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- Advancing internationalisation at the Polytechnic University of Viseu: Transforming challenges into opportunities with short-term mobilitiesPublication . MOTA ROBOREDO AMANTE, FÁTIMA SUSANA; Rodrigues, HelenaInternationalisation has become essential for higher education institutions (HEIs), driven by global collaboration demands. Blended Intensive Programmes (BIPs), Collaborative Online International Learning (COIL), and other European University initiatives (EUIs) offer new international opportunities. The Polytechnic University of Viseu (IPV) in Inner Portugal is embracing this trend. This study explores the potential of short-mobility initiatives in advancing internationalisation, focusing on opportunities for students, staff, and the broader institutional landscape. It highlights the role of the EUNICE European University Alliance (EUA), of which IPV is a member, in fostering international engagement. Through a qualitative analysis, the study examines IPV’s strategic planning, showing how it aligns with internationalisation goals. Organisational culture plays a key role in shaping IPV’s global perspective, and the institution’s resourceful approach and clear strategic direction have contributed to a thriving internationalisation framework. This research also addresses the unique challenges HEIs in Inner Portugal face, offering insights that can inform policymakers and practitioners aiming to enhance internationalisation efforts in similar contexts. Ultimately, it showcas
- AI-Powered Data Management to Optimize Data Collection and Processing in a Painting LaboratoryPublication . Pereira, Maria Teresa Ribeiro; Pereira, Marisa João Guerra; Tavares, Miguel Guedes; Guimarães, André; Vilarinho, HermilioIndustrial laboratories often remain under-digitized compared to production lines, creating a gap between data acquisition and analytical intelligence, critical for advanced quality control. This study addresses this gap by proposing and validating a novel framework that combines Low-Code digitalisation tools with Machine Learning (ML) and Causal Inference to optimise data collection and analysis in an automotive painting laboratory. A Microsoft Power Apps-based platform was developed in order to digitalise all measurement records, eliminating manual transcription errors (previously ≈ 40.01%) and reducing data-handling time by up to 34% of an operator’s shift, while enabling centralised, traceable storage and Power BI integration. Four datasets were used to assess predictive capacity with Random Forest, XGBoost and Neural Networks; Random Forest consistently provided the most stable results—Mean Absolute Error (MAE) of 0.972, Mean Absolute Percentage Error (MAPE) of 16.45%, and Root Mean Square Error (RMSE) of 1.307. Causal models (Linear Regression, DoWhy, Causal Forest, Double Machine Learning) consistently identified ultrafiltrate I solid content of the electrodeposition process as a dominant causal factor for defects. This study provides a novel framework that bridges digitalisation and ML-based causal reasoning in laboratory settings, offering a scalable approach that can be extended and replicated in other industrial sectors, aiming to develop smart, data-driven quality control systems.
- AI-Powered Data Management to Optimize Data Collection and Processing in a Painting LaboratoryPublication . Pereira, Teresa; Oliveira, Marisa; Tavares, Miguel; Guimarães, AndréIndustrial laboratories often remain under-digitized compared to production lines, creating a gap between data acquisition and analytical intelligence, critical for advanced quality control. This study addresses this gap by proposing and validating a novel framework that combines Low-Code digitalisation tools with Machine Learning (ML) and Causal Inference to optimise data collection and analysis in an automotive painting laboratory. A Microsoft Power Apps-based platform was developed in order to digitalise all measurement records, eliminating manual transcription errors (previously ≈ 40.01%) and reducing data-handling time by up to 34% of an operator’s shift, while enabling centralised, traceable storage and Power BI integration. Four datasets were used to assess predictive capacity with Random Forest, XGBoost and Neural Networks; Random Forest consistently provided the most stable results—Mean Absolute Error (MAE) of 0.972, Mean Absolute Percentage Error (MAPE) of 16.45%, and Root Mean Square Error (RMSE) of 1.307. Causal models (Linear Regression, DoWhy, Causal Forest, Double Machine Learning) consistently identified ultrafiltrate I solid content of the electrodeposition process as a dominant causal factor for defects. This study provides a novel framework that bridges digitalisation and ML-based causal reasoning in laboratory settings, offering a scalable approach that can be extended and replicated in other industrial sectors, aiming to develop smart, data-driven quality control systems.
