Advanced Engineering Informatics最新文献

{"title":"A hybrid data-physics framework with conformal GNN for enhanced damage identification","authors":"Armin Dadras Eslamlou ,&nbsp;Arshia Ghasemlou ,&nbsp;Brais Barros ,&nbsp;Belén Riveiro","doi":"10.1016/j.aei.2025.103718","DOIUrl":"10.1016/j.aei.2025.103718","url":null,"abstract":"<div><div>Structural damage identification is crucial for ensuring safety, yet existing data-driven and physics-based methods often suffer from accuracy and computational limitations. To address these issues, we propose a hybrid framework that integrates Graph Neural Networks (GNNs) with a physics-based Finite Element (FE) model updating approach. The first module employs a GNN trained on modal data from FE simulations to estimate the location and severity of structural damage, with an evolutionary AutoML framework optimizing the GNN’s architecture and hyperparameters. In the second module, a conformal prediction technique quantifies uncertainty in the GNN’s predictions, ensuring robust confidence bounds for damage estimations. These uncertainty-aware predictions initialize a warm-started FE model updating workflow, where the Water Strider Algorithm (WSA) efficiently minimizes a cost function based on limited modal data. The proposed methodology has been validated on benchmark structures, including the Louisville bridge, IASC-ASCE building and a dome structure, demonstrating a remarkable increase in damage identification accuracy compared to conventional approaches. Unlike pure data-driven and physics-based methods, this hybrid framework leverages their strengths while integrating uncertainty quantification, enhancing their efficiency. This hybrid approach is scalable to various structural configurations, making it a promising solution for enhanced structural health monitoring.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103718"},"PeriodicalIF":9.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D reconstruction and optimization system for underwater dam surface based on modified SfM and neural radiation fields","authors":"Huadu Tang ,&nbsp;Fei Kang ,&nbsp;Zaiming Geng ,&nbsp;Zhe Li","doi":"10.1016/j.aei.2025.103683","DOIUrl":"10.1016/j.aei.2025.103683","url":null,"abstract":"<div><div>Regular inspections are essential for dam operation and maintenance. However, conventional underwater inspections, typically relying on manual visual assessments, suffer from low efficiency and limited spatial coverage, which hinder comprehensive detection of dams. To address these limitations, this paper proposes a novel three-dimensional (3D) reconstruction and optimization system using modified structure from motion and neural radiation fields. The rapid generation of 3D point clouds from underwater imagery was achieved through the combination of DISK-NN and COLLISION-MAPping (COLMAP). The SeaThru-NeRF is employed for accurate 3D reconstruction, integrated with multiresolution hash encoding and fully fused neural networks to expedite the process. Bayes’ rays are introduced for efficient artifact removal, facilitating optimization for enhanced 3D reconstruction quality. Experimental results demonstrate that the proposed method achieves a 3.31% improvement in peak signal to noise ratio (PSNR) and a 24.15% acceleration in reconstruction speed. Post-processing optimization of the 3D reconstruction can be effectively controlled by adjusting the uncertainty threshold. The system’s applicability and scalability are validated through multi scenes experiments in engineering projects. This study provides a novel technical pathway for the intelligent inspection of water conservancy facilities, demonstrating significant practical value.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103683"},"PeriodicalIF":9.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A knowledge graphs construction method enhanced by multimodal large language model for industrial equipment operation and maintenance","authors":"Zhengping Zhang ,&nbsp;Junyuan Yu ,&nbsp;Bo Yang ,&nbsp;Kaze Du ,&nbsp;Shilong Wang ,&nbsp;Xing Qi","doi":"10.1016/j.aei.2025.103705","DOIUrl":"10.1016/j.aei.2025.103705","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The industrial equipment Operation and Maintenance (O&amp;M) is a core component in ensuring production safety and efficiency, urgently requiring the support of intelligent technologies. Knowledge graphs, which represent equipment and faults in graph structures, are widely utilized to enable efficient association and rapid retrieval of maintenance knowledge, thereby being extensively applied in intelligent decision-making for the equipment O&amp;M. Traditional knowledge graph construction methods, which rely on a single textual modality, are confronted with challenges such as the scarcity of annotated samples, difficulties in dynamically associating old and new equipment, and insufficient parsing of complex equipment relationships. As a result, issues like missing graph entities and broken causal chains are often encountered, thereby negatively impacting the quality of maintenance decision-making. Therefore, a dual-attention model enhanced by multimodal large language models (MllmDA-KGC) is proposed in this paper. Multimodal large language model(MLLM) is introduced to fully utilize multi-modal knowledge from the O&amp;M domain, thereby enabling a more effective understanding and modeling of complex O&amp;M knowledge. As a result, the quality of knowledge graph construction is significantly improved. In the MllmDA-KGC framework, first, QWEN2-VL is