Eating behaviours, social media usage, and its association: A cross-sectional study in Indian medical undergraduates.

Background: Despite increasing incidence, there is little data on abnormal eating behaviours or disorders in Indian youth, especially medical students. Additionally, little literature exists measuring the association of social media use with abnormal eating behaviours. Aim: To assess the prevalence of abnormal eating behaviours amongst medical students, social media usage, and any association of social media usage with eating behaviours. Materials and Methods: An online cross-sectional questionnaire-based study was conducted with 272 participants at a medical college, and two scales: the Three-Factor Eating Questionnaire-Revised 21items (TFEQ-R21) and the Scale of Effects of social media on Eating Behaviour (SESMEB) were used. Results: 22% of the participants reported abnormal eating behaviours. A significant difference in the effect of social media on eating behaviour according to the year of study [f = 3.08, P = 0.02] was seen with the final years having the lowest and the first years having the highest SESMEB scores. Students using more than 4 social media platforms had a higher SESMEB score [t = -2.02, P < 0.04]. A positive correlation was seen between TFEQ domains such as uncontrolled eating [r = 0.38, P = 0.01], emotional eating [r = 0.30, P = 0.01], and TFEQ total score [r = 0.40, P = 0.01] with SESMEB scores. Conclusion: This study finds a significant correlation between increased social media usage and developing abnormal eating behaviours in medical students. It highlights the need for the creation of policies regulating social media use with eating behaviours in mind.

Analysis of collision avoidance in honeybee flight.

Insects are excellent at flying in dense vegetation and navigating through other complex spatial environments. This study investigates the strategies used by honeybees (Apis mellifera) to avoid collisions with an obstacle encountered frontally during flight. Bees were trained to fly through a tunnel that contained a solitary vertically oriented cylindrical obstacle placed along the midline. Flight trajectories of bees were recorded for six conditions in which the diameter of the obstructing cylinder was systematically varied from 25 mm to 160 mm. Analysis of salient events during the bees' flight, such as the deceleration before the obstacle, and the initiation of the deviation in flight path to avoid collisions, revealed a strategy for obstacle avoidance that is based on the relative retinal expansion velocity generated by the obstacle when the bee is on a collision course. We find that a quantitative model, featuring a controller that extracts specific visual cues from the frontal visual field, provides an accurate characterization of the geometry and the dynamics of the manoeuvres adopted by honeybees to avoid collisions. This study paves the way for the design of unmanned aerial systems, by identifying the visual cues that are used by honeybees for performing robust obstacle avoidance flight.

Controlling a bio-inspired miniature blimp using a depth sensing neural-network camera.

Miniature blimps are lighter-than-air vehicles which have become an increasingly common unmanned aerial system research platform due to their extended endurance and collision tolerant design. The UNSW-C bio-inspired miniature blimp consists of a 0.5 m spherical mylar envelope filled with helium. Four fins placed along the equator provide control over the three translatory axes and yaw rotations. A gondola attached to the bottom of the blimp contains all the electronics and flight controller. Here, we focus on using the UNSW-C blimp as a platform to achieve autonomous flight in GPS-denied environments. The majority of unmanned flying systems rely on GPS or multi-camera motion capture systems for position and orientation estimation. However, such systems are expensive, difficult to set up and not compact enough to be deployed in real environments. Instead, we seek to achieve basic flight autonomy for the blimp using a low-priced and portable solution. We make use of a low-cost embedded neural network stereoscopic camera (OAK-D-PoE) for detecting and positioning the blimp while an onboard inertia measurement unit was used for orientation estimation. Flight tests and analysis of trajectories revealed that 3D position hold as well as basic waypoint navigation could be achieved with variance (<0.1 m). This performance was comparable to that when a conventional multi-camera positioning system (VICON) was used for localizing the blimp. Our results highlight the potentially favorable tradeoffs offered by such low-cost positioning systems in extending the operational domain of unmanned flight systems when direct line of sight is available.