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On May 12, the Institute for Research in Didactics and Education (IRDENa) at the University of Namur is organizing a seminar dedicated to a topic at the heart of current concerns in initial and continuing teacher education: training through professional practice.In a context where expectations of the teaching profession and the realities on the ground are evolving rapidly, and where policymakers are entrusting the field with a significant portion of the training of future teachers through the requirement for extended teaching placements, practical experience plays a decisive role in the development of professional competencies, professional skills, and educational approaches. This essential practice also raises numerous questions:What challenges does training in (with and through) professional practice face today?What obstacles still hinder its implementation or quality?What challenges must training institutions, partner schools, and trainers address?What concrete benefits does professional immersion offer for both aspiring and experienced teachers?To shed light on these questions, the event will feature two speakers:Catherine Van Nieuwenhoven, professor and international expert on teacher education and work-study programs;Sephora Boucenna, a researcher at IRDENa, whose work focuses on professional development through practice and the analysis of field experiences.Their combined perspectives, blending scientific expertise, institutional analysis, and a nuanced understanding of the field, will fuel a collective discussion on the levers to strengthen and the avenues to explore in order to support an ambitious, coherent, and efficient practical training program in light of current policy challenges.The program will continue with a roundtable bringing together colleagues from various universities, who will compare their perspectives on the training of internship supervisors. Their discussion will focus in particular on training needs, support models, institutional challenges, and the conditions necessary to organize this training effectively.Finally, the day will give significant attention to real-world practice through testimonials from several student teachers, who have been invited to share their experiences, their mentoring practices, as well as the tensions that arise in their daily professional lives. These accounts will help ground the discussion in the concrete realities of schools and highlight the importance of the partnership between academic institutions and K-12 education.This study day is intended for researchers, trainers, teachers, as well as anyone involved in teacher education who wishes to contribute to a shared reflection on the future of the profession. Program 8:30–9:00 a.m. – Welcome9:00 AM to 10:00 AM – Catherine Van Niewenhoven (UCLouvain) - The Role of Fieldwork in Teacher Education: Support and Professional Development10:00 AM to 10:20 AM - Coffee break10:20 AM to 11:20 AM – Research findings (Call for papers)11:20 AM to 12:20 PM - Presentation of mentor programs12:30 PM to 1:30 PM – Lunch1:30 PM to 2:00 PM - Research Incubator2:00 PM to 3:00 PM - Sephora Boucenna (UNamur) - Training through and within professional practice: what are the specific features?3:00 PM to 4:00 PM - RoundtableStarting at 4:00 PM - Closing reception

Invited speakers: Manuel GARCÍA-SANTANA (Pompeu Fabra University) and Eric VERHOOGEN (Columbia University)Audience: PhD studentsCall for papers: PhD students and postdocs are invited to submit a paper by April 15, 2026. Applicants will be notified by May 15, 2026. Accepted contributions will be presented in a poster session, with a subset selected for seminar-style presentations. Please send your paper to this address: nathan.jespere@unamur.be

Researchers, graduate students, artists, designers, teachers, and creatives from all walks of life: this is a unique opportunity to take your ideas beyond the usual framework and bring them to life in a new way. The goal of this first edition: to collaboratively design an educational treasure hunt in Namur, centered on the theme of misinformation, aimed at young people aged 15 to 18.Practical detailsWhen? June 1, 2, and/or 3, 2026 (9 a.m.–4 p.m.), flexible participation (a few hours, half a day, a full day, or all three days)Where? The TRAKK in NamurLunch provided by NADI + unlimited coffee/tea in a collaborativeatmosphere Everything is set up for experimenting, testing, and creatingRegistration: free, but please send an email to digifactory.asbl@gmail.comA chance to bridge disciplines, break out of silos…and invent new ways to tell stories, convey information, and share.Note: For doctoral students, participation counts toward your doctoral program (course credits).

