Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a mac...Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.展开更多
Mineralogical data are presented for the peridotite xenoliths from Miocene(~19 Ma)Qingyuan basalts in the eastern North China Craton(NCC),with the aim of constraining on property of the sub-continental lithospheric ma...Mineralogical data are presented for the peridotite xenoliths from Miocene(~19 Ma)Qingyuan basalts in the eastern North China Craton(NCC),with the aim of constraining on property of the sub-continental lithospheric mantle(SCLM)beneath the northern Tan-Lu fault zone(TLFZ)during the Cenozoic.The Qingyuan peridotites are dominated by spinel lherzolites with moderate-Mg^(#)olivines(89.4 to 91.2),suggesting that the regional SCLM is mainly transitional and fertile.Light rare earth element(LREE)-depleted,slightly depleted and enriched clinopyroxenes(Cpx)are identified in different peridotites.Chemical compositions of the LREE-enriched Cpx and the presence of phlogopite suggest that the Qingyuan SCLM has experienced silicate-related metasomatism.The synthesis of available mineral chemical data of the mantle xenoliths across the NCC confirms the SCLM beneath the NCC is highly heterogeneous in time and space.The Mesozoic–Cenozoic SCLM beneath the TLFZ and neighboring regions are more fertile and thinner than that beneath the region away from the fault zone.The fertile and refractory peridotite xenoliths experienced varying degrees of silicate and carbonatite metasomatism,respectively.The spatial-temporal lithospheric mantle heterogeneity in composition,age and thickness suggest that the trans-lithosphere fault zone played an important role in heterogeneous replacement of refractory cratonic lithospheric mantle.展开更多
Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despit...Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despite differences in the mechanisms of injury,both conditions share a high prevalence of motor and cognitive impairments.These deficits show only limited natural recovery.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
This study examined the influence of the built environment surrounding rail stations on rail transit ridership and its spatiotemporal variations,aiming to enhance rail transit operational efficiency and inform station...This study examined the influence of the built environment surrounding rail stations on rail transit ridership and its spatiotemporal variations,aiming to enhance rail transit operational efficiency and inform station planning and development.Data from 159 metro stations in Nanjing,collected over a 14-d period,were analyzed to identify changes in weekday and weekend ridership patterns.The analysis included explanatory variables grouped into three categories:urban spatial variables,socioeconomic vari-ables,and transit service variables.A geographically and temporally weighted regression(GTWR)model was developed,and its performance was compared with that of ordinary least squares(OLS)and geographically weighted regression(GWR)models.The results demonstrated that the GTWR model outperformed others in analyzing the relationship between rail transit ridership and the built environment.In addition,the coefficients of explanatory variables showed significant variation across spatiotemporal dimensions,revealing distinct patterns.Notably,the influence of commuter flows led to more pronounced temporal heterogeneity in the coefficients observed on weekdays.These findings offer valuable insights for optimizing urban public transportation systems and advancing integrated urban rail development.展开更多
Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications.However,effectively capturing spatio-temporal features for smo...Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications.However,effectively capturing spatio-temporal features for smoother and more precise human motion prediction remains a challenge.To address these issues,a robust human motion prediction method via integration of spatial and temporal cues(RISTC)has been proposed.This method captures sufficient spatio-temporal correlation of the observable sequence of human poses by utilizing the spatio-temporal mixed feature extractor(MFE).In multi-layer MFEs,the channel-graph united attention blocks extract the augmented spatial features of the human poses in the channel and spatial dimension.Additionally,multi-scale temporal blocks have been designed to effectively capture complicated and highly dynamic temporal information.Our experiments on the Human3.6M and Carnegie Mellon University motion capture(CMU Mocap)datasets show that the proposed network yields higher prediction accuracy than the state-of-the-art methods.展开更多
