To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily tota...To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.展开更多
BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects betw...BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.展开更多
Background Few studies have investigated the effects of air pollutants on children with acute bronchitis.This study aimed to explore the acute effects of four air pollutants[fine particulate matter(PM_(2.5)),inhalable...Background Few studies have investigated the effects of air pollutants on children with acute bronchitis.This study aimed to explore the acute effects of four air pollutants[fine particulate matter(PM_(2.5)),inhalable particulate matter(PM10),sulfur dioxide(SO_(2)),and nitrogen dioxide(NO_(2))]on the daily number of children admitted to the hospital for acute bronchitis in Sichuan Province,China.Methods The 49,975 records of hospitalized children with acute bronchitis from medical institutions in nine cities/prefectures,Sichuan Province,China,as well as the simultaneous meteorological data and air pollution data from 183 monitoring sites,were collected from 1 January 2017 to 31 December 2018.A generalized additive model was adopted to analyze the exposure–response and lag effects of hospitalizations of children with acute bronchitis to air pollutants.Stratified analyses were conducted based on sex,age,and season.Results The single-pollutant model showed that a 10µg/m3 increase at lag07 of PM_(2.5),PM10,SO_(2),and NO_(2) corresponded to an increase of 1.23%[95%confidence interval(CI)0.21–2.26%],1.33%(95%CI 0.62–2.05%),23.52%(95%CI 11.52–36.81%),and 12.47%(95%CI 8.46–16.64%)in daily hospitalizations for children with acute bronchitis,respectively.Children aged 0–2 were more prone to PM_(2.5)(P<0.05).Interestingly,the effects were stronger in the warm season than in transition seasons and the cool season for PM_(2.5) and PM10(P<0.05).Conclusion The higher daily average concentrations of four pollutants in Sichuan Province can result in an increased number of children hospitalized for acute bronchitis.展开更多
Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in...Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasti...The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.展开更多
In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring meth...In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.展开更多
It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and externa...It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.展开更多
Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused signif...Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
Dear Editor,Psoriasis,a chronic inflammatory cutaneous condition,is characterized by the development of red plaques with silvery scales,significantly affecting patients'quality of life and mental health[1].This co...Dear Editor,Psoriasis,a chronic inflammatory cutaneous condition,is characterized by the development of red plaques with silvery scales,significantly affecting patients'quality of life and mental health[1].This condition is thought to affect approximately 2%of the Western population,with diagnosis peaking in early adulthood[2].Vitamin D,a fat-soluble vitamin,is essential for phospho-calcium metabolism,calcium homeostasis,and bone health.展开更多
Objective Observational studies have found associations between inflammatory bowel disease(IBD)and the risk of dementia,including Alzheimer’s dementia(AD)and vascular dementia(VD);however,these findings are inconsist...Objective Observational studies have found associations between inflammatory bowel disease(IBD)and the risk of dementia,including Alzheimer’s dementia(AD)and vascular dementia(VD);however,these findings are inconsistent.It remains unclear whether these associations are causal.Methods We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia.Mendelian randomization(MR)analysis based on summary genome-wide association studies(GWASs)was performed.Genetic correlation and Bayesian colocalization analyses were used to provide robust genetic evidence.Results Ten observational studies involving 80,565,688 participants were included in this metaanalysis.IBD was significantly associated with dementia(risk ratio[RR]=1.36,95%CI=1.04-1.78;I2=84.8%)and VD(RR=2.60,95%CI=1.18-5.70;only one study),but not with AD(RR=2.00,95%CI=0.96-4.13;I^(2)=99.8%).MR analyses did not supported significant causal associations of IBD with dementia(dementia:odds ratio[OR]=1.01,95%CI=0.98-1.03;AD:OR=0.98,95%CI=0.95-1.01;VD:OR=1.02,95%CI=0.97-1.07).In addition,genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia.Conclusion Our study did not provide genetic evidence for a causal association between IBD and dementia risk.The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.展开更多
