Cleaner Energy Systems最新文献

{"title":"Examining social-cultural norms affecting the adoption of solar energy technologies at the household level","authors":"Robert Muwanga,&nbsp;Irene Namugenyi,&nbsp;Benard Musekese Wabukala,&nbsp;Warren Tibesigwa,&nbsp;Patsy Vincent Katutsi","doi":"10.1016/j.cles.2024.100164","DOIUrl":"10.1016/j.cles.2024.100164","url":null,"abstract":"<div><div>The relevance of social culture in determining energy consumption behaviour is repeatedly highlighted, yet it is still unclear how social-cultural factors affect the uptake of solar energy technologies- SETs. The study highlights social-cultural norms as one of the aspects rarely considered for solar energy adoption and utilises the Energy Cultures Framework to examine the influence of such norms on the acceptance and usage of solar energy technologies SETs at the household level. The study comprised a cross-sectional survey of households from three urban districts in central Uganda (<em>n</em> = 439), and data was analysed using partial least squares structural equation modelling method. The results showed that moral and subjective norms positively and significantly influence the adoption of SETs. However, the influence of moral norms on adoption is relatively stronger than that of subjective norms on adoption. These findings imply that individual-level concerns and a sense of responsibility towards energy usage are more important than the collective societal behavioural requirement for SETs adoption. This study distinctively highlights the relevance of the different types of norms and offers valuable insights into households’ energy usage behaviour, which is essential for designing successful promotion campaigns for SETs and increasing the adoption and usage of more sustainable energy alternatives. Increased adoption of SETs is an important part in the development, implementation, and achievement of energy policy goals.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743835","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":"Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm","authors":"Md. Omer Faruque ,&nbsp;Md. Alamgir Hossain ,&nbsp;Md. Rashidul Islam ,&nbsp;S.M. Mahfuz Alam ,&nbsp;Ashish Kumar Karmaker","doi":"10.1016/j.cles.2024.100129","DOIUrl":"10.1016/j.cles.2024.100129","url":null,"abstract":"<div><p>This paper proposes a new hybrid deep learning model to enhance the accuracy of forecasting very short-term wind power generation. The proposed model comprises a convolutional layer, a long-short-term memory (LSTM) unit, and fully connected neural network. Convolution layer can automatically learn complicated features from the raw input, whereas the LSTM layers can retain useful information through which gradient information may flow over extended periods. To obtain the best performance from the forecasting model, a random search optimization technique has been developed for tuning hyper-parameters of the model developed. The 5 min datasets from the White Rock wind farm, Australia are used to investigate the effectiveness of the proposed model as wind farms are participating in spot electricity market. To compare the effectiveness, the proposed model is compared with the existing models, such as convolution neural network (CNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), artificial neural network (ANN), and support vector machine (SVM). The root-mean-square error (RMSE), mean absolute error (MAE), and Theil’s inequality coefficient (TIC) are used to analyze and compare the performances of the predictive models. Based on RMSE and MAE, the proposed model exhibits a higher accuracy of approximately 23.79% and 28.63% compared to other forecasting methods, respectively.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000232/pdfft?md5=e04018332afecc00662ddb1533359b4e&pid=1-s2.0-S2772783124000232-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852523","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":"Enhancing exergy efficiency and environmental sustainability in pressure swing azeotropic distillation","authors":"Jonathan Wavomba Mtogo ,&nbsp;Gladys Wanyaga Mugo ,&nbsp;Peter Mizsey","doi":"10.1016/j.cles.2024.100134","DOIUrl":"10.1016/j.cles.2024.100134","url":null,"abstract":"<div><p>This study explores the economic, energetic, exergy efficiency, and environmental benefits of energy integration in pressure-swing distillation, focusing on the separation of tetrahydrofuran/water and acetone/chloroform azeotropes. Heat integration and heat pump techniques are applied to reduce energy consumption. Three energy-efficient configurations are examined, comparing total annual cost (TAC), total energy consumption (TEC), CO₂ emissions, and second-law efficiency. In the tetrahydrofuran/water system, heat integration and heat pump technologies outperform conventional processes, achieving up to 50.2% TAC reduction, 59.6% TEC reduction, 82.8% CO₂ emission reduction, and thermodynamic efficiencies up to 23.5%. In the acetone/chloroform system, similar improvements are observed, with up to 70.9% TAC reduction, 87.2% CO₂ emission reduction, and thermodynamic efficiencies up to 17.6%. These findings demonstrate the effectiveness of energy-saving strategies, endorsing process intensification for environmentally sustainable azeotropic mixture separations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000281/pdfft?md5=32b3a3a1060b31f4dbda00eec11c1694&pid=1-s2.0-S2772783124000281-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049097","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":"Optimization of a proposed biomass generator: Harnessing citizen waste with electric vehicle charging infrastructure","authors":"Akib Chowdhury ,&nbsp;Nusrat Chowdhury ,&nbsp;Wahiba Yaïci ,&nbsp;Michela Longo","doi":"10.1016/j.cles.2024.100146","DOIUrl":"10.1016/j.cles.2024.100146","url":null,"abstract":"<div><p>Global municipal solid waste production is rising, causing significant environmental, health, and economic issues. Developed countries have advanced recycling technologies, but cities like Dhaka, Bangladesh—among the most densely populated-struggle with inadequate waste management. This feasibility study aims to improve environmental protection and create new energy sources by proposing a waste management system across Dhaka, focusing on waste valorization for bioenergy with optimized efficiency and minimal impact. The study includes design and optimization of a biomass-based power plant to meet the energy needs of EV charging stations and the national grid, evaluating its economic performance through discounted cash flow and payback period analyses. The paper explores the integration of an EV charging station powered by biogas, addressing the growing need for EV infrastructure in Dhaka. By evaluating biomass generators as a greener alternative to fossil fuels, the study analyzes the technical, economic, and environmental feasibility, including CO2 emissions, using HOMER Pro.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000402/pdfft?md5=6819804d63e35d8d77776259364e361f&pid=1-s2.0-S2772783124000402-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242682","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 SWOT Analysis Approach for the Development of Photovoltaic (PV) Energy in Northern Nigeria","authors":"Anas A. Bisu ,&nbsp;Tariq G. Ahmed ,&nbsp;Umar S. Ahmad ,&nbsp;Abubakar D. Maiwada","doi":"10.1016/j.cles.2024.100128","DOIUrl":"10.1016/j.cles.2024.100128","url":null,"abstract":"<div><p>This research employs a comprehensive Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to investigate the advancement of photovoltaic (PV) energy in Northern Nigeria. The study delves into the intricacies of introducing PV systems within the context of economic challenges, including issues such as currency volatility and inflation, which amplify costs and impede capital investments. Environmental factors, such as dust and sandstorms, are identified as obstacles diminishing the efficiency of solar panels. Additionally, security concerns in remote areas elevate operational costs and influence investment decisions. This paper proposes effective mitigation strategies, encompassing widespread public awareness campaigns to augment market engagement, the establishment of mini-grid systems for enhanced energy distribution, customised on-the-job training programs to foster local expertise in PV technology, and the utilisation of micro-grid systems as experimental grounds for regulatory and policy testing. By synthesising these components, the study offers a comprehensive overview of the prerequisites essential for the successful proliferation of PV energy in Northern Nigeria. Emphasis is placed on the potential for solar energy to significantly contribute to the region's sustainable development and achieve energy independence when the identified strength, and opportunities are exploited. The key strength identified are the average Global horizontal irradiance (GHI) of 5.436 kWh/m², Direct Normal Irradiance (DNI) of 1534–1680 kWh/m², Levelised Cost of Electricity (LCoE) of $ 0.1, and an opportunity to fully utilise the over $ 7.88 million grant authorised by the African Development Bank (AfDB) from the Sustainable Energy Fund for Africa.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000220/pdfft?md5=5ad76d8050fa4517244a11767e5a1f55&pid=1-s2.0-S2772783124000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141961799","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":"Optimization and design to catalyze sustainable energy in Morocco’s Eastern Sahara: A hybrid energy system of PV/Wind/PHS for rural electrification","authors":"Norddine Oubouch,&nbsp;Abdelbari Redouane,&nbsp;Anouar Makhoukh,&nbsp;Abdennebi El Hasnaoui","doi":"10.1016/j.cles.2024.100141","DOIUrl":"10.1016/j.cles.2024.100141","url":null,"abstract":"<div><p>This paper conducts a comprehensive assessment of the potential of water, solar, and wind resources for sustainable energy generation. The study is situated in a Moroccan region within eastern Saharan Africa. It presents a detailed comparative analysis between a photovoltaic system (PV) integrated with a pumped hydro storage (PHS), a wind turbine, and a conventional grid, considering both energy production and economic analysis using HOMER software. Moreover, the paper provides an initial social impact assessment of hybrid energy systems integrating locally available water resources, especially during the winter season, alongside photovoltaic and wind technologies. This evaluation delves into aspects of rural electrification and community development. The findings underscore the potential of sustainable energy solutions to drive economic and social progress in the studied area by harnessing the region’s water resources. We proposed this technology because the owners of the area do not greatly benefit from the seasonal groundwater that passes through the valley, despite the presence of a dam. Accordingly, we will exploit this water to generate energy and achieve energy self-sufficiency. By harnessing this underutilized resource, we aim to provide sustainable energy solutions and drive economic and social progress in the region. The results given by HOMER identify the most cost-effective system capable of serving the load at the lowest cost of energy (COE) of about $0.03831 and net present cost (NPC) of about $262,596 under the modeled conditions, and the most satisfactory system chosen by the HOMER optimizer is a PV/Wind/PHS-based hybrid energy system.