Journal of Manufacturing Systems最新文献

{"title":"On the reusability of machine learning-based process monitoring systems for manufacturing digital twins","authors":"Jiarui Xie ,&nbsp;Zhuo Yang ,&nbsp;Haw-Ching Yang ,&nbsp;Yan Lu ,&nbsp;Yaoyao Fiona Zhao","doi":"10.1016/j.jmsy.2025.12.006","DOIUrl":"10.1016/j.jmsy.2025.12.006","url":null,"abstract":"<div><div>Advanced manufacturing is increasingly supported by machine learning (ML)-based digital twins (DTs) for real-time process monitoring and quality assurance. However, changes in physical domain configurations (e.g., machines, materials, and sensors) often cause domain shifts, limiting the reusability of existing DT components. Rebuilding DTs from scratch for each new configuration is costly and time-consuming. To address this challenge, we define DT reusability through three key criteria: FAIRness (findability, accessibility, interoperability, and reusability), transferability, and transfer efficiency. We propose a framework to systematically support the reuse of ML-based process modeling components in DTs, consisting of three phases: FAIR compliance, transferability analysis, and domain adaptation. To enhance transfer efficiency, we introduce the domain-adversarial and decision distribution alignment (DADDA) network, which enables class-conditional alignment and mitigates overfitting through competing domain alignment objectives. A case study on vision-based process monitoring in additive manufacturing was conducted to validate the proposed framework. A FAIR-compliant database of existing DT components was developed, and the most suitable source domain for the designated target domain was identified through an evaluation of semantic and statistical similarity. Leveraging the selected source dataset, DADDA achieved 84 % accuracy after unsupervised pre-training and 96.9 % after supervised fine-tuning with only 210 labeled examples. Further validation on acoustic-based monitoring systems demonstrated the applicability of DADDA to various modalities.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 333-356"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786606","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":"Deep unsupervised learning-based supplier selection and ranking for assembly manufacturing","authors":"Suyoung Park,&nbsp;Shreyes N. Melkote","doi":"10.1016/j.jmsy.2025.12.004","DOIUrl":"10.1016/j.jmsy.2025.12.004","url":null,"abstract":"<div><div>Conventional supplier selection methods for assembled products have primarily relied on qualitative or business-level assessments of supplier capabilities, since manufacturing-related metrics such as product geometry, cost, time, and tolerance are heterogeneous and difficult to integrate into a unified evaluation. This reliance makes the identification of suppliers with adequate manufacturing capability particularly challenging as global supply chains grow increasingly complex. To address this gap, we propose the Deep Unsupervised Assembly Supplier Matcher (DU-ASM), an integrated data-driven framework that jointly embeds geometry, topology, and quantitative manufacturing attributes into a unified latent space for assembly-level supplier selection and ranking. Leveraging a graph autoencoder, DU-ASM reconstructs manufacturing attributes and supports robust supplier selection even with incomplete inputs. Experimental validation across multiple case studies demonstrates that DU-ASM achieves over 95 % supplier selection accuracy under complete requirements and over 90 % with partially masked inputs, while attaining mean normalized Discounted Cumulative Gain scores at top-k positions (nDCG@k) exceeding 0.99 in ranking tasks. By linking geometric, topological, and quantitative data, DU-ASM demonstrates both methodological novelty and strong quantitative performance, providing a scalable foundation for supplier matching at the assembly level and supporting multi-tier decision-making in future manufacturing supply networks.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 173-188"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786758","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":"End-to-end multimodal knowledge graph construction for industrial exploded views via attention-guided expert chains","authors":"Xinxin Liang ,&nbsp;Zuoxu Wang ,&nbsp;Mingrui Li ,&nbsp;Chun-Hsien Chen ,&nbsp;Jihong Liu","doi":"10.1016/j.jmsy.2025.10.013","DOIUrl":"10.1016/j.jmsy.2025.10.013","url":null,"abstract":"<div><div>Industrial exploded views (IEVs) integrate images, text, and part–assembly relations, which is essential for advancing intelligent manufacturing. However, semantic ambiguities, structural inconsistencies, and fragmented annotations hinder effective knowledge extraction and reuse. We cast extraction from IEVs as constrained inference over scene graphs and present a Scene-aware Cascade Expert Chain (SACEC) that incrementally resolves entities, relations, and assembly context. A Visual–Structural–Rule (VSR) validator then enforces domain rules and semantic consistency on every triple. A dynamic triple-cutting strategy selects credible triples by jointly balancing local evidence, contextual coherence, and assembly order, yielding a multimodal knowledge graph (MMKG). We also introduce the Industrial Exploded-View (IEV) dataset, with fine-grained component and relation annotations and assembly-order metadata. Experiments on VRD, VG150, and the IEV dataset demonstrate significant improvements over state-of-the-art baselines, achieving R@100 of 73.2%, 63.9%, and 67.4%, and TripleAcc of 31.8%, 20.2%, and 24.9%. At the triple level, we further obtain P@100 of 54.9%, 39.8%, and 49.6%, and F1@100 of 46.2%, 34.1%, and 45.1%. Against strong path- and context-based baselines, our method improves by up to +7.4<!