This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural commu...This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural communication skills,in-depth regional and country knowledge,and practical expertise in ocean affairs.Additionally,the paper presents specific training pathways and policy recommendations for implementing this model.展开更多
In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audi...In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audience,imposes distinct requirements on children’s books,compelling translators to approach the text from a child’s perspective.“The Little Prince”has renowned both within and outside of China,and a careful reading of this work can provide us with much inspiration.To this end,the present study adopts the perspective of Gideon Toury’s Descriptive Translation Studies to conduct an in-depth analysis of the different English and Chinese translations in conjunction with the original French novel.This approach aims to better guide literary research and explores translation methods for children’s literature through the analysis of translation norms and rules.展开更多
Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribu...Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.展开更多
Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrat...Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrated significant associations between CRC risk and various serum metabolites as well as inflammatory cytokines;however,due to limitations in study design and potential confounding factors,the causal relationships remain unclear.This study aims to investigate the causal relationships between inflammatory cytokines,serum metabolites,and CRC risk,providing a theoretical basis for the development of novel early diagnostic biomarkers and therapeutic targets.Methods:A two-sample Mendelian randomization(MR)design was applied using summary statistics from genome-wide association studies(GWAS).Instrumental variables(IVs)were derived from:1)metabolomics GWAS data of 1400 serum metabolites(n=8299);2)cytokine GWAS data of 91 inflammatory factors(n=14824);and 3)CRC risk data from the FinnGen consortium(6847 cases and 314193 controls).The primary analysis was conducted using the inverse-variance weighted(IVW)method,with sensitivity analyses performed using MR Egger regression and the weighted median method.Effect estimates including odds ratios(OR),95%confidence intervals(CI),and false discovery rates(FDR)were calculated.Results:MR analysis indicated that higher levels of axin-1(AXIN1)(OR=0.84195%CI 0.714 to 0.991)and Fms-related tyrosine kinase 3 ligand(Flt3L)(OR=0.916,95%CI 0.844 to 0.994)were associated with a reduced risk of CRC.In contrast,higher levels of Delta/Notchlike epidermal growth factor-related receptor(DNER)(OR=1.119,95%CI 1.009 to 1.241)and vascular endothelial growth factor A(VEGF-A)(OR=1.078,95%CI 1.011 to 1.150)were associated with an increased risk of CRC(all P<0.05).Metabolomics association analysis further identified 144 serum metabolites significantly correlated with these four key inflammatory cytokines(FDR<0.05),suggesting that they may regulate CRC risk through inflammatory pathways.Conclusion:Specific inflammatory cytokines and serum metabolites have causal relationships with the risk of CRC.These findings provide insights for further exploration of potential risk factors and the development of effective prevention strategies for CRC.展开更多
In molybdenum chemistry,the oxidative addition of o-quinone or 1,2-dicarbonyl compounds to molybdenum has been widely used in Mo-catalyzed C—C bond construction.The carbonyl oxidative addition to Mo(0)or Mo(Ⅱ)is the...In molybdenum chemistry,the oxidative addition of o-quinone or 1,2-dicarbonyl compounds to molybdenum has been widely used in Mo-catalyzed C—C bond construction.The carbonyl oxidative addition to Mo(0)or Mo(Ⅱ)is the critical elementary reaction of molybdenum catalysis.However,the relevant density functional theory(DFT)studies are relatively scarce,especially regarding the rational selection of functionals.In this work,14 functionals were employed to investigate the Mo-catalyzed carbonyl oxidative addition step.A benchmark study was carried out to evaluate their performance in structure optimization and energy calculation.Analyses of mean absolute error(MAE)and mean squared error(MSE)indicated that the B3LYP-D3(BJ),TPSSh,and ωB97X-D functionals exhibited superior performance in structure optimization.Using the DLPNO-CCSD(T)functional as the reference,the M06,M06-L,and MN15-L functionals exhibited good performance for energy calculation based on the structures optimized using the B3LYP-D3(BJ)functional.In particular,MN15-L provided the best performance with the smallest MAE and MSE.展开更多
