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An exploratory model for the non-fatal drowning risks in children in Guangdong, China

BMC PUBLIC HEALTH 2019年第1期19卷 599-599页

作者： Xu, Haofeng Zhu, Xuhao Zhou, Zhishan Xu, Yanjun Zhu, Yongjian Lin, Lifeng Huang, Jinying Meng, Ruilin Inst Control & Prevent Chron Noninfect Dis Guangdong Prov Ctr Dis Control & Prevent Guangzhou Guangdong Peoples R China Qingyuan City Ctr Dis Control & Prevent Qingyuan 511515 Peoples R China Qingxin Dist Ctr Dis Control & Prevent Qingyuan 511000 Peoples R China Ctr Directors Off Guangdong Prov Ctr Dis Control & Prevent Guangzhou Guangdong Peoples R China

BackgroundDrowning is a leading cause of accidental death in children under 14years of age in Guangdong, China. We developed a statistical model to classify the risk of drowning among children based on the risk *** multiple-stage cluster random sampling was employed to select the students in Grades 3 to 9 in two townships in Qingyuan, Guangdong. Questionnaire was a self-reported measure consisting of general information, knowledge, attitudes and activities. A univariate logistic regression model was used to preliminarily select the independent variables at a P value of 0.1 for multivariable model. Three-quarters of the participants were randomly selected as a training sample to establish the model, and the remaining were treated as a testing sample to validate the *** total of 8390 children were included in this study, about 12.18% (1013) experienced drowning during the past one year. In the univariate logistic regression model, introvert personality, unclear distributions of water areas on the way to school, and bad relationships with their classmates and families were positively associated with drowning. However, females, older age and lower swimming skills were negatively associated with drowning. After employing the prediction model with these factors to estimate drowning risk of the students in the testing samples, the results of Hosmer-Lemeshow tests showed non-significant differences between the predictive results and actual risk (2=5.97, P=0.65).ConclusionsMale, younger children, higher swimming skills, bad relationship with their classmates and families, introvert personality and unclear distributions of water areas on the way to school were important risk factors of non-fatal drowning among children. The prediction model based on these variables has an acceptable predictive ability.

关键词： Drowning Children Risk factors logistic regression model Prediction

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Flood susceptibility modeling and hazard perception in Rwanda

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2019年 38卷 101211-000页

作者： Mind'je, Richard Li, Lanhai Amanambu, Amobichukwu Chukwudi Nahayo, Lamek Nsengiyumva, Jean Baptiste Gasirabo, Aboubakar Mindje, Mapendo Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol 818 South Beijing Rd Urumqi 830011 Xinjiang Peoples R China Chinese Acad Sci Ili Stn Watershed Ecosyst Res Urumqi 830011 Xinjiang Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China Univ Lay Adventists Kigali UNILAK Fac Environm Sci POB 6392 Kigali Rwanda CAS Res Ctr Ecol & Environm Cent Asia 818 South Beijing Rd Urumqi 830011 Xinjiang Peoples R China Chinese Acad Sci Xinjiang Reg Ctr Resources & Environm Sci Instrum Urumqi 830011 Peoples R China Univ Rwanda Coll Agr Anim Sci & Vet Med POB 117 Huye Rwanda

Flooding is a deleterious phenomenon that induces detrimental impacts on humans, properties and environment. As a result, the knowledge of susceptible places and hazard perception is increasingly pertinent. This study mainly aims at identifying areas susceptible to flood through the application of logistic regression model using remote sensing data (RS) and Geographical Information System (GIS). A flood inventory was generated using 153 historical flood locations and a total of 10 predicting factors (elevation, slope, aspect, profile curvature, distance from rivers, distance from roads, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI), the Topographic Wetness Index (TWI) and rainfall) were utilized. Flood points were randomly subdivided into training (75%) for model building and testing (25%) points for validation through the Area Under Curve (AUC) approach. The results indicated that NDVI and rainfall are the most influencing variables for estimating flood risk as they showed a high positive relationship with flood occurrence in the study area. Testing datasets disclosed 79.8% of prediction rate using the AUC. Moreover, the results have been linked with community perception on flood and the outcome revealed that the government is perceived as responsible for all flood mitigation measures instead of being a shared responsibility. This perception may contribute to the increase in susceptibility. The results of this study will be essential for upcoming development projects from different organizations operating in many developing countries and would assist as a baseline for flood risk reduction and management especially for Rwanda.

