Oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)are key catalytic processes in various renewable energy conversion and energy storage technologies.Herein,we systematically investigated the ORR and OER ...Oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)are key catalytic processes in various renewable energy conversion and energy storage technologies.Herein,we systematically investigated the ORR and OER catalytic activity of the single-atom catalysts(SACs)composed of 4d/5d period transition metal(TM)atoms embedded on MBene substrates(TM-M_(2)B_(2)O_(2),M=Ti,Mo,and W).We found that TM dominates the catalytic activity compared to the MBene substrates.The SACs embedded with Rh,Pd,Au,and Ir exhibit excellent ORR or OER catalytic activity.Specifically,Rh-Mo2B2O2and Rh-W2B2O2are promising bifunctional catalysts with ultra-low ORR/OER overpotentials of 0.39/0.21 V and0.19/0.32 V,respectively,lower than that of Pt/RuO_(2)(0.45/0.42 V).Importantly,through machine learning,the models containing 10 element features of SACs were developed to quickly and accurately identify the superior ORR and OER electrocatalysts.Our findings provide several promising SACs for ORR and OER,and offer effective models for catalyst design.展开更多
Synthetic Aperture Radar three-dimensional(3D)imaging enables the acquisition of more comprehensive information,making it a recent hotspot in radar imaging.Traditional 3D imaging methods have evolved from 2D and inter...Synthetic Aperture Radar three-dimensional(3D)imaging enables the acquisition of more comprehensive information,making it a recent hotspot in radar imaging.Traditional 3D imaging methods have evolved from 2D and interferometric imaging,combining elevation aperture extension with signal processing techniques.Limitations such as long acquisition or complex system from its imaging mechanism restrict its application.In recent years,rapid development of artificial intelligence has led to a swift advancement in radar,injecting new vitality into SAR 3D imaging.SAR microwave vision 3D imaging theory,which is built upon advanced technologies,has emerged as a new interdisciplinary field for radar imaging.This paper reviews SAR 3D imaging’s history and present situation,and introduces SAR microwave vision.We establish a theoretical framework covering representation models,computational models,processing paradigms and evaluation systems.Additionally,our research progress in this area is discussed,along with future prospects for SAR microwave vision 3D imaging.展开更多
In recent years,there has been growing interest in the study of chiral active materials,which consist of building blocks that show active dynamics featuring chiral symmetry breaking,e.g.,particles that rotate in a com...In recent years,there has been growing interest in the study of chiral active materials,which consist of building blocks that show active dynamics featuring chiral symmetry breaking,e.g.,particles that rotate in a common direction.These materials exhibit fascinating phenomena such as odd viscosity,odd diffusivity,active turbulence in fluids,vivid dislocation dynamics or odd elasticity in crystals or elastic materials,and hyperuniform states.The systematic study of soft chiral active matter systems is relatively new,starting around 2017,but has already shown promising applications in robust cargo transport,segregation and mixing dynamics,or manipulation of metamaterials.In this review,we summarize recent experimental and theoretical advances in this field,highlighting the emergence of anti-symmetric and odd stresses and ensuring effects such as odd viscosity or topologically protected edge modes.We further discuss the underlying mechanisms and provide insights into the potential of chiral active matter for various applications.展开更多
In the context of global climate warming,the propagation of meteorological drought(MD)may aggravate the devastating impact of hydrological drought(HD)on water security and sustainable development.There are challenges ...In the context of global climate warming,the propagation of meteorological drought(MD)may aggravate the devastating impact of hydrological drought(HD)on water security and sustainable development.There are challenges in accurately predicting the propagation of drought and effectively quantifying the effects of uncertainty,especially in data-deficient regions.In this study,a novel method called RFCFA is developed through integrating random forest(RF),copula,and factorial analysis(FA)into a general framework as well as applied to the Aral Sea Basin(a typical arid and data-scarce basin in Central Asia)under considering the impact of climate change.Several findings can be summarized:(1)the projected future drought propagation probability of ASB is 39.2%,which is about 8%higher than historical level;(2)drought propagation is mainly affected by mean climate condition,catchment characteristics(i.e.,elevation,LUCC,and slope),and human activities(i.e.,irrigation and reservoir operation);(3)the lower propagation probability in spring is expected under SSP1-2.6 due to increased snow meltwater,and the drought propagation probability in autumn is the highest(reaching 45.4%)under the influence of reservoir operation;(4)the combined effects of meteorological conditions and agricultural irrigation can lead to a higher probability of future propagation in the upper river basin in summer.Findings are valuable for predicting drought propagation risk,revealing main factors and inherent uncertainties,as well as providing support for drought management and disaster prevention.展开更多
