Advances in Applied Energy最新文献

{"title":"Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling","authors":"Ao Li ,&nbsp;Fu Xiao ,&nbsp;Ziwei Xiao ,&nbsp;Rui Yan ,&nbsp;Anbang Li ,&nbsp;Yan Lv ,&nbsp;Bing Su","doi":"10.1016/j.adapen.2024.100189","DOIUrl":"10.1016/j.adapen.2024.100189","url":null,"abstract":"<div><p>Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100189"},"PeriodicalIF":13.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000271/pdfft?md5=0f782334db1159003891c4cf1c77159d&pid=1-s2.0-S2666792424000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148847","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":"Toward global rooftop PV detection with Deep Active Learning","authors":"Matthias Zech ,&nbsp;Hendrik-Pieter Tetens ,&nbsp;Joseph Ranalli","doi":"10.1016/j.adapen.2024.100191","DOIUrl":"10.1016/j.adapen.2024.100191","url":null,"abstract":"<div><div>It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100191"},"PeriodicalIF":13.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705984","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":"Techno–Economic Modeling and Safe Operational Optimization of Multi-Network Constrained Integrated Community Energy Systems","authors":"Ze Hu ,&nbsp;Ka Wing Chan ,&nbsp;Ziqing Zhu ,&nbsp;Xiang Wei ,&nbsp;Weiye Zheng ,&nbsp;Siqi Bu","doi":"10.1016/j.adapen.2024.100183","DOIUrl":"10.1016/j.adapen.2024.100183","url":null,"abstract":"<div><p>The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100183"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000210/pdfft?md5=cd536c4a02a001e229c11a6ef7a1a59a&pid=1-s2.0-S2666792424000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711958","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":"Impact of forecasting on energy system optimization","authors":"Florian Peterssen ,&nbsp;Marlon Schlemminger ,&nbsp;Clemens Lohr ,&nbsp;Raphael Niepelt ,&nbsp;Richard Hanke-Rauschenbach ,&nbsp;Rolf Brendel","doi":"10.1016/j.adapen.2024.100181","DOIUrl":"10.1016/j.adapen.2024.100181","url":null,"abstract":"<div><p>Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100181"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000192/pdfft?md5=fbba7e83b4274182667c200c1582b508&pid=1-s2.0-S2666792424000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636813","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":"Optimal scheduling of smart home energy systems: A user-friendly and adaptive home intelligent agent with self-learning capability","authors":"Zhengyi Luo ,&nbsp;Jinqing Peng ,&nbsp;Xuefen Zhang ,&nbsp;Haihao Jiang ,&nbsp;Rongxin Yin ,&nbsp;Yutong Tan ,&nbsp;Mengxin Lv","doi":"10.1016/j.adapen.2024.100182","DOIUrl":"10.1016/j.adapen.2024.100182","url":null,"abstract":"<div><p>This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO₂ emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100182"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000209/pdfft?md5=1cedc846099a911397ba821e7c77e286&pid=1-s2.0-S2666792424000209-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623286","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":"The potential of radiative cooling enhanced photovoltaic systems in China","authors":"Maoquan Huang ,&nbsp;Hewen Zhou ,&nbsp;G.H. Tang ,&nbsp;Mu Du ,&nbsp;Qie Sun","doi":"10.1016/j.adapen.2024.100184","DOIUrl":"10.1016/j.adapen.2024.100184","url":null,"abstract":"<div><p>Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100184"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000222/pdfft?md5=0a129a711e045cc4c3d88283b1dec009&pid=1-s2.0-S2666792424000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848674","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":"Risk-aware microgrid operation and participation in the day-ahead electricity market","authors":"Robert Herding ,&nbsp;Emma Ross ,&nbsp;Wayne R. Jones ,&nbsp;Elizabeth Endler ,&nbsp;Vassilis M. Charitopoulos ,&nbsp;Lazaros G. Papageorgiou","doi":"10.1016/j.adapen.2024.100180","DOIUrl":"https://doi.org/10.1016/j.adapen.2024.100180","url":null,"abstract":"<div><p>This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the <span><math><mi>ɛ</mi></math></span>-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100180"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000180/pdfft?md5=96b7a1215928fbf42c89bb54f97b0164&pid=1-s2.0-S2666792424000180-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540414","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 probabilistic model for real-time quantification of building energy flexibility","authors":"Binglong Han ,&nbsp;Hangxin Li ,&nbsp;Shengwei Wang","doi":"10.1016/j.adapen.2024.100186","DOIUrl":"10.1016/j.adapen.2024.100186","url":null,"abstract":"<div><p>Buildings have great energy flexibility potential to manage supply-demand imbalance in power grids with high renewable penetration. Accurate and real-time quantification of building energy flexibility is essential not only for engaging buildings in electricity and grid service markets, but also for ensuring the reliable and optimal operation of power grids. This paper proposes a probabilistic model for rapidly quantifying the aggregated flexibility of buildings under uncertainties. An explicit equation is derived as the analytical solution of a commonly used second-order building thermodynamic model to quantify the flexibility of individual buildings, eliminating the need of time-consuming iterative and finite difference computations. A sampling-based uncertainty analysis is performed to obtain the distribution of aggregated building flexibility, considering major uncertainties comprehensively. Validation tests are conducted using 150 commercial buildings in Hong Kong. The results show that the proposed model not only quantifies the aggregated flexibility with high accuracy, but also dramatically reduces the computation time from 3605 s to 6.7 s, about 537 times faster than the existing probabilistic model solved numerically. Moreover, the proposed model is 8 times faster than the archetype-based model and achieves significantly higher accuracy.