Energy and AI最新文献

{"title":"Heat pump detection and load disaggregation in low-resolution smart meter data with convolutional neural networks","authors":"Olivier Gisiger,&nbsp;Andreas Melillo,&nbsp;Philipp Schuetz","doi":"10.1016/j.egyai.2026.100691","DOIUrl":"10.1016/j.egyai.2026.100691","url":null,"abstract":"<div><div>The transition from fossil heating systems to heat pumps introduces new challenges for energy grid management, particularly due to their contribution to peak loads. Demand-side flexibility offers a promising solution, but requires detailed monitoring of individual devices. This paper addresses two key tasks using residential smart meter data: detection of installed heat pumps and disaggregation of heat pump load profiles from total household consumption. Multiple machine learning models are trained and evaluated for both tasks using a large real-world dataset comprising more than 7000 heat pumps in Switzerland.</div><div>For heat pump detection, a rule-based approach achieves classification precision of over 89%, indicating that robust detection is possible even with low-resolution data. For load disaggregation, we develop a dedicated one-dimensional convolutional neural network with differential input and auxiliary features, achieving a root mean squared error below 0.18 kWh, outperforming reference models by more than 20%. The proposed disaggregation model further demonstrates robustness to variations in residual household load and benefits from increased training data, with performance gains saturating beyond approximately 200 heat pumps.</div><div>Finally, the combined detection and disaggregation framework is evaluated in a neighborhood-scale case study. On a representative winter day, the proposed approach estimates peak heat pump demand with a relative error of 6%, highlighting its potential to support artificial intelligence-enabled demand response energy management applications.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100691"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DFL-RUL: Decentralised Federated Learning for Battery Remaining Useful Life Estimation on Heterogeneous Edge-to-cloud","authors":"Jaber Pournazari ,&nbsp;Mo’ath El-Dalahmeh ,&nbsp;Dong-Hwan Park ,&nbsp;James Marco ,&nbsp;Truong Quang Dinh ,&nbsp;Jung-Hoon Ahn ,&nbsp;Mona Faraji Niri","doi":"10.1016/j.egyai.2026.100689","DOIUrl":"10.1016/j.egyai.2026.100689","url":null,"abstract":"<div><div>Accurate remaining useful life (RUL) prediction of lithium-ion batteries is essential for reliable and cost-effective electric vehicle operation, yet existing approaches largely rely on centralised training or overlook deployment constraints and data heterogeneity. This paper introduces DFL-RUL, a decentralised federated learning framework specifically designed to address feature-space inconsistency, temporal generalisation, and edge-level feasibility in real-world battery prognostics. Unlike prior federated RUL methods that assume aligned feature representations across clients, DFL-RUL integrates unsupervised, client-side PCA to automatically align heterogeneous sensor features before model aggregation. Local battery degradation is modelled using lightweight LSTM networks, while global knowledge is learned through FedAvg-based aggregation without sharing raw data. To reflect practical forecasting conditions, the framework is evaluated under a forward-in-time validation protocol, where only early-life cycles are available during training. Extensive experiments demonstrate that DFL-RUL achieves accuracy comparable to or exceeding local and centralised baselines, while significantly reducing communication cost and training latency. Moreover, runtime profiling on EV-class edge hardware confirms low inference latency and low energy consumption, validating the framework’s suitability for on-device deployment. These results show that reliable battery RUL estimation can be achieved in a privacy-preserving, hardware-aware, and temporally robust federated setting.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100689"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FlameRF: A fast time-resolved reconstruction technique for turbulent flame using Tensorial Radiance Fields","authors":"Shiyu Liu,&nbsp;Haiou Wang,&nbsp;Yuqi Huang,&nbsp;Kun Luo,&nbsp;Jianren Fan","doi":"10.1016/j.egyai.2026.100758","DOIUrl":"10.1016/j.egyai.2026.100758","url":null,"abstract":"<div><div>This work introduces a novel machine learning framework for reconstructing dynamic turbulent flames using Tensorial Radiance Fields (TensoRF), which is termed as FlameRF. This framework allows for a continuous spatial and temporal reconstruction of four-dimensional (4D) flame properties based on two-dimensional (2D) projections, overcoming the inherent limitations of tomography methods that rely on discretized volume representations. By factorizing the 4D flame scene tensor into compact low-rank components, FlameRF facilitates effective and fast flame reconstruction. The performance of FlameRF is evaluated on two different configurations of turbulent flames, including freely propagating planar premixed combustion and swirling premixed combustion. The reconstructed flame fields are compared with the high-fidelity direct numerical simulation (DNS) data and the predictions of the traditional Simultaneous Algebraic Reconstruction Technique (SART) algorithm. The results indicate that FlameRF exhibits superior performance in terms of both accuracy and efficiency compared to SART. FlameRF can faithfully recover the complex flame structures, particularly in the highly turbulent case of swirling flames, where the performance of SART deteriorates. Furthermore, FlameRF enables temporal interpolation for unseen time instants within the training range without 2D projections, and can also handle noisy measurements. This study highlights the great potential of FlameRF for dynamic flame diagnostics, and provides new insights for the development of complementary tools for conventional diagnostic techniques in combustion research.