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Association between frailty index and mortality in depressed patients: results from NHANES 2005–2018
Unveiling the biological activities of Heliotropium indicum L. plant extracts: anti-inflammatory activities, GC–MS analysis, and in-silico molecular docking
Flood change detection model based on an improved U-net network and multi-head attention mechanism
A comprehensive analysis of trends in the burden of gout in China and globally from 1990 to 2021
Interactions between DASH-style diet and ADIPOQ and ARL15 genes polymorphisms on blood pressure and central obesity in Iranian adults
Association of liver fibrosis with aneurysm size and mortality risk in patients undergoing open abdominal aortic aneurysm repair
Extreme migratory connectivity and apparent mirroring of non-breeding grounds conditions in a severely declining breeding population of an Afro-Palearctic migratory bird
Chain-mediated effect of physical activity between Chinese language-based L2 motivational self-system and intended effort
Evaluating solar power plant sites using integrated GIS and MCDM methods: a case study in Kermanshah Province
Predictive nomogram for postoperative lower-limb deep vein thrombosis in patients undergoing endoscopic endonasal surgery during hospitalization: a retrospective cohort study
Identification of a novel TOP2B::AFF2 fusion gene in B-cell acute lymphoblastic leukemia
Berberine decorated zinc oxide loaded chitosan nanoparticles a potent anti cancer agent against breast cancer
Transition zone-based prostate-specific antigen density for differentiating clinically significant prostate cancer in PI-RADS score 3 lesions
Optical band gap modulation in functionalized chitosan biopolymer hybrids using absorption and derivative spectrum fitting methods: A spectroscopic analysis
Overexpression of PLCG2 and TMEM38A inhibit tumor progression in clear cell renal cell carcinoma
The myometrial transcriptome changes in mares with endometrosis
A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM
Subclinical depressive symptoms and job stress differentially impact memory in working and retired older adults
Abstract Retirement has been associated with cognitive decline beyond normal age-related decline. However, there are many individual differences in retirement that can influence cognition. Subclinical depressive symptoms are common in late life and are associated with general memory decline and a bias towards remembering negative events (i.e., better memory for negative vs. positive or neutral stimuli), in opposition to a reported positivity bias (i.e., better memory for positive vs. negative or neutral stimuli) in aging. Furthermore, job stress is often a major contributor to retirement decisions and may impact cognition post-retirement. Here, we aimed to examine how subclinical depressive symptoms and job stress in working and retired older adults impacted emotional memory. We found that retired, but not working, older adults with greater depressive symptoms showed enhanced negative and impaired positive memory. Second, working older adults with moderately high current job stress showed better memory overall but a weaker positivity bias, while retired older adults with moderately high retrospective job stress showed worse memory overall and a stronger positivity bias. These findings suggest that subclinical depressive symptoms and job stress have differing impacts on emotional memory in late life depending on retirement status.
Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning
Abstract This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques. Convolutional Neural Networks (CNNs) architecture which includes ENet, PINet, ResNet-50, ResNet-101, SqueezeNet, and VGG16Net are employed in lane detection problems to construct segmentation models and demonstrate proficiency in distinct road conditions. However, the proposed MAB-Ensemble technique overcomes the limitations of individual models by dynamically selecting the most suitable CNN model based on prevailing environmental factors. The proposed technique optimizes the segmentation accuracy and treats the attained accuracy as a reward signal in the context of reinforcement learning by interacting with the environment through CNN model selection. The MAB-Ensemble achieved an overall accuracy of 90.28% in different road conditions. The results overcome the performance of the individual CNN models and state-of-the-art ensemble techniques. Also, it demonstrates superior performance which includes daytime, night-time, and abnormal road conditions. The MAB-Ensemble technique offers a promising solution for robust lane detection by harnessing the collective strengths of diverse CNN models.