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Avaliação do Risco Climático para a Rede Nacional de Distribuição de Eletricidade
No full textOs impactes das alterações climáticas serão transversais aos vários setores da sociedade. No setor da distribuição de energia elétrica, condições climáticas adversas são já a principal causa de interrupções no fornecimento, comprometendo a fiabilidade do serviço e aumentando consideravelmente os custo de manutenção da rede. Neste contexto, a caracterização do risco climático para a rede de distribuição assume uma importância crítica no suporte à tomada de decisão das estratégias de adaptação da rede, tendo em conta os elevados custos associados à modernização e manutenção das infraestruturas. Para os operadores das redes elétricas, a importância da adaptação é cada vez mais evidente, no entanto, são ainda limitados os conhecimentos sobre os potenciais impactes climáticos e os riscos associados para os ativos físicos. A presente dissertação tem por objetivo identificar a variação espacial e temporal do risco climático para a rede nacional de distribuição, tendo em consideração os principais perigos climáticos e os ativos da rede, constituindo assim um primeiro passo importante na definição de estratégias de adaptação. Face a estes objetivos, recorreu-se a uma metodologia de avaliação semi-quantitativa do risco, em linha com a abordagem conceptual de risco definida pelo Painel Intergovernamental para as Alterações Climáticas em que o risco é caracterizado por três componentes- perigo climático, exposição e vulnerabilidade. A metodologia é aplicada à Rede Nacional de Distribuição de Eletricidade (RND) e cada componente do risco é avaliada através de indicadores expressos espacialmente, com recurso a software SIG, permitindo a elaboração de mapas de risco. O risco foi avaliado individualmente para os diferentes tipos de ativos físicos da RND, em alta e média tensão, tendo sido considerados os principais perigos climáticos suscetíveis de afetar a rede- ventos fortes, incêndios rurais, cheias, galgamentos costeiros e nevões. Para avaliar o risco futuro foram considerados três cenários de emissões (RCP2.6, RCP4.5 e RCP8.5) em quatro períodos distintos. Os resultados obtidos evidenciaram a existência de variações espaciais significativas no risco entre os diferentes perigos climáticos e tipos de ativos. Foi possível, no entanto, aferir que as Regiões Norte e Centro do país concentram as classes de risco mais elevadas, nomeadamente no que diz respeito ao risco de ventos fortes, incêndios rurais e nevões. Em períodos futuros, os incêndios rurais surgem como o risco mais crítico para a rede de distribuição nos cenários RCP4.5 e RCP8.5. A avaliação do risco de cheias e galgamentos costeiros permitiu ainda identificar várias subestações e postos de transformação expostos. Entre tipos de ativos, destaca-se como crítica a rede de alta tensão, em particular a rede de 130kV situada entre as sub-regiões do Alto-Minho e Cávado, onde o risco elevado v resulta da sua exposição aos perigos climáticos e de uma vulnerabilidade superior comparativamente aos restantes níveis de tensão. Com base nos resultados obtidos, que permitiram identificar os principais riscos e pontos críticos de atuação, foram propostas medidas de adaptação para a RND que passam tanto pelo aumento da robustez física dos ativos como pelo aumento da capacidade operacional da rede, garantindo assim a sua resiliência.The impacts of climate change will be transversal to the various sectors of society. In the electricity distribution sector, adverse weather conditions are already the main cause of supply interruptions, compromising service reliability and considerably increasing network maintenance costs. In this context, the assessment of climate risk for the distribution network is of crucial importance in supporting decision-making on network adaptation strategies, given the high costs associated with modernising and maintaining infrastructure. For electricity network operators, the importance of adaptation is becoming increasingly evident, but knowledge about the potential climate impacts and associated risks for physical assets is still limited. The aim of this dissertation is to identify the spatial and temporal variation in climate risk for the national distribution network, considering the main climate hazards and network assets, thus constituting an important first step in defining adaptation strategies. To meet these objectives, a semi-quantitative risk assessment methodology was used, in line with the conceptual risk framework defined by the Intergovernmental Panel on Climate Change, where risk is defined by three components - climate hazard, vulnerability and exposure. The methodology is applied to the National Electricity Distribution Grid (RND) and each risk component is assessed using indicators expressed spatially using GIS software, allowing risk maps to be drawn up. The risk was assessed individually for the different types of physical assets in the RND, in high and medium voltage, taking into account the main climate hazards that can affect the grid - strong winds, wildfires, floods, storm surges and snowstorms. To assess future risk, three emissions scenarios (RCP2.6, RCP4.5 and RCP8.5) and four different time periods were considered. The results show that there are significant spatial variations in risk between the different climate hazards and asset types. However, it was possible to discern that the North and Centre Regions of the country concentrate the highest risk classes, particularly with regard to the risk of strong winds, wildfires and snowstorms. In future periods, wildfires appear as the most critical risk for the distribution grid in the RCP4.5 and RCP8.5 scenarios. The assessment of the risk of floods and storm surges also made it possible to identify several exposed substations and transformer stations. Among asset types, the high voltage grid stands out as critical, particularly the 130kV grid located between the Alto-Minho and Cávado sub-regions, where the high risk results from its exposure to the climate hazards and higher vulnerability compared to the other voltage levels. Based on the results obtained, which made it possible to identify the main risks and critical points of intervention, adaptation measures were proposed for the RND that include both increasing the physical robustness of the assets and increasing the operational capacity of the grid, thus guaranteeing its resilience.Plano de ação para a definição do plano de adaptação às alterações climáticas da E-REDES. Projeto financiado pela E-REDES
Understanding the drivers of news consumption through social media and its impact on citizens´ political trust
No full textDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis work aims to understand the drivers of using social media as a reliable news source and its consequent impact on political trust among citizens, studying how personality traits, political ideologies, cultural values, and social media use behaviors affect the consumption of news through social media. A conceptual model was developed based on and adapted from the model proposed by Gupta et al. (2023). We collected data from 451 respondents from two European countries, Portugal and Germany, and performed a PLS-SEM analysis. The results support the hypotheses that conservatism, frequent use of social media and agreeableness positively impact news-seeking on social media platforms, contrarily to consciousness and emotional stability which have a negative effect. People who rely on social media as a news source have less trust in political institutions. We found similar results for both countries, highlighting the possible generalization of this phenomenon among different socio-economic contexts
Shifting Perceptions of Delivery Logistics in Consumer Markets During Global Health Challenges
No full textDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceConsumer behavior changed dramatically during the COVID-19 pandemic. Online shopping has grown to unprecedented levels, which, in turn, has had an enormous impact on the delivery process of the purchased goods. This study evaluates how consumers perceive the delivery process, considering their shopping experience. A data-driven approach was employed to measure this impact. Sentiment analysis was performed in reviews from online purchases of Levi’s jeans, a “classic” and timeless product. Results showed that despite the perception, consumers did not become more demanding with the delivery process. Furthermore, results show that the delivery service became a key point during the health pandemic, which was fundamental in responding to demand and maintaining customer satisfaction. This study makes significant contributions to research and practitioners. Scientifically, this study shows how online review data can be used to access product delivery. For companies, the study shows that a good and well-established delivery process influences the user's emotional response, triggering a sense of satisfaction
Assessing the impact of news avoidance on an online newspaper performance: An applied case study of user engagement over time
No full textProject Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsThis project examines the interplay between news avoidance, reader engagement, and content production within the Luxemburger Wort website. Drawing on data-driven strategies to comprehend reader preferences, the study investigates the impact of news avoidance and selection on media businesses. Leveraging text mining, Natural Language Processing (NLP), and statistical analyses, the research delves into the correlation between content production, consumption patterns, and reader engagement on Wort's website across three years.
The study's methodology integrates Text Mining (TM), NLP techniques, and topic modeling, employing TF-IDF and Latent Dirichlet Allocation (LDA) models. These methodologies enable the extraction of sentiments associated with news articles and categorization into topics, shedding light on reader preferences and content resonance. Word cloud analyses further elucidate the emotional character of news content.
The findings underscore the need for newsrooms to align content production with reader preferences across various sections and topics, emphasizing the significance of reader-centric approaches in optimizing news production efforts. The thesis concludes with a comprehensive review of literature, methodology, results, and implications for both academic theory and practical considerations within the media industry
Application of deep learning method in automatically detecting rainfall-induced shallow landslides in a data-sparse context
No full textDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesDetecting rainfall-induced shallow landslides in data-sparse contexts has become an environmental concern in recent decades and is crucial for a comprehensive landslide disaster management plan (CLDMP). Most of the previous works have contributed to the development of automated methods for detecting earthquake-triggered landslides. Despite the substantial contributions of researchers in this field, gaps and uncertainties still exist in developing a method for automatically detecting rainfall-induced shallow landslides. To address this gap, the present study has utilized the deep learning (DL) based U-net model for automatically detecting rainfall-induced shallow landslides from multi-temporal, very high-resolution (VHR) PlanetScope, medium resolution (MR) Sentinel-2 imagery, and ALOS PALSAR-provided digital elevation model (DEM), collected from the years 2018, 2019, 2022, and 2023. Four different data sets have been prepared for this study: Dataset A, comprising red, green, blue (RGB), and near-infrared (NIR) bands of PlanetScope imagery; Dataset B, comprising RGB and NIR bands of PlanetScope imagery with the inclusion of the normalized difference vegetation index (NDVI) calculated from the red and NIR bands, elevation, and slope derived from DEM; Dataset C, comprising RGB and NIR bands