Unmanned aerial vehicle(UAV)array InSAR is a new type of single-flight 3D SAR imaging system with the advantages of high coherence and resolution.However,due to the low altitude of the platform,the elevation ambiguity...Unmanned aerial vehicle(UAV)array InSAR is a new type of single-flight 3D SAR imaging system with the advantages of high coherence and resolution.However,due to the low altitude of the platform,the elevation ambiguity of the system is smaller than the maximal terrain elevation.Since the spatial phase unwrapping is conducted based on the assumption of phase continuity,the inappropriate ambiguity will cause significant unwrapping errors.In this work,a 3D phase unwrapping algorithm assisted by image segmentation is proposed to address this issue.The Markov random field(MRF)is utilized for image segmentation.The optimal spatial phase unwrapping network is achieved based on the segmentation results.The asymptotic 3D phase unwrapping is adopted to get the refined 3D reconstruction.Results based on the real airborne array-InSAR data show that the proposed method effectively improves the elevation ambiguity.展开更多
Many cities are adopting low impact development(LID)technologies(a type of nature-based solution)to sustainably manage urban stormwater in future climates.LIDs,such as bioretention cells,green roofs,and permeable pave...Many cities are adopting low impact development(LID)technologies(a type of nature-based solution)to sustainably manage urban stormwater in future climates.LIDs,such as bioretention cells,green roofs,and permeable pavements,are developed and applied at small-scales in urban and peri-urban settings.There is an interest in the large-scale implementation of these technologies,and therefore assessing their performance in future climates,or conversely,their potential for mitigating the impacts of climate change,can be valuable evidence in support of stormwater management planning.This paper provides a literature review of the studies conducted that examine LID function in future climates.The review found that most studies focus on LID performance at over 5 km2scales,which is quite a bit larger than traditional LID sizes.Most paper used statistical downscaling methods to simulate precipitation at the scale of the modelled LID.The computer model used to model LIDs was predominantly SWMM or some hybrid version of SWMM.The literature contains examples of both vegetated and unvegetated LIDs being assessed and numerous studies show mitigation of peak flows and total volumes to high levels in even the most extreme climates(characterized by increasing rainfall intensity,higher temperatures,and greater number of dry days in the inter-event period).However,all the studies recognized the uncertainty in the projections with greatest uncertainty in the LID’s ability to mitigate storm water quality.Interestingly,many of the studies did not recognize the impact of applying a model intended for small-scale processes at a much larger scale for which it is not intended.To explore the ramifications of scale when modelling LIDs in future climates,this paper provides a simple case study of a large catchment on Vancouver Island in British Columbia,Canada,using the Shannon Diversity Index.PCSWMM is used in conjunction with providing regional climates for impacts studies(PRECIS)regional climate model data to determine the relationship between catchment hydrology(with and without LIDs)and the information loss due to PCSWMM’s representation of spatial heterogeneity.The model is applied to five nested catchments ranging from 3 to 51 km2and with an RCP4.5 future climate to generate peak flows and total volumes in 2022,and for the period of 2020–2029.The case study demonstrates that the science behind the LID model within PC stormwater management model(PCSWMM)is too simple to capture appropriate levels of heterogeneity needed at larger-scale implementations.The model actually manufactures artificial levels of diversity due to its landuse representation,which is constant for every scale.The modelling exercise demonstrated that a simple linear expression for projected precipitation vs.catchment area would provide comparable estimates to PCSWMM.The study found that due to the spatial representation in PCSWMM for landuse,soil data and slope,slope(an important factor in determining peak flowrates)had the highest level of information loss followed by soil type and then landuse.As the research scale increased,the normalized information loss index(NILI)value for landuse exhibited the greatest information loss as the catchments scaled up.The NILI values before and after LID implementation in the model showed an inverse trend with the predicted LID mitigating performance.展开更多
Understanding and controlling phase separation in nonequilibrium colloidal systems are of both fundamental and applied importance.In this article,we investigate the spatiotemporal control of phase separation in chemic...Understanding and controlling phase separation in nonequilibrium colloidal systems are of both fundamental and applied importance.In this article,we investigate the spatiotemporal control of phase separation in chemically active immotile colloids.We show that a population of silver colloids can spontaneously phase separate into dense clusters in hydrogen peroxide(H_(2)O_(2))due to phoretic attraction.The characteristic length of the formed pattern was quantified and monitored over time,revealing a growth and coarsening phase with different growth kinetics.By tuning the trigger frequency of light,the lengths and growth kinetics of the clusters formed by silver colloids in H_(2)O_(2)can be controlled.In addition,structured light was used to precisely control the shape,size,and contour of the phase-separated patterns.This study provides insight into the microscopic details of the phase separation of chemically active colloids induced by phoretic attraction,and presents a generic strategy for controlling the spatiotemporal evolution of the resulting mesoscopic patterns.展开更多
