Stroke is the third-leading cause of disabilityadjusted life years(DALYs)and poses a significant public health challenge worldwide~([1]).Developing countries,including China,continue to face a substantial burden from ...Stroke is the third-leading cause of disabilityadjusted life years(DALYs)and poses a significant public health challenge worldwide~([1]).Developing countries,including China,continue to face a substantial burden from stroke.Since 1990,China has reported the highest global stroke burden,with 2.19 million deaths and 45.9 million DALYs recorded in 2019~([2]).展开更多
Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from differ...Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from different seasons of satellite images and the seasonal sensitivity of different methods.Four Landsat ETM?images of four different seasons and two popular methods(i.e.artificial neural network(ANN)and support vector machine(SVM))are employed to estimate the impervious surface on the pixel level.Results indicate that winter(dry season)is the best season to estimate impervious surface even though plants are not in their growing season.Less cloud and less variable source areas(VSA)(seasonal water body)become the major advantages of winter for the ISE,as cloud is easily confusedwith bright impervious surfaces,andwater in VSA is confusedwith dark impervious surfaces due to their similar spectral reflectance.For the seasonal sensitivity of methods,ANN appears more stable as its accuracy varied less than that obtained with SVM.However,both the methods showed a general consistency of the seasonal changes of the accuracy,indicating that winter time is the best season for impervious surfaces estimation with optical satellite images in subtropical monsoon regions.展开更多
The statistical study of F2 layer critical frequency at Dakar station from 1971 to 1996 is carried out. This paper shows foF2 statistical diurnal for all geomagnetic activities and all seasons and that during solar ma...The statistical study of F2 layer critical frequency at Dakar station from 1971 to 1996 is carried out. This paper shows foF2 statistical diurnal for all geomagnetic activities and all seasons and that during solar maximum and minimum phases. It emerges that foF2 diurnal variation graphs at Dakar station exhibits the different types of foF2 profiles in African EIA regions. The type of profile depends on solar activity, season and solar phase. During solar minimum and under quiet time condition, data show?the signature of a strength electrojet that is coupled with intense counter electrojet in the afternoon. Under disturbed conditions,?mean intense electrojet is observed in winter?during fluctuating and recurrent activities. Intense counter electrojet is seen under fluctuating and shock activities in all seasons coupled with strength electrojet in autumn. In summer?and spring under all geomagnetic activity condition, there is intense counter electrojet. During solar maximum, in summer and spring there is no electrojet under geomagnetic activity conditions.?Winter shows a mean intense electrojet. Winter and autumn are marked by the signature of the reversal electric field.展开更多
On the basis of the emission data of the industrial sulphur dioxide (SO_2) and observed climate fields over East Asia, the distribution of anthropogenic sulfate aerosol(SO_4~2-) with seasonal variation in the troposph...On the basis of the emission data of the industrial sulphur dioxide (SO_2) and observed climate fields over East Asia, the distribution of anthropogenic sulfate aerosol(SO_4~2-) with seasonal variation in the troposphere is simulated and analyzed by a regional sulfur transport model, and the direct radiative effects of SO_4~2- under different weather conditions are also calculated using the discrete ordinate method. The results show that the concentration of SO_4~2- has significant seasonal and spatial variations resulting from the effects of SO_2 emission source and precipitation and wind fields. Both the concentration of SO_2 and its radiative forcing have the largest values in October and the lowest in July. SO_4~2- causes the decrease of the radiation flux absorbed by earth-atmosphere and the cooling of air temperature by scattering more solar radiation back into space. Besides, the radiative and climatic effects of SO_4~2- are related to the types and height and optical thickness, etc., of the clouds.展开更多
Taken the Dalian lake region as the study area,which represents the typical agriculture production mode and agricultural non-point source pollution （ANSP） in Dianshan lake area in Shanghai City,basis on the characte...Taken the Dalian lake region as the study area,which represents the typical agriculture production mode and agricultural non-point source pollution （ANSP） in Dianshan lake area in Shanghai City,basis on the characteristics of regional ANSP and combing with the seasonal water quality monitoring of Dalian Lake and reaches of its main influents,the laws of seasonal impact on the water environment were investigated.The results showed that,the seasonal change of TN and COD concentration of regional water had no significant correlation with the local ANSP emissions,while the seasonal changes of TP was consistent with seasonal emissions of regional TP pollution,and it had a significant correlation with Chl.a in four seasons,indicating that regional TP pollutant was the constriction factor influenced the eutrophication degree of Dalian lake.Because more than 80% of TP emissions came from the drainage of intensive pounds in winter,summer and fall,TP pollutant control should be adopted as the control target of regional ANSP control.展开更多
Wetland degradation is an escalating global challenge with profound impacts on animal diversity,particularly during successional processes.Birds,as highly mobile and environmentally sensitive organisms,serve as effect...Wetland degradation is an escalating global challenge with profound impacts on animal diversity,particularly during successional processes.Birds,as highly mobile and environmentally sensitive organisms,serve as effective indicators of ecological change.While previous studies have primarily focused on local community structures and species diversity during a specific season,there is a need to extend the research timeframe and explore broader spatial variations.Additionally,expanding from simple species diversity indices to more multidimensional diversity indices would provide a more comprehensive understanding of wetland health and resilience.To address these gaps,we investigated the effects of wetland degradation on bird diversity across taxonomic,phylogenetic,and functional dimensions in the Zoige Wetland,a plateau meadow wetland biodiversity hotspot.Surveys were conducted during both breeding(summer)and overwintering(winter)seasons across 20 transects in 5 sampling areas,representing 4 degradation levels(pristine,low,medium,and high).Our study recorded a total of 106 bird species from 32 families and 14 orders,revealing distinct seasonal patterns in bird community composition and diversity.Biodiversity indices were significantly higher in pristine and low-degraded wetlands,particularly benefiting waterfowl(Anseriformes,Ciconiiformes)and wading birds(Charadriiformes)in winter,when these areas provided superior food resources and habitat conditions.In contrast,medium and highly degraded wetlands supported increased numbers of terrestrial birds(Passeriformes)and raptors(Accipitriformes,Falconiformes).Seasonal differences in taxonomic,phylogenetic,and functional diversity indices highlighted the contrasting ecological roles of wetlands during breeding and overwintering periods.Furthermore,indicator species analysis revealed key species associated with specific degradation levels and seasons,providing valuable insights into wetland health.This study underscores the importance of spatiotemporal dynamics in understanding avian responses to wetland degradation.By linking seasonal patterns of bird diversity to habitat conditions,our findings contribute to conservation efforts and provide a framework for assessing wetland degradation and its ecological impacts.展开更多
In this paper,we present an attempt at analyzing the seasonal pattern of the Babesiosis transmission dynamics in bovine and tick populations.The infestation rate plays an important role in the variation of infection.I...In this paper,we present an attempt at analyzing the seasonal pattern of the Babesiosis transmission dynamics in bovine and tick populations.The infestation rate plays an important role in the variation of infection.In this logic,we set out a mathematical model with variable infestation rate for the evolution of babesiosis disease.Using the(0,2)-Jacobi multi-wavelets method combined with the decoupling and quasi-linearization technique,we demonstrate the validity and applicability of our model.Then,a set of experimental data is used to validate the proposed model under the same operating conditions.The results of numerical simulations are provided to show the impact of seasonality on the transmission dynamics of Babesiosis.Eventually,a numerical study of the model varying the control parameters of babesiosis shows different scenarios about the spread of the disease.展开更多
Using a newly reported Pacific sea surface temperature data set, we extend a prior study that assigned El Niño episodes to distinct sequences. Within these sequences the episodes are phase-locked to subharmoni...Using a newly reported Pacific sea surface temperature data set, we extend a prior study that assigned El Niño episodes to distinct sequences. Within these sequences the episodes are phase-locked to subharmonics of the annual solar irradiance cycle having two- or three-year periodicity. There are 40 El Niño episodes occurring since 1872, each found within one of eighteen such sequences. Our list includes all previously reported events. Three El Niño episodes have already been observed in boreal winters of 2009, 2012 and 2015, illustrating a sequence of 3-year intervals that began in 2008. If the climate system remains in this state, the next El Niño is likely to occur in boreal winter of 2018.展开更多
