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
目的构建留守与非留守中学生自伤的风险预测模型,为制定针对性的干预措施提供科学依据。方法2021年9月―2023年6月采用多阶段抽样方法,在留守儿童分布相对集中的6个省份中抽取14623名<18岁的中学生(留守8471名,非留守6152名)作为研...目的构建留守与非留守中学生自伤的风险预测模型,为制定针对性的干预措施提供科学依据。方法2021年9月―2023年6月采用多阶段抽样方法,在留守儿童分布相对集中的6个省份中抽取14623名<18岁的中学生(留守8471名,非留守6152名)作为研究对象。通过问卷调查收集研究对象的一般情况、创伤性事件和自伤发生情况。分析不同特征留守与非留守中学生自伤的发生情况。采用R 4.3.0软件按照7∶3的比例分别将留守与非留守中学生随机划分为训练集与测试集,构建logistic回归分析模型和随机森林模型,通过受试者工作特征曲线、灵敏度、特异度等指标评估模型性能。结果中学生自伤总体发生率为25.7%,留守中学生自伤发生率高于非留守中学生(χ^(2)=59.266,P<0.001)。Logistic回归分析模型分析结果显示,留守与非留守中学生预测模型训练集的曲线下面积(area under the curve,AUC)分别为0.745和0.756,测试集的AUC分别为0.721和0.726,Hosmer-Lemshow拟合优度检验P>0.05。随机森林模型中,留守中学生自伤的主要预测因素为经历创伤性事件、家庭氛围、和父亲/母亲关系等,模型的灵敏度、特异度、阳性预测值、阴性预测值和F1指数分别为0.740、0.591、0.822、0.470和0.779,Brier分数为0.212,训练集和测试集的AUC分别为0.800和0.729。非留守中学生则以经历创伤性事件、家庭氛围、父母感情状况等为主,模型的灵敏度、特异度、阳性预测值、阴性预测值和F1指数分别为0.785、0.519、0.850、0.411和0.816,Brier分数为0.188,训练集和测试集的AUC分别为0.845和0.724。结论留守中学生自伤风险高于非留守中学生,二者的预测因素虽有不同,但存在高度重叠,其中创伤经历和家庭因素是关键预测变量。两种模型对自伤的识别能力良好,但随机森林模型综合性能更优,本研究构建的预测模型可为早期识别高危人群提供科学依据。展开更多
【目的】拟预测江西省吉安市森林火灾发生概率,为吉安市森林火灾精准防控提供科学依据。【方法】基于2001—2020年MODIS火点数据,结合气象、地形、植被及人类活动等多维因子,分析江西省吉安市森林火灾的时空分布特征及其驱动机制。采用...【目的】拟预测江西省吉安市森林火灾发生概率,为吉安市森林火灾精准防控提供科学依据。【方法】基于2001—2020年MODIS火点数据,结合气象、地形、植被及人类活动等多维因子,分析江西省吉安市森林火灾的时空分布特征及其驱动机制。采用多重共线性诊断和相关性分析筛选关键影响因子,构建了Logistic回归(binary logistic regression)模型,预测森林火灾发生概率,并利用混淆矩阵和曲线下面积(area under the curve,AUC)评估模型性能。【结果】(1)吉安市森林火灾年际变化呈5年周期性波动,主要发生在9月至次年4月,空间分布呈现北多南少、西多东少的特征;(2)人口密度、上月植被指数、海拔、本月降雨量、上月温度和灯光指数是火灾发生的主要驱动因子,其中本月降雨量和灯光指数与火灾风险呈正相关,其余因子呈负相关;(3)火灾发生概率在0.2~0.7,永丰县、安福县、永新县、吉安县和遂川县为高风险区;(4)模型AUC值为0.748,具有较好的预测能力。【结论】研究可为吉安市森林火灾风险管理提供科学依据,建议在高风险区域加强监测预警,并针对不同驱动因子采取差异化防控措施。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.
文摘目的构建留守与非留守中学生自伤的风险预测模型,为制定针对性的干预措施提供科学依据。方法2021年9月―2023年6月采用多阶段抽样方法,在留守儿童分布相对集中的6个省份中抽取14623名<18岁的中学生(留守8471名,非留守6152名)作为研究对象。通过问卷调查收集研究对象的一般情况、创伤性事件和自伤发生情况。分析不同特征留守与非留守中学生自伤的发生情况。采用R 4.3.0软件按照7∶3的比例分别将留守与非留守中学生随机划分为训练集与测试集,构建logistic回归分析模型和随机森林模型,通过受试者工作特征曲线、灵敏度、特异度等指标评估模型性能。结果中学生自伤总体发生率为25.7%,留守中学生自伤发生率高于非留守中学生(χ^(2)=59.266,P<0.001)。Logistic回归分析模型分析结果显示,留守与非留守中学生预测模型训练集的曲线下面积(area under the curve,AUC)分别为0.745和0.756,测试集的AUC分别为0.721和0.726,Hosmer-Lemshow拟合优度检验P>0.05。随机森林模型中,留守中学生自伤的主要预测因素为经历创伤性事件、家庭氛围、和父亲/母亲关系等,模型的灵敏度、特异度、阳性预测值、阴性预测值和F1指数分别为0.740、0.591、0.822、0.470和0.779,Brier分数为0.212,训练集和测试集的AUC分别为0.800和0.729。非留守中学生则以经历创伤性事件、家庭氛围、父母感情状况等为主,模型的灵敏度、特异度、阳性预测值、阴性预测值和F1指数分别为0.785、0.519、0.850、0.411和0.816,Brier分数为0.188,训练集和测试集的AUC分别为0.845和0.724。结论留守中学生自伤风险高于非留守中学生,二者的预测因素虽有不同,但存在高度重叠,其中创伤经历和家庭因素是关键预测变量。两种模型对自伤的识别能力良好,但随机森林模型综合性能更优,本研究构建的预测模型可为早期识别高危人群提供科学依据。
文摘【目的】拟预测江西省吉安市森林火灾发生概率,为吉安市森林火灾精准防控提供科学依据。【方法】基于2001—2020年MODIS火点数据,结合气象、地形、植被及人类活动等多维因子,分析江西省吉安市森林火灾的时空分布特征及其驱动机制。采用多重共线性诊断和相关性分析筛选关键影响因子,构建了Logistic回归(binary logistic regression)模型,预测森林火灾发生概率,并利用混淆矩阵和曲线下面积(area under the curve,AUC)评估模型性能。【结果】(1)吉安市森林火灾年际变化呈5年周期性波动,主要发生在9月至次年4月,空间分布呈现北多南少、西多东少的特征;(2)人口密度、上月植被指数、海拔、本月降雨量、上月温度和灯光指数是火灾发生的主要驱动因子,其中本月降雨量和灯光指数与火灾风险呈正相关,其余因子呈负相关;(3)火灾发生概率在0.2~0.7,永丰县、安福县、永新县、吉安县和遂川县为高风险区;(4)模型AUC值为0.748,具有较好的预测能力。【结论】研究可为吉安市森林火灾风险管理提供科学依据,建议在高风险区域加强监测预警,并针对不同驱动因子采取差异化防控措施。
