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
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
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
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
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
Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However...Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However,the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood.In this study,the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor,Northwest China,a representative area of the Gobi desert ecosystems,were monitored using pitfall trapping during 2015-2020.The following results were showed:(1)monthly activity of tenebrionid beetles was observed from March to October,with monthly activity peaking in spring and summer,and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year;(2)spatial distribution of tenebrionid beetle community was influenced by structural factors.Specifically,at a spatial scale of 24.00 m,tenebrionid beetle community was strongly and positively correlated with the dominant species,with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica;(3)abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature,whereas monthly abundance of B.gobiensis and M.kraatzi alashanica was positively correlated with monthly mean precipitation;and(4)the cover of Reaumuria soongarica(Pall.)Maxim.and Nitraria sphaerocarpa Maxim.had a positive influence on the number of tenebrionid beetles captured.In conclusion,monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles,with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.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
Freezing–thawing indices serve as a comprehensive indicator of both the duration of the freezing/thawing periods and the degree of cold and heat in a given region.In-depth analysis of the freezing-thawing indices not...Freezing–thawing indices serve as a comprehensive indicator of both the duration of the freezing/thawing periods and the degree of cold and heat in a given region.In-depth analysis of the freezing-thawing indices not only enables the prediction of permafrost distribution and its dynamic changes,but also facilitates the assessment of damage risk to infrastructure under freeze-thaw action.In this paper,the air/ground freezing–thawing indices from 1987 to 2017,based on daily temperature observations from meteorological stations along the China–Nepal Highway(CNH),were calculated,and their spatial and temporal variation patterns were analyzed.The results showed that:(1)Both mean annual air temperature and mean annual ground surface temperature along the CNH fluctuated upward,with climate tendency rates of 0.43 and 0.52.C·(10a)~(-1),respectively;(2)The number of days with negative air temperature and ground temperature showed fluctuated downward,with change rates of-8.6 and-8.3 d·(10a)~(-1),respectively;(3)The ranges of air freezing index,air thawing index,ground freezing index,and ground thawing index over the years were 157.05-458.88°C·d,2034.20-2560.73°C·d,108.78-396.83°C·d,and 3515.25-4288.67°C·d,respectively.The climate tendency rates were-5.42,10.22,-6.79,and 12.14.C·d·a-1,respectively,showing a general warming trend;(4)The air freezing index,ground freezing index,and ground thawing index changed abruptly in 1999,2000,and 2002,respectively,evincing significant changes after 2002.The research results can provide a basis for the risk assessment of freezing–thawing erosion and the prevention and control of permafrost engineering diseases along the CNH.展开更多
The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainabl...The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainable development.While effectively enhancing WC necessitates a comprehensive understanding of its driving factors and corresponding intervention strategies,existing studies have largely neglected the spatiotemporal heterogeneity of both natural and socio-economic drivers.Therefore,this study explored the spatiotemporal heterogeneity of WC drivers in YRS using multi-scale geographically weighted regression(MGWR)and geographically and temporally weighted regression(GTWR)models from an eco-hydrological perspective.We discovered that downstream regions,which are more developed,achieved significantly better WC than upstream regions.The results also demonstrated that the influence of temperature and wind speed is consistently dominant and temporally stable due to climate stability,while the influence of vegetation shifted from negative to positive around 2010,likely indicating greater benefits from understory vegetation.Economic growth positively impacted WC in upstream regions but had a negative effect in the more developed downstream regions.These findings highlight the importance of targeted water conservation strategies,including locally appropriate revegetation,optimization of agricultural and economic structures,and the establishment of eco-compensation mechanisms for ecological conservation and sustainable development.展开更多
Eutrophic shallow lakes are generally considered as a contributor to the emission of nitrous oxide(N_(2)O),while regional and global estimates have remained imprecise.This due to a lack of data and insufficient unders...Eutrophic shallow lakes are generally considered as a contributor to the emission of nitrous oxide(N_(2)O),while regional and global estimates have remained imprecise.This due to a lack of data and insufficient understanding of the multiple contributing factors.This study characterized the spatiotemporal variability in N_(2)O concentrations and N_(2)O diffusive fluxes and the contributing factors in LakeWuliangsuhai,a typical shallow eutrophic and seasonally frozen lake in Inner Mongolia with cold and arid climate.Dissolved N_(2)O concentrations of the lake exhibited a range of 4.5 to 101.2 nmol/L,displaying significant spatiotemporal variations.The lowest and highest concentrations were measured in summer and winter,respectively.The spatial distribution of N_(2)Ofluxwas consistent with that of N_(2)O concentrations.Additionally,the hotspots of N_(2)O emissions were detected within close to the main inflow of lake.The wide spatial and temporal variation in N_(2)O emissions indicate the complexity and its relative importance of factors influencing emissions.N_(2)O emissions in different lake zones and seasons were regulated by diverse factors.Factors influencing the spatial and temporal distribution of N_(2)O concentrations and fluxes were identified as WT,WD,DO,Chl-a,SD and COD.Interestingly,the same factor demonstrated opposing effects on N_(2)O emission in various seasons or zones.This research improves our understanding of N_(2)O emissions in shallow eutrophic lakes in cold and arid areas.展开更多
Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within wate...Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.展开更多
The coupling of water and fertilizer is the only way for high yield, efficiency, sugar on sugarcane. On sugarcane production, the spatial and temporal controlling technology of fertigation is an important direction of...The coupling of water and fertilizer is the only way for high yield, efficiency, sugar on sugarcane. On sugarcane production, the spatial and temporal controlling technology of fertigation is an important direction of the sustainable and healthy development of ecological agriculture in cane area of China. This paper reviews main achievements and advances on the coupling effect of water and fertilizer on sugarcane from time and space at home or abroad in recent years, analyzes the application prospects of the temporal and spatial coupling effect of water and fertilizer on sugarcane and puts forward some problems which need further research in future.展开更多
The cloud-to-ground lightning data between 2007 and 2008 were collected by lightning detection and location system,which was composed of four lightning detectors in four different sites of Dalian area.The spatio-tempo...The cloud-to-ground lightning data between 2007 and 2008 were collected by lightning detection and location system,which was composed of four lightning detectors in four different sites of Dalian area.The spatio-temporal distribution of cloud-to-ground lightning in surrounding areas of Dalian was analyzed from several aspects of polarity distribution,diurnal variation,lightning intensity and lightning density.The results showed that the number of negative lightning accounted for 93.9% of the total number of lightning,and its average lightning intensity was 27.99 kA.The number of positive lightning accounted for 6.1% of the total number of lightning,and its average lightning intensity was 35.56 kA.The diurnal variation of lightning frequency showed an obvious structure of two peaks (17:00-18:00 and 04:00-05:00) and two valleys (09:00-10:00 and 00:00-01:00).The number of lightning between May and September was 91.5% of the annual number,and the lightning occurred the most frequently between June and August.Most of positive and negative lightning was at the intensity of 15-35 kA,80.0% lower than 40 kA,and 99.3% lower than 100 kA.The lightning density had obvious regional differences in distribution,high in the Liaodong Bay and the Dalian Bay and low in inland areas.Therefore,coastal areas should attract more attention in lightning disaster defense in the surrounding areas of Dalian.展开更多
Climate change will have important implications in water shore regions,such as Huang-Huai-Hai（3H） plain,where expected warmer and drier conditions might augment crop water demand.Sensitivity analysis is important in...Climate change will have important implications in water shore regions,such as Huang-Huai-Hai（3H） plain,where expected warmer and drier conditions might augment crop water demand.Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation in reference evapotranspiration（ET 0）.In this study,the 51-yr ET 0 during winter wheat and summer maize growing season were calculated from a data set of daily climate variables in 40 meteorological stations.Sensitivity maps for key climate variables were estimated according to Kriging method and the spatial pattern of sensitivity coefficients for these key variables was plotted.In addition,the slopes of the linear regression lines for sensitivity coefficients were obtained.Results showed that ET 0 during winter wheat growing season accounted for the largest proportion of annual ET 0,due to its long phenological days,while ET 0 was detected to decrease significantly with the magnitude of 0.5 mm yr-1in summer maize growing season.Solar radiation is considered to be the most sensitive and primarily controlling variable for negative trend in ET 0 for summer maize season,and higher sensitive coefficient value of ET 0 to solar radiation and temperature were detected in east part and southwest part of 3H plain respectively.Relative humidity was demonstrated as the most sensitive factor for ET 0 in winter wheat growing season and declining relativity humidity also primarily controlled a negative trend in ET 0,furthermore the sensitivity coefficient to relative humidity increased from west to southeast.The eight sensitivity centrals were all found located in Shandong Province.These ET 0 along with its sensitivity maps under winter wheat-summer maize rotation system can be applied to predict the agricultural water demand and will assist water resources planning and management for this region.展开更多
Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data fr...Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data from 114 agro-meteorological stations in four maize growing zones in China. Results showed that average air temperature and growing degree-days (GDD) during maize growing seasons showed an increasing trend from 1981 to 2010, while precipitation and sunshine duration showed a decreasing trend. Maize phenology has significantly changed under climate change: spring maize phenology was mainly advanced, especially in northwest and southwest maize zones, while summer and spring-summer maize phenology was delayed. The delay trend observed for summer maize in the northwest maize zone was more pronounced than in the Huang-Huai spring-summer maize zone. Variations in maize phenology changed the corresponding growth stages length: the vegetative growth period (days from sowing date to tasseling date) was generally shortened in spring, summer, and spring-summer maize, although to different degrees, while the reproductive growth period (days from tasseling date to mature date) showed an extension trend. The entire growth period(days from sowing date to mature date) of spring maize was extended, but the entire growth periods of summer and spring-summer maize were shortened.展开更多
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.