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100186"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000246/pdfft?md5=5103629f1a558de886b8f7db3d5993e4&pid=1-s2.0-S2666792424000246-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077117","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":"Planning reliable wind- and solar-based electricity systems","authors":"Tyler H. Ruggles ,&nbsp;Edgar Virgüez ,&nbsp;Natasha Reich ,&nbsp;Jacqueline Dowling ,&nbsp;Hannah Bloomfield ,&nbsp;Enrico G.A. Antonini ,&nbsp;Steven J. Davis ,&nbsp;Nathan S. Lewis ,&nbsp;Ken Caldeira","doi":"10.1016/j.adapen.2024.100185","DOIUrl":"10.1016/j.adapen.2024.100185","url":null,"abstract":"<div><p>Resource adequacy, or ensuring that electricity supply reliably meets demand, is more challenging for wind- and solar-based electricity systems than fossil-fuel-based ones. Here, we investigate how the number of years of past weather data used in designing least-cost systems relying on wind, solar, and energy storage affects resource adequacy. We find that nearly 40 years of weather data are required to plan highly reliable systems (e.g., zero lost load over a decade). In comparison, this same adequacy could be attained with 15 years of weather data when additionally allowing traditional dispatchable generation to supply 5 % of electricity demand. We further observe that the marginal cost of improving resource adequacy increased as more years, and thus more weather variability, were considered for planning. Our results suggest that ensuring the reliability of wind- and solar-based systems will require using considerably more weather data in system planning than is the current practice. However, when considering the potential costs associated with unmet electricity demand, fewer planning years may suffice to balance costs against operational reliability.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100185"},"PeriodicalIF":13.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000234/pdfft?md5=a1828cdb491a3fbda3e3e4a773b1bcba&pid=1-s2.0-S2666792424000234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048529","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":"Introducing sodium lignosulfonate as an effective promoter for CO2 sequestration as hydrates targeting gaseous and liquid CO2","authors":"Hailin Huang ,&nbsp;Xuejian Liu ,&nbsp;Hongfeng Lu ,&nbsp;Chenlu Xu ,&nbsp;Jianzhong Zhao ,&nbsp;Yan Li ,&nbsp;Yuhang Gu ,&nbsp;Zhenyuan Yin","doi":"10.1016/j.adapen.2024.100175","DOIUrl":"10.1016/j.adapen.2024.100175","url":null,"abstract":"<div><p>Hydrate-based CO₂ sequestration (HBCS) emerges as a promising solution to sequestrate CO₂ as solid hydrates for the benefit of reducing CO₂ concentration in the atmosphere. The natural conditions of high-pressure and low-temperature in marine seabed provide an ideal reservoir for CO₂ hydrate, enabling long-term sequestration. A significant challenge in the application of HBCS is the identification of an environmental-friendly promoter to enhance or tune CO₂ hydrate kinetics, which is intrinsically sluggish. In addition, the promoter identified should be effective in all CO₂ sequestration conditions, covering CO₂ injection as gas or liquid. In this study, we introduced sodium lignosulfonate (SL), a by-product from the papermaking industry, as an eco-friendly kinetic promoter for CO₂ hydrate formation. The impact of SL (0–3.0 wt.%) on the kinetics of CO₂ hydrate formation from gaseous and liquid CO₂ was systematically investigated. CO₂ hydrate morphology images were acquired for both gaseous and liquid CO₂ in the presence of SL for the explanation of the observed promotion effect. The promotion effect of SL on CO₂ hydrate formation is optimal at 1.0 wt.% with induction time reduced to 5.3 min and 21.1 min for gaseous and liquid CO₂, respectively. Moreover, CO₂ storage capacity increases by around two times at 1.0 wt.% SL, reaching 85.1 v/v and 57.1 v/v for gaseous and liquid CO₂, respectively. The applicability of SL as an effective kinetic promoter for both gaseous and liquid CO₂ was first demonstrated. A mechanism explaining how SL promotes CO₂ hydrate formation was formulated with additional nucleation sites by SL micelles and the extended contact surface offered by generated gas bubbles or liquid droplets with SL. The study demonstrates that SL as an effective promoter for CO₂ hydrate kinetics is possible for adoption in large-scale HBCS projects both nearshore and offshore.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000131/pdfft?md5=0849e60616fc3e08beffef6ac31ad037&pid=1-s2.0-S2666792424000131-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140792160","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}