Energy and AI最新文献

{"title":"Intelligent acoustic detection of blade icing on wind turbines: 600 W prototype study","authors":"Sun Bingchuan ,&nbsp;Cui Hongmei ,&nbsp;Su Mingxu","doi":"10.1016/j.egyai.2025.100556","DOIUrl":"10.1016/j.egyai.2025.100556","url":null,"abstract":"<div><div>Diagnosing wind turbine blade icing is crucial for enhancing the efficiency and reliability of wind power generation in cold regions. Current acoustic-based diagnostic techniques, while cost-efficient, face challenges in precision and signal processing within complex sound environments. For this reason, this paper proposes a new method for diagnosing blade icing, which includes an enhanced deep residual network based on densely connected modules and a data enhancement strategy to improve diagnostic results in complex environments. In particular, blade acoustic signatures, rich in spatial information, are captured using a microphone array. These signals are then processed by a model combining fixed-orientation delay-and-sum beamforming with the enhanced deep residual network. The performance of the proposed method for blade icing damage diagnosis has been evaluated through a 600 W wind turbine under different operating and measurement conditions, and experiments have been conducted under different blade icing positions. The results show that the proposed approach achieved high diagnostic precision, yielding F1-scores of 0.9354 and 0.9297. These scores indicate a substantial improvement in accurately identifying blade icing compared to existing other methods. Furthermore, the competitiveness of the proposed method is further demonstrated through ablation studies. This work makes an important contribution to the sustainable utilization of wind energy resources in cold regions.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100556"},"PeriodicalIF":9.6,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687166","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":"Machine learning-assisted optimization of CsPbI₃-based all-inorganic perovskite solar cells: A combined SCAPS-1D and XGBoost approach","authors":"Usama Ghulam Mustafa ,&nbsp;Wei Wu ,&nbsp;Mingqing Wang ,&nbsp;Adham Hashibon ,&nbsp;Hafeez Anwar","doi":"10.1016/j.egyai.2025.100559","DOIUrl":"10.1016/j.egyai.2025.100559","url":null,"abstract":"<div><div>The commercialization of perovskite solar cells (PSCs) is hindered by the instability of organic components and the resource-intensive nature of experimental optimization. Machine learning (ML) is revolutionizing the discovery and optimization of photovoltaic devices by reducing reliance on conventional trial-and-error approaches. This study aims to optimize the performance of CsPbI₃-based all-inorganic PSCs using a combined SCAPS-1D and machine learning (ML) approach. We generated 56,390 unique device configurations via SCAPS-1D simulations, varying layer thicknesses and defect densities. Five ML models were trained, with XGBoost achieving the highest accuracy (R² = 0.999). Feature importance was analyzed using SHAP. Optimization increased the PCE from 15.15 % to 19.16 %, with the perovskite layer thickness (2 µm) and defect density (&lt;10¹⁵ cm⁻³) identified as critical parameters. This study highlights the potential of ML-driven optimization in perovskite solar cells, offering a systematic and data-driven approach to enhancing device efficiency and accelerating the development of next-generation photovoltaics.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100559"},"PeriodicalIF":9.6,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679361","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":"Size or diversity? Synthetic dataset recommendations for machine learning heating energy prediction models in early design stages for residential buildings","authors":"Xinyue Wang ,&nbsp;Yinan Yu ,&nbsp;Robin Teigland ,&nbsp;Alexander Hollberg","doi":"10.1016/j.egyai.2025.100557","DOIUrl":"10.1016/j.egyai.2025.100557","url":null,"abstract":"<div><div>One promising means to reduce building energy for a more sustainable environment is to conduct early-stage building energy optimization using simulation, yet today’s simulation engines are computationally intensive. Recently, machine learning (ML) energy prediction models have shown promise in replacing these simulation engines. However, it is often difficult to develop such ML models due to the lack of proper datasets. Synthetic datasets can provide a solution, but determining the optimal quantity and diversity of synthetic data remains a challenging task. Furthermore, there is a lack of understanding of the compatibility between different ML algorithms and the characteristics of synthetic datasets. To fill these gaps, this study conducted multiple ML experiments using residential buildings in Sweden to determine the best-performing ML algorithm, as well as the characteristics of the corresponding synthetic dataset. A parametric model was developed to generate a wide range of synthetic datasets varying in size and building shape, referred to as diversity. Five ML algorithms selected through a literature review were trained using the different datasets. Results show that the Support Vector Machine performed the best overall. Multiple Linear Regression performed well with small and low-diverse datasets, while the Artificial Neural Network performed well with large and high-diverse datasets. We conclude that developers should focus more on increasing diversity instead of size once the dataset size reaches around 1440 when generating synthetic training datasets. This study offers insights for researchers and practitioners, such as software tool developers, when developing ML building energy prediction models in early-stage optimization.