Nowadays, technological tools greatly support the work of teaching-learning tasks. In this sense, there are various sources of information from which teachers and students rely on to complement their academic activities. Content is sought on the web, significantly updated and easy to understand, generally in the form of videos. As people progress in their learning, they face terms, concepts, and topics that they are not familiar with them. However, those topics are included in the video. In this context, a complex process is generated of alternating sections of the video with other sources of information that explain the related topics and contribute to the understanding of the topic discussed. In this regard, and considering the possibility of systematically consuming information from various sources, it is necessary to build a method and an application that orchestrates the contents of these sources in a convenient, fast and automatic way, according to the person's learning. This proposal contemplates the development of a Mashup. This mashup integrates different data sources in a single graphical interface. Also, it is considered the construction of a core software solution based on text mining techniques. This solution allows extracting the textual content from videos and identifying the terms that could support the knowledge of the topic. It would significantly contribute to the fact that related topics are presented unified in the same interface. At the same time, the learning experience is greatly improved, avoiding losing the common thread of the observed video. Therefore, this article presents a process of orchestrating various data sources in a Web Mashup application. It includes videos available on YouTube channels, with other sources (e.g., Wikipedia, Pinterest) that help understand the topic better, generating hypertext references based on the generation of terms through text mining techniques. A Mathematics Learning mashup has been built to show the proposal’s feasibility.

Several proposals on active aging have been addressed within the psychological field, conceptualizing it satisfactorily as a perspective of aging. Those proposals generate indicators that assess the level of physical health, psychological wellbeing, adequate social adaptation. Physical, cognitive, and functional faculties, interpersonal relationships, and productive activities have been evaluated. Although several technological approaches have been proposed to promote active aging, they have not included a deep understanding of the results obtained from solution implementations. Then, this paper presents the first step towards an approach that uses variables proposed by active aging models (e.g., health, cognition, activity, affection, fitness aspects) to generate knowledge through patterns. These patterns are identified using data obtained through several instruments (i.e., psychological evaluations, health studies, and human experts' contributions). Thus, selecting those variables and evaluating them as future models is necessary. Domain experts perform this evaluation. The evaluation of this proposal has been completed with participants belonging to the health area through a case study. This evaluation generates input data for engineers to apply data mining techniques to reveal strategic knowledge. Finally, from the psychologist's point of view, the results showed that the contribution results are appropriate for achieving healthy aging indicators.

Serious games are applications that pursue, on the one hand, the users' entertainment and, on the other hand, look to promote their learning, cognitive stimulation, among reaching other objectives. Moreover, data generated from those games (e.g., demographic information, gaming precision, user efficiency) provide insights helpful in improving certain aspects such as the attention and memory of the gamers. Therefore, applying data mining techniques over those data allows obtaining multiple patterns to improve the game interface, identify preferences, discover, predict, train, and stimulate the users' cognitive situation, among other aspects, to reach the games' objectives. Unfortunately, although several solutions have been addressed about this topic, no secondary studies have been found to condensate research that uses data mining to extract patterns from serious games. Thus, this paper presents a Systematic Literature Review (SLR) to extract such evidence from studies reported between 2001 and 2021. Besides, this SLR aims to answer research questions involving serious games solutions that train the cognitive functions of th

The Stroop test also called the colors and words test, is a widely used attention test to detect\nneuropsychological problems. Moreover, the stress test is a psychological instrument used to diagnose the\nlevel of stress and to identify the most common symptoms. This research aims to evaluate whether there is a\nrelationship between the score of the Stroop test and the participant's level of stress. Data are collected through\na web application, where participants answered the stress test and completed the Stroop test. Several variables\nwere collected, such as the precision of each answer, the time spent, and demographic information. The\nmachine learning technique called k-means was applied to process the collected data; the results include\nclusters of unlabeled data to find relationships. The main findings show that a person's stress level is directly\nlinked to the number of correct answers obtained in the Stroop test; according to the clusters that show higher\nstress levels, the number of correct answers decreased progressively

The control technique based on infinite-gain resonators also called Adaptive Feedforward Cancellation (AFC) has been used for some years, mainly in continuous time. In this article, finite-gain resonators in discrete time are analyzed and the advantages for their use in practical applications are discussed, from the perspectives of dynamical behavior and computational implementation. The existing design rules for optimizing system robustness are generalized to any kind of resonator, whether of infinite or finite gain. Nowadays, because most practical implementations are carried out by means of digital systems, such as Digital Signal Processors (DSPs), working directly in discrete time is justified, avoiding any problems that may appear when the resonant controllers are discretized by means of approximations from continuous time.

Controllers for ac/dc power converters are subject to many constraints: first, they must be reliable when working conditions are not ideal, i.e., when the grid voltages present sags or swells, big impedances, changes in the frequency or harmonics. Second, they must be robust since the plant is not perfectly know (parametric uncertainty), it may change over time (load changes) and they must reduce to the minimum the currents' harmonics. And third, they must operate using the minimum number or sensors to reduce the cost. On the other hand, the use of digital controllers is very common today since it makes easier the implementation and gives them flexibility. In this paper, a discrete-time control technique based on Adaptive Feed-forward Cancellation (AFC) is proposed for a full-bridge single-phase power rectifier. The controller has been entirely designed in discrete-time, avoiding approximations from continuous-time. The experimental results show that AFC controllers are not only robust, but also they present a very good performance.

The use of LCL filters with VSI converters is interesting since they present good attenuation of current ripple at high frequencies. Nevertheless, they also present a high resonance peak which can cause undesired oscillations, and even instability problems. Passive and Active Damping are methods which try to reduce those resonance effects. In this paper, Passive Damping is briefly reviewed and Active Damping is formally analyzed in continuous-time and, specially, in discrete-time. It will be shown that the one period delay included in sampled-data control systems and the grid impedance play a very important role in stability conditions. The presented results are useful to design discrete-time Active Damping controllers. Moreover, important implications for higher level controls can be deduced since the Active Damping is the first control loop in the power converter's controller.

In this work, the control system and fault detection algorithm applied on a real fatigue assay machine is addressed. The objective of the machine is to apply periodical forces to restored teeth and to detect the teeth collapse calculating the number of cycles until the fault occurs. There is a Scottish yoke mechanism that converts the torque of a brushless motor on linear forces. This motor is driven by a power drive that receives the torque reference from a computer operating on real-time, and where the fault detection algorithm is applied. There is also a user computer where the parameters of the assay are introduced and where the results are shown. In this work the control system that has been implemented is shown in detail and the developed fault algorithm with adaptive threshold for the system with several sampling times is addressed.