Electric faults in photovoltaic (PV) systems cause negative economic and safety impacts, reducing their performance and causing unwanted electric connections that can be dangerous for the user. Line to line, ground and open circuit faults, are three of the main faults that happen in a photovoltaic array system. This work proposes a characterization of the equivalent circuits and the voltage-power (VP) curves at the output of multiple PV arrays under different topological configurations and fault conditions to evaluate the effects of these three main faults on the performance of a photovoltaic array system, taking into account the temperature and solar radiation influence. This work presents a validation of the characterization by measuring the output VP curves of a low-power photovoltaic array system under real outdoors conditions. This method can be useful in future works to develop low cost systems capable of detecting and classifying electric faults in photovoltaic array systems.

Dynamic lighting is playing a key role in education, by considering the main photometric variables such as the correlated color temperature and the illuminance to increase student’s attention levels inside the classroom. In the case of design students, the project component is fundamental for teaching, where students mainly need to develop activities such as presentation to listen to the teacher instructions, ideation and sketching to propose a solution according to the problem being addressed, and exhibition to present the work that has been done. These activities require specific and adequate lighting conditions to generate a positive impact on the performance of students. This article presents the design of a dynamic lighting system capable of adjusting the correlated color temperature in a range from 2500 K to 6500 K and the illuminance levels in a range from 0 lx to 800 lx to enhance the sustained and fixed attention of design students inside the classroom according to the type of activity that is being developed. The performance of the system was evaluated experimentally by measuring student’s attention inside the ergonomics and usability laboratory by using the Gesell chamber, the Emotiv Epoc EEG Headset with 14 electrodes to measure the brain activity and obtain engagement and focus levels, the eye tracking Tobii glasses, and a protocol to evaluate performance including several surveys and camera observation. In conclusion, the dynamic lighting system can improve the attention of design students by configuring the photometric variables according to the type of activity that is being done.

The classification of activities of daily living in the home of older adults makes it possible to identify risk situations and changes in behavior that may be associated with some type of problem. This information allows caregivers and health professionals to take action when these types of situations are detected. Although many machine learning classification techniques have been proposed, the effectiveness of the solution in a real-world context remains unclear in most cases due to the large number of sensors required, the type of sensors used which may pose privacy issues, and the assumption of considering only segmented sensor events for each activity before training the models. This paper presents an evaluation of different machine learning techniques using fixed time windows to extract spatio-temporal features and classify 10 human activities in a real smart home with unobtrusive sensors using the Aruba CASAS dataset. The three classification techniques that achieved better performance were Random Forest, XGBoost and Support Vector Machine, achieving an accuracy of 97% with our best model, outperforming other approaches from the literature that were using the same dataset under similar conditions. The proposed classification techniques were also evaluated under a more realistic scenario by reducing the amount of hardware required and using an additional class labeled "Other" to consider all raw sensor events, including those that do not belong to any specific activity, achieving an accuracy of 89%, outperforming other approaches from the literature using the same dataset under similar conditions.

The Traditional Afro-Caucan farm - TAF is part of the northern Cauca culture in Colombia, but it is threatened. Mi Fink is a mobile phone app that vindicates the tradition of the farm as a form of community work an afro-descendant expression about their identity in this territory. This app is the result of a research-creation project with children from the municipality of Villa Rica in the northern region of the Department of Cauca in Colombia. Through collaborative design, a team of professionals from different areas and academic researchers investigated the memories of care and use of their territory. Despite expected limitations related to digital technologies access, this experience shows how easy children can get involved, collaborate and carry out innovative activities with digital devices such as stop motion animation. The project gives clues to the knowledge and recognition of spaces in which participatory design allows a transition from a passive use of mobile phones to a creative perspective using meaningful activities to transform realities. In this way, we are able to construct various stories about their reality, in this case, from children who are often marginalized and excluded from dominant cultural trajectories.

Meditation is a form of mental training that has therapeutic potential and cognitive benefits that may enhance attention, mental well-being, and neuroplasticity. However, the learning process is not easy because meditators do not receive immediate feedback that lets them know if they are correctly doing the activity. EEG Neurofeedback training is one of the techniques to train brain self-regulation and it has the potential to increase the effectiveness of meditation. However, the benefits greatly differ between subjects with a high percentage of inefficacy. In this work, an EEG Neurofeedback Training System is proposed based on user-centered design methodology to provide real-time performance feedback to meditators to increase levels of attention and relaxation through a visual, sound and smell stimuli interface. Levels of attention and relaxation of nine participants were measured with a mobile Neurosky EEG headset biosensor during meditation practice to analyze the incidence of each type of stimuli during activity. Visual stimuli feedback was able to increase attention levels of 78% of the participants by 11.8% compared to a meditation session without any stimuli. The sound stimuli feedback was able to increase the relaxation levels of 44.4% of the participants by 16% compared to a session without any stimuli. These results might bring new insights for the design of a neurofeedback system interface for meditation. Further research on neurofeedback training interfaces for meditators is suggested to validate these results with more participants.

This article presents the development of an algorithm capable of detecting and classifying electric faults in photovoltaic array systems by measuring the voltage-power curve. The algorithm was build based on a characterization method in which multiple photovoltaic arrays were evaluated under different fault conditions, by measuring and analyzing the voltage-power curves at the output of each array. The algorithm was evaluated experimentally in a controlled environment inside a laboratory under 59 different fault conditions obtaining an effectiveness of 100%. Then, the algorithm was evaluated experimentally outdoors under 124 different fault conditions, temperature and solar radiations, and was able to detect and classify electric faults in different photovoltaic arrays with an effectiveness of 94.4%. The proposed algorithm can be implemented with standard power-inverters as a low-cost solution and users can receive information on up-to date performance of their photovoltaic array systems through a mobile App. The design of a mobile app for the algorithm is proposed as well.