As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimen...Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and...Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.展开更多
The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate cha...The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.展开更多
Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-de...Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.展开更多
Water use efficiency(WUE),as a pivotal indicator of the coupling degree within the carbon–water cycle of ecosystems,holds considerable importance in assessment of the carbon–water balance within terrestrial ecosyste...Water use efficiency(WUE),as a pivotal indicator of the coupling degree within the carbon–water cycle of ecosystems,holds considerable importance in assessment of the carbon–water balance within terrestrial ecosystems.However,in the context of global warming,WUE evolution and its primary drivers on the Tibetan Plateau remain unclear.This study employed the ensemble empirical mode decomposition method and the random forest algorithm to decipher the nonlinear trends and drivers of WUE on the Tibetan Plateau in 2001–2020.Results indicated an annual mean WUE of 0.8088 gC/mm·m^(2)across the plateau,with a spatial gradient reflecting decrease from the southeast toward the northwest.Areas manifesting monotonous trends of increase or decrease in WUE accounted for 23.64%and 9.69%of the total,respectively.Remarkably,66.67%of the region exhibited trend reversals,i.e.,39.94%of the area of the Tibetan Plateau showed transition from a trend of increase to a trend of decrease,and 26.73%of the area demonstrated a shift from a trend of decrease to a trend of increase.Environmental factors accounted for 70.79%of the variability in WUE.The leaf area index and temperature served as the major driving forces of WUE variation.展开更多
This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data fr...This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental chall...Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.展开更多
Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored ...Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.展开更多
The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainabl...The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainable development.While effectively enhancing WC necessitates a comprehensive understanding of its driving factors and corresponding intervention strategies,existing studies have largely neglected the spatiotemporal heterogeneity of both natural and socio-economic drivers.Therefore,this study explored the spatiotemporal heterogeneity of WC drivers in YRS using multi-scale geographically weighted regression(MGWR)and geographically and temporally weighted regression(GTWR)models from an eco-hydrological perspective.We discovered that downstream regions,which are more developed,achieved significantly better WC than upstream regions.The results also demonstrated that the influence of temperature and wind speed is consistently dominant and temporally stable due to climate stability,while the influence of vegetation shifted from negative to positive around 2010,likely indicating greater benefits from understory vegetation.Economic growth positively impacted WC in upstream regions but had a negative effect in the more developed downstream regions.These findings highlight the importance of targeted water conservation strategies,including locally appropriate revegetation,optimization of agricultural and economic structures,and the establishment of eco-compensation mechanisms for ecological conservation and sustainable development.展开更多
As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly e...As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.展开更多
Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose a...Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.展开更多
The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed...The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed out from such logic system. This article aims to rearticulate the underlying lucid multi-dimensional logic system, which faded in obscurity only because of time-out loss of the mid-right concept. Retracing this past tacit but important concept could uncover a multi-dimensional system over a point relating to all matters while capturing the central core of the matter. The seemingly unmanageable multidimensional logic was strengthened by verification processes which affirmed its further extensions, and made up the language of the people, the concepts of yin-yang(阴阳), and the development of extensions of Ba Gua(八卦) derivatives, which furthered the interpretation of the space-time properties and Chinese medicine.展开更多
Benzene,toluene,ethylbenzene,and xylene(BTEX)pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects,carcinogenic properties,and adverse effects on air...Benzene,toluene,ethylbenzene,and xylene(BTEX)pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects,carcinogenic properties,and adverse effects on air quality.BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities,necessitating the assessment of atmospheric concentrations and their spatial variation.This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India.The study investigates the correlation of BTEX with other criteria pollutants andmeteorological parameters,aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions.Additionally,the paper categorizes various regions in India according to the Air Quality Index(AQI)based on BTEX pollution levels.The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central,eastern,and western regions.In contrast,the southern zone experiences the least pollution with BTEX.Seasonal analysis indicates that winter and postmonsoon periods,characterized by lower temperatures,are associated with higher BTEX levels due to the accumulation of localized emissions.When comparing the different zones in India,high traffic emissions and localized activities,such as solvent use and solvent evaporation,are found to be the primary sources of BTEX.The findings of the current study aid in source characterization and identification,and better understanding of the region’s air quality problems,which helps in the development of focused BTEX pollution reduction and control strategies.展开更多