We investigate the effective diffusion of a tracer immersed in an active particle bath consisting of self-propelled particles.Utilising the Dean's method developed for the equilibrium bath and extending it to the ...We investigate the effective diffusion of a tracer immersed in an active particle bath consisting of self-propelled particles.Utilising the Dean's method developed for the equilibrium bath and extending it to the nonequilibrium situation,we derive a generalized Langevin equation(GLE)for the tracer particle.The complex interactions between the tracer and bath particles are shown as a memory kernel term and two colored noise terms.To obtain the effective diffusivity of the tracer,we use path integral technique to calculate all necessary correlation functions.Calculations show the effective diffusion decreases with the persistent time of active force,and has rich behavior with the number density of bath particles,depending on different activities.All theoretical results regarding the dependence of such diffusivity on bath parameters have been confirmed by direct computer simulation.展开更多
Non-line-of-sight(NLOS)imaging is a novel radar sensing technology that enables the reconstruction of hidden targets.However,it may suffer from synthetic aperture length reduction caused by ambient occlusion.In this s...Non-line-of-sight(NLOS)imaging is a novel radar sensing technology that enables the reconstruction of hidden targets.However,it may suffer from synthetic aperture length reduction caused by ambient occlusion.In this study,a complex total variation(CTV)regularization-based sparse reconstruction method for NLOS three-dimensional(3-D)imaging by millimeter-wave(mm W)radar,named RM-CSTV method,is proposed to improve imaging quality and speed.In this scheme,the NLOS imaging model is first introduced,and associated geometric constraints for NLOS objects are established.Second,an effective high-resolution NLOS imaging method based on the range migration(RM)kernel and complex sparse joint total variation constraint,dubbed as modified RM-CSTV,is proposed for 3-D high-resolution imaging with edge information.The experiments with multi-type NLOS targets show that the proposed RM-CSTV method can provide effective and high-resolution NLOS targets 3-D imaging.展开更多
Climate change is a pressing global concern with far-reaching consequences that vary across sectors.Addressing the adverse impacts of climate change on various sectors is a challenging issue faced by countries worldwi...Climate change is a pressing global concern with far-reaching consequences that vary across sectors.Addressing the adverse impacts of climate change on various sectors is a challenging issue faced by countries worldwide,including China.It is imperative for China to address climate change to foster sustainable development and make meaningful contributions to global climate mitigation efforts.This paper presented a comprehensive analysis of the impacts of climate change on the electricity,agriculture,and industry sectors,which together account for over 80%of the greenhouse gas(GHG)emissions in China.Additionally,the strategies employed by these sectors to address climate change were reviewed,and potential future developments were explored.This review article could shine light on climate change practices and evidence-based policies aimed at addressing climate-related challenges across various sectors in China.展开更多
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 threshol...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 interactive mechanism is known in 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 in robotics and social studies.展开更多
Our ability to perceive the correlation of different substances in the world is one of the key aspects of human intelligence.The passing of this faculty to artificial intelligence(AI)represents arguably one of the lon...Our ability to perceive the correlation of different substances in the world is one of the key aspects of human intelligence.The passing of this faculty to artificial intelligence(AI)represents arguably one of the long-standing challenges in the application of AI to scientific problems.To meet this challenge in the burgeoning field of AI for chemistry,we may adopt the paradigm of knowledge graph.Herein,focusing on catalytic chemical reactions,we have developed a semantic knowledge graph framework based on both structured and unstructured data,the latter of which are extracted from the text of 220,000articles on catalysts for organic molecules.The framework captures the latent knowledge of reactant-catalyst-product relationships and can therefore provide accurate recommendation on potential catalysts for targeted reaction,which especially facilitates the research involving large molecules.This study presents a viable pathway towards the implementation of literature-based data management in a catalyst recommendation platform.展开更多
