Energy Informatics最新文献

{"title":"An AI and multi-criteria framework for siting renewable energy systems enhanced by graphene photovoltaics in a coastal City","authors":"Farshad Rafipour,&nbsp;Manouchehr Tabibian,&nbsp;Mehdi Saati","doi":"10.1186/s42162-026-00647-4","DOIUrl":"10.1186/s42162-026-00647-4","url":null,"abstract":"<div>\u0000 \u0000 <p>This research analyzed and evaluated the factors affecting the productivity of solar, wave, and wind renewable energy systems in the coastal city of Tonekabon. We employed statistical and machine learning methods, including Linear Regression and Random Forest, on a dataset of spatial, climatic, and physical data from the region. This analysis identified key indicators and predicted the optimal performance of energy systems. A correlation analysis was also conducted to examine the relationships between these indicators. For the first time in this region, the role of graphene-based photovoltaic (GPV) was investigated. The results indicate that indicators such as optimal building orientation, height, density, vegetation cover, and climatic parameters significantly impact renewable energy productivity. Furthermore, GPV demonstrated a 13.8% improvement in annual average efficiency compared to conventional silicon cells in Tonekabon’s humid climate. This research proposes a framework for improving urban design and promoting sustainable energy development in coastal cities like Tonekabon.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00647-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-assessment framework for evaluating efficiency of data centers","authors":"Mustafa Kuzay,&nbsp;Ender Demirel,&nbsp;Basak Bayraktar,&nbsp;Josef Vilestad,&nbsp;Axel Kärnebro,&nbsp;Cagatay Yilmaz,&nbsp;Simon Pommerencke Melgaard,&nbsp;Thomas Juul,&nbsp;Jesper Ellerbæk Nielsen,&nbsp;Reto Fricker,&nbsp;Sascha Stoller,&nbsp;Gabriele Humbert,&nbsp;Binod Prasad Koirala","doi":"10.1186/s42162-026-00652-7","DOIUrl":"10.1186/s42162-026-00652-7","url":null,"abstract":"<div>\u0000 \u0000 <p>Increasing global data center energy consumption, with the rapid digitalization of society, has driven the need for comprehensive assessment reports periodically. Current challenges include the complexity of collecting data and calculating Key Performance Indicators (KPI) within the corresponding reporting period. To address this, we introduce the Self-Assessment Tool (SAT) as a unified, modular, and extensible framework for the evaluation of thermal and energy performance of data centers based on IT and cooling data sourced from data monitoring systems. A historical dataset can also be imported to the SAT to assess the energy efficiency of a data center based on the data collected from external sensors. The KPI calculation module of SAT calculates a comprehensive set of standardized thermal (RCI, RHI, RTI, RI, and LI) and energy (PUE and COP) metrics based on both real-time and historical datasets, with a transparent and reproducible KPI computation workflow that enables independent implementation. As demonstrated and validated on two pilot data centers located in Denmark and Switzerland, the SAT automatically generates assessment reports including time-series visualizations, rack-level thermal maps and energy-efficiency classifications according to the KPIs. Finally, the proposed framework can be deployed either as a standalone or web-based service for the performance evaluation of data centers following the present assessment workflow.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00652-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating distributed optimization algorithms into blockchain for P2P energy trading","authors":"Edinson Benavides,&nbsp;Germán Obando,&nbsp;Andrés Pantoja","doi":"10.1186/s42162-026-00644-7","DOIUrl":"10.1186/s42162-026-00644-7","url":null,"abstract":"<div>\u0000 \u0000 <p>Peer-to-peer (P2P) energy trading represents a viable solution for the transition toward carbon-free energy systems, as they enable prosumers to exchange electricity at lower costs without the need for intermediaries. However, P2P networks require an infrastructure that supports the secure exchange and storage of information. In addition, a key challenge in P2P markets is ensuring economic balance among all network participants. This paper develops an integrated methodology for P2P energy trading by combining distributed optimization with <i>Blockchain</i> technology. To mitigate the challenges of economic balancing and data privacy, we implement an asynchronous distributed algorithm based on Replicator Dynamics within the Ethereum ecosystem. The proposed two-layer architecture utilizes smart contracts to facilitate optimal dispatch among prosumers while maintaining information immutability and security. Experimental implementation shows that the system achieves fast convergence in the optimization process without compromising agent privacy. The study concludes with an automated P2P market framework, demonstrating the potential of <i>Blockchain</i> as a robust infrastructure for decentralized energy management and distributed optimization.