Objective:To identify the root causes of typical adverse drug events through the lens of patient experiences proposing novel strategies to mitigate preventable harm.Methods:A qualitative case study leveraging in-depth...Objective:To identify the root causes of typical adverse drug events through the lens of patient experiences proposing novel strategies to mitigate preventable harm.Methods:A qualitative case study leveraging in-depth interviews with patients and families,anchored by Interactive Patient Par ticipation Theory,to analyze 4 high-severity adverse drug events(ADE)cases.Cases were purposively sampled from 8 communities in China's National Adverse Event Monitor Center(2018-2023).Semi-structured interviews explored patient perspectives,with data analyzed via thematic coding and triangulation against clinical records.Results:Five interconnected themes emerged:(1)erosion of trust,(2)communication breakdowns,(3)information asymmetry,(4)environmental inadequacies,and(5)technological alienation.Notably,75% of participants had≤high school education,and 50% used≥7 medications daily,compounding ADE risks.Conclusions:We considered elements mentioned by theory,exploring trust,communication,information,and suppor t as the root causes.In addition,we added“adaptability to new technology”as an impor tant and necessary component.It is impor tant and necessary to analyze typical adverse drug events from the perspectives of patients.展开更多
China ranked first worldwide in the production and export of electric bicycles.As an emerging market for electric bicycles,Malaysia holds significant potential for trade collabor ation with China in this sector.This s...China ranked first worldwide in the production and export of electric bicycles.As an emerging market for electric bicycles,Malaysia holds significant potential for trade collabor ation with China in this sector.This study presents a compar ative analysis of the national electric bicycle standards in China and Malaysia,offering technical insights from a standardization perspective.These insights aim to support Chinese enterprises in strategically positioning their technologies in the Malaysian market.The findings reveal significant differences in technical parameters,safety requirements,and testing methods,highlighting the need for tailored product adapt ation.展开更多
文摘To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.
基金This study was reviewed and approved by the Ethics Committee of The First Psychiatric Hospital of Harbin.
文摘BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.
基金The authors received financial support from the National Natural Science Foundation of China(No.72174032)the research projects of“Xinglin Scholars”Nursery Talent in 2021 Research Plan of Chengdu University of Traditional Chinese Medicine through grants MPRC2021013.
文摘Background Few studies have investigated the effects of air pollutants on children with acute bronchitis.This study aimed to explore the acute effects of four air pollutants[fine particulate matter(PM_(2.5)),inhalable particulate matter(PM10),sulfur dioxide(SO_(2)),and nitrogen dioxide(NO_(2))]on the daily number of children admitted to the hospital for acute bronchitis in Sichuan Province,China.Methods The 49,975 records of hospitalized children with acute bronchitis from medical institutions in nine cities/prefectures,Sichuan Province,China,as well as the simultaneous meteorological data and air pollution data from 183 monitoring sites,were collected from 1 January 2017 to 31 December 2018.A generalized additive model was adopted to analyze the exposure–response and lag effects of hospitalizations of children with acute bronchitis to air pollutants.Stratified analyses were conducted based on sex,age,and season.Results The single-pollutant model showed that a 10µg/m3 increase at lag07 of PM_(2.5),PM10,SO_(2),and NO_(2) corresponded to an increase of 1.23%[95%confidence interval(CI)0.21–2.26%],1.33%(95%CI 0.62–2.05%),23.52%(95%CI 11.52–36.81%),and 12.47%(95%CI 8.46–16.64%)in daily hospitalizations for children with acute bronchitis,respectively.Children aged 0–2 were more prone to PM_(2.5)(P<0.05).Interestingly,the effects were stronger in the warm season than in transition seasons and the cool season for PM_(2.5) and PM10(P<0.05).Conclusion The higher daily average concentrations of four pollutants in Sichuan Province can result in an increased number of children hospitalized for acute bronchitis.
基金supported by the National Natural Science Foundation of China (41471335, 41271407)the National Remote Sensing Survey and Assessment of Eco-Environment Change between 2000 and 2010, China (STSN-1500)+2 种基金the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2013BAD05B03)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050601)the International Science and Technology (S&T) Cooperation Program of China (2012DFG22050)
文摘Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
文摘The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.