Artificial active matters on a macroscopic scale,including vibrating particles,robots,and camphor boats,have attracted increasing attentions due to their uniform properties,rich and easily controllable parameters,conv...Artificial active matters on a macroscopic scale,including vibrating particles,robots,and camphor boats,have attracted increasing attentions due to their uniform properties,rich and easily controllable parameters,convenient observation,and the independence of biochemical processes from physical processes,especially providing these unique advantages for researching the collective behaviors under strong confinement and crowded surroundings.In this review,we present an overview of motion models,mechanisms,and dynamic characteristics of various active particles,both in free and complex media.Additionally,we delve into the collective behaviors of“dry”active matter,covering structural and dynamic properties observed in experiments and theoretical models.We summarize the impact of hydrodynamic interactions on the dynamics and structures of these active particles within hydrodynamic environments.Lastly,we discuss emerging opportunities and challenges for future advancement of macroscopic artificial active matter.展开更多
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 ce...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.展开更多
Time reversal asymmetry and spatial anisotropy are considered two prerequisites for Brownian ratchet.An intriguing realization can be achieved by placing an asymmetric gear in the suspension of motile rod-like bacteri...Time reversal asymmetry and spatial anisotropy are considered two prerequisites for Brownian ratchet.An intriguing realization can be achieved by placing an asymmetric gear in the suspension of motile rod-like bacteria.Usually,alignment interactions caused by anisotropic collisions or hydrodynamics would boost the ratchet effect.Here,we are concerned with a perfectly isotropic system,i.e.,symmetric gear immersed in a bath of spherical active Brownian particles.We find that,under certain conditions,kinetic symmetry-breaking arises spontaneously,i.e.,the symmetric gear keeps rotating in one direction.Unexpectedly,such ratchet phenomenon does not rely on the direct many-particle interactions and moreover the introduction of alignment interaction would counterintuitively prevent it from happening!Further investigation reveals that such spontaneous symmetry-breaking phenomenon shares similarities with the equilibrium phase transition of the Ising model.Our results provide new insights and enhance our understanding of the fundamental aspects of active ratchet phenomena.展开更多
Recently a new paradigm is emerging in synthetic aperture radar(SAR)three-dimensional(3D)imaging technology where the imaging performance is enhanced by exploiting SAR visual semantics.Here by“SAR visual semantics”,...Recently a new paradigm is emerging in synthetic aperture radar(SAR)three-dimensional(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 part 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.,facades and roofs)to sparse 3D points initially recovered via traditional TomoSAR methods.The second part is a case 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.展开更多
Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying...Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.展开更多
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 ...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 offering efficiency but limited reliability,and the latter providing accuracy but restricted to systems of relatively small sizes.Herein,we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a 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 effectively 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.展开更多
Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale s...Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.展开更多
The behavioral response of pollinators is significantly influenced by the prior experience of flower visiting.Learning of pollinators,including non-associative learning,associative learning,and operant conditioning,is...The behavioral response of pollinators is significantly influenced by the prior experience of flower visiting.Learning of pollinators,including non-associative learning,associative learning,and operant conditioning,is determined by the presence or absence of rewards during the flower visiting experience.Here,we indicate that process of non-rewarding flower(empty flower)visiting coincident well with the behavioral paradigm of non-associative learning.Habituation,one of non-associative learning,most likely modulates the pollinating behavior patterns of empty flower visitation.Moreover,we propose that the process of habituation recovery,including spontaneous recovery and dishabituation,may also modulate the behavior of pollinators,which leads to ecological consequences of long-distance pollen dispersal and high outcross pollination rate.We believe that utilizing the methodology of non-associative learning behavioral neurobiology paradigm to investigate pollinator behavior will establish novel insights into the sensory responses and neural activity of pollination behavior in the pollination systems.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3807200)
文摘Oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)are key catalytic processes in various renewable energy conversion and energy storage technologies.Herein,we systematically investigated the ORR and OER catalytic activity of the single-atom catalysts(SACs)composed of 4d/5d period transition metal(TM)atoms embedded on MBene substrates(TM-M_(2)B_(2)O_(2),M=Ti,Mo,and W).We found that TM dominates the catalytic activity compared to the MBene substrates.The SACs embedded with Rh,Pd,Au,and Ir exhibit excellent ORR or OER catalytic activity.Specifically,Rh-Mo2B2O2and Rh-W2B2O2are promising bifunctional catalysts with ultra-low ORR/OER overpotentials of 0.39/0.21 V and0.19/0.32 V,respectively,lower than that of Pt/RuO_(2)(0.45/0.42 V).Importantly,through machine learning,the models containing 10 element features of SACs were developed to quickly and accurately identify the superior ORR and OER electrocatalysts.Our findings provide several promising SACs for ORR and OER,and offer effective models for catalyst design.
