In-person versus virtual activities: Assessing the usefulness and impact of simulated experiments, a case study

In recent years, interactive computer simulations and virtual laboratories have proven to be the best alternative to conventional physics laboratories. Online physics simulations have significant educational advantages and are powerful tools that enable students to easily handle and interpret experimental data. © 2021 Elsevier Inc. All rights reserved.

The interactive computer simulation and learning activity for facilitating students' conceptual understanding on the buoyant force through the CoSci learning platform

Learning science, especially in the physics field, there are many varieties of invisible and phenomena that are hard and difficult for students to observe and learn. One of the tools that can help students to understand those phenomena in a better way is computer simulations. The computer simulations are usually used in both on-site classroom and on-line learning platforms. Learning in the COVID-19 pandemic era at present, the computer simulations are very important for helping students to understand the physics concept. Interactive computer simulation can be considered as one of the effective methods of facilitating inquiry learning in science, as it allows students to experience the scientific inquiry process and facilitates students to understand an conception and to understand the relationship between variables of invisible phenomena more clearly in reasonable ways. This study aimed to develop the interactive computer simulation and learning activity for enhancing students' conceptual understanding of the buoyant force on the CoSci learning platform. Totally eighteen participants were studied in the twelfth grade in science classrooms of a university-affiliated school project (SCiUS), Khon Kaen University, Thailand, in 2019. The learning activity was developed based on students' alternative concepts and used to facilitate students' conceptual understanding of the buoyant force. There were six basic concepts related to the buoyant force constructed based on the predict-observe-explain strategy (POE) with the interactive computer simulation (i.e., the CoSci learning platform) in the learning activity. The learning activity on the CoSci learning platform consisted of eight pie charts such as 1) main question pie chart, 2) density pie chart, 3) water level pie chart, 4) volume pie chart, 5) mass pie chart, 6) weight pie chart, 7) submerged depth pie chart, and 8) answer pie chart. There were six interactive computer simulations used in this research including 1) density simulation, 2) water level simulation, 3) volume simulation, 4) mass simulation, 5) submerged depth simulation, and 6) weight simulation. All of these simulations were developed on the CoSci learning platform (https://cosci.tw/). The findings showed that 72% of students performed better in the post-test scores than in the pre-test score in all six basic concepts related to the buoyant force after learning buoyant force on the CoSci platform. Furthermore, the most difficulty in changing misconception in learning of the buoyant force was the concept related to the mass of the object. © 2022 Institute of Physics Publishing. All rights reserved.

COVID-19 Lockdowns May Reduce Resistance Genes Diversity in the Human Microbiome and the Need for Antibiotics.

Recently, much attention has been paid to the COVID-19 pandemic. Yet bacterial resistance to antibiotics remains a serious and unresolved public health problem that kills hundreds of thousands of people annually, being an insidious and silent pandemic. To contain the spreading of the SARS-CoV-2 virus, populations confined and tightened hygiene measures. We performed this study with computer simulations and by using mobility data of mobile phones from Google in the region of Lisbon, Portugal, comprising 3.7 million people during two different lockdown periods, scenarios of 40 and 60% mobility reduction. In the simulations, we assumed that the network of physical contact between people is that of a small world and computed the antibiotic resistance in human microbiomes after 180 days in the simulation. Our simulations show that reducing human contacts drives a reduction in the diversity of antibiotic resistance genes in human microbiomes. Kruskal-Wallis and Dunn's pairwise tests show very strong evidence (p < 0.000, adjusted using the Bonferroni correction) of a difference between the four confinement regimes. The proportion of variability in the ranked dependent variable accounted for by the confinement variable was η2 = 0.148, indicating a large effect of confinement on the diversity of antibiotic resistance. We have shown that confinement and hygienic measures, in addition to reducing the spread of pathogenic bacteria in a human network, also reduce resistance and the need to use antibiotics.

Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials.

Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm 3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.

An analysis of a nonlinear susceptible-exposed-infected-quarantine-recovered pandemic model of a novel coronavirus with delay effect.

In the present study, a nonlinear delayed coronavirus pandemic model is investigated in the human population. For study, we find the equilibria of susceptible-exposed-infected-quarantine-recovered model with delay term. The stability of the model is investigated using well-posedness, Routh Hurwitz criterion, Volterra Lyapunov function, and Lasalle invariance principle. The effect of the reproduction number on dynamics of disease is analyzed. If the reproduction number is less than one then the disease has been controlled. On the other hand, if the reproduction number is greater than one then the disease has become endemic in the population. The effect of the quarantine component on the reproduction number is also investigated. In the delayed analysis of the model, we investigated that transmission dynamics of the disease is dependent on delay terms which is also reflected in basic reproduction number. At the end, to depict the strength of the theoretical analysis of the model, computer simulations are presented.