Climate change and occupational health and safety. Risk of injuries, productivity loss and the co-benefits perspective.

Background: Climate change is a fundamental threat to human health and outdoor workers are one of the most vulnerable population subgroups. Increasing heat stress and heatwaves are directly associated with the health and safety of workers for a large spectrum of occupations. Heat stress negatively affects labour supply, productivity, and workability.

Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.

Efficacy & safety of brolucizumab 6.0 mg versus 3.6 mg in diabetic macular edema.

Background: Management of Diabetic Macular edema (DME) requires repeated injections. Therefore newer Anti-VEGFs like Brolucizumab with longer durability have been introduced. We compared two different dosages of Brolucizumab, 6.

Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning.

Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection.