A study of federal incentives in the period of the Covid-19 pandemic

This work had the general objective of analyzing how the Brazilian Federal Government, through tax incentive laws, has enhanced the economic activity of companies, highlighting the current socioeconomic scenario caused by the COVID-19 pandemic. The research was classified as qualitative, documental, bibliographic, basic and descriptive. The federal rules on tax incentives that exist in Brazil until March 2020 were analyzed, totaling eleven rules. Then, it was verified if there were any changes in the period from March 2020 to December 2021, and if there were publications of new federal rules on tax incentives. The conclusions are that of the federal norms already instituted, in March 2020, six have undergone changes: Rouanet Law;Informatics Law;Audiovisual Law;Sports Incentive Law;Payroll Exemption Law;and Drawback Law. Regarding the federal regulations instituted in the period, there was no institution of tax incentives, which the companies needed to be able to maintain their cash flow, enhance their socio-economic activities and mitigate the impacts caused by the COVID-19 pandemic, which highlighted a certain slowness in the approval procedures of the Law projects. Possibly, the benefits already granted would need to be extended or adjusted.

COVID-19: how did the pandemic influence the quality of medical students' sleep?

Introduction: Sleep is considered a cyclical physiological state due to the alternation between sleep and wakefulness that occur in most living beings. It is characterized by five fundamental stages and the total or partial absence of quality nighttime sleep can cause sleep and wake disorders, which are related to damage to the health of the population. Relating the theme to medical students, it is known that this population experiences, since college, the reality of a few hours to sleep and rest. Also, by associating this issue in the current context of COVID-19, it is possible that the quality of sleep in this population has changed. Objective: To identify the prevalence of the occurrence of Sleep Disorders and associated factors among medical students from a higher education institution in the forest region of Minas Gerais. Methods: The research, with a cross-sectional design, was conducted online through a semi-structured questionnaire with social, personal, demographic variables and also using the Epworth Sleepiness Scale and the Pittsburg Sleep Quality Index. Results: Of the total of 340 academics invited to participate in the study, 63.8% participated in the survey. 41% of the participants stated that they had a sleep duration per night of less than six hours and in relation to the usual sleep efficiency, 50.7% certified that they have an efficacy of less than 84%, evidencing a high frequency of sleep disorders (94, 9%). Also regarding the analysis of the Pittsburg Scale for Assessment of Sleep Quality, comparing cycles (basic, clinical and internship), 14% of students in the basic cycle need to use medication to sleep 3 or more times a week, as opposed to 7% denounced by the analysis of both the clinical cycle and the internship. About 40% of basic cycle students, 22% of clinical cycle students and 4% of boarding students reported having daytime dysfunction 5 to 6 days a week, such as difficulty staying awake and focused on their daily activities, in as a result of disturbed nights of sleep. As for the Epworth sleepiness scale, only 31.7% of the sample had normal sleep and in 44% of students an abnormal (pathological) sleepiness was identified. Conclusion: It is concluded that the perception of poor sleep quality was high for all years of the medical graduation course. Regarding the comparison of course phases, students in the early years (basic cycle group) reported worse sleep quality and greater daytime dysfunction.

1-Year of COVID-19 pandemic: Changes in the epidemiology of maxillofacial trauma

Objectives: Due to COVID-19 pandemic, social distancing policies were enacted worldwide, including by the Portuguese official authorities. However, the impact of these measures on maxillofacial trauma and fracture surgical repair remains poorly understood. The aim of this study was to evaluate and compare the incidence and aetiology of facial fractures submitted to surgical repair during 1-year of COVID-19 pandemic versus the previous 4 years, in a level III Trauma Centre located in Lisbon, Portugal. Materials and methods: All emergency episodes registered in our hospital between March 2016 and February 2021 that resulted in patient admittance for surgical treatment of facial fracture were included. Comparative analysis was performed for variables such as fracture type and aetiology. Results: Analysis showed that surgeries performed during the 1st year of COVID-19 were reduced by 37.5 %. Considering only the 75-day lockdown period at the beginning of the pandemic, reduction was even more pronounced and reached-66.7 %. Significant differences in the aetiology were also found, with physical assault and sport accidents relative frequency decreasing. Moreover, despite being systematically the second most common cause of fracture, during lockdown, fall ranked first, over physical assault. The relative frequency of nasal fractures, the most common facial fracture treated in our hospital, decreased during both the 1st year of COVID-19 and the lockdown period, while mandible fractures ranked first during lockdown. Conclusions: Our study shows that COVID-19 pandemic and enacted policies have significantly changed the epidemiology of maxillofacial trauma. © 2022 Elsevier Espana S.L.. All rights reserved.

A cost-effective trilateration-based radio localization algorithm using machine learning and sequential least-square programming optimization

Wireless communication systems play an essential role in everyday life situations and enable a wide range of location-based services to their users. The imminent adoption of 5G networks worldwide and the future establishment of next-generation wireless networks will allow various applications, such as autonomous vehicles, connected robotics, and most recently, crowd monitoring for fighting infectious diseases, such as COVID-19. In this context, radio localization techniques have become an essential tool to provide solid performance for mobile positioning systems, through increased accuracy or less computational time. With this in mind, we propose a trilateration-based approach using machine learning (ML) and sequential least-square programming (SLSQP) optimization to estimate the outdoor position of mobile terminals in cellular networks. The ML technique employed is the k-nearest neighbors (k-NN). The optimization methods analyzed are Nelder–Mead (NM), genetic algorithms (GA), and SLSQP. Different environments (noise-free and noisy) and network scenarios (different numbers of base stations) are considered to evaluate the approaches. Numerical results indicate that the k-NN/SLSQP technique has similar accuracy compared to the k-NN/GA with eight generations. Both perform better than k-NN/NM in all scenarios and environments. When comparing computational times, our proposal is considerably more time-efficient. Aside from that, SLSQP computational time is less affected by network scenarios with more base stations in comparison with GA. That feature is significant considering the ultra-dense base station deployment forecasted for the next-generation cellular networks. © 2021 Elsevier B.V.