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A new platform dedicated to research in the humanities and social sciences (SHS) is being launched at UNamur. The aim? To offer SHS researchers methodological support tailored to their needs and strengthen SHS excellence at UNamur. This platform, SHS Impulse, will provide various services such as financial support for training, consultancy, access to resources, or co-financed software purchases.

For Bruno Dumas, computer science fits in with the principles of applied psychology Artificial intelligence (AI) is interfering in our professional as well as our private lives. It both seduces and worries us. On a global scale, it is at the heart of major strategic, societal or economic issues, still being debated in mid-February 2025, at the AI World Summit in Paris. But how can we, as users, avoid being subjected to it? How can we gain access to the necessary transparency of its workings? By placing his research prism on the user's side, Bruno Dumas is something of a "computer psychologist". An expert in human-computer interaction, co-president of the NaDI Institute (Namur Digital Institute), he defends the idea of a reasoned and enlightened use of emerging technologies.

The NaDI-CeRCLe research center has distinguished itself brilliantly on the international scene in recent weeks. Three young researchers from the EMCP Faculty have received prestigious recognition at leading international events for their research in service management: they are Floriane Goosse, Victor Sluÿters and Florence Nizette. This summer, let's discover the work of these PhD students and their significant contributions to the advancement of knowledge and practice in this field.

Beautiful victory for Margaux Mignolet, a researcher at the Faculty of Medicine's Unité de Recherche en Physiologie Moléculaire (URPhyM), who wins 1st prize in the Belgian inter-university final of the Ma thèse en 180 secondes (MT180) competition. Her research? To better understand the mechanisms of antibodies active in cases of long COVID. The second prize in this national competition was also won by a candidate from Namur. It was Petra Manja, from the Unité de Recherche en biologie des micro-organismes (URBM), Department of Biology, Faculty of Science, and is pursuing a thesis aimed at understanding resistance mechanisms in the bacterium E. coli. Both are also researchers at the NARILIS Institute.

A lifelong video game enthusiast, Fanny Barnabé, a researcher at the CRIDS research center (Namur Digital Institute) and lecturer at the University of Namur, explores behind the scenes of a major cultural phenomenon: video game streaming on Twitch. Between humor, irony and toxic discourse, she deciphers the issues at stake in a digital space in the throes of change.

Abstract Radiotherapy is a cornerstone of cancer treatment and is currently administered to approximately half of all cancer patients. However, the cytotoxic effects of ionizing radiation on normal tissues represent a major limitation, as they restrict the dose that can be safely delivered to patients and, consequently, reduce the likelihood of effective tumor control. In this context, delivering radiation at ultra-high dose rates (UHDR, > 40 Gy/s) is gaining increasing attention due to its potential to spare healthy tissues surrounding the tumor and to prevent radiation-induced side effects, as compared to conventional dose rates (CONV, on the order of Gy/min).The mechanism underlying this protective effect-termed the FLASH effect-remains elusive, driving intensive research to elucidate the biological processes triggered by this type of irradiation.In vitro models offer a valuable tool to support this research, allowing for the efficient screening of various beam parameters and biological responses in a time- and cost-effective manner. In this study, multicellular tumor spheroids and normal cells were exposed to proton irradiation at UHDR to evaluate its efficacy in controlling tumor growth and its cytotoxic impact on healthy tissues, respectively.We report that UHDR and CONV irradiation induced a comparable growth delay in 3D tumor spheroids, suggesting similar efficacy in tumor control. In normal cells, both dose rates induced similar levels of senescence; however, UHDR irradiation led to lower apoptosis induction at clinically relevant doses and early time points post-irradiation.Taken together, these findings further highlight the potential of UHDR irradiation to modulate the response of normal tissues while maintaining comparable tumor control.JuryProf. Thomas BALLIGAND (UNamur), PresidentProf. Stéphane LUCAS (UNamur), SecretaryProf. Carine MICHIELS (UNamur)Dr Sébastien PENNINCKX (Hôpital Universitaire de Bruxelles)Prof. Cristian FERNANDEZ (University of Bern)Dr Rudi LABARBE (IBA)

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

Hepatocellular carcinoma is the most common primary liver cancer. Unfortunately, this tumor still has a high mortality rate due to the lack of effective treatments for its most advanced or poorly localized forms. As part of a partnership with the CHU UCL Namur - site de Godinne and with the support of Roche Belgium, researchers in the Department of Biomedical Sciences are trying to understand why liver tumor cells are so resistant to treatment, and to identify therapeutic alternatives to better target them.

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

This summer's third and final focus on the NaDI-CeRCLe research center, which has gained international recognition in recent weeks thanks to awards won by three young researchers in service management. Following on from Floriane Goosse and Victor Sluÿters, we invite you to discover the work of Florence Nizette, a young researcher working on Artificial Intelligence technologies.

Researchers at the Universities of Namur (UNamur), Brussels (ULB) and Liège (ULiège) have just taken a key step towards understanding viral mechanisms. Their study, published in the international scientific journal PLOS Pathogens, focuses on a particular type of molecule produced by viruses, circular RNAs, and presents an innovative bioinformatics tool capable of better identifying them.

Abstract Primarily described as an alarmone, secondary messenger (p)ppGpp, when accumulated, binds to many targets involved in DNA replication, translation, and transcription. In the asymmetrically-dividing a-proteobacterium Caulobacter crescentus, (p)ppGpp has been shown to strongly impact cell cycle progression and differentiation, promoting the non-replicating G1/swarmer phase. Mutations in the major subunits of transcriptional complex, b or b' subunits, were able to display the (p)ppGpp-related phenotypes even in the absence of the alarmone. We identified that the transcriptional holo-enzyme, RNA polymerase (RNAP) is a primary target of (p)ppGpp in C. crescentus. Furthermore, mutations that inactivate (p)ppGpp binding to RNAP annihilated the (p)ppGpp-related phenotypes and phenocopied a (p)ppGpp0 strain. Our RNAseq analysis further elucidated the changes in the transcriptional landscape of C. crescentus cells displaying different (p)ppGpp levels and expressing RNAP mutants. Since the DNA replication initiation protein DnaA is required to exit the G1 phase, we observed that it was significantly less abundant in cells accumulating (p)ppGpp. We further explored its proteolysis under the influence of (p)ppGpp. Our work suggests that (p)ppGpp regulates cell cycle and differentiation in C. crescentus by reprogramming transcription and triggering proteolytic degradation of key cell cycle regulators by yet unknown mechanisms. In Part II, we identified two σ factors belonging to the ECF family that might be involved in this (p)ppGpp-accompanied phenotypes. In Part III, we propose an overlapping role of the ω subunit, RpoZ, and the heat shock subunit, RpoH, in carbon metabolism.JuryProf. Gipsi LIMA MENDEZ (UNamur), PresidentProf Régis HALLEZ (UNamur), SecretaryDr Emanuele BIONDI (CNRS-Université Paris-Saclay)Prof. Justine COLLIER (University of Lausanne)Dr Marie DELABY (Université de Montréal)