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100758"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geophysical decision transformer: Generative control for geological carbon storage","authors":"Kyubo Noh,&nbsp;Andrei Swidinsky","doi":"10.1016/j.egyai.2026.100737","DOIUrl":"10.1016/j.egyai.2026.100737","url":null,"abstract":"<div><div>Geophysical control uses time-lapse geophysical measurements to optimize decision-making in sustainable energy applications such as geological carbon storage and geothermal power generation. Deep reinforcement learning (DRL) provides a natural framework for such control, yet its real-world deployment is challenging due to the limited observability of the subsurface state. To address this problem, we hypothesize that utilizing DRL architectures capable of modeling temporal relationships in geophysical data can alleviate such partial observability issues. Specifically, we compare four DRL agent architectures: three value-based methods and one autoregressive generative policy model, the Online Decision Transformer (ODT), in the context of geological carbon storage optimization. Using time-lapse seismic and borehole gravity data as geophysical control measurements, the ODT outperforms value-based agents in both decision-making abilities and learning speed, even with uncertainties in the underlying geological model. We attribute such superior performance to a combination of a two-phase, offline-online training strategy, effective modeling of temporal dependencies using transformers, and the integration of multimodal inputs—including combined geophysical measurements (states), historical control parameters (actions), and conditioned feedback metrics (rewards or return-to-go). t-SNE analysis further demonstrates that incorporating sequential, multimodal data from multiphysical monitoring and control-feedback histories helps mitigate partial observability. Attention-based analysis reveals that the agent dynamically prioritizes geophysical observations—especially seismic measurements—during critical control periods while appropriately downweighting less informative signals. Interestingly, these patterns depart from traditional geophysical monitoring expectations (which typically emphasize higher sensitivity measurements at later time steps when leakage becomes more pronounced), and offer new insights into the temporal value of geophysical data for proactive decision-making in leakage prevention. These findings highlight the potential of transformer-based DRL agents for enabling cost-effective, high-performance geophysical control in realistic subsurface systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100737"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wind power forecasting with a VMD-LSTM-informer hybrid deep learning model","authors":"Qiantong Zheng ,&nbsp;Yubao Liu ,&nbsp;Tingting Gu","doi":"10.1016/j.egyai.2026.100765","DOIUrl":"10.1016/j.egyai.2026.100765","url":null,"abstract":"<div><div>The integration of wind power systems into power grids poses operational challenges due to the inherent intermittency of wind power generation. Accurate wind power prediction can help mitigate these problems and improve grid reliability. This study introduces an innovative wind power forecasting architecture, VMD-HybridNet, which can enhance wind power forecast accuracies across multiple forecast horizons. VMD-HybridNet disentangles complex wind power signals into predictable trend and fluctuation components with Variational Mode Decomposition (VMD) and applies a dual-path learning strategy. Specialized experts—an LSTM for long-term trend and an LSTM-Informer for transient volatility—are configured to achieve a hierarchical feature fusion that significantly enhances the robustness of the final aggregated forecast. The results show that VMD-HybridNet outperforms all benchmark models for 15 min, 30 min, 1 h, and 2 h ahead forecasts. For a 2000 kW wind turbine, the VMD-HybridNet achieves an MAE of 60.2 kW for 2 h ahead forecasts, which is 73.94% lower than the average of the simple machine learning models (linear regression, support vector regression, and LightGBM), and 71.84% lower than the average of the deep learning models (LSTM, Transformer, Informer, and LSTM-Informer). Furthermore, VMD-HybridNet outperforms the VMD-driven standalone deep learning models (VMD-LSTM, VMD-Transformer, VMD-Informer and VMD-LSTM-Informer) by 14.94% on the average MAE. These results indicate that the VMD-HybridNet effectively enhances 0–2 h operational wind power forecasting accuracy.