This study examined particulate matter with a diameter of 2.5μm or less(PM_(2.5))samples to investigate seasonal shifts in bacterial and fungal communities in Seoul,Republic of Korea.To assess these variations and th...This study examined particulate matter with a diameter of 2.5μm or less(PM_(2.5))samples to investigate seasonal shifts in bacterial and fungal communities in Seoul,Republic of Korea.To assess these variations and the influence of environmental factors,DNA was extracted from PM_(2.5) samples and subjected to sequencing analysis.The results showed distinct seasonal changes in microbial communities.Pseudarthrobacter dominated in winter,Arthrospira in spring,Rhodococcus in summer,and Pelomonas in autumn among the bacterial communities,while Candida in winter,Coprinopsis in spring,and Cutaneotrichosporon in both summer and autumn were prevalent in fungal communities.Bacterial richness peaked in spring,whereas fungal richness was highest in winter.These shifts were driven by environmental factors:air pollutants and chemical compositions had a greater influence in winter and spring,while meteorological conditions,such as temperature and humidity,were dominant in summer and autumn.Functional gene analysis revealed a prevalence of metabolic pathways essential for microbial survival,with fungi showing a higher proportion of saprotrophs,particularly in spring.This comprehensive analysis,considering a wide range of environmental factors including meteorological conditions,air pollutants,and atmospheric organic compounds such as polyaromatic hydrocarbons(PAHs)and dicarboxylic acids(DCAs),provides novel insights into the dynamic relationships between environmental factors and microbial communities in PM_(2.5),highlighting the significant role of anthropogenic influences.This research advances our understanding of atmospheric microbial ecosystems and their seasonal dynamics.展开更多
Accurate building energy simulation(BES)is essential for developing effective energy conservation strategies and implementing evidence-based policy interventions in the built environment.However,BES accuracy is often ...Accurate building energy simulation(BES)is essential for developing effective energy conservation strategies and implementing evidence-based policy interventions in the built environment.However,BES accuracy is often undermined by unrealistic weather data,as conventional Typical Meteorological Year(TMY)files fail to adequately capture urban microclimate variations.This study proposes a deep learning model that integrates wind-driven building morphology maps for high-resolution temporal microclimate prediction.By combining macro-scale wind dynamics with urban morphological features,encoded as frontal area maps,the model captures seasonal microclimate variations influenced by prevailing wind conditions.Validation conducted on a university campus demonstrates that the proposed model outperforms benchmark approaches in predicting air temperature and relative humidity(RH).The ground truth for validation is the real-time microclimate data collected by weather stations installed across the campus.Compared to TMY files,a standard deep learning model,and a deep learning model with wind directions,the proposed model reduces the root mean squared error(RMSE)in air temperature by 36.3%,14.2%,and 14.0%,and RMSE in RH by 30.5%,17.3%,and 17.3%,respectively.When integrated into BES for three test buildings,the model’s weather data enabled cooling energy prediction with less than 2%error,significantly outperforming alternative methods.Overall,the proposed model allows cross-building temporal microclimate prediction without requiring long-term weather data collection at the target building.展开更多
Ellagic acid （EA） has aroused great interest worldwide owing to its antioxidant, anti-inflammatory, and anticarcinogenetic properties. The EA content in pomegranate leaf was measured in this study using high perform...Ellagic acid （EA） has aroused great interest worldwide owing to its antioxidant, anti-inflammatory, and anticarcinogenetic properties. The EA content in pomegranate leaf was measured in this study using high performance liquid chromatography to investigate the effects of season, variety, and processing method on the EA level. The results show that the EA content in 11 varieties of pomegranate from the Zaozhuang region in China range from 1.30 mg · g^-1 to 6.46 mg · g^-1 of dry weight in five consecutive seasons from June to October. An analysis of variance （ANOVA） shows that the EA content is significantly dependent on the season （p〈0.05）. The EA content increases significantly during the growing season to the highest level in September and October. The effect of the leaf variety on the EA content is less significant than the season. The processing methods have different effects on the EA content. Soaking for 24 hours slightly increases the EA content （p〈0.05）. Heating at 80℃ or 100℃ for 1 h after soaking has little influence on the EA content, while slow-fired cooking at high temperature significantly elevates the EA content （p〈0.05）. To improve quality and stability, several parameters such as leaf collection time, slow-fired cooking, and cooking time should be strictly controlled during the processing of pomegranate leaf tea and its extract.展开更多