As part of the Inforums and CyberExcellence seminars, we are pleased to welcome Dr. Ryan Wails (Georgetown University), who will speak to us about cybersecurity. You will find the abstract of his presentation and his biography below.No registration required.We hope to see many of you at this event!On the Interplay of Modern Traffic Analysis and Internet Censorship & Circumvention TechniquesIn this talk, I will review the current state of real-world Internet censorship and some tools that network users employ to circumvent censorship. Then, I’ll take a forward-looking view on how censors might incorporate modern ML-based traffic analysis techniques to block users, highlighting the need for stronger circumvention tools. Finally, I’ll discuss our new internet censorship evasion technique called Unidentified Protocol Generation (published at USENIX Security 2025), which is capable of evading detection by state-of-the-art traffic analysis.Author bio: Ryan Wails is a postdoctoral researcher at Georgetown University studying network privacy and security. He completed his PhD while working at the U.S. Naval Research Laboratory. This lab is known for originating the Tor network and establishing the PETs research community, which is now among the top international research communities in computer security. Ryan is a core contributor to the Tor project and has published in leading conferences on network simulators, privacy attacks, privacy-preserving measurements, path selection algorithms, censorship circumvention, and website fingerprinting. He has received several international distinctions, including Best Paper awards at top conferences and recognition at community events for his contributions.

JuryProf. Johan WOUTERS (UNamur), ChairProf. Steve LANNERS (UNamur), secretaryProf. Stéphane VINCENT (UNamur)Prof. Johan WINNE (UGent)Prof. Andrew MITCHELL (Illinois State University)AbstractTaxpropellane is a taxane complex with a particularly elaborate structure. Although its biological properties are still unknown, its structural complexity makes it a synthetic target of choice. The approach developed in this thesis is based on a retrosynthetic simplification toward a bicyclo[5.4.0]undecane, the preparation of which requires new methodologies, in particular the development of an (5+2) oxydopyrylium cycloaddition using a temporary bridge to construct the required bicyclic compounds.Oxydopyrylium species are highly reactive aromatic intermediates, commonly used to synthesize 7-membered rings via cycloaddition reactions. Their use for intermolecular cycloadditions is severely limited due to their rapid dimerization when the dipolarophile is not sufficiently reactive. The strategy developed in this work relies on the use of a temporary ether-type linker to overcome this limitation. Numerous bicyclic compounds have thus been efficiently synthesized using this methodology. We have shown that this diastereoselectivity depends solely on how the two reactive fragments are linked. Thus, the proposed methodology allows for complete control of stereoselectivity. The cleavage of the linker has also been investigated. This can be achieved in two different ways. This work extends the use of oxidopyrylium ions in synthesis, and this methodology will be applied to the total synthesis of taxpropellane, following the synthesis of a suitable dipolarophile also described here.

Additional information Milan Van Steenvoort - milan.vansteenvoort@unamur.beNathan J'Espère - nathan.jespere@unamur.beGuilhem Cassan - guilhem.cassan@unamur.be View the program and list of speakers

Inspired by the Prologue to Nietzsche’s Thus Spoke Zarathustra, this play offers a theatrical experience that is at once vibrant, educational, and deeply philosophical. Through the stage, speech, and the body, we explore Nietzsche’s great questions—the Übermensch, the meaning of life, nihilism—in an original staging that also addresses contemporary issues in theatrical practice.We look forward to welcoming you to share this moment of thought in action, where philosophy is performed and seen as much as it is reflected upon.An event organized by: Nicolas Monseu, Fabien Robert, Jean Loubry, Chloé Guzman-Mardones, Lucien Flament, Marie Forget, Alixe Barbier, Joachim Rens, Nounou Remy, and Emilie Chasseur

JuryProf. Joseph WINKIN (UNamur), ChairProf. Timoteo CARLETTI (UNamur), SecretaryProf. Alexandre MAUROY (UNamur)Prof. Malbor ASLLANI (Florida State University)Dr. Maxime LUCAS (UNamur)Dr. Riccardo MUOLO (RIKEN Institute)AbstractSynchronization is a ubiquitous phenomenon in the world around us. It is a crucial feature that ensures the proper functioning of many complex systems. The various generators in a power grid must produce alternating current at a common frequency, and the brain’s cortical regions synchronize their activities to enable the brain to control the human body. These systems can be modeled as coupled oscillators, as in the famous Kuramoto model, where entities interact in pairs so that they synchronize globally.However, synchronization can also pose a problem. For instance, excessive synchronization of brain dynamics leads to pathological states such as epileptic seizures. It is therefore necessary to develop methods that reduce global synchronization by locally controlling the dynamics of certain oscillators. In particular, a control scheme based on a Hamiltonian framework has been designed to effectively desynchronize the Kuramoto model.Nevertheless, some limitations remain. First, the controlled nodes are selected at random without considering their specific characteristics. Second, this method is designed to control systems with a network structure—that is, with pairwise coupling—whereas many recent studies have demonstrated the importance of higher-order networks, i.e., group interactions, in modeling such systems.In this Ph.D. thesis, we aim to address these gaps through several studies. We explore the optimal method for selecting controlled nodes to maximize control efficiency, investigate the method’s ability to desynchronize systems with higher-order interactions, and develop a new control method tailored to this framework.Our results not only improve these control techniques but also offer novel perspectives on the synchronization of complex systems. They allow us to better understand the influence of each local entity on collective behavior and the role played by interactions of different orders. Among other things, they shed light on the non-monotonic relationship between synchronization capacity and the strength of higher-order interactions.