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000359/pdfft?md5=548cf620c1194c0562deca54a3257740&pid=1-s2.0-S2772783124000359-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096574","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":"Electromobility strategy on emerging economies: Beyond selling electric vehicles","authors":"Sérgio Roberto Knorr Velho ,&nbsp;Artur Santana Guedes Vanderlinde ,&nbsp;Antônio Henrique Aguiar Almeida ,&nbsp;Sanderson César Macêdo Barbalho","doi":"10.1016/j.cles.2024.100166","DOIUrl":"10.1016/j.cles.2024.100166","url":null,"abstract":"<div><div>Electromobility (EM) has emerged as a promising solution to achieve carbon neutrality goals by replacing traditional fossil fuel-powered transportation with electric vehicles (EVs). This sustainable transportation option significantly reduces energy consumption and eliminates greenhouse gas emissions, contributing to mitigating climate change and improving air quality. While some countries have implemented strategies to promote EM adoption, emerging economies like Brazil face complex challenges. This research employs Q-methodology to explore the viewpoints and opinions of Brazilian specialists in the field of EM, identifying challenges and opportunities for successful adoption. The study also examines the broader implications for emerging countries and their automotive industries. The findings emphasize the importance of addressing EV costs, propulsion technologies, and the need for government incentives and policies. Additionally, the research highlights the role of education and urban mobility in promoting EM. While the study offers valuable insights, it acknowledges limitations in the sample and suggests future research directions.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103041","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":"Analyzing the impact of temperature on PV module surface during electricity generation using machine learning models","authors":"S. M. Rezaul Karim ,&nbsp;Debasish Sarker ,&nbsp;Md. Monirul Kabir","doi":"10.1016/j.cles.2024.100135","DOIUrl":"10.1016/j.cles.2024.100135","url":null,"abstract":"<div><p>Use of fossil fuel in industries causes Carbon emission, which is mostly responsible for global warming. Another aspect is that environment friendly energy production and sustainable development goal is highly dependent on the production of clean energy. According to the IEA solar energy has a huge potential and will contribute up to 16 % of the global electricity by 2050. Hence, prediction of solar energy production has a great deal of demand in renewable energy sector. This paper compares machine-learning algorithms to evaluate the impact of PV module back surface temperature (degC) on the generated power. Support Vector Machine for Regression (SMOreg), Multilayer Perceptron (ANN), Linear Regression, M5 Rules, k-Nearest-Neighbor (Ibk) and Random Forest methods are employed to test their performance in different ratio of training and testing data. The dataset comprises five independent parameters such as PV module back surface temperature (degC), Dry bulb temperature (degC), Relative humidity (%RH), Atmospheric pressure (mb), and Precipitation (mm). The dependent parameter is Maximum power of PV module (W). The correlation coefficient was determined by varying the percentage of training data from 60 % to 85 %. The numerical tests were done for two data sets, one dataset includes all the independent variables and another one excluded the PV module back surface temperature. Except for M5 Rules, other models exhibit consistent correlation coefficients with several of training data. All models demonstrate a dependency on the PV module back surface temperature, with Random Forest surpassing others in overall performance with a correlation coefficient of 0.9713 at 75 % of training set.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000293/pdfft?md5=e94b18d5f59eadc7771707204dcf1063&pid=1-s2.0-S2772783124000293-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998606","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":"Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances","authors":"Abdulhalim Musa Abubakar ,&nbsp;Lukman Buba Umdagas ,&nbsp;Moses NyoTonglo Arowo ,&nbsp;Marwea Al-Hedrewy ,&nbsp;Mahlon Kida Marvin ,&nbsp;Noureddine Elboughdiri ,&nbsp;Aminullah Zakariyya Abdul ,&nbsp;Jenisus O. Dejarlo ,&nbsp;Rezkallah Chafika","doi":"10.1016/j.cles.2024.100156","DOIUrl":"10.1016/j.cles.2024.100156","url":null,"abstract":"<div><div>The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO₂) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO₂ and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO₂ purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO₂ while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664330","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":"Battery remaining useful life estimation based on particle swarm optimization-neural network","authors":"Zuriani Mustaffa ,&nbsp;Mohd Herwan Sulaiman","doi":"10.1016/j.cles.2024.100151","DOIUrl":"10.1016/j.cles.2024.100151","url":null,"abstract":"<div><div>Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO<img>NN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSO<img>NN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSO<img>NN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSO<img>NN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(<em>T</em> ≤ <em>t</em>)), all below the significance level of 0.05, indicate that the differences between PSO<img>NN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSO<img>NN model in predicting battery RUL. This study contributes to the field by presenting the PSO<img>NN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419857","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}