Nine year changes in sitting time in young and mid-aged Australian women: Findings from the Australian longitudinal study for women's health

- Objective To examine changes in sitting time (ST) in women over nine years and to identify associations between life events and these changes. - Methods Young (born 1973–78, n = 5215) and mid-aged (born 1946–51, n = 6973) women reported life events and ST in four surveys of the Australian Longitudinal Study on Women's Health between 2000 and 2010. Associations between life events and changes in ST between surveys (decreasers ≥ 2 h/day less, increasers ≥ 2 h/day more) were estimated using generalized estimating equations. - Results Against a background of complex changes there was an overall decrease in ST in young women (median change − 0.48 h/day, interquartile range [IQR] = − 2.54, 1.50) and an increase in ST in mid-aged women (median change 0.43 h/day; IQR = − 1.29, 2.0) over nine years. In young women, returning to study and job loss were associated with increased ST, while having a baby, beginning work and decreased income were associated with decreased ST. In mid-aged women, changes at work were associated with increased ST, while retiring and decreased income were associated with decreased ST. - Conclusions ST changed over nine years in young and mid-aged Australian women. The life events they experienced, particularly events related to work and family, were associated with these changes.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.