基金provided by the Program for New Century Excellent Talents in University (No. NCET-06-0477)the Independent Research Project of the State Key Laboratory of Coal Resources and Mine Safety of China University of Mining and Technology (No. SKLCRSM09X01)the Fundamental Research Funds for the Central Universities
文摘In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB2101003National Natural Science Foundation of China,Grant/Award Numbers:51991395,U1806226,51778033,51822802,71901011,U1811463,51991391Science and Technology Major Project of Beijing,Grant/Award Number:Z191100002519012。
文摘It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
基金The National Natural Science Foundation of China under contract No.42406188the Natural Science Foundation of Liaoning Province under contract No.2024-BS-022+1 种基金the Dalian High-Level Talent Innovation Program under contract No.2022RG02the Fundamental Research Funds for the Central Universities under contract No.3132025107.
文摘Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
基金supported by the National Natural Science Foundation of China(Grant Nos.82573974 and 82373475)to Z.Y.
文摘Dear Editor,Psoriasis,a chronic inflammatory cutaneous condition,is characterized by the development of red plaques with silvery scales,significantly affecting patients'quality of life and mental health[1].This condition is thought to affect approximately 2%of the Western population,with diagnosis peaking in early adulthood[2].Vitamin D,a fat-soluble vitamin,is essential for phospho-calcium metabolism,calcium homeostasis,and bone health.
基金supported by the China Postdoctoral Science Foundation(Grant No.2021M703366)Shenzhen Science and Technology Program(Grant No.KQTD20190929172835662).
文摘Objective Observational studies have found associations between inflammatory bowel disease(IBD)and the risk of dementia,including Alzheimer’s dementia(AD)and vascular dementia(VD);however,these findings are inconsistent.It remains unclear whether these associations are causal.Methods We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia.Mendelian randomization(MR)analysis based on summary genome-wide association studies(GWASs)was performed.Genetic correlation and Bayesian colocalization analyses were used to provide robust genetic evidence.Results Ten observational studies involving 80,565,688 participants were included in this metaanalysis.IBD was significantly associated with dementia(risk ratio[RR]=1.36,95%CI=1.04-1.78;I2=84.8%)and VD(RR=2.60,95%CI=1.18-5.70;only one study),but not with AD(RR=2.00,95%CI=0.96-4.13;I^(2)=99.8%).MR analyses did not supported significant causal associations of IBD with dementia(dementia:odds ratio[OR]=1.01,95%CI=0.98-1.03;AD:OR=0.98,95%CI=0.95-1.01;VD:OR=1.02,95%CI=0.97-1.07).In addition,genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia.Conclusion Our study did not provide genetic evidence for a causal association between IBD and dementia risk.The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.
基金supported by“Dalian Maritime University Teaching Reform Research Fund 2022 Annual Project”(Fund No.XJG2022-96).
文摘This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural communication skills,in-depth regional and country knowledge,and practical expertise in ocean affairs.Additionally,the paper presents specific training pathways and policy recommendations for implementing this model.
文摘In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audience,imposes distinct requirements on children’s books,compelling translators to approach the text from a child’s perspective.“The Little Prince”has renowned both within and outside of China,and a careful reading of this work can provide us with much inspiration.To this end,the present study adopts the perspective of Gideon Toury’s Descriptive Translation Studies to conduct an in-depth analysis of the different English and Chinese translations in conjunction with the original French novel.This approach aims to better guide literary research and explores translation methods for children’s literature through the analysis of translation norms and rules.
基金Under the auspices of National Natural Science Foundation of China(No.42171407,42077242)Key Program of National Natural Science Foundation of China(No.42330607)。
文摘Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.