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.
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 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funded by the National Natural Science Foundation of China(U23A2063)the Gansu Province Top-notch Leading Talents Project(E339040101)the National Natural Science Foundation of China(41771290,42377043,41773086).
文摘Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However,the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood.In this study,the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor,Northwest China,a representative area of the Gobi desert ecosystems,were monitored using pitfall trapping during 2015-2020.The following results were showed:(1)monthly activity of tenebrionid beetles was observed from March to October,with monthly activity peaking in spring and summer,and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year;(2)spatial distribution of tenebrionid beetle community was influenced by structural factors.Specifically,at a spatial scale of 24.00 m,tenebrionid beetle community was strongly and positively correlated with the dominant species,with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica;(3)abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature,whereas monthly abundance of B.gobiensis and M.kraatzi alashanica was positively correlated with monthly mean precipitation;and(4)the cover of Reaumuria soongarica(Pall.)Maxim.and Nitraria sphaerocarpa Maxim.had a positive influence on the number of tenebrionid beetles captured.In conclusion,monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles,with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.
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supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2021QZKK0202)Shaanxi Provincial Youth Science and Technology Rising Star Project(No.2022KJXX-85)+3 种基金Key Scientific Research Project of Shaanxi Provincial Department of Education(No.22JS041)Youth Innovation Team Research Project of Shaanxi Provincial Department of Education(Nos.22JP099,21JP137)The Youth Innovation Team of Shaanxi Universitiesthe Support Program for Outstanding Young Talents of Shaanxi Universities(Dr.Tao Luo)。
文摘Freezing–thawing indices serve as a comprehensive indicator of both the duration of the freezing/thawing periods and the degree of cold and heat in a given region.In-depth analysis of the freezing-thawing indices not only enables the prediction of permafrost distribution and its dynamic changes,but also facilitates the assessment of damage risk to infrastructure under freeze-thaw action.In this paper,the air/ground freezing–thawing indices from 1987 to 2017,based on daily temperature observations from meteorological stations along the China–Nepal Highway(CNH),were calculated,and their spatial and temporal variation patterns were analyzed.The results showed that:(1)Both mean annual air temperature and mean annual ground surface temperature along the CNH fluctuated upward,with climate tendency rates of 0.43 and 0.52.C·(10a)~(-1),respectively;(2)The number of days with negative air temperature and ground temperature showed fluctuated downward,with change rates of-8.6 and-8.3 d·(10a)~(-1),respectively;(3)The ranges of air freezing index,air thawing index,ground freezing index,and ground thawing index over the years were 157.05-458.88°C·d,2034.20-2560.73°C·d,108.78-396.83°C·d,and 3515.25-4288.67°C·d,respectively.The climate tendency rates were-5.42,10.22,-6.79,and 12.14.C·d·a-1,respectively,showing a general warming trend;(4)The air freezing index,ground freezing index,and ground thawing index changed abruptly in 1999,2000,and 2002,respectively,evincing significant changes after 2002.The research results can provide a basis for the risk assessment of freezing–thawing erosion and the prevention and control of permafrost engineering diseases along the CNH.
