Applied Energy最新文献

{"title":"Unsupervised and controllable synthesizing for imbalanced energy dataset based on AC-InfoGAN","authors":"Zhenghao Zhou ,&nbsp;Yiyan Li ,&nbsp;Runlong Liu ,&nbsp;Xiaoyuan Xu ,&nbsp;Zheng Yan","doi":"10.1016/j.apenergy.2025.126107","DOIUrl":"10.1016/j.apenergy.2025.126107","url":null,"abstract":"<div><div>Generating synthetic data has become a popular alternative solution to deal with the difficulties in accessing and sharing field measurement data in power systems. However, to make the generation results controllable, existing methods (e.g., Conditional Generative Adversarial Nets, cGAN) require labeled dataset to train the model, which is demanding in practice because many field measurement data lack descriptive labels. Meanwhile, real-world datasets are naturally imbalanced, causing bias in neural network training. In this paper, we introduce the Adaptive and Contrastive Information Maximizing Generative Adversarial Nets (AC-InfoGAN) to achieve controllable synthesizing for the unlabeled and imbalanced energy dataset. Features with physical meanings can be automatically extracted by maximizing the mutual information between the input latent code and the classifier output. Then the extracted features are used to control the generation results similar to a vanilla cGAN framework. We employ the Gumbel-Softmax distribution and frequency-based contrastive learning techniques to dynamically adapt to the imbalanced dataset to avoid the model training bias. Meanwhile, frequency-domain neural network modules are introduced to the AC-InfoGAN framework to enhance the model performances. Case study is based on the unlabeled and imbalanced energy datasets of power load and renewable energy output. Results demonstrate that AC-InfoGAN can extract both discrete and continuous features with certain physical meanings, as well as generating realistic synthetic energy data that satisfy given features</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126107"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinated Management of Mobile Charging Stations and Community Energy Storage for Electric Vehicle Charging","authors":"Akın Taşcıkaraoğlu ,&nbsp;Muhammed Ali Beyazıt ,&nbsp;Jan Kleissl ,&nbsp;Yuanyuan Shi","doi":"10.1016/j.apenergy.2025.126066","DOIUrl":"10.1016/j.apenergy.2025.126066","url":null,"abstract":"<div><div>Widespread adoption of electric vehicles (EVs) largely relies on the availability of a charging infrastructure. However, the significant installation costs, need for appropriate locations, congestion and lengthy queues at public fixed charging station (FCS), and the potential strain on the grid hinder the expansion of the charging station network, particularly in urban areas. To address these shortcomings associated with FCSs, mobile charging stations (MCSs) can be used as a supplementary solution. To this end, an optimization framework that incorporates FCSs and MCSs is proposed to meet the spatiotemporally distributed EV charging demands. A community energy storage system (CESS) is integrated into the system to enhance the flexibility and increase the use of renewable energy in EV charging. When the EV charging requests are received, the proposed framework determines the optimal charging technology and location for each charging demand by taking charging site and time preferences of EV users into account. The simulation studies and comparative analyses demonstrate that the proposed framework enhances benefits for both the operators and EV users, achieving a 90 % reduction in carbon emissions and reducing waiting times at FCSs to zero. Further analyses confirm its effectiveness across various conditions, leading to substantial reductions in emissions and costs, particularly in larger systems. Compared to an MCS scheduling method based on EV clustering, the proposed framework achieves lower emissions while slightly increasing battery degradation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126066"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Management of Electric Vehicle Charging Stations: Balancing user preferences and grid demands with energy storage systems and renewable energy","authors":"Anis Ur Rehman ,&nbsp;Junwei Lu ,&nbsp;Bo Du ,&nbsp;Feifei Bai ,&nbsp;Mohammad J. Sanjari","doi":"10.1016/j.apenergy.2025.126147","DOIUrl":"10.1016/j.apenergy.2025.126147","url":null,"abstract":"<div><div>Renewable energy sources (RESs), combined with energy storage systems (ESSs), are increasingly used in electric vehicle charging stations (EVCSs) due to their economic and environmental advantages. To highlight its advantages, extensive studies have been conducted on the techno-economic and environmental impacts of integrating RESs and ESSs into EVCSs. However, important aspects such as load management capabilities and the operational efficiency of these systems in EVCSs have received comparatively little attention. This paper addresses this gap by investigating the load management and operational efficiency of combining RESs and ESSs with ultra-fast direct current chargers (UFCs) in an EVCS. It conducts a hypothetical case study on a commercial Evie network (charging company) charging station having 4 ultra-fast charging ports, in Australia, to investigate three load management strategies: 1) user-preferred, 2) grid-preferred, and 3) renewable energy resources - battery energy storage integrated