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Percorrer Faculdade de Ciências e Tecnologia por Objetivos de Desenvolvimento Sustentável (ODS) "08:Trabalho Digno e Crescimento Económico"
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- Abordagem terapêutica da laminite em equinosPublication . Rito, Catarina de Seruca; Serralheiro, Ana Isabel AzevedoA laminite é uma doença que afeta os membros dos equinos, mais especificamente nos tecidos chamados lâminas, que unem a 3ª falange ao casco. Trata-se de uma doença que causa uma grave claudicação e mal-estar no animal, para além de estar associada a uma elevada taxa de mortalidade. Normalmente a carreira atlética do cavalo termina quando é realizado o diagnóstico, e no pior dos cenários, pode ser mesmo necessária a eutanásia do animal, se este se encontrar em grande sofrimento. Esta patologia pode ser de natureza aguda ou crónica. Atualmente não existe consenso quanto aos seus mecanismos fisiopatológicos, tendo sido propostas várias teorias, entre elas, a teoria inflamatória, a vascular, a enzimática, a metabólica e a traumática. Sabe-se que o seu desenvolvimento advém essencialmente da rutura da integridade estrutural da ligação derma-epidérmica, mais precisamente da perda de aderência das células epiteliais basais das lamelas epidérmicas às lamelas dérmicas subjacentes. A definição da terapêutica mais eficiente para o tratamento da laminite é das questões que mais levanta dúvidas entre médicos veterinários e ferradores. De entre as várias terapêuticas farmacológicas utilizadas destacam-se por exemplo, a terapêutica com fármacos anti-inflamatórios, onde a fenilbutazona e a flunixina meglumina se destacam; analgésicos, sendo os opioides os mais utilizados; fármacos com propriedades vasodilatadoras, como a acetilpromazina e fenoxibenzamina; e finalmente os anti-trombóticos, como a heparina e o ácido acetilsalicílico. Sob o ponto de vista não farmacológico, também são várias as medidas que se podem implementar, como por exemplo a crioterapia, alterações alimentares e suporte biomecânico dos cascos, todas com o intuito de se combinarem com a terapêutica farmacológica.
- Análise econômica das tecnologias de produção boas práticas para mitigar os efeitos das alterações climáticas e lutar contra a desertificaçãoPublication . Rodrigues, Alessandra; Freitas, Maria de Belém Ferreira da Silva Costa; Antunes, Carla Maria RoloA Região Mediterrânica tem enfrentado um agravamento e intensificação dos períodos de seca além da diminuição da precipitação. Nesse contexto, em 1/07/2022, foi implementado, o Programa +Solo +Mais Vida: Adaptação e Mitigação das Alterações Climáticas e Luta contra a Desertificação. O programa, com duração de 22 meses, foi desenvolvido numa área de 94 hectares no Parque Natural do Vale do Guadiana, e tinha por objetivo impulsionar a adaptação às alterações climáticas e o combate à desertificação dessa região através da adoção de 10 boas práticas agrosilvopastoris de combate a degradação do solo. As medidas que caracterizaram o programa foram: biodiversidade funcional, controle da erosão e aumento da infiltração, economia circular e carbon farming, gestão adaptativa do pastoreio, melhoramento de solo, melhoria do mosaico mediterrânico, pastagens permanentes, promoção da regeneração natural, restauro de linhas de água e retenção e conservação de água na paisagem. O intuito da dissertação, apresentada a seguir, foi desenvolver uma análise econômica das tecnologias de produção, e boas práticas inseridas nesse programa, buscando compreender seus resultados e oferecer aos agricultores uma ferramenta que auxilie a sua tomada de decisão. Foi possível observar, de forma imediata, os custos referentes à implementação de cada tecnologia e dados relativos à emissão de carbono das atividades anuais e dos investimentos (sementeira direta e convencional de pastagens, protetores de regeneração, implementação de bosquetes e restauro de linha de água); também se estabeleceu uma comparação entre a tecnologia de sementeira direta e convencional, onde ficaram claras as vantagens econômicas e ambientais da primeira.
- Application of semantic web techniques in DW/BI systems for strategic managementPublication . Antunes, António Lorvão; Barateiro, José; Cardoso, ElsaSemantic Web techniques, such as ontologies, facilitate data and knowledge sharing in Information Systems due to their semantic formalization and inference qualities. Integrating knowledge-based artifacts into Decision Support Systems, such as Data Warehouse and Business Intelligence (DW/BI) systems, can provide new information sources, enable new analytical capabilities, and enhance decision-making. In previous work, the Balanced Scorecard Ontology (BSO) was developed to represent knowledge related to the Balanced Scorecard framework, including its concepts and relationships. The BSO was used to assess strategy formulation, implementation, and execution in a public sector organization, demonstrating its impact on strategy management. However, manual ontology population, especially related to performance indicator values, can lead to challenges in data availability, acquisition frequency, and quality. This paper proposes a semantic approach to integrating, aligning, and ensuring traceability between strategy and DW/BI systems. The Light Data Warehouse Ontology is introduced to represent the DW conceptual and logical models and semantically connect them with strategic information using the BSO. This integration enriches strategy analysis by providing strategic context to the BI environment and enabling automatic retrieval of performance indicator values. The proposed framework improves decision-making efficiency, reliability, and timeliness, providing managers with a data-driven environment aligned with organizational strategy.
