Applied Energy最新文献

{"title":"Attack-resilient state estimation for cyber-physical power systems: A dynamic spatial-temporal redundancy reconfiguration framework for FDIA detection","authors":"Shutan Wu ,&nbsp;Qi Wang ,&nbsp;Jianxiong Hu ,&nbsp;Yujian Ye ,&nbsp;Yi Tang","doi":"10.1016/j.apenergy.2025.126330","DOIUrl":"10.1016/j.apenergy.2025.126330","url":null,"abstract":"<div><div>Modern power systems, as cyber-physical systems, increasingly rely on hybrid measurement data to improve the accuracy and resolution of state estimation (SE). However, the enhancement of SE functionality is accompanied by an increased reliance on measurement devices and external detection mechanisms, thereby expanding the attack surface and exposing SE to sophisticated cyber threats. This paper reveals security vulnerabilities in existing hybrid measurement-based SE frameworks, particularly under coordinated false data injection attacks (FDIAs) that manipulate both baseline and verification measurements to evade detection. To address this challenge, we propose an attack-resilient SE method based on dynamic spatial-temporal redundancy reconfiguration. By proactively injecting measurement uncertainty into the measurement process, the method enhances resilience against external attacks. A comprehensive detection index is introduced to jointly evaluate estimation accuracy and attack impact. Then, we develop an FDIA detection framework that integrates offline training and online adaptation. The offline phase optimizes the sensitivity parameter and initial measurement configurations, while the online phase dynamically updates measurement reconfiguration strategies and detection thresholds based on real-time feedback. Extensive validations on the IEEE 14-bus and 118-bus systems demonstrate that the proposed approach significantly improves the FDIA detection capability while maintaining estimation stability and computational efficiency, without requiring additional external security mechanisms.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126330"},"PeriodicalIF":10.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331034","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":"Recent advances in metal oxide reinforced polymer composites for efficient piezoelectric energy harvesting applications","authors":"V. Abhijith ,&nbsp;B. Akhila ,&nbsp;K. Pramod ,&nbsp;Tapas Ranjan Mohanty ,&nbsp;C. Sivakumar ,&nbsp;S. Ramakrishnan ,&nbsp;Sabu Thomas ,&nbsp;M.S. Sreekala","doi":"10.1016/j.apenergy.2025.126335","DOIUrl":"10.1016/j.apenergy.2025.126335","url":null,"abstract":"<div><div>In recent years, there has been a growing demand for miniaturized and multipurpose gadgets. Invention of wearable gadgets like smartwatches, rings, and IoT’s helps people to understand physical activities and health conditions. On the other hand, the environmental problems surrounding fossil fuel consumption and the limited battery lifespan made the researchers to develop clean and green energy sources. Piezoelectric nanogenerators (PENGs) having inherent ability to convert mechanical strains to electrical energy seek remarkable attention for energy harvesting applications. Nowadays, more researchers focus on polymer-based PENGs due to their excellent flexibility and compatibility, but the poor piezoelectric coefficient limit them to produce feeble electrical output, insufficient for wearable devices. Metal oxides improve the piezoelectric characteristics of polymer composites due to intrinsic piezoelectric properties and their ability to bind with polymers enhances its electrical output, morphological, mechanical, and thermal properties. This review lays out an extensive overview of the latest advancements in metal oxide-reinforced polymer composites for efficient energy harvesting applications. Here, essential characteristics of the PENGs, their structural properties, and their energy harvesting mechanism are discussed in detail. Furthermore, the review discovers the potential applications of PENGs, including wireless sensors like tire pressure monitoring systems, health monitoring systems, and wearable devices.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126335"},"PeriodicalIF":10.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331033","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":"State-of-health estimation for EV battery packs via incremental capacity curves and S-transform","authors":"Siyi Tao ,&nbsp;Jiangong Zhu ,&nbsp;Yuan Li ,&nbsp;Siyang Chen ,&nbsp;Xiuwu Wang ,&nbsp;Xueyuan Wang ,&nbsp;Bo Jiang ,&nbsp;Wei Chang ,&nbsp;Xuezhe Wei ,&nbsp;Haifeng Dai","doi":"10.1016/j.apenergy.2025.126334","DOIUrl":"10.1016/j.apenergy.2025.126334","url":null,"abstract":"<div><div>Accurate battery state-of-health (SOH) estimation in electric vehicles (EVs) plays a crucial role in mitigating user range anxiety. However, the suboptimal quality of cloud-based battery management system (BMS) data combined with the material heterogeneity of battery cathodes creates substantial barriers to developing universal SOH estimation methods for real-world EV applications. In this study, we propose a generalizable feature extraction framework based on the charging process. The method extracts time-domain features from incremental capacity (IC) curves and frequency-domain features using the S-transform, while also incorporating inter-cell inconsistency indicators. To assess the robustness of the extracted features, validation is conducted using laboratory data. Additionally, the influence of temperature on battery capacity and extracted features is analyzed through tests on batteries with varying capacities and cathode materials. Furthermore, real-world operational data from 37 EVs over a three-year period are employed to develop machine learning (ML) and deep learning (DL) models. Based on these results, a fusion model combining gated recurrent units (GRU) and LightGBM (LGB) is proposed, achieving material-independent battery SOH estimation with a mean absolute percentage error (MAPE) below 1.99 % and a maximum error (MAXE) under 6.57 %.