syN-BEATS for robust pollutant forecasting in data-limited context.

This research introduces syN-BEATS, a novel ensemble deep learning model tailored for effective pollutant forecasting under conditions of limited data availability. Based on the N-BEATS architecture, syN-BEATS integrates various configurations with differing numbers of stacks and blocks, effectively combining weak and strong learning approaches. Our experiments show that syN-BEATS outperforms standard models, especially when using Bayesian optimization to fine-tune ensemble weights.

[Painful diabetic polyneuropathy: focus on life quality improvement of patients].

Aim: To evaluate the efficacy and safety of cytoflavin as an additional agent in the treatment of painful diabetic polyneuropathy and to analyze changes in the life quality of the patients studied.

Material And Methods: An analysis of treatment data was carried out in 61 patients with verified painful diabetic polyneuropathy, who were divided into 2 groups depending on the therapy regimen. Patients of the main group (n=36) at the start of the therapy with gabapentin additionally received cytoflavin: intravenously, slowly, 10 ml diluted in 0.