"abstract":"<div><div>Artificial Intelligence Content Generation (AIGC) has revolutionized how content is produced worldwide for various types of data using AI tools. Identification of rephrased content and separating it from human written content is an active research area. However, several AI tools use various writing styles to rephrase AIGC which makes it more difficult to detect. To address this new research challenge, this study explores a comprehensive set of content‐based linguistic features ranging from raw quantity metrics to higher‐order measures of vocabulary complexity, grammatical complexity, and specificity-expressiveness to capture the complex patterns. The applied methodology explores transformer‐based model called Distillation Bidirectional Encoder Representations from Transformers (DistilBERT) that integrates with self‐attention mechanisms to encode long‐range dependencies within text. The empirical analysis demonstrates feature‐exploration by exploring parts of speech tagging diversity, Flesch–Kincaid readability scoring, word entropy calculations, and affective term counts. The data split carried out using holdout method by taking 80% training and 20% testing, ensuring that no rephrased variants of the same source appeared which preventing parallel-example leakage. Model performance is assessed by using accuracy, precision, recall, and F1-scores on the hold-out test set, with consistent results observed across repeated runs under fixed random seeds. Quantitatively, the DistilBERT model achieves the highest overall classification accuracy at 93%, outperforming both the classical transformer baseline and all sequential models. Qualitatively, to support model interpretability, explainable AI techniques including locally interpretable model-agnostic explanations produce local explanations that highlight the top six features influencing each style prediction.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114056"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175219","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":"Trajectory time impact on error stability for hyper-redundant continuum Manipulators: A comparative study","authors":"Elsayed Atif Aner,&nbsp;Mohamed Fawzy El-Khatib","doi":"10.1016/j.engappai.2026.114057","DOIUrl":"10.1016/j.engappai.2026.114057","url":null,"abstract":"<div><div>The precise trajectory tracking of hyper-redundant continuum manipulators is essential for applications requiring both high accuracy and adaptability, such as minimally invasive surgery and confined space exploration. However, existing Artificial Intelligence (AI)-based control strategies often struggle to maintain precision under dynamic conditions characterized by rapid motion transitions and complex trajectories, particularly in scenarios involving short durations and tight curves. This study addresses this challenge by evaluating the performance of two proposed controllers—Particle Swarm Optimization-based Fuzzy Logic Controller (PSO-FLC) and Sliding Mode Controller (SMC)—in tracking an infinity-shaped trajectory across three distinct durations: 8 s, 4 s, and 2 s. Performance metrics, including trajectory accuracy, end-effector position error, speed profiles, and statistical error analysis, are used to systematically evaluate the controllers. The results indicate that both controllers deliver reliable performance during slower trajectories (8 s); however, the proposed SMC demonstrates superior robustness at higher speeds. It achieves lower position errors, smoother speed profiles, and greater dynamic stability, whereas the PSO-FLC exhibits significant performance degradation under rapid motion constraints. The model was implemented in MATLAB (Matrix Laboratory) and Simulink (Simulation and Link Editor), validated for fidelity, and subsequently tested with the proposed controller under various time constraints. The findings of this study establish the proposed SMC as a robust and reliable solution for high-speed dynamic applications, while positioning the PSO-FLC as a viable option for scenarios with less demanding motion requirements. These insights contribute to the optimization of controller design and selection for hyper-redundant continuum manipulators operating in complex environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114057"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122758","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":"Uncertainty-aware data-driven three-dimensional turbine aerodynamic design system with transformer and multi-fidelity neural networks","authors":"Peng Ren,&nbsp;Xiangjun Fang,&nbsp;Junfeng Chen","doi":"10.1016/j.engappai.2026.114125","DOIUrl":"10.1016/j.engappai.2026.114125","url":null,"abstract":"<div><div>Gas turbines are widely used energy conversion devices, and secondary flows have a significant impact on their overall efficiency. Adjusting the stacking line through sweep and lean is an important method for controlling secondary flows. Traditional stacking line design methods typically rely on designers' experience and iterative processes, which are time-consuming, computationally expensive, and lack generalizable design guidelines. To address these challenges, this paper proposes a data-driven stacking line design method that integrates a transformer architecture with Deep Ensemble (DE) learning to model the relationship between optimal stacking lines and blade geometry under varying operating conditions. To reduce computational costs, a multi-fidelity network is employed to model the relationship between low- and high-fidelity data for predicting the intermediate physical feature of spanwise distributions of total pressure loss. Geometric and aerodynamic features are linearly transformed before being input into the transformer network to extract more informative representations, thereby enhancing the accuracy of a multilayer perceptron (MLP). Multiple transformer-based probabilistic neural networks are ensembled to estimate predictive uncertainty, which improves model robustness and extends its applicability to unseen data. Results show that the transformer-based models improve MLP performance in predicting both the spanwise distribution of total pressure loss and optimal stacking lines. The ensemble model exhibits high uncertainty in out-of-domain predictions, effectively capturing potential large prediction errors. Using a representative low-pressure turbine stage as a benchmark, the proposed method significantly reduces endwall secondary flows, resulting in a 0.61 ± 0.11% increase in stage efficiency compared to the baseline design, thereby validating the effectiveness of the approach.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114125"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175305","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":"Deep Potential Semantic-aware Hashing for Cross-modal Retrieval","authors":"Lei Wu ,&nbsp;Qibing Qin ,&nbsp;Jiangyan Dai ,&nbsp;Lei Huang ,&nbsp;Wenfeng Zhang","doi":"10.1016/j.engappai.2026.114155","DOIUrl":"10.1016/j.engappai.2026.114155","url":null,"abstract":"<div><div>Hashing learning has moved into the mainstream for multimedia retrieval because it offers the advantages of low storage cost and high retrieval efficiency. Currently, most cross-modal hashing methods commonly explore the similarity relations between samples by constructing pair-wise or triplet-wise constraints. However, these methods focus on the relative correct ranking of samples, ignore the potential semantic similarity of raw sample distribution, and generate sub-optimal hash codes. To resolve this issue, the novel Deep Potential Semantic-aware Hashing framework (DPSaH) is proposed to mine the local semantic structure of heterogeneous samples, maintaining inter-modality-consistent and cross-modality-correlated semantic relationships. Specifically, by exploring the potential local structure of the data, the multi-modal quadruple loss is extended to the cross-modal hashing framework, thereby preserving the potential semantic neighborhoods among raw samples in Hamming space. During model training, based on the average semantic labels, the label-averaged balanced strategy is developed to quantify the frequency difference between positive and negative samples. Besides, by injecting noise information into the generated discrete codes, the binary-injection loss is introduced to alleviate the over-activation of specific bits, decorrelating different bits in the Hamming space. Extensive experiments are performed on three public datasets, and the results verify the superiority of the DPSaH framework compared to the current mainstream cross-modal hashing frameworks. The source code for DPSaH is available at <span><span>https://github.com/QinLab-WFU/DPSaH</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114155"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175224","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 multi-agent stock trading decision support system based on deep reinforcement learning","authors":"Xu Yuan ,&nbsp;Jiaqiang Wang ,&nbsp;Shaokui Gu ,&nbsp;Yi Guo ,&nbsp;Ange Qi ,&nbsp;Shijin Li ,&nbsp;Liang Zhao","doi":"10.1016/j.engappai.2026.114130","DOIUrl":"10.1016/j.engappai.2026.114130","url":null,"abstract":"<div><div>The stock market is a highly dynamic, complex, and uncertain environment, where traditional investment strategies and technical analysis tools often fail to provide reliable guidance, leading to increased investment risk and uncertainty. This study aims to develop an adaptive multi-agent stock trading decision support system that can effectively respond to volatile market conditions while balancing returns and risk management. We propose a deep reinforcement learning framework based on the Dueling Deep Q-Network (Dueling DQN) algorithm, in which multiple agents independently make optimal trading decisions based on the constructed environment state. The system incorporates a redesigned reward function, a dynamic exploration strategy, and a risk management mechanism to ensure real-time adaptation to market feedback. Extensive experiments on domestic and international market data demonstrate that the proposed system outperforms existing models, effectively responds to market shocks, and exhibits superior adaptability across different market conditions. The proposed multi-agent trading system achieves a robust balance between profitability and risk control, indicating its potential economic value and applicability in dynamic financial markets.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114130"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175218","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":"Discrete physics-informed neural network with enforced interface constraint for domain decomposition","authors":"Jichao Yin ,&nbsp;Mingxuan Li ,&nbsp;Jianguang Fang ,&nbsp;Chi Wu ,&nbsp;Hu Wang ,&nbsp;Guangyao Li","doi":"10.1016/j.engappai.2026.114065","DOIUrl":"10.1016/j.engappai.2026.114065","url":null,"abstract":"<div><div>While domain decomposition method (DDM) constitutes an effective strategy for improving the training efficiency of physics-informed neural network (PINN), the approach simultaneously introduces an increased risk of training instability owing to the additional loss terms introduced. To address this issue, the work proposes an energy-based discrete PINN (dPINN) approach incorporating a proposed enforced interface constraint (EIC) mechanism within the context of the DDM. The dPINN builds upon the DDM with the EIC mechanism and will henceforth be referred to as EIC-DDM-dPINN. Within this framework, the dPINN computes the system energy in an element-wise fashion using Gaussian integration, guided by finite element-inspired formulations. Meanwhile, displacement continuity across subdomain interfaces is explicitly enforced through the EIC mechanism. This enforcement obviates the need to incorporate supplementary loss terms into the loss function, thereby substantially mitigating the risk of training instability. The integration of the EIC-based DDM facilitates simpler and more flexible subdomain mesh partitioning within the EIC-DDM-dPINN framework, thereby reducing the strong dependence on sampling strategies typically required in conventional DDM-based PINN. Beyond improving computational efficiency via parallelization, the DDM also helps decouple the weak spatial constraint (WSC) effect, which can otherwise result in spurious displacement continuity across geometrically discontinuous gaps. Comprehensive numerical experiments in both two- and three-dimensional settings are conducted to assess the accuracy and efficiency of the proposed approach, and the results demonstrate its scalability and robustness, highlighting its potential for application to large-scale problems with complex geometries.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114065"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175403","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":"Multivariate time series representation learning with multi-task graph neural network","authors":"Zhihui Gao ,&nbsp;Baomin Xu ,&nbsp;Jidong Yuan ,&nbsp;Jinfeng Wang ,&nbsp;Xu Li","doi":"10.1016/j.engappai.2026.113894","DOIUrl":"10.1016/j.engappai.2026.113894","url":null,"abstract":"<div><div>Multivariate time series (MTS) representation learning poses a significant challenge in data mining. Current deep learning-based MTS representation methods mostly utilize neural networks to model temporal dependencies within individual univariate sequences, while failing to adequately consider the spatial relationships among different channels within MTS data. While a few methods leverage graph neural networks (GNNs) to model spatial dependencies, but they often do not effectively capture both global and local features simultaneously, potentially limiting the quality of MTS data representations. To overcome these limitations, we present <strong>MTGL</strong>, a novel <strong>M</strong>ulti-<strong>T</strong>ask <strong>G</strong>raph Neural Network-based MTS Representation <strong>L</strong>earning Framework. It leverages MTS reconstruction, global-level graph learning, and local-level graph learning to capture latent spatio-temporal dependencies without relying on predefined graph structures. To obtain global graph-level representations, MTGL performs message-passing and graph pooling operations, and simultaneously leverages a dynamic graph mechanism to capture associations across different windows for local-level representations. By fusing global and local features in a unified framework, MTGL effectively supports a variety of MTS tasks. Extensive experiments show that the proposed method outperforms existing state-of-the-art baselines on benchmark MTS datasets and the tunnel boring machine dataset.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113894"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175342","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}