Recovery Patterns of COVID-19 Related Smell Disorders: An Analysis of the Available Evidence.

Recently acquired olfactory dysfunction (OD) has emerged as one of hallmark manifestations of the novel Corona virus disease (COVID-19), but the evolution of its spontaneous recovery has remained inconclusive, with reports of persistence of OD beyond six months of onset. We undertook this systematic review and meta-analysis with a view to generating a pooled recovery rate of COVID-19 associated olfactory dysfunctions and attempt to examine the predictors of olfactory recovery. Systematic review and meta-analysis.

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review.

Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems.

Partnership for International Development: Finland-Nigeria Conference on Climate, Food, Health and Entrepreneurship.

A joint collaboration between the Arctic Centre of the University of Lapland, Finland and the Federal Institute of Industrial Research, Oshodi, Lagos, Nigeria was organised as a hybrid conference on several topics that are related to climate, food, health and entrepreneurship. The utilisation of natural resources in both regions is an important theme in meeting the sustainable development goals agenda. The topics discussed were multidisciplinary, they include Nigerian indigenous foods, bioeconomy, circular economy, nutrition, health, innovation and entrepreneurship under four themes (Climate, Food, Health and Entrepreneurship).

COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing.

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques.

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach.

Background: The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally.