We describe an entomological dual-band 808 and 980 nm lidar system which has been implemented in a tropical cloud forest (Ecuador). The system was successfully tested at a sample rate of 5 kHz in a cloud forest during challenging foggy conditions (extinction coefficients up to 20 km ^–1 ). At times, the backscattered signal could be retrieved from a distance of 2.929 km. We present insect and bat observations up to 200 m during a single night with an emphasis on fog aspects, potentials, and benefits of such dual-band systems. We demonstrate that the modulation contrast between insects and fog is high in the frequency domain compared to intensity in the time domain, thus allowing for better identification and quantification in misty forests. Oscillatory lidar extinction effects are shown in this work for the first time, caused by the combination of dense fog and large moths partially obstructing the beam. We demonstrate here an interesting case of a moth where left- and right-wing movements induced oscillations in both intensity and pixel spread. In addition, we were able to identify the dorsal and ventral sides of the wings by estimating the corresponding melanization with the dual-band lidar. We demonstrate that the wing beat trajectories in the dual-band parameter space are complementary rather than covarying or redundant, thus a dual-band entomological lidar approach to biodiversity studies is feasible in situ and endows species specificity differentiation. Future improvements are discussed. The introduction of these methodologies opens the door to a wealth of possible experiments to monitor, understand, and safeguard the biological resources of one of the most biodiverse countries on Earth.

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis.

Cocoa is a major commodity in the global food industry. Heavy metal contamination, particularly cadmium (Cd), raises significant concerns. This work demonstrates the use of laser-induced breakdown spectroscopy (LIBS) for fast Cd quantification in commercial cocoa powder across a wide range of concentrations (70–5000 ppm). Cocoa powder presents unique challenges due to its physical properties, such as the tendency to soften and liquefy at elevated temperatures, which complicates sample preparation. To address these issues, a mechanical mixing and pelletization protocol was implemented to ensure uniformity. Pellets were doped with known cadmium concentrations for calibration. Cadmium atomic lines at 340.36 and 361.05 nm were used to construct quantification curves. A special algorithm for background subtraction was implemented, and the LIBS plasma was characterized to ensure local thermodynamic equilibrium conditions. Out of eighteen samples, five double-blinded unknowns were evaluated. The concentrations agreed well within normalized standard deviations of 9.73% and 5.88% for the two cadmium lines. The limits of detection for the lines were 0.4 and 0.08 μg/g, respectively. LIBS is confirmed as a rapid and versatile analytical tool for Cd detection and quantification in complex food matrices, with potential applications in field-based and industrial monitoring systems.