--> <!-->pp in recall@100, +2.7<!--> <!-->pp in TripleAcc, +15.8<!--> <!-->pp in Precision@100, and +13.5<!--> <!-->pp in F1@100. The approach reduces manual annotation and yields interpretable, audit-ready outputs for intelligent design and process planning, offering a practical route to automated and interpretable knowledge extraction in industrial environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 85-99"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682162","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":"Resilience-enhancing multi-strategy decision-making for dynamic scheduling in manufacturing systems","authors":"Xin Guo ,&nbsp;Mingyue Yang ,&nbsp;Pai Zheng ,&nbsp;Jiewu Leng ,&nbsp;Chong Chen ,&nbsp;Kai Zhang ,&nbsp;Jun Li ,&nbsp;Zechuan Huang","doi":"10.1016/j.jmsy.2025.12.009","DOIUrl":"10.1016/j.jmsy.2025.12.009","url":null,"abstract":"<div><div>High-impact disruptions can cause significant performance degradation and even failures in manufacturing systems. Resilient manufacturing systems can absorb such disruptions, adapt to changing environments, and accelerate recovery through strategy scheduling based on real-time performance data. However, the nonlinear nature of degradation processes can lead to deviations from expected recovery outcomes and delays in strategy scheduling, which makes strategy scheduling for repairing manufacturing systems a difficult decision-making problem. Therefore, a resilience-enhancing multi-strategy decision-making for dynamic scheduling model in manufacturing systems is proposed, aiming to determine the optimal strategy and reduce performance anomaly duration. First, a component-based evaluation method is proposed to measure the absorption, adaptation, and recovery capabilities of the system, achieving real-time analysis of resilience levels. Then, a dynamic strategy scheduling method based on Markov chains is proposed to plan strategies and predict trajectories based on the real-time performance status, disruption, and resilience level, which solves the nonlinearity changes of performance state. Finally, a multi-strategy decision-making method based on fuzzy-BWM is proposed to achieve the resilient-oriented multi-objective discrete strategy decision-making, considering cost, recovery time, and recovery degree. The die forging press is used to demonstrate the effectiveness of the proposed model. The results show that the strategy decided by the model enables the system to recover quickly to its expected state with an acceptable cost compared to other strategies.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 269-288"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786665","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":"Frequency-aware and bionic-aligned collaborative modeling for cross-domain tool wear monitoring under small-sample conditions","authors":"Yezhen Peng ,&nbsp;Weimin Kang ,&nbsp;Qirui Hu ,&nbsp;Fengwen Yu ,&nbsp;Wenhong Zhou ,&nbsp;Xinhua Yao ,&nbsp;Congcong Luan ,&nbsp;Songyu Hu ,&nbsp;Jianzhong Fu","doi":"10.1016/j.jmsy.2025.11.025","DOIUrl":"10.1016/j.jmsy.2025.11.025","url":null,"abstract":"<div><div>Tool wear monitoring is crucial for optimizing CNC machining processes in next-generation intelligent manufacturing systems. However, existing methods struggle to capture the dynamic relationship between high-frequency features and wear evolution. Small-sample training and the uneven distribution of labels across the domain exacerbate bias in feature migration, limiting model generalizability and adaptability. To address this, a frequency domain-aware and bionic-aligned collaborative modeling approach for domain shift mitigation is proposed. Firstly, a smoothed wavelet convolution feature extraction method is introduced, enhancing the capture of sensitive frequency bands and stabilizing gradient propagation through a Softplus smoothing mechanism. The method’s ability to suppress domain offset during the initial feature extraction stage is validated by comparing feature activation distributions across two domains. Inspired by bat echolocation, an attention mechanism is proposed that integrates energy guidance, echo alignment, and time-frequency focusing modules to enhance high-frequency signal mapping and mitigate domain shift. The method's effectiveness in high-frequency feature response is validated through enhancement metrics and variance distribution within the attention focus region. Additionally, interpretability of dual-domain feature alignment is improved by calculating working condition similarity, integrating a priori knowledge, and optimizing the MMD loss function. Systematic ablation experiments demonstrate that the proposed method achieves average RMSE, MAE, and R² values