关键词： Flood susceptibility GIS Hazard perception logistic regression model Remote sensing technique

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Investigation of house dust mite induced allergy using logistic regression in West Bengal, India

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WORLD ALLERGY ORGANIZATION JOURNAL 2019年第12期12卷 100088页

作者： Mondal, Priti Dey, Debarati Saha, Nimai Chandra Moitra, Saibal Saha, Goutam Kumar Bhattacharya, Srijit Podder, Sanjoy Barasat Govt Coll Post Grad Dept Zool Allergol & Appl Entomol Res Lab Kolkata 700124 W Bengal India Univ Calcutta Dept Zool Kolkata 700019 W Bengal India Univ Burdwan Burdwan 713104 W Bengal India Allergy & Asthma Res Ctr Kolkata W Bengal India Barasat Govt Coll Post Grad Dept Phys Kolkata 700124 W Bengal India

Background: The diagnosis of house dust mite (HDM) allergy based on Skin prick test (SPT) is not accurate, especially in lower risk cases. Our aim is to develop and validate a predictive model to diagnose the HDM allergic symptoms (urticaria, allergic rhinitis, asthma). Methods: A forward-step logistic regression model was developed using a data set of 537 patients of West Bengal, India consisting of clinical variables (SPT based on 6 allergens of house dust and house dust mites, total IgE) and demographic characteristics (age, sex, house conditions). The output probability was estimated from the allergic symptoms shown by the patients. We finally prospectively validated a data set of 600 patients. Results: The gradual inclusion of the variables increased the correlation between observed and predicted probabilities (correlation coefficient (r(2)) = 0.97). The model development using group-1 showed an accuracy rate of 99%, sensitivity and specificity of 99.7% and 88.6% respectively and the area under the receiver operating characteristics (ROC) curve (AUC) of 99%. The corresponding numbers for the validation of our model with group-2 were 87%, 95.6% and 66% and 86% respectively. The model predicted the probability of symptoms better than SPTs in combination (accuracy rate 0.76-0.80), especially in lower risk cases (probability < 0.8) that are highly difficult to diagnose. Conclusion: This is perhaps the first attempt to model the outcome of HDM allergy in terms of symptoms, which could open up an alternative but highly efficient way for accurate diagnosis of HDM allergy enhancing the efficiency of immunotherapy.

关键词： Asthma House dust mite allergy logistic regression model ROC SPT

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Reliability estimation for cutting tools based on logistic regression model using vibration signals

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2011年第7期25卷 2526-2537页

作者： Chen, Baojia Chen, Xuefeng Li, Bing He, Zhengjia Cao, Hongrui Cai, Gaigai Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

As an important part of CNC machine, the reliability of cutting tools influences the whole manufacturing effectiveness and stability of equipment. The present study proposes a novel reliability estimation approach to the cutting tools based on logistic regression model by using vibration signals. The operation condition information of the CNC machine is incorporated into reliability analysis to reflect the product time-varying characteristics. The proposed approach is superior to other degradation estimation methods in that it does not necessitate any assumption about degradation paths and probability density functions of condition parameters. The three steps of new reliability estimation approach for cutting tools are as follows. First, on-line vibration signals of cutting tools are measured during the manufacturing process. Second, wavelet packet (WP) transform is employed to decompose the original signals and correlation analysis is employed to find out the feature frequency bands which indicate tool wear. Third, correlation analysis is also used to select the salient feature parameters which are composed of feature band energy, energy entropy and time-domain features. Finally, reliability estimation is carried out based on logistic regression model. The approach has been validated on a NC lathe. Under different failure threshold, the reliability and failure time of the cutting tools are all estimated accurately. The positive results show the plausibility and effectiveness of the proposed approach, which can facilitate machine performance and reliability estimation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Reliability estimation Cutting tool logistic regression model Wavelet packet decomposition Correlation analysis

