In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
Bangladesh has a denser population in comparison with many other countries. Though the rate of population increase has been regarded as a concerning issue, estimation of the population instability in the upcoming year...Bangladesh has a denser population in comparison with many other countries. Though the rate of population increase has been regarded as a concerning issue, estimation of the population instability in the upcoming years may be useful for national planning. To predict Bangladesh’s future population, this study compares the estimated populations of two popular population models, the Malthusian and the logistic population models, with the country’s census population published by BBS. We also tried to find out which model gives a better approximation for forecasting the past, present, and future population between these two models.展开更多
This study proposed a new and more flexible S-shaped rock damage evolution model from a phenomenological perspective based on an improved Logistic function to describe the characteristics of the rock strain softening ...This study proposed a new and more flexible S-shaped rock damage evolution model from a phenomenological perspective based on an improved Logistic function to describe the characteristics of the rock strain softening and damage process.Simultaneously,it established a constitutive model capable of describing the entire process of rock pre-peak compaction and post-peak strain softening deformation,considering the nonlinear effects of the initial compaction stage of rocks,combined with damage mechanics theory and effective medium theory.In addition,this research verified the rationality of the constructed damage constitutive model using results from uniaxial and conventional triaxial compression tests on Miluo granite,yellow sandstone,mudstone,and glutenite.The results indicate that based on the improved Logistic function,the theoretical damage model accurately describes the entire evolution of damage characteristics during rock compression deformation,from maintenance through gradual onset,accelerated development to deceleration and termination,in a simple and unified expression.At the same time,the constructed constitutive model can accurately simulate the stress-strain process of different rock types under uniaxial and conventional triaxial compression,and the theoretical model curve closely aligns with experimental data.Compared to existing constitutive models,the proposed model has significant advantages.The damage model parameters a,r and β have clear physical meanings and interact competitively,where the three parameters collectively determine the shape of the theoretical stress−strain curve.展开更多
目的基于LASSO-Logistic回归分析影响慢性鼻窦炎伴鼻息肉(chronic rhinosinusitis with nasal polyps,CRSwNP)术后嗅觉功能恢复的危险因素,构建预测模型。方法收集2022年9月至2024年12月于本院行鼻内镜手术的CRSwNP患者270例,依据术后...目的基于LASSO-Logistic回归分析影响慢性鼻窦炎伴鼻息肉(chronic rhinosinusitis with nasal polyps,CRSwNP)术后嗅觉功能恢复的危险因素,构建预测模型。方法收集2022年9月至2024年12月于本院行鼻内镜手术的CRSwNP患者270例,依据术后嗅觉功能恢复情况分为良好组(n=207)和不良组(n=63),分析影响CRSwNP术后嗅觉功能恢复的危险因素,并构建预测模型。结果270例患者术后嗅觉功能恢复不良发生率为23.33%(63/270)。LASSO回归结合多因素Logistic回归分析显示,嗅觉功能障碍持续时间、合并变应性鼻炎、病变类型、疾病分型以及分期、Lund-Kennedy评分、复诊次数是独立影响因素(P均<0.001)。基于此构建的列线图预测模型内部验证一致性指数为0.731,校准良好;ROC曲线下面积(AUC)为0.783(95%CI:0.677~0.889),敏感度和特异性分别为81.25%与67.50%。结论影响CRSwNP患者术后嗅觉功能恢复的危险因素包括嗅觉功能障碍持续时间≥2年、合并变应性鼻炎、嗅裂区息肉、疾病分型及分期≥Ⅱ型2期、Lund-Kennedy评分≥2分以及复诊次数>3次,基于上述因素所构建的预测模型,可为临床医生提供术后嗅觉功能恢复的预测依据。展开更多
The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understandi...The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.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
[ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observ...[ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observed and recorded. Impulsive Logistic Model was used to simulate the development process of the disease, which was compared with actual incidence. [ Result] Artificial inoculation tests showed that impulsive Logistic Model could reflect time dynamic of C. zeae-maydi. Through derivation, exponential growth phase was from maize seedling emergence to eady July in each year, logistic phase was from early July to late August, terminal phase was from eady September to the end of maize growth stage. [ Conclusion] The derivation result from model was consistent with the development biological laws of C. zeae-maydi.展开更多
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘Bangladesh has a denser population in comparison with many other countries. Though the rate of population increase has been regarded as a concerning issue, estimation of the population instability in the upcoming years may be useful for national planning. To predict Bangladesh’s future population, this study compares the estimated populations of two popular population models, the Malthusian and the logistic population models, with the country’s census population published by BBS. We also tried to find out which model gives a better approximation for forecasting the past, present, and future population between these two models.