基金supported by the funding provided by the State Key Laboratory of Hydraulics and Mountain River Engineering(SKHL2210)National Natural Science Foundation of China(42171304)+1 种基金the Sichuan Science and Technology Program(2023YFS0380)Natural Science Foundation of Jiangsu Province of China(BK20242018)。
文摘The Yellow River Basin in Sichuan Province(YRS)is undergoing severe soil erosion and exacerbated ecological vulnerability,which collectively pose formidable challenges for regional water conservation(WC)and sustainable development.While effectively enhancing WC necessitates a comprehensive understanding of its driving factors and corresponding intervention strategies,existing studies have largely neglected the spatiotemporal heterogeneity of both natural and socio-economic drivers.Therefore,this study explored the spatiotemporal heterogeneity of WC drivers in YRS using multi-scale geographically weighted regression(MGWR)and geographically and temporally weighted regression(GTWR)models from an eco-hydrological perspective.We discovered that downstream regions,which are more developed,achieved significantly better WC than upstream regions.The results also demonstrated that the influence of temperature and wind speed is consistently dominant and temporally stable due to climate stability,while the influence of vegetation shifted from negative to positive around 2010,likely indicating greater benefits from understory vegetation.Economic growth positively impacted WC in upstream regions but had a negative effect in the more developed downstream regions.These findings highlight the importance of targeted water conservation strategies,including locally appropriate revegetation,optimization of agricultural and economic structures,and the establishment of eco-compensation mechanisms for ecological conservation and sustainable development.
基金supported by the National Natural Science Foundation of China(Nos.52260028,52060022,52260029,and 52160021)the National Key Research and Development Program of China(Nos.2017YFE0114800 and 2019YFC0409200)+1 种基金Inner Mongolia Autonomous Region Science and Technology Plan(No.2021GG0089)personal grant to Guohua Li by China Scholarship Council(CSC).
文摘Eutrophic shallow lakes are generally considered as a contributor to the emission of nitrous oxide(N_(2)O),while regional and global estimates have remained imprecise.This due to a lack of data and insufficient understanding of the multiple contributing factors.This study characterized the spatiotemporal variability in N_(2)O concentrations and N_(2)O diffusive fluxes and the contributing factors in LakeWuliangsuhai,a typical shallow eutrophic and seasonally frozen lake in Inner Mongolia with cold and arid climate.Dissolved N_(2)O concentrations of the lake exhibited a range of 4.5 to 101.2 nmol/L,displaying significant spatiotemporal variations.The lowest and highest concentrations were measured in summer and winter,respectively.The spatial distribution of N_(2)Ofluxwas consistent with that of N_(2)O concentrations.Additionally,the hotspots of N_(2)O emissions were detected within close to the main inflow of lake.The wide spatial and temporal variation in N_(2)O emissions indicate the complexity and its relative importance of factors influencing emissions.N_(2)O emissions in different lake zones and seasons were regulated by diverse factors.Factors influencing the spatial and temporal distribution of N_(2)O concentrations and fluxes were identified as WT,WD,DO,Chl-a,SD and COD.Interestingly,the same factor demonstrated opposing effects on N_(2)O emission in various seasons or zones.This research improves our understanding of N_(2)O emissions in shallow eutrophic lakes in cold and arid areas.
基金supported by the National Natural Science Foundation of China(No.31660234).
文摘Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.
基金Supported by Open Fund of Key Laboratory of Plant Nutrition and Fertilizer,Ministry of Agriculture(2013-1)Guangxi Natural Science Foundation for Youths(2011GXNSFB018026)+2 种基金Guangxi Scientific Research and Technological Development Program(14125008-2-15)Scientific and Technological Development Fund for Youths of Guangxi Academy of Agricultural Sciences(2013YQ18,2013YF06)the State Key Program of National Natural Science of China(U1033004-06)~~
文摘The coupling of water and fertilizer is the only way for high yield, efficiency, sugar on sugarcane. On sugarcane production, the spatial and temporal controlling technology of fertigation is an important direction of the sustainable and healthy development of ecological agriculture in cane area of China. This paper reviews main achievements and advances on the coupling effect of water and fertilizer on sugarcane from time and space at home or abroad in recent years, analyzes the application prospects of the temporal and spatial coupling effect of water and fertilizer on sugarcane and puts forward some problems which need further research in future.