of Sentinel-2 imagery; and Dataset D, comprising RGB and NIR bands of Sentinel-2 imagery with the inclusion of NDVI, elevation, and slope. As a case study, the Chittagong Hill Tracts (CHT) of Bangladesh have been selected. For training the U-net model with ground truth data, 181 landslide polygons have been created from Google Earth Pro, which is a small set of ground truth data. So, the horizontal flip technique has been applied to augment the dataset, effectively doubling the entire dataset. Each dataset (A, B, C, and D) has been experimented with in 4 different trials utilizing the repeated stratified hold-out validation method so that all data is used as test data, to avoid biased results. Comparatively, Trials 1 and 2 contain a larger set of landslide training samples than Trials 3 and 4. Thus, 16 different experiments have been conducted in the present study. The performance of the U-net model is evaluated by precision, recall, F1 score, loss, and accuracy metrics. It is explored from the experiment that Datasets A and B perform the best; however, the integration of the DEM data does not enhance the accuracy of the model. The datasets comprised of Sentinel-2 imagery (Datasets C and D) exhibited very poor performance in all trials (4) in detecting rainfall-induced shallow landslides. Among the four Trials, utilizing Dataset A and B, Trials 1 and 2 outperformed, indicating the necessity of using larger training samples for DL model implementation. The mean precision, recall, F1 score, loss, and accuracy based on Trials 1 and 2 are 1, 0.625, 0.625, 0.380, and 0.999, respectively (same results found in both Datasets A and B). Overall, the performance of the model indicates that the U-net model can be used to detect rainfall-induced shallow landslides across similar geographic regions and temporal contexts around the worl
Brand loyalty in new generations: Investigating the impact of brand image, perceived quality, and customer experience on brand loyalty among Generation Z consumers
No full textDissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing IntelligenceOver the past few decades, brand loyalty has aroused significant interest, drawing the
attention of both scholars and practitioners. This research explores the factors contributing to brand
loyalty among Generation Z consumers: brand image, perceived quality, and customer experience. A
quantitative analysis was conducted through a questionnaire administered to Generation Z consumers
to assess their perceptions of brand image, quality, and customer experience in the cosmetics industry
and how these factors influence their loyalty. The model was estimated using partial least squares
(PLS), considering 220 valid responses. Findings suggest that consumers with high perceptions of
quality and customer experience may show greater brand loyalty in the skincare market. This study
contributes to the existing literature by addressing research gaps and providing insights into brand
loyalty and the factors that influence it, especially in the context of Generation Z. The research not
only contributes to the theory of brand loyalty but also provides recommendations for marketers to
improve practices in the competitive skincare market, highlighting critical areas for strategies to
strengthen loyalty in this demographic
Inovação no setor de construção: o full package da Bcc Bridge Construction Consulting em grandes projetos de infraestruturas
No full textAlterations of Mitochondrial related processes during influenza A vírus infection
No full text"In this PhD thesis, we investigated how influenza A virus (IAV) reprograms host
mitochondrial dynamics, bioenergetics, metabolism, and immune responses to support
viral replication and evade immune defenses. To address these questions, we utilized 2
IAV strains: the wild-type (WT) A/Puerto Rico/8/34 (PR8), which expresses a full-length
non-structural protein 1 (NS1) capable of suppressing host immune responses, and the
mutant PR8 NS1 N81 strain, which carries a truncation in the NS1 protein at amino acid
81. This mutation impairs the virus's immune evasion capabilities and results in
enhanced activation of the host innate immune response, particularly through the
mitochondrial antiviral signaling protein (MAVS)-mediated signaling pathway. By
comparing these 2 strains, we were able to explore how the presence or absence of
effective immune suppression influences mitochondrial dynamics, bioenergetics, and
metabolism.(...)
Improved Particle Engineering and DPI Formulation for Optimal Pulmonary Delivery
No full textThe pharmaceutical interest to deliver drugs to the lungs has been increasing to
treat local diseases like asthma or for systemic treatment, as insulin for diabetics.
Pulmonary drug delivery enables a rapid drug action and bioavailability while avoiding
the first pass metabolism and reducing the drug load required, minimizing adverse side
effects and creating a more efficient therapy.
The main goal of this work was to investigate, benchmark and optimize the
different formulations and process parameters of the particle engineering technologies
in order to optimize pulmonary drug delivery in dry powder inhalers.(...
Developing an immersive digital education platform: an exploratory study to providing digital education tools to medical students *in Germany
No full textThis work projects extends the previous business project by investigating whether an
immersive digital education platform offering applications to the B2C segment in Germany
represents a sound decision for Siemens Healthineers and identifies elements of a suitable
business model. Precisely, the business model was explored along value creation, value
capture, and value delivery by following a qualitative approach yielding insights from 9 semi structured interviews with German medical students. This thesis concludes that the interviewed
students are highly interested in such a platform if it creates superior value to learning
outcomes, features a tiered pricing model, and implements physical marketing initiatives