O contributo do estudo material para a desconstrução do códice 99 do fundo da Manizola

UIDB/04666/2020 UIDP/04666/2020publishersversionpublishe

A 20-year analysis in Portugal

Publisher Copyright: © 2024 Sociedade Portuguesa de CardiologiaIntroduction and objectives: Sudden cardiac death (SCD) in athletes is a tragic event, with some evidence remaining controversial. The aim of this study was to evaluate cases of SCD in athletes in Portugal within the last 20 years. Methods: An advanced Google search using a combination of several keywords and systematic searches on websites of national newspapers/television stations was conducted. Additionally, 54 Portuguese sports federations and the Portuguese Institute of Sports and Youth were contacted by email and/or phone. All sports-related SCD cases in competitive athletes, occurring between 2003 and 2023 in Portugal, were included. The total number of athletes at risk used for the calculation of SCD incidence, was collected from official national records. Results: A total of 42 SCD cases in athletes were identified, with a median age of 27 [18;42] years, and the great majority were male (n=39; 93%). Most events occurred in outdoor sports (n=28; 67%), especially in football (n=13; 31%), athletics (n=4; 10%) and trail running (n=4; 10%), and during competition or training sessions (n=27; 64%). The higher number of cases were reported in 2021 and 2022, while in several years no occurrences were found. The yearly average SCD incidence was 0.39 cases per 100 000 athletes/year. Conclusions: The incidence of SCD in athletes in Portugal is very low, mainly occurring in male, outdoor sports and during competitions or training sessions. Due to the limitations of passive data collection, prospective registries are needed, with standardization of the most relevant data, especially regarding their etiology and circumstances.publishersversionpublishe

A lab-in-the-field experiment in Cape Verde

Funding Information: The authors gratefully acknowledge comments from Toman Barsbai, Cara Ebert, Eliana La Ferrara, David Lagakos, Ben Marx, David McKenzie, Caroline Theoaharides and other participants at several seminars and conferences. We are grateful to our project coordinator Gon\u00E7alo Gameiro for superb work on all aspects of project implementation and data management, as well as to Isabel Gouveia, Mariana Parente and the local team of enumerators for their dedication and careful fieldwork. Funding was provided by the Aga Khan Foundation, grant FCT-AGAKHAN 15417394041-2019. Publisher Copyright: © 2024 The AuthorsStudy abroad migration is the fastest growing international migration flow. However, the college completion rates of students from low-income countries are often modest in OECD countries, raising the hypothesis that these migrants are poorly informed about the costs and benefits of their decision. Our work tests this hypothesis by running a lab-in-the-field experiment where graduating high school students in Cape Verde are faced with incentivized decisions to apply for college studies abroad. Our results show that potential migrants react strongly to information about the availability of financial support and about college completion rates. Since subjects’ prior beliefs on availability of financial support are overestimated, it is likely that study migrants need to shift their time from study to work after uninformed migration, which likely harms their scholar performance. Policies that inform potential migrants of actual study funding possibilities should decrease study migration flows but may improve successful graduation.publishersversionpublishe

Evaluation of the cyto- and genotoxicity of two types of cellulose nanomaterials using human intestinal cells and in vitro digestion simulation