of 0.078, 0.063, and 0.817, respectively. It outperforms all ablation models, yielding average reductions of 31.6 % and 32.5 % in RMSE and MAE, and an average improvement of 42.7 % in R². Furthermore, the proposed method outperforms the best-performing method among the four mainstream methods, reducing RMSE and MAE by 13.3 % and 2.5 %, and improving R² by 5.1 %. This method effectively suppresses domain bias in feature extraction, mapping, and training under small sample conditions, providing critical technical support for intelligent manufacturing in complex, variable working environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 223-241"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786669","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":"Physics-informed multi-task heterogeneous transfer learning network for robust milling tool wear and RUL prediction under anomalous data conditions","authors":"Jinrui Liu ,&nbsp;Pei Wang ,&nbsp;Kesong Zhou ,&nbsp;Qinfeng Wang ,&nbsp;Hui Wang","doi":"10.1016/j.jmsy.2025.12.011","DOIUrl":"10.1016/j.jmsy.2025.12.011","url":null,"abstract":"<div><div>Milling is widely employed in precision component manufacturing for aerospace and other high-precision industries. Indirect tool wear monitoring via machining signals provides critical references for timely tool replacement in real-world, ensuring production quality and cost efficiency. However, due to practical constraints (e.g., sensor costs), some machining signal channels may be missing, while sensor failures (e.g., poor contact or damage) can lead to abnormal signals in other channels. Consequently, historical models trained on intact data often fail when confronted with such heterogeneous data. To address this issue, this paper proposes a physics-informed multi-task heterogeneous transfer learning network for robust milling tool wear prediction under anomalous data conditions. The framework employs channel-specific autoencoders, jointly pre-trained via inter-channel adaptation on intact historical data, along with physics-informed attention, to homogenize heterogeneous data. Furthermore, a Frank-Wolfe solver based multi-task loss weighting strategy, inter-domain adaptation, and soft/hard physical constraints guide the predictor's learning process, ensuring consistency with prior physical knowledge and enabling parallel prediction of multi-blade wear. Finally, the max predicted wear is utilized to estimate remaining useful life. Comparative studies with state-of-the-art (SOTA) models in related domains validate the superior accuracy and robustness of the proposed method. Extensive experiments on two public datasets further demonstrate that the proposed method achieves high generalizability. Taking PHM2010 dataset as an example, compared to the best-performing comparative model, the MAE of the proposed model tested on normal or heterogeneous data are 13.8 % and 25.8 % lower, respectively. And the degradation rates of MAE, RMSE and R2 are 0.15, 3.12, and 3.86 % points lower, respectively, when tested on simulated channel-failure data.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 403-425"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786672","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 mining-driven modeling and simulation to enhance fault diagnosis in cyber–physical systems","authors":"Francesco Vitale ,&nbsp;Nicola Dall’Ora ,&nbsp;Sebastiano Gaiardelli ,&nbsp;Enrico Fraccaroli ,&nbsp;Nicola Mazzocca ,&nbsp;Franco Fummi","doi":"10.1016/j.jmsy.2025.12.005","DOIUrl":"10.1016/j.jmsy.2025.12.005","url":null,"abstract":"<div><div>Cyber–Physical Systems (CPSs) tightly interconnect digital and physical operations within production environments, enabling real-time monitoring, control, optimization, and autonomous decision-making that directly enhance manufacturing processes and productivity. The inherent complexity of these systems can lead to faults that require robust and interpretable diagnoses to maintain system dependability and operational efficiency. However, manual modeling of faulty behaviors requires extensive domain expertise and cannot leverage the low-level sensor data of the CPS. Furthermore, although powerful, deep learning-based techniques produce black-box diagnostics that lack interpretability, limiting their practical adoption. To address these challenges, we set forth a method that performs unsupervised characterization of system states and state transitions from low-level sensor data, uses several process mining techniques to model faults through interpretable stochastic Petri nets, simulates such Petri nets for a comprehensive understanding of system behavior under faulty conditions, and performs Petri net-based fault diagnosis. The method begins with detecting collective anomalies involving multiple samples in low-level sensor data. These anomalies are then transformed into structured event logs, enabling the data-driven discovery of interpretable Petri nets through process mining. By enhancing these Petri nets with timing distributions, the approach supports the simulation of faulty behaviors. Finally, faults can be diagnosed online by checking collective anomalies with the Petri nets and the corresponding simulations. The method is applied to the Robotic Arm Dataset (RoAD), a benchmark collected from a robotic arm deployed in a scale-replica smart manufacturing assembly line. The application to RoAD demonstrates the method’s effectiveness in modeling, simulating, and classifying faulty behaviors in CPSs. The modeling results demonstrate that our method achieves a satisfactory interpretability-simulation accuracy trade-off with up to 0.676 arc-degree simplicity, 0.395 R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and 0.088 RMSE. In addition, the fault identification results show that the method achieves an F1 score of up to 98.925%, while maintaining a low conformance checking time of 0.020 s, which competes with other deep learning-based methods.