文摘The cloud-to-ground lightning data between 2007 and 2008 were collected by lightning detection and location system,which was composed of four lightning detectors in four different sites of Dalian area.The spatio-temporal distribution of cloud-to-ground lightning in surrounding areas of Dalian was analyzed from several aspects of polarity distribution,diurnal variation,lightning intensity and lightning density.The results showed that the number of negative lightning accounted for 93.9% of the total number of lightning,and its average lightning intensity was 27.99 kA.The number of positive lightning accounted for 6.1% of the total number of lightning,and its average lightning intensity was 35.56 kA.The diurnal variation of lightning frequency showed an obvious structure of two peaks (17:00-18:00 and 04:00-05:00) and two valleys (09:00-10:00 and 00:00-01:00).The number of lightning between May and September was 91.5% of the annual number,and the lightning occurred the most frequently between June and August.Most of positive and negative lightning was at the intensity of 15-35 kA,80.0% lower than 40 kA,and 99.3% lower than 100 kA.The lightning density had obvious regional differences in distribution,high in the Liaodong Bay and the Dalian Bay and low in inland areas.Therefore,coastal areas should attract more attention in lightning disaster defense in the surrounding areas of Dalian.
基金supported by the Key Technologies R&D Program of China during the 12th Five-Year Plan period(2012BAD09B01)the National 973 Program of China(2012CB955904)the Project of Food Security and Climate Change in the Asia-Pacific Region:Evaluating Mismatch between Crop Development and Water Availability and Project of National Non-profit Institute Fund,China-Australia(BSRF201206)
文摘Climate change will have important implications in water shore regions,such as Huang-Huai-Hai（3H） plain,where expected warmer and drier conditions might augment crop water demand.Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation in reference evapotranspiration（ET 0）.In this study,the 51-yr ET 0 during winter wheat and summer maize growing season were calculated from a data set of daily climate variables in 40 meteorological stations.Sensitivity maps for key climate variables were estimated according to Kriging method and the spatial pattern of sensitivity coefficients for these key variables was plotted.In addition,the slopes of the linear regression lines for sensitivity coefficients were obtained.Results showed that ET 0 during winter wheat growing season accounted for the largest proportion of annual ET 0,due to its long phenological days,while ET 0 was detected to decrease significantly with the magnitude of 0.5 mm yr-1in summer maize growing season.Solar radiation is considered to be the most sensitive and primarily controlling variable for negative trend in ET 0 for summer maize season,and higher sensitive coefficient value of ET 0 to solar radiation and temperature were detected in east part and southwest part of 3H plain respectively.Relative humidity was demonstrated as the most sensitive factor for ET 0 in winter wheat growing season and declining relativity humidity also primarily controlled a negative trend in ET 0,furthermore the sensitivity coefficient to relative humidity increased from west to southeast.The eight sensitivity centrals were all found located in Shandong Province.These ET 0 along with its sensitivity maps under winter wheat-summer maize rotation system can be applied to predict the agricultural water demand and will assist water resources planning and management for this region.
基金National Natural Science Foundation of China,No.41671037Youth Innovation Promotion Association of CAS,No.2016049+1 种基金Key Research Program of Frontier Sciences,CAS,No.QYZDB-SSW-DQC005Program for "Kezhen" Excellent Talents in IGSNRR,CAS,No.2017RC101
文摘Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data from 114 agro-meteorological stations in four maize growing zones in China. Results showed that average air temperature and growing degree-days (GDD) during maize growing seasons showed an increasing trend from 1981 to 2010, while precipitation and sunshine duration showed a decreasing trend. Maize phenology has significantly changed under climate change: spring maize phenology was mainly advanced, especially in northwest and southwest maize zones, while summer and spring-summer maize phenology was delayed. The delay trend observed for summer maize in the northwest maize zone was more pronounced than in the Huang-Huai spring-summer maize zone. Variations in maize phenology changed the corresponding growth stages length: the vegetative growth period (days from sowing date to tasseling date) was generally shortened in spring, summer, and spring-summer maize, although to different degrees, while the reproductive growth period (days from tasseling date to mature date) showed an extension trend. The entire growth period(days from sowing date to mature date) of spring maize was extended, but the entire growth periods of summer and spring-summer maize were shortened.