Tuberculosis(TB)remained the first leading cause of death from a single infectious agent worldwide in 2023,resulting in nearly twice as many deaths as those caused by the human immunodeficiency virus/acquired immune d...Tuberculosis(TB)remained the first leading cause of death from a single infectious agent worldwide in 2023,resulting in nearly twice as many deaths as those caused by the human immunodeficiency virus/acquired immune deficiency syndrome.An estimated 10.8 million TB cases were reported globally in 2023,with approximately 1.25 million associated deaths.In China,which ranks third in the global TB burden,there were approximately 741,000 new cases and 25,000 deaths in 2023^([1]).TB poses a significant threat to human health worldwide.展开更多
Based on the monitoring data of PM 10 concentration from six environmental monitoring stations and the ground meteorological observation data in Yantai City from 2019 to 2021,the spatial and temporal variation of PM 1...Based on the monitoring data of PM 10 concentration from six environmental monitoring stations and the ground meteorological observation data in Yantai City from 2019 to 2021,the spatial and temporal variation of PM 10 concentration and its relationship with meteorological factors were studied.The results show that from the perspective of temporal variation,the annual average of PM 10 concentration in Yantai City tended to decrease year by year.It was high in winter and spring and low in summer and autumn.In terms of monthly variation,the changing curve is U-shaped,and it was high in December and January but low in July and August.During a day,PM 10 concentration had two peaks.The first peak appeared approximately from 09:00 to 11:00,and the second peak can be found from 21:00 to 23:00.From the perspective of spatial distribution,PM 10 concentration was the highest in the development area and Fushan District.It was the highest in the west,followed by the east,while it was the lowest in the middle.The spatial difference rate was the highest in summer.Average temperature,relative humidity,wind speed and precipitation were the main meteorological factors influencing PM 10 concentration in Yantai area.PM 10 concentration was negatively correlated with average temperature and relative humidity,and the correlation was the most significant from June to October.It was negatively correlated with wind speed and precipitation,and the correlation was different in various months.The negative correlation was significant in summer and winter.展开更多
Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies a...Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames.These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns.To address the above problems,we proposed a novel multi-level spatial and temporal learning model,which consists of a Cross Spatial Dependencies Encoding Module(CSM)and a Dynamic Temporal Connection Encoding Module(DTM).Specifically,the CSM is designed to capture complementary local and global spatial dependent information at both the joint level and the joint pair level.We further present DTM to encode diverse temporal evolution contexts and compress motion features to a deep level,enabling the model to capture both short-term and long-term dependencies efficiently.Extensive experiments conducted on the Human 3.6M and CMU Mocap datasets demonstrate that our model achieves state-of-the-art performance in both short-term and long-term predictions,outperforming existing methods by up to 20.3% in accuracy.Furthermore,ablation studies confirm the significant contributions of the CSM and DTM in enhancing prediction accuracy.展开更多
This study investigated the spatio-temporal variation characteristics of ecosystem service value(ESV)alongside its driving influencing factors,thereby offering valuable theoretical insights for the sustainable develop...This study investigated the spatio-temporal variation characteristics of ecosystem service value(ESV)alongside its driving influencing factors,thereby offering valuable theoretical insights for the sustainable development of Jingzhou City,Hubei Province.Based on the land use data for Jingzhou City from 2000 to 2020,this study quantified the value of the ecological environment using the equivalent factor method.Furthermore,it analyzed and elucidated the spatio-temporal heterogeneity and driving mechanisms of ecosystem services in Jingzhou City.The results indicated that between 2000 and 2020,cultivated land(66.40%)and water area(18.82%)were the predominant land use types in Jingzhou City.The areas of water area and construction land exhibited a growth trend during this period.Construction land had the highest rate of land use change,followed by water area and cultivated land.Land use transitions primarily occurred between cultivated land and water area,as well as between cultivated land and construction land.The total value of ecosystem services in Jingzhou City increased by 165.07%from 2000 to 2020.ESV exhibited an upward trend from 2000 to 2015,followed by a gradual decline from 2015 to 2020.The ranking of individual ecosystem services,in descending order,was as follows:regulation services,supporting services,provisioning services,and cultural services.High-value ESV areas were predominantly situated in the water area of Lake Honghu,while low-value regions were mainly found in the cultivated land in the central and western parts of Jingzhou City.The spatial differentiation of ESV in Jingzhzou City was influenced by both natural and socio-economic factors,with natural factors exerting a more significant impact than socioeconomic factors.Specifically,the Normalized Difference Vegetation Index(NDVI)was the dominant environmental factor,while GDP plays a synergistic role.展开更多
