Tobacco use and second-hand smoke exposure in young adolescents aged 12–15 years: data from 68 low-income and middle-income countries

Tobacco use is an important risk factor for non-communicable diseases worldwide. However, the global extent and prevalence of tobacco use in adolescents is poorly described. Using previously collected survey data, we aimed to assess tobacco use and second-hand smoke exposure in young adolescents aged 12–15 years in 68 low-income and middle-income countries. Methods We used data from the Global School-based Student Health Survey (2006–13) and the China Global Tobacco Youth Survey (2013), which are school-based surveys of young adolescents aged 12–15 years that assess health behaviours using a standardised, anonymous, self-reported questionnaire. We calculated the prevalence of current tobacco use and exposure to second-hand smoke in young adolescents from 68 low-income and middle-income countries that collected these data in the surveys. We used a multilevel model to estimate the association between parental tobacco use, second-hand smoke, and adolescent tobacco use, adjusting for sex, age, school, school class, country's purchasing power parity, smoking initiation age, national prevalence of tobacco use among adults, year the WHO FCTC was ratified for each country, proxy of socioeconomic status, and survey year. Findings The mean prevalence of current tobacco use was 13·6%, ranging from 2·8% in Tajikistan to 44·7% in Samoa. In most countries, the prevalence of tobacco use was higher for boys than girls, and higher for adolescents aged 14–15 years than for those aged 12–13 years. The overall prevalence of second-hand smoke exposure was 55·9%, ranging from 16·4% in Tajikistan to 85·4% in Indonesia. Parental tobacco use (as reported by the young adolescents), especially maternal use, was associated with tobacco use in young adolescents (odds ratio 2·06, 95% CI 1·93–2·19, for maternal and 1·29, 1·23–1·35 for paternal use). Second-hand smoke exposure was also a risk factor for young adolescents' tobacco use (2·56, 2·43–2·69). However, the prevalence of tobacco use was not associated with a country's purchasing power parity. Interpretation Tobacco use and second-hand smoke exposure were frequent among young adolescents aged 12–15 years in low-income and middle-income countries. Parental tobacco use and second-hand smoke exposure were strongly associated with young adolescents' tobacco use. The data emphasise the need to strengthen tobacco control interventions and programmes in young adolescents from low-income and middle-income countries

Prevalence of waterpipe smoking and its associated factors among adolescents aged 12-16 years in 73 countries/territories

IntroductionTo describe the recent prevalence of, and trends in, waterpipe smoking and to examine its associated factors among adolescents aged 12-16 years in 73 countries/territories (hereafter "countries"). MethodsData from 72 countries that had conducted a Global Youth Tobacco Survey (GYTS) in 2010-2019 and from the National Youth Tobacco Survey in the United States in 2019 were used to assess the recent prevalence of waterpipe smoking and to examine its associated factors among adolescents aged 12-16 years. Data from 38 countries that had carried out at least 2 surveys from 2000 to 2019 were used to determine trends in the prevalence of waterpipe smoking among adolescents.ResultsThe recent prevalence of current waterpipe smoking (on 1 day during the past 30 days) among adolescents was 6.9% (95%CI 6.4-7.5). The prevalence was highest in the European region (10.9%, 9.9-11.8) and Eastern Mediterranean region (10.7%, 9.5-11.9), but lowest in the Western Pacific region (1.9%, 1.4-2.4). The prevalence of current waterpipe smoking increased or remained unchanged in 19 (50%) of 38 countries, but decreased in the remaining 19 countries (50%). Parental smoking, closest friends' smoking, secondhand smoke exposure, tobacco advertisement exposure, not being taught the dangers of smoking, particularly cigarette smoking, were positively associated with adolescent waterpipe smoking.DiscussionWaterpipe smoking among adolescents remains a major public health issue worldwide, especially in the regions of Europe and the Eastern Mediterranean. Effective prevention and control strategies and measures are needed to curb the epidemic of adolescent waterpipe smoking.</p

LiDAR-IMU-UWB-Based Collaborative Localization

This article introduced a positioning system composed of different sensors, such as LiDAR, IMU, and ultra-wideband (UWB), for the positioning method in autonomous driving technology under closed coal mine tunnels. First, we processed the LiDAR data, extracted its feature points and merged the extracted feature point clouds to generate a skewed combined feature point cloud. Then, we used the skew combined feature point clouds for feature matching, performed pre-integration processing on the IMU sensor data, and completed the LiDAR-IMU odometer with the LiDAR. Finally, we added UWB data to IMU pose node as a one-dimensional over-edge constraint. By updating the sliding window, the positioning accuracy was further improved. Moreover, we have conducted experiments to verify the proposed positioning system in a simulated roadway. The experimental results showed that the method proposed in this paper is superior to the single LiDAR method and the single UWB method in terms of positioning accuracy

