Advanced Engineering Informatics最新文献

{"title":"RLLM-SS: A knowledge-guided simplex search method integrating large language model and reinforcement learning for injection molding quality control","authors":"Haipeng Zou ,&nbsp;Xinyu Li ,&nbsp;Yongkuan Yang ,&nbsp;Ke Yao ,&nbsp;Xiangsong Kong ,&nbsp;Zhijiang Shao ,&nbsp;Furong Gao","doi":"10.1016/j.aei.2026.104372","DOIUrl":"10.1016/j.aei.2026.104372","url":null,"abstract":"<div><div>In injection molding (IM), product quality and process stability are highly dependent on the setting of key process parameters, making efficient parameter tuning essential for achieving reliable and consistent production. However, the tuning process is traditionally guided by expert experience and trial-and-error methods, which often lead to low efficiency and prolonged adjustment cycles. To address this challenge, we propose a knowledge-guided simplex search method that integrates a large language model (LLM) with the Soft Actor–Critic (SAC) reinforcement learning algorithm in a collaborative optimization framework, called RLLM-SS. In RLLM-SS, a quasi-gradient mechanism leverages historical data to dynamically estimate the step size and gradient compensation direction of the simplex search method. These estimated variables, integrated with domain knowledge, are encoded into structured prompts that guide the injection molding quality LLM in dynamically adjusting simplex coefficients through natural language reasoning. This enables the simplex search to overcome fixed-coefficient limitation and avoid local optima to the maximum extent. To mitigate the drawbacks of the LLM, such as its tendency to generate hallucinated outputs and lack of memory of past tuning adjustments, a SAC-based evaluation module is introduced. It assigns rewards based on optimization performance, thereby reinforcing effective strategies and fostering continuous policy improvement when similar conditions recur. Experimental evaluations first verified LLM-SS on standard high-dimensional benchmark functions, confirming its effectiveness in complex search spaces, and were then conducted on an injection molding quality simulation platform built on a neural network trained with practical IM process data. Results show that RLLM-SS outperforms several advanced methods, reducing the average number of iterations by 27.6% and the final Euclidean distance to the target quality curve by 68.3%. It also maintains strong robustness under Gaussian noise perturbations.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104372"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023024","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 general and efficient approach for uncertainty quantification in neural networks: Identifying risky decisions in AI systems","authors":"Zhao Zhang,&nbsp;Senlin Luo,&nbsp;Xiaolong Wu,&nbsp;Xikai Gao,&nbsp;Jiawei Pi,&nbsp;Limin Pan","doi":"10.1016/j.aei.2026.104343","DOIUrl":"10.1016/j.aei.2026.104343","url":null,"abstract":"<div><div>Uncertainty quantification in neural networks enables the assessment of predictive reliability in artificial intelligence systems, thereby reducing the risk of unsafe decisions. Existing approaches rely heavily on ensemble construction to sample the model parameter space and capture decision variability. However, under realistic resource constraints, small-scale sampling leads to insufficient evidence sources and inaccurate uncertainty estimates. In addition, the design of uncertainty metrics significantly influences estimation accuracy and may limit applicability across different types of machine learning (ML) tasks. In this paper, a Systematic Reusable Ensemble (SRE) framework is proposed for uncertainty quantification. The approach reuses and shares neural network components during retraining to efficiently generate multiple model instances within a single training process. Furthermore, a compounded ensemble pruning strategy is introduced to promote more uniform sampling in parameter space. A general fusion metric is then developed based on evidence theory with a redesigned trust allocation mechanism. Experimental results demonstrate that the proposed framework systematically reduces ensemble construction overhead while improving the reliability of uncertainty estimation. The generalization capability of the SRE is further validated through its effectiveness in identifying high-risk decisions across at least five categories of ML tasks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104343"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023035","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":"An adaptive spatial–temporal encoder with gated multi-convolutions for remaining useful life prediction","authors":"Wen Liu ,&nbsp;Jyun-You Chiang ,&nbsp;Yi Li ,&nbsp;Haobo Zhang","doi":"10.1016/j.aei.2026.104369","DOIUrl":"10.1016/j.aei.2026.104369","url":null,"abstract":"<div><div>Accurate estimation of Remaining Useful Life (RUL) is essential for safe and economical operation of turbofan engines. This paper introduces an Adaptive Spatial-Temporal Encoder with Gated Multi-Convolutions (GMC-ASTE), a novel approach that simultaneously models temporal dynamics and inter-sensor relationships to enhance RUL prediction accuracy. The methodology employs a multi-scale gated convolution module to extract refined features from raw multi-sensor data, effectively reducing noise while retaining transient signals. These features are subsequently processed through an adaptive spatial–temporal encoder, which utilizes graph attention mechanisms to dynamically adjust sensor connections and multi-head temporal attention to capture long-term dependencies parallelly. Extensive experiments on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) benchmark demonstrate that GMC-ASTE achieves superior performance, with the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Scoring metrics across all four sub-datasets. The results confirm the effectiveness and interpretability of the proposed model, providing an advanced framework that improves engine prognostic theory and offering airlines a practical tool to reduce downtime and maintenance costs.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104369"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023134","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":"Discovering interpretable blast Loading equations from Black-Box Machine learning models","authors":"Zifan Shi ,&nbsp;Qilin Li ,&nbsp;Yanda Shao ,&nbsp;Ling Li ,&nbsp;Hong Hao","doi":"10.1016/j.aei.2025.104244","DOIUrl":"10.1016/j.aei.2025.104244","url":null,"abstract":"<div><div>Boiling Liquid Expanding Vapour Explosion (BLEVE) is a high-energy event characterised by intense blast waves that pose serious safety risks. Accurate prediction of the resulting overpressure wave is essential for knowledge-intensive engineering analysis and decision support. While empirical methods are available for predicting overpressure in simple BLEVE scenarios, they fail to capture nonlinear relationships in multi-feature and complex conditions. Computational Fluid Dynamics (CFD) methods offer high accuracy in overpressure wave prediction but are computationally intensive, expensive to use and difficult to integrate into automated or real-time engineering workflows. Machine learning models offer a promising alternative for rapid predictions, but their limited interpretability, particularly in deep learning architectures, poses a significant barrier to integration into real-world engineering systems. This study proposes a systematic approach combining machine learning, explainable artificial intelligence, and symbolic regression for BLEVE overpressure prediction. A feedforward neural network model is developed and interpreted using SHapley Additive exPlanations (SHAP). Global SHAP analysis identified nine features with the most significant contributions, which were subsequently used to train a global surrogate model via symbolic regression. This approach yielded an explicit mathematical expression that approximates the behaviour of the original neural network. The derived equation achieved a relative error of 15.73% on simulated data and 35.45% on experimental data, outperforming existing empirical formulas. This research demonstrates the potential of combining black-box machine learning models with xAI techniques to develop interpretable and reliable equations for blast load prediction. More importantly, it introduces a novel data-driven methodology of data-model-interpretation-equation that formalises engineering knowledge by transforming black-box models into explicit and interpretable computational representations.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104244"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792146","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":"Integration of LiDAR scan-to-IFC and UWB real-time positioning for automated construction monitoring: a precast module case study","authors":"Maggie Y. Gao,&nbsp;Chengjia Han,&nbsp;Zhen Peng,&nbsp;Yiqing Dong,&nbsp;Robert L.K. Tiong,&nbsp;Yaowen Yang","doi":"10.1016/j.aei.2025.104247","DOIUrl":"10.1016/j.aei.2025.104247","url":null,"abstract":"<div><div>This paper presents a novel framework for construction monitoring, focusing on efficient as-built model registration through the integration of LiDAR scanning and Ultra-Wideband (UWB) positioning technology. The proposed approach leverages UWB positioning data as preliminary spatial references for precise alignment of as-built model for precast module and components from LiDAR point cloud processing. This integrated framework addresses the critical gap in construction monitoring by integrating multiple technologies into a cohesive system, overcoming the limitations of fragmented approaches. During these procedures, the study presents a systematic targeted partial transformation to correct angular misalignments during point cloud registration. This framework employs BIMCrossNet, a custom deep learning architecture specifically designed for point cloud segmentation in construction scenarios. At last, the study enables automated updating of semantic enriched as-built BIM models with real-time validation of component placement, making it particularly valuable for quality control and progress monitoring in modular construction applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104247"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792145","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":"Dynamic reliability informed adaptive task scheduling for multirobot manufacturing system","authors":"Jian Zhou ,&nbsp;Hang Zhang ,&nbsp;Buyun Tang ,&nbsp;Lianyu Zheng ,&nbsp;Yiwei Wang","doi":"10.1016/j.aei.2026.104335","DOIUrl":"10.1016/j.aei.2026.104335","url":null,"abstract":"<div><div>Efficient and reliable scheduling is critical for multirobot manufacturing systems, yet existing performance diagnosis and reliability evaluation methods fail to capture the dynamic evolution of system states, and current scheduling often neglects reliability information. This paper proposes a collaborative