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Percorrer Teses de Mestrado ESTiG por Objetivos de Desenvolvimento Sustentável (ODS) "03:Saúde de Qualidade"
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- A relação dos fatores psicossociais na saúde e produtividade dos trabalhadores de uma IPSSPublication . Vilela, Catarina Machado; Oliveira, Rui; Cardim, SofiaO mundo laboral está em constante transformação, o que acarreta uma série de desafios, com aspetos tanto positivos como negativos. Os riscos associados à forma como o trabalho é planeado, organizado e gerido, bem como ao seu contexto económico e social, podem dar origem a sérios problemas de saúde física e mental, conhecidos como riscos psicossociais. Considerando que os profissionais das instituições de solidariedade social lidam diariamente com populações vulneráveis, como idosos e pessoas em situação de exclusão social, o contacto contínuo com esta realidade de sofrimento, doença, carência e até lidar com a morte pode provocar um desgaste emocional, tornando-os mais propensos a enfrentar riscos psicossociais. Neste sentido, o objetivo principal do estudo foi identificar os fatores psicossociais que afetam o seu dia a dia e como estes lidam com tais desafios. A metodologia utilizada teve por base uma revisão bibliográfica, que deu fundamentação teórica para conceitos fundamentais sobre a temática, foi também utilizada para o desenvolvimento dos inquéritos, para a sua aplicação também foi utilizado o método quantitativo descritivo. Os resultados obtidos demonstraram que, de uma forma geral, os riscos psicossociais apresentam níveis reduzidos, demonstrando, contudo, maior vulnerabilidade entre os ajudantes de ação direta, seguidos pelos profissionais de cozinha e, por último, pela equipa técnica. As exigências emocionais e físicas, o ritmo de trabalho, a sobrecarga de tarefas e a falta de reconhecimento foram identificados como fatores de maior impacto. Em suma, o estudo demonstra a importância de promover ambientes laborais saudáveis e emocionalmente equilibrados, de forma a promover o bem-estar dos trabalhadores, pois são o pilar essencial da eficácia e eficiência das organizações do setor social.
- Aplicação de identificação de episódios de fibrilação atrialPublication . Guerreiro, Nathan Antonio; Teixeira, João Paulo; Dajer, Maria EugeniaAs Doenças Cardiovasculares (DCVs) causam cerca de 18 milhões de mortes por ano, segundo a Organização Mundial da Saúde (OMS). Na Europa, mais de 10 milhões de pessoas são afetadas anualmente, com 3 milhões de óbitos registrados em 2021. No Brasil, são aproximadamente 400 mil mortes por ano, resaltando arritmias cardíacas. A fibrilação atrial (FA), arritmia mais comum, é caracterizada por ritmo cardíaco irregular. Diante desse cenário e alinhado ao papel social do engenheiro e ao Objetivo de Desenvolvimento Sustentável (ODS) “Garantir saúde e bem-estar para todos” da Organização das Nações Unidas (ONU), este trabalho apresenta o desenvolvimento de uma interface gráfica do usuário (GUI) para aquisição e classificação de eletrocardiogramas (ECG). O sistema foi implementado em MATLAB, integrando aquisição em tempo real com a plataforma BITalino c (derivação I, via Bluetooth), detecção dos picos R e classificação automática de episódios de FA com redes neurais LSTM. São extraídas quatro características principais a cada 60 ciclos cardíacos: intervalos RR e entropias de Shannon das ondas T, U e P. Após normalização, essas variáveis compõem os vetores de entrada da rede, que classifica como Outro Ritmo, Ritmo Normal e Ritmo FA. A aplicação permite ainda a visualização dos sinais em tempo real e a geração automática de relatórios em PDF. A validação com sinais da base de dados MIT-BIH Atrial Fibrillation demonstrou que a interface é funcional, e a acurácia de 98,17%, obtida em estudo anterior, evidencia seu potencial como ferramenta auxiliar na análise de ECGs em ambientes clínicos e domiciliares.
