This paper presents a case study on the IPUMS NHIS database,which provides data from censuses and surveys on the health of the U.S.population,including data related to COVID-19.By addressing gaps in previous studies,w...This paper presents a case study on the IPUMS NHIS database,which provides data from censuses and surveys on the health of the U.S.population,including data related to COVID-19.By addressing gaps in previous studies,we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms.Our experiments focus on four groups of factors:demographic,socio-economic,health condition,and related to COVID-19 vaccination.By analysing the sensitivity of the variables used to train the models and the VEC(variable effect characteristics)analysis on the variable values,we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.展开更多
On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted...On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.展开更多
Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ri...Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.展开更多
Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct ...Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.展开更多
文摘This paper presents a case study on the IPUMS NHIS database,which provides data from censuses and surveys on the health of the U.S.population,including data related to COVID-19.By addressing gaps in previous studies,we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms.Our experiments focus on four groups of factors:demographic,socio-economic,health condition,and related to COVID-19 vaccination.By analysing the sensitivity of the variables used to train the models and the VEC(variable effect characteristics)analysis on the variable values,we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.
基金supported by the National College Students Innovation and Entrepreneurship Training Programs(CN)(Grant Nos.2021J00054&2019J00127)
文摘On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.
基金This paper was financially supported by NSC96-2628-E-366-004-MY2 and NSC96-2628-E-132-001-MY2
文摘Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.
文摘Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.
