{"title":"Building decarbonization based on building loads flexibility and clusters’ collaboration","authors":"Jian Ge, Guoquan Lv, Jiahuan Tang, Kang Zhao","doi":"10.1360/nso/20230074","DOIUrl":"https://doi.org/10.1360/nso/20230074","url":null,"abstract":"","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"1217 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Wang, Haixia Wang, Liankun Yu, Qiulei Dong, Zhanyi Hu
: Recently a new paradigm is emerging in SAR (Synthetic Aperture Radar) 3D imaging technology where the imaging performance is enhanced by exploiting SAR visual semantics. Here by “SAR visual semantics”, we mean primarily the scene conceptual structural information extracted directly from SAR images. Under this paradigm, a paramount open problem lies in what and how the SAR visual semantics could be extracted and used at different levels associated with different structural information. This work is a tentative attempt to tackle the above what-and-how problem, and it mainly consists of the following two parts: The first one is a sketchy description of how three-level (low, middle, and high) SAR visual semantics could be extracted and used in SAR Tomography (TomoSAR), including an extension of SAR visual semantics analysis (e.g., façades and roofs) to sparse 3D points initially recovered via traditional TomoSAR methods. The second part is a case of study on two open source TomoSAR datasets to illustrate and validate the effectiveness and efficiency of SAR visual semantics exploitation in TomoSAR for box-like 3D building modeling. Due to the space limit, only main steps of the involved methods are reported, and we hope, such neglects of technical details will not severely compromise the underlying key concepts and ideas.
{"title":"Exploiting SAR Visual Semantics in TomoSAR for 3D Modeling of Buildings","authors":"Wei Wang, Haixia Wang, Liankun Yu, Qiulei Dong, Zhanyi Hu","doi":"10.1360/nso/20230067","DOIUrl":"https://doi.org/10.1360/nso/20230067","url":null,"abstract":": Recently a new paradigm is emerging in SAR (Synthetic Aperture Radar) 3D imaging technology where the imaging performance is enhanced by exploiting SAR visual semantics. Here by “SAR visual semantics”, we mean primarily the scene conceptual structural information extracted directly from SAR images. Under this paradigm, a paramount open problem lies in what and how the SAR visual semantics could be extracted and used at different levels associated with different structural information. This work is a tentative attempt to tackle the above what-and-how problem, and it mainly consists of the following two parts: The first one is a sketchy description of how three-level (low, middle, and high) SAR visual semantics could be extracted and used in SAR Tomography (TomoSAR), including an extension of SAR visual semantics analysis (e.g., façades and roofs) to sparse 3D points initially recovered via traditional TomoSAR methods. The second part is a case of study on two open source TomoSAR datasets to illustrate and validate the effectiveness and efficiency of SAR visual semantics exploitation in TomoSAR for box-like 3D building modeling. Due to the space limit, only main steps of the involved methods are reported, and we hope, such neglects of technical details will not severely compromise the underlying key concepts and ideas.","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"879 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: We propose a simple model of colloidal suspension, whereby individual particles change their diffusivity from high (hot) to low (cold), as the local concentration of their closest peers grows larger than a certain threshold. Such a non-reciprocal interaction mechanism is known from biology as quorum sensing. Upon tuning the parameters of the adopted quorum sensing protocol, the suspension is numerically shown to go through a variety of two-phase (hot and cold) configurations. This is an archetypal model with potential applications to robotics and social studies.
{"title":"Clustering of quorum sensing colloidal particles","authors":"Yuxin Zhou, Yunyun Li, F. Marchesoni","doi":"10.1360/nso/20230081","DOIUrl":"https://doi.org/10.1360/nso/20230081","url":null,"abstract":": We propose a simple model of colloidal suspension, whereby individual particles change their diffusivity from high (hot) to low (cold), as the local concentration of their closest peers grows larger than a certain threshold. Such a non-reciprocal interaction mechanism is known from biology as quorum sensing. Upon tuning the parameters of the adopted quorum sensing protocol, the suspension is numerically shown to go through a variety of two-phase (hot and cold) configurations. This is an archetypal model with potential applications to robotics and social studies.","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"1218 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xueqing Jin, Haixiao Wan, Zheng Jiao, Jiaqi Li, Li-Tang Yan
: Active matter is characterized by out-of-equilibrium behaviors, offering an attractive, alternative route for revolutionizing disease diagnostics and therapy. A better understanding of how active matter interacts with cell membranes is critical to elucidating the underlying physical mechanisms and broadening the potential biomedical applications. This review provides a conceptual framework on the physiochemical mechanisms underlying active matter-biomembrane interactions. We briefly introduce the physical models of active matter and lipid membranes, and summarize the typical phenomena emerging from various active matter, including artificial active particles, cellular cytoskeletons, bacteria, and membrane proteins. Moreover, the remaining challenges and future perspectives of such non-equilibrium systems in living organisms are discussed. The findings and fundamental principles discussed in this review shed light on the rational design of activity-mediated cellular interaction, and could trigger better strategies to design and develop novel functional systems and materials toward advantageous biomedical applications.