To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily e...To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily emotion recognition.First,ResNet50,DNN and spatial ransformer models are used to capture facial texture vectors,bodily skeleton vectors and wholebody geometric vectors,and an intraframe correlation attention guidance(S-CAG)mechanism,which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector,is designed to exploit the spatial potential emotional correlation between face and posture.Second,an interframe significant segment enhancement(T-SSE)structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony.Finally,an adaptive weight assignment(M-AWA)strategy is constructed to realise facial-bodily fusion.The experimental results on the BabyRobot Emotion Dataset(BRED)and Context-Aware Emotion Recognition(CAER)dataset indicate that the proposed network reaches accuracies of 81.61%and 89.39%,which are 9.61%and 9.46%higher than those of the baseline network,respectively.Compared with the state-of-the-art methods,the proposed method achieves 7.73%and 20.57%higher accuracy than single-modal methods based on facial expression or bodily posture,respectively,and 2.16%higher accuracy than the dual-modal methods based on facial-bodily fusion.Therefore,the proposed method,which adaptively fuses the complementary information of face and posture,improves the quality of emotion recognition in real-world scenarios.展开更多
Based on the data of meteorological elements and concentration of negative ions in the county town station,Luguhe station and Yunjishan station during 2020-2024,the temporal and spatial variations in the concentration...Based on the data of meteorological elements and concentration of negative ions in the county town station,Luguhe station and Yunjishan station during 2020-2024,the temporal and spatial variations in the concentration of negative ions and their influencing factors in Xinfeng County were analyzed.The results show that the concentration of negative ions was the highest in summer,followed by spring;it was lower in autumn and the lowest in winter.In terms of diurnal variations,it was higher in the early morning and night,and lower in the noon and afternoon,which was closely related to the diurnal variations of human activities and meteorological conditions.The factors that affect the concentration of negative ions in the air are more complex.Besides meteorological factors,vegetation,altitude,human activities and other factors should be considered.展开更多
Recently,information acquired at the canopy top,such as spectral and textural data,has been widely used to estimate plant nitrogen(N)accumulation(PNA).The response of crops to N uptake involves not only changes in hor...Recently,information acquired at the canopy top,such as spectral and textural data,has been widely used to estimate plant nitrogen(N)accumulation(PNA).The response of crops to N uptake involves not only changes in horizontal canopy top information but also an increase in vertical plant height(PH).It remains unclear whether the fusion of spectral indices with PH can improve the estimation performance of PNA models based on spectral remote sensing across different growth stages.展开更多
High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiri...High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.展开更多
Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In respon...Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.展开更多
Ensuring a harmonious coexistence between man and nature is crucial for China’s economic and social development.However,with increasing industrialization and urbanization,there is a growing mismatch between China’s ...Ensuring a harmonious coexistence between man and nature is crucial for China’s economic and social development.However,with increasing industrialization and urbanization,there is a growing mismatch between China’s ecological resilience(ER)and economic level(EL)of development,which poses a notable social threat.Currently,the link between ER and EL in China remains unclear,especially in terms of spatial dislocation(SD),referring to the disconnect between the locations where environmental impacts occur and those where economic benefits or activities are concentrated.Therefore,this paper aims to provide theoretical support and an empirical basis for policy-based solutions to address this gap.Based on the SD theory,this study systematically discusses the temporal changes,spatial patterns,and SD characteristics of China’s ER and EL using spatial auto-correlation and barycentric analysis to analyze data from 30 provinces covering the period 