A virtual approach: Systematic review and meta-analysis of virtual reality-based therapies for convergence insufficiency.

Convergence insufficiency (CI) is a common binocular vision disorder that significantly impacts visual comfort and quality of life. It has been hypothesized that CI therapy can also be provided effectively with Virtual reality (VR). Recently, a few studies were conducted on virtual reality-based CI therapy.

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier.

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet.

Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models.

"Epilepsy is a chronic brain disorder that affects people of all ages. The cause of epilepsy is often unknown and its effect in different age groups is not yet investigated. The main objective of this study is to introduce a novel approach that successfully detects epilepsy even from noisy EEG signals.

Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19.

Background: The coronavirus disease 2019 (COVID-19) pandemic is a worldwide epidemiological emergency, and the risk factors for the multiple waves with new COVID-19 strains are concerning. This study aims to identify the most significant risk factors for spreading COVID-19 to help policymakers take early measures for the next waves.

Methods: We conducted the study on randomly selected 29 countries where the pandemic had a downward trend in the daily active cases curve as of June 10, 2020.