Energy and Buildings最新文献

{"title":"Rethinking building energy performance gap: a workflow for bridging measured, modeled, designed, and optimized strategies – a review with real-world examples","authors":"Shoumik Desai ,&nbsp;Burak Gunay ,&nbsp;Mohamed Ouf ,&nbsp;Weihao Liu ,&nbsp;Joe Coady","doi":"10.1016/j.enbuild.2026.117065","DOIUrl":"10.1016/j.enbuild.2026.117065","url":null,"abstract":"<div><div>The significant gap between a building’s predicted and actual energy use, known as the energy performance gap (EPG), remains a persistent barrier to achieving decarbonization goals. Conventional analyses often attribute this gap to single causes, such as operational faults or modelling inaccuracies, failing to capture the interplay of issues across the building lifecycle. This paper introduces a structured diagnostic workflow designed to deconstruct the EPG by systematically examining four lenses of performance: As-Operated, As-Modeled, As-Designed, and As-Prescribed Intended Performance. The workflow is demonstrated through a detailed case study of a large healthcare facility. The analysis reveals that an observed 48% overconsumption of natural gas was not a simple operational error but is symptomatic of a cascading discrepancy. The workflow traces the issue back to its source, demonstrating how a suboptimal design (Lens 3) was inaccurately represented in a simplified energy model (Lens 2), which in turn failed to predict the inefficient operational reality (Lens 1). The key contribution of this work is developing a robust, diagnostic method that allows stakeholders to move beyond merely quantifying the EPG to identify its distinct root causes in design, modeling, and operation. This approach provides a more granular understanding of the problem, enabling targeted and effective mitigation strategies to EPG.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117065"},"PeriodicalIF":7.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Powered dynamic Façades using thermoelectric generators and phase change materials","authors":"Zia Mohajerzadeh ,&nbsp;Mohammad Elmi ,&nbsp;Amin Nozariasbmarz ,&nbsp;Julian Wang ,&nbsp;Rahman Azari","doi":"10.1016/j.enbuild.2026.117066","DOIUrl":"10.1016/j.enbuild.2026.117066","url":null,"abstract":"<div><div>Residential and commercial buildings in the U.S. are major energy consumers, with demand projected to rise significantly in the coming decades. This research develops a self-powered dynamic façade that integrates thermoelectric generators (TEGs) with phase change materials (PCMs) to convert solar-driven temperature gradients into electricity for automated shading. While TEG performance is constrained by thermal management, PCMs act as latent-heat buffers that stabilize the cold side and help maintain larger temperature differences.</div><div>A prototype combining two 4 cm × 4 cm TEG modules with a 29 °C BioPCM was designed and evaluated in both controlled solar chamber conditions and outdoor testing. In the solar chamber, the measured surface temperature difference between the sun-exposed aluminum fin and the outer PCM container reached approximately 24 °C under 800 W/m² irradiance with PCM integration. This enabled a 1.5 V supercapacitor to charge to 1.0 V and intermittently drive a 3 V DC motor to rotate a 39 cm × 7 cm aluminum louver, consuming an average of 19.5 mW. Outdoor tests in a humid continental climate (ASHRAE 5A) showed strong weather dependence and demonstrated that black-coated fins achieved stored voltages above 400 mV.</div><div>To increase thermal input, a Fresnel lens and acrylic dome were added. This optical–thermal configuration improved voltage generation by ∼ 20% by concentrating solar flux and reducing convective losses. Under these enhanced conditions, the 29 °C PCM did not always fully melt, prompting evaluation of a 20 °C PCM to activate the phase-change process more completely. The 20 °C PCM fully melted and produced higher early-stage stored voltage (∼550 mV) compared with the 29 °C PCM (∼450 mV), although its cooling duration was shorter once fully liquid. Overall, the results show that melting-point selection, optical concentration, and thermal storage can be combined to advance façade-integrated thermoelectric systems toward autonomous operation and lower building energy demand.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117066"},"PeriodicalIF":7.