{"title":"Physical principles of bio-nano interfaces with active matter","authors":"Xueqing Jin, Haixiao Wan, Zheng Jiao, Jiaqi Li, Li-Tang Yan","doi":"10.1360/nso/20230079","DOIUrl":"https://doi.org/10.1360/nso/20230079","url":null,"abstract":": Active matter is characterized by out-of-equilibrium behaviors, offering an attractive, alternative route for revolutionizing disease diagnostics and therapy. A better understanding of how active matter interacts with cell membranes is critical to elucidating the underlying physical mechanisms and broadening the potential biomedical applications. This review provides a conceptual framework on the physiochemical mechanisms underlying active matter-biomembrane interactions. We briefly introduce the physical models of active matter and lipid membranes, and summarize the typical phenomena emerging from various active matter, including artificial active particles, cellular cytoskeletons, bacteria, and membrane proteins. Moreover, the remaining challenges and future perspectives of such non-equilibrium systems in living organisms are discussed. The findings and fundamental principles discussed in this review shed light on the rational design of activity-mediated cellular interaction, and could trigger better strategies to design and develop novel functional systems and materials toward advantageous biomedical applications.","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"96 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Radiative cooling for long-term building energy efficiency: an experimental comparison of seven coatings","authors":"Yue He, Biao Lu, Jinzhong Fang, Yue Lei, Shan Gao, Chi Feng","doi":"10.1360/nso/20230065","DOIUrl":"https://doi.org/10.1360/nso/20230065","url":null,"abstract":"","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140465217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations, with the former o ff ering e ffi ciency but limited reliability, and the latter providing accuracy but restricted to systems of relatively small size. Herein, we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within the training dataset. This includes small nanoclusters, nanoparticles, as well as surface and bulk systems with periodic boundary conditions. The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes. Through rigorous validation, we have shown that our trained machine learning potential is capable of e ff ectively driving molecular dynamics simulations of nanoparticles across a wide temperature range, especially within the nanoscale regime. Remarkably, this is achieved while preserving the accuracy typically associated with ab initio methods.
{"title":"Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping monte carlo and active learning","authors":"Fu-Qiang Gong, Ke Xiong, Jun Cheng","doi":"10.1360/nso/20230088","DOIUrl":"https://doi.org/10.1360/nso/20230088","url":null,"abstract":": Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations, with the former o ff ering e ffi ciency but limited reliability, and the latter providing accuracy but restricted to systems of relatively small size. Herein, we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within the training dataset. This includes small nanoclusters, nanoparticles, as well as surface and bulk systems with periodic boundary conditions. The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes. Through rigorous validation, we have shown that our trained machine learning potential is capable of e ff ectively driving molecular dynamics simulations of nanoparticles across a wide temperature range, especially within the nanoscale regime. Remarkably, this is achieved while preserving the accuracy typically associated with ab initio methods.","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"120 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140462750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Special Topic: Energy Systems of Low Carbon Buildings
专题：低碳建筑的能源系统低碳建筑的能源系统
{"title":"pH-sensitive tunable thermochromic hydrogel with carbon quantum dots for smart windows","authors":"T. Jiang, Gang Tan","doi":"10.1360/nso/20230071","DOIUrl":"https://doi.org/10.1360/nso/20230071","url":null,"abstract":"Special Topic: Energy Systems of Low Carbon Buildings","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"177 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Overview of Research and Development of Nearly Zero Energy Buildings in China","authors":"Zhen Yu, Caifeng Gao, Jiaxin Yang, Jianlin Wu, Huan Zhang","doi":"10.1360/nso/20230083","DOIUrl":"https://doi.org/10.1360/nso/20230083","url":null,"abstract":"","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"461 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ci Chen, Lihua Xie, Kan Xie, Frank L. Lewis, Yilu Liu, Shengli Xie
The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences. Seeking an interpretable reward for decision-making that largely shapes the system’s behavior has always been a challenge in reinforcement learning. In this work, we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical laws. We find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral rewards. We apply this finding to solve output-feedback design problems in power systems. The results reveal that our approach removes an intermediate stage of identifying dynamical models. Our work suggests that the discrete-time reward is efficient in search of the desired decision law, which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.
{"title":"Learning the Continuous-Time Optimal Decision Law from Discrete-Time Rewards","authors":"Ci Chen, Lihua Xie, Kan Xie, Frank L. Lewis, Yilu Liu, Shengli Xie","doi":"10.1360/nso/20230054","DOIUrl":"https://doi.org/10.1360/nso/20230054","url":null,"abstract":"The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences. Seeking an interpretable reward for decision-making that largely shapes the system’s behavior has always been a challenge in reinforcement learning. In this work, we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical laws. We find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral rewards. We apply this finding to solve output-feedback design problems in power systems. The results reveal that our approach removes an intermediate stage of identifying dynamical models. Our work suggests that the discrete-time reward is efficient in search of the desired decision law, which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.","PeriodicalId":444662,"journal":{"name":"National Science Open","volume":"375 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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