Abstract
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. These compressed models are especially essential when implementing multi-model fusion solutions where multiple models are required to operate simultaneously. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.

Abstract
We propose an approach to cluster and classify compositional data. We transform the compositional data into directional data using the square root transformation. To cluster the compositional data, we apply the identification of a mixture of Watson distributions on the hypersphere and to classify the compositional data into predefined groups, we apply Bayes rules based on the Watson distribution to the directional data. We then compare our clustering results with those obtained in hierarchical clustering and in the K-means clustering using the log-ratio transformations of the data and compare our classification results with those obtained in linear discriminant analysis using log-ratio transformations of the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The challenge of engineering education is to transform engineering students into agents of innovation and well-being. In addition to solid scientific and technical knowledge, critical thinking, problem-solving and interpersonal competencies, it implies the ability to design and implement solutions supported by ethical and sustainability principles. With this goal in mind, the European Project Semester (EPS) provides a student-centred project-based learning framework. It is offered by a group of European higher education institutions, including the Instituto Superior de Engenharia do Porto (ISEP), the engineering school of the Polytechnic of Porto. Students work in teams of four to six, from different fields of study and nationalities, to design solutions to problems that affect individuals, society or the planet, taking into account the state of the art, the market and the ethical and sustainability implications of their decisions. These solutions are then implemented in a proof-of-concept prototype. Most of the projects address problems in education, the environment, food production and smart cities and have a strong educational, ethical and sustainability drive, encouraging students to develop sustainability competencies. This work analyses team papers of illustrative EPS@ISEP projects searching for evidences of the development of sustainability competencies. The proposed method maps keywords related to the sixteen United Nations Sustainable Development Goals to the contents of team papers by applying natural language processing and reusing the list of SDG keywords proposed by Auckland University. The results confirm EPS@ISEP fosters sustainability competencies in engineering undergraduates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The main aim of this chapter is to explore the role of social media in the internationalization of business-to-business (B2B) companies, addressing a remaining gap in the literature. It includes a qualitative study, where data from semi-structured interviews was subject to thematic content analysis. The study found that social media acts as a facilitator of internationalization in the B2B setting. While social media alone does not determine the success of internationalization efforts, it has become an essential tool for firms seeking to engage with global markets and maintain a competitive edge. The study provides relevant insights for managers, recommending that businesses should track social media performance to understand its impact on internationalization efforts and adjust strategies accordingly. © 2025 Elsevier B.V., All rights reserved.

Abstract
Hash maps are a widely used and efficient data structure for storing and accessing data organized as key-value pairs. Multithreading with hash maps refers to the ability to concurrently execute multiple lookup, insert, and delete operations, such that each operation runs independently while sharing the underlying data structure. One of the main challenges in hash map implementation is the management of collisions. Arguably, separate chaining is among the most well-known strategies for collision resolution. In this paper, we present a comprehensive study comparing two common approaches to implementing separate chaining-linked lists and dynamic arrays-in a multithreaded environment using a lock-based concurrent hash map design. Our study includes a performance evaluation covering parameters such as cache behavior, energy consumption, contention under concurrent access, and resizing overhead. Experimental results show that dynamic arrays maintain more predictable memory access and lower energy consumption in multithreaded environments.

Abstract
Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&Estains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms in ANNs, the scientific understanding of these methods should first be addressed.