- Competitiveness of portuguese montado ewe production systems among the european ewe production systemsPublication . Ferreira da Silva da Costa Freitas, Maria de Belém; Ventura-Lucas, Maria Raquel; Izquierdo, Lola; Deblitz, ClausThe number of ewes in Portugal registers a decrease since 1998. This decrease is felt particularly in the south of the country, which concentrates almost half of the existing ewes, mainly for meat production. One of the most important ewe production systems is the Montado, a High Nature Value ecosystem, occupying ca. 1.2 million ha in Portugal. The competitiveness of this system among the European ewe production systems is an important issue for the future of the Montado ecosystem. So, the objective of this paper is to analyze the ewe production systems in the Montado, using the agri benchmark database, and compare these systems with other European countries’ systems, ranking their competitiveness and e_ciency among other systems in the European Union. We concluded that this methodology facilitated an in-depth understanding not only of the competitiveness and e_ciency of ewe production systems in Portugal but also of their positioning regarding other systems in the European Union. The pattern of returns assures that these farms are competitive in the sense that they depend on the market on their decisions, and thus it is important that market values sheep products. Nevertheless, the diversification to other income sources would be a good option for the future sustainability of these farms and the opportunities and risks that these systems will deal with in a new green economy, with probable new functions and new opportunities for land, will be a challenge for the future.
- Continual learning for object classification: integrating AutoML for binary classification tasks within a modular dynamic architecturePublication . Turner, Daniel; Cardoso, Pedro; Rodrigues, JoaoFor humans it is quite easy to identify a new object after learning to identify existing ones, but not for a machine. Deep neural networks (DNN) are the foundation of the current state-of-the-art methods for training machines to recognize sets of objects. The issue is that any modification to the DNN weights that were trained to classify an initial set of objects has the potential to seriously impair the network’s ability to make those initial classifications; this behaviour is referred to as catastrophic forgetting (CF). This paper presents a continual learning (CL) architecture that can deal with CF. The architecture is composed of two primary parts: (i) The feature extraction component, which is based on the ResNet50 backbone and (ii) the modular dynamic classification (MDC) component. The latter is made up of multiple sub-networks that gradually assemble themselves into a tree-like structure that reorganizes itself as it learns over time, so that each sub-network can operate independently. The MDC relies heavily on binary classification, and here the application of automated machine learning (AutoML) was introduced, where each binary classifier is tailored on-the-fly, and is/can be different from object to object. The strategy involves a calculated selection from a predefined list of model types and parameters, optimizing them for their respective tasks. Results demonstrate that we advanced the adaptability and performance of the network, emphasizing the transformative potential of AutoML in modular CL approaches. Tests on the CORe50 dataset showed accuracy results of 81.1%, which are above the state of the art for CL architectures.
- Mono-trophic seaweed polyculture of sea grapes (Caulerpa lentillifera) and Kappaphycus alvarezii: a case study from Van Phong Bay, Viet NamPublication . Stuthmann, Lara Elisabeth; Costa, Beatrice Brix da; Cordes, Aaron Johannes; Du, Hoang Trung; Kunzmann, Andreas; Springer, KarinKappaphycus alvarezii and Caulerpa lentillifera are economical important seaweeds cultivated in Van Phong Bay, Viet Nam, respectively. The complementary light and nitrogen requirements of the seaweeds introduce the opportunity for a mono-trophic seaweed polyculture. Three different set-ups were tested, namely the integration of K. alvarezii in sea grape ponds, the integration of sea grape plastic cages on longlines and the polyculture of both species in net cages. The relative growth rates (RGRs) of K. alvarezii were highest on longlines, compared to net cages in mono- and polyculture (4.4 ± 0.8 % day− 1 vs 2.1 ± 0.6, 0.6 ± 0.5 % day− 1 ), whereas fragments died off due to warm temperatures and absence of water movement in ponds. Strong recurring water movements at the experimental site caused high losses of K. alvarezii fragments (39 % of initial) and impaired growth of delicate C. lentillifera causing negative RGRs in all treatments (plastic cages without gauze: − 9.8 ± 0.6 % day− 1 , net cages: − 6.4 ± 0.9 % day− 1 ), but with least loss in plastic cages with gauze wrapping (-1.3 ± 0.8 % day− 1 ). Fv´/ Fm´values of both species showed typical midday depression and C. lentilliferas´Fv/Fm were influenced especially by gauze wrapping. Here, we show that the K. alvarezii cultivation on longlines with C. lentillifera integrated below in inexpensive, self-made, customizable plastic cages with additional gauze protection is the most promising set-up from a physiological and economic point of view for Van Phong Bay and beyond. However, further research is needed before implementation of the system.
- On farm non-agricultural activities: recent evolution and dynamics in PortugalPublication . Xavier, António; Rosário, Maria do Socorro; Carvalho, Maria Leonor Silva; Ferreira da Silva da Costa Freitas, Maria de Belémwhich constitute a complement to the farmer’s income and can function as a factor for the development of farms, enhancing the endogenous resources of the territories and contributing to the multifunctionality of rural areas. Therefore, it is relevant to understand the importance of these non-agricultural activities in the territory, their diversification, spatial trends at local level and the relation with farm’s orientation. This paper intends to analyse the OFNAA, using as object of study the Portuguese municipalities. To analyse the diversification of the OFNAA, a diversification index based on entropy is proposed. The relationships between OFNAA diversification and the farms’ technical-economic orientation (TEO) are also analysed using correlation matrixes, while the spatial patterns are studied, using the global Moran I and local Moran-LISA. The results provide important insights of the OFNAA dynamics and diversification. Therefore, this study provides an important tool for policy management and implementation.
- Physiological and psychological benefits of exposure to nature during work in a military bunker—a pilot experimental studyPublication . Silva Fernandes, Maria Jacinta; Bento, Ana Teresa; Gonçalves, Gabriela; Campos, ClariceThe present controlled experimental research addresses the effects of exposure to nature on workers’ well-being and job performance in a work-confined setting. Ten individuals working in an open-space office inside a Portuguese military bunker were exposed to simulated nature (audio sounds and/or video images of nature). Quantitative physiological (heart rate) and self-reported measures (perceived positive and negative emotions, environment restorativeness, and work performance) were taken. Results indicate that exposure to nature during working time in confined places, through simulating a window with a view of nature and/or by introducing sounds of nature, promotes physiological and emotional well-being at work (heart rate significantly decreases, positive emotions significantly increase, and negative emotions decrease), and significantly increases employees’ perception of workplace restorative qualities. The results on work performance were non-significant. The present findings contribute to the evidence of the restorative effects of nature exposure during work. The research bridges a gap by considering workplaces where real nature exposure is not feasible and examining the evidence on the beneficial biophilic interventions (the restorative effects of simulated nature) within confined environments. The strategy to use videos and audio of nature may improve the structural conditions of work, benefiting well-being in these types of work settings.
- Printed circuit boards leaching followed by synthesis of gold nanoparticle clusters using plant extractsPublication . Nobahar, Amir; Lourenço, João P.; Costa, Maria Clara; Carlier, JorgeThis work investigates the potential of 70% ethanolic leaf extracts of Rubus idaeus L., Cistus ladanifer L. and Erica andevalensis in the metal separation from synthetic unimetallic solutions of different metals and a leachate obtained from the leaching of PCBs. Results from the experiments with unimetallic solutions revealed R. idaeus and E. andevalensis extracts induced separation of more than 95% of the initial Au(III), while C. ladanifer separated ~78% of this metal. Thereafter, application of three plant extracts to real Au bearing leachate obtained from PCBs leaching, revealed about 96, 95 and 90% Au recovery with R. idaeus, C. ladanifer and E. andevalensis extracts, respectively with 15–60% co-removal of Pb and less than 15% of other metals. The reduction of Au(III) ions into Au(0) nanoparticles by R. idaeus extract was confirmed by molecular UV–Visible, and FT-IR analysis showed the involvement of plant secondary metabolites in Au bio-reduction and bio-stabilization. Particles obtained from the application of R. idaeus extract to the leachate were initially analyzed with XRD and results confirmed the presence of Au(0) with contamination of PbSO4, which was completely removed by washing with 1 M HCl. Thereafter, results from STEM-EDS analysis showed the presence of Au particles conjugated with organic material and other metals. Consequently, particles were subjected to another washing step with acetone. Afterwards, STEMEDS showed pure Au microparticle clusters (~0.8 μm) with flower-shaped or apparently cubic morphologies, and HRSTEM showed the tiny nanoparticles (~20 nm), which form the clusters.
- Recent advances in quantum machine learning: a survey with a comparative analysisPublication . Privalihhin, Vassili; Oliveira , José Valente de; Sousa, Joana CoutinhoQuantum mechanics, a fundamental theory in physics that describes the behavior of nature at atomic and subatomic scales, offers significant advantages over classical physics in various contexts. These advances have laid the foundation for the field of quantum computing, which leverages the unique properties of quantum mechanics to solve complex computational problems more efficiently than classical computers. At the core of quantum computing are qubits, the quantum analogs of classical bits. Unlike classical bits, which are restricted to binary states (0 or 1), qubits utilize superposition and entanglement, enabling them to exist in multiple states simultaneously and interact in ways that amplify computational capabilities. This study presents an overview of quantum algorithms and tools for quantum machine learning, with a focus on recent advancements. Through a comparative analysis and empirical evidence, the research highlights the potential advantages of quantum algorithms in various applications, such as data processing, pattern recognition, and algorithmic complexity. The findings suggest that quantum methods may surpass their classical counterparts in certain domains. However, a key challenge remains: the current limitations of quantum hardware. Despite the theoretical benefits, practical implementation is constrained by the noisy and error-prone nature of quantum devices. Consequently, the experiments conducted in this study were performed in simulation environments, which demonstrated potential improvements when applying quantum paradigms.