- Binary classification of cardiac pathologies using deep learning: a PTB-XL dataset approachPublication . Chaabani, Mohamed Khalil; Teixeira, João Paulo; Slim , Mohamed AymenCardiovascular diseases, including myocardial infarction, remain among the leading causes of mortality worldwide. Timely and accurate diagnosis is critical for effective treatment but often requires labour-intensive manual analysis of clinical-grade electrocardiograms (ECGs). This dissertation proposes a novel deep learning-based approach for binary classification of cardiac pathologies, using the PTB-XL dataset. The final model architecture integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies between ECG leads, the results prove its adaptability for ECG image classification tasks. Extensive experimentation and iterative model development were conducted to reach the final design. Early trials involved exploring different hyperparameter tuning like Optuna and Adam optimizer and a wide range of hyperparameter configurations, including different learning rates, dropout rates, batch sizes, and numbers of Linformer layers. These experiments were critical in finding the optimal combination of parameters that balanced computational efficiency and model accuracy. Comprehensive details of these trials and evaluations are provided in the report. The preprocessing pipeline involves selecting the ECGs and converting them to 11 images (aVR was excluded) each representing a lead, converting RGBA ECG images to RGB format and applying normalization to ensure compatibility with model input requirements. This preprocessing step addresses the unique format of the dataset and prepares it for high-performance neural network training. Initial results from the finalized model architecture have demonstrated promising performance, achieving an AUC (Area Under the Curve) of 85.02% and a F1-score of 78.94%. The achieved results are comparable to recent state-of-the-art models reported on the PTB-XL dataset, which typically range between 85% and 95% AUC for similar binary classification tasks. These results indicate that the model's AUC of 85.02% is promising but on the edge of the current state-of-the-art. These findings indicate strong potential for the system to support clinical decision-making by automating the classification of ECG data. Ongoing research aims to extend the current binary classification framework to multi-class scenarios, further enhancing its clinical applicability. Additionally, efforts are being made to improve the model for faster inference times, enabling real-time ECG analysis and improving its feasibility for deployment in healthcare settings.
- Classificação de sinais de ECG com técnicas explicáveis de inteligência artificialPublication . Martins, Hugo Fidalgo Oliveira; Teixeira, João PauloCardiovascular diseases are among the main causes of premature mortality, with atrial flutter representing a clinically relevant arrhythmia due to its association with stroke and heart failure. The eletrocardiogram (ECG) is the most suitable diagnostic method for evaluating chardiac rhytms. Automatic ECG interpretations have attempted to improve clinical practice. However, the lack of interpretability of existing models has limited their acceptance. This dissertation presents a framework for atrial flutter classification using raw 12- lead ECG signals from PTB-XL Database, Georgia 12-Lead ECG Challenge Database and Large 12-Lead ECG Database for Arrhythmia Study. A hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks was developed and trained under a 10-fold cross-validation scheme. To ensure model transparency, explainable artificial intelligence (XAI) mehods were applied: Shapley Additive Explanations (SHAP) was used to quantify the contribution of each lead, and Local Interpretable Model-Agnostic Explanations (LIME) was employed to highlight the most informative temporal segments at the patient level. The results demonstrate that the proposed approach achieves competitive performance while improving interpretability, thus contributing to more reliable and clinically meaningful applications of artificial intelligence in cardiology.