- Artificial intelligence, ethics and sports: a narrative reviewPublication . Oliveira, Sofia; Morgado, Elsa; Leonido, Levi; Pereira, AntoninoArtificial intelligence (AI) is currently being increasingly utilised in various contexts within society. In this regard, several tools employing this same technology are also used in sports. This study had two main objectives: to identify the potentialities of AI usage in high-performance sports for athletes, coaches, and referees/judges, and to understand the ethical issues that its use may cause. To achieve this, a narrative review was conducted through research in the databases PubMed, ResearchGate, B-On, and RCAAP in March and April of this year, resulting in a total of 11 studies. The data obtained indicate that the tools used in high-performance sports indeed offer a series of benefits to the three groups of sports agents mentioned earlier. However, their use can also lead to ethical problems, such as a loss of privacy and data security, as well as the possible creation of inequalities between athletes and/or sports teams, among other concerns. The use of AI in high-performance sports is quite advantageous, but considering the potential issues it may raise, it is necessary to establish well-defined limits and criteria for its use, so that its benefits can be maximised without compromising the ethical values of sport.
- Assessing Q Fever Exposure in Veterinary Professionals: A Study on Seroprevalence and Awareness in Portugal, 2024Publication . Guilherme Moreira; Mário Ribeiro; Miguel Martins; José Maria Cardoso; Esteves, Fernando; Sofia Anastácio; Sofia Duarte; Vala Correia, Helena Maria; Cruz, Rita; Mesquita, João R.Due to their frequent contact with animals, veterinarians may be at preferential risk of Coxiella burnetii exposure due to occupational contact with livestock. This study assesses the seroprevalence and risk factors associated with C. burnetii seropositivity in Portuguese veterinarians. A cross-sectional study compared IgG anti-C. burnetii in veterinarians’ sera to a demographically matched control group. Univariate and multivariate logistic regression analyses evaluated associations between the demographic, occupational, and biosecurity factors and seropositivity. Seroprevalence among veterinarians was 33.7%, significantly higher (p = 0.0023) than in the controls (17.39%). Univariate analysis identified higher seropositivity in the northern region (p = 0.03), though this association was not significant after adjustment (p = 0.07). Protective measures, including isolating aborting animals from the rest of the herd (adjusted OR [aOR]: 0.35, p = 0.03) and wearing gloves during sample collection (OR: 0.28, p = 0.009), were significantly associated with lower infection risk. Veterinarians face increased C. burnetii exposure, but specific biosecurity practices reduce risk. Strengthening preventive measures, including personal protective equipment (PPE) use and biosecurity training, is essential to mitigate occupational and public health risks. Further research should explore vaccination strategies and molecular epidemiology to improve risk reduction efforts.
- Bibliometric Analysis of Studies on Industry 4.0 Maturity Assessment in SMEsPublication . Guimarães, André; Reis, Pedro; Antonio J. Marques Cardoso; Autor correspondente: Guimarães, A..Introdução: Esta pesquisa pretende contribuir para a organização e análise da literatura científica relacionada com a Avaliação do Nível de Maturidade Digital da Indústria 4.0 (I4.0) em pequenas e médias empresas (PMEs). Destaca-se a relevância contínua da transformação digital, impactando as PMEs e oferecendo oportunidades de integração na economia global. Objetivo: O objetivo principal é utilizar técnicas bibliométricas para analisar e organizar a literatura científica disponível na avaliação do Nível de Maturidade Digital da I4.0 em PMEs. Pretende-se contribuir para compreender as tendências de pesquisa, identificar lacunas de conhecimento e fornecer orientações para futuras investigações. Métodos: Realização de uma revisão abrangente da literatura, abrangendo artigos publicados entre 2011 e 2023 nas plataformas Web of Science (WoS) e SciVerse Scopus (Scopus), pela forte reputação, extenso conteúdo e citações globais. Utilização de técnicas bibliométricas facilitadas pelo VOSviewer e pelo software R-studio´s Bibliometrix R para processamento e análise de dados. Resultados: A análise da literatura revelou insights significativos, incluindo a escassez de pesquisas recentes sobre a avaliação do nível de maturidade digital de PMEs no contexto da I4.0. Identificação de tendências de pesquisa, artigos notáveis com base em citações e publicações, bem como reconhecimento de autores frequentemente citados. Conclusão: A importância do estudo reside na análise minuciosa da literatura existente, na avaliação de tendências de pesquisa chave e na identificação de lacunas, fornecendo insights valiosos. As direções propostas e as prioridades para futuras pesquisas destacam a necessidade de investigações adicionais sobre o nível de maturidade digital das PMEs no contexto da I4.0 e áreas como avaliação de desempenho e competências de gestão.
