Variations in land surface temperatures in small-scale urban areas in Vietnam during Covid-19 restrictions: Case studies from Da Nang, Hue and Vinh City.

Land surface temperature (LST) is an important variable in urban microclimate research. At the end of 2019, the emergence of Covid-19 pandemic has changed the world in a manner that forced many countries to impose restrictions in human activities. As a measure to prevent the expansion of Covid-19 infections, most of the major cities have entered a prolonged lockdown period and reduction in human activities between the early 2020 and the late 2021. These restrictions were strict in most of the cities in Southeast Asia, particularly in Vietnam. The present study investigated the variations in LST and NDVI observed in three rapidly growing urban areas, namely Da Nang, Hue and Vinh, in Vietnam using Landsat-8 imagery acquired between 2017 and 2022. There has been a slight reduction in LST in the study sites, particularly in Da Nang City, during the lockdown period but not as high as observed in recently conducted studies from big metropolitan cities, including in Vietnam. It is also observed that LST estimated from built-up areas and other impervious surfaces remained relatively stable during the study period which is similar to the results from recent studies.

A study on the sentiments and psychology of twitter users during COVID-19 lockdown period.

The outbreak of the novel Coronavirus in late 2019 brought severe devastation to the world. The pandemic spread across the globe, infecting more than ten million people and disrupting several businesses. Although social distancing and the use of protective masks were suggested all over the world, the cases seem to rise, which led to worldwide lockdown in different phases. The rampant escalation in the number of cases, the global effects, and the lockdown may have a severe effect on the psychology of people. The emergency protocols implemented by the authorities also lead to increased use in the number of multimedia devices. Excessive use of such devices may also contribute to psychological disorders. Hence, hence it is necessary to analyze the state of mind of people during the lockdown. In this paper, we perform a sentiment analysis of Twitter data during the pandemic lockdown, i.e., two weeks and four weeks after the lockdown was imposed. Investigating the sentiments of people in the form of positive, negative, and neutral tweets would assist us in determining how people are dealing with the pandemic and its effects on a psychological level. Our study shows that the lockdown witnessed more number positive tweets globally on multiple datasets. This is indicative of the positivity and optimism based on the sentiments and psychology of Twitter users worldwide. The study will be effective in determining people's mental well-being and will also be useful in devising appropriate lockdown strategies and crisis management in the future.

Differentiation of Diabetic Status Using Statistical and Machine Learning Techniques on Optical Coherence Tomography Angiography Images.

Purpose: To investigate the potential of statistical and machine learning approaches to determine the diabetic status of patients from optical coherence tomography angiography (OCT-A) images. Methods: This was a retrospective cross-sectional observational study based at Manchester Royal Eye Hospital, United Kingdom. OCT-A scans were sequentially selected from one eye of each of 182 patients who were either not diabetic, diabetic without retinopathy, or diabetic with retinopathy requiring hospital follow-up. Eligible images were analyzed by expert purpose-built automated algorithms to calculate clinically relevant outcome measures. These were used in turn as inputs to machine learning and statistical procedures to derive algorithms to perform clinically relevant classifications of patient images into the clinical groups. Receiver operating characteristic curves for the classifiers were evaluated and predictive accuracy assessed using area under curve (AUC). Results: For distinguishing diabetic patients from those without diabetes, the Random Forest classifier provided the highest AUC (0.8). For distinguishing diabetic patients with significant retinopathy from those with no retinopathy, the highest AUC was represented by logistic regression (0.91). Conclusions: The study demonstrates the potential of novel techniques using automated analysis of OCT-A scans to diagnose patients with diabetes, or when diabetic status is known, to automatically determine those that require hospital input. Translational Relevance: This work advances the concept of a rapid and noninvasive clinical screening tool using OCT-A to determine a patient's diabetic status.