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126334"},"PeriodicalIF":10.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331021","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":"Combined real-time and scheduling methodology for operation of battery swapping stations considering energy flexibility and grid support","authors":"Hericles Eduardo Oliveira Farias ,&nbsp;Camilo Alberto Sepulveda Rangel ,&nbsp;Bernardo Ziquinatti Franciscatto ,&nbsp;Henrique Klein ,&nbsp;Luciane Silva Neves ,&nbsp;Victor Gomes","doi":"10.1016/j.apenergy.2025.126332","DOIUrl":"10.1016/j.apenergy.2025.126332","url":null,"abstract":"<div><div>Battery Swapping Stations (BSSs) offer a viable alternative to Electric Vehicle Charging Stations (EVCSs) in electric mobility. However, due to their higher investment costs, primarily associated with battery inventory costs, their economic and technical feasibility still lacks improvements for a wider adoption. In contrast, the electric micro-mobility sector, with smaller EVs, simpler battery requirements and charging complexity, emerges as a promising field for BSS studies and applications. Therefore, this paper presents a methodology, termed MH-RB-ARW, for optimizing BSS operations within electric micro-mobility while supporting grid services during Flexible Response to Demand (FRD) events. The approach integrates rule-based (RB) algorithms and a meta-heuristic (MH) optimizer within an adaptive rolling window (ARW) approach. This enables real-time coordination of BSS operations for scheduled and opportunistic users, aligning preparation (short-term) and operation phases. FRD events are classified into power absorption (PA), where the BSS absorbs excess grid energy (valley filling service), and power injection (PI), where the BSS injects energy into the grid (peak shaving service), both adhering to predefined demand contracts. While supporting the grid, the BSS simultaneously manages battery swapping operations. RB algorithms address real-time and scheduled requests, while the MH optimizer minimizes recharging costs for depleted batteries (DBs). Case study results demonstrate that the proposed methodology allows the BSS to provide demand response services without compromising its primary operations. Furthermore, the MH optimizer significantly reduces energy purchase costs for recharging DBs, enhancing economic benefits during both PA and PI events.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126332"},"PeriodicalIF":10.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320842","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":"Techno-economic feasibility study of imported green hydrogen via inter-continental route: From Australia to South Korea","authors":"Gye Hyun Park ,&nbsp;Sanghyun Hong ,&nbsp;Steven Percy ,&nbsp;Seung Wan Kim","doi":"10.1016/j.apenergy.2025.126288","DOIUrl":"10.1016/j.apenergy.2025.126288","url":null,"abstract":"<div><div>Green hydrogen is crucial for South Korea’s low-carbon transition. However, due to limited domestic production capacity, importing hydrogen from countries with abundant renewable energy resources, such as Australia, is essential. This study develops a model to evaluate the techno-economic feasibility of importing green hydrogen from Australia. The analysis considers importing stages, including hydrogenation, shipping, port, dehydrogenation, and inland distribution, evaluating multiple pathways with, liquid hydrogen and ammonia carriers, and various transportation methods. The results demonstrate that ammonia (USD 3.345–8.008 kg-H₂⁻¹) is a more economically viable hydrogen carrier compared to liquid hydrogen (USD 6.456–10.656 kg-H₂⁻¹). Furthermore, importing costs are projected to decline by approximately 12.5 %–37.3 % by 2030, aligning with South Korea’s national hydrogen technology development targets. The study also finds that even under a global carbon price scenario of up to USD 200 ton-CO₂⁻¹, carbon-free ships struggle to achieve cost parity with fossil-fueled ships. Achieving cost parity for carbon-free ships by 2040 would specifically require a carbon price approximately 38.5 % higher than this maximum scenario. For inland transportation, battery-electric vehicles become economically favorable at carbon prices up to USD 86 ton-CO₂⁻¹. This research also emphasizes the need for strategic investments in energy-efficient ammonia cracking technologies, clean transportation methods such as battery-electric vehicles and ammonia-powered ships, and comprehensive hydrogen supply planning to enable a cost-effective, low-carbon economy. It additionally highlights the importance of embedding carbon pricing within the hydrogen import process.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126288"},"PeriodicalIF":10.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321525","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":"Development of a real-time link-based predictive energy management strategy for extending FCEV lifespan using an experiment-driven degradation model","authors":"Geunyoung Park ,&nbsp;Kyunghwan Choi ,&nbsp;Minjun Kim ,&nbsp;EunAe Cho ,&nbsp;Kyungsub Sung ,&nbsp;Dongsuk Kum","doi":"10.1016/j.apenergy.2025.126246","DOIUrl":"10.1016/j.apenergy.2025.126246","url":null,"abstract":"<div><div>Fuel cell electric vehicles (FCEVs) face durability challenges primarily due to cell degradation influenced by power variations and operational ranges. This issue can be mitigated through an energy management strategy (EMS), with many durability-focused studies employing predictive EMS (P-EMS) for high performance. However, existing strategies often rely on highly uncertain future vehicle trajectories, such as velocity or power demand, leading to a shortened horizon length and significant loss of optimality. This study proposes a novel link-based, durability-focused P-EMS optimized on a per-link basis, achieving near-optimal performance. The key innovation lies in reformulating the problem from trajectory optimization to parameter optimization, expressed as a quadratic programming (QP) problem, which enables real-time implementation. The degradation model consists of dynamic and quasi-static operations, where the quasi-static model is developed based on experimental data. A multi-objective optimal control problem is then formulated, revealing a Pareto optimal relationship between degradation and system efficiency through a dynamic programming (DP) algorithm that ensures global optimality. Building on insights from DP results, the proposed approach analytically reformulates the problem, requiring easily predictable driving parameters such as travel time and energy demand that represent link conditions. The simulation results reveal that, when prioritizing cell degradation protection, the proposed method achieves high performance comparable to DP, with a minimal loss of optimality (1.5 % in fuel economy and 6.7 % in fuel cell degradation) while showing an impressive average computational time of merely 2.5 ms.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126246"},"PeriodicalIF":10.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321529","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 suggested methodological framework to enhance comparability and reliability for life cycle assessment of CO2 adsorption in energy-intensive industries","authors":"Yipeng Yao,&nbsp;Marie-Eve Duprez,&nbsp;Guy De Weireld","doi":"10.1016/j.apenergy.2025.126240","DOIUrl":"10.1016/j.apenergy.2025.126240","url":null,"abstract":"<div><div>Among the various CO₂ capture technologies, adsorption capture appears to be an emerging and promising technology characterised by operational flexibility, low pollutant emissions, and low energy consumption. It is expected to be crucial in carbon capture and storage systems. Whilst Life Cycle Assessment (LCA) has emerged as the consensus methodology for evaluating the environmental impacts of this technology, methodological heterogeneity in LCA applications has limited the comparability and credibility of research findings. This study systematically reviews 31 LCA studies published between 2006 and 2025, examining methodological commonalities and differences across four aspects: goal and scope definition, inventory analysis, impact assessment and interpretation. Current LCA methodological standards and guidelines were used as benchmarks to analyse the challenges and opportunities in CO₂ adsorption LCA methodology and to propose a methodological framework. The findings reveal that commonalities (e.g., functional unit) and differences (e.g., system boundary, life cycle stages and process stage alignments) exist among LCA methodologies. Moreover, compared to existing standards and guidelines, current methodological applications demonstrate notable gaps (e.g., the lack of data quality evaluation and the classification of significance levels). Consequently, we propose a hierarchical improvement framework comprising three levels based on required additional effort levels— minor, moderate, and major efforts. This framework aims to systematically enhance the comparability and reliability of LCA studies. This work contributes to establishing common LCA application protocols and provides methodological guidance for future environmental assessments of CO₂ adsorption technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126240"},"PeriodicalIF":10.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321567","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":"Hierarchical energy management for heterogeneous multi-energy microgrid community: Integrating hydrogen into the water-energy nexus","authors":"Muhammad Ahsan Khan ,&nbsp;Talha Rehman ,&nbsp;Hak-Man Kim","doi":"10.1016/j.apenergy.2025.126254","DOIUrl":"10.1016/j.apenergy.2025.126254","url":null,"abstract":"<div><div>The global shift towards green hydrogen production increases the interdependency on the water sector. To facilitate this transition, this study proposes a novel multi-energy microgrid (MEMG) community model, where a coastal MEMG leverages seawater resources to support hydrogen production in interconnected hydrogen-based MEMGs. To this end, a three-step hierarchical energy management framework is developed to realize the water and electric energy transactions among them. The