Abstract
High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-driven approach, where models generate executable applications, usually with an automated approach. As these applications might execute in different parallel environments, their behavior becomes very hard to predict, and making the verification of non-functional requirements complicated. In this regard, it is crucial to analyse and understand the impact that the mapping and scheduling of computation have on the real-time response of the applications. In fact, different strategies in these steps of the parallel orchestration may produce significantly different interference, leading to different timing behaviour. Tuning the application parameters and the system configuration proves to be one of the most fitting solutions. The design space can however be very cumbersome for a developer to test manually all combinations of application and system configurations. This paper presents a methodology and a toolset to profile, analyse, and configure the timing behaviour of highperformance cyber-physical applications and the target platforms. The methodology leverages on the possibility of generating a task dependency graph representing the parallel computation to evaluate, through measurements, different mapping configurations and select the one that minimizes response time.

Abstract
Computational thinking is a fundamental competence for the 21st century. It refers to a set of capacities and skills that can be stimulated to facilitate the teaching-learning process in a wide range of fields, including Science, Technology, Engineering and Mathematics (STEM). Experts in information technology argue that the earlier children are exposed to programming through digital platforms appropriate for their age, the easier it will be for them to assimilate their concepts in the future. This effort should be continued throughout the educational stages of children and youth to increase students' interest in pursuing STEM studies and careers. This paper describes the Scratch4All project promoted by the consortium CASPAE ( a Private Social Solidarity Institution) and Inova-Ria, with technical assistance from professors at the public higher education institution Coimbra Institute of Engineering. Scratch4All Project includes the activities Scratch on Road, Programming and Robotics Lab, and the Scratch4All Digital Platform. According to the impact assessment for the school year 2020-2021, the Scratch4All project promotes school success and true equality in access to new technologies for students in the 1st, 2nd, and 3rd cycles of elementary school, developing essential skills for their academic and professional future such as computational thinking, STEM competencies and social skills. By encouraging young girls to participate in technological projects, this project also aims to combat gender stereotypes.

Abstract
Boarding time constitutes a critical element of turnaround time, which is used to measure the efficiency of airline operations. Therefore, to reduce boarding time, it is imperative to reconsider traditional passenger boarding strategies to make them more efficient. In this sense, this study seeks to analyze the impact of different strategies on boarding times using discrete event simulation on an Airbus 320. Seven boarding strategies have been identified and considered in our study, as follows: random, back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal. The impact of carrying hand luggage and the presence of priority passengers has been considered, as well as the impact of having a continuous arrival of passengers during the boarding process versus having all passengers available at boarding time. In general, simulation results have pointed out that the outside-in and reverse pyramid strategies are the most effective, improving boarding time by up to 15%, when compared to the random strategy. Moreover, the back-to-front strategy, which is generally implemented by airline companies, has been shown to be the most inefficient strategy. Efficient boarding strategies are expected to contribute to the sustainability of air travel by minimizing the turnaround time, improving operational efficiency, and reducing emissions.

Abstract
Traditional use of predictive control techniques require the knowledge of the systems model to control and the use of constant cycle-time. In the case of a switched reluctance motor its model is highly nonlinear and time-varying with current magnitude and rotor position. The use of look-up tables has been one solution, but requires a complete knowledge of the motor and mismatches from the original model used in the design can happen due temperature variation or changes in operating regimes. To address these issues as well as to increase the tracking performance of current control, a model-free predictive algorithm is developed by updating the next cycle time of the next time step of the predictive control. A new parameter estimation method is proposed that identifies the parameters of the switched reluctance model with low computational burden. Based on knowledge of the parameters at real time, not only the ideal voltage vector is applied at each cycle but the ideal time that each cycle must have is also calculated. As result, the advanced current controller requires almost no knowledge of the motor in use. The performance of the proposed schemes is validated through simulation and by a prototype experimental setup. Experimental data shows a decreasing in prediction error around 78 per cent, when comparing to the predefined model controller.

Abstract
The digitalization of energy generation and distribution systems opens new opportunities for devising network operation and traffic engineering strategies capable of adapting to the energy availability and sources. Despite the potential, developing and testing new approaches are challenging in production environments. Furthermore, no simulators support such integration between the communication infrastructure and the power grid. Thus, this paper introduces Flexcomm Simulator, a tool based on ns-3 that supports developing and assessing multiple strategies toward green networking and communications driven by real-time information from the power grid (i.e., Energy Flexibility). The proof-of-concept results demonstrate this contribution's potential by implementing an energy-aware routing algorithm that adapts to real-world Energy Flexibility data in a Metropolitan Area Network (MAN). Also, it showcases the simulator's capacity to deal with large-scale simulations through MPI-based distributed environments.

Abstract
Taxi demand prediction in a city is a highly demanded smart city research application for better traffic strategies formulation. It is essential for the interest of the commuters and the taxi companies both to have an accurate measure of taxi demands at different regions of a city and at varying time intervals. This reduces the cost of resources, efforts and meets the customers' satisfaction at its best. Modern predictive models have shown the potency of Deep Neural Networks (DNN) in this domain over any traditional, statistical, or Tensor-Based predictive models in terms of accuracy. The recent DNN models using leading technologies like Convolution Neural Networks (CNN), Graph Convolution Networks (GCN), ConvLSTM, etc. are not able to efficiently capture the existing spatio-temporal characteristics in taxi demand time-series. The feature aggregation techniques in these models lack channeling and uniqueness causing less distinctive but overlapping feature space which results in a compromised prediction performance having high error propagation possibility. The present work introduces Spatio-Temporal Aggregator Predictor (ST-A(G)P), a DNN model which aggregates spatio-temporal features into (1) non-redundant and (2) highly distinctive feature space and in turn helps (3) reduce noise propagation for a high performing multi-step predictive model. The proposed model integrates the effective feature engineering techniques of machine learning approach with the non-linear capability of a DNN model. Consequently, the proposed model is able to use only the informative features responsible for the objective task with reduce noise propagation. Unlike, existing DNN models, ST-A(G)P is able to induce these qualities of feature aggregation without the use of Multi-Task Learning (MTL) approach or any additional supervised attention that existing models need for their notable performance. A considerable high-performance gain of 25 - 37% on two real-world city taxi datasets by ST-A(G)P over the state-of-art models on standard benchmark metrics establishes the efficacy of the proposed model over the existing ones.