基金supported by the National Natural Science Foundation of China(61991420,61991421 and 61991424)
文摘Synthetic Aperture Radar three-dimensional(3D)imaging enables the acquisition of more comprehensive information,making it a recent hotspot in radar imaging.Traditional 3D imaging methods have evolved from 2D and interferometric imaging,combining elevation aperture extension with signal processing techniques.Limitations such as long acquisition or complex system from its imaging mechanism restrict its application.In recent years,rapid development of artificial intelligence has led to a swift advancement in radar,injecting new vitality into SAR 3D imaging.SAR microwave vision 3D imaging theory,which is built upon advanced technologies,has emerged as a new interdisciplinary field for radar imaging.This paper reviews SAR 3D imaging’s history and present situation,and introduces SAR microwave vision.We establish a theoretical framework covering representation models,computational models,processing paradigms and evaluation systems.Additionally,our research progress in this area is discussed,along with future prospects for SAR microwave vision 3D imaging.
基金the National Natural Sience Foundation of China for supporting this project within the Research Fund for International Young Scientists(12350410368)financial support from the Natural Science Foundation of Guangdong Province(2024A1515011343)the Key Project of Guangdong Provincial Department of Education(2023ZDZX3021)
文摘In recent years,there has been growing interest in the study of chiral active materials,which consist of building blocks that show active dynamics featuring chiral symmetry breaking,e.g.,particles that rotate in a common direction.These materials exhibit fascinating phenomena such as odd viscosity,odd diffusivity,active turbulence in fluids,vivid dislocation dynamics or odd elasticity in crystals or elastic materials,and hyperuniform states.The systematic study of soft chiral active matter systems is relatively new,starting around 2017,but has already shown promising applications in robust cargo transport,segregation and mixing dynamics,or manipulation of metamaterials.In this review,we summarize recent experimental and theoretical advances in this field,highlighting the emergence of anti-symmetric and odd stresses and ensuring effects such as odd viscosity or topologically protected edge modes.We further discuss the underlying mechanisms and provide insights into the potential of chiral active matter for various applications.
基金supported by the Innovative Research Group of the National Natural Science Foundation of China(52221003)the Strategic Priority Research Program of Chinese Academy of Sciences(XDA20060302)the National Natural Science Foundation of China(52279003 and 52279002)
文摘In the context of global climate warming,the propagation of meteorological drought(MD)may aggravate the devastating impact of hydrological drought(HD)on water security and sustainable development.There are challenges in accurately predicting the propagation of drought and effectively quantifying the effects of uncertainty,especially in data-deficient regions.In this study,a novel method called RFCFA is developed through integrating random forest(RF),copula,and factorial analysis(FA)into a general framework as well as applied to the Aral Sea Basin(a typical arid and data-scarce basin in Central Asia)under considering the impact of climate change.Several findings can be summarized:(1)the projected future drought propagation probability of ASB is 39.2%,which is about 8%higher than historical level;(2)drought propagation is mainly affected by mean climate condition,catchment characteristics(i.e.,elevation,LUCC,and slope),and human activities(i.e.,irrigation and reservoir operation);(3)the lower propagation probability in spring is expected under SSP1-2.6 due to increased snow meltwater,and the drought propagation probability in autumn is the highest(reaching 45.4%)under the influence of reservoir operation;(4)the combined effects of meteorological conditions and agricultural irrigation can lead to a higher probability of future propagation in the upper river basin in summer.Findings are valuable for predicting drought propagation risk,revealing main factors and inherent uncertainties,as well as providing support for drought management and disaster prevention.
