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
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
目的基于Logistic回归和随机森林算法构建全身麻醉复苏延迟的预判模型并验证。方法选择2021—2023年浙江某三甲医院复苏室收治的1177例全麻患者作为研究对象,按7︰3的比例随机分为训练组和验证组两组,采用Logistic单因素+多因素回归分析...目的基于Logistic回归和随机森林算法构建全身麻醉复苏延迟的预判模型并验证。方法选择2021—2023年浙江某三甲医院复苏室收治的1177例全麻患者作为研究对象,按7︰3的比例随机分为训练组和验证组两组,采用Logistic单因素+多因素回归分析,构建全身麻醉复苏延迟的预判模型并用列线图展示。利用随机森林算法筛选全身麻醉患者复苏延迟的影响因素并按重要性排序。采用受试者操作特征曲线(Receiver operating characteristic curve,ROC)下面积(Area of the under curve,AUC)检验模型的预测效果,采用校准曲线以及决策曲线综合评价模型的预测性能。结果1177例患者复苏延迟发生99例,发生率为8.41%。Logistic回归显示性别、ASA分级、年龄、手术时间、手术种类、输液量是全麻患者复苏延迟的独立危险因素。随机森林算法结果显示复苏延迟各变量的重要性排序为手术种类、年龄、手术时间、输液量、ASA分级、性别。Logistic回归模型的训练组AUC为0.87(95%CI 0.83~0.91),验证组为0.86(95%CI 0.81~0.91)。随机森林模型训练组AUC为0.85(95%CI 0.49~1.00),验证组AUC为0.76(95%CI 0.26~1.00)。提示模型具有良好的区分能力,预测能力较高,具有一定的临床价值。结论手术种类、年龄、手术时间、输液量、ASA分级、性别是全麻患者复苏延迟的独立危险因素,根据此构建预判模型的区分度与校准度较高,有助于预测全麻患者苏醒延迟的发生,可以为临床护理干预措施的制定与实施提供参考。展开更多
Bedding structural planes significantly influence the mechanical properties and stability of engineering rock masses.This study conducts uniaxial compression tests on layered sandstone with various bedding angles(0...Bedding structural planes significantly influence the mechanical properties and stability of engineering rock masses.This study conducts uniaxial compression tests on layered sandstone with various bedding angles(0°,15°,30°,45°,60°,75°and 90°)to explore the impact of bedding angle on the deformational mechanical response,failure mode,and damage evolution processes of rocks.It develops a damage model based on the Logistic equation derived from the modulus’s degradation considering the combined effect of the sandstone bedding dip angle and load.This model is employed to study the damage accumulation state and its evolution within the layered rock mass.This research also introduces a piecewise constitutive model that considers the initial compaction characteristics to simulate the whole deformation process of layered sandstone under uniaxial compression.The results revealed that as the bedding angle increases from 0°to 90°,the uniaxial compressive strength and elastic modulus of layered sandstone significantly decrease,slightly increase,and then decline again.The corresponding failure modes transition from splitting tensile failure to slipping shear failure and back to splitting tensile failure.As indicated by the modulus’s degradation,the damage characteristics can be categorized into four stages:initial no damage,damage initiation,damage acceleration,and damage deceleration termination.The theoretical damage model based on the Logistic equation effectively simulates and predicts the entire damage evolution process.Moreover,the theoretical constitutive model curves closely align with the actual stress−strain curves of layered sandstone under uniaxial compression.The introduced constitutive model is concise,with fewer parameters,a straightforward parameter determination process,and a clear physical interpretation.This study offers valuable insights into the theory of layered rock mechanics and holds implications for ensuring the safety of rock engineering.展开更多
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.
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.