2011-2021.The key results are as follows.China’s ER shows a general trend of growth;however,its distribution is uneven.The spatial pattern generally decreases from the southeastern coastal provinces to the northwest.Moreover,a gradually increasing positive correlation is observed between the ER and EL,but this correlation varies by region,with some showing regional linkages and others developing independently.Finally,the dislocation index of ER and EL presents divergent results based on region-the eastern and central regions primarily show a high level of dislocation,whereas the western and northeastern regions show a low level of dislocation.The results provide a comprehensive overview of the spatiotemporal patterns in the association between ER and EL in China.The results emphasize that to balance sustainable regional development and ecological governance,a region-specific approach must be employed,prioritizing innovation-driven strategies for high ER in more developed regions and market-oriented strategies in less developed regions.展开更多
Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal ...Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.展开更多
To conduct a comprehensive analysis of the current status of water environment quality in Yilong Lake,a systematic study was undertaken to characterize the evolution of water quality.This study utilized monthly data o...To conduct a comprehensive analysis of the current status of water environment quality in Yilong Lake,a systematic study was undertaken to characterize the evolution of water quality.This study utilized monthly data on water quality indicators collected from three monitoring sections of Yilong Lake between 2016 and 2023,employing the Mann-Kendall trend test and ArcGIS spatial interpolation technique.The results indicated that the five-day biochemical oxygen demand(BOD5),total nitrogen(TN),and chlorophyll a(Chla)exhibited an overall increasing trend,whereas other indicators demonstrated a decreasing trend.The permanganate index(PI),chemical oxygen demand(COD),TN,and Chla were observed in the following order:east of the lake>middle of the lake>west of the lake.In contrast,the BOD5 and total phosphorus(TP)were ranked as west of the lake>east of the lake>middle of the lake.Additionally,ammonia nitrogen(NH3-N)was found to be in the order of east of the lake>west of the lake>middle of the lake,while transparency was ranked as west of the lake>middle of the lake>east of the lake.Urban domestic sewage,effluent from industrial parks,domestic waste generated by rural residents’production and daily activities,agricultural waste,wastewater from decentralized farming,domestic sewage,and point source discharges from the soybean processing industry are the primary contributors to the exceedance of water quality standards.The enhancement of a precise pollution control system,along with the regulation of pollution sources and the interception of pollutants,can significantly diminish the pollution load entering the lake.This approach is essential for the protection and restoration of river and lake ecosystems,thereby facilitating the gradual recovery of their ecological functions.Additionally,the implementation of ecological water replenishment and the recycling of water resources can improve the capacity of the water environment.Furthermore,bolstering scientific and technological support,as well as comprehensive supervision and assurance measures,is crucial to ensuring that water quality remains stable and adheres to established standards.展开更多
To understand the spatial and temporal variation characteristics of blue algae in summer in Lake Dianshan,the phytoplankton in Lake Dianshan from June to September in 2009 was surveyed. It found 11 genera and 28 speci...To understand the spatial and temporal variation characteristics of blue algae in summer in Lake Dianshan,the phytoplankton in Lake Dianshan from June to September in 2009 was surveyed. It found 11 genera and 28 species blue algae in total. Microcystis,Oscillatoria and Chroococcus were the main composition communities of blue algae in Lake Dianshan in summer. In the survey period,the average density of blue algae in Lake Dianshan was 16.48×106 cells/L which changed during 1.01×106-59.76×106 cells/L. The characteristics were:September > July > August > June. The mass propagation and aggregation of Microcystis in September caused that the water blooms phenomenon in the partial water areas was serious. In the space,the average density of blue algae in the west and southwest parts of Lake Dianshan was bigger than in the east and southeast. When the nutritive matter was sufficient,the temperature was the main factor which affected the generation and disappearance of blue algae water blooms. The wind direction was also an important factor which affected the distribution of blue algae.展开更多
基金Sponsored by the Transportation Science and Technology Planning Project of Henan Province,China(Grant No.2019G-2-2).
文摘Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.
