目的探讨与成人腰痛(low back pain,LBP)相关的行为因素,包括日常运动、饮酒、吸烟、睡眠时间和肥胖等情况。方法纳入2017年9月-2019年3月在本院因长期慢性腰痛就诊的LBP人群60例,作为LBP组;另按1:2比例随机纳入同时期于本院行常规体检...目的探讨与成人腰痛(low back pain,LBP)相关的行为因素,包括日常运动、饮酒、吸烟、睡眠时间和肥胖等情况。方法纳入2017年9月-2019年3月在本院因长期慢性腰痛就诊的LBP人群60例,作为LBP组;另按1:2比例随机纳入同时期于本院行常规体检的健康人群120例,作为健康对照组。统计两组人群的人口学资料,以及日常运动、饮酒、吸烟、睡眠时间等日常行为情况并进行赋值。组间单因素分析采用卡方检验,多因素分析采用多元Logistic回归分析,以探讨成人LBP与上述行为因素之间的关系。结果 LBP的人口学特征以女性为主,多见于40岁以上人群,且其经济收入水平偏低;单因素分析表明,两组人群的日常运动、吸烟、饮酒、睡眠和体质量指数均有显著性差异(P<0.05);Logsitic回归分析显示,缺乏运动(OR=2.253)、经常吸烟(OR=3.968)、经常饮酒(OR=2.546)、睡眠3-4 h(OR=2.528)、体型肥胖(OR=2.635),均是成人腰痛的行为风险因素。结论成人腰痛与其日常行为因素有密切的相关性,运动量不足、经常吸烟、饮酒和严重缺乏睡眠、体型肥胖,均是其致病的独立风险因素。展开更多
In this study,I explore smoking behavior among pregnant U.S.women using the 1979 cohort of the National Longitudinal Survey of Youth.The key aspect of this study is the availability of smoking participation data befor...In this study,I explore smoking behavior among pregnant U.S.women using the 1979 cohort of the National Longitudinal Survey of Youth.The key aspect of this study is the availability of smoking participation data before and during pregnancy.I consider the probabilities of smoking cessation while pregnant as the outcome.I find that pregnant women who smoke are less responsive to price changes when they are more future-oriented.Women who are more present-oriented are more likely to smoke and consume more cigarettes given that they smoke more than those who are future-oriented.Moreover,those who discount the future more heavily are more sensitive to the money price of cigarettes than those who are more future-oriented.I focus on the role of time preference and the interaction between time preference and price in determining these outcomes.展开更多
Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of ...Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios.With the developments of closed circuit television(CCTV)system,vision-based methods for object detection,mostly driven by deep learning techniques,were introduced recently.However,the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed.This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,high accuracy on micro-objects and low computing budget,i.e.,it could be deployed on the edge device such as NVIDIA JETSON TX2.We designed a new framework named RTVBS based on yolov3 and made a smoking dataset to train our model.We raised several methods to improve detection accuracy during the training step.The validation results show our model has excellent performance in smoking detection.展开更多
文摘目的探讨与成人腰痛(low back pain,LBP)相关的行为因素,包括日常运动、饮酒、吸烟、睡眠时间和肥胖等情况。方法纳入2017年9月-2019年3月在本院因长期慢性腰痛就诊的LBP人群60例,作为LBP组;另按1:2比例随机纳入同时期于本院行常规体检的健康人群120例,作为健康对照组。统计两组人群的人口学资料,以及日常运动、饮酒、吸烟、睡眠时间等日常行为情况并进行赋值。组间单因素分析采用卡方检验,多因素分析采用多元Logistic回归分析,以探讨成人LBP与上述行为因素之间的关系。结果 LBP的人口学特征以女性为主,多见于40岁以上人群,且其经济收入水平偏低;单因素分析表明,两组人群的日常运动、吸烟、饮酒、睡眠和体质量指数均有显著性差异(P<0.05);Logsitic回归分析显示,缺乏运动(OR=2.253)、经常吸烟(OR=3.968)、经常饮酒(OR=2.546)、睡眠3-4 h(OR=2.528)、体型肥胖(OR=2.635),均是成人腰痛的行为风险因素。结论成人腰痛与其日常行为因素有密切的相关性,运动量不足、经常吸烟、饮酒和严重缺乏睡眠、体型肥胖,均是其致病的独立风险因素。
文摘In this study,I explore smoking behavior among pregnant U.S.women using the 1979 cohort of the National Longitudinal Survey of Youth.The key aspect of this study is the availability of smoking participation data before and during pregnancy.I consider the probabilities of smoking cessation while pregnant as the outcome.I find that pregnant women who smoke are less responsive to price changes when they are more future-oriented.Women who are more present-oriented are more likely to smoke and consume more cigarettes given that they smoke more than those who are future-oriented.Moreover,those who discount the future more heavily are more sensitive to the money price of cigarettes than those who are more future-oriented.I focus on the role of time preference and the interaction between time preference and price in determining these outcomes.
文摘Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios.With the developments of closed circuit television(CCTV)system,vision-based methods for object detection,mostly driven by deep learning techniques,were introduced recently.However,the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed.This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,high accuracy on micro-objects and low computing budget,i.e.,it could be deployed on the edge device such as NVIDIA JETSON TX2.We designed a new framework named RTVBS based on yolov3 and made a smoking dataset to train our model.We raised several methods to improve detection accuracy during the training step.The validation results show our model has excellent performance in smoking detection.