systems (ReBIS). The study investigates the load management and operational effectiveness of these strategies in combination with techno-economic analysis. It highlights that the ReBIS effectively reduces grid peak demand, maximizes charging sessions, alleviates grid strains, and balances both user and grid charging preferences. Simulation results indicate that ReBIS with UFCs lowers energy costs by 40 %, supports 99.4 % of user-preferred charging sessions, and cuts carbon emissions by 63.2 % and 65.2 % compared to user-preferred and grid-preferred modes, respectively. The results validate ReBIS with UFCs as the optimal load management strategy for EVs, balancing charging demand with grid stability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126147"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PV Segmenter: A frequency-guided edge-aware network for distributed photovoltaic segmentation in remote sensing imagery","authors":"Siyuan Wang ,&nbsp;Zhenfeng Shao ,&nbsp;Dongyang Hou ,&nbsp;Bowen Cai","doi":"10.1016/j.apenergy.2025.126137","DOIUrl":"10.1016/j.apenergy.2025.126137","url":null,"abstract":"<div><div>Accurate localization and sizing of distributed photovoltaic (PV) systems using remote sensing imagery are critical for assessing installed capacity and forecasting solar generation potential. However, existing PV extraction methods predominantly rely on spatial-domain learning strategies, which struggle to capture the complex boundaries and fine details of small-scale PV systems. In this paper, we propose PV Segmenter, a frequency-guided edge-aware network that employs frequency-domain learning to improve edge detection and pattern recognition in distributed PV systems. Specifically, a frequency-enhanced edge detection module is designed to leverage frequency-domain decoupling for the extraction of edge semantics related to PV boundaries. An edge-guided feature discrimination module subsequently injects edge cues into multi-level semantic features to refine structural semantic representation. Furthermore, a context-aware cross-layer fusion module is designed to preserve critical details of small PV panels, facilitating robust edge detection. Finally, we introduce an object-edge hybrid loss function with deep supervision that jointly optimizes PV object and edge features. Experimental results on two distributed PV datasets demonstrate that PV Segmenter improves the Intersection over Union (IoU) by 1.96 % to 9.61 % compared to nine benchmark methods. The proposed method shows promise for accurately identifying small-scale PV systems and effectively defining complex boundaries, offering a viable solution for renewable energy assessment and smart grid planning.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126137"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From theory to practice: A critical review of model predictive control field implementations in the built environment","authors":"Etienne Saloux ,&nbsp;José A. Candanedo ,&nbsp;Charalampos Vallianos ,&nbsp;Navid Morovat ,&nbsp;Kun Zhang","doi":"10.1016/j.apenergy.2025.126091","DOIUrl":"10.1016/j.apenergy.2025.126091","url":null,"abstract":"<div><div>While the potential of model-based predictive control (MPC) to improve building operation is widely acknowledged, its implementation has not yet become a mainstream practice in the building operation industry. This review paper explores the scientific literature documenting MPC field implementations in actual buildings. The goal is twofold: (a) to identify critical features in the deployment of MPC strategies, including the targeted building types and applications, systems controlled, expected benefits, software used, as well as common issues encountered (and successful measures to overcome these issues); (b) to evaluate the benefits of MPC based on the reported information from real-life implementations. Aspects analyzed include drivers and energy contexts, control-oriented modelling approaches, optimization routines, and performance evaluation methods. Results show that most practical studies focussed on buildings with a floor area under 10,000 m², often even less than 1000 m². MPC applications were varied, ranging from setpoint tracking and building conditioning to the optimization of the operation of thermal energy storage and photovoltaic panels and/or battery systems. MPC consistently yields significant benefits, with average savings of 30 % for thermal energy, 25 % for electricity use, 25 % for energy costs, 26 % for peak power and 17 % for GHG emissions, obtained under an average field-testing duration of 41 days.