Coastal ecosystems are plagued by high levels of microplastic pollution.Conducting baseline surveys is crucial to comprehend the distribution and influencing factors of this pollution.The present study investigates th...Coastal ecosystems are plagued by high levels of microplastic pollution.Conducting baseline surveys is crucial to comprehend the distribution and influencing factors of this pollution.The present study investigates the spatiotemporal variation and diversity of microplastic on the coastal beaches in Xiamen City,China,considering the combined effects of seasons,human activities,and physicochemical properties of sediments.It is detected that the abundance of microplastics in Xiamen beaches was 0.271±0.01 items/g.The abundance of microplastics in dry season was significantly higher than in rainy season.In terms of spatial variation,the beaches that attracted a larger number of tourists exhibited significantly higher microplastic abundance.The temporal pattern of microplastic distribution on different beaches varied greatly due to region-specific human activities(e.g.,mangrove restoration project)and sedimentary properties(e.g.,bulk density).When the assemblage of microplastics in the coastal area was viewed as a biological community,the Shannon-Wiener index and Pielou's index were higher in rainy season and in the beaches with high intensity of tourist activities,which suggests that the human activities and the surface runoff may contribute to the diversity of microplastics on coastal beaches.Future investigations are encouraged to combine controlled experiments and long-term monitoring at multiple scales to elucidate the underlying mechanisms and factors associated with microplastic pollution in coastal zone.展开更多
Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial...Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial for acquiring stable NiSe2-based materials for sodium-ion batteries(SIBs),Herein,a stress dissipation strategy driven by architecture engineering is proposed,which can achieve ultrafast and ultralong sodium storage properties.Different from the conventional sphere-like or rod-like architecture,the three-dimensional(3D)flower-like NiSe_(2)@C composite is delicately designed and assembled with onedimensional nanorods and carbon framework.More importantly,the fundamental mechanism of improved structure stability is unveiled by simulations and experimental results simultaneously.It demonstrates that this designed multidimensional flower-like architecture with dispersed nanorods can balance the structural mismatch,avoid concentrated local strain,and relax the internal stress,mainly induced by the unavoidable volume variation during the repeated conversion processes.Moreover,it can provide more Na^(+)-storage sites and multi-directional migration pathways,leading to a fast Na^(+)-migration channel with boosted reaction kinetic.As expected,it delivers superior rate performance(441 mA h g^(-1)at 5.0 A g^(-1))and long cycling stability(563 mA h g^(-1)at 1.0 A g^(-1)over 1000 cycles)for SIBs.This work provides useful insights for designing high-performance conversion-based anode materials for SIBs.展开更多
This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses ...This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses the construction of multi-dimensional collaborative theoretical models,public space streamline organization,and other aspects,emphasizing the importance of multi-dimensional collaboration.Additionally,it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.展开更多
Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic ...Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic obstacles are considered as the dominant inhibition for attaining satisfactory energy-conversion efficiency.The complexity in light absorption and carrier transfer behaviors has remained to be further clearly illuminated.It is challenging to trace the fast evolution of charge carriers involved in transfer migration and interfacial reactions within a micro–nano-single-particle photocatalyst,which requires spatiotemporal high resolution.In this review,comprehensive dynamic descriptions including irradiation field,carrier separation and transfer,and interfacial reaction processes have been elucidated and discussed.The corresponding mechanisms for revealing dynamic behaviors have been explained.In addition,numerical simulation and modeling methods have been illustrated for the description of the irradiation field.Experimental measurements and spatiotemporal characterizations have been clarified for the reflection of carrier behavior and probing detection of interfacial reactions.The representative applications have been introduced according to the reported advanced research works,and the relationships between mechanistic conclusions from variable spatiotemporal measurements and photocatalytic performance results in the specific photocatalytic reactions have been concluded.This review provides a collective perspective for the full understanding and thorough evaluation of the primary dynamic processes,which would be inspired for the improvement in designing solar-driven energy-conversion systems based on nanoscale particulate photocatalysts.展开更多
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
基金supported by the National Natural Science Foundation of China(22072048)the Guangdong Provincial Department of Science and Technology(2021A1515010128 and 2022A0505050013).
文摘Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
基金supported by the National Natural Science Foundation of China(82574173,82003516)Jiangsu Provincial Natural Science Foundation(BK20251958)+2 种基金Jiangsu Provincial Medical Key Discipline(ZDXK202250)Top Talent Awards Project Fund(RDF-TP-0023,RDF-TP-0030)Postgraduate Research Fund(PGRS2112022)at Xi'an Jiaotong-Liverpool University.
文摘Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008202)the National Natural Science Foundation of China(No.42406019)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202353066)。
文摘The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.
基金funding from Grant No. HIDSS-0002 DASHH (Data Science in Hamburg-Helmholtz Graduate School for the Structure of Matter)partially supported by the Helmholtz Imaging platform through the project “Smart Phase.”
文摘Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.
基金National Nonprofit Institute Research Grant of CAF,No.CAFYBB2018ZA004,No.CAFYBB2023ZA009Fengyun Application Pioneering Project,No.FY-APP-ZX-2023.02。
文摘Water use efficiency(WUE),as a pivotal indicator of the coupling degree within the carbon–water cycle of ecosystems,holds considerable importance in assessment of the carbon–water balance within terrestrial ecosystems.However,in the context of global warming,WUE evolution and its primary drivers on the Tibetan Plateau remain unclear.This study employed the ensemble empirical mode decomposition method and the random forest algorithm to decipher the nonlinear trends and drivers of WUE on the Tibetan Plateau in 2001–2020.Results indicated an annual mean WUE of 0.8088 gC/mm·m^(2)across the plateau,with a spatial gradient reflecting decrease from the southeast toward the northwest.Areas manifesting monotonous trends of increase or decrease in WUE accounted for 23.64%and 9.69%of the total,respectively.Remarkably,66.67%of the region exhibited trend reversals,i.e.,39.94%of the area of the Tibetan Plateau showed transition from a trend of increase to a trend of decrease,and 26.73%of the area demonstrated a shift from a trend of decrease to a trend of increase.Environmental factors accounted for 70.79%of the variability in WUE.The leaf area index and temperature served as the major driving forces of WUE variation.
基金supported by the Joint Funds of the National Natural Science Foundation of China(No.U21A2027)the New Cornerstone Science Foundation through the XPLORER PRIZE(2023-1033).
文摘This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to 2020.Utilizing data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and 2020.The spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural activities.NH_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to 2020.The highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to 2020.Agricultural emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones.Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)VCDs.The rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model.The concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)VCDs.This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases.Effective emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
文摘Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.
基金supported by the National Key Research and Development Program of China (Nos.2022YFC3702000 and 2022YFC3703500)the Key R&D Project of Zhejiang Province (No.2022C03146).
文摘Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.
基金supported by the funding provided by the State Key Laboratory of Hydraulics and Mountain River Engineering(SKHL2210)National Natural Science Foundation of China(42171304)+1 种基金the Sichuan Science and Technology Program(2023YFS0380)Natural Science Foundation of Jiangsu Province of China(BK20242018)。
文摘The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainable development.While effectively enhancing WC necessitates a comprehensive understanding of its driving factors and corresponding intervention strategies,existing studies have largely neglected the spatiotemporal heterogeneity of both natural and socio-economic drivers.Therefore,this study explored the spatiotemporal heterogeneity of WC drivers in YRS using multi-scale geographically weighted regression(MGWR)and geographically and temporally weighted regression(GTWR)models from an eco-hydrological perspective.We discovered that downstream regions,which are more developed,achieved significantly better WC than upstream regions.The results also demonstrated that the influence of temperature and wind speed is consistently dominant and temporally stable due to climate stability,while the influence of vegetation shifted from negative to positive around 2010,likely indicating greater benefits from understory vegetation.Economic growth positively impacted WC in upstream regions but had a negative effect in the more developed downstream regions.These findings highlight the importance of targeted water conservation strategies,including locally appropriate revegetation,optimization of agricultural and economic structures,and the establishment of eco-compensation mechanisms for ecological conservation and sustainable development.