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future building cooling energy demand under climate change in the Mediterranean region","authors":"Lei Zhu ,&nbsp;Lingye Leng ,&nbsp;Yong Huang ,&nbsp;Sun Lei","doi":"10.1016/j.enbuild.2026.117058","DOIUrl":"10.1016/j.enbuild.2026.117058","url":null,"abstract":"<div><div>The objective of the research team in the current study was to establish the presence and the percentage change in cooling energy usage in Italian residential buildings in relation to climate change. It is a specific intervention to establish cooling demands, as it analyses historical trends and projections of Cooling Degree Hours (CDHs). The study will also focus on identifying high-priority areas for energy actions by comparing population patterns with CDHs. Based on hourly outdoor temperature predictions from the Weather Research and Forecasting (WRF) Model, both CDHs for the cooling seasons between 2002 and 2021 and for 2050 and 2080, according to the IPCC predictions, were calculated. Due to the Mediterranean region’s geographic vulnerability and Italy’s diverse climate, Italy was selected as a case study. The data on CDH were clustered into 4 5-year intervals for analytical convenience. The median CDHs have increased twice between 2012 and 2016 and four times between 2017 and 2020; nevertheless, the levels remained unchanged between 2002 and 2011. Following that, it increased exponentially. It is projected to rise the most in 2080 under the RCP8.5 scenario, with the highest concentrations found in urban areas, the Po Valley, and the southern coastal regions. These findings highlight the importance of modernizing Italy’s building stock to make buildings more resilient and efficient, as well as the anticipated significant growth in cooling requirements in buildings in the future.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117058"},"PeriodicalIF":7.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Of demographics, technology, and geography: The social determinants of energy consumption patterns and user behaviour in Saudi Arabia’s residential sector","authors":"Muhammad Asif ,&nbsp;Benjamin K. Sovacool ,&nbsp;Zulfiqar Ali ,&nbsp;Erin Heinz ,&nbsp;Thomas Alan Kwan ,&nbsp;Johan Nordensvärd ,&nbsp;Arunima Krishna ,&nbsp;Patrick Thollander ,&nbsp;Patrik Rohdin ,&nbsp;Weimin Zhang","doi":"10.1016/j.enbuild.2026.117061","DOIUrl":"10.1016/j.enbuild.2026.117061","url":null,"abstract":"<div><div>Driven by increased exposure to climate change hazards and energy price reforms, the Kingdom of Saudi Arabia (KSA) is keen to transform its energy-intensive building sector, with air-conditioning (AC) accounting for 70% of the energy used in buildings. While much past research has focused on technological solutions, this study investigates some of the critical AC usage patterns and energy conservation behavior in the Saudi residential sector. Harnessing a novel and original public survey with spatial granularity, this study explores socio-demographic, economic, and behavioral determinants of AC usage, thermostat preferences, and energy conservation attitudes. The study identifies household income and energy expenditure as among the more influential predictors of user behavior. Lower-income households are significantly less likely to use AC extensively, which may indicate potential equity and affordability concerns, while higher-income groups exhibit extended usage patterns, particularly year-round use and daily operation exceeding 18 h. Education, dwelling type, and ownership status are also influential factors, though with a modest effect. Regional differences, particularly in Makkah and Riyadh, reveal further contextual variations in behavior. AC switch-off and thermostat adjustment attitudes reflect a mix of economic constraints and habitual behavior. Drawing from these findings, the study underscores the need for integrated policy frameworks that combine efficiency measures with targeted behavioral interventions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117061"},"PeriodicalIF":7.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate model evaluation and building energy benchmarking for commercial buildings","authors":"Venkatesh Chinde ,&nbsp;Rohit Chintala,&nbsp;Janghyun Kim,&nbsp;Alex Chapin,&nbsp;Jie Xiong,&nbsp;Katherine Fleming,&nbsp;Brian L. Ball","doi":"10.1016/j.enbuild.2026.117033","DOIUrl":"10.1016/j.enbuild.2026.117033","url":null,"abstract":"<div><div>Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio™/EnergyPlus™ to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117033"},"PeriodicalIF":7.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate models for evaluating HVAC retrofit options in multi-unit residential buildings","authors":"Harry Vallianos,&nbsp;Costa Kapsis","doi":"10.1016/j.enbuild.2026.117060","DOIUrl":"10.1016/j.enbuild.2026.117060","url":null,"abstract":"<div><div>This study investigates the use of surrogate modeling to optimize retrofit strategies in multi-unit residential buildings (MURBs), including HVAC systems. A comprehensive synthetic dataset was generated using EnergyPlus simulations, parameterized across a wide range of building and system variables, including ten distinct HVAC configurations. Multiple surrogate modeling approaches were evaluated, including single-output and multi-output artificial neural networks (ANNs) as well as Light Gradient Boosting Machine (LGBM) models. The models were trained to predict key performance metrics: Energy Use Intensity (EUI), Thermal Energy Demand Intensity (TEDI), and Cooling Energy Demand Intensity (CEDI). Results show that multi-output ANN models, with HVAC system as a categorical input, achieved high accuracy (R² &gt; 0.997 and RMSE&lt;2.2kWh/m²/year) and superior generalization compared to both single-output ANNs and LGBM models, while also reducing computational effort. The findings underscore the effectiveness of ANN-based surrogate models for rapid and accurate evaluation of retrofit scenarios involving diverse HVAC systems, supporting more efficient decision-making in building energy retrofits.