The recurrent extreme El Niño events are commonly linked to reduced vegetation growth and the land carbon sink over many but discrete regions of the Northern Hemisphere(NH).However,we reported here a pervasive an...The recurrent extreme El Niño events are commonly linked to reduced vegetation growth and the land carbon sink over many but discrete regions of the Northern Hemisphere(NH).However,we reported here a pervasive and continuous vegetation greening and no weakened land carbon sink in the maturation phase of the 2015/2016 El Niño event over the NH(mainly in the extra-tropics),based on multiple evidences from remote sensing observations,global ecosystem model simulations and atmospheric CO_(2)inversions.We discovered a significant compensation effect of the enhanced vegetation growth in spring on subsequent summer/autumn vegetation growth that sustained vegetation greening and led to a slight increase in the land carbon sink over the spring and summer of 2015(average increases of 23.34%and 0.63%in net ecosystem exchange from two independent datasets relative to a 5-years average before the El Niño event,respectively)and spring of 2016(6.82%),especially in the extra-tropics of the NH,where the water supply during the pre-growing-season(November of the previous year to March of the current year)had a positive anomaly.This seasonal compensation effect was much stronger than that in 1997 and 1998 and significantly alleviated the adverse impacts of the 2015/2016 El Niño event on vegetation growth during its maturation phase.The legacy effect of water supply during the pre-growing-season on subsequent vegetation growth lasted up to approximately six months.Our findings highlight the role of seasonal compensation effects on mediating the land carbon sink in response to episodic extreme El Niño events.展开更多
Leaf senescence is a complex phenomenon occurring in all plant species, but it is still poorly understood in plants grown in Mediterranean field conditions and well-adapted to harsh climatic conditions. To better unde...Leaf senescence is a complex phenomenon occurring in all plant species, but it is still poorly understood in plants grown in Mediterranean field conditions and well-adapted to harsh climatic conditions. To better understand the physiological processes underlying leaf senescence in mastic trees （Pistacia lentiscus L.）, we evaluated leaf growth, water and N content, photosystem II （PSII） photochemistry, lipid peroxidation and levels of photosynthetic pigments, antioxidants, abscisic acid, and salicylic acid and jasmonic acid during the complete leaf lifespan, from early expansion to late senescence in relation to natural climatic conditions in the field. While mature leaves suffered from water and N deficit during late spring and summer, both young （emerging） and old （senescing） leaves were most sensitive to photo- oxidative stress, as indicated by reductions in the Fv/Fm ratio and enhanced lipid peroxidation during late autumn and winter. Reductions in the FvlFm ratio were associated with low ^-tocopherol （vitamin E） levels, while very old, senescing leaves additionally showed severe anthocyanin losses. We have concluded that both young （emerging） and old （senescing） leaves suffer oxidative stress in mastic trees, which may be linked in part to suboptimal temperatures during late autumn and winter as well as to low vitamin E levels.展开更多
The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the for...The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.展开更多
This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future hi...This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.展开更多
基金Supported by Qingdao Key Medical and Health Discipline Project(2025060)Qingdao Municipal Science and Technology Special Program for the Public(23-2-8-smjk-18-nsh)+1 种基金Shandong Public Health Association Program(No.SGWXH202303)Qingdao Outstanding Health Professional Development(2020-2022.2022-2024)。
文摘Stroke is the third-leading cause of disabilityadjusted life years(DALYs)and poses a significant public health challenge worldwide~([1]).Developing countries,including China,continue to face a substantial burden from stroke.Since 1990,China has reported the highest global stroke burden,with 2.19 million deaths and 45.9 million DALYs recorded in 2019~([2]).