基金supported by funds from the Ministry of Science and Technology of the People's Republic of China(No.2019YFA0708603)NSFC(Nos.41973050,42288201,41930215)the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2019ZD0202)。
文摘Mineralogical data are presented for the peridotite xenoliths from Miocene(~19 Ma)Qingyuan basalts in the eastern North China Craton(NCC),with the aim of constraining on property of the sub-continental lithospheric mantle(SCLM)beneath the northern Tan-Lu fault zone(TLFZ)during the Cenozoic.The Qingyuan peridotites are dominated by spinel lherzolites with moderate-Mg^(#)olivines(89.4 to 91.2),suggesting that the regional SCLM is mainly transitional and fertile.Light rare earth element(LREE)-depleted,slightly depleted and enriched clinopyroxenes(Cpx)are identified in different peridotites.Chemical compositions of the LREE-enriched Cpx and the presence of phlogopite suggest that the Qingyuan SCLM has experienced silicate-related metasomatism.The synthesis of available mineral chemical data of the mantle xenoliths across the NCC confirms the SCLM beneath the NCC is highly heterogeneous in time and space.The Mesozoic–Cenozoic SCLM beneath the TLFZ and neighboring regions are more fertile and thinner than that beneath the region away from the fault zone.The fertile and refractory peridotite xenoliths experienced varying degrees of silicate and carbonatite metasomatism,respectively.The spatial-temporal lithospheric mantle heterogeneity in composition,age and thickness suggest that the trans-lithosphere fault zone played an important role in heterogeneous replacement of refractory cratonic lithospheric mantle.
基金supported by the Defitech Foundation(Morges,CH)to FCHthe Bertarelli Foundation-Catalyst program(Gstaad,CH)to FCH+2 种基金the Wyss Center for Bio and Neuroengineering the Lighthouse Partnership for AI-guided Neuromodulation to FCHthe Fonds de recherche du Quebec-Sante(FRQS#342969)to CEPthe Neuro X Postdoctoral Fellowship Program to CEP。
文摘Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despite differences in the mechanisms of injury,both conditions share a high prevalence of motor and cognitive impairments.These deficits show only limited natural recovery.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金The National Key Research and Development Program of China(No.2022YFC3800201).
文摘This study examined the influence of the built environment surrounding rail stations on rail transit ridership and its spatiotemporal variations,aiming to enhance rail transit operational efficiency and inform station planning and development.Data from 159 metro stations in Nanjing,collected over a 14-d period,were analyzed to identify changes in weekday and weekend ridership patterns.The analysis included explanatory variables grouped into three categories:urban spatial variables,socioeconomic vari-ables,and transit service variables.A geographically and temporally weighted regression(GTWR)model was developed,and its performance was compared with that of ordinary least squares(OLS)and geographically weighted regression(GWR)models.The results demonstrated that the GTWR model outperformed others in analyzing the relationship between rail transit ridership and the built environment.In addition,the coefficients of explanatory variables showed significant variation across spatiotemporal dimensions,revealing distinct patterns.Notably,the influence of commuter flows led to more pronounced temporal heterogeneity in the coefficients observed on weekdays.These findings offer valuable insights for optimizing urban public transportation systems and advancing integrated urban rail development.
基金supported by the National Key R&D Program of China(No.2018YFB1305200)the Natural Science Foundation of Zhejiang Province(No.LGG21F030011)。
文摘Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications.However,effectively capturing spatio-temporal features for smoother and more precise human motion prediction remains a challenge.To address these issues,a robust human motion prediction method via integration of spatial and temporal cues(RISTC)has been proposed.This method captures sufficient spatio-temporal correlation of the observable sequence of human poses by utilizing the spatio-temporal mixed feature extractor(MFE).In multi-layer MFEs,the channel-graph united attention blocks extract the augmented spatial features of the human poses in the channel and spatial dimension.Additionally,multi-scale temporal blocks have been designed to effectively capture complicated and highly dynamic temporal information.Our experiments on the Human3.6M and Carnegie Mellon University motion capture(CMU Mocap)datasets show that the proposed network yields higher prediction accuracy than the state-of-the-art methods.
文摘Tuberculosis(TB)remained the first leading cause of death from a single infectious agent worldwide in 2023,resulting in nearly twice as many deaths as those caused by the human immunodeficiency virus/acquired immune deficiency syndrome.An estimated 10.8 million TB cases were reported globally in 2023,with approximately 1.25 million associated deaths.In China,which ranks third in the global TB burden,there were approximately 741,000 new cases and 25,000 deaths in 2023^([1]).TB poses a significant threat to human health worldwide.
