BACKGROUND The lack of specific predictors for type-2 diabetes mellitus(T2DM)severely impacts early intervention/prevention efforts.Elevated branched-chain amino acids(BCAAs:Isoleucine,leucine,valine)and aromatic amin...BACKGROUND The lack of specific predictors for type-2 diabetes mellitus(T2DM)severely impacts early intervention/prevention efforts.Elevated branched-chain amino acids(BCAAs:Isoleucine,leucine,valine)and aromatic amino acids(AAAs:Tyrosine,tryptophan,phenylalanine)show high sensitivity and specificity in predicting diabetes in animals and predict T2DM 10-19 years before T2DM onset in clinical studies.However,improvement is needed to support its clinical utility.AIM To evaluate the effects of body mass index(BMI)and sex on BCAAs/AAAs in new-onset T2DM individuals with varying body weight.METHODS Ninety-seven new-onset T2DM patients(<12 mo)differing in BMI[normal weight(NW),n=33,BMI=22.23±1.60;overweight,n=42,BMI=25.9±1.07;obesity(OB),n=22,BMI=31.23±2.31]from the First People’s Hospital of Yunnan Province,Kunming,China,were studied.One-way and 2-way ANOVAs were conducted to determine the effects of BMI and sex on BCAAs/AAAs.RESULTS Fasting serum AAAs,BCAAs,glutamate,and alanine were greater and high-density lipoprotein(HDL)was lower(P<0.05,each)in OB-T2DM patients than in NW-T2DM patients,especially in male OB-T2DM patients.Arginine,histidine,leucine,methionine,and lysine were greater in male patients than in female patients.Moreover,histidine,alanine,glutamate,lysine,valine,methionine,leucine,isoleucine,tyrosine,phenylalanine,and tryptophan were significantly correlated with abdominal adiposity,body weight and BMI,whereas isoleucine,leucine and phenylalanine were negatively correlated with HDL.CONCLUSION Heterogeneously elevated amino acids,especially BCAAs/AAAs,across new-onset T2DM patients in differing BMI categories revealed a potentially skewed prediction of T2DM development.The higher BCAA/AAA levels in obese T2DM patients would support T2DM prediction in obese individuals,whereas the lower levels of BCAAs/AAAs in NW-T2DM individuals may underestimate T2DM risk in NW individuals.This potentially skewed T2DM prediction should be considered when BCAAs/AAAs are to be used as the T2DM predictor.展开更多
The synergy between corrosion protection and wear resistance is an effective strategy for the development of multifunctional coating to withstand complex working conditions.This study reports an epoxy resin coating fi...The synergy between corrosion protection and wear resistance is an effective strategy for the development of multifunctional coating to withstand complex working conditions.This study reports an epoxy resin coating filled with benzotriazole loaded metal-organic frameworks(BTA-MOFs)functionalized graphene oxide nanoribbons(GONR)that exhibit active anti-corrosion,act as a barrier to corrosive ion,and enhance wear resistance.The GONR@BTA-MOFs composite is synthesized through chemically etching multi-walled carbon nanotubes and subsequent electrostatic self-assembly corrosion inhibitors loaded MOFs onto the GONR.The composite demonstrates improved compatibility with epoxy resins compared to carbon nanotubes.The anti-corrosion performance of the composite coating is investigated using electrochemical impedance spectroscopy.After immersing in a 3.5 wt.%NaCl solution for 25 d,the alternating current impedance of the composite coating is three orders of magnitude higher than that of pure epoxy resin.Simultaneously,the controlled release of the corrosion inhibitor retards the deterioration of the coating after localized damage occurrence,which functions as active corrosion protection.The GONR@BTA-MOFs/EP composite coating exhibits the highest corrosion potential of-0.188 V and the lowest corrosion current of 3.162×10^(−9)A cm^(−2)in the Tafel test.Tribological studies reveal a reduction in the friction coefficient from 0.62 to 0.08 after incorporating GONR@BTA-MOFs in the coating,with the wear volume being seven times lower than that of pure epoxy resin.The excellent lubrication effect of the nanomaterials reduces the coefficient of friction of the coating,thereby improving the abrasion resistance of the coating.The synergy between the self-lubrication of the two-dimensional layered fillers and the corrosion resistance of the smart inhibitor containers suggests a promising strategy for enhancing the performance of epoxy resins under complex working conditions.展开更多
Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies ...Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data.This comprehensive survey reviews state-of-the-art DL architectures forTSF,focusing on four core paradigms:(1)ConvolutionalNeuralNetworks(CNNs),adept at extracting localized temporal features;(2)Recurrent Neural Networks(RNNs)and their advanced variants(LSTM,GRU),designed for sequential dependency modeling;(3)Graph Neural Networks(GNNs),specialized for forecasting structured relational data with spatial-temporal dependencies;and(4)Transformer-based models,leveraging self-attention mechanisms to capture global temporal patterns efficiently.We provide a rigorous analysis of the theoretical underpinnings,recent algorithmic advancements(e.g.,TCNs,attention mechanisms,hybrid architectures),and practical applications of each framework,supported by extensive benchmark datasets(e.g.,ETT,traffic flow,financial indicators)and standardized evaluation metrics(MAE,MSE,RMSE).Critical challenges,including handling irregular sampling intervals,integrating domain knowledge for robustness,and managing computational complexity,are thoroughly discussed.Emerging research directions highlighted include diffusion models for uncertainty quantification,hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability,quantile regression with Transformers for riskaware forecasting,and optimizations for real-time deployment.This work serves as an essential reference,consolidating methodological innovations,empirical resources,and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.展开更多
The effective recovery of water level is a crucial measure of the success of comprehensive groundwater over-exploitation management actions in North China.However,traditional evaluation method do not directly capture ...The effective recovery of water level is a crucial measure of the success of comprehensive groundwater over-exploitation management actions in North China.However,traditional evaluation method do not directly capture the relationship between mining and other equilibrium elements.This study presents an innovative evaluation method to assess the water level recovery resulting from mining reduction based on the relationship between variation in exploitation and recharge.Firstly,the recharge variability of source and sink terms for both the base year and evaluation year is calculated and the coefficient of recharge variationβis introduced,which is then used to calculate the effective mining reduction and solve the water level recovery value caused by the effective mining reduction,and finally the water level recovery contribution by mining reduction is calculated by combining with the actual volume of mining reduction in the evaluation area.This research focuses on Baoding and Shijiazhuang Plain area,which share similar hydrogeological conditions but vary in groundwater exploitation and utilization.As the effect of groundwater level recovery with mining reduction was evaluated in these two areas as case study.In 2018,the results showed an effective water level recovery of 0.17 m and 0.13 m in the shallow groundwater of Shijiazhuang and Baoding Plain areas,respectively.The contributions of recovery from mining reduction were 76%and 57.98%for these two areas,respectively.It was notable that the water level recovery was most prominent in the foothill plain regions.From the evaluation results,it is evident that water level recovery depends not only on the intensity of groundwater mining reduction,but also on its effectiveness.The value of water level recovery alone cannot accurately indicate the intensity of mining reduction,as recharge variation significantly influences water level changes.Therefore,in practice,it is crucial to comprehensively assess the impact of mining reduction on water level recovery by combining the coefficient of recharge variation with the contribution of water level recovery from mining reduction.This integrated approach provide a more reasonable and scientifically supported basis,offering essential data support for groundwater management and conservation.To improve the accuracy and reliability of evaluation results,future work will focus on the standardizing and normalizing raw data processing.展开更多
基金Supported by the Open Project Grant for Clinical Medical Center of Yunnan Province,No.2019LCZXKF-NM03Medical Leader Training Grant,No.L-201624and Yunnan Province Ten Thousand Talents:“Medical Expert”grant,No.YNWR-MY-2019-020.