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

Program Overview This event will take place over two exciting days, featuring a diverse program designed to offer a dynamic and interactive experience for all participants. The entire symposium will be conducted in English.On Wednesday, December 9, and Thursday, December 10, two days of scientific and technical sessions will be dedicated to the topics of alternative proteins, the microbiome, and functional ingredients.On Wednesday, December 9, there will be a public lecture (in English) presented by Dr. Patrice Cani on the topic “Nourishing Your Gut: Nutrition, Microbiota, and Health.”For experienced researchers and group leaders, we are organizing a third day on December 11 dedicated to international collaboration, including laboratory visits and a workshop focused on establishing structured collaborative projects. Separate registration is requiredOverview of the ThemesThis symposium is structured around the three projects in the FoodWal portfolio, while pushing their boundaries and framing them within a “One Health” approach.The session titled “Building Sustainable Value Chains for Alternative Proteins: from protein sources to the development of healthy food products” will provide an opportunity to present scientific and technological advances in the creation and characterization of alternative protein sources and products, as well as socioeconomic perspectives on the development, maintenance, and growth of sustainable value chains for alternative proteins.The session titled “Research on the Microbiome and Microbiotics: Innovation in Nutrition for Better Health” will present scientific advances in the characterization, understanding, and modulation of the microbiome, as well as cutting-edge technologies aimed at developing innovative microbiotics.Finally, the session titled “Functional Ingredients and Bioactive Compounds: Food Science and Biotechnology for Health” will focus on scientific advances in the identification, characterization, and understanding of the mechanisms of action of functional ingredients, as well as cutting-edge technologies designed to develop and produce innovative functional ingredients. More information on the FoodWal website Je m'inscris

JuryProf. Steve LANNERS (UNamur), ChairProf. Johan WOUTERS (UNamur), SecretaryProf. Pierre FRANCOTTE (ULiège)Dr. Marie HAUFROID (UCB)Prof. Lionel POCHET (UNamur)AbstractCurrently, Mycobacterium tuberculosis remains the second deadliest infectious agent in the world, responsible for 1.6 million deaths in 2021. The burden and cost of current treatment (6 months and 4,000 euros), coupled with the alarming emergence of antibiotic-resistant strains, underscore the absolute urgency of developing new therapeutic molecules. This study focuses on the Mycobacterium tuberculosis phosphoserine phosphatase (MtSerB2), an enzyme essential for serine biosynthesis and vital to the pathogen’s survival. Furthermore, this protein plays a key role in host invasion (through its interactions with the NF-κB factor and the cellular cytoskeleton), making MtSerB2 a prime therapeutic target for the development of new, potent anti-tuberculosis drugs.One of the innovative strategies explored in this thesis is based on destabilizing the protein’s structure (disruption of protein structure). The goal is to design a molecule capable of disrupting the enzyme’s conformation, thereby causing it to lose its catalytic activity. This new class of molecules is expected to exhibit significantly higher selectivity for MtSerB2 compared to its human homolog, human phosphoserine phosphatase (hPSP).To this end, the Mycobacterium avium phosphoserine phosphatase (MaSerB) was initially used as a model system, justified by its 83% sequence identity with MtSerB2 and its propensity to crystallize rapidly. Initially, a virtual screening of drugs already available on the market was conducted to identify potential inhibitors of MaSerB. Enzymatic assays based on malachite green detectionwere then performed to evaluate the inhibitory activity of the various candidates. The results demonstrated increased selectivity of these compounds for dimeric proteins (MtSerB2 and MaSerB) compared to the human enzyme hPSP.Notably, subsequent enzymatic assays conducted directly on MtSerB2 revealed response profiles that differed from those observed with the MaSerB model. To elucidate the molecular basis of these differences, the structure of the protein in its ligand-bound state is currently being investigated. To this end, protein-inhibitor complexes have been crystallized and will be analyzed by X-ray diffraction.