The use of vibration monitors is a well-established practice in industrial maintenance, usually vibration sensors are positioned at specific points on the monitored machinery and data are continuously collected to feed a machine operating health control system. Nevertheless, the technology for obtaining the signal, its treatment and analysis is generally expensive, and the financial return is not evident, which justifies the development of low-cost alternatives technologies. In this work was performed an analysis of the responses of two Micro-Electro-Mechanical accelerometers, models ADXL345 and MPU6050, exposed to a low intensity random signal and standard operating frequency. The objective of the analysis was to verify the capacity of these devices to be used as mechanical vibration sensors for rotating machines. For this purpose, offset shift analyzes of the sensors due to the Earth's gravitational field were performed, as well as vibrational spectrum and rectification errors analysis under multiple conditions. The data pointed to a greater uniformity of the MPU6050 response, while several behavioral anomalies were seen in the ADXL345, when these sensors are exposed to the same mechanical signal. The qualitative and quantitative behavior of MPU6050 rectification error was consistent with reported in the literature. It was noted that the methodology used can profile the behavior of sensors, however, it is not sufficient to safely justify the inaccuracies, requiring that the tests be performed on a statistically representative number of sensors from different manufacturers and batches.

This study provides a scientometric analysis of research focused on energy theft detection and load profiling in smart grid networks. Data were retrieved from the Web of Science and Scopus databases, covering publications from 2003 to April 2024. Using the Bibliometrix package and VOSviewer software, we analyzed trends in publications, author productivity, collaborative networks, and key journals. The study highlights significant growth in the research field, with China and the USA emerging as the most productive countries, with strong international collaboration. Nadeem Javaid is identified as a leading author, contributing to publications with a strong focus on the application of deep learning techniques for energy consumption analysis in smart grids. Key journals such as IEEE Access, Applied Energy, and Energies were found to be central to this research area. Our findings highlighted the importance of this area, as smart grid technologies continue to evolve, requiring advanced methodologies to detect non-technical losses and analyze consumption patterns. This research supports the United Nations’ (UN) Sustainable Development Goals (SDGs), particularly goals related to sustainable energy and infrastructure development, by emphasizing the importance of technological innovation and collaboration in tackling energy theft.

A inteligência artificial tem transformado a educação, o mundo do trabalho e a sociedade. A introdução desta tecnologia na educação não está associada apenas a uma mera mudança tecnológica, mas a mudanças nas metodologias de ensino-aprendizagem e nas habilidades e competências a serem desenvolvidas pelos estudantes. Neste contexto, este trabalho tem como objetivo explorar as perspectivas para o aprendizado com a introdução da IA na educação profissional e tecnológica. Considerando como objetos do estudo, as metodologias de ensino-aprendizagem e as exigências do mundo do trabalho foi realizado um levantamento de 4 cursos ofertados na Rede Federal de Educação Profissional, Científica e Tecnológica do Brasil de forma a verificar a aderência destes a estes objetos.

Os estudantes da Rede Federal de Educação Profissional, Científica e Tecnológica, mesmo geralmente ingressando após processos seletivos concorridos, também enfrentam dificuldades para manterem um bom desempenho. Estudos no campo, com destaque aos de Felder e Silverman, apontam que parte significativa disso se relaciona diretamente com os estilos de aprendizagem, que são padrões comportamentais individuais que podem implicar em maior ou menor facilidade para aprender determinado assunto. O presente estudo apresenta os resultados de uma investigação que buscou identificar influências de estilos de aprendizagem de professor e estudantes sobre o desempenho escolar, levando-se em consideração também os métodos didáticos utilizados. O contexto analisado foi o do Curso Técnico Integrado em Edificações do IFMG Campus Santa Luzia, especificamente nas disciplinas de Estabilidade das Construções e Matemática. A metodologia consistiu fundamentalmente na aplicação de questionário adaptado ao contexto brasileiro para caracterização dos estilos a estudantes e professores. Dentre os resultados, destaca-se que os melhores desempenhos estiveram mais ligados a estilos mais flexíveis dos estudantes do que a uma convergência em relação ao estilo do professor.

This study provides a scientometric analysis of research focused on energy theft detection and load profiling in smart grid networks. Data were retrieved from the Web of Science and Scopus databases, covering publications from 2003 to April 2024. Using the Bibliometrix package and VOSviewer software, we analyzed trends in publications, author productivity, collaborative networks, and key journals. The study highlights significant growth in the research field, with China and the USA emerging as the most productive countries, with strong international collaboration. Nadeem Javaid is identified as a leading author, contributing to publications with a strong focus on the application of deep learning techniques for energy consumption analysis in smart grids. Key journals such as IEEE Access, Applied Energy, and Energies were found to be central to this research area. Our findings highlighted the importance of this area, as smart grid technologies continue to evolve, requiring advanced methodologies to detect non-technical losses and analyze consumption patterns. This research supports the United Nations’ (UN) Sustainable Development Goals (SDGs), particularly goals related to sustainable energy and infrastructure development, by emphasizing the importance of technological innovation and collaboration in tackling energy theft.

Validação de um modelo computacional de acelerador linear VARIAN CLINAC 2100 utilizando o código EGSnrc para utilização em dosimetria de pequenos campos.