Prevalence and factors associated with depression and anxiety among older adults: A large-scale cross-sectional study in China.

BACKGROUND: To determine the prevalence of depression and anxiety among older adults in China, and explore the associated factors. METHODS: This cross-sectional study recruited participants between October 2022 and December 2022. The sample collection utilized a multi-stage stratified equal probability random sampling method. This study included 8436 older adults who underwent interviews utilizing standardized assessment instruments. The assessment of depressive symptoms employed the Patient Health Questionnaire 9, while the evaluation of anxiety utilized the Generalized Anxiety Disorder 7. Multivariate logistic regression was conducted to determine the odds ratio and 95 % confidence interval (CI). RESULTS: The weighted prevalence rates for depression and anxiety were 2.79 % (95 % CI: 2.38 %-3.28 %) and 1.39 % (95 % CI: 1.12 %-1.74 %), respectively. Older adults who were female, widowed, had irregular dietary habits, spent <1 h per day using electronic devices for socializing and entertainment, engaged in >8 h of sedentary behavior per day, and had chronic diseases (cardiovascular disease, cerebrovascular disease, insomnia, and Chronic gastroenteritis) displayed a higher likelihood of encountering symptoms indicative of depression and anxiety. Conversely, older adults living in rural areas and those who walked daily were less prone to experience symptoms of depression and anxiety. CONCLUSIONS: This study suggests that the psychological well-being of older adults should be cared for when treating chronic diseases. Moreover, families, communities, and clinics should recognize that supporting regular diets, providing social engagement and recreational activities, encouraging physical activity, and minimizing sedentary behavior can reduce the risk of depression and anxiety.

ScatterHough: Automatic Lane Detection from Noisy LiDAR Data.

Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, ρ neighbor voting and ρ neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors' voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods.