Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malays...Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malaysia,using Shannon Entropy-weighted bivariatemodels(i.e.,Frequency Ratio,Information Value,andWeight of Evidence),in comparison with Logistic Regression.Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity:slope gradient,slope aspect,curvature,vegetation cover,lineament density,terrain ruggedness index,and flow accumulation.Each model generated susceptibility maps,which were validated using Receiver Operating Characteristic curves and Area Under the Curve metrics.Logistic Regression yielded the highest predictive accuracy,reflecting its strength in capturing interactions among variables.Among the bivariate models,Frequency Ratio performed best,slightly outperforming the other two methods.Zones of high susceptibility were consistently located along steep slopes,high lineament density areas,and near built environments.The study demonstrates that incorporating Shannon Entropy improves the performance of conventional bivariate methods and provides a useful framework for spatial susceptibility modeling in data-constrained environments.The comparison with Logistic Regression highlights the advantages ofmultivariate modeling in capturing complex spatial relationships.Limitations of the study include the use of secondary spatial data and the exclusion of dynamic parameters such as rainfall intensity.Future research should incorporate temporal datasets and investigate machine learning techniques to enhance model generalizability and predictive capability.展开更多
Bigeye tuna is a protein-rich fish that is susceptible to spoilage during cold storage,however,there is limited information on untargeted metabolomic profiling of bigeye tuna concerning spoilage-associated enzymes and...Bigeye tuna is a protein-rich fish that is susceptible to spoilage during cold storage,however,there is limited information on untargeted metabolomic profiling of bigeye tuna concerning spoilage-associated enzymes and metabolites.This study aimed to investigate how cold storage affects enzyme activities,nutrient composition,tissue microstructures and spoilage metabolites of bigeye tuna.The activities of cathepsins B,H,L increased,while Na^(+)/K^(+)-ATPase and Mg^(2+)-ATPase decreased,α-glucosidase,lipase and lipoxygenase first increased and then decreased during cold storage,suggesting that proteins undergo degradation and ATP metabolism occurs at a faster rate during cold storage.Nutrient composition(moisture and lipid content),total amino acids decreased,suggesting that the nutritional value of bigeye tuna was reduced.Besides,a logistic regression equation has been established as a food analysis tool and assesses the dynamics and correlation of the enzyme of bigeye tuna during cold storage.Based on untargeted metabolomic profiling analysis,a total of 524 metabolites were identified in the bigeye tuna contained several spoilage metabolites involved in lipid metabolism(glycerophosphocholine and choline phosphate),amino acid metabolism(L-histidine,5-deoxy-5′-(methylthio)adenosine,5-methylthioadenosine),carbohydrate metabolism(D-gluconic acid,α-D-fructose 1,6-bisphosphate,D-glyceraldehyde 3-phosphate).The results of tissue microstructures of tuna showed a looser network and visible deterioration of tissue fiber during cold storage.Therefore,metabolomic analysis and tissue microstructures provide insight into the spoilage mechanism investigations on bigeye tuna during cold storage.展开更多
Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee th...Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.展开更多
BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate ...BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate risk prediction is essential for optimized clinical management.AIM To develop and validate a logistic regression-based risk prediction model for aortic adverse remodeling following TEVAR in patients with TBAD.METHODS This retrospective observational cohort study analyzed 140 TBAD patients undergoing TEVAR at a tertiary center(2019–2024).Based on European guidelines,patients were categorized into adverse remodeling(aortic growth rate>2.9 mm/year,n=45)and favorable remodeling groups(n=95).Comprehensive variables(clinical/imaging/surgical)were analyzed using multivariable logistic regression to develop a predictive model.Model performance was assessed via receiver operating characteristic-area under the curve(AUC)and Hosmer-Lemeshow tests.RESULTS Multivariable analysis identified several strong independent predictors of negative aortic remodeling.Larger false lumen diameter at the primary entry tear[odds ratio(OR):1.561,95%CI:1.197–2.035;P=0.001]and patency of the false lumen(OR:5.639,95%CI:4.372-8.181;P=0.004)were significant risk factors.False lumen involvement extending to the thoracoabdominal aorta was identified as the strongest predictor,significantly increasing the risk of adverse remodeling(OR:11.751,95%CI:9.841-15.612;P=0.001).Conversely,false lumen involvement confined to the thoracic aorta demonstrated a significant protective effect(OR:0.925,95%CI:0.614–0.831;P=0.015).The prediction model exhibited excellent discrimination(AUC=0.968)and calibration(Hosmer-Lemeshow P=0.824).CONCLUSION This validated risk prediction model identifies aortic adverse remodeling with high accuracy using routinely available clinical parameters.False lumen involvement thoracoabdominal aorta is the strongest predictor(11.751-fold increased risk).The tool enables preoperative risk stratification to guide tailored TEVAR strategies and improve long-term outcomes.展开更多
文摘Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malaysia,using Shannon Entropy-weighted bivariatemodels(i.e.,Frequency Ratio,Information Value,andWeight of Evidence),in comparison with Logistic Regression.Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity:slope gradient,slope aspect,curvature,vegetation cover,lineament density,terrain ruggedness index,and flow accumulation.Each model generated susceptibility maps,which were validated using Receiver Operating Characteristic curves and Area Under the Curve metrics.Logistic Regression yielded the highest predictive accuracy,reflecting its strength in capturing interactions among variables.Among the bivariate models,Frequency Ratio performed best,slightly outperforming the other two methods.Zones of high susceptibility were consistently located along steep slopes,high lineament density areas,and near built environments.The study demonstrates that incorporating Shannon Entropy improves the performance of conventional bivariate methods and provides a useful framework for spatial susceptibility modeling in data-constrained environments.The comparison with Logistic Regression highlights the advantages ofmultivariate modeling in capturing complex spatial relationships.Limitations of the study include the use of secondary spatial data and the exclusion of dynamic parameters such as rainfall intensity.Future research should incorporate temporal datasets and investigate machine learning techniques to enhance model generalizability and predictive capability.
