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JuryProf. Max COLLINET (UNamur), ChairProf. Johan YANS (UNamur), secretaryProf. Flavien CHOULET (Marie and Louis Pasteur University)Dr. Alexandre FELTEN (UNamur)Prof. Mohammed BOUABDELLAH (Mohammed IV Polytechnic University)Prof. Nadine MATTIELLI (Free University of Brussels)Dr. Augustin DEKONINCK (UMons)AbstractSupergene processes are responsible for the redistribution of metals near the surface and can form economically significant mineral deposits. This PhD thesis investigates the evolution (genesis and timing) of supergene mineralization in polymetallic systems from Morocco (Anti-Atlas and Atlas) and France (Provence). The study combines field observations, petrography, geochemistry, stable isotope analyses, and experimental oxidation to provide a multiscale understanding ranging from microscopic characterization to regional geological evolution.Stable Cu and Fe isotopes show unique fractionation in each deposit, which primarily depends on the primary ore’s isotopic composition. In addition, specific minerals (e.g., arsenates) may strongly influence the Cu fractionation of later-formed minerals (e.g., malachite), which can result in highly variable Cu isotope compositions across deposits. Therefore, Cu and Fe isotopes must be considered site-specific. Experimental investigations complement geological data by quantifying the oxidation rates of pyrite and galena under various conditions. These results highlight that the timing of weathering is reproducible and consistent with natural examples studied in this thesis via (U–Th)/He and K–Ar geochronology. However, pyrite oxidation (4.3 µm/year) is faster than that of galena, which may have a catalytic effect on other sulfides in polymetallic deposits.Overall, supergene mineralization reflects combined controls from mineralogy, host rocks, fluids, climate, and tectonics. This work refines genetic models and provides new tools to describe and constrain secondary mineralization, as well as their potential impact on metallurgical processes.

Flamure Ibrahimi is a Ph.D. student in service and marketing management at the NaDI-CeRCLe Research Center at the University of Namur (Belgium) within the EMCP Faculty, under the supervision of Prof. Dr. Wafa Hammedi (University of Namur) and Prof. Dr. Linda Alkire (Texas State University). She has just been awarded the prestigious ServCollab Scholarship 2026, an international distinction that recognizes and supports doctoral students whose work falls within the field of Transformative Service Research (TSR)—doctoral projects with high impact on society and humanity.

Abstract Predictive maintenance (PdM) is increasingly being used to improve system reliability, reduce downtime, and optimize operating costs in complex industrial environments through anomaly detection. As industrial systems become more complex, with components interacting with one another and monitored by ever-expanding sensor networks, anomaly detection faces new methodological challenges. In particular, there is a gap in the literature regarding anomaly detection using traditional machine learning (ML) methods based on a Single Label Classification (SLC) approach, which is unsuitable when multiple failures can occur simultaneously.Traditional anomaly detection approaches based on ML methods using an SLC formulation assume that each observation belongs to a single anomaly category. This assumption does not hold when multiple failures occur simultaneously on the same equipment, resulting in a loss of information. We begin by illustrating this limitation through a concrete example showing that using an SLC approach instead of a multi-label formulation degrades the classification of simultaneous anomalies. To address this issue, anomaly detection is reformulated as a Multi-Label Classification (MLC) problem, enabling the detection of multiple failures within a single time step. Following this reformulation, the issue of selecting multi-label classifiers suited to the context of Predictive Maintenance (PdM) must be examined. The general conclusion from the literature is that the performance of classifiers depends heavily on the context and that while there are many detection methods, none is universally dominant. Although in-depth comparative studies exist in the field of MLC, datasets from complex systems are absent from these comparisons, even though they exhibit specific characteristics such as multivariate time-series structures, significant label imbalance, and complex interactions between components.To address this gap, a structured and reproducible evaluation protocol is proposed to evaluate eight state-of-the-art ML methods in an MLC framework across three public industrial datasets. The results confirm that the performance of classifiers depends heavily on the characteristics of the dataset and that no single method consistently outperforms others across all scenarios. However, conducting this type of comparison for each new industrial context is costly in terms of time, computational resources, and data requirements, which limits its feasibility for real-world industrial deployment. The thesis also addresses this issue through dimensionality reduction and variable selection methods. PM systems generate large multivariate datasets in which only a subset of the variables is actually relevant for anomaly detection. A self-adaptive evolutionary strategy is proposed to perform wrapper-style variable selection and obtain a subset containing only the most informative variables. Experimental results show that reducing the variable space improves computational efficiency and, in most cases, anomaly prediction performance. A comparison of the proposed method with state-of-the-art metaheuristic approaches on three PdM datasets and five anomaly detection methods confirms its competitiveness in terms of predictive performance and the optimal size of the selected feature subset. Finally, feature space reduction not only optimizes the resources required for detection but also reduces the number of signals that technicians must analyze during root cause investigations. This simplification aims to strengthen confidence in the system. Finally, the thesis addresses the practical aspects of deploying detection methods in complex systems. Among these, there is a lack of trust and transparency among maintenance technicians and decision-makers. An analysis of the interpretability and robustness of anomaly detection methods is provided to directly contribute to their deployment in real-world conditions. First, the proposed variable selection strategy is evaluated on a public PdM dataset using SHAP-based explanations to verify the consistency between the selected variables and those identified as the most important by tools dedicated to interpretability. The results show that the selected variables are consistent with those identified as the most important by these tools. Furthermore, visual analysis of the most influential variables reveals that anomalies are associated with a limited and well-defined subset of sensor signals, which helps improve transparency and strengthen confidence in automatic detection systems. At the same time, it is necessary to conduct a study of the robustness of multi-label classifiers in controlled sensor degradation scenarios, including different types of degradation and various levels of severity. The results show that the performance of classifiers can deteriorate significantly, particularly in the presence of gradual drifts. To mitigate this effect, introducing sensor degradation during training appears to be a relevant strategy. Finally, this thesis proposes a structured framework for multi-label anomaly detection in complex systems, covering classification methods, feature space optimization, interpretability, and robustness. For each of these aspects, the thesis adopts an industrial perspective and takes into account the differences between controlled laboratory experimental conditions and the additional constraints of real-world complex environments. The jury Prof. Katrien Beuls - University of Namur, BelgiumProf. Elio Tuci - University of Namur, BelgiumProf. Patrick Heymans - University of Namur, BelgiumProf. Jenni Raitoharju - University of Jyväskylä, FinlandMr. Fabio Pinna - Telespazio, BelgiumMr. Vito Trianni - ISTC-CNR, Italy Sign me up

