We present AQuality32, an open-source air quality monitoring device designed to address the challenges faced by small research teams in accessing affordable and versatile air quality monitoring solutions. The device utilizes an ESP32 System on Module (SoM) and integrates sensors for CO₂ (SDC30 from Sensirion) and particulate matter (HM3301 from Seeed) measurement, along with temperature, relative humidity, and extensive sensing possibilities. It can be powered by a battery and is suitable for both stationary and on-the-go measurements. The paper details the hardware description, building instructions, programming, calibration procedures, and data collection setup for AQuality32. Validation experiments assess communication stability, geolocation accuracy, and environmental monitoring capabilities. The results demonstrate the device’s reliability, affordability, and suitability for various applications in environmental sciences, public health, and indoor/outdoor air quality monitoring. The paper emphasizes the importance of robust solutions, openness, and easy documentation for widespread adoption and impact in air quality research and monitoring.

The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of dropout risk. The methodological design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms. The results demonstrate that (1) the top features identified by feature selection techniques, i.e., those constructed through feature engineering, proved to be among the most effective in classifying student dropout; (2) the F-score of the best model increased by 5% with feature selection techniques; and (3) depending on the type of feature selection, the performance of the machine learning algorithm can vary, potentially increasing or decreasing based on the sensitivity of features with higher noise. At the same time, metaheuristic algorithms demonstrated significant precision improvements, but there was a risk of increasing errors and reducing recall.

The aim of this paper is to provide a review on verification and validation of embedded software. An embedded software is a product that contains a microprocessor and software to perform certain function. The growing demand for new features and functionalities of embedded systems makes the design and implementation reach a higher level of complexity. We focused on identifying research trends in order to provide a review of the challenges that emerge during the verification and validation processes of embedded software. The research works are grouped by related themes in order to find research problems that persist nowadays.