基金supported by the Natural Science Foundation of Hunan Province (2022JJ30987)the Key Research and Development Project of Hunan Province (2024JK2107),China。
文摘Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrated significant associations between CRC risk and various serum metabolites as well as inflammatory cytokines;however,due to limitations in study design and potential confounding factors,the causal relationships remain unclear.This study aims to investigate the causal relationships between inflammatory cytokines,serum metabolites,and CRC risk,providing a theoretical basis for the development of novel early diagnostic biomarkers and therapeutic targets.Methods:A two-sample Mendelian randomization(MR)design was applied using summary statistics from genome-wide association studies(GWAS).Instrumental variables(IVs)were derived from:1)metabolomics GWAS data of 1400 serum metabolites(n=8299);2)cytokine GWAS data of 91 inflammatory factors(n=14824);and 3)CRC risk data from the FinnGen consortium(6847 cases and 314193 controls).The primary analysis was conducted using the inverse-variance weighted(IVW)method,with sensitivity analyses performed using MR Egger regression and the weighted median method.Effect estimates including odds ratios(OR),95%confidence intervals(CI),and false discovery rates(FDR)were calculated.Results:MR analysis indicated that higher levels of axin-1(AXIN1)(OR=0.84195%CI 0.714 to 0.991)and Fms-related tyrosine kinase 3 ligand(Flt3L)(OR=0.916,95%CI 0.844 to 0.994)were associated with a reduced risk of CRC.In contrast,higher levels of Delta/Notchlike epidermal growth factor-related receptor(DNER)(OR=1.119,95%CI 1.009 to 1.241)and vascular endothelial growth factor A(VEGF-A)(OR=1.078,95%CI 1.011 to 1.150)were associated with an increased risk of CRC(all P<0.05).Metabolomics association analysis further identified 144 serum metabolites significantly correlated with these four key inflammatory cytokines(FDR<0.05),suggesting that they may regulate CRC risk through inflammatory pathways.Conclusion:Specific inflammatory cytokines and serum metabolites have causal relationships with the risk of CRC.These findings provide insights for further exploration of potential risk factors and the development of effective prevention strategies for CRC.
基金Project supported by the Fundamental Research Funds for the Central Universities(No.2042025kf0052)。
文摘In molybdenum chemistry,the oxidative addition of o-quinone or 1,2-dicarbonyl compounds to molybdenum has been widely used in Mo-catalyzed C—C bond construction.The carbonyl oxidative addition to Mo(0)or Mo(Ⅱ)is the critical elementary reaction of molybdenum catalysis.However,the relevant density functional theory(DFT)studies are relatively scarce,especially regarding the rational selection of functionals.In this work,14 functionals were employed to investigate the Mo-catalyzed carbonyl oxidative addition step.A benchmark study was carried out to evaluate their performance in structure optimization and energy calculation.Analyses of mean absolute error(MAE)and mean squared error(MSE)indicated that the B3LYP-D3(BJ),TPSSh,and ωB97X-D functionals exhibited superior performance in structure optimization.Using the DLPNO-CCSD(T)functional as the reference,the M06,M06-L,and MN15-L functionals exhibited good performance for energy calculation based on the structures optimized using the B3LYP-D3(BJ)functional.In particular,MN15-L provided the best performance with the smallest MAE and MSE.
基金supported by the Science and Technology Fund Project of Guizhou Health Commission(gzwkj2025-163)。
文摘Objective:To identify the root causes of typical adverse drug events through the lens of patient experiences proposing novel strategies to mitigate preventable harm.Methods:A qualitative case study leveraging in-depth interviews with patients and families,anchored by Interactive Patient Par ticipation Theory,to analyze 4 high-severity adverse drug events(ADE)cases.Cases were purposively sampled from 8 communities in China's National Adverse Event Monitor Center(2018-2023).Semi-structured interviews explored patient perspectives,with data analyzed via thematic coding and triangulation against clinical records.Results:Five interconnected themes emerged:(1)erosion of trust,(2)communication breakdowns,(3)information asymmetry,(4)environmental inadequacies,and(5)technological alienation.Notably,75% of participants had≤high school education,and 50% used≥7 medications daily,compounding ADE risks.Conclusions:We considered elements mentioned by theory,exploring trust,communication,information,and suppor t as the root causes.In addition,we added“adaptability to new technology”as an impor tant and necessary component.It is impor tant and necessary to analyze typical adverse drug events from the perspectives of patients.
文摘China ranked first worldwide in the production and export of electric bicycles.As an emerging market for electric bicycles,Malaysia holds significant potential for trade collabor ation with China in this sector.This study presents a compar ative analysis of the national electric bicycle standards in China and Malaysia,offering technical insights from a standardization perspective.These insights aim to support Chinese enterprises in strategically positioning their technologies in the Malaysian market.The findings reveal significant differences in technical parameters,safety requirements,and testing methods,highlighting the need for tailored product adapt ation.
