Places where preschoolers are (in)active: an observational study on Latino preschoolers and their parents using objective measures

abstract: Background To combat the disproportionately higher risk of childhood obesity in Latino preschool-aged children, multilevel interventions targeting physical (in) activity are needed. These require the identification of environmental and psychosocial determinants of physical (in) activity for this ethnic group. The objectives were to examine differences in objectively-measured physical activity and sedentary behavior across objectively-determined types of locations in Latino preschool-aged children; and determine whether the differences in physical activity by location were greater in children of parents with higher neighborhood-safety perceptions and physical activity-supportive parenting practices. Methods An observational field study was conducted in Houston (Texas, USA) from August 2011 to April 2012. A purposive sample of Latino children aged 3–5 years and one of their parents (n = 84) were recruited from Census block groups in Houston (Texas) stratified by objectively-assessed high vs. low traffic and crime safety. Seventy-three children provided valid data. Time spent outdoors/indoors tagged with geographic locations was coded into location types based on objective data collected using Global Positioning Systems units that children wore >8 hr/day for a week. Physical activity parenting practices, perceived neighborhood-safety, and demographics were reported by parents. Time spent in sedentary behavior and moderate-to-vigorous physical activity was measured based on objective data collected using accelerometers (motion sensors) that children wore >8 hr/day for a week. Results The odds of children engaging in moderate-to-vigorous physical activity were 43 % higher when outdoors than indoors (95 % confidence interval: 1.30, 1.58), and the odds of being sedentary were 14 % lower when outdoors compared to indoors (95 % confidence intervals: 0.81, 0.91). This difference depended on parental neighborhood-safety perceptions and parenting practices. Children were most active in parks/playgrounds (30 % of the time spent in moderate-to-vigorous physical activity) and least active in childcare/school settings (8 % of the time spent in moderate-to-vigorous physical activity). Conclusions Objectively-assessed time spent in specific locations is correlated with physical activity and sedentary behavior in Latino preschoolers. Interventions and policies should identify ways to engage Latino preschool-aged children in more physical activity and less sedentary behavior while in childcare, and encourage parents to spend more time with their young children in parks/playgrounds and other safe outdoor places.The electronic version of this article is the complete one and can be found online at: http://ijbnpa.biomedcentral.com/articles/10.1186/s12966-016-0355-

The chemical reaction optimization approach to solving the environmentally sustainable network design problem

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Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions

To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans. So the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to re-charge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being re-charged. Based on these new perspectives, we formulate the Electric Vehicle Charging Station Placement Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.Comment: Submitted to IEEE Transactions on Smart Grid, revise

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201

An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario

Electric Vehicle Routing Problem in Urban Logistics

Due to the impact of global warming, diesel locomotives that use fossil energy as fuel are gradually being replaced by electric vehicles. At present, many countries at home and abroad are actively promoting the development of the electric vehicle industry in response to the call of the Paris Agreement. However, electric vehicles have a maximum mileage limit, so the reasonable layout of electric vehicle charging stations is also a problem to be solved today. In this article, the author analyzes the research background of the electric vehicle routing problem. After introducing several new research directions in the current electric vehicle routing problem, we propose an optimization algorithm for solving those types of problem. It brings certain theoretical significance for future generations to solve the problem of electric vehicle routing in real life

Modified DDPG car-following model with a real-world human driving experience with CARLA simulator

In the autonomous driving field, fusion of human knowledge into Deep Reinforcement Learning (DRL) is often based on the human demonstration recorded in a simulated environment. This limits the generalization and the feasibility of application in real-world traffic. We propose a two-stage DRL method to train a car-following agent, that modifies the policy by leveraging the real-world human driving experience and achieves performance superior to the pure DRL agent. Training a DRL agent is done within CARLA framework with Robot Operating System (ROS). For evaluation, we designed different driving scenarios to compare the proposed two-stage DRL car-following agent with other agents. After extracting the "good" behavior from the human driver, the agent becomes more efficient and reasonable, which makes this autonomous agent more suitable for Human-Robot Interaction (HRI) traffic

Adaptive Power Level for DSRC Congestion Control

Vehicular industries and researchers have invested efforts to reduce avoidable accidents through the means of Vehicle to Vehicle (V2V) wireless communication using Vehicular Ad Hoc Networks (VANETs) through the periodic exchange of Basic Safety Messages (BSMs). The transmission rate of BSMs is defined by IEEE 1609 to be 10 Hz. With a high vehicular density, Network Congestion can quickly arise in the 5.9 GHz spectrum, rendering the system as unreliable because safety messages are not delivered on time. Researchers have focused on altering the rate of transmission and/or power of transmission in congestion control algorithms. The rate of transmission dictates how many messages each vehicle sends per second. Further, the transmission power dictates how far each message travels; it is known that messages transmitted with higher power will reach further distances. Based on that, our algorithm performs two operations to mitigate channel congestion; a) we send a number of low powered packets based on the node’s velocity, the higher the velocity then the higher transmission power, then followed by a high powered packet to maintain awareness for distant vehicles, b) we increase the power of transmission in a cyclic fashion. By doing so, we can maintain necessary level of awareness for closer vehicles, while sacrificing some awareness for distant ones. The goal is to provide adequate awareness for all vehicles, while reducing the overall congestion of the wireless channel

Geospatial analyses in support of heavy metal contamination assessments of soil and grass along highways at Mafikeng, South Africa

Heavy metals in the environment are of concern due to detrimental effects, which include disturbance of plant physiology. This paper presents an exploratory assessment of heavy metal contamination along the main highways in Mafikeng, and illustrates how spatial analyses of the contamination for environmental management purposes can be supported by GIS and Remote Sensing. Roadside soil and grass (Stenotaphrum sp.) samples were analysed for total content per heavy metal. Spatial patterns in soil metal concentrations were evaluated using IDW interpolation. Effects of the contamination on the vigour of roadside grass were assessed using NDVI transects within 30m of the roads, on a pan-sharpened 5m resolution SPOT 5 HRG multispectral image. The results showed that NDVI values increased with distance from roads (R2 0.508-0.965; p < 0.05), indicating that proximity to roads reduced grass vigour. Metal concentrations in grass tissue were lower than in soil by an average factor of nine, but varied as the soil concentrations. The concentrations of the heavy metals that are associated with motor vehicles along roads were in the order [Fe]>[Mn]>[Zn]>[Pb]>[Ni]>[Cu]>[Cr]>[Cd], but were much lower than in cities that have higher motor vehicle traffic. IDW interpolation of metal concentrations revealed traffic-related spatial variations that can support environmental management. In this limestone mineralogy soil the relative abundance of Mn (range 2.4-11.4mg/kg) is attributable to lead replacement fuels that are in use, while the Pb concentrations (range 0.20-1.29mg/kg) indicate persistence of Pb in the urban environment some ten years after the phasing out of leaded petrol