Pub Date : 2026-01-30DOI: 10.1016/j.enbuild.2026.117079
Luyao Li, Jichao Zhao, Zhiyong Tian, Xinyu Chen, Dilshod Jalilov, Tukhtamurod Juraev, Akbar Halimov, Michael T.F. Owen
{"title":"Synergistic deployment of electric heat pumps and pit thermal energy storage for renewable energy integration and heating decarbonization","authors":"Luyao Li, Jichao Zhao, Zhiyong Tian, Xinyu Chen, Dilshod Jalilov, Tukhtamurod Juraev, Akbar Halimov, Michael T.F. Owen","doi":"10.1016/j.enbuild.2026.117079","DOIUrl":"https://doi.org/10.1016/j.enbuild.2026.117079","url":null,"abstract":"","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"28 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071621","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}
Pub Date : 2026-01-30DOI: 10.1016/j.enbuild.2026.117082
Henrik Alexander Nissen Søndergaard, Hamid Reza Shaker, Bo Nørregaard Jørgensen
{"title":"Multi-Method Fault Detection Considering Uncertainty through MC Dropout for Enhanced Voting","authors":"Henrik Alexander Nissen Søndergaard, Hamid Reza Shaker, Bo Nørregaard Jørgensen","doi":"10.1016/j.enbuild.2026.117082","DOIUrl":"https://doi.org/10.1016/j.enbuild.2026.117082","url":null,"abstract":"","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"15 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071925","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}
Pub Date : 2026-01-28DOI: 10.1016/j.enbuild.2026.117071
E. Belloni, Ann M. Anderson, Mary K. Carroll, B. Frizzi, T. Pierini, C. Buratti
{"title":"Innovative glazing systems composed of photovoltaic cells and monolithic aerogel layers: Electrical and thermal experimental characterization and simulation analysis","authors":"E. Belloni, Ann M. Anderson, Mary K. Carroll, B. Frizzi, T. Pierini, C. Buratti","doi":"10.1016/j.enbuild.2026.117071","DOIUrl":"https://doi.org/10.1016/j.enbuild.2026.117071","url":null,"abstract":"","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"44 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071950","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}
Pub Date : 2026-01-28DOI: 10.1016/j.enbuild.2026.117064
M. Marquardt, J. Jürgensen, T. Boldt, R. Kulenovic, J. Starflinger, K. Terheiden
{"title":"Novel thermally activated building system with bi-metal heat pipes as reinforcement for optimal passive heat transport","authors":"M. Marquardt, J. Jürgensen, T. Boldt, R. Kulenovic, J. Starflinger, K. Terheiden","doi":"10.1016/j.enbuild.2026.117064","DOIUrl":"https://doi.org/10.1016/j.enbuild.2026.117064","url":null,"abstract":"","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"1 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071949","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}
Pub Date : 2026-01-27DOI: 10.1016/j.enbuild.2026.117046
Aadit Malla , Lukas Kranzl , Andreas Müller
Space cooling demand in Europe is on the rise, yet systematic assessments of how passive measures can moderate this growth remain scarce. Passive measures such as shading, glazing, and ventilation reduce cooling demand by limiting heat gains without relying on active energy use. This study evaluates their techno-economic potential across the EU-27 using a bottom-up building stock model. By linking energy savings with investment needs, we construct country-level cost curves that establish a merit order for implementation. Results show that shading and glazing upgrades are the most cost-effective measures, with multi-family buildings delivering higher savings at lower costs than single-family houses. Regional differences are notable: Mediterranean countries show limited additional headroom, while Central and Northern Europe retain larger unrealized potentials. The findings provide policymakers with a practical roadmap for integrating passive cooling into national renovation plans and local energy plans, thereby supporting the long-term objectives of European energy and building directives.
