This paper proposes a non-intrusive, multimodal Internet of Things (IoT) framework designed for the continuous monitoring of mood and behavioral patterns in adolescents. The system integrates two complementary sensing modalities: a passive smart-chair subsystem and an active gamified behavioral probe. The smart chair utilizes a grid of Force-Sensing Resistors (FSRs) and an ESP32 microcontroller to capture real-time motor activity and posture dynamics, which are interpreted through a fuzzy-logic inference model to identify states of arousal or restlessness. Simultaneously, a mobile videogame-based system elicits and records active decision-making and motivational metrics, such as reward sensitivity and persistence, grounded in foraging theory. By employing an edge-cloud-fusion architecture, the framework synchronizes these data streams to identify complex mood constructs (e.g., anxiety or apathy) while prioritizing interpretability and privacy-by-design. The proposed approach avoids intrusive technologies like cameras or microphones, offering a scalable and ethically grounded tool for early intervention in behavioral and mood disorders within naturalistic environments such as study or gaming contexts.
The presence of a fertile queen is essential for the stability and productivity of a honey bee colony. However, her status is generally assessed through visual hive inspections, which are invasive, time-consuming, and performed at non-continuous intervals. For this reason, within the framework of the so-called precision beekeeping, there is growing interest in remote monitoring systems. The analysis of hive acoustics represents a promising approach for detecting variations in colony state. In this study, the potential of acoustic analysis to discriminate against the presence or absence of the queen in colonies of Apis mellifera ligustica Spin. was evaluated. Audio recordings were collected inside the hive during two experimental summer and autumn trials. The queen bee absence was simulated by isolating them in the secondary queen-rearing compartment of a French-like breeding hive. The acoustic signal was analyzed using Short-Time Fourier Transform (STFT) and spectral peak tracking within three selected frequency bands (80-140 Hz, 200-270 Hz, and 380-455 Hz). The results show a consistent increase in peak frequency within the middle band (200-270 Hz) following queen isolation, with a shift of approximately 20 Hz observed in three out of four colonies. In contrast, the lower and higher frequency bands investigated did not show systematic differences between simulated queenright and queenless conditions. These findings suggest that monitoring the peak frequency in the middle band may provide useful information on colony/queen status and represent a promising basis for the development of lightweight acoustic tools for queen monitoring in precision beekeeping. The ongoing study involving a larger number of colonies is however necessary to confirm the robustness and generalizability of such acoustic indicator.
In the context of Industry 4.0, monitoring and controlling environmental parameters and volatile organic compounds (VOCs) has become central to improving operational efficiency, ensuring product quality, and safety in production environments. The following article aims to develop a versatile, low-cost hardware interface for MOX (Metal-Oxide Semiconductor) gas sensors. The proposed architecture is based on the Arduino Giga R1 Wi-Fi and a custom-designed board developed for resistance measurement (0-500 MΩ) and temperature control of MOX gas sensors. The system also allows data to be stored locally using an SD module or transmitted wirelessly, responding to the growing demand for integrated and connected solutions. Validation was performed by comparing the response of a TiO2 film-based MOX sensor at 400 °C with reference laboratory instrumentation at different ethanol concentrations. The results obtained demonstrate a good level of agreement under the target operating conditions.
Accurate dimensional measurement plays a key role in modern manufacturing, particularly as Industry 4.0 drives the adoption of automated and non-contact inspection solutions. Among the available technologies, vision-based measurement systems have gained increasing attention thanks to their flexibility, high acquisition speed, and relatively easy integration into production environments. At the same time, their measurement performance is known to depend strongly on imaging conditions, optical configuration, and the specific measurement strategy adopted. This paper presents an uncertainty analysis of a stationary vision-based measurement system designed for the dimensional analysis of ropes using an industrial camera and controlled illumination. The experimental setup uses a camera with a telecentric lens. A dedicated calibration procedure is employed to establish the pixel-to-metric conversion. The proposed methodology includes image acquisition, feature extraction, and dimensional computation, followed by an uncertainty analysis based on repeated measurements. Results from the sensitivity analysis show repeatable dimensional measurements, with a propagated lay length uncertainty of approximately 0.76% for a lay angle uncertainty of 0.06◦ and a diameter uncertainty of 0.01 mm.
In the evaluation of high-value edible oils, increasing attention is devoted to non-destructive techniques capable of supporting authenticity assessment and quality control. Variations in the thermal behaviour of oils under controlled excitation can provide indirect but informative indicators of compositional changes and adulteration. In this study, an infrared (IR) thermography-based approach combined with deep learning is proposed for the analysis of tomato seed oil (TSO) and sunflower oil (SO). Thermographic sequences acquired during repeatable heating–cooling cycles were processed as spatiotemporal thermal signals reflecting heat absorption and dissipation dynamics. A convolutional neural network–long short-term memory (CNN–LSTM) model was employed to classify TSO, SO, and adulterated oil samples. The proposed system achieved an overall classification accuracy of 95.1% and a macro F1score of 94.8% in three-class classification. The results confirm that thermographic fingerprints capture stability-related differences induced by oil composition and blending, highlighting the potential of IR thermography coupled with CNN-based modelling as a rapid, non-contact decision support tool for edible oil authenticity screening.