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 189-206"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786757","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":"Towards perceptive assembly: An edge vision network-enabled augmented reality (AR) monitoring method for global shape and mechanical information in large aerospace components","authors":"Yang Zhang,&nbsp;Xu Wang,&nbsp;Jiacheng Cui,&nbsp;Yulin Jin,&nbsp;Qihang Chen,&nbsp;Yongkang Lu,&nbsp;Wei Liu","doi":"10.1016/j.jmsy.2025.11.017","DOIUrl":"10.1016/j.jmsy.2025.11.017","url":null,"abstract":"<div><div>Real-time, high-fidelity, and interactive monitoring of global mechanical responses during the assembly of large-scale, flexible aerospace structures remains a critical and unresolved challenge. Here, we present a perceptive assembly framework that integrates a distributed edge vision network, physics-informed sparse sensing, and immersive augmented reality (AR) visualization to enable full-field structural state monitoring. A modular edge sensing system is deployed to achieve fast, high-precision measurement of distributed displacements across meter-scale components. To overcome view discontinuities, a hierarchical coordinate transformation pipeline is introduced for global registration under non-overlapping camera views. Building on sparse displacement data, we develop a constrained sensor optimization strategy that enables real-time reconstruction of global displacement and strain fields. Through HoloLens 2, the system provides intuitive AR overlays that deliver immersive, in-situ mechanical feedback during assembly. Validation experiments on composite panels demonstrate sub-millimeter reconstruction accuracy and real-time performance, significantly enhancing transparency and decision-making in the assembly process. This work establishes a scalable AR-based perception infrastructure for next-generation intelligent manufacturing of large aerospace structures.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 117-134"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786759","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":"Designing Synthetic Active Learning for model refinement in manufacturing parts detection","authors":"Xiaomeng Zhu ,&nbsp;Jacob Henningsson ,&nbsp;Pär Mårtensson ,&nbsp;Lars Hanson ,&nbsp;Mårten Björkman ,&nbsp;Atsuto Maki","doi":"10.1016/j.jmsy.2025.11.023","DOIUrl":"10.1016/j.jmsy.2025.11.023","url":null,"abstract":"<div><div>This paper introduces Synthetic Active Learning (SAL), a fully automatic model refinement strategy for manufacturing parts detection using only synthetic data actively generated with domain randomization for training. SAL iteratively updates the detection model by identifying its weaknesses, such as in specific categories, materials, or object sizes, using custom evaluators, and generating targeted synthetic data to address them; it selectively synthesizes new useful data with respect to active learning, where traditionally humans in the loop select data to label. During each iteration, model training and data generation occur simultaneously to improve efficiency. Evaluated on four use cases from two industrial datasets, SAL achieved mAP@50 improvements of 2 to 6% percentage points over static learning, which refers to training on a fixed, pre-generated dataset. It also showed notable gains in underperforming categories, leading to more balanced performance across classes. Another benefit is that it uses a consistent configuration across multiple use cases, avoiding the need for extensive hyperparameter tuning common in prior domain randomization studies. Given its encouraging performance across diverse scenarios, we believe that SAL can scale to broader industrial applications where training can be fully or mostly based on synthetic data.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 68-84"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682163","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":"Corrigendum to “Designing Synthetic Active Learning for model refinement in manufacturing parts detection [Volume 84, February 2026, Pages 68–84]”","authors":"Xiaomeng Zhu ,&nbsp;Jacob Henningsson ,&nbsp;Pär Mårtensson ,&nbsp;Lars Hanson ,&nbsp;Mårten Björkman ,&nbsp;Atsuto Maki","doi":"10.1016/j.jmsy.2025.12.012","DOIUrl":"10.1016/j.jmsy.2025.12.012","url":null,"abstract":"","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Page 646"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924722","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}