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117060"},"PeriodicalIF":7.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 personalized federated meta-learning approach for distributed load forecasting of buildings","authors":"Qi Chang ,&nbsp;Jie Kang ,&nbsp;Lingfeng Tang","doi":"10.1016/j.enbuild.2026.117055","DOIUrl":"10.1016/j.enbuild.2026.117055","url":null,"abstract":"<div><div>Building load forecasting is crucial to its operation optimization. Federated learning enables distributed building load forecasting while preserving data privacy. However, a global model via directly aggregating local models cannot effectively capture personalized energy consumption patterns. Moreover, fine-tuning the global model may face the risk of overwriting globally shared knowledge. To address this issue, this paper proposes a personalized federated <em>meta</em>-learning approach for distributed building load forecasting. The framework consists of two parts. The first is a federated <em>meta</em>-learning-based global model that incorporates <em>meta</em>-learned auxiliary variables to extract global model parameters optimized across buildings, thereby acquiring globally shared knowledge. The second is a multi-channel residual compensation model, trained locally to capture residuals between the global prediction and actual loads, which acquires personalized knowledge not represented by the global model. The final prediction is obtained by summing the outputs of the global and personalized models, effectively balancing global generalization and local personalization. The proposed method is validated on the real-world dataset Building Data Genome Project 2, with conformal prediction employed to quantify the model uncertainty. Experimental results demonstrate that the proposed model not only improves prediction accuracy but also provides reliable uncertainty estimation through conformal prediction-based intervals.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117055"},"PeriodicalIF":7.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building climate zoning in the sea-land interlaced region based on the clustering method","authors":"Jingjie Tan ,&nbsp;Xiaojing Zhang ,&nbsp;Ziyang Hao ,&nbsp;Jingchao Xie ,&nbsp;Jiaping Liu","doi":"10.1016/j.enbuild.2026.117040","DOIUrl":"10.1016/j.enbuild.2026.117040","url":null,"abstract":"<div><div>To address the boundary ambiguity issue in building climate zoning for sea-land interlaced region at low-latitudes in China, this study proposes a collaborative zoning method that integrates density-based clustering with subsequent classification. The method enables the identification of arbitrarily shaped climate clusters and the precise treatment of noisy samples in sea-land transition zones, thereby overcoming the limitations of K-means and hierarchical clustering approaches, which assume spherical clusters and rigid boundaries. The ERA5 high-resolution meteorological data (with a spatial resolution of 0.25° × 0.25°) is used to construct a zoning index system incorporating three elements: temperature, precipitation, and radiation. By optimizing the dual-index joint classification, the proportion of boundary-disputed samples is reduced to 0.76%, with key thresholds identified as an annual total horizontal solar radiation of 1573 kWh/m² and a coldest-month mean temperature of 15 °C. The zoning results show that the low-latitude regions in China can be distinctly partitioned along the coastline into a <em>New Hot-Summer and Warm-Winter Zone</em> (wherein buildings must adequately address the heat prevention requirements and rain protection during summer) and an <em>Extreme Hot-Humid Zone</em> (wherein buildings require year-round heat prevention, rain protection, and full-shading design). Building energy simulations across 19 typical cities reveal that the average building energy consumption is significantly higher in the <em>Extreme Hot-Humid Zone</em> (101.27 kWh·m⁻²·a⁻¹) than in the <em>New Hot-Summer and Warm-Winter Zone (</em>57.07 kWh·m⁻²·a⁻¹<em>)</em>. Moreover, the rate of energy consumption changes peaks across the climate zone boundary. These simulation results effectively validate the accuracy of the new building climate zoning.