Response Surface Methodology was used for optimizing operating variables for a multi-frequency ultrasound reactor using BP-3 as a model compound. The response variable was the Triclosan degradation percent after 10 sonication min. Frequency at levels from 574, 856 and 1134 kHz were used. Power density, pulse time (PT), silent time (ST) and PT/ST ratio effects were also analyzed. 22 and 23 experimental designs were used for screening purposes and a central composite design was used for optimization. An optimum value of 79.2% was obtained for a frequency of 574 kHz, a power density of 200 W/L, and a PT/ST ratio of 10. Significant variables were frequency and power level, the first having an optimum value after which degradation decreases while power density level had a strong positive effect on the whole operational range. PT, ST, and PT/ST ratio were not significant variables although it was shown that pulsed mode ultrasound has better degradation rates than continuous mode ultrasound; the effect less significant at higher power levels.

The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring.

Even though waste pickers play a crucial role in the urban solid waste management system in developing countries, their social, familial, and labor conditions remain uncertain. In this study, we analyzed the profiles of waste pickers in Colombia using multivariate statistical methods and cluster analysis. Our findings indicate that the majority of waste pickers have only completed elementary or high school education, and most of them are not associated with any organization and belong to the subsidized health regime. We identified five profiles of waste pickers in the population. The first consists of the most experienced individuals, while the second comprises individuals with the lowest salaries. The third includes older individuals who work more hours per day. The fourth is made up of individuals who work in the informal sector, and the fifth includes individuals who work in the formal sector. Our study highlights specific actions to be taken for each profile. Improving family and household conditions, social and educational status, and job conditions can have a significant impact on their lives.

The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.