introduced into a dual-stream Transformer architecture to achieve dynamic alignment between images and text while supplementing semantics. As a result, the precision of identifying relationships between parts and problem entities in the O&amp;M domain is significantly enhanced; second, the MT-Transformer module is proposed, which integrates causal convolution, dilated convolution, and Memory_Bank mechanisms to achieve cross-modal temporal embedding fusion. As a result, the continuity of associations between new and old parts, as well as the precision of causal chain embeddings, is significantly improved; third, a multimodal dynamic weight attention-guided module is designed, in which weighted key-value guided attention mechanisms are introduced to focus on critical aspects. Schematic diagrams and textual features are fused to enhance the precision of entity relationship modeling between parts and faults; finally, to fully leverage the multimodal understanding capabilities of the MLLM, the image embeddings and positional embeddings generated and marked by MLLM are integrated into the feature embeddings of RoBERTa. Subsequently, CRF and Softmax are combined to accomplish the MNER (Multimodal Named Entity Recognition) and MRE (Multimodal Relation Extraction) tasks, thereby enabling the construction of a multimodal equipment O&amp;M knowledge graph. In this paper, the vehicle O&amp;M dataset from an automobile company was utilized to validate the proposed method. The experimental results showed that the F1 scores of the model in the MNER and MRE tasks reached 88.40% and 93.79%, respectively, demons","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103705"},"PeriodicalIF":9.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing PCB assemblies reliability: An adaptive CatBoost-MOA approach integrated with Hu-Washizu variational principle for fatigue life prediction","authors":"Akashjyoti Barman ,&nbsp;Konkimalla Venkata Sai Varun ,&nbsp;Sudha Radhika ,&nbsp;GR Sabareesh ,&nbsp;Veerabhadra Reddy","doi":"10.1016/j.aei.2025.103720","DOIUrl":"10.1016/j.aei.2025.103720","url":null,"abstract":"<div><div>Fatigue life prediction is crucial in ensuring the reliability and longevity of electronic components, particularly in high-stakes industries such as aerospace and defence, where premature failures can lead to catastrophic consequences, safety risks, and high operational costs. The current research introduces a novel adaptive model for predicting the fatigue lifetime of printed circuit board assemblies (PCBAs) under vibrational loading conditions. The model integrates adaptive versions of the Categorical Boosting (CatBoost) model and the Mother Optimization Algorithm (MOA) with the Hu-Washizu variational principle, addressing the limitations of conventional machine learning models by combining both data-driven and physics-informed components, improving accuracy and efficiency. The adaptive CatBoost-MOA-HuWashizu model significantly outperforms traditional methods, achieving a coefficient of determination (R²) of 0.9982 and a mean absolute percentage error (MAPE) of 0.0832 for single-component PCBs, and an R² of 0.9839 and MAPE of 0.0995 for multicomponent PCBs, demonstrating superior predictive capability for non-linear degradation behaviours under vibrational loading conditions. The integration of MOA optimises hyperparameters by balancing exploration and exploitation, reducing computational load and accelerating convergence, while the Hu-Washizu principle ensures efficient parameter tuning and resource allocation. Comparative results show reduced computational time, with faster convergence and no loss in prediction accuracy. The model’s robustness was validated through cross-validation, yielding an average R² of 0.9001 and an average MAPE of 0.3495 for single-component PCBs, and an average R² of 0.9084 and an average MAPE of 0.3466 for multicomponent PCBs, confirming its generalizability across varied operational scenarios. The proposed novel adaptive model achieves near-perfect alignment between predicted and numerically estimated fatigue lifetimes, with minimal residual errors. The research offers a promising solution for real-time fatigue life estimation in aerospace, defence, and related fields. Future research will expand the model’s scalability to other complex loading conditions and component types to enhance its applicability in broader reliability assessment frameworks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103720"},"PeriodicalIF":9.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed design optimisation in collaborative product development by integrating analytical target cascading with kriging","authors":"Kai-Wen Tien ,&nbsp;Chih-Hsing Chu","doi":"10.1016/j.aei.2025.103708","DOIUrl":"10.1016/j.aei.2025.103708","url":null,"abstract":"<div><div>In the current era of economic globalization, small and medium-sized enterprises have increasingly recognized the imperative of inter-company collaboration across the supply chain to enhance competitiveness. The effective utilization of distributed design resources has become crucial to address product complexity and shorter life cycles. Collaborative product development (CPD) has thus emerged as a common practice to achieve this goal, in which design teams, possibly dispersed across different companies, negotiate engineering solutions that not only fulfil the overall product development goal but also align with their own interests. This research proposes a novel approach by integrating Analytical Target Cascading (ATC) with Kriging to solve distributed optimal design problems in the context of CPD. The focus is to improve the iterative process of the ATC coordination strategy by incorporating the Kriging model to address the situation of limited information disclosure. Case studies of real-world engineering problems validate the effectiveness of the proposed approach. Test results show that it contributes not only to increasing the efficiency of the design process but also to improving the overall design quality in CPD.