Binocular vision-based displacement detection method for anchor digging robot

The problem of low detection accuracy of driving displacement exists in the driving process of anchor digging robots. In order to solve the above problem, taking the supporting bolt as the positioning benchmark, by analyzing the distance relationship between the anchor digging robot and the supporting bolt, the positioning model of 'anchor digging robot-supported anchor' is established. This paper proposes a binocular vision-based displacement detection method for anchor digging robots. Due to the complexity of the underground coal mine environment, the disparity map obtained by using the traditional Census transform algorithm has limitations. By analyzing the binocular vision ranging principle, an improved Census transform algorithm is proposed to obtain the disparity map of the anchor and the depth information of the anchor image. This paper presents a method of anchor feature recognition and positioning, and uses edge detection algorithm to extract the anchor contour in disparity map. The minimum circumscribed rectangle and the maximum circumscribed rectangle algorithm are used to frame the anchor outline and extract the pixel coordinates of anchor feature points. By analyzing coordinate conversion relationships, the pixel coordinates of feature points are converted to world coordinates. By using the least square method, the spatial coordinates of feature points are fitted into a straight line. The plane parallel to the roadway section is established through the straight line. The distance between the binocular camera and the plane is calculated, and then the distance between the anchor digging robot and the plane is obtained. A mobile robot platform is set up to carry out the displacement detection experiment of the anchor digging robot. The results show the following points. The improved Census transform algorithm reduces the mismatch rate from 19.85% to 11.52%, which is 41.96% lower than the traditional Census transform algorithm. The method of anchor feature point recognition and positioning can effectively extract the spatial coordinates of anchor feature points. The distance between the camera and the three parallel sections is 3 010.428, 2 215.910, 1 415.127 mm respectively through straight line fitting. In the robot positioning experiment, the real calculated displacement is compared with the theoretical displacement. The results show that the real calculated displacement curve coincides with the theoretical displacement curve basically. The error between the theoretical displacement and the calculated displacement is less than 20 mm. The autonomous, accurate and real-time displacement detection of the anchor digging robot can be realized

FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot

Conveyor belts are key pieces of equipment for bulk material transport, and they are of great significance to ensure safe operation. With the development of belt conveyors in the direction of long distances, large volumes, high speeds, and high reliability, the use of inspection robots to perform full inspections of belt conveyors has not only improved the efficiency and scope of the inspections but has also eliminated the dependence of the traditional method on the density of sensor arrangement. In this paper, relying on the wireless-power-supply orbital inspection robot independently developed by the laboratory, aimed at the problem of the deviation of the belt conveyor, the methods for the diagnosis of the deviation of the conveyor belt and FPGA (field-programmable gate array) parallel computing technology are studied. Based on the traditional LSD (line segment detection) algorithm, a straight-line extraction IP core, suitable for an FPGA computing platform, was constructed. This new hardware linear detection algorithm improves the real-time performance and flexibility of the belt conveyor diagnosis mechanism

A Tightly Coupled LiDAR-Inertial SLAM for Perceptually Degraded Scenes

Realizing robust six degrees of freedom (6DOF) state estimation and high-performance simultaneous localization and mapping (SLAM) for perceptually degraded scenes (such as underground tunnels, corridors, and roadways) is a challenge in robotics. To solve these problems, we propose a SLAM algorithm based on tightly coupled LiDAR-IMU fusion, which consists of two parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly, on the front end, an iterative Kalman filter is established to construct a tightly coupled LiDAR-Inertial Odometry (LIO). The state propagation process for the a priori position and attitude of a robot, which uses predictions and observations, increases the accuracy of the attitude and enhances the system robustness. Second, on the back end, we deploy a keyframe selection strategy to meet the real-time requirements of large-scale scenes. Moreover, loop detection and ground constraints are added to the tightly coupled framework, thereby further improving the overall accuracy of the 6DOF state estimation. Finally, the performance of the algorithm is verified using a public dataset and the dataset we collected. The experimental results show that for perceptually degraded scenes, compared with existing LiDAR-SLAM algorithms, our proposed algorithm grants the robot higher accuracy, real-time performance and robustness, effectively reducing the cumulative error of the system and ensuring the global consistency of the constructed maps

A Tightly Coupled LiDAR-Inertial SLAM for Perceptually Degraded Scenes

Realizing robust six degrees of freedom (6DOF) state estimation and high-performance simultaneous localization and mapping (SLAM) for perceptually degraded scenes (such as underground tunnels, corridors, and roadways) is a challenge in robotics. To solve these problems, we propose a SLAM algorithm based on tightly coupled LiDAR-IMU fusion, which consists of two parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly, on the front end, an iterative Kalman filter is established to construct a tightly coupled LiDAR-Inertial Odometry (LIO). The state propagation process for the a priori position and attitude of a robot, which uses predictions and observations, increases the accuracy of the attitude and enhances the system robustness. Second, on the back end, we deploy a keyframe selection strategy to meet the real-time requirements of large-scale scenes. Moreover, loop detection and ground constraints are added to the tightly coupled framework, thereby further improving the overall accuracy of the 6DOF state estimation. Finally, the performance of the algorithm is verified using a public dataset and the dataset we collected. The experimental results show that for perceptually degraded scenes, compared with existing LiDAR-SLAM algorithms, our proposed algorithm grants the robot higher accuracy, real-time performance and robustness, effectively reducing the cumulative error of the system and ensuring the global consistency of the constructed maps

Numerical Investigation of a Novel Heat Exchanger in a High-Temperature Thermoelectric Generator

A cylindrical thermoelectric power generator for high-temperature flue gas was designed, and a distributor was installed to enhance heat transfer by affecting the jet on the hot side. The influence of the different distributor diameters and jet hole diameters on the temperature distribution of the hot and cold sides of the thermoelectric module was studied. The corresponding temperature field, velocity field, and exhaust pressure drop of the device were also obtained. The results indicated that the temperature difference between the hot and cold ends of the thermoelectric module was increased, and the uniformity of the temperature distribution was improved with the increasing diameter of the distributor and the decreasing diameter of the jet hole. The performance of the thermoelectric power generator was further improved by the jet hole with a gradient diameter. The number and distance between jet holes were sensitive to pressure drop