This study evaluated spatial relationships between features of the built environment and youth depressive symptoms. Data used in this study came from the 2008 Boston Youth Survey Geospatial Dataset, which includes Boston high school students with complete residential information (n=1170). Features of the built environment (such as access to walking destinations and community design features) were created for 400- and 800-m street network buffers of the youths' residences. We computed standard Ordinary Least Squares (OLS) regression and spatial simultaneous autoregressive models. We found significant positive spatial autocorrelation in all of the built environment features at both spatial scales (all p=0.001), depressive symptoms (p=0.034) as well as in the OLS regression residuals (all p<0.001), and, therefore, fit spatial regression models. Findings from the spatial regression models indicate that the built environment can have depressogenic effects, which can vary by spatial scale, gender and race/ethnicity (though sometimes in unexpected directions, i.e. associations opposite to our expectations). While our results overall suggest that the built environment minimally influences youth depressive symptoms, additional research is needed, including to understand our results in the unexpected direction.
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http://dx.doi.org/10.1016/j.sste.2013.03.001 | DOI Listing |
iScience
January 2025
Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia.
Achieving lightweight real-time object detection necessitates balancing model compression with detection accuracy, a difficulty exacerbated by low redundancy and uneven contributions from convolutional layers. As an alternative to traditional methods, we propose Rigorous Gradation Pruning (RGP), which uses a desensitized first-order Taylor approximation to assess filter importance, enabling precise pruning of redundant kernels. This approach includes the iterative reassessment of layer significance to protect essential layers, ensuring effective detection performance.
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January 2025
Department of Architecture, University of Cambridge, Cambridge, United Kingdom.
Nat Rev Neurol
January 2025
Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.
Evidence suggests that anthropogenic climate change is accelerating and is affecting human health globally. Despite urgent calls to address health effects in the context of the additional challenges of environmental degradation, biodiversity loss and ageing populations, the effects of climate change on specific health conditions are still poorly understood. Neurological diseases contribute substantially to the global burden of disease, and the possible direct and indirect consequences of climate change for people with these conditions are a cause for concern.
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January 2025
Henan International Joint Laboratory of Machine Vision and Intelligent Systems, Department of Information Engineering, Pingdingshan University, Pingdingshan, 467000, Henan, China.
Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power system.The aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected.
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January 2025
School of Architecture and Built Environment, Queensland University of Technology, Brisbane, 4000, Australia.
The rise of robotics in aged care is transforming how older adults are cared for, addressing staff shortages and workload. Daily interactions with staff and residents highlight an urgent need to better understand and improve human-robot interactions. This study investigates how 34 staff interacted with 10 service robots in a regional Australian aged care facility over 5 years, focusing primarily on their experience with service robots that transported meals and laundry.
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