Objective:To reveal the distribution characteristics and demographic factors of traditional Chinese medicine(TCM)constitution among elderly individuals in China.Methods: Elderly individuals from seven regions in China...Objective:To reveal the distribution characteristics and demographic factors of traditional Chinese medicine(TCM)constitution among elderly individuals in China.Methods: Elderly individuals from seven regions in China were selected as samples in this study using a multistage cluster random sampling method.The basic information questionnaire and Constitution in Chinese Medicine Questionnaire(Elderly Edition)were used.Descriptive statistical analysis,chi-squared tests,and binary logistic regression analysis were used.Results: The single balanced constitution(BC)accounted for 23.9%.The results of the major TCM constitution types showed that BC(43.2%)accounted for the largest proportion and unbalanced constitutions ranged from 0.9%to 15.7%.East China region(odds ratio[OR]=2.097;95%confidence interval[CI],1.912 to 2.301),married status(OR=1.341;95%CI,1.235 to 1.457),and managers(OR=1.254;95%CI,1.044 to 1.505)were significantly associated with BC.Age>70 years was associated with qi-deficiency constitution and blood stasis constitution(BSC).Female sex was significantly associated with yang-deficiency constitution(OR=1.646;95%CI,1.52 to 1.782).Southwest region was significantly associated with phlegm-dampness constitution(OR=1.809;95%CI,1.569 to 2.086).North China region was significantly associated with inherited special constitution(OR=2.521;95%CI,1.569 to 4.05).South China region(OR=2.741;95%CI,1.997 to 1.3.763),Central China region(OR=8.889;95%CI,6.676 to 11.835),senior middle school education(OR=2.442;95%CI,1.932 to 3.088),and managers(OR=1.804;95%CI,1.21 to 2.69)were significantly associated with BSC.Conclusions: This study defined the distribution characteristics and demographic factors of TCM constitution in the elderly population.Adjusting and improving unbalanced constitutions,which are correlated with diseases,can help promote healthy aging through the scientific management of these demographic factors.展开更多
The hadal zone(ocean depths of 6 – 11 km) is one of the least-understood habitats on Earth because of its extreme conditions such as high pressure, darkness, and low temperature. With the development of deep-sea vehi...The hadal zone(ocean depths of 6 – 11 km) is one of the least-understood habitats on Earth because of its extreme conditions such as high pressure, darkness, and low temperature. With the development of deep-sea vehicles such as China's 7000 m manned submersible Jiaolong, abyssal science has received greater attention. For decades, gravity-piston corers have been widely used to collect loose subsea-sediment long-core samples. However, the weight and length of the gravity sampler cables and the operating environment limit sampling capacity at full ocean depths. Therefore, a new self-floating sediment sampler with a spring-loaded auto-trigger release and that incorporates characteristics from traditional gravity-driven samplers is designed. This study analyzes the process by which a gravity-piston corer penetrates the sediment and the factors that affect it. A formula for obtaining the penetration depth is deduced. A method of optimizing the sampling depth is then developed based on structure design and parametric factor modeling. The parameters considered in the modeling include the sampling depth, balance weight, ultimate stress friction coefficient, dimensions of the sampler, and material properties. Thus, a new deep-sea floating parametric sampler designed based on virtual prototyping is proposed. Accurate values for all the design factors are derived from calculations based on the conservation of energy with penetration depth, analyses of the factors affecting the penetration depth, and analyses of the pressure bar stability. Finally, experimental data are used to verify the penetration-depth function and to provide theoretical guidance for the design of sediment samplers.展开更多
