Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as "dietary lapses" can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.

Wind disturbance presents a formidable challenge to the flight performance of multi-rotor small unmanned aerial vehicles (sUAVs). This paper presents a comprehensive review of techniques for measuring wind speed and airspeed for multi-rotor sUAVs. Three categories of sensing techniques are reviewed: flow sensors, anemometers, and tilt-angle based approaches. We also review techniques for generating wind disturbances in simulation. Wind simulation techniques that use power spectral density (PSD) functions, computational fluid dynamics (CFD), and probabilistic models are examined. Finally, we provide an open-source Python implementation of the Dryden wind turbulence model and embedded code to interface with an ultrasonic anemometer.INDEX TERMS Unmanned aerial vehicles, measurement, simulation.

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The sustained growth of the solar energy industry has created the need to formalize solar energy education. This paper elucidates a novel, cloud-based, virtual reality (VR) pedagogical system that provides students with the fundamentals of solar or Photovoltaic (PV) cells, solar (PV) modules, and solar (PV) array installation configurations. The VR system was developed using Unity3d software and was integrated with a Learning Management System (LMS). The system was hosted on the Google Cloud Platform. Students/users accessed the VR system using a standard web browser, making it widely accessible. The VR system consisted of self-guided laboratory modules covering electrical engineering fundamentals of solar (PV) cells such as output power losses as a function of finger length, width, depth, and spacing. Additionally, series and parallel solar (PV) cell connections to create desired voltage and current output, and orientation/tilt considerations of solar (PV) array installations were covered. Physics models to incorporate realistic solar energy behavior were programmed in the VR system. Live graphs showing how parameters affected total power output provided instant feedback to students. An adaptive formative assessment system that tested students' application of electrical engineering fundamentals along with design skills was implemented. The system was used in a first year engineering fundamentals undergraduate class to make solar energy education accessible to early state engineering students. Data collected from the cloud system and student survey indicate growth in student engagement and students' knowledge of introductory solar and electrical engineering topics.INDEX TERMS Electrical engineering education, solar energy, virtual reality, educational technology.

This paper presents a real-time method to detect and track multiple mobile ground robots using event cameras. The method uses density-based spatial clustering of applications with noise (DBSCAN) to detect the robots and a single k-dimensional (k − d) tree to accurately keep track of them as they move in an indoor arena. Robust detections and tracks are maintained in the face of event camera noise and lack of events (due to robots moving slowly or stopping). An off-the-shelf RGB camera-based tracking system was used to provide ground truth. Experiments including up to 4 robots are performed to study the effect of i) varying DBSCAN parameters, ii) the event accumulation time, iii) the number of robots in the arena, iv) the speed of the robots, v) variation in ambient light conditions on the detection and tracking performance, and vi) the effect of alternative clustering algorithms on detection performance. The experimental results showed 100% detection and tracking fidelity in the face of event camera noise and robots stopping for tests involving up to 3 robots (and upwards of 93% for 4 robots). When the lighting conditions were varied, a graceful degradation in detection and tracking fidelity was observed.