基金The ETM+data from USGS are highly appreciated.This study is jointly supported by the CUHK Direct Grants(2021103)Hong Kong Research Grants Council(RGC)General Research Grants(GRF)project(CUHK 459210 and 457212)+2 种基金Hong Kong Innovation and Technology Fund(GHP/002/11GD)the funding of Shenzhen Municipal Science and Technology Innovation Council(JCYJ20120619151239947)the National Key Technol-ogies R&D Program in the 12th Five Year Plan of China(2012BAH32B03).
文摘Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from different seasons of satellite images and the seasonal sensitivity of different methods.Four Landsat ETM?images of four different seasons and two popular methods(i.e.artificial neural network(ANN)and support vector machine(SVM))are employed to estimate the impervious surface on the pixel level.Results indicate that winter(dry season)is the best season to estimate impervious surface even though plants are not in their growing season.Less cloud and less variable source areas(VSA)(seasonal water body)become the major advantages of winter for the ISE,as cloud is easily confusedwith bright impervious surfaces,andwater in VSA is confusedwith dark impervious surfaces due to their similar spectral reflectance.For the seasonal sensitivity of methods,ANN appears more stable as its accuracy varied less than that obtained with SVM.However,both the methods showed a general consistency of the seasonal changes of the accuracy,indicating that winter time is the best season for impervious surfaces estimation with optical satellite images in subtropical monsoon regions.
文摘The statistical study of F2 layer critical frequency at Dakar station from 1971 to 1996 is carried out. This paper shows foF2 statistical diurnal for all geomagnetic activities and all seasons and that during solar maximum and minimum phases. It emerges that foF2 diurnal variation graphs at Dakar station exhibits the different types of foF2 profiles in African EIA regions. The type of profile depends on solar activity, season and solar phase. During solar minimum and under quiet time condition, data show?the signature of a strength electrojet that is coupled with intense counter electrojet in the afternoon. Under disturbed conditions,?mean intense electrojet is observed in winter?during fluctuating and recurrent activities. Intense counter electrojet is seen under fluctuating and shock activities in all seasons coupled with strength electrojet in autumn. In summer?and spring under all geomagnetic activity condition, there is intense counter electrojet. During solar maximum, in summer and spring there is no electrojet under geomagnetic activity conditions.?Winter shows a mean intense electrojet. Winter and autumn are marked by the signature of the reversal electric field.
文摘On the basis of the emission data of the industrial sulphur dioxide (SO_2) and observed climate fields over East Asia, the distribution of anthropogenic sulfate aerosol(SO_4~2-) with seasonal variation in the troposphere is simulated and analyzed by a regional sulfur transport model, and the direct radiative effects of SO_4~2- under different weather conditions are also calculated using the discrete ordinate method. The results show that the concentration of SO_4~2- has significant seasonal and spatial variations resulting from the effects of SO_2 emission source and precipitation and wind fields. Both the concentration of SO_2 and its radiative forcing have the largest values in October and the lowest in July. SO_4~2- causes the decrease of the radiation flux absorbed by earth-atmosphere and the cooling of air temperature by scattering more solar radiation back into space. Besides, the radiative and climatic effects of SO_4~2- are related to the types and height and optical thickness, etc., of the clouds.