文摘目的基于Logistic回归和随机森林算法构建全身麻醉复苏延迟的预判模型并验证。方法选择2021—2023年浙江某三甲医院复苏室收治的1177例全麻患者作为研究对象,按7︰3的比例随机分为训练组和验证组两组,采用Logistic单因素+多因素回归分析,构建全身麻醉复苏延迟的预判模型并用列线图展示。利用随机森林算法筛选全身麻醉患者复苏延迟的影响因素并按重要性排序。采用受试者操作特征曲线(Receiver operating characteristic curve,ROC)下面积(Area of the under curve,AUC)检验模型的预测效果,采用校准曲线以及决策曲线综合评价模型的预测性能。结果1177例患者复苏延迟发生99例,发生率为8.41%。Logistic回归显示性别、ASA分级、年龄、手术时间、手术种类、输液量是全麻患者复苏延迟的独立危险因素。随机森林算法结果显示复苏延迟各变量的重要性排序为手术种类、年龄、手术时间、输液量、ASA分级、性别。Logistic回归模型的训练组AUC为0.87(95%CI 0.83~0.91),验证组为0.86(95%CI 0.81~0.91)。随机森林模型训练组AUC为0.85(95%CI 0.49~1.00),验证组AUC为0.76(95%CI 0.26~1.00)。提示模型具有良好的区分能力,预测能力较高,具有一定的临床价值。结论手术种类、年龄、手术时间、输液量、ASA分级、性别是全麻患者复苏延迟的独立危险因素,根据此构建预判模型的区分度与校准度较高,有助于预测全麻患者苏醒延迟的发生,可以为临床护理干预措施的制定与实施提供参考。
基金Projects(52074299,41941018)supported by the National Natural Science Foundation of ChinaProject(2023JCCXSB02)supported by the Fundamental Research Funds for the Central Universities,China。
文摘Bedding structural planes significantly influence the mechanical properties and stability of engineering rock masses.This study conducts uniaxial compression tests on layered sandstone with various bedding angles(0°,15°,30°,45°,60°,75°and 90°)to explore the impact of bedding angle on the deformational mechanical response,failure mode,and damage evolution processes of rocks.It develops a damage model based on the Logistic equation derived from the modulus’s degradation considering the combined effect of the sandstone bedding dip angle and load.This model is employed to study the damage accumulation state and its evolution within the layered rock mass.This research also introduces a piecewise constitutive model that considers the initial compaction characteristics to simulate the whole deformation process of layered sandstone under uniaxial compression.The results revealed that as the bedding angle increases from 0°to 90°,the uniaxial compressive strength and elastic modulus of layered sandstone significantly decrease,slightly increase,and then decline again.The corresponding failure modes transition from splitting tensile failure to slipping shear failure and back to splitting tensile failure.As indicated by the modulus’s degradation,the damage characteristics can be categorized into four stages:initial no damage,damage initiation,damage acceleration,and damage deceleration termination.The theoretical damage model based on the Logistic equation effectively simulates and predicts the entire damage evolution process.Moreover,the theoretical constitutive model curves closely align with the actual stress−strain curves of layered sandstone under uniaxial compression.The introduced constitutive model is concise,with fewer parameters,a straightforward parameter determination process,and a clear physical interpretation.This study offers valuable insights into the theory of layered rock mechanics and holds implications for ensuring the safety of rock engineering.
文摘目的 在登革热疾病进展的早期阶段通过建立预测模型来评估重症登革热及登革热伴预警出现的风险,以避免重症登革热在疾病发展早期得不到有效防治,并降低登革热死亡率。方法 回顾性收集云南省瑞丽市人民医院2019—2023年临床及实验室检查等831例患者资料,按7∶3比例分为训练集和验证集。训练集进行统计描述、单因素分析,LASSO回归筛选变量,logistic回归开发登革热重症风险预警模型;训练集和验证集进行ROC曲线模型性能验证。结果 本研究共纳入831名登革热患者,年龄为(44.20±15.02)岁,52.59%为男性,5.42%为缅甸籍;发生重症登革热或登革热伴预警122例,占比14.68%,女性为主(58.20%)。训练集采用LASSO回归筛选发生重症登革热或登革热伴预警的相关变量11个:年龄、头晕、呕吐、凝血酶原时间、部分活化凝血活酶时间、红细胞压积、血小板、单核细胞百分比、单核细胞绝对值、血红蛋白、C反应蛋白(λmin=0.011 59);logistic回归建立重症登革热及登革热伴预警模型,具有统计学意义的变量为年龄[OR=1.034(95%CI:1.016~1.053)]、红细胞压积[OR=1.258(95%CI:1.143~1.519)]、血小板[OR=0.991(95%CI:0.985~0.997)]、血红蛋白[OR=0.919(95%CI:0.873~0.950)]、C反应蛋白[OR=1.019(95%CI:1.004~1.034)]。训练集ROC曲线下面积(area under the curve, AUC)值为0.894(95%CI:0.796~0.867),验证集中AUC值为0.862(95%CI:0.709~0.827)。最佳阈值点(Cut-off)取0.197,灵敏度为0.850,特异度为0.743。结论 本研究建立了LASSO-logistic回归风险预测模型,可在登革热患者患病早期预测发生重症登革热及登革热伴预警风险,提高医院重症登革热的防治能力,有助于指导临床治疗决策。