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Study on quick judgment of power system stability using improved k-NN and LASSO method

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 686-689页

作者： Shi, Dongyu Tian, Fang Li, Tie Meng, Xianbo Tang, Junci Huang, Yanhao Cui, Dai Zhang, Yushi Zeng, Hui China Elect Power Res Inst Power Syst Dept East Rd Qinghe Xiaoying 15 Beijing 100192 Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Dispatching Ctr Ningbo Rd 18 Shenyang 110006 Liaoning Peoples R China Res Inst Liaoning Power Co Ltd Power Syst Dept Siping Rd 39 Shenyang 110006 Liaoning Peoples R China

Dynamic security assessment is widely used in dispatching operation systems, and calculation speed is one of its most important performance indices. In this study, an improved k-nearest neighbour (k-NN) method is proposed aiming to predict the stability indicators of power system, for example, critical clearing time. The method is much faster than the simulation and suitable for online analysis. Firstly, a simulation sample database is constructed based on historical online data and a logistic regression model with least absolute shrinkage and selection operator is trained to pick the stability features, which are chosen from static quantities like running state and active power of electric elements. While a new operation mode needs to be evaluated, a weighted k-NN is implemented to obtain the most familiar samples in the database using the chosen features;the final result will be determined comprehensively by the familiar samples. The validity of the proposed method is verified by simulation using online data of State Grid Corp of China and different key faults. It is proved that the method meets the requirements for speed and accuracy of online analysis system.

关键词： power grids regression analysis power system transient stability power system security power system control load dispatching power system stability power engineering computing quick judgment power system stability NN LASSO method dynamic security assessment operation systems calculation speed important performance indices nearest neighbour stability indicators critical clearing time simulation sample database historical online data logistic regression model absolute shrinkage selection operator stability features static quantities running state active power electric elements operation mode familiar samples chosen features online analysis system

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Analysis of Willingness and Influences for Preventive Resettlement in China——The Three Gorges Reservoir Case

Analysis of Willingness and Influences for Preventive Resett...

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作者： Zijiang Hu Guoqing Shi Nanjing University of Finance and Economics Hohai University

In this paper,the formation mechanism of population migration dynamics is analyzed based on the "Push-pull Theory".Combining with the case study of preventive resettlement in the Three Gorges Reservoir Area,the influencing variables of migration intention are *** logistic regression model was used to analyze the migration intention,and the important influencing factors were obtained from the positive and negative correlative *** conclusion of the analysis has great significance for improving the effectiveness of the implementation of disaster-avoidance migrants.

关键词： Preventive Resettlement Willingness and Influences logistic regression model

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Developing a Risk Group Predictive model for Korean Students Falling into Bad Debt

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ASIAN ECONOMIC JOURNAL 2018年第1期32卷 3-14页

作者： Han, Jun-Tae Choi, Jae-Seok Kim, Myeon-Jung Jeong, Jina Korea Student Aid Fdn Dept Student Aid Policy Res 125 Sinam Ro Daegu 41200 South Korea Korea Student Aid Fdn Dept Student Loan Repayment 125 Sinam Ro Daegu 41200 South Korea

Using direct loan data for 2012 to 2014 from the Korea Student Aid Foundation, we develop a risk group predictive model for borrowers defaulting on their loans. We used a logistic regression model and the Cox proportional hazards model to develop the risk predictive model. We verified the validity of the models using a receiver operating characteristic curve and a validation dataset. The present study shows that area under the receiver operating characteristic curves is similar for the models and that the major influencing factors for defaulting on their loans are household income, whether a national grant was received, age, whether more than two accounts are overdue, field of study and the monthly repayment amount. The risk group predictive model in this study will be the basis for more efficient management of direct student loans.

关键词： Cox proportional hazards model direct student loan logistic regression model

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An Improvement to Test Case Failure Prediction in the Context of Test Case Prioritization 18

An Improvement to Test Case Failure Prediction in the Contex...