文摘BACKGROUND The lack of specific predictors for type-2 diabetes mellitus(T2DM)severely impacts early intervention/prevention efforts.Elevated branched-chain amino acids(BCAAs:Isoleucine,leucine,valine)and aromatic amino acids(AAAs:Tyrosine,tryptophan,phenylalanine)show high sensitivity and specificity in predicting diabetes in animals and predict T2DM 10-19 years before T2DM onset in clinical studies.However,improvement is needed to support its clinical utility.AIM To evaluate the effects of body mass index(BMI)and sex on BCAAs/AAAs in new-onset T2DM individuals with varying body weight.METHODS Ninety-seven new-onset T2DM patients(<12 mo)differing in BMI[normal weight(NW),n=33,BMI=22.23±1.60;overweight,n=42,BMI=25.9±1.07;obesity(OB),n=22,BMI=31.23±2.31]from the First People’s Hospital of Yunnan Province,Kunming,China,were studied.One-way and 2-way ANOVAs were conducted to determine the effects of BMI and sex on BCAAs/AAAs.RESULTS Fasting serum AAAs,BCAAs,glutamate,and alanine were greater and high-density lipoprotein(HDL)was lower(P<0.05,each)in OB-T2DM patients than in NW-T2DM patients,especially in male OB-T2DM patients.Arginine,histidine,leucine,methionine,and lysine were greater in male patients than in female patients.Moreover,histidine,alanine,glutamate,lysine,valine,methionine,leucine,isoleucine,tyrosine,phenylalanine,and tryptophan were significantly correlated with abdominal adiposity,body weight and BMI,whereas isoleucine,leucine and phenylalanine were negatively correlated with HDL.CONCLUSION Heterogeneously elevated amino acids,especially BCAAs/AAAs,across new-onset T2DM patients in differing BMI categories revealed a potentially skewed prediction of T2DM development.The higher BCAA/AAA levels in obese T2DM patients would support T2DM prediction in obese individuals,whereas the lower levels of BCAAs/AAAs in NW-T2DM individuals may underestimate T2DM risk in NW individuals.This potentially skewed T2DM prediction should be considered when BCAAs/AAAs are to be used as the T2DM predictor.
基金supported by the National Natural Science Foundation of China(No.52475216)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515240030)+2 种基金the Natural Science Foundation of Shaanxi Province(No.2024RSCXTD-62)the Research Fund of the State Key Laboratory of Solidification Processing(NPU)(No.2022-QZ-04)We would like to thank the Analytical&Testing Center of Northwestern Polytechnical University and the Shaanxi Materials Analysis and Research Center.
文摘The synergy between corrosion protection and wear resistance is an effective strategy for the development of multifunctional coating to withstand complex working conditions.This study reports an epoxy resin coating filled with benzotriazole loaded metal-organic frameworks(BTA-MOFs)functionalized graphene oxide nanoribbons(GONR)that exhibit active anti-corrosion,act as a barrier to corrosive ion,and enhance wear resistance.The GONR@BTA-MOFs composite is synthesized through chemically etching multi-walled carbon nanotubes and subsequent electrostatic self-assembly corrosion inhibitors loaded MOFs onto the GONR.The composite demonstrates improved compatibility with epoxy resins compared to carbon nanotubes.The anti-corrosion performance of the composite coating is investigated using electrochemical impedance spectroscopy.After immersing in a 3.5 wt.%NaCl solution for 25 d,the alternating current impedance of the composite coating is three orders of magnitude higher than that of pure epoxy resin.Simultaneously,the controlled release of the corrosion inhibitor retards the deterioration of the coating after localized damage occurrence,which functions as active corrosion protection.The GONR@BTA-MOFs/EP composite coating exhibits the highest corrosion potential of-0.188 V and the lowest corrosion current of 3.162×10^(−9)A cm^(−2)in the Tafel test.Tribological studies reveal a reduction in the friction coefficient from 0.62 to 0.08 after incorporating GONR@BTA-MOFs in the coating,with the wear volume being seven times lower than that of pure epoxy resin.The excellent lubrication effect of the nanomaterials reduces the coefficient of friction of the coating,thereby improving the abrasion resistance of the coating.The synergy between the self-lubrication of the two-dimensional layered fillers and the corrosion resistance of the smart inhibitor containers suggests a promising strategy for enhancing the performance of epoxy resins under complex working conditions.
