Abstract
One strategy for constructing an artificial neural network with multiple hidden layers is to insert layers incrementally in stages. However, for this approach to be effective, each newly added layer must be properly aligned with the previous layers to avoid degradation of the network output and preserve the already learned knowledge. Ideally, inserting new layers should expand the network's search space, enabling it to explore more complex representations and ultimately improve overall performance. In this work, we present a novel method for layer insertion in stacked autoencoder networks. The method developed maintains the learning obtained before the layer insertion and allows the acquisition of new knowledge; therefore, it is denoted collaborative. This approach allows this kind of neural network to evolve and learn effectively, while significantly reducing the design time. Unlike traditional methods, it addresses the common challenges associated with manually defining the number of layers and the number of neurons in each layer. By automating this aspect of network design, the proposed method promotes scalability and adaptability between tasks. The effectiveness of the approach was validated on multiple binary classification datasets using neural networks initialized with various architectures. The experimental results demonstrate that the method maintains performance while streamlining the architectural design process.

Abstract
Group fairness in machine learning is an important area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple clients, amplifies the need for fairness methodologies due to its inherent heterogeneous data distributions that can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 48 research works specifically dedicated to addressing this issue. However, no comprehensive survey has specifically focused on group fairness in Federated Learning. In this work, we analyze the key challenges of this topic, propose practices for its identification and benchmarking, and create a novel taxonomy based on criteria such as data partitioning, location, and strategy. Furthermore, we analyze broader concerns, review how different approaches handle the complexities of various sensitive attributes, examine common datasets and applications, and discuss the ethical, legal, and policy implications of group fairness in FL. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.

Abstract
The increasing deployment of solar photovoltaic technologies has intensified concerns regarding end-of-life waste management and the recovery of critical raw materials. Given the socio-environmental and economic significance of photovoltaic panels’ waste, their integration into circular economy and industrial symbiosis strategies is becoming imperative. However, current sustainability assessment methods remain fragmented, with few frameworks adequately supporting informed decision-making across sustainability dimensions. This study conducts a literature review of existing decision-support frameworks that integrate Life Cycle Assessment and Multi-criteria Decision Analysis in the context of industrial symbiosis. The results reveal limited applications of this hybrid methodology specifically targeting photovoltaic waste streams. Key challenges, gaps, and trends were identified being shared into particular inputs and holistic outputs. Based on this synthesis, the paper proposes foundational features for a robust framework tailored to the industrial symbiosis of the photovoltaic waste context, emphasizing dynamic modeling and the critical role of digital tools. This work contributes both a conceptual roadmap and a practical foundation for researchers, policymakers, and industry actors seeking to enhance the sustainability and circularity of photovoltaic waste through industrial symbiosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
PurposeDetecting anomalous access to electronic health records (EHRs) is critical for safeguarding patient privacy and ensuring compliance with healthcare regulations. Traditional anomaly detection methods often struggle in this domain due to extreme class imbalance, limited labelled data and the subtlety of insider threats. This study proposes a lightweight, hybrid anomaly detection framework that integrates unsupervised, supervised and rule-based approaches using a meta-classifier architecture.Design/methodology/approachAn experimental and model-development approach is employed, combining machine learning techniques with domain-inspired rule modelling to construct a hybrid anomaly detection framework for healthcare access logs. Performance of the algorithm is measured using standard classification metrics such as precision, recall, F1-score and accuracy.FindingsEvaluated on a synthetic but realistic dataset of 50.000 normal and 500 labelled anomalous healthcare access events, the proposed framework achieved superior performance compared to standalone models as well as other hybrid models, with an F1-score of 0.8989 and recall of 0.8180. It also maintained low inference latency (0.028 ms) and energy consumption (4.03e-07 kg CO2), making it suitable for deployment in resource-constrained clinical environments.Originality/valueThis study highlights the potential of a hybrid meta-classifier to enhance anomaly detection in healthcare access logs, capturing both subtle and obvious anomalies while outperforming conventional models and remaining efficient, scalable and practical for real-time monitoring.

Abstract
This report describes the ICDAR 2025 Competition on Automatic Classification of Literary Epochs (ICDAR 2025 CoLiE), which consisted of two tasks focused on automatic prediction of the time in which a book was written (date of first publication). Both tasks comprised two sub-tasks, where a related fine-grained classification was addressed. Task 1 consisted of the identification of literary epochs, such as Romanticism or Modernism (sub-task 1.1), and a more precise classification of the period within the epoch (sub-task 1.2). Task 2 addressed the chronological identification of century (sub-task 2.1) or decade (sub-task 2.2). The compiled dataset and the reported findings are valuable to the scientific community and contribute to advancing research in the automatic dating of texts and its applications in digital humanities and temporal text analysis.

Abstract
The use of process improvement methodologies to assist and support the improvement of processes has proven to be an important mechanism for effectively implementing these improvements. However, there is difficulty in choosing the best methodology and to ensure that it will lead to the best improvement results. In this sense, the research questions of this work can be formulated as the following: H1-There are differences between the major process improvement methodologies and gaps not covered by them; H2-A new process improving methodology may mitigate the gaps identified in the existing process improvement methodologies. Comparing the main process improvement methodologies available in the literature, namely, PDCA, Six Sigma, DMAIC, QC Story, 8D, TOC and Lean, it was proven the research question H1. To validate the research question H2 a new process improvement methodology, the MAPIC, was then proposed and compared with the other methodologies. From a theoretical view point, the research question H2 was validated, because the MAPIC covers the existed gaps from the others methodologies, namely, that there is no phase to promote proactive continuous improvement, nor to validate the proposed improvement before its implementation. As for its practical validation, the MAPIC is being applied in a case study and the results will be presented in further work.