{"title":"Passive measures for reducing space cooling demand in the EU-27: Potentials and costs","authors":"Aadit Malla , Lukas Kranzl , Andreas Müller","doi":"10.1016/j.enbuild.2026.117046","DOIUrl":"10.1016/j.enbuild.2026.117046","url":null,"abstract":"<div><div>Space cooling demand in Europe is on the rise, yet systematic assessments of how passive measures can moderate this growth remain scarce. Passive measures such as shading, glazing, and ventilation reduce cooling demand by limiting heat gains without relying on active energy use. This study evaluates their techno-economic potential across the EU-27 using a bottom-up building stock model. By linking energy savings with investment needs, we construct country-level cost curves that establish a merit order for implementation. Results show that shading and glazing upgrades are the most cost-effective measures, with multi-family buildings delivering higher savings at lower costs than single-family houses. Regional differences are notable: Mediterranean countries show limited additional headroom, while Central and Northern Europe retain larger unrealized potentials. The findings provide policymakers with a practical roadmap for integrating passive cooling into national renovation plans and local energy plans, thereby supporting the long-term objectives of European energy and building directives.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117046"},"PeriodicalIF":7.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071959","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}
Pub Date : 2026-01-27DOI: 10.1016/j.enbuild.2026.117070
Arya Parsaei, Andre Markus, Burak Gunay, William O’Brien, Ricardo Moromisato, Jayson Bursill
{"title":"A system-level auto-commissioning framework for proactive detection of hard and soft faults in VAV AHU systems","authors":"Arya Parsaei, Andre Markus, Burak Gunay, William O’Brien, Ricardo Moromisato, Jayson Bursill","doi":"10.1016/j.enbuild.2026.117070","DOIUrl":"https://doi.org/10.1016/j.enbuild.2026.117070","url":null,"abstract":"","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"43 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071957","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}
Pub Date : 2026-01-27DOI: 10.1016/j.enbuild.2026.117067
Qinye Lu , Tengyao Jiang , Guojian Yang , Tianyu Cai , Ye Yang , Gang Tan
Mitigating energy consumption for transparent building envelopes has emerged as a crucial strategy for improving building energy efficiency in recent years. Although electrochromic (EC) windows dynamically regulate their Solar Heat Gain Coefficient (SHGC) to reduce heating and cooling loads, the comprehensive optimization of control strategies remains underexplored in architectural implementations. This study integrates building energy simulations with the Entropy Weight Method (EWM) to assess the EC glazing performance under diverse control strategies through multi-objective evaluation. Three strategies of solar radiation-based, window surface temperature-based, and indoor daylight illuminance-based control were implemented to dynamically modulate the optical properties of EC glass. Key metrics including building energy consumption, daylight performance, and thermal comfort indices were analyzed to establish practical guidelines for varied building orientations across the climatic zones in China. Results demonstrates that under optimized control conditions, annual cooling energy consumption decreased by 32%, daylight glare index (DGI) satisfaction increased threefold and predicted mean vote compliance improved by 10% compared to conventional glazing in Shanghai. Notably, control strategies based on indoor illuminance with a colorization threshold of 300 lx achieved superior daylight comfort and glare reduction across most orientations. Rational control strategies enable EC smart window to outperform conventional counterparts in most climatic regions, providing dual benefits of enhanced occupant comfort and building energy efficiency.
{"title":"Comprehensive evaluation of different control strategies of electrochromic smart window on improving the building energy, visual and thermal comfort","authors":"Qinye Lu , Tengyao Jiang , Guojian Yang , Tianyu Cai , Ye Yang , Gang Tan","doi":"10.1016/j.enbuild.2026.117067","DOIUrl":"10.1016/j.enbuild.2026.117067","url":null,"abstract":"<div><div>Mitigating energy consumption for transparent building envelopes has emerged as a crucial strategy for improving building energy efficiency in recent years. Although electrochromic (EC) windows dynamically regulate their Solar Heat Gain Coefficient (SHGC) to reduce heating and cooling loads, the comprehensive optimization of control strategies remains underexplored in architectural implementations. This study integrates building energy simulations with the Entropy Weight Method (EWM) to assess the EC glazing performance under diverse control strategies through multi-objective evaluation. Three strategies of solar radiation-based, window surface temperature-based, and indoor daylight illuminance-based control were implemented to dynamically modulate the optical properties of EC glass. Key metrics including building energy consumption, daylight performance, and thermal comfort indices were analyzed to establish practical guidelines for varied building orientations across the climatic zones in China. Results demonstrates that under optimized control conditions, annual cooling energy consumption decreased by 32%, daylight glare index (DGI) satisfaction increased threefold and predicted mean vote compliance improved by 10% compared to conventional glazing in Shanghai. Notably, control strategies based on indoor illuminance with a colorization threshold of 300 lx achieved superior daylight comfort and glare reduction across most orientations. Rational control strategies enable EC smart window to outperform conventional counterparts in most climatic regions, providing dual benefits of enhanced occupant comfort and building energy efficiency.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117067"},"PeriodicalIF":7.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071948","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}
Pub Date : 2026-01-27DOI: 10.1016/j.enbuild.2026.117072
Johnson Kehinde Abifarin
This study aims to optimise the thermal performance and space efficiency of Cold Thermal Energy Storage (CTES) systems using a beneficial and unbeneficial probability utility index analysis. The optimisation framework simultaneously minimises complete solidification time (St) and complete melting time (Mt), while maximising the compactness factor (C). The effects of fin height, fin spacing, and fin thickness on heat transfer performance and system compactness are systematically evaluated. The optimisation results identify an optimal configuration with a fin height of 10 mm, a fin spacing of 2.4 mm, and a fin thickness of 0.75 mm, resulting in an enhancement in heat transfer efficiency and a reduction in both solidification and melting times compared to the baseline design reported in the literature. Sensitivity analysis indicates that fin height is the dominant parameter, contributing 95.8% to overall thermal performance, followed by fin spacing (2.75%) and fin thickness (0.94%). The proposed methodology provides a robust and systematic framework for balancing thermal efficiency, compactness, and response time in CTES systems. These findings offer practical design guidelines for high-performance CTES applications in refrigeration, air conditioning, and peak-load shifting, and support future advancements in cold thermal energy storage technologies.
