Public Service Interpreters Education in French-Speaking Belgium

peer reviewedOver the past three decades, the professionalisation of public service interpreters has increased with training increasingly targeting specific settings, and pedagogical approaches being more and more reflexive (e.g. Cirillo/Niemants 2017). Given the dramatic rise in migratory movements in recent years (UNHCR 2022), the current major issue is to develop effective university-level curricula for interpreters in languages of lesser diffusion (LLD): the aim is not only to provide training, i.e. practical-only learning, but also and above all to provide education, i.e. to equip them intellectually by enhancing their capacity for critical analysis, reasoning, autonomous judgement and informed decision-making (cf. Merlini 2017: 139). In French-speaking Belgium, as no doubt elsewhere in the world, the main challenges were as follows: to gain access to the target audience, in this case interpreters in LLD; to design a curriculum that closely meets the needs of the stakeholders in the field; to give access to higher education to people who may not have had the opportunity to complete their school education; to support them in their learning while taking into account their personal situation (the majority of them are adults with families who have to earn a living while studying); to develop interpreting skills in multilingual classrooms. To meet these challenges, the University of Mons has established close links with community associations, developed modular programmes that evolve according to their needs, valued experience as much as a diploma to access higher education, implemented a four-level training out-of-school-hours programme that allows progressive learning at different paces, and introduced non-language-specific learning methods (cf. Balogh et al. 2016), such as specific role-play writing, peer co-construction of knowledge, and peer feedback using a criterion-based assessment grid. This chapter details the challenges, solutions, educational content, certification system, assessment grid and main learning methods of this programme and outlines future developments. The region has stepped up its efforts since the 2000s and now has a pool of trained PS interpreters, who can further their professional development by taking part in a tailor-made process of continuing education at university level.10. Reduced inequalitie

CO2-binding alcohols as potential candidates for poly(vinyl chloride) upcycling

peer reviewedDespite the increasing global production of poly(vinyl chloride) (PVC), its recycling remains a major challenge, primarily due to its high chlorine content and limited compatibility with conventional recycling processes. This study explores the use of 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD)-based CO2-binding alcohols (CO2BALs) as nucleophiles for PVC functionalization, aiming to enhance its upcycling potential. The impact of solvent polarity, CO2BAL conversion, and reaction time on the substitution-to-elimination ratio was systematically investigated. Although the degree of substitution remained below 10 wt%, a promising SN2/E2 selectivity of 94/6 was achieved. The functionalized materials were characterized using 1H NMR, FT-IR, SEC, and TGA, confirming the successful grafting of carbonate moieties and highlighting thermal stability trends. While CO2BAL stabilization in polar solvents may limit reactivity, alternative approaches, such as flow chemistry, are currently under consideration to improve substitution efficiency. This work provides new insights into CO2-based strategies for PVC modification, bridging the gap between polymer upcycling and sustainable chemistry

Heterovalent-doping-induced ultrasensitive and highly exclusive ethylene sensor: Application to crop quality inspection

peer reviewedA promising ethylene sensor based on Sb2MoO6 (SMO) with a permeable lamellar structure and tunable W dopants is proposed. The optimal 5 mol% W-doped SMO featuring atomically distributed heterovalent doping sites enables the ideal combination of high response (121.26/2.6 for 10/0.5 ppm), short response/recovery time (180 s/54 s for 10 ppm), low limit of detection (LoD) (23.18 ppb), excellent selectivity, good long-term stability (45 days), and robust performance in high humidity (LoD of 31.5 ppb at 80 % relative humidity). The rich W4+ doping-induced active sites are primarily responsible for the strengthened gas-sensing performances. Theoretical simulations reveal that W doping modulates the SMO lattice through substitutional and interstitial mechanisms, optimizing adsorption energy and charge transfer between ethylene and Mo sites, thereby resolving the trade-off between high response and recovery speed. Furthermore, the real-world application in detecting and differentiating moldy rice across storage periods underscores its potential for on-site quality monitoring in the grain industry. This work highlights the significant role of heteroatom doping in tailoring material properties, positioning W-doped SMO as a highly effective gas-sensing material for agricultural and environmental applications