基金Supported by Science and Technology Support Program in Shanghai Science and Technology Committee (08DZ1203200, 08DZ1203205)~~
文摘Taken the Dalian lake region as the study area,which represents the typical agriculture production mode and agricultural non-point source pollution （ANSP） in Dianshan lake area in Shanghai City,basis on the characteristics of regional ANSP and combing with the seasonal water quality monitoring of Dalian Lake and reaches of its main influents,the laws of seasonal impact on the water environment were investigated.The results showed that,the seasonal change of TN and COD concentration of regional water had no significant correlation with the local ANSP emissions,while the seasonal changes of TP was consistent with seasonal emissions of regional TP pollution,and it had a significant correlation with Chl.a in four seasons,indicating that regional TP pollutant was the constriction factor influenced the eutrophication degree of Dalian lake.Because more than 80% of TP emissions came from the drainage of intensive pounds in winter,summer and fall,TP pollutant control should be adopted as the control target of regional ANSP control.
基金supported by the Southwest Minzu University Research Startup Funds (No.16011221038,RQD2022021)Double World-Class Project (No.CX2023010)。
文摘Wetland degradation is an escalating global challenge with profound impacts on animal diversity,particularly during successional processes.Birds,as highly mobile and environmentally sensitive organisms,serve as effective indicators of ecological change.While previous studies have primarily focused on local community structures and species diversity during a specific season,there is a need to extend the research timeframe and explore broader spatial variations.Additionally,expanding from simple species diversity indices to more multidimensional diversity indices would provide a more comprehensive understanding of wetland health and resilience.To address these gaps,we investigated the effects of wetland degradation on bird diversity across taxonomic,phylogenetic,and functional dimensions in the Zoige Wetland,a plateau meadow wetland biodiversity hotspot.Surveys were conducted during both breeding(summer)and overwintering(winter)seasons across 20 transects in 5 sampling areas,representing 4 degradation levels(pristine,low,medium,and high).Our study recorded a total of 106 bird species from 32 families and 14 orders,revealing distinct seasonal patterns in bird community composition and diversity.Biodiversity indices were significantly higher in pristine and low-degraded wetlands,particularly benefiting waterfowl(Anseriformes,Ciconiiformes)and wading birds(Charadriiformes)in winter,when these areas provided superior food resources and habitat conditions.In contrast,medium and highly degraded wetlands supported increased numbers of terrestrial birds(Passeriformes)and raptors(Accipitriformes,Falconiformes).Seasonal differences in taxonomic,phylogenetic,and functional diversity indices highlighted the contrasting ecological roles of wetlands during breeding and overwintering periods.Furthermore,indicator species analysis revealed key species associated with specific degradation levels and seasons,providing valuable insights into wetland health.This study underscores the importance of spatiotemporal dynamics in understanding avian responses to wetland degradation.By linking seasonal patterns of bird diversity to habitat conditions,our findings contribute to conservation efforts and provide a framework for assessing wetland degradation and its ecological impacts.
文摘In this paper,we present an attempt at analyzing the seasonal pattern of the Babesiosis transmission dynamics in bovine and tick populations.The infestation rate plays an important role in the variation of infection.In this logic,we set out a mathematical model with variable infestation rate for the evolution of babesiosis disease.Using the(0,2)-Jacobi multi-wavelets method combined with the decoupling and quasi-linearization technique,we demonstrate the validity and applicability of our model.Then,a set of experimental data is used to validate the proposed model under the same operating conditions.The results of numerical simulations are provided to show the impact of seasonality on the transmission dynamics of Babesiosis.Eventually,a numerical study of the model varying the control parameters of babesiosis shows different scenarios about the spread of the disease.
文摘Using a newly reported Pacific sea surface temperature data set, we extend a prior study that assigned El Niño episodes to distinct sequences. Within these sequences the episodes are phase-locked to subharmonics of the annual solar irradiance cycle having two- or three-year periodicity. There are 40 El Niño episodes occurring since 1872, each found within one of eighteen such sequences. Our list includes all previously reported events. Three El Niño episodes have already been observed in boreal winters of 2009, 2012 and 2015, illustrating a sequence of 3-year intervals that began in 2008. If the climate system remains in this state, the next El Niño is likely to occur in boreal winter of 2018.