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14th International Conference on Predictive models and Data Analytics in Software Engineering (PROMISE)

作者： Palma, Francis Abdou, Tamer Bener, Ayse Maidens, John Liu, Stella Ryerson Univ Data Sci Lab Toronto ON Canada IBM Canada Lab Toronto ON Canada

ISBN: (纸本)9781450365932

Aim: In this study, we aim to re-evaluate research questions on the ability of a logistic regression model proposed in a previous work to predict and prioritize the failing test cases based on some test quality metrics. Background: The process of prioritizing test cases aims to come up with a ranked test suite where test cases meeting certain criteria are prioritized. One criterion may be the ability of test cases to find faults that can be predicted a priori. Ranking test cases and executing the top-ranked test cases is particularly beneficial when projects have tight schedules and budgets. Method: We performed the comparison by first rebuilding the predictive models using the features from the original study and then we extended the original work to improve the predictive models using new features by combining with the existing ones. Results: The results of our study, using a dataset of five open-source systems, confirm that the findings from the original study hold and that our predictive models with new features outperform the original models in predicting and prioritizing the failing test cases. Conclusions: We plan to apply this method to a large-scale dataset from a large commercial enterprise project, to better demonstrate the improvement that our modified features provide and to explore the model's performance at scale.

关键词： Test Case Prioritization Prediction logistic regression model Machine Learning Test Quality Metrics

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New insights on relationships between street crimes and ambient population: Use of hourly population data estimated from mobile phone users' locations

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ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE 2018年第2期45卷 295-311页

作者： Hanaoka, Kazumasa Ritsumeikan Univ Dept Geog Kyoto Japan

The purpose of this research is to examine relationships between occurrences of snatch-and-run offences and hourly population estimated from mobile phone users' locations, with particular focus on differences between daytime and nighttime. Using an hourly population dataset allows us to count the so-called 'ambient population' by hour of day to accurately quantify the influence of such population as capable guardians and suitable targets in a framework of routine activity theory. Our major findings based on logistic regression models are that (1) the effects of ambient population and (2) its temporal change are large, and the effects differ between daytime and nighttime. During the daytime, snatch-and-run offences are less likely to occur in areas where hourly population density is expect to increase, possibly because offenders are highly sensitive to the risk of being detected by other people. On the other hand, offences at night occur even in relatively crowded areas, and they are only weakly related to population change. In addition, our study found that (3) snatch-and-run offences are more likely to occur in or near local town centres and (4) socially vulnerable neighbourhoods are only targeted at night. We attempted to explain this in terms of offenders' characteristics and motivations depending on time of day.

关键词： Snatch-and-run offence routine activity theory hourly population logistic regression model big data

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Factors influencing rural households' willingness of centralized residence: Comparing pure and nonpure farming areas in China

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HABITAT INTERNATIONAL 2018年 73卷 25-33页

作者： Zhang, Zhengfeng Wen, Yangyang Wang, Ruonan Han, Wenjing Renmin Univ China Sch Publ Adm & Policy Beijing 100872 Peoples R China

In recent years, Chinese governments began exploring centralized residence of rural households, with a view to protecting farming land through the incorporation and reduction of rural construction land, building new countryside, and solving the problem of insufficient construction land quotas for urban development. In the centralized residence process, it is important to study the willingness of rural households and factors influencing their willingness. This empirical study used Panshan County and Jiangshan City as representatives of pure and nonpure farming areas, respectively. Three logistic regression models were applied, to explore differences between pure and nonpure farming areas and the factors influencing rural households' willingness to accept centralized residence according to five aspects: households individual characteristics, family economy, policy perception, housing conditions, and social environment. The results showed that, for nonpure farming areas, such as Jiangshan City, when there is more trust in the village collective, less arable area, increased satisfaction with infrastructure and higher non-agricultural income, the more willing rural households are to accept centralized residence. In pure farming areas, such as Panshan County, when there are greater expectations of policy, the safer environments and higher non-agricultural income, the more willing rural households are to accept centralized residence. By comparison, rural households in nonpure farming areas are more concerned with fairness future quality of life, while those in pure farming areas are more concerned with implementation and guaranteed compensation. China's centralized residence policy should focus on the objective conditions of each region, and appropriately differentiate based on different areas and categories.

关键词： Centralized residence logistic regression model Rural household willingness Pure farming area Nonpure farming area logistic regression model

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