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100557"},"PeriodicalIF":9.6,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665537","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":"HEAT: Hierarchical-constrained Encoder-Assisted Time series clustering for fault detection in district heating substations","authors":"Jonne van Dreven ,&nbsp;Abbas Cheddad ,&nbsp;Ahmad Nauman Ghazi ,&nbsp;Sadi Alawadi ,&nbsp;Jad Al Koussa ,&nbsp;Dirk Vanhoudt","doi":"10.1016/j.egyai.2025.100548","DOIUrl":"10.1016/j.egyai.2025.100548","url":null,"abstract":"<div><div>Fault detection in district heating (DH) substations is crucial for maintaining energy efficiency. However, existing methods often fall short and rely on labelled data or global analysis that may miss subtle anomalies. We introduce HEAT, a Hierarchical-constrained Encoder-Assisted Time series clustering method designed to enhance fault detection in DH substations. HEAT operates in a two-phase approach: first, it approximates a relative network topology using a constraint hierarchical clustering algorithm on supply temperature profiles. HEAT incorporates a Convolutional AutoEncoder (CAE) for dimensionality reduction of the time series data and uses adaptive soft constraints in the linkage function, enabling both minimum and maximum cluster size constraints while supporting domain knowledge, e.g., must-link and cannot-link constraints, using a constraint matrix. Second, we use the topology approximation to perform intra-cluster analysis using Mean Absolute Deviation (MAD) z-scores, with neighbouring substations serving as a validation mechanism, allowing for robust analysis without requiring labelled data. Experimental results demonstrate that HEAT outperforms conventional clustering methods while achieving 74.1% sensitivity and 95.5% specificity in fault detection, significantly improving over typical global analysis. HEAT not only identified major faults (e.g., sensor issues, valve failures) but also detected subtle anomalies (e.g., secondary leakages) while minimising false positives. This unsupervised method offers a viable and flexible solution for DH networks, improving operational efficiency and energy sustainability without disclosing sensitive information.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100548"},"PeriodicalIF":9.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634141","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":"Achieving high precision and balanced multi-energy load forecasting with mixed time scales: a multi-task learning model with stacked cross-attention","authors":"Yunfei Zhang ,&nbsp;Jun Shen ,&nbsp;Jian Li ,&nbsp;Mingzhe Yu ,&nbsp;Xu Chen ,&nbsp;Ziyong Yin","doi":"10.1016/j.egyai.2025.100561","DOIUrl":"10.1016/j.egyai.2025.100561","url":null,"abstract":"<div><div>Accurate multi-energy load forecasting is a prerequisite for on-demand energy supply in integrated energy systems. However, due to differences in response characteristics and load patterns among electrical, heating, and cooling loads, multi-energy load forecasting faces the challenges of heterogeneous time scales and imbalanced forecasting accuracy across load types. To address these challenges, this paper proposes a multi-task learning model with stacked cross-attention. This model incorporates a time scale alignment module to align the time scales of different loads, and employs Informer encoders as experts to extract load-specific features. Stacked cross-attention as the soft sharing mechanism dynamically fuses expert features at the sequence level, enhancing inter-task collaboration and adaptability. This design improves the overall accuracy of multi-energy load forecasting with mixed time scales while reducing forecasting imbalance across load types. Comparison results demonstrate that the model with the stacked cross-attention achieves the best forecasting performance and lowers the imbalance index by 79.17 %. Furthermore, the experts based on Informer encoders yield over a 30.09 % MAPE reduction compared to alternative expert architectures. Compared to the multi-gate mixture-of-experts based models, classical forecasting models, and recent advanced models, the proposed model achieves superior forecasting accuracy, validating its effectiveness and advancement.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100561"},"PeriodicalIF":9.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632061","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 Hybrid machine learning–statistical based method for short-term energy consumption prediction in residential buildings","authors":"Kamran Hassanpouri Baesmat,&nbsp;Emma E. Regentova,&nbsp;Yahia Baghzouz","doi":"10.1016/j.egyai.2025.100552","DOIUrl":"10.1016/j.egyai.2025.100552","url":null,"abstract":"<div><div>Accurate short-term load forecasting is essential for modern power systems, enabling efficient energy management and supporting grid reliability amid increasing demand and variable weather conditions. This study addresses the challenge of forecasting household electricity consumption by proposing SSRXLR—a novel hybrid method that integrates statistical and machine learning techniques including a sparse, Seasonal Autoregressive Integrated Moving Average Exogenous model, Random Forest, Extreme Gradient Boosting, Long Short-Term Memory, and a Residual Correction step to leverage both linear trends and complex nonlinear relationships. We have analyzed one year of high-resolution (5-minute interval) energy