41 a （1961 - 2001） seasonal Z index series of 25 representative weather stations are investigated by virtue of EOF, FFT, continuous wavelet transformation （CWT） and orthogonai wavelet transformation （OWT）. It sh...41 a （1961 - 2001） seasonal Z index series of 25 representative weather stations are investigated by virtue of EOF, FFT, continuous wavelet transformation （CWT） and orthogonai wavelet transformation （OWT）. It shows that： （1） Fujian drought/flood （DF） has a significant 2 - 3a cycle for the periods 1965 - 1975 and 1990＇s; （2） the pattern, which represents the opposite DF trend between the southern and northem parts, has la and 3 - 4a cycles since the middle of 1980＇s; （3） EOF3, which denotes the reverse change between the middle-west region and other areas, has significant 1 - 2a cycle for the period from 1985 to 1998 and 9 - 13a cycle since 1980s; （4） there is an obvious drought trend for the last 40a （especially in the 1990＇s）, which is more outstanding in the south （east） than in the north （west）; （5） the 1960＇s and 1980＇s are in relatively wet phases and the 1970＇s and 1990＇s are in drought spells.展开更多
Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and dr...Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.展开更多
The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about ...The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about whether the LDG remains stable over time as climate warming progresses and whether MTE remains applicable to clarify this pattern.In this study,forest data spanning temperate,subtropical,and tropical zones across China were used to analyze long-term changes in the LDG of tree species over 2005-2020.Based on the MTE framework,spatial scales were considered to assess temperature dependence of typical forest trees species.Our results show that species richness decreased with increasing latitude,and that temperature was the primary driver of this change.Although temperature in China has significantly increased over the past two decades,the LDG of tree species has remained stable.However,there was a decrease in species richness in tropical regions over time.With predictions of the MTE,the logarithm of typical forest tree species richness exhibited negative linear relationships with the inverse of ambient temperature,indicating temperature dependence of species richness.However,the relationship remained stable and was strongly influenced by spatial scale,intensifying as spatial scale increased.The findings emphasize the important role of temperature in shaping the LDG.The effects of spatial scale,in particular,should be considered when biodiversity management plans are developed for future climate change.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
High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining prec...High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining precise photon frequencies,especially in the ultraviolet or even extreme ultraviolet regimes,is a key goal in both light–matter interaction experiments and engineering applications.High-order harmonic generation(HHG)is an ideal light source for producing such photons.In this work,we propose an optical temporal interference model(OTIM)that establishes an analogy with multi-slit Fraunhofer diffraction(MSFD)to manipulate fine-frequency photon generation by exploiting the temporal coherence of HHG processes.Our model provides a unified physical framework for three distinct non-integer HHG generation schemes:single-pulse,shaped-pulse,and laser pulse train approaches,which correspond to single-MSFD-like,double-MSFD-like,and multi-MSFD-like processes,respectively.Arbitrary non-integer HHG photons can be obtained using our scheme.Our approach provides a new perspective for accurately measuring and controlling photon frequencies in fields such as frequency comb technology,interferometry,and atomic clocks.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
End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods th...End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.展开更多
Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major ...Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major satellite precipitation products(CHIRPS,PERSIANN-CDR,IMERG-F v07,and GSMaP)over Isfahan province,Iran,over a 9-year period(2015-2023).The performance of these products was benchmarked against a dense network of 98 rain gauges using a suite of continuous and categorical statistical metrics,following a two-stage quality control protocol to remove outliers and false alarms.The results revealed that the performance of all products improves with temporal aggregation.At the daily level,GSMaP performed marginally better,although all products were linked with considerable uncertainty.At the monthly and annual levels,the GPM-era products(IMERG and GSMaP)clearly beat the other two,establishing themselves as dependable tools for long-term hydro-climatological studies.Error analysis revealed that topography is the dominant regulating factor,creating a systematic elevationdependent bias,largely characterized by underestimation from most products in high-elevation areas,though the PERSIANN-CDR product exhibited a contrasting overestimation tendency.Finally,the findings highlight the importance of implementing local,elevation-dependent calibration before deploying these products in hydrological modeling.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
Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbani...Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.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
Based on the data of NDVI and meteorological factors in Siziwang Banner from 2000 to 2021,the temporal and spatial variation characteristics of NDVI in the grassland of Siziwang Banner and its responses to climate cha...Based on the data of NDVI and meteorological factors in Siziwang Banner from 2000 to 2021,the temporal and spatial variation characteristics of NDVI in the grassland of Siziwang Banner and its responses to climate change were analyzed.The results show that the NDVI of grassland in Siziwang Banner tended to rise overall,with the average tendency rate of 0.05/10 a.The annual variation of NDVI was mainly driven by precipitation,and there was an extremely significant positive correlation between the two.During the growing season,temperature was positively correlated with NDVI in May,but then the correlation gradually turned negative.NDVI was generally positively correlated with precipitation,and there was a significant lag.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
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
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
基金Project from the Ministry of Science and Technology of China (2001DIB20116)open projectfor KLME of Nanjing Institute of Meteorology (KJS02108)
文摘41 a （1961 - 2001） seasonal Z index series of 25 representative weather stations are investigated by virtue of EOF, FFT, continuous wavelet transformation （CWT） and orthogonai wavelet transformation （OWT）. It shows that： （1） Fujian drought/flood （DF） has a significant 2 - 3a cycle for the periods 1965 - 1975 and 1990＇s; （2） the pattern, which represents the opposite DF trend between the southern and northem parts, has la and 3 - 4a cycles since the middle of 1980＇s; （3） EOF3, which denotes the reverse change between the middle-west region and other areas, has significant 1 - 2a cycle for the period from 1985 to 1998 and 9 - 13a cycle since 1980s; （4） there is an obvious drought trend for the last 40a （especially in the 1990＇s）, which is more outstanding in the south （east） than in the north （west）; （5） the 1960＇s and 1980＇s are in relatively wet phases and the 1970＇s and 1990＇s are in drought spells.
