The São Paulo State Technological Colleges (FATECs, Portuguese: Faculdades de Tecnologia do Estado de São Paulo) are public institutions of higher education maintained by the State Center of Technological Education (CEETEPS). FATECs are important Brazilian institutions of higher education, being pioneers in the graduation of technologists. They are located in several cities of the São Paulo state, with four campuses in the capital (Bom Retiro, Campos Elíseos, East Zone and South Zone), and several other units in the metropolitan region of São Paulo, countryside and seashore.The 46 FATECs offer high degree careers in virtually all areas of knowledge. In most of the units, are offered courses of higher education in technology, focused in the training of technologists. The units of São Caetano do Sul, Ourinhos, Carapicuíba and Americana, however, offer the option of bachelorship and licentiate degree in the career of System Analysis and Information Technology, starting the tradition of FATECs to train, too, bachelors and licentiates.More than 28 thousand students are currently enrolled in FATECs. For the formation of this quota is annually invested more than R$1 billion (US$420,000 mi).
The use of orbiting space mirrors to reflect sunlight onto the Earth's surface has been contemplated in numerous applications, including illumination from space, large terrestrial solar power farms, space-based solar power (collecting solar power directly from space), geoengineering, and terraforming schemes. Previous studies have demonstrated the feasibility of using space-based solar reflectors for mega-projects. In this study, an Earth-space mirror two-body analysis is performed, considering the Solar Radiation Pressure and.. 2 Earth oblateness perturbation, such that when sunlight strikes the perfectly reflecting space mirror surface, the ray reflects off the mirror in the direction of the Earth's center. In this study, two candidate orbits for solar reflectors were considered: a polar orbit perpendicular to the ecliptic plane of Earth and an in-plane orbit in the ecliptic plane of Earth. We assumed that the ecliptic and equatorial planes of the Earth coincide. Using Gauss' perturbing equations, a set of orbital parameters is provided to achieve a Sun-synchronous Solar Reflector Orbit that distributes the solar energy intercepted by the mirror azimuthally. The characteristic acceleration of the mirror was also selected appropriately from the sun-synchronous condition. Additionally, it was shown that in this scenario, a Low Sun-Synchronous Earth Orbit can be accomplished only for polar orbits, but not for in-plane motion. In this scenario, only Medium Sun-Synchronous Earth Orbits can be achieved.
PurposeThe purpose of this study is to develop and evaluate a data-driven framework for vibration-based condition monitoring aimed at supporting predictive maintenance of polymeric components in rotating mechanical systems. The work seeks to demonstrate how machine learning techniques can be used to automatically identify crack-related faults based on vibration signals, contributing to improved reliability, early fault detection, and maintenance decision-making in engineering applications.Design/methodology/approachAn experimental setup was developed to acquire multi-axial vibration signals from a rotating system containing polymeric components under controlled operating conditions. The collected signals were normalized and processed using principal component analysis for dimensionality reduction. A random forest classifier was then trained to distinguish between intact and crack-induced damaged conditions. Multiple datasets were analyzed to evaluate the robustness and repeatability of the proposed machine learning-based monitoring framework.FindingsThe results show that the proposed approach can reliably identify crack-related damage based on vibration data, achieving high classification accuracy across multiple experimental datasets. The framework demonstrated robustness to signal variability and noise, indicating its suitability for practical condition monitoring applications. The findings confirm that vibration measurements combined with machine learning can effectively support automated fault detection in polymeric components within rotating systems.Research limitations/implicationsThis study is limited to controlled laboratory experiments and focuses on binary classification of intact and damaged conditions. The proposed framework does not explicitly model crack propagation or fatigue mechanisms. Future research may extend the approach to different materials, operating conditions, damage severities and multi-class fault scenarios, as well as investigate scalability to industrial environments.Practical implicationsFrom a maintenance engineering perspective, the proposed framework provides a low-cost and automated solution for vibration-based condition monitoring. By enabling early identification of crack-related faults, the method supports predictive maintenance strategies, reduces unplanned downtime and enhances maintenance planning and asset reliability in rotating machinery applications.Social implicationsThe adoption of data-driven predictive maintenance strategies can contribute to safer and more reliable industrial operations by reducing the likelihood of unexpected failures. Improved maintenance efficiency also supports more sustainable use of resources, lower maintenance costs and reduced environmental impact associated with premature component replacement.Originality/valueThis study provides an original contribution by integrating vibration analysis and machine learning into a practical framework tailored for predictive maintenance of polymeric components. The value of the work lies in demonstrating how data-driven methods can enhance condition monitoring and maintenance decision-making without requiring complex physical modeling, offering a practical solution for maintenance engineering applications.
Este artigo tem como objetivo analisar a relação entre o uso excessivo de telas digitais e a saúde mental, discutindo como a hiperconectividade pode agravar os quadros de ansiedade, depressão, insônia e isolamento social. Este estudo parte de resultados de relatórios internacionais e artigos científicos recentes, os quais apresentam uma relação consistente entre o aumento do tempo de exposição digital e o crescimento dos transtornos mentais em escala global. Entre 2015 e 2024, o tempo médio diário de uso de tecnologias praticamente duplicou, enquanto o número de pessoas diagnosticadas com transtornos mentais passou de aproximadamente 700 milhões para 1,3 bilhão. Os resultados parciais desta pesquisa, a partir dos dados analisados, indicam que a relação a hiperconectividade funciona como um fator de risco significativo para doenças mentais, intensificando as vulnerabilidades emocionais já existentes nos indivíduos. A partir disso, este trabalho reforça a necessidade do uso consciente das tecnologias digitais que deve ser compreendido como uma prática coletiva e preventiva que busca equilibrar inovação, saúde emocional e bem-estar.
The expansion of investments with a passive management strategy has contributed to the growth of the investment robot market, transforming investors, laymen in finance, into rational economic actors, since the algorithms follow the prescriptions of the Modern Portfolio Theory (MPT). This article aims to analyze the business model of robots operating in the United States of America (US) and Brazil. In the US, clients' investment portfolios are built only with exchange traded funds (ETFs) of different financial asset classes. In Brazil, the robots, despite starting with a strategy similar to the North American one, started to build investment portfolios with an active management strategy, with non-indexed products. Investment robots operating in the Brazilian market, by abandoning the use of ETFs, lose the legitimacy guaranteed by MPT and expose customers to the risks of active management.
The development of effective and safe drugs is a complex and resource-intensive process that often relies on uncertain trial-and-error methods. Predicting pharmacokinetic properties, such as drug delivery, is decisive for accelerating drug discovery and enhancing therapeutic outcomes. This paper presents a Machine Learning (ML) and Deep Learning (DL) based approach utilizing open pharmacological databases to predict properties associated with drug distribution, with a focus on bioavailability and the octanol-water partition coefficient (LogP). The study encompasses data preprocessing, molecular representation via SMILES encoding, and model evaluation utilizing regression and classification metrics. Results show promising predictive performance, suggesting that ML and DL techniques can optimize early drug discovery stages and support decision-making.