With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec...With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.展开更多
滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距...滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子,使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子;基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling,简称TSES)选择负样本,作为性能优异的随机森林模型的输入变量;之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性,并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性,采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型,与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明,环境因子优化的TSES法构建的随机森林模型的评价效果较好,其ACC为93.7%、AUC为0.987,均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度,解决多因子作为约束条件取样中因子选取的问题,为滑坡易发性评价采集负样本提供了新的思路。展开更多
不同环境因子组合和负样本选取策略对崩岗易发性评价结果存在较多不确定性。为探究其对评价结果的影响,该研究以江西省兴国县花岗岩区为例,利用地理探测器探测17个环境因子的统计量q值,根据累计q值百分比大小依次选择4、7、10和17个环...不同环境因子组合和负样本选取策略对崩岗易发性评价结果存在较多不确定性。为探究其对评价结果的影响,该研究以江西省兴国县花岗岩区为例,利用地理探测器探测17个环境因子的统计量q值,根据累计q值百分比大小依次选择4、7、10和17个环境因子进行组合;利用单随机欠采样、频率比法及改进频率比法等负样本选取策略构建与正样本等量的负样本数据集;采用随机森林模型进行易发性评价,并对评价结果进行对比分析。结果表明:1)3种负样本选取策略下的模型精度随着因子数量的增加先下降再上升,考虑4个环境因子的模型AUC(area under curve)值分别为0.729、0.909和0.909,较最优环境因子组合仅相差0.020~0.038,说明考虑主控环境因子,即可得到较为理想的精度;2)通过频率比法选取的负样本数据集更具合理性;3)研究区内高和极高易发区主要分布在兴国县西南部,而极低易发区主要分布在兴国县北部及东部,这与实际情况较吻合。该研究通过探究不同环境因子组合和负样本选取策略对崩岗易发性评价的影响,可为花岗岩区崩岗的防灾减灾提供科学依据。展开更多
基金supported by the National Key R&D Program of China(2020YFC2003102).
文摘Objective:To reveal the distribution characteristics and demographic factors of traditional Chinese medicine(TCM)constitution among elderly individuals in China.Methods: Elderly individuals from seven regions in China were selected as samples in this study using a multistage cluster random sampling method.The basic information questionnaire and Constitution in Chinese Medicine Questionnaire(Elderly Edition)were used.Descriptive statistical analysis,chi-squared tests,and binary logistic regression analysis were used.Results: The single balanced constitution(BC)accounted for 23.9%.The results of the major TCM constitution types showed that BC(43.2%)accounted for the largest proportion and unbalanced constitutions ranged from 0.9%to 15.7%.East China region(odds ratio[OR]=2.097;95%confidence interval[CI],1.912 to 2.301),married status(OR=1.341;95%CI,1.235 to 1.457),and managers(OR=1.254;95%CI,1.044 to 1.505)were significantly associated with BC.Age>70 years was associated with qi-deficiency constitution and blood stasis constitution(BSC).Female sex was significantly associated with yang-deficiency constitution(OR=1.646;95%CI,1.52 to 1.782).Southwest region was significantly associated with phlegm-dampness constitution(OR=1.809;95%CI,1.569 to 2.086).North China region was significantly associated with inherited special constitution(OR=2.521;95%CI,1.569 to 4.05).South China region(OR=2.741;95%CI,1.997 to 1.3.763),Central China region(OR=8.889;95%CI,6.676 to 11.835),senior middle school education(OR=2.442;95%CI,1.932 to 3.088),and managers(OR=1.804;95%CI,1.21 to 2.69)were significantly associated with BSC.Conclusions: This study defined the distribution characteristics and demographic factors of TCM constitution in the elderly population.Adjusting and improving unbalanced constitutions,which are correlated with diseases,can help promote healthy aging through the scientific management of these demographic factors.