- Bibliometric Analysis of Studies on Lean Maturity Assessment in SMEsPublication . Guimarães, André; Reis, Pedro; Antonio J. Marques Cardoso; Autor correspondente: Guimarães, A..Introdução: Os princípios Lean têm contribuído significativamente para a eficiência e competitividade das pequenas e médias empresas (PMEs). Contudo, persistem lacunas na literatura científica, particularmente na avaliação da implementação do Lean, nos seus impactos no desempenho e na análise das competências de gestão e conhecimento relacionados. Objetivo: Identificar e analisar as tendências de investigação sobre os princípios Lean e a maturidade Lean em PMEs, avaliando as principais contribuições científicas, os artigos mais citados, os autores mais influentes e as lacunas de investigação que possam orientar trabalhos futuros. Métodos: Foi realizada uma análise bibliométrica de artigos publicados entre 2010 e 2024, disponíveis nas bases Web of Science e Scopus. A análise foi suportada pelos softwares Bibliometrix, em R Studio, e VOSviewer, permitindo a visualização de redes de citações, coautorias e palavras-chave. Resultados: São evidenciadas tendências emergentes e lacunas significativas na investigação sobre maturidade Lean em PMEs. Foram identificados artigos, autores e tópicos de maior destaque, bem como a necessidade de mais estudos sobre a avaliação da maturidade Lean e os seus impactos nas práticas organizacionais e no desempenho das PMEs. Conclusão: Este estudo fornece uma visão abrangente do estado da arte sobre os princípios Lean e a maturidade Lean em PMEs. Ao identificar lacunas e propor direções futuras de investigação, contribui para o avanço das práticas Lean, promovendo uma gestão mais eficiente e competitiva.
- Business Productivity and the Adoption of Lean and Industry 4.0 Tools: A Regional StudyPublication . Guimarães, André; Rosivalda Pereira; Pereira, Marisa; Pereira,Maria TeresaThis study investigates the relationship between the adoption levels of Lean and Industry 4.0 (I4.0) tools and business productivity among 140 industrial companies in the Central Region of Portugal. Lean and I4.0 adoption indices were constructed and categorized into tertiles. Productivity data were retrieved from the SABI database. Statistical analysis using non-parametric methods revealed a marginally significant association between Lean tool adoption and productivity (Kruskal-Wallis H(2) = 5.30, p = 0.071), indicating a positive trend. In contrast, a statistically significant relationship was found for Industry 4.0 adoption (H(2) = 8.39, p = 0.015, Dunn’s p = 0.039), with companies with low adoption levels underperforming those with medium and high levels. No significant productivity differences were observed by firm size (p = 0.154). These findings highlight the relevance of Lean and I4.0 tools as productivity drivers, regardless of company size, and underscore the importance of promoting structured digital and operational transformation strategies in low digital maturity regions.
- Carbon footprint calculator for the Portuguese textile and clothing industry: development, application and validationPublication . C. Duarte; Ferreira, José; Lopes Brás, Isabel Paula; Ferreira Silva, Maria Elisabete
- Characterization of Lignocellulosic Byproducts from the Portuguese Forest: Valorization and Sustainable UsePublication . Macena, Morgana; Gonçalves Oliveira Valente da Cruz-Lopes, Luísa Paula; Grosche, Lucas; Santos-Vieira, Isabel; Esteves, Bruno; Pereira, HelenaThe increasing emphasis on environmental sustainability has placed biomass as a versatile and renewable resource, while the management and disposal of forest byproducts remain a significant challenge. This study explores the valorization of forest biomass residues derived from Pinus pinaster, Pinus pinea, and the invasive species Acacia dealbata, with a focus on their potential application as bioadsorbents. A comprehensive physicochemical characterization was conducted for different biomass fractions (leaves, needles, and branches of varying diameters). Leaves and needles contained higher amounts of extractives (from 7.7% in acacia leaves to 18.8% in maritime pine needles) and ash (3.4 and 4.2% in acacia leaves and stone pine needles, respectively), whereas branches contained more holocellulose (from 59.6% in P. pinea small branches to 79.2% in P. pinaster large branches). ATR-FTIR and pHpzc analyses indicated compositional and surface charge differences, with higher pHpzc values in A. dealbata relative to Pinus. TG analysis showed that acacia large branches degraded at a lower temperature (320 °C) compared to Pinus species (440–450 °C). Overall, the findings highlight the suitability of these underutilized forest byproducts as bioadsorbents, contributing to the advancement of circular economy practices.