基金funded by the National Natural Science Foundation of China(42571311).
文摘Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.
基金supported by the Key Program of National Science of China(Grant No.:42030509 and 42141005)。
文摘The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about whether the LDG remains stable over time as climate warming progresses and whether MTE remains applicable to clarify this pattern.In this study,forest data spanning temperate,subtropical,and tropical zones across China were used to analyze long-term changes in the LDG of tree species over 2005-2020.Based on the MTE framework,spatial scales were considered to assess temperature dependence of typical forest trees species.Our results show that species richness decreased with increasing latitude,and that temperature was the primary driver of this change.Although temperature in China has significantly increased over the past two decades,the LDG of tree species has remained stable.However,there was a decrease in species richness in tropical regions over time.With predictions of the MTE,the logarithm of typical forest tree species richness exhibited negative linear relationships with the inverse of ambient temperature,indicating temperature dependence of species richness.However,the relationship remained stable and was strongly influenced by spatial scale,intensifying as spatial scale increased.The findings emphasize the important role of temperature in shaping the LDG.The effects of spatial scale,in particular,should be considered when biodiversity management plans are developed for future climate change.
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 National Natural Science Foundation of China(Grant No.12304379)the Natural Science Foundation of Liaoning Province(Grant No.2024BS-269)the Guangdong Basic and Applied Basic Research Foundation(Grant No.025A1515011117)。
文摘High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining precise photon frequencies,especially in the ultraviolet or even extreme ultraviolet regimes,is a key goal in both light–matter interaction experiments and engineering applications.High-order harmonic generation(HHG)is an ideal light source for producing such photons.In this work,we propose an optical temporal interference model(OTIM)that establishes an analogy with multi-slit Fraunhofer diffraction(MSFD)to manipulate fine-frequency photon generation by exploiting the temporal coherence of HHG processes.Our model provides a unified physical framework for three distinct non-integer HHG generation schemes:single-pulse,shaped-pulse,and laser pulse train approaches,which correspond to single-MSFD-like,double-MSFD-like,and multi-MSFD-like processes,respectively.Arbitrary non-integer HHG photons can be obtained using our scheme.Our approach provides a new perspective for accurately measuring and controlling photon frequencies in fields such as frequency comb technology,interferometry,and atomic clocks.
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.
基金supported by the National Natural Science Foundation of China(Grant No.62266054)the Major Science and Technology Project of Yunnan Province(Grant No.202402AD080002)the Scientific Research Fund of the Yunnan Provincial Department of Education(Grant No.2025Y0302).
文摘End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.
文摘Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major satellite precipitation products(CHIRPS,PERSIANN-CDR,IMERG-F v07,and GSMaP)over Isfahan province,Iran,over a 9-year period(2015-2023).The performance of these products was benchmarked against a dense network of 98 rain gauges using a suite of continuous and categorical statistical metrics,following a two-stage quality control protocol to remove outliers and false alarms.The results revealed that the performance of all products improves with temporal aggregation.At the daily level,GSMaP performed marginally better,although all products were linked with considerable uncertainty.At the monthly and annual levels,the GPM-era products(IMERG and GSMaP)clearly beat the other two,establishing themselves as dependable tools for long-term hydro-climatological studies.Error analysis revealed that topography is the dominant regulating factor,creating a systematic elevationdependent bias,largely characterized by underestimation from most products in high-elevation areas,though the PERSIANN-CDR product exhibited a contrasting overestimation tendency.Finally,the findings highlight the importance of implementing local,elevation-dependent calibration before deploying these products in hydrological modeling.
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supported by the National Natural Science Foundation of China(Grants No.42301226,42271209 and 42471199)the Fundamental Research Funds for the Central Universities(Grant No.2024CDJXY014)+2 种基金the Natural Science Foundation of Jiangxi Province(Grant No.20242BAB25170)Special Funds for Water Resources in Jiangxi Province(Science and Technology Projects)(Grant No.202425YBKT16)the Young Talent Cultivation and Innovation Fund Project of Nanchang University(Grant No.XX202506030028).
文摘Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
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.
文摘Based on the data of NDVI and meteorological factors in Siziwang Banner from 2000 to 2021,the temporal and spatial variation characteristics of NDVI in the grassland of Siziwang Banner and its responses to climate change were analyzed.The results show that the NDVI of grassland in Siziwang Banner tended to rise overall,with the average tendency rate of 0.05/10 a.The annual variation of NDVI was mainly driven by precipitation,and there was an extremely significant positive correlation between the two.During the growing season,temperature was positively correlated with NDVI in May,but then the correlation gradually turned negative.NDVI was generally positively correlated with precipitation,and there was a significant lag.
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.