基金supported by the Shanghai Sailing Program(22YF1416300)Youth Fund Project of National Natural Science Foundation of China(32202117)+1 种基金National Key Research and Development Program of China(2022YFD2100104)the China Agriculture Research System(CARS-47).
文摘Bigeye tuna is a protein-rich fish that is susceptible to spoilage during cold storage,however,there is limited information on untargeted metabolomic profiling of bigeye tuna concerning spoilage-associated enzymes and metabolites.This study aimed to investigate how cold storage affects enzyme activities,nutrient composition,tissue microstructures and spoilage metabolites of bigeye tuna.The activities of cathepsins B,H,L increased,while Na^(+)/K^(+)-ATPase and Mg^(2+)-ATPase decreased,α-glucosidase,lipase and lipoxygenase first increased and then decreased during cold storage,suggesting that proteins undergo degradation and ATP metabolism occurs at a faster rate during cold storage.Nutrient composition(moisture and lipid content),total amino acids decreased,suggesting that the nutritional value of bigeye tuna was reduced.Besides,a logistic regression equation has been established as a food analysis tool and assesses the dynamics and correlation of the enzyme of bigeye tuna during cold storage.Based on untargeted metabolomic profiling analysis,a total of 524 metabolites were identified in the bigeye tuna contained several spoilage metabolites involved in lipid metabolism(glycerophosphocholine and choline phosphate),amino acid metabolism(L-histidine,5-deoxy-5′-(methylthio)adenosine,5-methylthioadenosine),carbohydrate metabolism(D-gluconic acid,α-D-fructose 1,6-bisphosphate,D-glyceraldehyde 3-phosphate).The results of tissue microstructures of tuna showed a looser network and visible deterioration of tissue fiber during cold storage.Therefore,metabolomic analysis and tissue microstructures provide insight into the spoilage mechanism investigations on bigeye tuna during cold storage.
基金National Key R&D Program of China under Grant Nos.2018YFC1504504 and 2018YFC0809404。
文摘Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.
基金Supported by Zhangjiajie"Xiao He(Young Talent)"Project,No.2024XHRC03Jishou University School-Level Research Project.
文摘BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate risk prediction is essential for optimized clinical management.AIM To develop and validate a logistic regression-based risk prediction model for aortic adverse remodeling following TEVAR in patients with TBAD.METHODS This retrospective observational cohort study analyzed 140 TBAD patients undergoing TEVAR at a tertiary center(2019–2024).Based on European guidelines,patients were categorized into adverse remodeling(aortic growth rate>2.9 mm/year,n=45)and favorable remodeling groups(n=95).Comprehensive variables(clinical/imaging/surgical)were analyzed using multivariable logistic regression to develop a predictive model.Model performance was assessed via receiver operating characteristic-area under the curve(AUC)and Hosmer-Lemeshow tests.RESULTS Multivariable analysis identified several strong independent predictors of negative aortic remodeling.Larger false lumen diameter at the primary entry tear[odds ratio(OR):1.561,95%CI:1.197–2.035;P=0.001]and patency of the false lumen(OR:5.639,95%CI:4.372-8.181;P=0.004)were significant risk factors.False lumen involvement extending to the thoracoabdominal aorta was identified as the strongest predictor,significantly increasing the risk of adverse remodeling(OR:11.751,95%CI:9.841-15.612;P=0.001).Conversely,false lumen involvement confined to the thoracic aorta demonstrated a significant protective effect(OR:0.925,95%CI:0.614–0.831;P=0.015).The prediction model exhibited excellent discrimination(AUC=0.968)and calibration(Hosmer-Lemeshow P=0.824).CONCLUSION This validated risk prediction model identifies aortic adverse remodeling with high accuracy using routinely available clinical parameters.False lumen involvement thoracoabdominal aorta is the strongest predictor(11.751-fold increased risk).The tool enables preoperative risk stratification to guide tailored TEVAR strategies and improve long-term outcomes.