{"title":"High-performance cold thermal energy storage for clean and efficient cooling","authors":"Johnson Kehinde Abifarin","doi":"10.1016/j.enbuild.2026.117072","DOIUrl":"10.1016/j.enbuild.2026.117072","url":null,"abstract":"<div><div>This study aims to optimise the thermal performance and space efficiency of Cold Thermal Energy Storage (CTES) systems using a beneficial and unbeneficial probability utility index analysis. The optimisation framework simultaneously minimises complete solidification time (St) and complete melting time (Mt), while maximising the compactness factor (C). The effects of fin height, fin spacing, and fin thickness on heat transfer performance and system compactness are systematically evaluated. The optimisation results identify an optimal configuration with a fin height of 10 mm, a fin spacing of 2.4 mm, and a fin thickness of 0.75 mm, resulting in an enhancement in heat transfer efficiency and a reduction in both solidification and melting times compared to the baseline design reported in the literature. Sensitivity analysis indicates that fin height is the dominant parameter, contributing 95.8% to overall thermal performance, followed by fin spacing (2.75%) and fin thickness (0.94%). The proposed methodology provides a robust and systematic framework for balancing thermal efficiency, compactness, and response time in CTES systems. These findings offer practical design guidelines for high-performance CTES applications in refrigeration, air conditioning, and peak-load shifting, and support future advancements in cold thermal energy storage technologies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117072"},"PeriodicalIF":7.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071961","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}
Renovation of existing buildings to meet Nearly Zero Energy Building (NZEB) standards is essential for reducing energy consumption and CO2 emissions in the residential sector, making it a central strategy for achieving national climate goals. While commonly applied renovation measures effectively reduce operational energy consumption and CO2 emissions, they may increase the building’s environmental impact due to embodied emissions from construction materials and equipment. To address these opposing effects, this study employs a life cycle-oriented multi-objective optimization (MOO) approach to identify optimal renovation strategies for a representative collective residential building. A Definitive Screening Design (DSD) is used to create a minimal simulation experiment, and regression analysis is applied to develop surrogate models that define the relationships between primary energy consumption (), life cycle emissions (), life cycle cost (), and the considered renovation measures. These surrogate models are integrated into the MOO model, which is optimized using the NSGA-III algorithm to simultaneously minimize , , and . The resulting Pareto-optimal solutions reveal distinct trade-offs, characterized by a non-linear positive relationship between and , a linear negative relationship between and , and a non-linear negative relationship between and . These relationships indicate that improvements in energy and environmental performance are associated with significant cost increases. To evaluate and rank the Pareto-optimal solutions, the hybrid Entropy-TOPSIS method is applied, resulting in the selection of an optimal renovation strategy with its corresponding set of renovation measures. The proposed methodology provides an efficient decision-support approach for renovating collective residential buildings following NZEB standards.
