Abstract
A learning path is the implementation of a curriculum design. It consists of a set of learning activities that help users achieve particular learning goals. Personalizing these paths became a significant task due to differences in users' limitations, backgrounds, goals, etc. Since the last decade, researchers have proposed a variety of learning path personalization methods using different techniques and approaches. In this paper, we present an overview of the methods that are applied to personalize learning paths as well as their advantages and disadvantages. The main parameters for personalizing learning paths are also described. In addition, we present approaches that are used to evaluate path personalization methods. Finally, we highlight the most significant challenges of these methods, which need to be tackled in order to enhance the quality of the personalization.

Abstract
In previous works, an educational platform based on Matlab/Simulink/Lego EV3 has been developed that allows interacting in real time with the robot. However, this platform is limited to the capacity of Simulink to adapt to different robotic devices, limiting its use to LEGO and Arduino or Rasberry Pi robots. In this paper we present a generalization of this platform to any type of robot, based on Matlab/ROS/Robot. Robot Operating System (ROS) is a robotic middleware, that is, a collection of frameworks for software development of robots [4]. Despite not being an operating system, ROS provides standard services such as hardware abstraction, control of low-level devices, etc. MATLAB (R) support for ROS is a library of functions that allows you to exchange data with ROS-enabled physical robots. Matlab/ROS/Robot interaction is tested in a generic robot with Arduino Mega and Raspberry Pi 3, demonstrating the viability of the presented educational robotic platform.

Abstract
The teaching process of auscultation is complex in itself, and difficult to operate since it requires a wide spectrum of patients with the most diverse cardiopulmonary pathologies, readily available during teaching and assessment hours, for an ever-growing number of medical students. In this paper we will focus on how virtual patient technologies can promote the evolution of the current teaching methodologies, promoting better learning. The chosen methodology was: a) a review of available medical simulation technologies for auscultation teaching; b) a case study illustrating how a virtual patient simulation technology has been successfully used to teach and certify auscultation skills. Results show the positive impact and high acceptability of virtual patient simulation technologies in the teaching of auscultation to medical students.

Abstract
We demonstrated in this work a filterless, multi-point and temperature-independent FBG (fiber Bragg grating) dynamical demodulator using pulse-width-modulation (PWM). In this approach, the FBG interrogation system is composed of a tunable laser and a demodulator that is designed to detect the wavelength shift of the FBG sensor without any optical filter making it very suitable to be used in harsh environments. In this work, we applied the proposed method that uses the PWM technique for FBG sensors placed in high pressure and high-temperature environments. The proposed method was characterized in the laboratory using an FBG sensor modulated in a frequency of 6 Hz, with a 1 kHz sweeping frequency in the wavelength range from 1527 to 1534 nm. Also, the method was evaluated in a field test in an engine of a thermoelectric power plant.

Abstract
Social media platforms have been increasingly used by companies, over the past few years, in order to make their recruitment and selection process more efficient. The aim is to gather more data about the applicants’ competences and personality traits, assuring that they are hiring the right candidates. Nevertheless, this method may be controversial since it can lead to different legal and ethical issues such as discrimination and invasion of privacy. Additionally, some problems may arise regarding the validity and fairness of the information found in such platforms. As Facebook is the most frequently used social platform among students and recently employed workers, we chose it as the main focus of our survey (which had 212 answers), in order to understand the applicants’ point of view about this subject. The main conclusions were that the majority of the respondents affirmed being aware of this practice, despite not agreeing with it; and, also, they do not believe that this is a good tool to evaluate their potential. Finally, our participants’ answers also led us to conclude that there could be contradictions regarding their judgement about the accessing of personal information by firms on Facebook. 76,9% of our respondents affirmed that they usually assess the profile of people who send them friend requests. Our respondents consider the use of Facebook during the selection process as being unethical, even though they assess others’ profiles and search for information themselves. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The Sequential Monte Carlo Simulation (SMCS) is a powerful and flexible method commonly used for generating system adequacy assessment. By sampling outage events in sequence and their respective duration, this method can easily incorporate time-dependent issues such as renewable power production, the capacity of hydro units, scheduled maintenance, complex correlated load models, etc, and is the only method that provides probability distributions for the reliability indexes. Despite these advantages, the SMCS method requires considerably more simulation time than the Non-sequential Monte Carlo Simulation approach to provide accurate estimates for the reliability indexes. In an attempt to reduce the simulation time, the SMCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms. Two parallelization strategies are proposed: Strategy A, which creates and evaluates yearly samples in a completely parallel approach and while the estimates of the reliability indexes are computed in the CPU; and Strategy B, which consists on concurrently sampling the outage events for the generating units while the state evaluation and the index estimation stages are executed in serial. Simulation results for the IEEE RTS 79, IEEE RTS 96, and the new IEEE RTS GMLC test systems, show that both implementations lead to a significant acceleration of the SMCS method while keeping all its advantages. In addition, it was observed that Strategy B results in less simulation time than Strategy A for generation system adequacy assessment. © 2020 IEEE.