and weather data from a household in Las Vegas, Nevada. Through a rigorous feature selection process, we have identified the four most influential features, i.e., sea level pressure, temperature, feels-like temperature, and dew point. The proposed method has demonstrated strong prediction performance across multiple metrics. Compared to well-known models, the proposed method achieved a root mean square logarithmic error of 0.043, which surpassed the Random Forest method by 0.066 and the Seasonal Autoregressive Integrated Moving Average Exogenous model by 0.106 in reducing the Root Mean Square Logarithmic Error (RMSLE). The coefficient of determination for the proposed method attained a 0.97 value, outperforming Random Forest (0.92) and the Seasonal Autoregressive Integrated Moving Average Exogenous model (0.67). These results highlight the effectiveness of combining advanced statistical modeling, machine learning, and targeted feature selection for precise short-term load forecasting. The proposed framework offers a scalable solution for smart grid operations, resource planning, and integration of renewable energy in diverse environments.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100552"},"PeriodicalIF":9.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655151","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":"Predicting future polarization curves from operating data: Machine learning-based investigation of degradation modeling concepts for PEM water electrolysis","authors":"Janis Woelke,&nbsp;Alexander Rex,&nbsp;Christoph Eckert,&nbsp;Boris Bensmann,&nbsp;Richard Hanke-Rauschenbach","doi":"10.1016/j.egyai.2025.100547","DOIUrl":"10.1016/j.egyai.2025.100547","url":null,"abstract":"<div><div>Electrolysis is expected to play an essential role in future energy systems that rely on renewable energy sources, especially in achieving climate neutrality in sectors that are challenging to electrify directly. The economic success of this technology is largely dependent on effective predictive maintenance, which requires a clear understanding of the systems’ current and future state-of-health to ensure adequate replacement planning with decreasing efficiency and prevent undesired aging-related failures. Given the incomplete physical understanding and mathematical description of degradation processes, while more and more data is becoming available, data-driven machine learning models are increasingly moving into focus. These models can learn underlying relationships from data without necessitating prior knowledge. Therefore, this study concentrates on predicting the degradation trend of a proton exchange membrane water electrolysis cell using a data-driven machine learning approach. To this end, a comprehensive data-driven modeling matrix is proposed and evaluated through selected practically relevant modeling concepts, which are characterized by different combinations of available training data and desired model outputs. Experimentally, this is facilitated by a targeted accelerated stress test consisting of operating and characterization phases. The applied machine learning pipeline, covering the hierarchical sequence of necessary data preprocessing and modeling steps, is presented in detail to ensure the traceability and reproducibility of the methodology from data collection to model testing and evaluation. As a major finding, it was demonstrated that the degradation trend prediction for the entire polarization curve can be realized using only typical operating data. This represents an initial step toward predicting the complete cell characteristic without interrupting ongoing operation for corresponding measurements.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100547"},"PeriodicalIF":9.6,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687168","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 Deep-learning based Model with Intra- and Inter-Well Constraints for Intelligent Identification of Stratigraphic Layers","authors":"Jinghua Yang ,&nbsp;Bin Gong ,&nbsp;Hu Huang ,&nbsp;Heng Zhao ,&nbsp;Haoqiang Wu ,&nbsp;Chen Liu ,&nbsp;Shifan Zhang ,&nbsp;Hui Li","doi":"10.1016/j.egyai.2025.100546","DOIUrl":"10.1016/j.egyai.2025.100546","url":null,"abstract":"<div><div>Geological stratification interpretation is a critical task in oil and gas exploration, aiming to delineate subsurface formations based on well logging data and provide a structural framework for drilling and reservoir development. Traditional stratification methods rely heavily on manual interpretation, which is subjective, labor-intensive, and often inconsistent, making it inadequate for complex geological settings. To address these limitations, this study proposes a fine-scale stratification method based on a Multi-Layer Perceptron (MLP), incorporating both intra-well and inter-well stratigraphic constraints into the model architecture. The proposed approach begins with principal component analysis (PCA) to reduce the dimensionality of logging parameters while retaining key geological features. Selected input features include well location, depth, drilling time, gamma ray logs, and lithology logs. An MLP model is constructed, and a custom loss function integrates stratigraphic consistency both within single wells and across multiple wells to improve formation boundary prediction. Furthermore, the study introduces a spatial segmentation strategy based on well locations to evaluate both interpolation performance within known areas and extrapolation capability to unseen regions. A case study in a coalbed methane block of the Ordos Basin demonstrates the effectiveness of the method. The model achieves a prediction accuracy of up to 95.04% in stratigraphic regions similar to the training data. Even when applied to extrapolated areas with well distances of approximately 1500–2000 meters from the nearest training point, the model maintains an accuracy of 85.36%. These results indicate that the proposed method not only delivers high precision in familiar formations but also generalizes well to new drilling areas. In conclusion, the MLP-based stratification model developed in this study reduces reliance on expert knowledge and exhibits strong performance in both precision and generalization. It provides a practical and reliable tool for automated stratigraphic interpretation and can support the planning of infill wells and the development of new blocks.