scheduling method integrating dynamic reliability assessment. First, multisource operational signals are collected and fused to identify degradation stages. Based on a classification probability mapping mechanism, the service state is then converted into probabilistic attributes applicable to modeling and scheduling. Subsequently, a system model incorporating structure, state, and behavior is constructed, and reliability indicators of the system are dynamically evaluated through logical model evolution. On this basis, a heuristic multiagent reinforcement learning scheduling algorithm is designed, using reliability attributes and constraint graph structures as inputs to achieve collaborative scheduling of multiple robots. Finally, during task execution, real-time state changes are dynamically perceived, and the scheduling plan is adaptively updated by triggering rescheduling based on the real-time evaluation results, thus forming a reliability-informed closed-loop scheduling mechanism. Case studies demonstrate 18.2% reduction in task completion time for static allocation compared to four baseline methods, along with 17.1% improvement for dynamic rescheduling against engineering practices. These quantitative results confirm the method’s significant enhancements in scheduling responsiveness to degradation, adaptive task optimization, and overall system stability and efficiency.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104335"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977718","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 transformer-based framework for cross-material in situ monitoring in extrusion-based bioprinting","authors":"Jiayi Zhang ,&nbsp;Kaicheng Yu ,&nbsp;Yifeng Yao ,&nbsp;Lihua Lu ,&nbsp;Qiang Gao ,&nbsp;Peng Zhang ,&nbsp;Guoyin Shang ,&nbsp;Swee Leong Sing","doi":"10.1016/j.aei.2026.104323","DOIUrl":"10.1016/j.aei.2026.104323","url":null,"abstract":"<div><div>Additive manufacturing is advancing toward intelligent and functionally reliable fabrication, particularly in biomedical applications. In extrusion-based three-dimensional (3D) bioprinting, machine learning (ML)-enabled <em>in situ</em> monitoring is crucial for improving print quality and ensuring the functional performance of tissue engineering constructs. This study proposes a transformer-based transfer learning framework for cross-material monitoring that efficiently transfers knowledge across diverse polymer systems under limited-data conditions. The model extracts geometric features of extruded filaments from in situ monitoring images and achieves 99.55% classification accuracy on PLCL and over 98% accuracy for PCL/<span><math><mi>β</mi></math></span>-TCP and GelMA datasets with less than 0.1% trainable parameters. Beyond visual monitoring, the predicted filament process states were quantitatively correlated with downstream mechanical performance, demonstrating a 24.6% improvement in tensile strength and enhanced geometric fidelity under optimal-heating conditions. Furthermore, <em>in vivo</em> wound-healing experiments using the bioprinted constructs verified the biological relevance and translational potential of the proposed monitoring strategy. Constructs fabricated under optimal conditions promoted accelerated tissue regeneration and vascularization, achieving faster wound closure within 10 days compared with suboptimal printing conditions. Overall, the proposed transformer-based cross-material framework establishes a generalizable and biologically validated paradigm for vision-guided process monitoring, providing a key step toward intelligent and adaptive bioprinting.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104323"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977715","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":"Causal feature selection framework for stable soft sensor modeling based on time-delayed cross mapping","authors":"Shi-Shun Chen ,&nbsp;Xiao-Yang Li ,&nbsp;Enrico Zio","doi":"10.1016/j.aei.2026.104337","DOIUrl":"10.1016/j.aei.2026.104337","url":null,"abstract":"<div><div>Soft sensor modeling plays a crucial role in process monitoring. Causal feature selection can enhance the performance of soft sensor models in industrial applications. However, existing methods ignore two critical characteristics of industrial processes. Firstly, causal relationships between variables always involve time delays, whereas most causal feature selection methods investigate causal relationships in the same time dimension. Secondly, variables in industrial processes are often interdependent, which contradicts the decorrelation assumption of traditional causal inference methods. Consequently, soft sensor models based on existing causal feature selection approaches often lack sufficient accuracy and stability. To overcome these challenges, this paper proposes a causal feature selection framework based on time-delayed cross mapping. Time-delayed cross mapping employs state space reconstruction to effectively handle interdependent variables in causality analysis, and considers varying causal strength across time delay. Time-delayed convergent cross mapping (TDCCM) is introduced for total causal inference, and time-delayed partial cross mapping (TDPCM) is developed for direct causal inference. Then, in order to achieve automatic feature selection, an objective feature selection strategy is presented. The causal threshold is automatically determined based on the model performance on the validation set, and the causal features are then selected. Two real-world case