first two steps involve information sharing and local-level optimization while the final step includes the energy trading decision and surplus energy allocation. To ensure fairness in the community, the proportional energy allocation technique is used, with fairness measured using Jain’s fairness index. A mixed integer linear programming model is developed to reduce community costs and emissions, and a bi-variate piecewise McCormick envelope technique is proposed to deal with model non-linearities. The computational efficiency, as well as the economic and environmental impact of the proposed model, is evaluated through various case studies. The results indicate that the proposed framework achieves a 32.3 % cost reduction and a 20.7 % emissions reduction through multi-energy trading. Furthermore, the contribution of electric and water trading alone within the community in cost reduction is 16.73 % and 22.45 %. The proposed method ensures maximum fairness in surplus energy distribution, with a Jain’s fairness index value of 1. In addition, the computational comparison with a centralized strategy shows a considerable reduction in processing time, averaging 669.5 s. Sensitivity analysis further validates the model’s applicability, particularly in assessing the cost and emissions contributions of water in hydrogen production.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126254"},"PeriodicalIF":10.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314281","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":"PVMTF: End-to-end long-sequence time-series forecasting frameworks based on patch technique and information fusion coding for mid-term photovoltaic power forecasting","authors":"Zhirui Tian ,&nbsp;Bingjie Liang","doi":"10.1016/j.apenergy.2025.126263","DOIUrl":"10.1016/j.apenergy.2025.126263","url":null,"abstract":"<div><div>Accurate photovoltaic power forecasting can alleviate the impact on grid stability. Most existing photovoltaic power prediction models rely on increasing model complexity or increasing the size of the look-back window to expand the amount of extracted information, but this often leads to catastrophic forgetting of learned information or the introduction of excessive redundant noise. In addition, some models predict by decomposing data and using non end-to-end learning, which may lead to inconsistent information and cumulative errors, limiting the improvement of prediction accuracy. To address the aforementioned challenges, we propose end-to-end PVMTF frameworks consisting of two models, PatchGRU and PatchGRU_h. This study is divided into two modules. In the data preprocessing module, we use Isolation Forest for outlier detection and replace outliers with window averages. Grey relational analysis is used for feature selection to reduce training complexity. In the photovoltaic power forecasting module, the PVMTF frameworks are used to directly achieve photovoltaic power forecasting. Firstly, based on the patch technique, the data is divided into independent short patches for separate learning, which can effectively preserve and learn historical information, avoiding catastrophic forgetting of important information that has already been learned as the look-back window grows. Specifically, for each patch, parameter sharing or independent parameter training Gated Recurrent Units (GRUs) are introduced to adapt to different computing needs, extract features within the patches, and achieve feature fusion. Next, a neural network-based gating mechanism is introduced to nonlinearly learn hidden states and fuse information. Finally, based on the above information fusion coding, accurate photovoltaic power forecasting is achieved by extracting the relationships between patches. Strict numerical verification indicates that PVMTF outperforms various state-of-the-art (SOTA) time series forecasting models in the three PV forecasting tasks (1-step, 384-step (4 days-ahead) and 672-step (7 days-ahead)), which provides an effective tool for PV power management and dispatch.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"396 ","pages":"Article 126263"},"PeriodicalIF":10.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322598","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":"Health-aware energy management for multiple stack hydrogen fuel cell and battery hybrid systems","authors":"Junzhe Shi ,&nbsp;Ulf Jakob Flø Aarsnes ,&nbsp;Shengyu Tao ,&nbsp;Ruiting Wang ,&nbsp;Dagfinn Nærheim ,&nbsp;Scott Moura","doi":"10.1016/j.apenergy.2025.126257","DOIUrl":"10.1016/j.apenergy.2025.126257","url":null,"abstract":"<div><div>Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i.e., MIQP-based individual stack control (ISC), with significant fuel cost reductions, FC and battery degradations. The proposed method is compared with classical dynamic programming (DP), with a 2243 times faster computational speed than the DP method while maintaining near-optimal performance. The case study results show that ISC achieves a 64.68 % total cost reduction compared to CSC in the examined scenario, with substantial reductions across key metrics including battery degradation (4 %), hydrogen fuel consumption (22 %), fuel cell idling loss (99 %), and fuel cell load-change loss (41 %)</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126257"},"PeriodicalIF":10.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314283","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}