Advances in Applied Energy最新文献

{"title":"Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases","authors":"B. Koirala ,&nbsp;H. Cai ,&nbsp;F. Khayatian ,&nbsp;E. Munoz ,&nbsp;J.G. An ,&nbsp;R. Mutschler ,&nbsp;M. Sulzer ,&nbsp;C. De Wolf ,&nbsp;K. Orehounig","doi":"10.1016/j.adapen.2024.100196","DOIUrl":"10.1016/j.adapen.2024.100196","url":null,"abstract":"<div><div>Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100196"},"PeriodicalIF":13.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578934","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":"Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China","authors":"Haizhi Luo ,&nbsp;Yiwen Zhang ,&nbsp;Xinyu Gao ,&nbsp;Zhengguang Liu ,&nbsp;Xiangzhao Meng ,&nbsp;Xiaohu Yang","doi":"10.1016/j.adapen.2024.100197","DOIUrl":"10.1016/j.adapen.2024.100197","url":null,"abstract":"<div><div>The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R² = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km² or between 288.2 and 657.3 km²; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R² &gt; 0.80). Restricting the scale to 11.3 km² could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km², separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100197"},"PeriodicalIF":13.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593033","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":"Hydrogen production via solid oxide electrolysis: Balancing environmental issues and material criticality","authors":"Elke Schropp,&nbsp;Gabriel Naumann,&nbsp;Matthias Gaderer","doi":"10.1016/j.adapen.2024.100194","DOIUrl":"10.1016/j.adapen.2024.100194","url":null,"abstract":"<div><div>Hydrogen is considered an essential component in mitigating climate change. Water electrolysis technologies present the potential for generating environmentally friendly hydrogen. The solid oxide water electrolysis attracts attention due to its high-temperature operation, leading to an unsurpassed efficiency. Nevertheless, high-temperature operation requires special materials, raising material criticality concerns. This study aims to determine the optimum current density for future solid oxide water electrolysis operation. To this end, the energetic performance of solid oxide electrolysis is assessed under different current densities with a numerical simulation. Consequently, prospective life cycle assessments and product-level material criticality assessments are performed. These dimensions are combined in a multi-criteria optimization. The environmental impacts strongly depend on electricity and heat generation, whereas manufacturing and the feed water supply play a minor role. Heat integration, a unique feature of solid oxide water electrolysis, is beneficial if heat carries less environmental impact than electricity. Then, the solid oxide electrolysis should be operated at relatively low current densities. In contrast, the material criticality decreases with increasing current densities. The multi-criteria optimization reveals that if minimizing environmental impacts and material criticality is equally vital, solid oxide water electrolysis should be operated at 0.955 A/cm², whereas a focus on environmental impacts leads to lower current densities. In conclusion, the energy supply situation affects the operational current density from an environmental perspective. In contrast, the material criticality favors high current densities for solid oxide water electrolysis. When combining both, medium current densities lead to minimum environmental and material criticality issues.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100194"},"PeriodicalIF":13.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653887","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":"Green light for bidirectional charging? Unveiling grid repercussions and life cycle impacts","authors":"Daniela Wohlschlager ,&nbsp;Janis Reinhard ,&nbsp;Iris Stierlen ,&nbsp;Anika Neitz-Regett ,&nbsp;Magnus Fröhling","doi":"10.1016/j.adapen.2024.100195","DOIUrl":"10.1016/j.adapen.2024.100195","url":null,"abstract":"<div><div>Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100195"},"PeriodicalIF":13.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571186","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":"MANGOever: An optimization framework for the long-term planning and operations of integrated electric vehicle and building energy systems","authors":"Alicia Lerbinger ,&nbsp;Siobhan Powell ,&nbsp;Georgios Mavromatidis","doi":"10.1016/j.adapen.2024.100193","DOIUrl":"10.1016/j.adapen.2024.100193","url":null,"abstract":"<div><div>The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for <strong>e</strong>lectric <strong>v</strong>ehicles and <strong>e</strong>nergy <strong>r</strong>etrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100193"},"PeriodicalIF":13.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535898","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":"Reviewing the complexity of endogenous technological learning for energy system modeling","authors":"Johannes Behrens ,&nbsp;Elisabeth Zeyen ,&nbsp;Maximilian Hoffmann ,&nbsp;Detlef Stolten ,&nbsp;Jann M. Weinand","doi":"10.1016/j.adapen.2024.100192","DOIUrl":"10.1016/j.adapen.2024.100192","url":null,"abstract":"<div><div>Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100192"},"PeriodicalIF":13.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586498","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 review of mixed-integer linear formulations for framework-based energy system models","authors":"Maximilian Hoffmann ,&nbsp;Bruno U. Schyska ,&nbsp;Julian Bartels ,&nbsp;Tristan Pelser ,&nbsp;Johannes Behrens ,&nbsp;Manuel Wetzel ,&nbsp;Hans Christian Gils ,&nbsp;Chuen-Fung Tang ,&nbsp;Marius Tillmanns ,&nbsp;Jan Stock ,&nbsp;André Xhonneux ,&nbsp;Leander Kotzur ,&nbsp;Aaron Praktiknjo ,&nbsp;Thomas Vogt ,&nbsp;Patrick Jochem ,&nbsp;Jochen Linßen ,&nbsp;Jann M. Weinand ,&nbsp;Detlef Stolten","doi":"10.1016/j.adapen.2024.100190","DOIUrl":"10.1016/j.adapen.2024.100190","url":null,"abstract":"<div><div>Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits their added value for users who not only want to use models but also program them themselves.</div><div>To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.</div><div>Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.</div><div>To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100190"},"PeriodicalIF":13.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419110","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":"Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data","authors":"Kosaku Nakano ,&nbsp;Sophia Vögler ,&nbsp;Kenji Tanaka","doi":"10.1016/j.adapen.2024.100188","DOIUrl":"10.1016/j.adapen.2024.100188","url":null,"abstract":"<div><p>The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100188"},"PeriodicalIF":13.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266679242400026X/pdfft?md5=d77f0e82e70762f99f4b385e9253c7d3&pid=1-s2.0-S266679242400026X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164649","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":"Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling","authors":"Ao Li ,&nbsp;Fu Xiao ,&nbsp;Ziwei Xiao ,&nbsp;Rui Yan ,&nbsp;Anbang Li ,&nbsp;Yan Lv ,&nbsp;Bing Su","doi":"10.1016/j.adapen.2024.100189","DOIUrl":"10.1016/j.adapen.2024.100189","url":null,"abstract":"<div><p>Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100189"},"PeriodicalIF":13.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000271/pdfft?md5=0f782334db1159003891c4cf1c77159d&pid=1-s2.0-S2666792424000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148847","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 probabilistic model for real-time quantification of building energy flexibility","authors":"Binglong Han ,&nbsp;Hangxin Li ,&nbsp;Shengwei Wang","doi":"10.1016/j.adapen.2024.100186","DOIUrl":"10.1016/j.adapen.2024.100186","url":null,"abstract":"<div><p>Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100186"},"PeriodicalIF":13.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000246/pdfft?md5=5103629f1a558de886b8f7db3d5993e4&pid=1-s2.0-S2666792424000246-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077117","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}