Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was 27.4%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

Polycyclic aromatic hydrocarbons (PAHs) are among the most toxic air pollutants in China. However, because there are unsubstantial data on indoor and outdoor particulate PAHs, efforts in assessing inhalation exposure and cancer risk to PAHs are limited in China. This study measured 12 individual PAHs in indoor and outdoor environments at 36 homes during the non‐heating period and heating period in 2009. Indoor PAH concentrations were comparable with outdoor environments in the non‐heating period, but were lower in the heating period. The average indoor/outdoor ratios in both sampling periods were lower than 1, while the ratios in the non‐heating period were higher than those in the heating period. Correlation analysis and coefficient of divergence also verified the difference between indoor and outdoor PAHs, which could be caused by high ventilation in the non‐heating period. To support this conclusion, linear and robust regressions were used to estimate the infiltration factor to compare outdoor PAHs to indoor PAHs. The calculated infiltration factors obtained by the two models were similar in the non‐heating period but varied greatly in the heating period, which may have been caused by the influence of ventilation. Potential sources were distinguished using a diagnostic ratio and a mixture of coal combustion and traffic emission, which are major sources of PAHs.

Physical activity (PA) is widely acknowledged to be beneficial to health and wellbeing, and is potentially influenced by a variety of environmental factors such as ambient temperature, weather conditions and air pollution levels. Since these factors vary seasonally, physical activity participation may also respond seasonally. Current population studies to profile physical activity often sample individuals only once, and this may result in biased estimates if there is strong seasonal variation. We conducted a study of 40 Han Chinese adults living in Beijing using GT3X accelerometers. We measured PA levels every two months across a complete year, while simultaneously monitoring ambient temperatures and air pollution levels. Average hourly vector magnitude (VM) and percentage time spent at each PA intensity (sedentary to light, moderate, vigorous and very vigorous) were measured. General Linear models (GLMs) were used to explore the effects of time of day, temperature and PM 2.5 levels on PA. One way ANOVA was used to test whether there were seasonal differences in body weight and body fatness. The main factors influencing activity levels were the time of day and individual characteristics including age and body fatness, but there was no significant difference between the months. In addition, there was no significant impact of either ambient temperature or air pollution levels (PM2.5). There were also no significant differences over the year in the time spent at sedentary-light, moderate and very vigorous PA levels, but for vigorous PA level which occupied less than 0.5% daily physical activity, both month and individual were significant factors. The relatively constant pattern of urban daily life, independent of time of year, may override the potential impacts of environmental factors that would be anticipated to impact PA levels. These subjects did not specifically avoid activity coincident with elevated air pollution levels (PM2.5). Single week long measurements of physical activity could provide a representative measurement of the physical active levels in this population.

Aiming at the problems of low degree of automation, troublesome technical measurement method and low efficiency in the current elevator track deformation detection method, a high degree of automation elevator track quality detection robot is designed and developed in this paper. The robot fully considers practicability and high efficiency in mechanical design and adds a feedback mechanism to the control unit for control optimization, so that it can climb the elevator track quickly and smoothly, and uses robot vision to detect the track deformation, main control principle is to use the computer to smoothly regulate the speed of the motor, to detect the posture of the robot in the climbing process in real-time, and to feed it back to the micro industrial computer for autonomous adjustment, to ensure that the robot runs smoothly on the elevator guide rail at all times.