基金Project(52074299)supported by the National Natural Science Foundation of ChinaProjects(2023JCCXSB02,BBJ2024083)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This study proposed a new and more flexible S-shaped rock damage evolution model from a phenomenological perspective based on an improved Logistic function to describe the characteristics of the rock strain softening and damage process.Simultaneously,it established a constitutive model capable of describing the entire process of rock pre-peak compaction and post-peak strain softening deformation,considering the nonlinear effects of the initial compaction stage of rocks,combined with damage mechanics theory and effective medium theory.In addition,this research verified the rationality of the constructed damage constitutive model using results from uniaxial and conventional triaxial compression tests on Miluo granite,yellow sandstone,mudstone,and glutenite.The results indicate that based on the improved Logistic function,the theoretical damage model accurately describes the entire evolution of damage characteristics during rock compression deformation,from maintenance through gradual onset,accelerated development to deceleration and termination,in a simple and unified expression.At the same time,the constructed constitutive model can accurately simulate the stress-strain process of different rock types under uniaxial and conventional triaxial compression,and the theoretical model curve closely aligns with experimental data.Compared to existing constitutive models,the proposed model has significant advantages.The damage model parameters a,r and β have clear physical meanings and interact competitively,where the three parameters collectively determine the shape of the theoretical stress−strain curve.
文摘目的基于LASSO-Logistic回归分析影响慢性鼻窦炎伴鼻息肉(chronic rhinosinusitis with nasal polyps,CRSwNP)术后嗅觉功能恢复的危险因素,构建预测模型。方法收集2022年9月至2024年12月于本院行鼻内镜手术的CRSwNP患者270例,依据术后嗅觉功能恢复情况分为良好组(n=207)和不良组(n=63),分析影响CRSwNP术后嗅觉功能恢复的危险因素,并构建预测模型。结果270例患者术后嗅觉功能恢复不良发生率为23.33%(63/270)。LASSO回归结合多因素Logistic回归分析显示,嗅觉功能障碍持续时间、合并变应性鼻炎、病变类型、疾病分型以及分期、Lund-Kennedy评分、复诊次数是独立影响因素(P均<0.001)。基于此构建的列线图预测模型内部验证一致性指数为0.731,校准良好;ROC曲线下面积(AUC)为0.783(95%CI:0.677~0.889),敏感度和特异性分别为81.25%与67.50%。结论影响CRSwNP患者术后嗅觉功能恢复的危险因素包括嗅觉功能障碍持续时间≥2年、合并变应性鼻炎、嗅裂区息肉、疾病分型及分期≥Ⅱ型2期、Lund-Kennedy评分≥2分以及复诊次数>3次,基于上述因素所构建的预测模型,可为临床医生提供术后嗅觉功能恢复的预测依据。
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,project number MoE-IF-UJ-R2-22-20772-1.
文摘The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.
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.
基金Supported by Doctoral Fundation of Liaoning Province(20081064)Liaoning BaiQianWan Talents Program(2009921072)Ministry of Agriculture,National Research Subject(2004BA520A11)~~
文摘[ Objectlve] Impulsive Logistic Model was used to simulate epidemic process of Gray Leaf Spots caused by C. zeae-maydi. [ Method] The pathogen was inoculated in different maize varieties, and the incidence were observed and recorded. Impulsive Logistic Model was used to simulate the development process of the disease, which was compared with actual incidence. [ Result] Artificial inoculation tests showed that impulsive Logistic Model could reflect time dynamic of C. zeae-maydi. Through derivation, exponential growth phase was from maize seedling emergence to eady July in each year, logistic phase was from early July to late August, terminal phase was from eady September to the end of maize growth stage. [ Conclusion] The derivation result from model was consistent with the development biological laws of C. zeae-maydi.