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00644-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147559760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protection challenges and emerging solutions in renewable-integrated microgrids: a critical review","authors":"Nouman Liaqat,&nbsp;Naceur Chihaoui,&nbsp;Muhammad Nasir,&nbsp;Ahmad Subhi Salem Mufleh,&nbsp;Shadi Majed Alshraah,&nbsp;Aashir Waleed","doi":"10.1186/s42162-026-00646-5","DOIUrl":"10.1186/s42162-026-00646-5","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a critical and structured review of protection challenges and emerging solutions in renewable-integrated microgrids. The proliferation of distributed energy resources (DERs) has introduced complex protection issues, including bidirectional power flow, low fault currents, mode-dependent dynamics, and communication dependencies. This review systematically examines fault detection, classification, and coordination strategies for both grid-connected and islanded operating conditions from 2020 to 2025. A qualitative comparative analysis is conducted across diverse protection approaches, including conventional relay-based methods, artificial intelligence (AI)-based schemes, fuzzy logic systems, time-frequency analysis, phasor measurement unit (PMU)-assisted techniques, and communication-assisted multi-agent frameworks. The analysis identifies dominant trends toward hybrid, adaptive, and data-driven protection strategies while highlighting persistent gaps in scalability, experimental validation, cybersecurity, and real-world deployment. By synthesizing current research and identifying unresolved challenges, this review provides a clear roadmap for future work toward robust, standardized, and practically deployable microgrid protection systems.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00646-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An online optimization control method for flexible DC distribution networks based on visual programming and its engineering applications","authors":"Lei Su,&nbsp;Kan Cao,&nbsp;Haoyu Ma,&nbsp;Runfa Wu,&nbsp;Mingjiang Wei,&nbsp;Ziya Chen,&nbsp;Junda Qin","doi":"10.1186/s42162-026-00649-2","DOIUrl":"10.1186/s42162-026-00649-2","url":null,"abstract":"<div>\u0000 \u0000 <p>With the large-scale integration of high-throughput renewable energy into the power system, the architecture of alternating and direct current interconnection of the distribution network has become increasingly complex and critical. This paper conducts an in-depth analysis of the popular AC/DC hybrid topology according to the scenario of high penetration of renewable energy and clarifies its structural features and operating mechanism. On this basis, an innovative virtual impedance management strategy is introduced, which integrates the mechanism of influence and dynamic characteristics of several variables. An online optimization control method of a flexible DC distribution network based on visual programming is proposed. This method uses the intuitiveness and accessibility of visual programming to achieve fast and efficient online optimization and parameter configuration in network debugging and real engineering applications, greatly improving debugging efficiency and ease of operation. To verify the effectiveness of this method, an advanced hardware-in-the-loop (HIL) testing platform was built using RT-LAB. Strict experimental results show that this method realizes fast online adjustment of DC voltage parameters and active power settings, and has excellent dynamic response performance. In addition, this method is also implemented in the electrical network of Guangshui District of Hubei province. Long-term operation monitoring and data analysis show that the DC voltage fluctuation is kept below 2%, and the active power adjustment error is always controlled within 1.5% to ensure the stability of the electrical network. In addition, this method facilitates trouble-free switching operations in field applications and effectively mitigates the transitions of the electrical network. Compared with traditional methods, the visual programming-based method improves parameter tuning efficiency—measured in terms of commissioning time, trial-and-error iterations—by 40%, and the degree of voltage distortion during the conversion of the system is kept below 3%, which significantly improves the quality of power. This method provides strong technical support for the stable and efficient operation of distribution networks with high throughput of renewable energy and has theoretical significance and practical technical value. It has great potential for widespread adoption in future power grid upgrades and smart grid development.