Abstract
Ventricular tachyarrhythmia (VTA), mainly ventricular tachycardia (VT) and ventricular fibrillation (VF) are the major causes of sudden cardiac death in the world. This work uses deep learning, more precisely, LSTM and biLSTM networks to predict VTA events. The Spontaneous Ventricular Tachyarrhythmia Database from PhysioNET was chosen, which contains 78 patients, 135 VTA signals, and 135 control rhythms. After the pre-processing of these signals and feature extraction, the classifiers were able to predict whether a patient was going to suffer a VTA event or not. A better result using a biLSTM was obtained, with a 5-fold-cross-validation, reaching an accuracy of 96.30%, 94.07% of precision, 98.45% of sensibility, and 96.17% of F1-Score. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
STEM areas (Science, Technology, Engineering and Math) are continuously growing but the number of technical workers do not accompany that growth. As the 21st century brings new challenges, students should be prepared for an increasingly complex life and work environments that will privilege proficiency in Learning and Innovation Skills that include Creativity and Innovation, Critical Thinking and Problem Solving, Communication and Collaboration. Also, the need to continuously explore new pedagogical practices in teaching and learning creates an opportunity to build new contents by balancing a stable and tested curriculum with new tools that stimulate creativity, allowing students to better understand the world they live in. This article describes the development of an educational robotics kit, aimed at children and teens from 8 to 18 years old, meant to work as an interdisciplinary teaching tool that can be applied directly in a curriculum.

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.

Abstract
The need for sustainable power production has led to the development of more innovative approaches to production and storage. In light of this hydrogen production through wind power has emerged as sufficient in ensuring that the objectives of the Paris Agreement are made. This paper discusses the state-of-art models and controls used in ensuring that greater efficiency is achieved in the processes of energy to hydrogen transformation. The paper concludes with a comparison of the models and determination of one which suffices in ensuring that hydrogen/energy transformation is more efficient.

Abstract
Nowadays the world faces the challenge to rapidly diminish the use of fossil fuels in order to reduce pollutants and the emission of greenhouse gases and to mitigate the global warming. Renewable energies, such as solar radiation, among others, are playing a relevant role in this context. Namely, the use of thermal energy storage systems in buildings and industry is increasing enabling to reduce operational costs and carbon dioxide emissions. Heat storage systems based in solar thermal panels for heating water in buildings are industrially mature but some improvements can be made to improve their efficiencies. In this work are presented the methods and the results achieved to model the dynamic behavior of the hot water temperature as function of the weather, operating conditions and technical parameters of the thermal solar system. This type of dynamic models will enable to optimize the efficiency of this type of systems regarding the use of auxiliary energy sources to heat the water whenever the temperature in the storage tank falls below a defined threshold level. As future work it is intended to use adaptive control algorithms to reduce the use of backup power sources (electricity, oil, gas) by using the information of the system status as well predictions for hot water consumption profiles and solar radiation.

Abstract
Wastewater treatment systems are major consumers of electricity being responsible for 3 to 5% of global energy consumption, and 56% of greenhouse gas emissions into the atmosphere in the water treatment sector. Climate change currently imposes the definition of a new pattern of human behavior in the defense and sharing of a common space that is the planet, so the optimization of water treatment models plays a crucial role in the definition of sustainability strategies as part of the challenges for decarbonization by 2050. The physical-chemical characteristics of the influent, the treatment techniques and associated technologies and the unpredictability of external phenomena of inefficiency transform wastewater treatment plants (WWTPs) into complex systems, sometimes difficult to understand. The study of energy efficiency plays an important role in the emergence of a standard behavior model, which allows the correction of unbalanced situations in the expected energy consumption. Given the importance of the topic, the present review aims to study energy auditing techniques and benchmarking tools developed for the wastewater treatment sector to reduce the current electricity consumption, which could represent up to 90% of total energy consumption. The result of the research was organized according to the criteria defined for the characterization of auditing techniques and benchmarking tools. A review was conducted from 51 scientific papers from different reference research platforms published in the last 20 years according to the keywords. This literature review has shown that there are, in the classification of consumption reduction, energy auditing and benchmarking tools; energy management techniques and methods directed to the energy efficiency of the treatment stages and specific equipment; and, finally, decision support tools. According to the methodology followed, it was possible to conclude that although the concern is not recent, there are techniques and tools for assessing energy performance more suitable for the wastewater sector. However, the authors recognize that associated with the complexity of wastewater treatment systems, inefficiency phenomena still strongly impact energy efficiency assessment, so the contributions for their identification and quantification may represent an added value for data analysis, systematization, and optimization methodologies.