基金funded by Natural Science Foundation of Heilongjiang Province,grant number LH2023F020.
文摘Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data.This comprehensive survey reviews state-of-the-art DL architectures forTSF,focusing on four core paradigms:(1)ConvolutionalNeuralNetworks(CNNs),adept at extracting localized temporal features;(2)Recurrent Neural Networks(RNNs)and their advanced variants(LSTM,GRU),designed for sequential dependency modeling;(3)Graph Neural Networks(GNNs),specialized for forecasting structured relational data with spatial-temporal dependencies;and(4)Transformer-based models,leveraging self-attention mechanisms to capture global temporal patterns efficiently.We provide a rigorous analysis of the theoretical underpinnings,recent algorithmic advancements(e.g.,TCNs,attention mechanisms,hybrid architectures),and practical applications of each framework,supported by extensive benchmark datasets(e.g.,ETT,traffic flow,financial indicators)and standardized evaluation metrics(MAE,MSE,RMSE).Critical challenges,including handling irregular sampling intervals,integrating domain knowledge for robustness,and managing computational complexity,are thoroughly discussed.Emerging research directions highlighted include diffusion models for uncertainty quantification,hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability,quantile regression with Transformers for riskaware forecasting,and optimizations for real-time deployment.This work serves as an essential reference,consolidating methodological innovations,empirical resources,and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.
基金supported by National Natural Science Foundation of China(41972262)Hebei Natural Science Foundation for Excellent Young Scholars(D2020504032).
文摘The effective recovery of water level is a crucial measure of the success of comprehensive groundwater over-exploitation management actions in North China.However,traditional evaluation method do not directly capture the relationship between mining and other equilibrium elements.This study presents an innovative evaluation method to assess the water level recovery resulting from mining reduction based on the relationship between variation in exploitation and recharge.Firstly,the recharge variability of source and sink terms for both the base year and evaluation year is calculated and the coefficient of recharge variationβis introduced,which is then used to calculate the effective mining reduction and solve the water level recovery value caused by the effective mining reduction,and finally the water level recovery contribution by mining reduction is calculated by combining with the actual volume of mining reduction in the evaluation area.This research focuses on Baoding and Shijiazhuang Plain area,which share similar hydrogeological conditions but vary in groundwater exploitation and utilization.As the effect of groundwater level recovery with mining reduction was evaluated in these two areas as case study.In 2018,the results showed an effective water level recovery of 0.17 m and 0.13 m in the shallow groundwater of Shijiazhuang and Baoding Plain areas,respectively.The contributions of recovery from mining reduction were 76%and 57.98%for these two areas,respectively.It was notable that the water level recovery was most prominent in the foothill plain regions.From the evaluation results,it is evident that water level recovery depends not only on the intensity of groundwater mining reduction,but also on its effectiveness.The value of water level recovery alone cannot accurately indicate the intensity of mining reduction,as recharge variation significantly influences water level changes.Therefore,in practice,it is crucial to comprehensively assess the impact of mining reduction on water level recovery by combining the coefficient of recharge variation with the contribution of water level recovery from mining reduction.This integrated approach provide a more reasonable and scientifically supported basis,offering essential data support for groundwater management and conservation.To improve the accuracy and reliability of evaluation results,future work will focus on the standardizing and normalizing raw data processing.