基金support for this research from the Natural Science Foundation of Henan Province(252300421005).
文摘As advancements in the Internet of Things(IoT)and unmanned technologies continues to progress,the development of unmanned system of systems(USS)has reached unprecedented levels.While prior research has predominantly examined temporal variations in USS resilience,spatial changes remain underexplored.However,USS may involve kinetic engagements and frequent spatial changes during mission execution,affecting signal interference in data layer communications.Although time-dependent factors primarily govern mission effectiveness of the USS,spatial factors influence the transmission stability of the data layer.Consequently,assessing spatiotemporal variations in USS performance is critical.To address these challenges,this study introduces a spatiotemporal resilience assessment framework,which evaluates USS resilience across both temporal and spatial dimensions.Furthermore,we propose a spatiotemporal resilience optimization scheme that enhances system adaptability throughout the mission lifecycle,with a particular emphasis on prevention and recovery strategies.Finally,we validate the validity of the proposed concepts and methods with a case study featuring a regular hexagonal deployment of USS.The results show that the spatiotemporal resilience can better reflect the spatial change characteristics of USS,and the proposed optimization strategy improves the prevention spatiotemporal resilience,recovery spatiotemporal resilience,and entire-process spatiotemporal resilience of USS by 0.22%,8.39%,and 11.29%,respectively.
基金supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(NO.SML2021SP201)the National Natural Science Foundation of China(Grant No.42306200 and 42306216)+2 种基金the National Key Research and Development Program of China(Grant No.2023YFC3008100)the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311021004)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(Project No.SL2021ZD203)。
文摘Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.
文摘The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed out from such logic system. This article aims to rearticulate the underlying lucid multi-dimensional logic system, which faded in obscurity only because of time-out loss of the mid-right concept. Retracing this past tacit but important concept could uncover a multi-dimensional system over a point relating to all matters while capturing the central core of the matter. The seemingly unmanageable multidimensional logic was strengthened by verification processes which affirmed its further extensions, and made up the language of the people, the concepts of yin-yang(阴阳), and the development of extensions of Ba Gua(八卦) derivatives, which furthered the interpretation of the space-time properties and Chinese medicine.
文摘Benzene,toluene,ethylbenzene,and xylene(BTEX)pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects,carcinogenic properties,and adverse effects on air quality.BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities,necessitating the assessment of atmospheric concentrations and their spatial variation.This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India.The study investigates the correlation of BTEX with other criteria pollutants andmeteorological parameters,aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions.Additionally,the paper categorizes various regions in India according to the Air Quality Index(AQI)based on BTEX pollution levels.The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central,eastern,and western regions.In contrast,the southern zone experiences the least pollution with BTEX.Seasonal analysis indicates that winter and postmonsoon periods,characterized by lower temperatures,are associated with higher BTEX levels due to the accumulation of localized emissions.When comparing the different zones in India,high traffic emissions and localized activities,such as solvent use and solvent evaporation,are found to be the primary sources of BTEX.The findings of the current study aid in source characterization and identification,and better understanding of the region’s air quality problems,which helps in the development of focused BTEX pollution reduction and control strategies.