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126091"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the impact of agricultural economic quality and energy consumption on greenhouse gas emissions: Evidence from China's major grain-producing regions","authors":"Yanqi Xie ,&nbsp;Aftab Khan ,&nbsp;Yumeng Wang ,&nbsp;Muhammad Waqas ,&nbsp;Shuifa Ke","doi":"10.1016/j.apenergy.2025.126121","DOIUrl":"10.1016/j.apenergy.2025.126121","url":null,"abstract":"<div><div>Our current study employs the entropy method to construct agricultural economic quality indicators based on three aspects: economic scale, economic structure, and economic growth rate. Using the panel data of 13 main grain-producing provinces of China from 1995 to 2020. We used the panel autoregressive distributed lag model (ARDL) and heterogeneous panel Granger causality test to empirically analyze the relationship between agricultural economic quality, agricultural energy consumption, and agricultural greenhouse gas emissions while also exploring the regional heterogeneity. The results show that (1) The Environmental Kuznets Curve is observed in the pattern of agricultural greenhouse gas emissions in major grain producing areas. (2) Agricultural energy consumption positively impacts greenhouse gas emissions in both the short and long term, with a more substantial impact over time. (3) The heterogeneous panel Granger causality test reveals two-way causal relationship between agricultural energy consumption and greenhouse gas emissions, and between agricultural energy consumption and agricultural economic growth quality. Additionally, there is one-way causal relationship from agricultural economic quality to agricultural greenhouse gas emissions. (4) Regional heterogeneity analysis indicates that agricultural energy consumption contributes to reduction in agricultural greenhouse gas emissions in the northern region, whereas in southern regions, agricultural economic quality is positively associated with agricultural greenhouse gas emissions. This research highlights the importance of optimizing the agricultural energy structure, promoting high-quality development of the agricultural economy, and developing region-specific emission reduction strategies to achieve sustainable agricultural development.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126121"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cybersecurity in smart microgrids using blockchain-federated learning and quantum-safe approaches: A comprehensive review","authors":"Jameel Ahmad ,&nbsp;Muhammad Rizwan ,&nbsp;Syed Farooq Ali ,&nbsp;Usman Inayat ,&nbsp;Hafiz Abdul Muqeet ,&nbsp;Muhammad Imran ,&nbsp;Tabbi Awotwe","doi":"10.1016/j.apenergy.2025.126118","DOIUrl":"10.1016/j.apenergy.2025.126118","url":null,"abstract":"<div><div>Smart microgrids are decentralized energy systems that effectively integrate renewable energy sources, energy storage technologies, and advanced communication and control frameworks, thereby facilitating efficient and sustainable energy distribution while enhancing grid resilience. These systems empower active participation from consumers and prosumers in energy trading, which significantly transforms traditional energy management practices. However, the increased connectivity and dependence on digital infrastructure inherent in smart microgrids introduce substantial cybersecurity vulnerabilities, underscoring the necessity for robust security protocols. This article provides a comprehensive review of cybersecurity threats directed at distributed generation in both AC and DC microgrids, energy trading platforms, and transactive energy management frameworks within the broader context of the smart grid. We systematically analyze both conventional and sophisticated stealth cyberattacks, identifying critical countermeasures essential for safeguarding modern smart grids. Furthermore, we explore the integration of emerging technologies, including machine learning, federated learning, blockchain security, and quantum-safe cryptographic mechanisms, as synergistic strategies to enhance cyber resilience in smart microgrids. Ultimately, this study identifies existing research gaps, barriers to adopting emerging technologies and proposes future research directions, with the goal of advancing the cybersecurity of these complex and evolving energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126118"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A methodology for simulation of power generation characteristics and enhancement of MPPT performance of offshore floating photovoltaic arrays","authors":"Hui Jin ,&nbsp;Xiangyu Kong ,&nbsp;Chunjie Wang ,&nbsp;Delong Zhang ,&nbsp;Ye Yao","doi":"10.1016/j.apenergy.2025.126129","DOIUrl":"10.1016/j.apenergy.2025.126129","url":null,"abstract":"<div><div>The irradiance distribution conditions and power generation characteristics of offshore floating photovoltaic (PV) arrays on multiple types of floating body structures are complex due to the influence of spatially and temporally variable wave environments, which greatly increases the difficulty of maximum power point tracking and hinders the efficient and economic operation of offshore floating PV power plants. Considering the influence of different scales of floating body structures, PV module arrangements and wiring methods on the irradiance distribution conditions and power generation characteristics of offshore floating PV arrays, this paper proposes a simulation method for power generation characteristics of offshore floating PV systems adapted to multiple types of floating body structures. The proposed method is validated in laboratory and actual operation scenarios using the six-degree-of-freedom motion experimental platform and the actual sea-measured irradiance data of the floating PV power generation unit. Based on two types of floating structure power generation units of offshore floating PV power plant in Bohai Bay, the multimodal uniform and non-uniform irradiance conditions and power generation characteristics of offshore PV arrays are generated, and the performance test of the proposed hybrid improved MPPT technique is carried out. The results show that by using a large hexagonal floating structure unit and centralizing the modules so that the PV arrays connected to the same MPPT control are spread out over as few floating structures as possible, fast and efficient MPPT can be achieved using the classical MPPT technique. The number of local maximal power points in the <em>P</em><img><em>V</em> curves is larger in number and speed of change with the small rectangular floating structure, but better MPPT performance can be ensured by adopting the proposed MPPT technique. The proposed MPPT technique has a tracking time of no more than 20 ms and a tracking efficiency of no less than 99.85 % under uniform and non-uniform irradiance conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126129"},"PeriodicalIF":10.