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100765"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network-driven multi-objective optimization for solid-state hydrogen sources dead-ended proton exchange membrane fuel cell power systems","authors":"Shaocong Wang ,&nbsp;Jiawei Chen ,&nbsp;Chunlin Hu ,&nbsp;Yunbo Wang ,&nbsp;Zhiyi Xu ,&nbsp;Xiongfei Liu ,&nbsp;Jianfei Xie ,&nbsp;Lei Xing ,&nbsp;Pengfei Zhu ,&nbsp;Fusheng Yang ,&nbsp;Zaoxiao Zhang ,&nbsp;Zhen Wu","doi":"10.1016/j.egyai.2026.100767","DOIUrl":"10.1016/j.egyai.2026.100767","url":null,"abstract":"<div><div>To overcome the dual challenges of short endurance and poor environmental adaptability faced by portable mobile devices, this study proposes a dead-ended anode and cathode (DEAC), air-cooled proton exchange membrane fuel cell (PEMFC) power system based on online hydrolysis hydrogen generation. By integrating solid sodium borohydride hydrolysis hydrogen generation technology with a DEAC mode PEMFC, the power system constructs an internal \"water-hydrogen-electricity\" cycle, enabling the closed-loop utilization of reaction products. The cycle significantly enhances the system's energy density and liberates the system from dependence on external air. An artificial neural network-driven surrogate model is developed based on system simulation data. This model is coupled with a multi‑objective genetic algorithm to synergistically optimize key operating parameters: current density, temperature, purge duration, and purge interval. This multi-objective optimization framework is designed to simultaneously optimize three conflicting targets: electrochemical performance, water recovery, and oxygen utilization. Under the resulting optimal conditions, the proposed system outperforms traditional open‑cathode PEMFCs in dynamic voltage output, and its electrical efficiency is approximately 24.7% higher than that of traditional systems. Furthermore, in fixed‑endurance scenarios, the proposed system achieves a 66.55% higher gravimetric energy density than conventional high‑pressure hydrogen storage. This work provides theoretical and methodological support for developing next‑generation portable hydrogen power systems with high energy density and broad environmental adaptability.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100767"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement data recovery for power distribution systems based on physics-guided spatio-temporal graph neural network and online data fusion","authors":"Wei Wei ,&nbsp;Pan Huang ,&nbsp;Tao Xu ,&nbsp;Lingxu Guo ,&nbsp;Xu Huang ,&nbsp;Yang Wang","doi":"10.1016/j.egyai.2026.100754","DOIUrl":"10.1016/j.egyai.2026.100754","url":null,"abstract":"<div><div>Real-time operation control and simulation of power distribution systems are crucial for supporting their platform and intelligent development, requiring reliable online measurement data support. However, resource constraints and cross-system interoperability issues often result in missing or delayed data transmission, demanding efficient recovery solutions. This paper proposes a physics-guided spatio-temporal graph neural network with online data fusion framework for missing data recovery of heterogeneous measurements in power distribution networks, including multiple types such as current, voltage, and power. The framework first establishes the physics-guided spatio-temporal graph neural network-based preliminary recovery model, where a multi-channel spatio-temporal prediction block is employed to extract spatio-temporal features from the measurement data of each channel in parallel, thereby enabling accurate real-time measurement forecasting. Building upon this, a physics-guided module incorporating physical constraints (Ohm's Law and Kirchhoff's Laws) is constructed for post-processing optimization, which couples different types of measurement data through physical constraints to enable mutual calibration and joint recovery, significantly improving recovery accuracy. Subsequently, an online data fusion-based secondary recovery model employs a transparency mask mechanism to effectively utilize available online measurements, further refining accuracy. Finally, extensive simulations on the IEEE 33-node distribution system and actual power distribution systems demonstrate that the physics-guided spatio-temporal graph neural network with online data fusion model outperforms conventional methods in recovering missing heterogeneous measurement data, with mean absolute percentage error values reduced by 3.41–1.04% relative to the best existing approaches.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100754"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variational digital twins","authors":"Logan A. Burnett ,&nbsp;Umme Mahbuba Nabila ,&nbsp;Majdi I. Radaideh","doi":"10.1016/j.egyai.2026.100756","DOIUrl":"10.1016/j.egyai.2026.100756","url":null,"abstract":"<div><div>While digital twins (DT) hold promise for providing real-time insights into complex energy assets, much of the current literature either does not offer a clear framework for information exchange between the model and the asset, lacks key features needed for real-time implementation, or gives limited attention to model uncertainty. Here, we aim to address these gaps by proposing a variational digital twin (VDT) framework that augments standard neural architectures with a single Bayesian output layer. This lightweight addition, along with a novel VDT updating algorithm, lets a twin update in seconds on commodity GPUs while producing calibrated uncertainty bounds that can inform experiment design, control algorithms, and model reliability.