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100546"},"PeriodicalIF":9.6,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597492","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":"Physics-informed deep convolutional hierarchical encoder-decoder neural network for flow field prediction in wind farms","authors":"Saeede Hasanpoor ,&nbsp;David A. Romero ,&nbsp;Joaquin E. Moran ,&nbsp;Cristina H. Amon","doi":"10.1016/j.egyai.2025.100553","DOIUrl":"10.1016/j.egyai.2025.100553","url":null,"abstract":"<div><div>Wind Farm Layout Optimization (WFLO) is a critical step in wind farm design, focusing on determining the optimal placement of turbines to maximize the annual energy production (AEP) of wind farms. Calculating AEP as an objective function in WFLO often relies on computationally expensive computational fluid dynamics (CFD) simulations to calculate the flow field within the farm. In this study, we propose PI-DeepWFLO, a physics-informed deep convolutional hierarchical encoder-decoder neural network, as a surrogate model to predict flow fields for various turbine configurations, significantly reducing dependence on costly CFD simulations. PI-DeepWFLO is trained on labeled data using a customized physics-informed loss function that incorporates mass and momentum conservation laws. Our results show that the proposed PI-DeepWFLO accurately predicts spanwise and streamwise velocity fields (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.955</mn></mrow></math></span>), effectively capturing wake interactions between turbines. Furthermore, results show that PI-DeepWFLO is less sensitive to variations in network weight initialization and training datasets than purely data-driven alternatives, exhibiting a ten-fold lower <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> variance over different re-samplings of the training dataset. A comparison of AEP values calculated from PI-DeepWFLO and CFD-generated flow fields demonstrates a median error of 1.25 % across test cases. Importantly, the Spearman’s Rank Correlation Coefficient between AEPs from CFD and PI-DeepWFLO flow fields is 1.0, confirming the PI-DeepWFLO’s suitability for AEP estimation in optimization studies. We illustrate PI-DeepWFLO’s performance in an application context by employing it as a surrogate model for a WFLO task.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100553"},"PeriodicalIF":9.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597405","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":"Active-learning-driven error control for data-driven state of charge estimation across the lithium battery lifecycle","authors":"Jinwei Xue ,&nbsp;Xuzhi Du ,&nbsp;Lei Zhao ,&nbsp;Zhigang Yang ,&nbsp;Chao Xia ,&nbsp;Yuan Ma ,&nbsp;Muhammad Jahidul Hoque ,&nbsp;Wuchen Fu ,&nbsp;Xiao Yan ,&nbsp;Nenad Miljkovic","doi":"10.1016/j.egyai.2025.100549","DOIUrl":"10.1016/j.egyai.2025.100549","url":null,"abstract":"<div><div>Accurate estimation of lithium-ion battery state of charge (SOC) is crucial for the safe and efficient operation of electric vehicles (EVs). However, both data-driven and model-driven SOC estimation methods face significant challenges under battery aging, which alters internal resistance and electrochemical properties, especially across complex aging trajectories. Most existing deep learning and model-based approaches operate in an open-loop manner, lacking mechanisms for uncertainty quantification, accuracy prediction, or adaptive correction—leading to uncontrolled estimation errors during aging. To address this, we propose an innovative closed-loop SOC estimation framework that integrates active learning with uncertainty-aware correction into deep learning networks, enabling real-time feedback on SOC prediction confidence levels without the need for additional sensors or reference data. Specifically, we quantify the performance degradation of mainstream data-driven methods, including long short-term memory (LSTM) networks and Gaussian process regression (GPR), under complex aging paths. We demonstrate that our model-disagreement-based active learning correction strategy maintains robustness throughout the battery lifecycle. Experimental results show that with only four active retraining sessions over the full aging process, our method reduces average SOC estimation error to below 1.5 %, and maximum cycle-based average error to below 2 %. This work establishes a path toward uncertainty-informed, lifecycle-resilient, and data-efficient SOC estimation, marking a significant advancement in battery management systems for real-world EV applications.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"21 ","pages":"Article 100549"},"PeriodicalIF":9.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614248","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}