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Academic entrepreneurship is becoming increasingly important to the field of research as well as to policy makers due to its ability to contribute to the economic, technological, and social development of regions and countries. This research aims to evaluate the determinants that influence the interest of Portuguese higher education students (HEI's) to become entrepreneurs. The methodology used is quantitative and uses structural model equations. The results obtained demonstrate that the student's perception of business skills, business growth skills, strategy, and successful business are key factors that students take into account in their entrepreneurial orientation. The research contributes to this theory by adding new knowledge to the literature on the perception of the HEI's students to become entrepreneurs, specifically the students of Portuguese universities. In practical terms, the contributions offered within this research are based on suggestions for the third mission of universities, explicitly knowledge transfer to the community, business groups, and policy makers, as well as the creation of the essentials within university boundaries to promote entrepreneurship amongst its students. The research is original and innovative, as no research on this field with all the aggregated elements under study has been previously performed in Portugal. Furthermore, the obtained results can translate into ideas that potentially create jobs.

The present research aims to compare the entrepreneurial intention of university students before and during the Covid-19 pandemic. For this purpose, some dimensions were analyzed, such as the availability of this target audience to undertake an activity at their own risk, the preference for a future while employed by others, their perception of the values that society places on entrepreneurship, and the entrepreneurial abilities/skillsets. A comparative study of a quantitative nature was used, associating two samples composed of students of higher education in Portugal, the data were obtained before and during the COVID-19 pandemic. The analysis of the results permits us to conclude: (1) in the circumstances of macroeconomic changes resulting from the COVID-19 pandemic, entrepreneurial activity does not decrease; (2) respondents are less interested in being employed by others and more attracted to being entrepreneurs; (3) there is a growing concordance with the values that society places on entrepreneurship; and (4) there is now a greater aptitude for entrepreneurial activity. The present research is original, as it compares data obtained in different contexts of economic and social stability. It contributes to theory and practice, in the sense that it points to conclusions in the opposite direction of other studies carried out in situations of disasters of another nature, and can serve as a reference for the development of strategies to promote entrepreneurship, within higher education institutions and official entities to publicize and promote new public policies.

Background: Handgrip strength (HGS) is an indicator of muscular strength, used in the diagnosis of sarcopenia, undernutrition, and physical frailty as well as recovery. Typically, the maximum HGS value is used; however, recent evidence suggests the exploration of new indicators provided based on the force–time curve to achieve a more comprehensive assessment of muscle function. Therefore, the objective was to identify indicators of the HGS profile beyond maximum HGS, based on force–time curves, and to systematize knowledge about their applications to various types of samples, health issues, and physical performance. Methods: A systematic review was performed including studies whose participants' HGS was assessed with a digital or adapted dynamometer. The outcome measures were HGS profile indicators calculated from the force–time curve. Results: a total of 15 studies were included, and the following indicators were identified: grip fatigue, fatigability index, fatigue rate, fatigue resistance, time to 80% maximal voluntary contraction, plateau coefficient of variability, time to maximum value, T-90%, release rate, power factor, grip work, average integrated area, endurance, cycle duration, time between cycles, maximum and minimum force–velocity, rate of grip force, final force, inflection point, integrated area, submaximal control, and response time. Conclusions: Various indicators based on the force–time curve can be assessed through digital or adapted dynamometers. Future research should analyze these indicators to understand their implications for muscle function assessment, to standardize evaluation procedures, to identify clinically relevant measures, and to clarify their implications in clinical practice. [ABSTRACT FROM AUTHOR]