Energy and Buildings最新文献

{"title":"Synergistic optimization of plant-based natural insulation materials with starch and waste-expanded polystyrene composite binders: Advancing thermo-acoustic efficiency for sustainable building envelopes","authors":"Fatima Mouguen ,&nbsp;Mouatassim Charai ,&nbsp;Said Oubaha ,&nbsp;Mohamed Oualid Mghazli ,&nbsp;Ahmed Mezrhab ,&nbsp;Yassine Taha ,&nbsp;Mostafa Benzazoua","doi":"10.1016/j.enbuild.2025.115834","DOIUrl":"10.1016/j.enbuild.2025.115834","url":null,"abstract":"<div><div>Developing sustainable binders and recycling waste materials are key to achieving carbon–neutral building materials. This study optimizes a composite binder using waste-expanded polystyrene (EPS) and cornstarch for bio-based insulation, enhancing thermal and acoustic performance while improving resource efficiency. A Doehlert experimental design optimized the fiber-to-binder (F/B) ratio (40–100 %) and EPS-to-starch (E/S) ratio (0–100 %), also assessing fiber length (short vs. long). For short fibers, the optimal formulation (F/B = 96.74 %, E/S = 27.34 %) achieved a density of 210.9 kg/m³ and thermal conductivity of 0.087 W/m·K. Long fibers (F/B = 89.72 %, E/S = 50 %) resulted in 144.2 kg/m³ and 0.068 W/m·K, meeting DIN 4108–2 standards. Acoustic tests showed strong absorption (49.5 % at 1600 Hz) and a transmission loss of 31.85 dB. This study highlights the potential of combining natural and recycled materials to develop high-performance bio-based insulation for thermal and acoustic applications.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115834"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931371","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":"Hybrid AI chiller model using transfer learning: Aleatoric and epistemic uncertainty","authors":"Sunghyun Kim ,&nbsp;Jin-Hong Kim ,&nbsp;Young Sub Kim ,&nbsp;Seon-Young Heo ,&nbsp;Cheol Soo Park","doi":"10.1016/j.enbuild.2025.115840","DOIUrl":"10.1016/j.enbuild.2025.115840","url":null,"abstract":"<div><div>This study proposed a hybrid AI chiller model using transfer learning (TL) to improve prediction accuracy and extrapolation reliability beyond the measured operating conditions. The model integrates physics-based knowledge from chiller specifications with measured operational data, thereby enabling physically consistent predictions while leveraging data-driven insights. An artificial neural network (ANN) was pre-trained with synthetic data generated from an empirical chiller model and fine-tuned using measured data from five chillers. Model accuracy was assessed using the coefficient of variation of the root mean square error, whereas uncertainty was quantified using Bayesian deep learning with Monte Carlo dropout to evaluate the aleatoric (AU) and epistemic uncertainties (EU). The results demonstrated that both the ANN and TL models achieved acceptable accuracy within the ASHRAE guideline limit of 30%. However, the TL model exhibited significantly lower uncertainty, with the AU, EU, and total uncertainty (TU) reduced by 73.4%, 70.4%, and 72.2%, respectively. The reduced AU was attributed to the pre-training on refined specification data, whereas the lower EU resulted from physics-based knowledge that improved extrapolation in regions with limited data. Overall, the proposed TL-based hybrid AI model offers a practical and reliable solution for chiller performance prediction, supporting energy-efficient system operation and enhancing reliability under unmeasured operating conditions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115840"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935623","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":"Diagnostic Bayesian network in building energy systems: Current insights, practical challenges, and future trends","authors":"Chujie Lu,&nbsp;Ziao Wang,&nbsp;Martín Mosteiro-Romero,&nbsp;Laure Itard","doi":"10.1016/j.enbuild.2025.115845","DOIUrl":"10.1016/j.enbuild.2025.115845","url":null,"abstract":"<div><div>Many buildings suffer from operational inefficiencies, leading to uncomfortable indoor environments, poor air quality, and significant energy waste. Developing automatic fault detection and diagnosis (FDD) tools in building energy systems is essential to mitigate these issues, reducing both energy waste and maintenance costs. Diagnostic Bayesian networks (DBNs), as probabilistic graphical models, offer a promising solution due to their interpretability, robustness to uncertainty, scalability, and flexibility. In this paper, the practical applications of DBNs for FDD in building energy systems are comprehensively reviewed. The generic modeling procedure is systematically examined and summarized, covering problem formulation, structure modeling, parameter modeling, and fault isolation and evaluation. Then, the paper provides insights into DBN modeling objectives, modeling types, diagnostic samples, and modeling software based on the 43 key relevant papers. Furthermore, the paper discusses practical challenges such as sensor configuration, baseline estimation, threshold determination, and expert knowledge integration. Finally, the recommendations are provided to guide further research, aiming to enhance DBN implementation for building energy systems in real-world scenarios, thereby supporting the transformation of the building service industry into a smart sector and ultimately improving building energy performance.