基金Supported by the Natural Science Foundation of Fujian Province(No.2022J05278)the Marine and Fishery Development Special Fund of Xiamen(No.23YYST064QCB36)。
文摘Coastal ecosystems are plagued by high levels of microplastic pollution.Conducting baseline surveys is crucial to comprehend the distribution and influencing factors of this pollution.The present study investigates the spatiotemporal variation and diversity of microplastic on the coastal beaches in Xiamen City,China,considering the combined effects of seasons,human activities,and physicochemical properties of sediments.It is detected that the abundance of microplastics in Xiamen beaches was 0.271±0.01 items/g.The abundance of microplastics in dry season was significantly higher than in rainy season.In terms of spatial variation,the beaches that attracted a larger number of tourists exhibited significantly higher microplastic abundance.The temporal pattern of microplastic distribution on different beaches varied greatly due to region-specific human activities(e.g.,mangrove restoration project)and sedimentary properties(e.g.,bulk density).When the assemblage of microplastics in the coastal area was viewed as a biological community,the Shannon-Wiener index and Pielou's index were higher in rainy season and in the beaches with high intensity of tourist activities,which suggests that the human activities and the surface runoff may contribute to the diversity of microplastics on coastal beaches.Future investigations are encouraged to combine controlled experiments and long-term monitoring at multiple scales to elucidate the underlying mechanisms and factors associated with microplastic pollution in coastal zone.
基金the financial support from the Guangxi Natural Science Foundation(grant no.2021GXNSFDA075012,2023GXNSFGA026002)National Natural Science Foundation of China(52104298,22075073,52362027,52462029)Fundamental Research Funds for the Central Universities(531107051077).
文摘Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial for acquiring stable NiSe2-based materials for sodium-ion batteries(SIBs),Herein,a stress dissipation strategy driven by architecture engineering is proposed,which can achieve ultrafast and ultralong sodium storage properties.Different from the conventional sphere-like or rod-like architecture,the three-dimensional(3D)flower-like NiSe_(2)@C composite is delicately designed and assembled with onedimensional nanorods and carbon framework.More importantly,the fundamental mechanism of improved structure stability is unveiled by simulations and experimental results simultaneously.It demonstrates that this designed multidimensional flower-like architecture with dispersed nanorods can balance the structural mismatch,avoid concentrated local strain,and relax the internal stress,mainly induced by the unavoidable volume variation during the repeated conversion processes.Moreover,it can provide more Na^(+)-storage sites and multi-directional migration pathways,leading to a fast Na^(+)-migration channel with boosted reaction kinetic.As expected,it delivers superior rate performance(441 mA h g^(-1)at 5.0 A g^(-1))and long cycling stability(563 mA h g^(-1)at 1.0 A g^(-1)over 1000 cycles)for SIBs.This work provides useful insights for designing high-performance conversion-based anode materials for SIBs.
文摘This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses the construction of multi-dimensional collaborative theoretical models,public space streamline organization,and other aspects,emphasizing the importance of multi-dimensional collaboration.Additionally,it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.
基金supported by the Project of National Natural Science Foundation of China(22102095,21773153)the National Key Basic Research and Development Program(2018YFB1502001)financial support from the program of China Scholarships Council(No.202306230242).
文摘Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic obstacles are considered as the dominant inhibition for attaining satisfactory energy-conversion efficiency.The complexity in light absorption and carrier transfer behaviors has remained to be further clearly illuminated.It is challenging to trace the fast evolution of charge carriers involved in transfer migration and interfacial reactions within a micro–nano-single-particle photocatalyst,which requires spatiotemporal high resolution.In this review,comprehensive dynamic descriptions including irradiation field,carrier separation and transfer,and interfacial reaction processes have been elucidated and discussed.The corresponding mechanisms for revealing dynamic behaviors have been explained.In addition,numerical simulation and modeling methods have been illustrated for the description of the irradiation field.Experimental measurements and spatiotemporal characterizations have been clarified for the reflection of carrier behavior and probing detection of interfacial reactions.The representative applications have been introduced according to the reported advanced research works,and the relationships between mechanistic conclusions from variable spatiotemporal measurements and photocatalytic performance results in the specific photocatalytic reactions have been concluded.This review provides a collective perspective for the full understanding and thorough evaluation of the primary dynamic processes,which would be inspired for the improvement in designing solar-driven energy-conversion systems based on nanoscale particulate photocatalysts.
