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Flexibilidade laboral e well-being: um estudo com enfoque sobre a relação trabalho/vida
( 2026 ) Gordo, Maria Antunes Bertão Ferreira; Santos, Gina Maria Gaio dos
Num contexto marcado pela digitalização, globalização e reconfiguração dos modelos organizacionais, a
flexibilidade laboral tem assumido crescente relevância nas políticas de Gestão de Recursos Humanos
(GRH), sendo frequentemente associada à promoção do Bem-estar dos colaboradores e do equilíbrio
entre a vida profissional e a pessoal (work-life balance). Contudo, a evidência empírica tem apresentado
resultados ambivalentes, sugerindo que os efeitos da flexibilidade dependem das condições de
implementação e do suporte organizacional.
A presente investigação analisa a relação entre flexibilidade laboral, nas suas dimensões temporal e
espacial, Bem-estar no trabalho e equilíbrio entre vida profissional e pessoal, propondo e testando
empiricamente um modelo conceptual de mediação em que o Bem-estar assume um papel mediador
nessa relação. O estudo integra duas perspetivas complementares: a dos colaboradores e a dos
Gestores/Profissionais de Recursos Humanos (RH).
Adotou-se uma abordagem quantitativa, com recolha de dados através da aplicação de dois questionários
online a 180 colaboradores e 76 Gestores/Profissionais de RH. A análise estatística permitiu testar as
hipóteses de investigação e analisar as relações entre as variáveis em estudo.
Os resultados evidenciam associações positivas e estatisticamente significativas entre flexibilidade
laboral, Bem-estar no trabalho e equilíbrio entre vida profissional e pessoal. A análise dos modelos de
mediação demonstra, contudo, que o papel mediador do Bem-estar não se manifesta de forma uniforme
em todas as dimensões analisadas, verificando-se padrões diferenciados em função da população
estudada e da dimensão de Bem-estar considerada. Estes resultados contribuem para aprofundar a
compreensão da articulação entre práticas de flexibilidade e de equilíbrio trabalho/vida, oferecendo
implicações relevantes para o desenvolvimento de políticas de GRH orientadas para a promoção do Bemestar
e para a implementação de modelos de trabalho mais sustentáveis.
AI-driven mobile solution for early detection and management of diabetic foot ulcers
( 2026 ) Chaves, António Jorge Monteiro; Ganança, Rúben; PELLER, TAYAN; Abelha, António; Machado, José Manuel; Peixoto, Hugo
Diabetic Foot Ulcers (DFUs) are one of the most serious and common complications of diabetes mellitus, with an estimated 15% to 25% of people with diabetes developing a DFUs during their lifetime. To combat misinformation and promote treatment adherence, it is proposed to develop an integrated follow-up framework, capable of intelligent treatment monitoring. The application integrates a Deep Learning (DL) approach to analyse images submitted by patients and provide personalized feedback to help adapt and optimize treatment. The proposed tool aims not only to provide educational information but also facilitate remote communication between the patient and the healthcare professional, contributing to the improvement of existing ulcers and the early detection of new lesions. The architecture of a mobile application for this purpose is outlined, and the routing of information in the application via APIs is also explained so that data can be recorded and captured efficiently. The joint implementation of the Deep Learning, YOLO/RetinaNet, and Segment Anything Model (SAM) models to classify and segment, respectively, the images submitted to the application is likewise described. Preliminary results indicate that the YOLOv11n and RetinaNet with resnet50+FPN backbone models achieved mAP@50 of 0.844 and 0.811, respectively.
Carbon based cortisol sensor for chronic stress assessment
( 2026 ) Santos, Ana Raquel Ribeiro; Viana, J. C.; Silva, Alexandre Ferreira da; Paiva, Maria C.
Chronic stress has become a major global health challenge, highlighting the need for low-cost and portable tools capable of continuously monitoring cortisol, its main biological marker. This thesis focuses on the development of non-affinity electrochemical sensors based on screen-printed carbon electrodes (SPCEs), following a simple, material-driven approach that avoids the complexity of antibody- or aptamerbased systems. Covalent functionalization of multi-walled carbon nanotubes with anhydride groups (MWCNT-ANI) was initially explored. thermogravimetric analysis (TGA) and x-ray photoelectron spectroscopy (XPS) confirmed successful modification, although the electrochemical response to cortisol remained limited. A pyrene derivative (PY) bearing amide and carboxylic groups was subsequently investigated to promote interactions with cortisol while leveraging pyrene’s strong affinity for carbon surfaces to enable straightforward SPCE modification. Two approaches were implemented to develop the PY-based platform. In the first approach, a hydroalcoholic solvent system was used, resulting in a sensor with a detection range from 3 to 500 nM, a limit of detection of 3 nM and a sensitivity of 2.21 μA/dec. Molecular dynamics (MD) and density functional theory (DFT) simulations were used to understand the sensing mechanism. The results showed that cortisol interacts with the PY layer via multiple hydrogen bonds, stabilizing the molecule and decreasing its intrinsic reduction peak at –1.1 V. In the second stage, an alkaline PY aqueous solution (pH > 7) was used to modify the electrode, resulting in improved surface uniformity and droplet confinement. This led to a clear improvement in analytical performance, with a sensitivity of 4.36 μA/dec. Hybrid carbon systems were also explored by combining PY with multi-walled carbon nanotubes (MWCNTs) and carbon black (CB). The PY/CB configuration reached a sensitivity of 4.13 μA/dec, supported by its high electroactive surface area and efficient electron transfer. This work demonstrates that controlling the surface chemistry and morphology of carbon-based interfaces enables detection of cortisol at nanomolar levels using a straightforward, scalable fabrication process. These results support the development of low-cost wearable devices for real-time stress monitoring.