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00649-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147559222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review of ontologies in the energy domain to enable knowledge representation in local electricity markets","authors":"Julio Costa,&nbsp;Gabriel Roma,&nbsp;Paulo Matos,&nbsp;Ângela Ferreira,&nbsp;Maria João Varanda Pereira","doi":"10.1186/s42162-026-00636-7","DOIUrl":"10.1186/s42162-026-00636-7","url":null,"abstract":"<div><p>This paper presents a systematic review of ontologies in the energy domain, focusing on their applicability to knowledge representation in local electricity markets. Using a rigorous methodology aligned with the PRISMA framework, a comprehensive literature search was conducted across multiple digital libraries in which 78 works were read and 26 ontologies were evaluated in depth. The review examines ontologies from diverse application areas including smart grids, energy consumption and management, renewable energy, electricity markets and policy, as well as industrial and building systems. Key characteristics of these ontologies are analysed, encompassing conceptual coverage, reuse, modularity, formal representation, development tools, and reasoning support. Adoption rates and use cases are also discussed to highlight practical implications. The study identifies research gaps, limitations, and challenges in current ontology development, offering insights and opportunities for future work to enhance semantic interoperability and knowledge integration in local electricity markets. Finally we discuss advancing ontology engineering practices to the evolving needs of the electricity sector and supports the development of intelligent energy systems specially in local and community based markets.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00636-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147559242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capacity optimization allocation and stability of new energy wind power generation and hybrid energy storage systems","authors":"Linyan Tang,&nbsp;Zhiming Huang","doi":"10.1186/s42162-026-00638-5","DOIUrl":"10.1186/s42162-026-00638-5","url":null,"abstract":"<div><p>To solve the power fluctuation and system stability problems caused by large-scale wind power grid connection, this study proposes a capacity optimization configuration and stability improvement strategy for wind power generation and battery-supercapacitor hybrid energy storage systems. In view of the shortcomings of traditional energy storage configuration methods that fail to take into account full life cycle economics and dynamic stability, this study established a two-layer optimization model with the goal of lowest annual average cost throughout the life cycle and minimal grid-connected power fluctuations, and designed a set of power allocation strategies based on frequency decoupling. The results revealed that the model proposed in the study was better than four advanced algorithms in optimization performance, and the Pareto front obtained reached the highest 0.782 in the hypervolume index. The optimized configuration achieves a minimum annual average cost of 1.887 million yuan and limits the grid-connected power fluctuation standard deviation to 124.6 kW. Compared with a single battery energy storage solution, the optimally configured hybrid energy storage system could not only further reduce the standard deviation of grid-connected power by 6.7%, but also reduce the average daily equivalent number of battery cycles by 38.5%. Stability analysis verified that the hybrid energy storage system solution could increase the damping ratio of the system’s dominant oscillation mode from 0.058 for a single battery to 0.124, and shorten the voltage recovery time by 21.2% under a three-phase short-circuit fault. The method proposed in the study provides a systematic solution that takes into account both economy and safety for energy storage system planning under high-proportion wind power access, and has important theoretical value and engineering application prospects.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00638-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable hybrid transformer–BiLSTM framework for multivariate household energy demand forecasting with weather integration","authors":"Michael Marko Sesay,&nbsp;Antony Ngunyi,&nbsp;Herbert Imboga","doi":"10.1186/s42162-026-00635-8","DOIUrl":"10.1186/s42162-026-00635-8","url":null,"abstract":"<div><h3>Purpose</h3><p>Accurate and explainable household load forecasting is critical for demand-side management, tariff-aware scheduling, and reliable smart grid operation. This study introduces a leakage-controlled multi-horizon forecasting pipeline that integrates predictive accuracy with statistical validation, interpretability, robustness, and operational relevance.</p><h3>Methods</h3><p>We model multivariate household demand using an hourly smart-meter dataset spanning 14 months (Nov 2022-Jan 2024; N=10,234 time steps), incorporating aligned local weather covariates. A hybrid Transformer-BiLSTM is trained using a multi-output configuration to predict 24-hour and 168-hour trajectories. Hyperparameters are optimized via Bayesian optimization (Optuna) employing chronological train/validation/test splits and a rolling-origin evaluation protocol. Performance is assessed using MAE, RMSE, and MAPE, while pairwise forecast differences are validated using the Diebold–Mariano procedure. Model explanations are generated through SHAP and attention analyses, further complemented by robustness testing (noise and feature dropout) and inference-efficiency measurements.