Publisher Copyright: © The Author(s) 2024.Emerging cellulose nanomaterials (CNMs) may have commercial impacts in multiple sectors, being their application particularly explored in the food sector. Thus, their potential adverse effects in the gastrointestinal tract should be evaluated before marketing. This work aimed to assess the safety of two CNMs (CNF–TEMPO and CMF–ENZ) through the investigation of their cytotoxicity, genotoxicity (comet and micronucleus assays), and capacity to induce reactive oxygen species in human intestinal cells, and their mutagenic effect using the Hprt gene mutation assay. Each toxicity endpoint was analysed after cells exposure to a concentration-range of each CNM or to its digested product, obtained by the application of a standardized static in vitro digestion method. The results showed an absence of cytotoxic effects in intestinal cells, up to the highest concentration tested (200 µg/mL or 25 µg/mL, for non-digested and digested CNMs, respectively). Of note, the cytotoxicity of the digestion control limited the top concentration of digested samples (25 µg/mL) for subsequent assays. Application of a battery of in vitro assays showed that CNF–TEMPO and CMF–ENZ do not induce gene mutations or aneugenic/clastogenic effects. However, due to the observed DNA damage induction, a genotoxic potential cannot be excluded, even though in vitro digestion seems to attenuate the effect. The lowest digested CNF–TEMPO concentration induced chromosomal damage in Caco-2 cells, leading to an equivocal outcome. Ongoing research on epigenotoxic effects of these CNMs samples may strengthen the lines of evidence on their safety when ingested, paving the way for their innovative application in the food industry.publishersversionpublishe

Gamified Chatbots: How They Impact Customer Engagement, Brand Image, and Purchase Intention

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing IntelligenceGrowing integration of artificial intelligence in marketing enables unique opportunities to enhance user experience and drive business outcomes in gamified chatbots. This Master thesis explores the impact of gamified chatbots on brand image, customer engagement and purchase intention, focusing on points and rewards as gamification elements. The research employed a mixed-method approach, gathering data from 228 participants interacting with gamified and non-gamified chatbots on Telegram. Results demonstrate that gamification significantly enhances cognitive, emotional, and behavioral customer engagement, with behavioral engagement showing the strongest influence on purchase intention. Additionally, both points and rewards positively contribute to brand image, a key mediator between engagement and purchase intention. This study contributes to the research in area of gamification for marketing by addressing its application in social media contexts, extending insights beyond traditional e-commerce platforms. Practical implications include designing gamified chatbots prioritizing interactive experiences to drive customer engagement and conversions. This research underscores the transformative potential of gamified chatbots as strategic tools for enhancing customer-brand relationships and achieving marketing objectives

Application of Blockchain in e-commerce Markets: The impact of Blockchain on e-commerce and consumer trust

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsThe main aim of this work is to explore whether blockchain technology has the potential to increase consumer confidence, and if so, how. Some key constructs such as privacy, transparency, and costsaving were studied to try to understand whether they affect consumer trust. A literature review was carried out followed by an empirical analysis using the SmartPLS 4 programme. In order to carry out the analysis, data was collected through a questionnaire aimed at all online shoppers, obtaining a total of 166 responses. The results revealed that transparency plays a significant role in increasing consumer confidence and the perception of security, while cost-saving has a significant impact on consumers' perceived value. Overall, it was possible to conclude that the structural model highlights that these mechanisms contribute to increasing consumer confidence, thus increasing the value proposition of blockchain-enabled platforms on e-commerce. The study provides actionable insights for e-commerce platforms, emphasizing the importance of transparency, cost-saving, and privacy strategies to cultivate trust and optimize user experiences

Multitask Symbolic Regression in Genetic Programming

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceMultitask Learning (MTL) is the process in which multiple problems are solved simultaneously, usually with shared representations or parameters. This approach is known to improve generalisation, performance and training time. MTL in Genetic Programming (GP) has received limited attention, due to the assumption that GP tailors solutions to a specific task and thus, that it is challenging to share these amongst different problems. This work introduces a novel method that evolves a tree that is shared between two tasks (also called problems), in which its terminals are pairs of the original features of the datasets. The fitness function of the common evolution is the average performance on each task, as an attempt to balance the improvement of both. The impact of different metrics for the common fitness function was studied, in specific the difference between Root Mean Squared Error (RMSE) and Relative Squared Error (RSE). The results revealed that the RSE is a more adequate measure than the other, as it achieves a better balance between the minimisation of error across both tasks. The effectiveness of the Common Tree (CT) in reducing overfitting and program size was verified for some cases, but it is not evident under what conditions this effect is predictable. In addition, across all datasets, the common evolution produces trees that for the same level of fitness, are smaller in size, indicating that it is preventing bloat. Other conclusions relate to the nature of the relationship between tasks. It was verified that the domain of problems is not sufficient to justify if a joint evolution will be successful. Additionally, some problems were verified to be better pairs than others and that their effectiveness is not symmetric. The CT is not expected to capture the complexity of each problem, leading to poorer results when compared with Standard Genetic Programming (StdGP). This was verified for all cases with the exception of the Istanbul dataset. To tackle this issue, the CT is transformed into a new feature that is later inserted into the dataset of each problem. A final evolutionary process is conducted to leverage the information from both evolutions. The final results yield no difference when compared with StdGP