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data center load modeling through optimal energy consumption characteristics: A path to simultaneously enhance energy efficiency and demand response quality","authors":"Yongcheng Zhou ,&nbsp;Fanchao Wei ,&nbsp;Shuangxiu Li ,&nbsp;Zhonghao Wang ,&nbsp;Jinfu Liu ,&nbsp;Daren Yu","doi":"10.1016/j.apenergy.2025.126095","DOIUrl":"10.1016/j.apenergy.2025.126095","url":null,"abstract":"<div><div>In an era defined by the rapid advancement of artificial intelligence and the global pursuit of “carbon neutrality,” data centers face the dual challenge of enhancing energy efficiency while ensuring high-quality participation in power system demand response. However, conventional linear load models used in demand response programming often force data centers into a trade-off: sacrificing energy efficiency to ensure response quality, or vice versa. This paper presents a hierarchical load modeling framework that captures the optimal energy consumption characteristics of data centers to mitigate this conflict. At the foundational layer, a fine-grained, cross-system energy consumption model is developed to capture the intricate electrical-thermal-performance interactions among the computing, cooling, and power conditioning systems within the data center. Solving the energy optimization problem at this layer yields the optimal energy consumption characteristics of the data center. At the upper layer, these characteristics are analyzed and abstracted into a weakly nonlinear demand response-oriented load model, composed of four patterns that together form a piecewise function—two linear and two nonlinear regions—each corresponding to distinct workload conditions. The nonlinear relations are simplified from cubic to quadratic forms without significant loss of accuracy. Experimental results show that the linear regions achieve <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mn>0.9999</mn></math></span> with mean relative errors below 0.1404 %, while the quadratic regions reach <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>≥</mo><mn>0.9982</mn></math></span> with mean relative errors under 0.6259 %. Applied to a typical demand response program, the proposed model reduces electricity costs by 13.40 % to 30.21 %, energy consumption by 24.19 % to 38.31 %, and cumulative curtailment deficit by 98.09 %, compared to conventional linear models.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126095"},"PeriodicalIF":10.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation and performance optimization of a novel hybrid CCHP system based on the prime movers of internal combustion engine and Stirling engine","authors":"Mohammad Sheykhi ,&nbsp;Mahmood Mehregan ,&nbsp;Saeed Ghorbani ,&nbsp;Amin Emamian ,&nbsp;Mohammad Hassan Kayhani ,&nbsp;Amin Amiri Delouei ,&nbsp;Shahabodin Kharazmi ,&nbsp;Mohammad Kazem Sheykhian ,&nbsp;Shunmin Zhu","doi":"10.1016/j.apenergy.2025.126103","DOIUrl":"10.1016/j.apenergy.2025.126103","url":null,"abstract":"<div><div>Combined cooling, heating, and power systems (CCHP) could increase the efficiency of conventional energy supply systems and mitigate carbon emissions. In this paper, a novel arrangement of a combined cooling, heating, and power (CCHP) system is presented with prime movers of internal combustion and Stirling engines, which are numerically simulated by Range-Kutta method and optimized with the genetic algorithm technique. The influence of some key parameters such as Stirling engine speed, phase angle, length and porosity of Stirling engine's regenerator, and also speed and spark timing of the internal combustion engine, on the capacity, efficiency, primary energy saving and the investment payback period of the CCHP system has been discussed. The results illustrated that using the CCHP system with hybrid prime movers, due to the appropriate efficiency of the combustion engine, allows the Stirling engine to be started at high speeds. In this condition, the overall efficiency of the hybrid system is increased by 12 % compared to using the CCHP system with only the Stirling engine. Additionally, the payback period of the CCHP system with combined prime movers at 3500 rpm for the two engines is approximately 4.4 years, which is about 1.6 years shorter than the payback period of the CCHP system based solely on the internal combustion engine. This work provides valuable insights into the design and optimization of hybrid CCHP systems with two different combustion-based prime movers.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 126103"},"PeriodicalIF":10.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}