</div><div>The VDT is evaluated on four energy-sector problems. For critical-heat-flux prediction, uncertainty-driven active learning reaches <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>98</mn></mrow></math></span> using 47% fewer experiments and one-third the training time of random sampling. A three-year renewable-generation twin maintains <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>95</mn></mrow></math></span> for solar output and curbs error growth for volatile wind forecasts via monthly updates that process only one month of data at a time. A nuclear reactor transient cooldown twin reconstructs thermocouple signals with <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>99</mn></mrow></math></span> and preserves accuracy after 50% sensor loss, demonstrating robustness to degraded instrumentation. Finally, a physics-informed Li-ion battery twin, retrained after every ten discharges, lowers voltage mean-squared error by an order of magnitude relative to the best static model while adapting its credible intervals as the cell approaches end-of-life. These results demonstrate that combining modest Bayesian augmentation with efficient update schemes turns conventional surrogates into uncertainty-aware, data-efficient, and computationally tractable DTs, paving the way for dependable models across industrial and scientific energy systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100756"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OVEN: A deep learning framework for photovoltaic site identification","authors":"João Oliveira ,&nbsp;Miguel S.E. Martins ,&nbsp;Ricardo Gomes ,&nbsp;Susana Vieira ,&nbsp;João M.C. Sousa ,&nbsp;Paulo Ferrão","doi":"10.1016/j.egyai.2026.100759","DOIUrl":"10.1016/j.egyai.2026.100759","url":null,"abstract":"<div><div>Given current global energy demand, a substantial expansion of solar panel deployment in urban areas, across both developing and developed nations, is essential to meet the net-zero emissions targets outlined in the Paris Agreement. To maximize energy output, it is critical to identify photovoltaic (PV) installation sites that offer optimal conditions for high solar efficiency. This article introduces the OVEN architecture (dO eVErything oNce) which simultaneously identifies potential PV installation sites and extracts parameterized representations of roof topologies. Unlike previous methods that decompose this problem in multiple stages, our YOLO-based approach requires a single pass to estimate roof characteristics. The model enables wider accessibility and scalability while significantly reducing operational costs, with a 28 fold reduction in execution time and three times smaller memory footprint. The model has an mAP50 of 0.9586 for each building with per-direction cardinal accuracies between 0.68 and 0.74. Discrepancies in predicted inclination and area result in annual discrepancies of electric generation of <span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>5</mn><mtext>%</mtext></mrow></math></span> and <span><math><mrow><mo>+</mo><mn>2</mn><mo>.</mo><mn>5</mn><mtext>%</mtext></mrow></math></span> according to LiDAR ground truth.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100759"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-task learning framework for real-time integrated FDIA localization and state correction of cyber–physical power systems","authors":"Xueyang Zhang,&nbsp;Shengjun Huang,&nbsp;Bo Jiang,&nbsp;Rui Wang,&nbsp;Tao Zhang","doi":"10.1016/j.egyai.2026.100769","DOIUrl":"10.1016/j.egyai.2026.100769","url":null,"abstract":"<div><div>The improved situational awareness capabilities of the power system have also exposed significant cybersecurity vulnerabilities, underscoring the critical need for developing effective approaches against attacks. However, existing methods fail to provide any effective information other than classification results during the detection stage, making it difficult to directly guide decision-making. In this paper, a computationally efficient false data injection attack (FDIA) model is formulated based on a modified AC power flow, which constructs stealthy attack vectors that cannot be recognized by any residual-based detection methods through refined physical constraints. Then, in order to diagnose the corruption on node states caused by data manipulation, a spatiotemporal graph encoder–decoder (ST-GED) is proposed based on a multi-task learning framework. The model performs real-time contamination detection and state correction simultaneously, which is completely immune to erroneous measurements. Specifically, in the spatiotemporal blocks of the encoder and decoder, spatial information is captured through an edge-aware message passing module that introduces a multi-head self-attention mechanism, and GRU is leveraged to learn temporal features. Numerical experiments demonstrate that the proposed ST-GED has excellent performance on both detection and state correction tasks.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"24 ","pages":"Article 100769"},"PeriodicalIF":9.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}