文摘Unmanned aerial vehicle(UAV)array InSAR is a new type of single-flight 3D SAR imaging system with the advantages of high coherence and resolution.However,due to the low altitude of the platform,the elevation ambiguity of the system is smaller than the maximal terrain elevation.Since the spatial phase unwrapping is conducted based on the assumption of phase continuity,the inappropriate ambiguity will cause significant unwrapping errors.In this work,a 3D phase unwrapping algorithm assisted by image segmentation is proposed to address this issue.The Markov random field(MRF)is utilized for image segmentation.The optimal spatial phase unwrapping network is achieved based on the segmentation results.The asymptotic 3D phase unwrapping is adopted to get the refined 3D reconstruction.Results based on the real airborne array-InSAR data show that the proposed method effectively improves the elevation ambiguity.
基金supported by the National Science and Engineering Research Council of Canada(RGPIN-2022-04352)
文摘Many cities are adopting low impact development(LID)technologies(a type of nature-based solution)to sustainably manage urban stormwater in future climates.LIDs,such as bioretention cells,green roofs,and permeable pavements,are developed and applied at small-scales in urban and peri-urban settings.There is an interest in the large-scale implementation of these technologies,and therefore assessing their performance in future climates,or conversely,their potential for mitigating the impacts of climate change,can be valuable evidence in support of stormwater management planning.This paper provides a literature review of the studies conducted that examine LID function in future climates.The review found that most studies focus on LID performance at over 5 km2scales,which is quite a bit larger than traditional LID sizes.Most paper used statistical downscaling methods to simulate precipitation at the scale of the modelled LID.The computer model used to model LIDs was predominantly SWMM or some hybrid version of SWMM.The literature contains examples of both vegetated and unvegetated LIDs being assessed and numerous studies show mitigation of peak flows and total volumes to high levels in even the most extreme climates(characterized by increasing rainfall intensity,higher temperatures,and greater number of dry days in the inter-event period).However,all the studies recognized the uncertainty in the projections with greatest uncertainty in the LID’s ability to mitigate storm water quality.Interestingly,many of the studies did not recognize the impact of applying a model intended for small-scale processes at a much larger scale for which it is not intended.To explore the ramifications of scale when modelling LIDs in future climates,this paper provides a simple case study of a large catchment on Vancouver Island in British Columbia,Canada,using the Shannon Diversity Index.PCSWMM is used in conjunction with providing regional climates for impacts studies(PRECIS)regional climate model data to determine the relationship between catchment hydrology(with and without LIDs)and the information loss due to PCSWMM’s representation of spatial heterogeneity.The model is applied to five nested catchments ranging from 3 to 51 km2and with an RCP4.5 future climate to generate peak flows and total volumes in 2022,and for the period of 2020–2029.The case study demonstrates that the science behind the LID model within PC stormwater management model(PCSWMM)is too simple to capture appropriate levels of heterogeneity needed at larger-scale implementations.The model actually manufactures artificial levels of diversity due to its landuse representation,which is constant for every scale.The modelling exercise demonstrated that a simple linear expression for projected precipitation vs.catchment area would provide comparable estimates to PCSWMM.The study found that due to the spatial representation in PCSWMM for landuse,soil data and slope,slope(an important factor in determining peak flowrates)had the highest level of information loss followed by soil type and then landuse.As the research scale increased,the normalized information loss index(NILI)value for landuse exhibited the greatest information loss as the catchments scaled up.The NILI values before and after LID implementation in the model showed an inverse trend with the predicted LID mitigating performance.
基金supported by the Shenzhen Science and Technology Program(RCYX20210609103122038 and JCYJ20210324121408022)the National Natural Science Foundation of China(T2322006,T2325027,12274448,12225410 and 12074243)
文摘Understanding and controlling phase separation in nonequilibrium colloidal systems are of both fundamental and applied importance.In this article,we investigate the spatiotemporal control of phase separation in chemically active immotile colloids.We show that a population of silver colloids can spontaneously phase separate into dense clusters in hydrogen peroxide(H_(2)O_(2))due to phoretic attraction.The characteristic length of the formed pattern was quantified and monitored over time,revealing a growth and coarsening phase with different growth kinetics.By tuning the trigger frequency of light,the lengths and growth kinetics of the clusters formed by silver colloids in H_(2)O_(2)can be controlled.In addition,structured light was used to precisely control the shape,size,and contour of the phase-separated patterns.This study provides insight into the microscopic details of the phase separation of chemically active colloids induced by phoretic attraction,and presents a generic strategy for controlling the spatiotemporal evolution of the resulting mesoscopic patterns.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0450402)the National Natural Science Foundation of China(32090040 and 22373090)
文摘We investigate the effective diffusion of a tracer immersed in an active particle bath consisting of self-propelled particles.Utilising the Dean's method developed for the equilibrium bath and extending it to the nonequilibrium situation,we derive a generalized Langevin equation(GLE)for the tracer particle.The complex interactions between the tracer and bath particles are shown as a memory kernel term and two colored noise terms.To obtain the effective diffusivity of the tracer,we use path integral technique to calculate all necessary correlation functions.Calculations show the effective diffusion decreases with the persistent time of active force,and has rich behavior with the number density of bath particles,depending on different activities.All theoretical results regarding the dependence of such diffusivity on bath parameters have been confirmed by direct computer simulation.
