The effect of indoor airflow has been confirmed on the diffusion and transmission of droplets generated when talking or sneezing by a person with a viral respiratory infection such as COVID-19. The present study to investigate the effect of airflow in an indoor environment (a classroom) on the distribution and transmission of droplets emitted from speaking and cough by an infected person. A numerical analysis to investigate the persistence and deposition of particles on the surfaces of desks and the faces of residents (teacher and students) under various scenarios, including the opening of windows. This study puts forward two types of conditions while the teacher is speaking and the cough of some students for the distribution of pathogenic particles. Computational Fluid Dynamics used to conduct the study, using the Euler-Lagrange approach to capture the transport of the particles, and the RANS equations to compute the airflow field in the classroom. The results indicate the significant effect of air conditioning and open window close to the infected person in reducing environmental pathogens. Moreover, the concentrations of virus particles increase greatly near the output; hence, the presence of people in these areas increases the risk of contracting the disease. Furthermore, when all the windows are closed, due to the low output capacity, the particles spread in all areas of the domain and increase the risk of infection. Therefore, it is recommended that the window be open in indoors environment especially the window next to the speaker.
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http://dx.doi.org/10.1016/j.jclepro.2021.128147 | DOI Listing |
Sci Rep
January 2025
Khuzestan Water & Power Authority (KWPA), Ahvaz, Iran.
Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energy schedules once operating in grid-connected mode, such systems are vulnerable to malicious attacks from the viewpoint of cybersecurity. With this in mind, this paper explores a novel advanced attack model named the false transferred data injection (FTDI) attack aiming to manipulatively alter the power flowing from the microgrid to the upstream grid to raise voltage usability probability.
View Article and Find Full Text PDFEnviron Health Perspect
January 2025
Scripps Institution of Oceanography, San Diego, California, USA.
Background: The increasing frequency and severity of extreme heat events due to climate change present unique risks to children and adolescents. There is a lack of evidence regarding how heat's impacts on pediatric patients vary spatially and how structural and sociodemographic factors drive this heterogeneity.
Objectives: We examined the association between extreme heat events and pediatric acute care utilization in California for 19 distinct health conditions.
Interest in carbon dioxide (CO) sensors is growing rapidly due to the increasing awareness of the link between air quality and health. Indoor, high CO levels signal poor ventilation, and outdoor the burning of fossil fuels and its associated pollution. CO gas sensors based on integrated optical waveguides are a promising solution due to their excellent gas sensing selectivity, compact size, and potential for mass manufacturing large volumes at low cost.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Condensation is a vital process integral to numerous industrial applications. Enhancing condensation efficiency through dropwise condensation on hydrophobic surfaces is well-documented. However, no surfaces have been able to repel liquids with extremely low surface tension, such as fluorinated solvents, during condensation, as they nucleate and completely wet even the most hydrophobic interfaces.
View Article and Find Full Text PDFAir conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems.
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