{"title":"Integrated life cycle assessment and multi-objective optimization of residential building renovation strategies to achieve NZEB standards","authors":"Džana Kadrić , Amar Aganović , Sanita Džino , Una Smailbegović , Ajdin Vatreš , Edin Kadrić","doi":"10.1016/j.enbuild.2026.117068","DOIUrl":"10.1016/j.enbuild.2026.117068","url":null,"abstract":"<div><div>Renovation of existing buildings to meet Nearly Zero Energy Building (NZEB) standards is essential for reducing energy consumption and CO<sub>2</sub> emissions in the residential sector, making it a central strategy for achieving national climate goals. While commonly applied renovation measures effectively reduce operational energy consumption and CO<sub>2</sub> emissions, they may increase the building’s environmental impact due to embodied emissions from construction materials and equipment. To address these opposing effects, this study employs a life cycle-oriented multi-objective optimization (MOO) approach to identify optimal renovation strategies for a representative collective residential building. A Definitive Screening Design (DSD) is used to create a minimal simulation experiment, and regression analysis is applied to develop surrogate models that define the relationships between primary energy consumption (<span><math><mrow><mi>PE</mi></mrow></math></span>), life cycle emissions (<span><math><mrow><mi>LCE</mi></mrow></math></span>), life cycle cost (<span><math><mrow><mi>LCC</mi></mrow></math></span>), and the considered renovation measures. These surrogate models are integrated into the MOO model, which is optimized using the NSGA-III algorithm to simultaneously minimize <span><math><mrow><mi>PE</mi></mrow></math></span>, <span><math><mrow><mi>LCE</mi></mrow></math></span>, and <span><math><mrow><mi>LCC</mi></mrow></math></span>. The resulting Pareto-optimal solutions reveal distinct trade-offs, characterized by a non-linear positive relationship between <span><math><mrow><mi>PE</mi></mrow></math></span> and <span><math><mrow><mi>LCE</mi></mrow></math></span>, a linear negative relationship between <span><math><mrow><mi>PE</mi></mrow></math></span> and <span><math><mrow><mi>LCC</mi></mrow></math></span>, and a non-linear negative relationship between <span><math><mrow><mi>LCE</mi></mrow></math></span> and <span><math><mrow><mi>LCC</mi></mrow></math></span>. These relationships indicate that improvements in energy and environmental performance are associated with significant cost increases. To evaluate and rank the Pareto-optimal solutions, the hybrid Entropy-TOPSIS method is applied, resulting in the selection of an optimal renovation strategy with its corresponding set of renovation measures. The proposed methodology provides an efficient decision-support approach for renovating collective residential buildings following NZEB standards.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117068"},"PeriodicalIF":7.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048119","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}
Pub Date : 2026-01-26DOI: 10.1016/j.enbuild.2026.117063
Yiqian Zheng , Biao Yang , Miaomiao Hou , Yi Zhang , Yuekuan Zhou , Xing Zheng , Pengyuan Shen
The global push for carbon neutrality has intensified the need for rapid and accurate energy prediction methods for BIPV-integrated modular buildings. Traditional physics-based simulation approaches suffer from excessive computational burden. This study presents a novel machine learning-based rapid energy prediction methodology specifically designed for modular buildings with building-integrated photovoltaics. A comprehensive feature engineering framework captures the unique thermal and geometric characteristics of modular construction through six-surface property encoding, geometric parameters, and solar irradiance calculations. The methodology employs a modular building decomposition strategy that enables individual module analysis while maintaining system-level accuracy. An XGBoost-based prediction model achieves superior performance across four representative climate zones. The model achieves R2 values exceeding 0.93 for heating loads, cooling loads, and total energy consumption. Experimental validation using a real-world BIPV-integrated modular building demonstrates prediction accuracy within industry-acceptable limits, with mean absolute errors below 1.5°C. The computational efficiency assessment shows prediction speeds over 2,000 × faster than traditional simulation approaches, enabling real-time design iteration. Successful integration with Grasshopper parametric design platforms facilitates immediate energy feedback during conceptual design phases. This advancement removes computational barriers to energy performance optimization and supports the broader adoption of sustainable modular construction practices by providing practical tools for energy-informed design decision-making.
{"title":"A machine learning based rapid thermal performance modeling method for modular buildings with BIPV: A novel decomposition strategy with real-time prediction capabilities","authors":"Yiqian Zheng , Biao Yang , Miaomiao Hou , Yi Zhang , Yuekuan Zhou , Xing Zheng , Pengyuan Shen","doi":"10.1016/j.enbuild.2026.117063","DOIUrl":"10.1016/j.enbuild.2026.117063","url":null,"abstract":"<div><div>The global push for carbon neutrality has intensified the need for rapid and accurate energy prediction methods for BIPV-integrated modular buildings. Traditional physics-based simulation approaches suffer from excessive computational burden. This study presents a novel machine learning-based rapid energy prediction methodology specifically designed for modular buildings with building-integrated photovoltaics. A comprehensive feature engineering framework captures the unique thermal and geometric characteristics of modular construction through six-surface property encoding, geometric parameters, and solar irradiance calculations. The methodology employs a modular building decomposition strategy that enables individual module analysis while maintaining system-level accuracy. An XGBoost-based prediction model achieves superior performance across four representative climate zones. The model achieves R<sup>2</sup> values exceeding 0.93 for heating loads, cooling loads, and total energy consumption. Experimental validation using a real-world BIPV-integrated modular building demonstrates prediction accuracy within industry-acceptable limits, with mean absolute errors below 1.5°C. The computational efficiency assessment shows prediction speeds over 2,000 × faster than traditional simulation approaches, enabling real-time design iteration. Successful integration with Grasshopper parametric design platforms facilitates immediate energy feedback during conceptual design phases. This advancement removes computational barriers to energy performance optimization and supports the broader adoption of sustainable modular construction practices by providing practical tools for energy-informed design decision-making.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117063"},"PeriodicalIF":7.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048122","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}
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