Spatial Suitability Analysis of Mars for Robotic Colonization and Future Human Settlement

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe exploration and potential colonization of Mars have long fascinated scientists and the public, driven by the goal of extending human presence beyond Earth. However, Mars’ harsh environment—thin atmosphere, extreme temperatures, high radiation, and lack of breathable oxygen—poses significant challenges. To establish a sustainable human presence, robotic colonies must serve as precursors, conducting scientific research, resource extraction, infrastructure development, and environmental monitoring to prepare viable locations for future missions. This study integrates geospatial technologies and Multi-Criteria Decision Analysis (MCDA) to identify optimal regions for robotic colonies and future human settlement, using the Analytic Hierarchy Process (AHP) combined with engineering constraints from the Perseverance (Mars 2020) mission. Engineering constraints—slope, elevation, latitude, surface reflectivity, and load-bearing properties—delineated non-viable areas, while factors including surface temperature, water-equivalent hydrogen, elevation, and terrain stability, determined suitable locations for human habitability. The integrated suitability map highlights certain regions at the four-region intersection between Oxia Palus–Margaritifer–Arabia–Sinus Sabaeus (area ≈ 600’000 km²), Memnonia (area 30’000 km2 ) and Aeolis (area ≈160’000 km²) as the most promising sites, with the first one emerging as the preferred option due to its larger contiguous terrain, reducing landing uncertainties and enhancing mission flexibility. This study provides a systematic and scalable framework for selecting robotic colony sites while ensuring safe and sustainable operations, ultimately supporting long-term human exploration. Beyond planetary exploration, these findings contribute to humanity’s pursuit of interplanetary expansion—securing survival, advancing scientific frontiers, and positioning Mars as a gateway for deep-space exploration

evidence from Portugal

© The Author(s) 2024.We analyse the PISA-reported convergence in the performance of private and public schools in Portugal. When PISA sampling weights are used, the number of students enrolled in those types of schools and specific grades/tracks of study differs significantly from official population figures. To account for those differences, we apply a post-stratification adjustment; however, sample sizes are small, resulting in estimates with low precision for several subgroups. We propose recommendations for improving the handling of these issues in future PISA cycles. In an additional analysis, we also account for changes in the composition of the student population. When all factors are considered, the convergence in scores is far less impressive than reported. For instance, in Science, after adjusting the sampling weights and removing population composition effects, the reported convergence of 46 points between private and public schools from 2015 to 2018 amounts to only 9 points. The decomposition and sample adjustment methods used in this paper can be easily adapted to other contexts.publishersversionpublishe

Design of a pedagogical tool in python to price and hedge European options

Pricing and hedging options are concepts that are difficult to grasp for many students. The fact that ”the price” means the cost of setting-up a self-financing trading strategy is difficult to visualize for students and manual computations are both unpractical and very limiting. In this thesis, we develop and implement Python web applications to price and hedge options. Based on user-chosen inputs, the applications output the delta-hedge strategies in plots and allow data exportation. In their development, robust code architecture and strong pedagogical explanations were emphasized. The web applications can be found at: hedging-derivatives.herokuapp.com This work main deliverable is the web applications. The present document’s purpose is to support them: its contents are meant to be used when users are in need of supplementary mathematical details or explanations, or simply if they are interested in the apps’ development and implementation