基金supported by the National Natural Science Foundation of China(62271108)
文摘Non-line-of-sight(NLOS)imaging is a novel radar sensing technology that enables the reconstruction of hidden targets.However,it may suffer from synthetic aperture length reduction caused by ambient occlusion.In this study,a complex total variation(CTV)regularization-based sparse reconstruction method for NLOS three-dimensional(3-D)imaging by millimeter-wave(mm W)radar,named RM-CSTV method,is proposed to improve imaging quality and speed.In this scheme,the NLOS imaging model is first introduced,and associated geometric constraints for NLOS objects are established.Second,an effective high-resolution NLOS imaging method based on the range migration(RM)kernel and complex sparse joint total variation constraint,dubbed as modified RM-CSTV,is proposed for 3-D high-resolution imaging with edge information.The experiments with multi-type NLOS targets show that the proposed RM-CSTV method can provide effective and high-resolution NLOS targets 3-D imaging.
基金supported by China Scholarship Council(CSC)during the doctoral studies of the Kaixuan Wang(202206300030)and Jiatai Wang(202208440019)in University of Surrey
文摘Climate change is a pressing global concern with far-reaching consequences that vary across sectors.Addressing the adverse impacts of climate change on various sectors is a challenging issue faced by countries worldwide,including China.It is imperative for China to address climate change to foster sustainable development and make meaningful contributions to global climate mitigation efforts.This paper presented a comprehensive analysis of the impacts of climate change on the electricity,agriculture,and industry sectors,which together account for over 80%of the greenhouse gas(GHG)emissions in China.Additionally,the strategies employed by these sectors to address climate change were reviewed,and potential future developments were explored.This review article could shine light on climate change practices and evidence-based policies aimed at addressing climate-related challenges across various sectors in China.
基金supported by the National Natural Science Foundation of China(12375037 and 11935010)
文摘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 interactive mechanism is known in 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 in robotics and social studies.
基金supported by Guangdong Basic and Applied Basic Research Foundation(2023A1515011391 and 2020A1515110843)the Soft Science Research Project of Guangdong Province(2017B030301013)+2 种基金the National Key Research and Development Program of China(2022YFB2702301)the Key-Area Research and Development Program of Guangdong Province(2020B0101090003)the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen
文摘Our ability to perceive the correlation of different substances in the world is one of the key aspects of human intelligence.The passing of this faculty to artificial intelligence(AI)represents arguably one of the long-standing challenges in the application of AI to scientific problems.To meet this challenge in the burgeoning field of AI for chemistry,we may adopt the paradigm of knowledge graph.Herein,focusing on catalytic chemical reactions,we have developed a semantic knowledge graph framework based on both structured and unstructured data,the latter of which are extracted from the text of 220,000articles on catalysts for organic molecules.The framework captures the latent knowledge of reactant-catalyst-product relationships and can therefore provide accurate recommendation on potential catalysts for targeted reaction,which especially facilitates the research involving large molecules.This study presents a viable pathway towards the implementation of literature-based data management in a catalyst recommendation platform.
