Recursive Detection and Analysis of Nanoparticles in Scanning Electron Microscopy Images

In this study, we present a computational framework tailored for the precise detection and comprehensive analysis of nanoparticles within scanning electron microscopy (SEM) images. The primary objective of this framework revolves around the accurate localization of nanoparticle coordinates, accompanied by secondary objectives encompassing the extraction of pertinent morphological attributes including area, orientation, brightness, and length. Constructed leveraging the robust image processing capabilities of Python, particularly harnessing libraries such as OpenCV, SciPy, and Scikit-Image, the framework employs an amalgamation of techniques, including thresholding, dilating, and eroding, to enhance the fidelity of image processing outcomes. The ensuing nanoparticle data is seamlessly integrated into the RStudio environment to facilitate meticulous post-processing analysis. This encompasses a comprehensive evaluation of model accuracy, discernment of feature distribution patterns, and the identification of intricate particle arrangements. The finalized framework exhibits high nanoparticle identification within the primary sample image and boasts 97\% accuracy in detecting particles across five distinct test images drawn from a SEM nanoparticle dataset. Furthermore, the framework demonstrates the capability to discern nanoparticles of faint intensity, eluding manual labeling within the control group.Comment: 9 pages, 10 figure

The Impact of the COVID-19 Pandemic on College Student’s Stress and Physical Activity Levels

Background: The coronavirus disease 2019 (COVID-19) pandemic adversely disrupted university student educational experiences worldwide, with consequences that included increased stress levels and unhealthy sedentary behavior. Aim: This study aimed to quantify the degree of impact that COVID-19 had on the levels of physical activity and stress of university students by utilizing wearable fitness tracker data and standard stress survey instrument scores before and during the pandemic. Methods: We collected Fitbit heart rate and physical activity data, and the results of a modified Social Readjustment Rating Scale (SRRS) stress survey from 2,987 university students during the Fall 2019 (residential instruction; before COVID-19) and Fall 2020 (hybrid instruction; during COVID-19) semesters. Results: We found indicators of increased sedentary behavior during the pandemic. There was a significant decrease in both the levels of physical activity as measured by mean daily step count (↓636 steps/day; p = 1.04 · 10-9) and minutes spent in various heart rate zones (↓58 minutes/week; p = 2.20 · 10-16). We also found an increase in stressors during the pandemic, primarily from an increase in the number of students who experienced the “death of a close family member” (38.8%), with the number even higher for the population of students who opted to stay home and attend classes virtually (41.4%). Conclusions: This study quantifies the decrease in levels of physical activity and notes an increase in the number of students who experienced the death of a close family member, a known stressor, during the first year of the COVID-19 pandemic. These findings allow for more informed student-health-focused interventions related to the COVID-19 pandemic disruptions experienced by academic communities worldwide