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103708"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process mechanisms fusion enhanced spatially scalable convolution network for multi-indicator prediction in process industries","authors":"Ming Wang ,&nbsp;Jie Zhang ,&nbsp;Youlong Lyu ,&nbsp;Peng Zhang ,&nbsp;Cheng Li","doi":"10.1016/j.aei.2025.103684","DOIUrl":"10.1016/j.aei.2025.103684","url":null,"abstract":"<div><div>In process industries, the detection of interrelated production indicators, including throughput, quality, etc., is often delay due to the inherent continuity, causing production disruptions and quality issues. Accurate prediction of multi-indicator is crucial, but intricate nonlinear correlations among parameters and indicators pose significant challenges. Data-driven prediction can overcome the challenge of accurately constructing process mechanism models covering the entire production process and all elements, but struggle to infer cross-space migration patterns of parameters’ impacts on indicators. To address this issue, this study proposes a process mechanism fusion enhanced intelligent multi-indicator prediction method for process industries, using the polyester fiber polymerization process as an illustrative case. Firstly, a process mechanism model is established to generate mechanism data encapsulating process mechanisms like coupled relationships and spatial correlations, and these mechanisms are extracted as mechanism features, which are fused with data features to enhance the model’s performance. Secondly, a spatially scalable convolutional neural network is raised, which extracts the implicit deep data features and mechanism features between parameters and indicators from both within-process and cross-process dimensions, utilizing both real and mechanism data. Furthermore, a multi-head self-attention mechanism is employed to adaptively adjust the self-attention weights of the fused features, guiding the model to learn the complex relationships between fused features and enhancing the ability to learn complex coupled relationships and spatial correlations. Finally, the proposed prediction method is validated using polymerization process data and demonstrated superior performance in achieving accurate multi-indicator prediction compared to both efficient machine learning and advanced deep learning methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103684"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A safety risk formation model for intelligent human–machine interaction based on computational neuroscience","authors":"Yining Zeng ,&nbsp;Youchao Sun ,&nbsp;Xia Zhang ,&nbsp;Yuanyuan Guo ,&nbsp;Heming Wu","doi":"10.1016/j.aei.2025.103668","DOIUrl":"10.1016/j.aei.2025.103668","url":null,"abstract":"<div><div>The widespread application of intelligent technologies in cockpits enhances the efficiency of human–machine systems, introduces new challenges for safety risk analysis. Safety risk analysis focuses on explaining the safety risk formation process from a macro perspective. However, the neurocognitive mechanisms of pilots during this process are still unclear. This paper proposes a safety risk formation model for intelligent human–machine interaction based on computational neuroscience. Specifically, the safety risk factors affecting pilot behavior during intelligent human–machine interaction are identified. The regulatory mechanisms involving dopamine, acetylcholine, and the thalamus corresponding to different safety risk factors are elucidated. The structure and function of the Cortico-Basal Ganglia-Thalamus-Cortical (CBTC) neural circuit are introduced. To quantitatively describe the dynamic characteristics of neurons, a computational model of CBTC is established. Experimental validation of the proposed model is conducted using a cockpit intelligent interaction platform. The results indicate that the established computational model of CBTC effectively simulates the impact of varying levels of safety risk factors on pilots.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103668"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controllable and flexible residential floor plan layout design based on multi-agent deep reinforcement learning with layout prior size and similar experience abandon","authors":"Gan Luo ,&nbsp;Xuhong Zhou ,&nbsp;Liang Feng ,&nbsp;Jiepeng Liu ,&nbsp;Pengkun Liu ,&nbsp;Yunzhu Liao ,&nbsp;Wenchen Shan ,&nbsp;Hongtuo Qi","doi":"10.1016/j.aei.2025.103702","DOIUrl":"10.1016/j.aei.2025.103702","url":null,"abstract":"<div><div>Automated floor plan generation can significantly enhance the efficiency of designers and reduce associated design costs. Nonetheless, ensuring the model’s controllability and flexibility is crucial for its practical applications, presenting distinct challenges for current methods. This paper presents an automated residential layout design method utilizing multi-agent deep reinforcement learning (MADRL) to address the challenges of spatial variability and customization requirements