Abstract

Abstract
The frequency security problem becomes a critical concern in power systems when the system inertia is lowered due to the high penetration of renewable energy sources (RESs). A wind-storage system (WSS) controlled by power electronics can provide the virtual inertia to guarantee the fast frequency response after a disturbance. However, the provision of virtual inertia might be affected by the variability of wind power generation. To address this concern, we propose a two-stage chance-constrained stochastic optimization (TSCCSO) model to find the optimal thermal unit commitment (i.e., economic operation) and the optimal placement of virtual inertia (i.e., frequency stability) in a power grid using representative power system operation scenarios. An enhanced bilinear Benders decomposition method is employed with strong valid cuts to effectively solve the proposed optimization model. Numerical results on a practical power system show the effectiveness of the proposed model and solution method.

Abstract
Innovative teaching-learning methodologies in the fields of Control, Automation and Robotics are of great interest to researchers, educators and students. Nowadays there is a wide range of technological options available that can be used to improve learning and motivate students in their knowledge acquisition and skills development. Concepts such as Pocket-Sized Labs, Virtual and Remote Labs, as well as Web-Based Learning, are increasingly included in the teaching-learning processes, where students are expected to acquire their knowledge as active and central elements in the entire process. This article focuses on the review of various teaching-learning methodologies in the fields of Control, Automation and Robotics, taking several aspects into account: the portability and low cost of devices and applications, the possibility of autonomous and distance learning and centering of the learning process in the student. The conclusions drawn allow us to state that it is possible to apply innovative, effective and motivating methodologies with tools, devices and applications that are both low-cost and easy to access. It can also be inferred that the future of teaching demands a radical departure from the traditional methodologies, as well as taking advantage of technologies and students' skills to use and put them into practice.

Abstract
Lung cancer treatments that are accurate and effective are urgently needed. The diagnosis of advanced-stage patients accounts for the majority of the cases, being essential to provide a specialized course of treatment. One emerging course of treatment relies on target therapy through the testing of biomarkers, such as the Epidermal Growth Factor Receptor (EGFR) gene. Such testing can be obtained from invasive methods, namely through biopsy, which may be avoided by applying machine learning techniques to the imaging phenotypes extracted from Computerized Tomography (CT). This study aims to explore the contribution of ensemble methods when applied to the prediction of EGFR mutation status. The obtained results translate in a direct correlation between the semantic predictive model and the outcome of the combined ensemble methods, showing that the utilized features do not have a positive contribution to the predictive developed models.

Abstract
The actual learning process in a school, college or university should take full advantage of the digital transformation. Computers, mobile phones, tablets or other electronic devices can be used in learning environments to improve learning experience and students performance. However, in a university campus, there are some activities where the use of connected devices, might be discouraged or even forbidden. Students should be discouraged to use their own devices in classes where they may become alienated or when their devices may cause any disturbance. Ultimately, their own devices should be forbidden in activities such as closed-book exams. This paper proposes a system architecture to detect or block unwanted wireless signals by students' mobile phones in a classroom. This architecture focuses on specific wireless signals from Wi-Fi and Bluetooth interfaces, and it is based on Software-Defined Radio (SDR) modules and a set of antennas with two configuration modes: detection mode and blocking mode. When in the detection mode, the architecture processes signals from the antennas, detects if there is any signal from Wi-Fi or Bluetooth interfaces and infers a position of the unwanted mobile device. In the blocking mode, the architecture generates noise in the same frequency range of Wi-Fi or Bluetooth interfaces, blocking any possible connection. The proposed architecture is designed to be used by professors to detect or block unwanted wireless signals from student devices when supervising closed-book exams, during specific periods of time. © Daniel Barros, Paulo Barros, Emanuel Lomba, Vítor Ferreira, and Pedro Pinto; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).

Abstract
Regional income inequality is a topic of increasing relevance worldwide that has received considerable scientific attention. However, a clear-cut, comprehensive view has yet to be put forward of the main determinants of regional income inequality. Indeed, the extant empirical literature on the topic has reported differing results. Thus, this study develops a comprehensive meta-analysis using 33 comparable empirical studies spanning 29 years of research, involving 28 main determinants of which the most frequently mentioned were regional development, human capital, manufacturing/industry share, unemployment, financial development, and trade openness. After adjusting for publication bias and heterogeneity in the results reported by the primary studies, we conclude that the not very frequently addressed institutional related determinants (financial development, fiscal policies and public sector size), substantially contribute to reduce within-region income inequality, particularly in lower-income settings. In a smaller extent, human capital and trade openness also mitigate within-region income inequality. Region level of development, urbanization and, in a lesser extent, technological intensity aggravate within-region income inequality. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.