基金the Science and Technology Research Project of Shandong Meteorological Bureau(2022SDQN11)Science and Technology Research Project of Yantai Meteorological Bureau(2024ytcx07).
文摘Based on the monitoring data of PM 10 concentration from six environmental monitoring stations and the ground meteorological observation data in Yantai City from 2019 to 2021,the spatial and temporal variation of PM 10 concentration and its relationship with meteorological factors were studied.The results show that from the perspective of temporal variation,the annual average of PM 10 concentration in Yantai City tended to decrease year by year.It was high in winter and spring and low in summer and autumn.In terms of monthly variation,the changing curve is U-shaped,and it was high in December and January but low in July and August.During a day,PM 10 concentration had two peaks.The first peak appeared approximately from 09:00 to 11:00,and the second peak can be found from 21:00 to 23:00.From the perspective of spatial distribution,PM 10 concentration was the highest in the development area and Fushan District.It was the highest in the west,followed by the east,while it was the lowest in the middle.The spatial difference rate was the highest in summer.Average temperature,relative humidity,wind speed and precipitation were the main meteorological factors influencing PM 10 concentration in Yantai area.PM 10 concentration was negatively correlated with average temperature and relative humidity,and the correlation was the most significant from June to October.It was negatively correlated with wind speed and precipitation,and the correlation was different in various months.The negative correlation was significant in summer and winter.
基金supported by the Urgent Need for Overseas Talent Project of Jiangxi Province(Grant No.20223BCJ25040)the Thousand Talents Plan of Jiangxi Province(Grant No.jxsg2023101085)+3 种基金the National Natural Science Foundation of China(Grant No.62106093)the Natural Science Foundation of Jiangxi(Grant Nos.20224BAB212011,20232BAB212008,20242BAB25078,and 20232BAB202051)The Youth Talent Cultivation Innovation Fund Project of Nanchang University(Grant No.XX202506030015)funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R759),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Predicting human motion based on historical motion sequences is a fundamental problem in computer vision,which is at the core of many applications.Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames.These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns.To address the above problems,we proposed a novel multi-level spatial and temporal learning model,which consists of a Cross Spatial Dependencies Encoding Module(CSM)and a Dynamic Temporal Connection Encoding Module(DTM).Specifically,the CSM is designed to capture complementary local and global spatial dependent information at both the joint level and the joint pair level.We further present DTM to encode diverse temporal evolution contexts and compress motion features to a deep level,enabling the model to capture both short-term and long-term dependencies efficiently.Extensive experiments conducted on the Human 3.6M and CMU Mocap datasets demonstrate that our model achieves state-of-the-art performance in both short-term and long-term predictions,outperforming existing methods by up to 20.3% in accuracy.Furthermore,ablation studies confirm the significant contributions of the CSM and DTM in enhancing prediction accuracy.
文摘This study investigated the spatio-temporal variation characteristics of ecosystem service value(ESV)alongside its driving influencing factors,thereby offering valuable theoretical insights for the sustainable development of Jingzhou City,Hubei Province.Based on the land use data for Jingzhou City from 2000 to 2020,this study quantified the value of the ecological environment using the equivalent factor method.Furthermore,it analyzed and elucidated the spatio-temporal heterogeneity and driving mechanisms of ecosystem services in Jingzhou City.The results indicated that between 2000 and 2020,cultivated land(66.40%)and water area(18.82%)were the predominant land use types in Jingzhou City.The areas of water area and construction land exhibited a growth trend during this period.Construction land had the highest rate of land use change,followed by water area and cultivated land.Land use transitions primarily occurred between cultivated land and water area,as well as between cultivated land and construction land.The total value of ecosystem services in Jingzhou City increased by 165.07%from 2000 to 2020.ESV exhibited an upward trend from 2000 to 2015,followed by a gradual decline from 2015 to 2020.The ranking of individual ecosystem services,in descending order,was as follows:regulation services,supporting services,provisioning services,and cultural services.High-value ESV areas were predominantly situated in the water area of Lake Honghu,while low-value regions were mainly found in the cultivated land in the central and western parts of Jingzhou City.The spatial differentiation of ESV in Jingzhzou City was influenced by both natural and socio-economic factors,with natural factors exerting a more significant impact than socioeconomic factors.Specifically,the Normalized Difference Vegetation Index(NDVI)was the dominant environmental factor,while GDP plays a synergistic role.