基金supported by the National Natural Science Foundation of China(12374205,12304245 and 12364029)the Science Foundation of China University of Petroleum,Beijing(2462023YJRC031 and 2462024BJRC010)+4 种基金the Beijing Institute of Technology Research Fund Program for Young Scholars,the Young Elite Scientist Sponsorship Program by BAST(BYESS2023300)the Natural Science Foundation of Inner Mongolia Autonomous Region(2023QN01015)the Beijing National Laboratory for Condensed Matter Physics(2023BNLCMPKF014)the Academic Research Fund from the Singapore Ministry of Education Tier 1 Gant(RG59/21)the National Research Foundation,Singapore,under its 29th Competitive Research Programme(CRP)Call(Award ID NRF-CRP29-2022-0002)
文摘Artificial active matters on a macroscopic scale,including vibrating particles,robots,and camphor boats,have attracted increasing attentions due to their uniform properties,rich and easily controllable parameters,convenient observation,and the independence of biochemical processes from physical processes,especially providing these unique advantages for researching the collective behaviors under strong confinement and crowded surroundings.In this review,we present an overview of motion models,mechanisms,and dynamic characteristics of various active particles,both in free and complex media.Additionally,we delve into the collective behaviors of“dry”active matter,covering structural and dynamic properties observed in experiments and theoretical models.We summarize the impact of hydrodynamic interactions on the dynamics and structures of these active particles within hydrodynamic environments.Lastly,we discuss emerging opportunities and challenges for future advancement of macroscopic artificial active matter.
基金supported by the National Science Foundation of China(22025302,21873053 and 22202049)the financial support from the Ministry of Science and Technology of China(2022YFA1203203)the State Key Laboratory of Chemical Engineering(SKL-Ch E-23T01)
文摘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.
基金supported by the National Natural Science Foundation of China(21774091(K.C.)and 21674078(W.T.))
文摘Time reversal asymmetry and spatial anisotropy are considered two prerequisites for Brownian ratchet.An intriguing realization can be achieved by placing an asymmetric gear in the suspension of motile rod-like bacteria.Usually,alignment interactions caused by anisotropic collisions or hydrodynamics would boost the ratchet effect.Here,we are concerned with a perfectly isotropic system,i.e.,symmetric gear immersed in a bath of spherical active Brownian particles.We find that,under certain conditions,kinetic symmetry-breaking arises spontaneously,i.e.,the symmetric gear keeps rotating in one direction.Unexpectedly,such ratchet phenomenon does not rely on the direct many-particle interactions and moreover the introduction of alignment interaction would counterintuitively prevent it from happening!Further investigation reveals that such spontaneous symmetry-breaking phenomenon shares similarities with the equilibrium phase transition of the Ising model.Our results provide new insights and enhance our understanding of the fundamental aspects of active ratchet phenomena.
基金supported by the National Natural Science Foundation of China(61991423,62376269 and 62472464)the Key Scientific and Technological Project of Henan Province(232102321068)
文摘Recently a new paradigm is emerging in synthetic aperture radar(SAR)three-dimensional(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 part 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.,facades and roofs)to sparse 3D points initially recovered via traditional TomoSAR methods.The second part is a case 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.
基金supported by the National Key Research and Development Program of China(2022YFA1004302)
文摘Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.
基金supported by the National Science Fund for Distinguished Young Scholars(22225302)the National Natural Science Foundation of China(92161113,21991151,21991150 and 22021001)+2 种基金the Fundamental Research Funds for the Central Universities(20720220008,20720220009 and 20720220010)the Laboratory of AI for Electrochemistry(AI4EC)IKKEM(RD2023100101 and RD2022070501)
文摘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 offering efficiency but limited reliability,and the latter providing accuracy but restricted to systems of relatively small sizes.Herein,we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a 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 effectively 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.
基金supported by the National Key R&D Program of China(2022ZD0117501)the National Natural Science Foundation of China(62201441)
文摘Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.
基金supported by the National Natural Science Foundation of China(32070255 and 32200186).
文摘The behavioral response of pollinators is significantly influenced by the prior experience of flower visiting.Learning of pollinators,including non-associative learning,associative learning,and operant conditioning,is determined by the presence or absence of rewards during the flower visiting experience.Here,we indicate that process of non-rewarding flower(empty flower)visiting coincident well with the behavioral paradigm of non-associative learning.Habituation,one of non-associative learning,most likely modulates the pollinating behavior patterns of empty flower visitation.Moreover,we propose that the process of habituation recovery,including spontaneous recovery and dishabituation,may also modulate the behavior of pollinators,which leads to ecological consequences of long-distance pollen dispersal and high outcross pollination rate.We believe that utilizing the methodology of non-associative learning behavioral neurobiology paradigm to investigate pollinator behavior will establish novel insights into the sensory responses and neural activity of pollination behavior in the pollination systems.
