This work studies the disparities found in higher education institutions across Portugal -in terms of geographical location, being polytechnics institutes or universities, and operating in the private or public sector -in relation to their offering of 1 st , 2 nd and 3 rd cycle study programmes and scientific areas.Bearing in mind that, on the one hand, any education institution must adapt to the surrounding population, and that, on the other hand, the population also ends up adapting to the existing educational offer, there is always some synergy between the characteristics of higher education institutions and local population/social context. Therefore, it is of surmount importance to characterize the educational system and verify the presence of asymmetries, by evaluation of the way it relates and is related to the physical and social setting.In Portugal, every study programme (newly submitted or running) is subject to evaluation by the Agency for Assessment and Accreditation of Higher Education -A3ES. At the initial stage of the evaluation process, Higher Education Institutions (HEIs) submit a proposal to A3ES. The analysis of the characteristics associated with both the study programme and HEI submitting it, held by A3ES, allow an indirect characterization of the national HE landscape.Based on a descriptive analysis of the data associated with all the study programmes submitted to A3ES from 2009 to 2014 ( = 2961), it was possible to cross-tabulate several variables and to do a thorough discussion, highlighting the influence of different internal aspects of the Portuguese Higher Education System. This data-based reflection is a contribute for future works aiming to understand the underlying dynamics behind such asymmetries. Knowing the regional asymmetries may provide opportunities to find innovative solutions that foster education on disadvantaged regions.

This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold–Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.

Considerando um número complexo z=x+iy, com x,y∈R,o seu conjugado, escrito na forma algébrica, é o número complexo z¯=x−iy. Geometricamente: Nota • O conjugado de um número complexo cuja parte imaginária é nula (número real) é o próprio número, pois sendo z=x, temos z¯=x. • O conjugado de um número complexo cuja parte real é nula (imaginário puro), z=iy, é z¯=−iy. Se z é um número complexo não nulo e θ=arg(z) tem-se, na forma trigonométrica, z=|z|(cosθ+isinθ) e z¯=|z|(cosθ−isinθ). Como |z|=|z¯|, sin(−θ)=−sinθ (a função seno é ímpar) e cos(−θ)=cosθ (a função cosseno é par), tem-se: z¯=|z|(cosθ−isinθ) = z¯=|z¯|(cos(−θ)+isin(−θ)), pelo que (−θ) é um argumento de z¯.

INTRODUCTION: The impact of the COVID-19 pandemic on orthopedic trauma surgery is not well characterized in the literature. We provided an evaluation of such impact in a tertiary hospital in Portugal.MATERIAL AND METHODS: Retrospective analysis of the patients admitted for surgical treatment due to acute orthopedic trauma from March to December of 2019 and March to December of 2020.RESULTS: A total of 794 patients in 2019 and 728 in 2020 were included. Although the mean time to surgery was shorter the hospital stay was longer in 2020. Infected patients had a longer hospital stay and longer time until surgery compared to non-infected. There was no significant difference between the mortality and need for treatment in the intensive care unit (ICU) in both years. There was a significant increase in the number of polytrauma patients treated in our institution in 2020 compared to 2019.CONCLUSION: Orthopedic trauma surgery was globally influenced by the pandemic. Safe and effective measures should be adopted in the treatment of trauma patients, to reduce the clinical and economic impact.

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