The importance of educating the next generations in the understanding of the fundamentals of the upcoming scientific and technological innovations that will force a broad social and economical paradigm change can not be overstressed. One such breakthrough technologies is Artificial Intelligence (AI), specifically machine learning algorithms. Nowadays, the public has little understanding of the workings and implications of AI techniques that are already entering their lives in many ways. We aim to achieve widespread public understanding of these issues in an experiential learning framework. Following a design based research approach, we propose to implement program coding scaffoldings to teach and experiment some basic mechanisms of AI systems. Such experiments would be shedding new light into AI potentials and limitations. In this paper we focus on innovative ways to introduce high school students to the fundamentals and operation of two of the most popular AI algorithms. We describe the elements of a workshop where we provide an academic use-create-modify scaffolding where students work on the Scratch partial coding of the algorithms so they can explore the behavior of the algorithm, gaining understanding of the underlying computational thinking of AI processes. The extent of the impact on the students of this experience is measured through questionnaires filled before and after participation in the workshop. Preliminary experiments offer encouraging results, showing that the workshop has differential impact on the way students understand AI.

Payload transportation and manipulation by rotorcraft drones are receiving a lot of attention from the military, industrial and logistics research areas. The interactions between the UAV and the payload, plus the means of object attachment or manipulation (such as cables or anthropomorphic robotic arms), may be nonlinear, introducing difficulties in the overall system performance. In this paper, we focus on the current state of the art of aerial transportation systems with suspended loads by a single UAV and a team of them and present a review of different dynamic cable models and control systems. We cover the last sixteen years of the existing literature, and we add a discussion for evaluating the main trends in the referenced research works.

This paper presents a numerical solution to optimize RTM processes based on a DDDAS methodology, taking permeability as stochastic. The model proposed in this investigation allows real-time accurate prediction of the filling rate by adjusting the parameters with the on-line captured process data. The suitability of the proposed model was tested and validated under different process parameters variations like pressure and preform permeability.