studies show that TDCCM achieves the highest average performance, while TDPCM improves soft sensor stability and performance in the worst scenario. On average over the two cases, TDCCM decreases root mean square error (RMSE) by about 8.93% compared with the best existing feature selection method, and TDPCM further decreases RMSE in the worst scenario by about 7.69% relative to TDCCM. The code is publicly available at <span><span>https://github.com/dirge1/TDPCM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104337"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977713","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":"Asynchronous multi-agent based differential evolution for assembly hybrid differentiation flowshop scheduling with variable sub-lot and limited buffer","authors":"Yiling Lu ,&nbsp;Qiuhua Tang","doi":"10.1016/j.aei.2025.104266","DOIUrl":"10.1016/j.aei.2025.104266","url":null,"abstract":"<div><div>Lot-streaming enables a discrete supply of components to multiple assembled products, while the limited buffer controls the fluency of the production flow. Thus, integrating differentiation processing with assembly, this work addresses an assembly hybrid differentiation flowshop scheduling with variable sub-lot and limited buffer (AHDFSP-VS-LB). Focusing on makespan minimization, a mixed-integer linear programming model is established and an asynchronous multi-agent based differential evolution (AMDE) is developed. Constrained by the limited buffer capacity, the AMDE incorporates a deadlock pre-detection strategy to quickly identify infeasible solutions during encoding and a dynamic adjustment strategy to ensure a high-quality feasible solution during decoding. Further, derived from processing-assembly coordination and buffer constraint, four problem-specific properties are extracted and embedded into the initialization and improvement strategy to optimize the performance of the algorithm. To achieve fast and sufficient convergence of the algorithm, an asynchronous multi-agent cooperative learning mechanism is designed to dynamically control the evolution power and direction for each individual by identifying the critical encoding layer and its suitable parameters at different search phases. Comprehensive experiments demonstrate that the designed operators excel in efficiency and coordination, and the proposed algorithm is superior to five state-of-the-art algorithms in solving this new problem.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104266"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978251","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":"Efficiency-aware seismic fragility analysis of super-high arch dam using unsupervised ground motion clustering with probabilistic representation","authors":"Yingbo Chen ,&nbsp;Mingchao Li ,&nbsp;Qiubing Ren ,&nbsp;Zhiyong Qi ,&nbsp;Hui Liang","doi":"10.1016/j.aei.2026.104318","DOIUrl":"10.1016/j.aei.2026.104318","url":null,"abstract":"<div><div>Machine Learning (ML)-driven approaches have been employed to replace computationally intensive seismic simulations of hydraulic engineering structures. For the complex seismic responses of arch dams, constructing a metamodel that captures the nonlinear relationship between ground motion inputs and structural response outputs using a limited set of numerical simulations can significantly reduce the computational cost. However, conventional deterministic predictions and fragility analyses fail to account for the high aleatory and epistemic uncertainties inherent in the seismic response of arch dams. To this end, this paper proposes an efficient fragility analysis method for arch dams that integrates probabilistic ML algorithms with the traditional Incremental Dynamic Analysis. By constructing a Natural Gradient Boosting (NGBoost) metamodel for the arch dam dynamic response, not only can the predicted mean value of each response sample be obtained, but also its conditional probability distribution. Superimpose the simulation data with the response distribution predicted by NGBoost, and the binary parameters of the fragility function are estimated, thereby generating both the fragility curve and the uncertain fragility interval of arch dam. Additionally, representative Ground Motion Records (GMRs) for the arch dam are selected using the Partitioning Around Medoids (PAM) unsupervised clustering technique, determining the minimum subset proportion that effectively represents the whole GMR dataset. The effectiveness of the proposed method is validated in a super-high arch dam. The 40% GMR proportion is found to adequately reproduce the fragility curves of the whole dataset, with the reference curve falling within the derived uncertainty interval, achieving a 56.8% reduction in computational cost. The 60% GMR proportion ensured fragility curves with balanced accuracy and effectiveness, exhibiting maximum mean differences of 0.058 and maximum standard deviation differences of 0.031 from reference curves across all damage levels, while reducing computational cost by 39.7%. Comparative results demonstrate the superiority of NGBoost and PAM over existing deterministic metamodels and GMRs selection techniques, respectively. The efficient fragility analysis method proposed in this study ultimately enables the direct characterization of uncertainties in arch dam seismic responses.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104318"},"PeriodicalIF":9.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978250","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}