基金National Natural Science Foundation of China,Grant/Award Number:62176084,Natural Science Foundation of Anhui Province of China,Grant/Award Number:1908085MF195,Natural Science Research Project of the Education Department of Anhui Province of China Grant/Award Numbers:2022AH051038,2023AH050474 and 2023AH050490.
文摘To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily emotion recognition.First,ResNet50,DNN and spatial ransformer models are used to capture facial texture vectors,bodily skeleton vectors and wholebody geometric vectors,and an intraframe correlation attention guidance(S-CAG)mechanism,which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector,is designed to exploit the spatial potential emotional correlation between face and posture.Second,an interframe significant segment enhancement(T-SSE)structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony.Finally,an adaptive weight assignment(M-AWA)strategy is constructed to realise facial-bodily fusion.The experimental results on the BabyRobot Emotion Dataset(BRED)and Context-Aware Emotion Recognition(CAER)dataset indicate that the proposed network reaches accuracies of 81.61%and 89.39%,which are 9.61%and 9.46%higher than those of the baseline network,respectively.Compared with the state-of-the-art methods,the proposed method achieves 7.73%and 20.57%higher accuracy than single-modal methods based on facial expression or bodily posture,respectively,and 2.16%higher accuracy than the dual-modal methods based on facial-bodily fusion.Therefore,the proposed method,which adaptively fuses the complementary information of face and posture,improves the quality of emotion recognition in real-world scenarios.
文摘Based on the data of meteorological elements and concentration of negative ions in the county town station,Luguhe station and Yunjishan station during 2020-2024,the temporal and spatial variations in the concentration of negative ions and their influencing factors in Xinfeng County were analyzed.The results show that the concentration of negative ions was the highest in summer,followed by spring;it was lower in autumn and the lowest in winter.In terms of diurnal variations,it was higher in the early morning and night,and lower in the noon and afternoon,which was closely related to the diurnal variations of human activities and meteorological conditions.The factors that affect the concentration of negative ions in the air are more complex.Besides meteorological factors,vegetation,altitude,human activities and other factors should be considered.
基金supported by the National Key Research and Development Plan Project Sub-Topic of China(Grant Nos.2022YFD1901500 and 2022YFD1901505-07)the National Natural Science Foundation of China(Grant No.32260531)+1 种基金the Key Laboratory of Molecular Breeding for Grain and Oil Crops in Guizhou Province,China(Grant No.Qiankehezhongyindi[2023]8)the Key Laboratory of Functional Agriculture of Guizhou Provincial Higher Education Institutions,China(Grant No.Qianjiaoji[2023]007).
文摘Recently,information acquired at the canopy top,such as spectral and textural data,has been widely used to estimate plant nitrogen(N)accumulation(PNA).The response of crops to N uptake involves not only changes in horizontal canopy top information but also an increase in vertical plant height(PH).It remains unclear whether the fusion of spectral indices with PH can improve the estimation performance of PNA models based on spectral remote sensing across different growth stages.
文摘High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.
文摘Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.
基金funded by the National Natural Science Foundation of China[Grant No.71963030]a subproject of China’s third comprehensive scientific expedition to Xinjiang[Grant No.SQ2021xjkk01800]+1 种基金a major science and technology project in the Xinjiang Uygur Autonomous Region[Grant No.2022A01003]a scientific research innovation project for excellent doctoral students of Xinjiang University[Grant No.XJU2022BS010].