基金supported by the National research Foundation of the Republic Korea(NRF)grant funded by the Republic of Korea government,the Ministry of Science and ICT(MSIT)(No.2022R1A2C2006615 and RS2023-00217228)supported by the Particulate Matter Management Specialized Graduate Program through the Republic Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘This study examined particulate matter with a diameter of 2.5μm or less(PM_(2.5))samples to investigate seasonal shifts in bacterial and fungal communities in Seoul,Republic of Korea.To assess these variations and the influence of environmental factors,DNA was extracted from PM_(2.5) samples and subjected to sequencing analysis.The results showed distinct seasonal changes in microbial communities.Pseudarthrobacter dominated in winter,Arthrospira in spring,Rhodococcus in summer,and Pelomonas in autumn among the bacterial communities,while Candida in winter,Coprinopsis in spring,and Cutaneotrichosporon in both summer and autumn were prevalent in fungal communities.Bacterial richness peaked in spring,whereas fungal richness was highest in winter.These shifts were driven by environmental factors:air pollutants and chemical compositions had a greater influence in winter and spring,while meteorological conditions,such as temperature and humidity,were dominant in summer and autumn.Functional gene analysis revealed a prevalence of metabolic pathways essential for microbial survival,with fungi showing a higher proportion of saprotrophs,particularly in spring.This comprehensive analysis,considering a wide range of environmental factors including meteorological conditions,air pollutants,and atmospheric organic compounds such as polyaromatic hydrocarbons(PAHs)and dicarboxylic acids(DCAs),provides novel insights into the dynamic relationships between environmental factors and microbial communities in PM_(2.5),highlighting the significant role of anthropogenic influences.This research advances our understanding of atmospheric microbial ecosystems and their seasonal dynamics.
基金supported by the National University of Singapore Start-Up Grant(A-0009876-00-00)the Ministry of Education Singapore under the Academic Research Fund Tier 1(A-8003235-00-00).
文摘Accurate building energy simulation(BES)is essential for developing effective energy conservation strategies and implementing evidence-based policy interventions in the built environment.However,BES accuracy is often undermined by unrealistic weather data,as conventional Typical Meteorological Year(TMY)files fail to adequately capture urban microclimate variations.This study proposes a deep learning model that integrates wind-driven building morphology maps for high-resolution temporal microclimate prediction.By combining macro-scale wind dynamics with urban morphological features,encoded as frontal area maps,the model captures seasonal microclimate variations influenced by prevailing wind conditions.Validation conducted on a university campus demonstrates that the proposed model outperforms benchmark approaches in predicting air temperature and relative humidity(RH).The ground truth for validation is the real-time microclimate data collected by weather stations installed across the campus.Compared to TMY files,a standard deep learning model,and a deep learning model with wind directions,the proposed model reduces the root mean squared error(RMSE)in air temperature by 36.3%,14.2%,and 14.0%,and RMSE in RH by 30.5%,17.3%,and 17.3%,respectively.When integrated into BES for three test buildings,the model’s weather data enabled cooling energy prediction with less than 2%error,significantly outperforming alternative methods.Overall,the proposed model allows cross-building temporal microclimate prediction without requiring long-term weather data collection at the target building.