in architectural layouts. By simulating the collaborative design process through multiple agents, the proposed method effectively accommodates diverse layout environments while ensuring valid and personalized designs. A refined reward system was developed to guide agents in generating rational room arrangements and meeting different custom constraints. Additionally, layout prior size (LPS) was proposed to address the size selection challenge, effectively reducing the action space and enhancing layout quality. To further improve diversity, a similar experience abandon (SEA) mechanism was proposed, allowing efficient experience interaction among agents and eliminating redundant exploration of similar layouts. Experimental results demonstrate the proposed method’s capability to generate valid floor plans and provide diversified layout options under various design inputs and custom tasks. Meanwhile, the agent achieves a design consistent with the real layout in 1187 episodes, demonstrating the method’s compatibility. This paper highlights the potential of MADRL in advancing the automation and efficiency of architectural layout design, offering a novel solution for the integration of flexibility and controllability in residential planning.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103702"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operation twins-driven human-centric replenishment-kitting synchronization for smart customized production logistics","authors":"Mingxing Li ,&nbsp;Fen Liu ,&nbsp;Ming Li ,&nbsp;Qu Zhou ,&nbsp;Shiquan Ling ,&nbsp;Ting Qu ,&nbsp;Zhen He","doi":"10.1016/j.aei.2025.103687","DOIUrl":"10.1016/j.aei.2025.103687","url":null,"abstract":"<div><div>Customized manufacturing mode is characterized by a wide variety of products and materials, mixed production volume, and order-driven operations. This study focuses on replenishment and kitting operations in a mass customization workshop under assemble-to-order (ATO) mode. Field investigation reveals that the lack of coordination between replenishment and kitting operations can increase the operation time and ergonomic risks of operators, thereby leading to low overall efficiency, increased operational costs, and reduced health levels. To address this issue, this paper proposes a novel operation twin driven human-centric replenishment-kitting synchronization framework (HCRK-Sync) for coordinated operations. The proposed operation twin framework consists of vertical twinning and horizontal twining, in which vertical twinning leverages Industry 5.0 technologies and human digital twin for real-time operational status perception and horizontal twining focuses on HCRK-Sync decision mechanism. The HCRK-Sync mechanism aims to minimize order operation time and human ergonomic risks, thereby achieving a harmonious balance between replenishment tasks and picking efficiency. Experiment results indicate that compared to traditional methods, HCRK-Sync shows significant advantages in improving picking efficiency and reducing ergonomic risks, with an average reduction of 9%-35% in total order operation time and 8%-50% in ergonomic risks, demonstrating stability and adaptability across different production scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103687"},"PeriodicalIF":8.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The transformative roles of generative artificial intelligence in vision techniques for structural health monitoring: A state-of-the-art review","authors":"Shundi Duan ,&nbsp;Xiao Tan ,&nbsp;Pengwei Guo ,&nbsp;Yurong Guo ,&nbsp;Yi Bao","doi":"10.1016/j.aei.2025.103719","DOIUrl":"10.1016/j.aei.2025.103719","url":null,"abstract":"<div><div>As urbanization accelerates, aging infrastructure demands more advanced inspection methods for structural health monitoring. The growing integration of artificial intelligence (AI) and computer vision technologies has significantly enhanced damage detection accuracy while simultaneously reducing inspection time and operational costs. Despite these advantages, the adoption of AI-based technologies in infrastructure maintenance remains limited due to challenges related to data. One major issue is the lack of comprehensive, task-specific annotated datasets. Another is the poor quality of images captured by drones or mobile devices, which are often affected by noise, blurring, and inconsistent lighting. Although recent advances in generative AI offer promising support for structural health monitoring, it remains unclear which models are best suited for specific tasks.</div><div>This study examines the use of generative AI in structural health monitoring, focusing on key challenges such as limited datasets and low-quality image restoration. The review covers a range of generative AI technologies, outlining their principles, strengths, limitations, and representative applications to support the selection of appropriate tools for specific tasks. Generative AI models enable accurate image segmentation and structural anomaly detection using limited training data. The paper also explores new opportunities for integrating multi-modal generative AI to enhance human–computer interaction in support of structural health monitoring. A framework is proposed to streamline the use of generative AI technologies for data augmentation, image restoration, damage inspection, and human–computer interaction in structural health monitoring.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103719"},"PeriodicalIF":9.9,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}