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115845"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924322","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":"Cross-unit soft fault diagnosis for VRF systems using deep transfer learning: a comparative study across multiple scenarios","authors":"Yuxuan He,&nbsp;Wei Gou,&nbsp;Huanxin Chen,&nbsp;Yuanyi Xu","doi":"10.1016/j.enbuild.2025.115811","DOIUrl":"10.1016/j.enbuild.2025.115811","url":null,"abstract":"<div><div>Soft faults in VRF systems are difficult to detect, often resulting in air conditioning systems operating in a “sick operation” state, which leads to significant energy waste. This study aims to develop a cross-unit soft fault diagnosis method for VRF systems based on deep transfer learning, addressing limitations in handling cross-condition and cross-unit scenarios. Two distinct transfer learning approaches were investigated and compared for different diagnostic scenarios. First, using 1-D CNN as the base classifier, parameter-based models (FE and FT) were constructed and evaluated under conditions with minimal target domain samples. The FT model achieved an accuracy of 77.4 %. Second, a feature-based domain-adversarial neural networks (DANN) model was constructed with unlabeled target domain data, achieving approximately a 25 % improvement in accuracy over traditional classifiers. These results highlight the potential of deep transfer learning methods for improving diagnostic performance and their applicability in real-world VRF system scenarios.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"342 ","pages":"Article 115811"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070661","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":"Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis","authors":"Darice Guittet ,&nbsp;Eric Bonnema ,&nbsp;Matt Mitchell ,&nbsp;Allison Mahvi ,&nbsp;Jason Woods","doi":"10.1016/j.enbuild.2025.115828","DOIUrl":"10.1016/j.enbuild.2025.115828","url":null,"abstract":"<div><div>Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. This study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115828"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942590","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":"PSAT: A knowledge-based decision support tool for selecting passive energy consumption optimisation strategies in buildings","authors":"Amirhossein Balali,&nbsp;Akilu Yunusa-Kaltungo","doi":"10.1016/j.enbuild.2025.115849","DOIUrl":"10.1016/j.enbuild.2025.115849","url":null,"abstract":"<div><div>Achieving net-zero energy targets is closely linked to the optimisation of building-related activities, given their significant share in global energy consumption (approximately 40%) and greenhouse gas emissions (around 33%). The substantial energy demands of active systems, such as energy recovery ventilation systems for heating and cooling, have increasingly directed research efforts towards the investigation of passive design strategies in recent years. Selecting the most sustainable passive energy consumption optimisation strategy for buildings is a complex and challenging task for practitioners, as it requires the consideration of multiple criteria, including technical, economic, and social factors. Therefore, this study aims to develop a knowledge-based decision support tool titled the PSAT for the selection of passive strategies for buildings. Initially, drivers and barriers to the adoption of passive strategies, the criteria involved in the selection of passive strategies, and the existing passive strategies were identified. This was followed by generating a novel multiple criteria decision-making (MCDM) algorithm by hybridising evaluation based on distance from average solution (EDAS) and criteria importance through inter criteria correlation (CRITIC) methods. Then, graphical user interface (GUI) was developed for the PSAT using CustomTkinter, an innovative and customisable Python UI library which allows for the design of modern interfaces, providing a more user-friendly environment for decision-makers. The PSAT was then validated by experts and obtained the efficiency score of 4.76 out of 5. Finally, a thematic analysis was performed to identify the key themes within the suggestions provided by the validating experts regarding future development of the PSAT. This tool can considerably facilitate the selection of passive strategies for practitioners, consequently enhancing the realisation of critical Sustainable Development Goals (SDGs), especially “Sustainable Cities and Communities”.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115849"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935621","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":"Using machine learning algorithm to predict lighting energy consumption of daylight-linked lighting systems from spatial daylight autonomy","authors":"Yu Bian ,&nbsp;Yuan Zhou ,&nbsp;Shiying Yang ,&nbsp;Dandan Lin ,&nbsp;Yuan Ma","doi":"10.1016/j.enbuild.2025.115847","DOIUrl":"10.1016/j.enbuild.2025.115847","url":null,"abstract":"<div><div>Assessing the energy savings from daylight linked control (DLC) for lighting systems in a generalized form is challenging. However, novel approaches, such as the Machine learning algorithm (MLA), have the potential to address this challenge and are worth further investigation. This study aims to predict the energy consumption of lighting systems with DLC from the daylighting performance metric: spatial daylight autonomy (sDA), along with several necessary design features. A parametric room model with single side-lit window is established, and four DLC modes are set. From these, around sixteen thousand data sets comprising sDA, room design features and lighting energy consumption are collected for training the algorithm model. The XGBoost model is selected as it outperforms other algorithms by accuracy and efficiency. The results of data analysis demonstrate that the prediction model developed with sDA and several design features exhibits commendable predictive performance, and these features include Room Area, Room Length, Room Height, WWR, and Room Width. The following conclusions can be drawn: sDA along with four to six design features, depending on control mode, are effective for predicting the energy consumption of a lighting system applying DLC in rooms of varied dimensions. The XGBoost has demonstrated efficacy in addressing regression issues and managing the complex nonlinear relationships inherent in dynamic daylighting related issues. The model produced decisive data and provided a rapid method that assists decision-makers in choosing between DLC and conventional lighting control systems. It is also a meaningful exploration of AI applications for building daylighting performance analysis.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115847"},"PeriodicalIF":6.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942676","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":"Characterization and optimization of amorphous alumina-doped silica thin layer material of low emissivity coating technology for energy-saving applications","authors":"S. Ibrahim ,&nbsp;A.M. Fayad ,&nbsp;A.A. El-Kheshen ,&nbsp;Y.M. Hamdy ,&nbsp;Mohamed M. Ibrahim ,&nbsp;M.A. Marzouk","doi":"10.1016/j.enbuild.2025.115836","DOIUrl":"10.1016/j.enbuild.2025.115836","url":null,"abstract":"<div><div>Alumina-doped silica thin layers were synthesized through the sol–gel route and coatings on soda lime substrates. The alumina-doped silica films were reactively deposited from pure 97.5SiO₂–2.5Al₂O₃, 95SiO₂–5Al₂O₃, 90SiO₂–10Al₂O₃, and 85SiO₂–15Al₂O₃ mol.% targets. The densified xerogel were characterized by thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM) supplemented with EDS, and UV–vis spectroscopy (UVIS). The three crystalline phases andalusite (Al₂SiO₅), sillimanite (Al₂SiO₅), and silicon oxide (SiO₂) were identified as having an orthorhombic structure, as confirmed by XRD measurements. SEM characterization shows that the surface morphology of silica and alumina particles is composed of polygonal-shaped particles of non-uniform size. As the doping content of Al₂O₃ increases, the optical bandgap increases from 2.544 to 2.879 eV. Urbach energy(ΔE) and refractive index (n)were determined and lies within the range of 0.185 – 0.165 eV and 2.53 to 2.43, respectively. The increase of Al₂O₃ introduction led to the continuous increase of the tetrahedral proportion of [AlO₄] and strengthened the degree of glass network connection. Based on measured properties of all fabricated thin film glass samples, the sample code ASG4 (85SiO₂–15Al₂O₃ mol %) can be used for numerical simulation. Results indicate that the advanced thin layer coating can effectively reduce the solar heat gain and offer the possibility of significant energy savings in buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115836"},"PeriodicalIF":6.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929440","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":"Research on the feature of thermal environment and auxiliary behavior of air-conditioning in buildings with substandard central heating","authors":"Ying Ji ,&nbsp;Miao Zhao ,&nbsp;Wei Tian ,&nbsp;Xinyue Wang ,&nbsp;Jingchao Xie ,&nbsp;Jiaping Liu","doi":"10.1016/j.enbuild.2025.115827","DOIUrl":"10.1016/j.enbuild.2025.115827","url":null,"abstract":"<div><div>The occurrence of extreme cold weather poses significant challenges to the operation of urban heating systems and indoor thermal comfort in buildings. This situation not only increases the demand for heating energy, but also puts forward new requirements for building thermal management. This study investigated the use of auxiliary heating devices (such as air-conditioning, abbreviated as AC) to maintain indoor thermal comfort in extreme cold conditions with insufficient central heating. Field surveys and equipment monitoring were conducted in 13 residential buildings and 13 office spaces in Beijing in winter from 2020 to 2022. The research results indicated that almost all auxiliary heating equipment used by users were ACs. Based on investigation and on-site measurement, the usage modes of AC for auxiliary heating were constructed and the energy usage under different modes had also been evaluated. Users were categorized into six groups, 4 types in residential buildings and 2 types in office buildings, working households (A1), mixed households (workers and students) (A2), retiree households (A3), and households with infant (A4), high-occupancy offices (B1), and low-occupancy offices (B2). Analysis showed that outdoor and indoor temperatures significantly influenced AC usage in household A1, A3 and A4, while household A2, office B1 and B2 were only affected by indoor temperature. Binary logistic regression models are developed to correlate temperature with AC usage probability, revealing critical indoor temperature thresholds for 80% AC usage. Results indicated that pre-adjustment conditions in residences were largely outside the comfort zone than offices, improving marginally after adjustment. This study highlights the critical role of auxiliary devices in enhancing indoor comfort and provides valuable insights for optimizing heating strategies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115827"},"PeriodicalIF":6.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929441","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":"Energy performance and life-cycle analyses of advanced terminal devices and heat pumps for a medium-sized office building in different climate zones in China","authors":"Shikang Wen,&nbsp;Bingyang Shen,&nbsp;Yuyue Yao,&nbsp;Qingyan Chen","doi":"10.1016/j.enbuild.2025.115835","DOIUrl":"10.1016/j.enbuild.2025.115835","url":null,"abstract":"<div><div>China targets carbon neutrality by 2060, yet high emissions from buildings’ operational energy pose significant challenges. Radiant ceiling panel (RCP) and gravity cabinet unit (GCU) terminals as a part of heating, ventilation, and air conditioning (HVAC) system, which rely on natural convection and radiation, have been proposed to reduce building energy consumption. However, limited research on their energy performance and economic feasibility across China’s diverse climates hinders widespread adoption. This study compared RCP and GCU terminals integrated with ground source heat pumps (GSHP) to traditional fan coil units (FCU) in a medium-sized office building across the five climate zones in China. To mitigate soil heat imbalance, a heating/cooling tower heat pump (HTHP) and a water-cooled chiller and boiler (WB) with the FCU were also applied. Energy simulations performed with EnergyPlus and life-cycle cost analyses evaluated energy consumption, payback periods, and net present values (NPV). Results showed RCP and GCU systems reduced energy consumption by 16–24 % and 8–11 %, respectively, compared to the FCU, primarily due to reduced fan energy use. Both GCU and RCP systems exhibited greater efficiency in colder climates, with the RCP achieving up to 23.8 % energy reduction in severe cold climate. The RCP had higher energy efficiency by employing low-lift heat pumps but incurred higher initial costs. The RCP’s NPV ranged from 70.5 to 288.2 CNY/m², whereas GCU’s NPV ranged between 100 and 200 CNY/m² across analyzed climates. RCP systems achieved payback of 10–17 years, while GCU systems recovered costs in under 12 years. A sensitivity analysis of internal loads revealed that high occupancy density reduced energy savings for RCP systems by increasing the reliance on high-lift heat pumps. In hot summer and warm winter climate, the GSHP coupled FCU and GCU terminals outperformed the HTHP in efficiency but faced thermal imbalance challenges and higher costs, especially the FCU-GSHP with a 43-year payback. In severe cold climate, the RCP-GSHP achieved a 38.6 % energy reduction versus the FCU-WB, with reasonable payback periods of 9 years. The findings provide insights into selecting suitable HVAC systems for varied climates across China.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"342 ","pages":"Article 115835"},"PeriodicalIF":6.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947617","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}