基金the National Natural Science Foundation of China (Nos. 30572340 and 30500651)the Fund of Technology Develop-ment of Tshinghua University (No. A2002162)the Doctoral Fund of the Education Ministry of China (No. 20060003072)
文摘Ellagic acid （EA） has aroused great interest worldwide owing to its antioxidant, anti-inflammatory, and anticarcinogenetic properties. The EA content in pomegranate leaf was measured in this study using high performance liquid chromatography to investigate the effects of season, variety, and processing method on the EA level. The results show that the EA content in 11 varieties of pomegranate from the Zaozhuang region in China range from 1.30 mg · g^-1 to 6.46 mg · g^-1 of dry weight in five consecutive seasons from June to October. An analysis of variance （ANOVA） shows that the EA content is significantly dependent on the season （p〈0.05）. The EA content increases significantly during the growing season to the highest level in September and October. The effect of the leaf variety on the EA content is less significant than the season. The processing methods have different effects on the EA content. Soaking for 24 hours slightly increases the EA content （p〈0.05）. Heating at 80℃ or 100℃ for 1 h after soaking has little influence on the EA content, while slow-fired cooking at high temperature significantly elevates the EA content （p〈0.05）. To improve quality and stability, several parameters such as leaf collection time, slow-fired cooking, and cooking time should be strictly controlled during the processing of pomegranate leaf tea and its extract.
基金This study was financially supported by the National Key Research and Development Program of China(Grant No.2022YFF0801802)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0306)+2 种基金the National Natural Science Foundation of China(Grant No.42171050)the China Postdoctoral Science Foundation(Grant No.2023M730281)the State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University(Grant No.2023-KF-07).
文摘The recurrent extreme El Niño events are commonly linked to reduced vegetation growth and the land carbon sink over many but discrete regions of the Northern Hemisphere(NH).However,we reported here a pervasive and continuous vegetation greening and no weakened land carbon sink in the maturation phase of the 2015/2016 El Niño event over the NH(mainly in the extra-tropics),based on multiple evidences from remote sensing observations,global ecosystem model simulations and atmospheric CO_(2)inversions.We discovered a significant compensation effect of the enhanced vegetation growth in spring on subsequent summer/autumn vegetation growth that sustained vegetation greening and led to a slight increase in the land carbon sink over the spring and summer of 2015(average increases of 23.34%and 0.63%in net ecosystem exchange from two independent datasets relative to a 5-years average before the El Niño event,respectively)and spring of 2016(6.82%),especially in the extra-tropics of the NH,where the water supply during the pre-growing-season(November of the previous year to March of the current year)had a positive anomaly.This seasonal compensation effect was much stronger than that in 1997 and 1998 and significantly alleviated the adverse impacts of the 2015/2016 El Niño event on vegetation growth during its maturation phase.The legacy effect of water supply during the pre-growing-season on subsequent vegetation growth lasted up to approximately six months.Our findings highlight the role of seasonal compensation effects on mediating the land carbon sink in response to episodic extreme El Niño events.
基金supported by the Spanish Government (BFU2009-07294)the research was also received through the prize ICREA Academia given to S.M.-B., funded by the Generalitat de Catalunya
文摘Leaf senescence is a complex phenomenon occurring in all plant species, but it is still poorly understood in plants grown in Mediterranean field conditions and well-adapted to harsh climatic conditions. To better understand the physiological processes underlying leaf senescence in mastic trees （Pistacia lentiscus L.）, we evaluated leaf growth, water and N content, photosystem II （PSII） photochemistry, lipid peroxidation and levels of photosynthetic pigments, antioxidants, abscisic acid, and salicylic acid and jasmonic acid during the complete leaf lifespan, from early expansion to late senescence in relation to natural climatic conditions in the field. While mature leaves suffered from water and N deficit during late spring and summer, both young （emerging） and old （senescing） leaves were most sensitive to photo- oxidative stress, as indicated by reductions in the Fv/Fm ratio and enhanced lipid peroxidation during late autumn and winter. Reductions in the FvlFm ratio were associated with low ^-tocopherol （vitamin E） levels, while very old, senescing leaves additionally showed severe anthocyanin losses. We have concluded that both young （emerging） and old （senescing） leaves suffer oxidative stress in mastic trees, which may be linked in part to suboptimal temperatures during late autumn and winter as well as to low vitamin E levels.
基金supported by the science and technology project of State Grid Corporation of China(Project Code:1400-202157207A-0-0-00)the National Natural Science Foundation of China[grant numbers 72273137].
文摘The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.
文摘This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.
