Abstract
Phonology is a linguistic discipline that is naturally computational. However, as many researchers are not familiar with the use of digital methods, most of the computation required is still performed by humans. This article presents a training experiment of master's students of the phonology seminar at the University of Porto, bringing the research process directly to the classroom. The experiment was designed to raise students' awareness of the potentialities of combining human and machine computation in phonology. The Centre for Digital Culture and Innovation (CODA) readily embraced this project to showcase the application of digital humanities as humanities in both research and training activities. During this experiment, students were trained to collect and process phonological data using various open-source and free web-based resources. By combining a strict protocol with some individual research freedom, the students were able to make valuable contributions towards Creolistic Studies, while enriching their individual skills. Finally, the interdisciplinary nature of the approach has demonstrated its potential within and beyond the humanities and social sciences fields (e.g., linguistics, archaeology, history, geography, ethnology, sociology, and genetics), by also introducing the students to basic concepts and practices of Open Science and FAIR principles, including Linked Open Data.

Abstract
For decades, the collaborative networks community has studied supply chains, focusing on trust, visibility, collaboration, and innovation, with emergent technologies being a key area of research. The rise of digital technologies has led to extensive studies on supply chain digital transformation. With the surge of AI-based technologies, there is an increasing body of research on AI's human and social impact on Supply Chain Management (SCM). However, while Socio-Technical Systems (STS) thinking has been applied to digital transformations, it has not yet addressed AI-induced changes in supply chains. This paper synthesises recent research on AI integration in SCM and the use of STS thinking in AI systems design. We propose a mapping approach for profiling AI-induced supply chain transformations for strategic design. We also present the Supply Chain Socio-Technical AI (SC-STAI) profiling tool in practice, demonstrating how it maps supply chain participants' current and desired states regarding AI integration.

Abstract
This paper introduces a deep learning approach for detecting plant leaf diseases. The objective is to develop robust models capable of accurately identifying plant diseases across various image backgrounds, thereby overcoming the limitations of existing methods that often rely on controlled laboratory conditions. To achieve this, a combination of the PlantDoc dataset and Web-sourced data of plant images from online platforms was used. This paper implemented and compared state-of-the-art *cnn architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, all fine-tuned specifically for leaf disease classification. A significant contribution is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. Results indicated varied performance across the datasets, with EfficientNet models generally outperforming others. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved the highest accuracy of 73.31%. In cross-dataset evaluation, EfficientNet-B3 reached 76.77% accuracy when trained on PlantDoc and tested on the Web-sourced dataset. The best performance occurred when training on the combined dataset and testing on the Web-sourced data, resulting in an accuracy of 80.19%. Class-wise F1-scores revealed consistently high performance (>0.90) for diseases such as apple rust leaf and grape leaf across models. This paper contributes to the comparative analysis of various datasets and model architectures for effective leaf disease detection

Abstract
Considering the rising energy needs and the depletion of conventional energy sources, microgrid systems combining wind energy and solar photovoltaic power with diesel generators are promising and considered economically viable for usage. To evaluate system cost and dependability, optimizing the size of microgrid system elements, including energy storage systems connected with the principal network, is crucial. In this line, a study has already been performed using a uni-objective optimization approach for the techno-economic sizing of a microgrid. It was noted that, despite the economic criterion, the environmental criterion can have a considerable impact on the elements constructing the microgrid system. In this paper, two multi-objective optimization approaches are proposed, including a non-dominated sorting genetic algorithm (NSGA-II) and the Pareto Search algorithm (PS) for the eco-environmental design of a microgrid system. The k-means clustering of the non-dominated point on the Pareto front has delivered three categories of scenarios: best economic, best environmental, and trade-off. Energy management, considering the three cases, has been applied to the microgrid over a period of 24 h to evaluate the impact of system design on the energy production system's behavior.

Abstract
Lock-freedom offers significant advantages in terms of algorithm design, performance and scalability. A fundamental building block in software development is the usage of hash map data structures. This work extends a previous lock-free hash map to support a new simplified design that is able to take advantage of most state-of-the-art safe memory reclamation methods, thus outperforming the previous design. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
PurposeThe purpose of the article is to develop an innovative pedagogic tool: an escape room board game to be played in-class, targeting an introduction to an ethics course for engineering students. The design is student-centred and aims to increase students' appreciation, commitment and motivation to learning ethics, a challenging endeavour for many technological students.Design/methodology/approachThe methodology included the design, development and in-class application of the mentioned game. After application, perception data from students were collected with pre- and post-action questionnaire, using a quasi-experimental method.FindingsThe results allow to conclude that the developed game persuaded students be in class in an active way. The game mobilizes body and mind to the learning process with many associated advantages to foster students' motivation, curiosity, interest, commitment and the need for individual reflection after information search.Research limitations/implicationsThe main limitation of the game is its applicability to large classes (it has been successfully tested with a maximum of 65 students playing simultaneously in the same room).Originality/valueThe originalities and contributions include the presented game that helped to captivate students to ethics area, a serious problem felt by educators and researchers in this area. This study will be useful to educators of ethics in engineering and will motivate to design tools for a similar pedagogical approach, even more so in areas where students are not especially motivated. The developed tool is available from the authors at no expense.