基金jointly supported by the Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology (No. JCKYS2019604SXJQR-06)the National Natural Science Foundation of China-Marine Science Research Center of Shandong Provincial Government Joint Funding Project (No. U1606401)+3 种基金the National Natural Science Foundation of China (No. 61603108)the Taishan Scholar Project Funding (No. tspd20161007)the National Key Research and Development Plan (Nos. 2016YFC03007042017YFC030660)。
文摘The hadal zone(ocean depths of 6 – 11 km) is one of the least-understood habitats on Earth because of its extreme conditions such as high pressure, darkness, and low temperature. With the development of deep-sea vehicles such as China's 7000 m manned submersible Jiaolong, abyssal science has received greater attention. For decades, gravity-piston corers have been widely used to collect loose subsea-sediment long-core samples. However, the weight and length of the gravity sampler cables and the operating environment limit sampling capacity at full ocean depths. Therefore, a new self-floating sediment sampler with a spring-loaded auto-trigger release and that incorporates characteristics from traditional gravity-driven samplers is designed. This study analyzes the process by which a gravity-piston corer penetrates the sediment and the factors that affect it. A formula for obtaining the penetration depth is deduced. A method of optimizing the sampling depth is then developed based on structure design and parametric factor modeling. The parameters considered in the modeling include the sampling depth, balance weight, ultimate stress friction coefficient, dimensions of the sampler, and material properties. Thus, a new deep-sea floating parametric sampler designed based on virtual prototyping is proposed. Accurate values for all the design factors are derived from calculations based on the conservation of energy with penetration depth, analyses of the factors affecting the penetration depth, and analyses of the pressure bar stability. Finally, experimental data are used to verify the penetration-depth function and to provide theoretical guidance for the design of sediment samplers.
基金This research was funded by Innovation and Entrepreneurship Training Program for College Students in Hunan Province in 2022(3915).
文摘With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.
文摘滑坡易发性评价是滑坡灾害防治的重要手段之一,而不合理的滑坡负样本会影响滑坡易发性评价,从而影响到滑坡灾害的防治,因此提供一种合理的负样本选取方法变得尤为关键。以西藏米林市的古滑坡为例,选择高程、坡度、坡向、坡位、距道路距离、距断层距离、距水系距离、地形起伏度、地层岩性、土地利用类型10类环境因子,使用Relief算法计算环境因子的贡献值并依据贡献值优化选择环境因子;基于环境因子优化的目标空间外向化采样法(target space exteriorization sampling,简称TSES)选择负样本,作为性能优异的随机森林模型的输入变量;之后结合优化的环境因子和正或负样本预测米林市的滑坡易发性,并用混淆矩阵和ROC曲线评价构建模型的性能。为检验环境因子优化的TSES法的有效性和先进性,采用耦合信息量法和TSES法选择滑坡负样本并构建随机森林模型,与环境因子优化的TSES法构建的随机森林模型进行对比研究。结果表明,环境因子优化的TSES法构建的随机森林模型的评价效果较好,其ACC为93.7%、AUC为0.987,均高于耦合信息量、TSES法构成的模型。环境因子优化的TSES法能够提高模型的精度,解决多因子作为约束条件取样中因子选取的问题,为滑坡易发性评价采集负样本提供了新的思路。
文摘不同环境因子组合和负样本选取策略对崩岗易发性评价结果存在较多不确定性。为探究其对评价结果的影响,该研究以江西省兴国县花岗岩区为例,利用地理探测器探测17个环境因子的统计量q值,根据累计q值百分比大小依次选择4、7、10和17个环境因子进行组合;利用单随机欠采样、频率比法及改进频率比法等负样本选取策略构建与正样本等量的负样本数据集;采用随机森林模型进行易发性评价,并对评价结果进行对比分析。结果表明:1)3种负样本选取策略下的模型精度随着因子数量的增加先下降再上升,考虑4个环境因子的模型AUC(area under curve)值分别为0.729、0.909和0.909,较最优环境因子组合仅相差0.020~0.038,说明考虑主控环境因子,即可得到较为理想的精度;2)通过频率比法选取的负样本数据集更具合理性;3)研究区内高和极高易发区主要分布在兴国县西南部,而极低易发区主要分布在兴国县北部及东部,这与实际情况较吻合。该研究通过探究不同环境因子组合和负样本选取策略对崩岗易发性评价的影响,可为花岗岩区崩岗的防灾减灾提供科学依据。