</p><h3>Results</h3><p>At the 24-hour horizon, the hybrid model achieves MAE and RMSE values of 0.0539/0.0701 (MAPE 29.7%), yielding performance comparable to N-BEATS (MAE/RMSE 0.0531/0.0684). For the 168-hour horizon, the model attains superior performance among evaluated baselines (MAE/RMSE/MAPE 0.0566/0.0746/30.1%) and demonstrates statistically significant improvements over the Transformer, BiLSTM, and TCN models (Diebold-Mariano, <span>(p&lt;0.001)</span>). SHAP analysis identifies electrical indicators (e.g., voltage, power factor) and meteorological variables (e.g., pressure, temperature statistics) as the dominant drivers of medium-term predictions. Median inference latency remains in the tens-of-milliseconds range per sample, facilitating near real-time application.</p><h3>Conclusion</h3><p>Beyond improving forecast accuracy, the proposed framework provides statistically supported and interpretable attributions, remains stable under input degradation, and demonstrates operational value in a tariff-based battery scheduling case study, reducing energy cost by approximately 2.29% over an aggregated multi-week evaluation.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00635-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147441267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid global and sub-domain approach for accurate hourly cooling load forecasting in short, medium, and long-term horizons","authors":"Hangyu Che,&nbsp;Masafumi Kinoshita,&nbsp;Shiyu Lu","doi":"10.1186/s42162-026-00619-8","DOIUrl":"10.1186/s42162-026-00619-8","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate cooling load forecasting is critical for optimizing heating, ventilation, and air conditioning (HVAC) system operations, reducing energy consumption, and advancing building sustainability objectives. This paper introduces the Hybrid Global and Sub-domain Approach (HGSA), a novel forecasting framework that synergistically combines advanced feature engineering, domain-specific data segmentation, and ensemble learning to deliver robust predictions across multiple time horizons. The methodology addresses the inherent complexity of cooling load dynamics by partitioning historical data into complementary domains—global, hourly, day of week, monthly, and temperature-based, each capturing distinct temporal and climatic patterns. HGSA incorporates four feature categories: temporal, weather-related, historical, and periodic factors, with the latter computed at both global and sub-domain levels to enhance pattern recognition. Multiple regression models are trained within each domain, and top-performing models are fused through weighted ensemble optimization to maximize predictive accuracy. Validated on 18 months of real-world data from a commercial building in Hong Kong, HGSA demonstrates substantial improvements over state-of-the-art methods including LSTM, LightGBM, Prophet, Autoregressive models, and Informer across four forecasting scenarios: 1-hour (CV-RMSE: 0.09), 24-hour (CV-RMSE: 0.161), 7-day (CV-RMSE: 0.157), and 1-month (CV-RMSE: 0.188) ahead predictions. The framework’s model-agnostic design ensures flexibility and practical deployability, while its consistently superior performance across short-, medium-, and long-term horizons establishes HGSA as a comprehensive solution for building energy management, HVAC optimization, and strategic energy planning applications.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00619-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated risk scoring and exploit prediction for cyber-physical power system vulnerabilities","authors":"Firdous Kausar,&nbsp;Lisette Batiste,&nbsp;Asmah Muallem,&nbsp;Sajid Hussain","doi":"10.1186/s42162-026-00640-x","DOIUrl":"10.1186/s42162-026-00640-x","url":null,"abstract":"<div>\u0000 \u0000 <p>Cyber-Physical Power Systems (CPPS) increasingly inherit cybersecurity vulnerabilities from industrial control components, yet practitioners lack a CPPS-focused dataset and a consistent way to prioritize remediation beyond generic severity scores. This paper presents a cohesive methodology for collecting, enriching, and modeling CPPS-related CVEs to predict their risk and prioritize remediation. We aggregate over 4,030 ICS-relevant CVEs from public sources (2020–2025) and enrich each with CVSS severity, exploitation data (CISA Known Exploited Vulnerabilities, Exploit Prediction Scoring System), and OT/ICS contextual attributes. Based on the dataset, we develop the two-stage learning framework that achieves the following two goals: (i) the provision of a risk score specific to the CPPS and the indication of the priority of the vulnerabilities, and (ii) an estimated likelihood of exploitation, combining structured indicators with features derived from CVE text. These rankings make triage possible by identifying a set of high priority vulnerabilities while reducing the priority of many others, allowing identification of CPPS components with high-risk issues not accounted for by KEV. The analysis of proposed method is shown to yield more informative prioritization than the severity-only baselines by distinguishing between operationally urgent and non-urgent vulnerabilities. The produced risk levels are intended to be interpretable and deployable, serving as a practical decision support tool for CPPS vulnerability management with the understanding that the true label is uncertain.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00640-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}