This research focuses on addressing critical driving safety issues on university campuses, particularly vehicular congestion, inadequate parking, and hazards arising from the interaction between vehicles and pedestrians. These challenges are common across campuses and demand effective solutions to ensure safe and efficient mobility. To address these issues, the study developed detailed microsimulation models tailored to the Victor Levi Sasso campus of the Technological University of Panama. The primary function of these models is to evaluate the effectiveness of various safety interventions, such as speed reducers and parking reorganization, by simulating their impact on traffic flow and accident risk. The models provide calculations of traffic parameters, including speed and travel time, under different safety scenarios, allowing for a comprehensive assessment of potential improvements. The results demonstrate that the proposed measures significantly enhance safety and traffic efficiency, proving the model’s effectiveness in optimizing campus mobility. Although the model is designed to tackle specific safety concerns, it also offers broader applicability for addressing general driving safety issues on university campuses. This versatility makes it a valuable tool for campus planners and administrators seeking to create safer and more efficient traffic environments. Future research could expand the model’s application to include a wider range of safety concerns, further enhancing its utility in promoting safer campus mobility.

This research study explores the addition of chromium (Cr⁶⁺) ions as a nucleating agent in the alumino-silicate-glass (ASG) system (i.e., Al₂O₃-SiO₂-MgO-B₂O₃-K₂O-F). The important feature of this study is the induction of nucleation/crystallization in the base glass matrix on addition of Cr⁶⁺ content under annealing heat treatment (600 ± 10 °C) only. The melt-quenched glass is found to be amorphous, which in the presence of Cr⁶⁺ ions became crystalline with a predominant crystalline phase, Spinel (MgCr₂O₄). Microstructural experiment revealed the development of 200–500 nm crystallite particles in Cr⁶⁺-doped glass-ceramic matrix, and such type microstructure governed the mechanical properties. The machinability of the Cr-doped glass-ceramic was thereby higher compared to base alumino-silicate glass (ASG). From the nano-indentation experiment, the Young’s modulus was estimated 25(±10) GPa for base glass and increased to 894(±21) GPa for Cr-doped glass ceramics. Similarly, the microhardness for the base glass was 0.6(±0.5) GPa (nano-indentation measurements) and 3.63(±0.18) GPa (micro-indentation measurements). And that found increased to 8.4(±2.3) (nano-indentation measurements) and 3.94(±0.20) GPa (micro-indentation measurements) for Cr-containing glass ceramic.

This work shows the results of the biosynthesis of silver nanoparticles using the microalga Chlorella sp, using growth media with different concentrations of glycerol, between 5%–20%, and different light and temperature conditions. The synthesis of nanoparticles was studied using supernatants and pellets from autotrophic, heterotrophic and mixotrophic cultures of the microalga. The presence of nanoparticles was verified by ultraviolet-visible spectroscopy and the samples showing the highest concentration of nanoparticles were characterized by scanning electron microscopy. The mixotrophic growth conditions favored the excretion of exopolymers that enhanced the reduction of silver and thus the formation of nanoparticles. The nanoparticles obtained presented predominantly ellipsoidal shape with dimensions of 108 nm × 156 nm and 87 nm × 123 nm for the reductions carried out with the supernatants of the mixotrophic cultures with 5% and 10% glycerol, respectively.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.