Sara Belghiti, a doctoral student at the University of Namur (NaDI-CeRCLe Institute) and a teaching and research assistant at the IÉSEG School of Management (France), has just been recognized by the AMS Review – Sheth Foundation Doctoral Competition for Conceptual Articles (DoCCA) 2026, one of the world’s most prestigious doctoral competitions in theoretical and conceptual marketing and management. 

Brendan Reid has just joined the Environmental and Evolutionary Biology Research Unit (URBE) team in the Department of Biology, Faculty of Science. This unit functions as a collaborative ecosystem, bringing together skills and expertise to advance research on organisms and their dynamic interactions with the environment. Dive into aquatic and semi-aquatic research!

A project submitted by Professor Frédéric Silvestre's Laboratoire de Physiologie Évolutive et Adaptative (LEAP) at the University of Namur has been ranked among the top 6 research projects funded by the FNRS and the Fonds de recherche du Québec (FRQ) for scientific collaboration between Wallonia and Quebec. The aim? To understand the impact of human activities on St. Lawrence Estuary (SLE) belugas, using interdisciplinary approaches to help improve conservation strategies for this threatened species..

This is a great recognition of research at UNamur: three Marie Skłodowska-Curie Doctoral Networks (DN) projects have just been awarded, with a key contribution from researchers in Namur! The first, in chemistry, involves Professor Stéphane Vincent; the second, focused on ecosystem resilience, involves Professor Frédérik de Laender; and the third, in the field of photonics, benefits from the expertise of FNRS-qualified researcher Michaël Lobet.

Do you know the rivulus? It is a small fish that lives in the Caribbean and has some amazing characteristics. It is indeed capable of self-fertilisation! But in this case, what happens to genetic diversity, which is essential for the evolution of a species? Welcome in the mangroves of Florida and Belize to find an answer.

An international team of researchers, including Prof Frederik De Laender, from the University of Namur, publish in Nature Communications. The editor highlights that the authors use theoretical models and field data to show how eco-evolutionary processes can force species to develop more similar characteristic traits in more species-rich communities to avoid competition. Which goes against what we intuitively perceive.

In a new publication, the European Reference Genome Atlas (ERGA) announces the success of its pilot project. This pioneering initiative has brought together a vast collaborative network of researchers and institutions in 33 countries to produce high-quality reference genomes of 98 European species. This continental effort paves the way for a new, inclusive and equitable model of biodiversity genomics.

At a time when around a fifth of Europe's 200,000 species are threatened with extinction, researchers from the University of Namur are taking part in a pan-European consortium to act fast and together to generate high-quality genome resources on a large scale.

A team of American researchers, with the help of Frédérik De Laender, professor in the Department of Biology at UNamur, has just published in the prestigious journal Nature. Their study describes how changing stream temperatures and human introductions of fish can alter river biodiversity in the USA.