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117040"},"PeriodicalIF":7.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain adaptation-enhanced transfer learning framework for cross-building cooling load forecasting: Case studies in metro stations","authors":"Panlong Liu ,&nbsp;Yinghuai Liang ,&nbsp;Shuhong Li ,&nbsp;Yanjun Li ,&nbsp;Wei Sheng","doi":"10.1016/j.enbuild.2026.117059","DOIUrl":"10.1016/j.enbuild.2026.117059","url":null,"abstract":"<div><div>Cooling load forecasting of central air-conditioning systems is a critical prerequisite for improving building energy efficiency and optimizing system control. Although existing data-driven prediction methods do not require complex physical modeling, their performance is still limited by the quality and quantity of historical data. Based on transfer learning, this study proposed a cross-building domain-adaptive cooling load forecasting framework and developed two domain adaptation models for cooling load forecasting: the CORAL-LSTM model and the DANN-LSTM model. These two models are a <strong>L</strong>ong <strong>S</strong>hort-<strong>T</strong>erm <strong>M</strong>emory (LSTM) model coupled with <strong>Cor</strong>relation <strong>Al</strong>ignment (CORAL) and an LSTM model coupled with <strong>D</strong>omain <strong>A</strong>dversarial <strong>N</strong>eural <strong>N</strong>etworks (DANN), respectively. Through transfer learning, both models integrate domain adaptation enhancement capabilities to address the scarcity of historical data. The models were evaluated using real building data under simulated data scarcity conditions. Experimental results show that in data-scarce scenarios, when the number of source domain samples reaches 4 times or more that of the target domain, compared with traditional LSTM and Gated Recurrent Unit (GRU) models, the overall Performance Improvement Rate (PIR) of the CORAL-LSTM model ranges from 70.8% to 92.62%, and that of the DANN-LSTM model ranges from 23.8% to 68.57%. Meanwhile, the Mean Absolute Percentage Error (MAPE) of the CORAL-LSTM model ranges from 0.99% to 1.15%, and its Coefficient of Determination (R²) ranges from 0.97 to 0.99. Furthermore, this study creatively introduces equivalent parameters to solve the problem of inconsistent feature dimensions when applying transfer learning models to heterogeneous systems. It also uses the Shapley Additive Explanations (SHAP) model to quantify the impact of input features on model outputs, verifying the effectiveness of the equivalent parameters. These findings confirm the feasibility of improving the performance of transfer learning models by enhancing domain adaptation in cooling load forecasting of central air-conditioning systems and provide a new technical approach to addressing data limitations in building energy system modeling.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117059"},"PeriodicalIF":7.1,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environment driven consumer psychological behavior based MPC energy model: a multi-dimensional digital twins framework using deep learning","authors":"Bilal Khan ,&nbsp;Sahibzada Muhammad Ali ,&nbsp;Zahid Ullah","doi":"10.1016/j.enbuild.2026.117035","DOIUrl":"10.1016/j.enbuild.2026.117035","url":null,"abstract":"<div><div>The paper proposes a Model Predictive Control (MPC) energy model for environment-driven Multi-Dimensional Digital Twins (MDDTs) powered by consumer psychological behaviour accomplished via Deep Learning (DL) to minimise energy consumption. This real-time integration of environmental factors, temperature, humidity, and lighting, with consumer behaviour patterns and physiological responses, provides the basis for a new integrated model for the dynamic control of energy systems. The proposed model relies on IoT sensors and real-time data aggregation in making predictions and optimising energy consumption, considering the environmental impacts on consumer comfort. The use of DL models improves MPC by uncovering non-linear correlations in the data and having the ability to predict future energy demands. The MPC architecture operates under a closed-loop operating system and, therefore, enables adjustment of real-time feedback according to the space, environmental, and consumer behaviour changes. Due to its predictive nature, MPC can make anticipatory changes to energy systems, which will save energy without compromising comfort. The proposed model is validated using extensive simulation to respond to dynamic situations with optimal energy consumption while ensuring adequate user comfort. The real-time application of multi-dimensional heterogeneous data proves the applicability and robustness of the proposed system in real-world environments.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117035"},"PeriodicalIF":7.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}