文摘Ensuring a harmonious coexistence between man and nature is crucial for China’s economic and social development.However,with increasing industrialization and urbanization,there is a growing mismatch between China’s ecological resilience(ER)and economic level(EL)of development,which poses a notable social threat.Currently,the link between ER and EL in China remains unclear,especially in terms of spatial dislocation(SD),referring to the disconnect between the locations where environmental impacts occur and those where economic benefits or activities are concentrated.Therefore,this paper aims to provide theoretical support and an empirical basis for policy-based solutions to address this gap.Based on the SD theory,this study systematically discusses the temporal changes,spatial patterns,and SD characteristics of China’s ER and EL using spatial auto-correlation and barycentric analysis to analyze data from 30 provinces covering the period 2011-2021.The key results are as follows.China’s ER shows a general trend of growth;however,its distribution is uneven.The spatial pattern generally decreases from the southeastern coastal provinces to the northwest.Moreover,a gradually increasing positive correlation is observed between the ER and EL,but this correlation varies by region,with some showing regional linkages and others developing independently.Finally,the dislocation index of ER and EL presents divergent results based on region-the eastern and central regions primarily show a high level of dislocation,whereas the western and northeastern regions show a low level of dislocation.The results provide a comprehensive overview of the spatiotemporal patterns in the association between ER and EL in China.The results emphasize that to balance sustainable regional development and ecological governance,a region-specific approach must be employed,prioritizing innovation-driven strategies for high ER in more developed regions and market-oriented strategies in less developed regions.
基金supported by the National Natural Science Foundation of China(Grant Nos.12090052,U24A2014,and 12325405).
文摘Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.
文摘To conduct a comprehensive analysis of the current status of water environment quality in Yilong Lake,a systematic study was undertaken to characterize the evolution of water quality.This study utilized monthly data on water quality indicators collected from three monitoring sections of Yilong Lake between 2016 and 2023,employing the Mann-Kendall trend test and ArcGIS spatial interpolation technique.The results indicated that the five-day biochemical oxygen demand(BOD5),total nitrogen(TN),and chlorophyll a(Chla)exhibited an overall increasing trend,whereas other indicators demonstrated a decreasing trend.The permanganate index(PI),chemical oxygen demand(COD),TN,and Chla were observed in the following order:east of the lake>middle of the lake>west of the lake.In contrast,the BOD5 and total phosphorus(TP)were ranked as west of the lake>east of the lake>middle of the lake.Additionally,ammonia nitrogen(NH3-N)was found to be in the order of east of the lake>west of the lake>middle of the lake,while transparency was ranked as west of the lake>middle of the lake>east of the lake.Urban domestic sewage,effluent from industrial parks,domestic waste generated by rural residents’production and daily activities,agricultural waste,wastewater from decentralized farming,domestic sewage,and point source discharges from the soybean processing industry are the primary contributors to the exceedance of water quality standards.The enhancement of a precise pollution control system,along with the regulation of pollution sources and the interception of pollutants,can significantly diminish the pollution load entering the lake.This approach is essential for the protection and restoration of river and lake ecosystems,thereby facilitating the gradual recovery of their ecological functions.Additionally,the implementation of ecological water replenishment and the recycling of water resources can improve the capacity of the water environment.Furthermore,bolstering scientific and technological support,as well as comprehensive supervision and assurance measures,is crucial to ensuring that water quality remains stable and adheres to established standards.
基金Supported by The Project of Shanghai Scientific and Technological Commission(08DZ1203102,08dz1203002,08dz1203101)
文摘To understand the spatial and temporal variation characteristics of blue algae in summer in Lake Dianshan,the phytoplankton in Lake Dianshan from June to September in 2009 was surveyed. It found 11 genera and 28 species blue algae in total. Microcystis,Oscillatoria and Chroococcus were the main composition communities of blue algae in Lake Dianshan in summer. In the survey period,the average density of blue algae in Lake Dianshan was 16.48×106 cells/L which changed during 1.01×106-59.76×106 cells/L. The characteristics were:September > July > August > June. The mass propagation and aggregation of Microcystis in September caused that the water blooms phenomenon in the partial water areas was serious. In the space,the average density of blue algae in the west and southwest parts of Lake Dianshan was bigger than in the east and southeast. When the nutritive matter was sufficient,the temperature was the main factor which affected the generation and disappearance of blue algae water blooms. The wind direction was also an important factor which affected the distribution of blue algae.