- Cyber–Ed A digital hands-on platform for learning cybersecurityPublication . Rocha, Alexandra Sofia Dias Alves; Pedrosa, Tiago; Lopes, Rui PedroA cibersegurança afirma-se como uma área crítica na sociedade contemporânea, especialmente após a pandemia de COVID-19, que acelerou a transformação digital e expôs vulnerabilidades crescentes. A falta de profissionais qualificados para enfrentar estes desafios demonstra a necessidade de metodologias educativas que promovam competências práticas, adaptativas e alinhadas com tecnologias emergentes. O estudo inclui uma revisão sistemática que fundamenta as opções metodológicas, analisando vantagens e limitações de abordagens tradicionais e inovadoras, com foco na gamificação, na Inteligência Artifical (IA) e em ambientes virtuais. Esta dissertação propõe uma plataforma educativa automatizada que combina laboratórios virtuais (Labtainers) e competições Capture The Flag (CTF) em ambientes dinâmicos, seguros e acessíveis via virtual private network (VPN). A solução integra princípios de gamificação e automação, utilizando tecnologias como Terraform, Ansible, React, FastAPI e Proxmox, com o objetivo de proporcionar experiências de aprendizagem realistas, diversificadas e inclusivas na formação em cibersegurança. Foram implementados e testados alguns cenários de aprendizagem prática, abrangendo ataques simulados, como SQL Injection (SQLI), e exercícios de administração de sistemas. Os resultados demonstram eficácia na criação de cenários e potencial de aplicação em contextos académicos e empresariais. Apesar de limitações, como a capacidade atual de utilizadores simultâneos e a diversidade restrita de cenários, a plataforma constitui uma base sólida para futuras expansões, incluindo a migração para infraestruturas cloud e a personalização mediada por IA. Este trabalho contribui, assim, para o avanço do ensino prático de cibersegurança, preparando profissionais para um panorama digital em rápida transformação.
- Development of an intelligent agent for knowledge extraction in the pathogens in foods (PIF) database with machine learningPublication . Silva, Lucas Ribeiro; Alves, Paulo; Cadavez, VascoScientific databases like the Pathogens in Foods (PIF) Database hold valuable public health data but are often inaccessible to experts lacking programming skills. This research addresses this gap by developing and evaluating a novel Visual Natural Language Interface (V-NLI) for the PIF database. The resulting PIF Intelligent Agent empowers users to perform complex queries, conduct meta-analyses, and generate dynamic reports using natural language. The agent uses a hybrid, dual-mode architecture separating language interpretation from statistical computation. An "Open Chat Mode" offers a flexible exploratory interface via a tool-calling Small Language Model (SLM) with Retrieval-Augmented Generation (RAG). A "Guided Meta-Analysis Mode" provides a structured workflow for generating reproducible scientific reports through a dedicated Rserver backend. A comprehensive evaluation benchmarked five SLMs: Phi-4 Mini (3.8B), MFDoom/deepseek-r1-tool-calling (14B), Cogito (14B), Qwen 3 (8B), and Gemini 2.5 Pro. While all models achieved flawless functional accuracy, their effectiveness was determined by interpretive quality. The ability to generate concise, factually coherent text was the key differentiator, with smaller, instruction-tuned models showing performance comparable or superior in conciseness to larger models. The end-to-end system proved highly reliable, validating the architecture and establishing interpretive fidelity as a critical benchmark for domain-specific agents.
- Development of cosmetic functional formulations incorporating hyaluronic acidPublication . Silva, Thaís Rossetto Cordeiro da; Barreiro, M.F.; Echart, Arantzazu Santamaria Echart; Düsman, ElisângelaThe cosmetic industry has been expanding continuously, driven by demand for innovative, sustainable, and high-performance formulations. In this context, this work aimed to develop particles composed of hyaluronic acid (HA) and Collagen type I (Col) to act as Pickering stabilisers in cosmetic emulsions. The particles were prepared using different methods (direct mixing - mix and droping - drop), with the most promising dispersions obtained after high-shear homogenisation: P_17_Mix_Ultra and P_17_Drop_Ultra. These formulations exhibited particle sizes of 12.30 ± 3.73 μm and 2.55 ± 0.08 μm, respectively. Both showed highly negative zeta potentials (–37.73 ± 1.50 mV for the mix and –37.72 ± 0.56 mV for the drop), confirming colloidal stability. Wettability tests demonstrated strong oil affinity, with contact angles of 128.30 ± 1.05° and 137.22 ± 3.20°. The drop method stood out for producing smaller, more homogeneous particles. Pickering emulsions were then prepared using sweet almond oil, Miglyol 812, and olive oil. Olive oil originated the most stable systems, and emulsions with a higher oil fraction (70:30 oil-to-aqueous ratio) presented superior stability compared to those with lower oil content. Analyses of emulsion type, colourimetry, creaming index, droplet size, microscopy, zeta potential, and rheology confirmed the better performance of olive oil-based formulations obtained by the drop method. The formulation E_70_OO_17d (drop method, olive oil, 70:30 ratio) achieved the most promising overall results, with a creaming index of 10% after 30 days, homogeneous droplets, and rheological behaviour exhibiting viscoelastic properties comparable to those of commercial products. In conclusion, HA-Col particles proved to be efficient Pickering stabilisers, enabling the development of stable emulsions. The most promising formulation, E_70_OO_17d, was particularly suitable for moisturising and anti-ageing cosmetic applications, where hyaluronic acid provides hydration and regeneration while Col enhances elasticity and repair.
- Development of functional emulsions based on humic acids for cosmetic applicationPublication . Lopes, Nayra Piscoso Saes; Barreiro, M.F.; Santamaria-Echart , Arantzazu; Sipoli, Caroline Casagrande; Demczuk Junior, BogdanThe cosmetics market continues to grow, especially dermocosmetics, which combine aesthetic and therapeutic care. This growth is driving the development of more effective and sustainable formulations, such as Pickering emulsions, which utilize solid particles instead of synthetic surfactants. These latter are responsible for causing environmental impacts and adverse effects on the skin, including irritation and allergies. Humic acid, a natural active ingredient with antioxidant, anti-inflammatory, and photoprotective properties, is an innovative alternative for this type of system. In this context, this work aims to study Pickering emulsions stabilized with humic acid nanoparticles for the development of a dermocosmetic product. Different conditions for producing the particles were analyzed, including the solvent and anti-solvent used, concentration, and pH. The optimized nanoparticles were obtained by acid precipitation, using 0.5 M sodium hydroxide to solubilize the humic acid (final concentration of 10 g/L) and 0.5 M citric acid as the precipitating agent. The optimized particles were used to formulate Pickering emulsions, with an oil volume percentage of 65% showing the best results. The humic acid particles had an average particle size of 73.9 ± 9.7 nm and a three-phase contact angle (particles-water-sweet almond oil) of 65 ± 3.5º, revealing a hydrophilic character. The optimized emulsion consisted of round-shaped droplets with an average size of 44.2 ± 0.3 μm, resulting in an emulsified layer of over 95%, which indicated high stability after 30 days of storage. In addition, the emulsion exhibited a brown color, 14.8% inhibition of antioxidant activity, and, according to rheological analysis, a pseudoplastic, non-Newtonian fluid behavior with gel-like characteristics. It had a smooth texture to the touch, was easy to spread, and had low tackiness. These results demonstrate not only the feasibility of using humic acid particles as stabilizers in Pickering emulsions but also highlight their multifunctional potential for cosmetic applications. This is an unprecedented approach in the field of dermocosmetics, with scientific and technological relevance. Overall, this study presents an innovative, ecological, and effective alternative to the use of synthetic surfactants, offering new perspectives for the development of sustainable cosmetic products based on physical stabilization technologies utilizing nanoparticles.
- Development of functional films based on antimicrobial photodynamic inactivationPublication . Carrasqueiras, Miguel Fernandes; Crugeira, Pedro; Barreiro, M.F.Microbial contamination remains a major food safety challenge, requiring innovative and sustainable control strategies beyond traditional thermal or chemical methods. Antimicrobial photodynamic inactivation (aPDI) has emerged as a promising alternative, based on the activation of photosensitizers that induce the formation of reactive oxygen species (ROS) capable of eliminating microbial cells. In this work, κ-carrageenan-based functional films incorporating curcumin solid dispersions at 1% and 5 wt% concentrations, designated CF_1%_1Y and CF_5%_1Y, were evaluated as photoactive systems for food applications. The solid dispersion approach improved the stability, amorphization, and homogeneous distribution of curcumin in the hydrophilic polymeric matrix. The FTIR and DSC analyses confirmed the preservation of the polymer network and the amorphous state of curcumin. The absorption spectrum at 445 nm, characteristic of curcumin, was identified by UV-Vis spectrophotometry. Evaluation of the mechanical properties demonstrated the structural integrity and strength of the CF_5%_1Y film, yielding a tensile modulus of 196.5 ± 15.6 MPa, a tensile strength at break of 3.22 MPa, and a strain at break of 4.03%. In planktonic cells, CF_5%_1Y under LED irradiation (450 ± 10 nm), at an energy density of 100 J/cm², reductions of 99.9% for Escherichia coli and 96.7% for Listeria monocytogenes were achieved, confirming the synergistic action of light, oxygen, and curcumin in ROS generation. Decontamination tests performed on cream cheese and mayonnaise matrices confirmed the antimicrobial properties of aPDI, previously identified in planktonic cells. When using irradiated CF_5%_1Y films, microbial reductions of 98.5% for E. coli and 99.4% for L. monocytogenes were observed in the mayonnaise matrix. In contrast, in cream cheese, reductions reached 91.2% for E. coli and 92.8% for L. monocytogenes. In 15-day preservation tests, samples of mayonnaise and cream cheese with irradiated CF_5%_1Y films maintained the integrity of the food matrices, with constant microbial concentrations of 1.7-1.8 log CFU/g. At the same time, the controls showed increased contamination, reaching approximately 4.3 log CFU/g in mayonnaise and 2.3 log CFU/g in cream cheese. These results demonstrate the effectiveness of functionalization and photoactivity, corroborating the development of stable, efficient, and sustainable films with antimicrobial properties for food decontamination and preservation, offering an eco-friendly path.
- Estrogen removal through adsorption using carbon materials prepared from biomass wastePublication . Exposto, Bruno Marques; Queiroz, A.M.; Ribeiro, António E.; Brito, PauloEndocrine disruptors are class of micropollutants that can influence and deregulate the endocrine system in humans and animals. Endocrine disruptors can consist of natural estrogenic hormones, such as E1 and E2, and synthetic estrogenic hormones, such as EE2, that are not easily removed by conventional treatment processes in water and sewage treatment plants, becoming dangerous emerging pollutants. This work addresses the removal of such compounds from aqueous matrices through adsorption processes onto activated carbon produced from biomass waste. The materials selected were almond shell in natura (ASiN) and cork in natura (CiN). These were activated through carbonization at 550ºC for 1 h, obtaining carbonized almond shell (CAS) and carbonized cork (CC). These materials were characterized by carbonization yield, moisture and ash content, PZC value, quantification of acidic and basic sites and FTIR analysis. For the adsorption assays, the impact of conditions such as adsorbent type, temperature and medium pH was assessed in relation to removal performance. Kinetic and isotherm assays were also performed in batch mode, together with the assessment of the thermodynamic data. The results show that equilibrium time was 16 h for ASiN, 4 h for CAS, 6 h for CiN and 24 h for CC. The adsorption process was regulated through the Elovich model for all adsorbents except for CiN, where the best model was PSO. Moreover, cork-based adsorbents presented higher adsorption capacities and activation through carbonization did not contribute to an increase in estrogen removal. Therefore, CiN was selected as the best material for estrogen removal to the posterior assays. A temperature increase did not affect CiN’s adsorption capacity, and Ea was determined to be 136.15, 80.84 and 146.81 kJ/mol for E2, EE2 and E1, respectively. Moreover, pH change only negatively affects adsorption when in extreme basic media. CiN’s adsorption process has been characterized by the Sheindorf-Rebuhn-Sheintuch multicomponent isotherm for all estrogens, with maximum adsorption capacities of 3.75, 7.23 and 3.82 mg/g achieved for estrogens E2, EE2 and E1, respectively, at 25ºC. Average removal percentage increased with the adsorbent’s dosage until it reached a maximum of 66.1% at 1 g/L. Both the adsorption capacity and removal percentage decreased with the increase in temperature. Thermodynamic data was determined and negative values for ΔGº and ΔHº and positive values for ΔSº were obtained, meaning that adsorption was characterized as spontaneous, exothermic and entropically favourable. It was verified that estrogen adsorption occurred through a combination of chemisorption and physisorption phenomena. Chemisorption occurred mainly through pEDA interactions and hydrogen bridges between estrogen and CiN’s functional groups and aromatic rings, while physisorption occurred mainly through pore filling. pEDA bonding assisted by hydrophobic interactions could be an explanation for the higher adsorption rates verified for EE2 in CiN.
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