Scalable Deep Learning for Industry 4.0: Speedup with Distributed Deep Learning and Environmental Sustainability Considerations

peer reviewedDeep learning (DL) is increasingly used in industry, especially in industry 4.0. Thanks to DL, it possible to better prevent breakdowns and manufacturing defects. DL models are becoming more and more complex and efficient, requiring significant compute resources and compute time. The use of Graphic Processing Units (GPUs) makes it possible to speed up processing but at a higher cost. An alternative to them is the use of distributed DL (DDL) which differs from Federated Deep Learning in that it focuses on accelerating calculations and does not address data privacy. DLL requires having several computing nodes. This is where cloud computing comes in. Cloud computing allows resources or virtual machines to be allocated on demand, which reduces costs. However, the allocation of GPU resources has a higher cost than CPU resources, which can be problematic for small businesses. This article proposes to exploit the DDL on CPUs via the on-demand allocation of virtual machines in order to reduce costs. In addition, a solution for deploying the software stack necessary for proper operation is proposed. This is achieved using a containerization which is only composed of the software suites needed to run the DDL to minimize the container transfer size and consequently minimize the container deployment time

A strategy based on hybrid 0D Chemical Reactor Networks and 1D Flame predictions for flashback prevention in an original H2 fueled micro Gas Turbine combustor without any redesign

peer reviewedHydrogen combustion is well-known to lead to flame instabilities, potentially resulting in flashback. Performing air humidification or Exhaust Gas Recirculation (EGR) alters the combustor inlet conditions, slowing down the flame speed and reducing the reaction rate and temperature. Nevertheless, these solutions are currently less considered for safe hydrogen combustion, and no prediction methodology exists. Therefore, the main goal of this work is thus to provide a fast prediction and low-computational complexity methodology to prevent flashback in a micro Gas Turbine (mGT) without any combustor redesigning. A parametric study is thus performed to find the minimal dilution levels to lead to stable combustion for several CH4/H2 blends, using a hybrid model, combining a 0D Chemical Reactor Network with 1D laminar flame calculations. The 0D/1D approach allows predetermining the inlet conditions to reduce the laminar flame speed down to the one of pure methane combustion flame. The results obtained using this hybrid methodology show that safe and complete combustion is possible for 0 to 100% hydrogen when performing water dilution, but limited to 50–55%vol when performing EGR. The 0D/1D analysis shows that a CH4/H2 blend of 50/50%vol requires either a water-to-air ratio of Ω=3.4%, or an EGR ratio of 77 % for flame stabilization. Burning up to 100 % H2 involves Ω=10.25%, while no solution exists when performing EGR

Two-photon 3D printing optical Fabry-Perot microcavity for non-contact pressure detection

peer reviewedIn this work, a polymer Fabry-Perot interferometer-based microcavity via two-photon direct laser writing is proposed, designed, and experimentally demonstrated as an optical pressure sensor. The sensor comprises a film-based hollow cavity with a diameter of 350 μm and an annular flow channel with four drain holes, which is further sealed for pressure sensing. As the applied pressure varies, the cavity length changes accordingly. Here, a non-contact optical spectral demodulation system integrated with an optical microscopy was designed. An illuminated light was used for localizing the device and a broadband near-infrared light was coupled into an objective lens and the transmitted light was reflected by the device and detected by a spectral demodulation system. To demodulate the spectral signal, a Fourier demodulation algorithm was used to track the wavelength. The results showed that the sensor exhibited a high sensitivity of 398 pm/kPa and good stability. This work can be used for non-contact pressure detection in the critical field of biomedical and aerospace applications

Genomics vs. AI-enhanced electrocardiogram: predicting atrial fibrillation in the era of precision medicine

peer reviewedAtrial fibrillation (AF) is the most prevalent cardiac arrhythmia and can lead to severe complications such as stroke. Artificial intelligence (AI) has emerged as a vital tool in predicting and detecting AF, with machine learning (ML) models trained on electrocardiogram (ECG) data now capable of identifying high-risk patients or predicting the imminent onset of AF. Precision medicine aims to tailor medical interventions for specific sub-populations of patients who are most likely to benefit, utilizing large genomic datasets. Genetic studies have identified numerous loci associated with AF, yet translating this knowledge into clinical practice remains challenging. This paper explores the potential of AI in precision medicine for AF and examines its advantages, particularly when integrated with or compared to genomics. AI-driven ECG analysis provides a practical and cost-effective method for early detection and personalized treatment, complementing genomic approaches. AI-based diagnosis of AF allows for near-certain prediction, effectively relieving cardiologists of this task. In the context of preventive identification, AI enhances the accuracy of predictive models from 75% to 85% when ML is employed. In predicting the exact onset of AF—where human capability is virtually nonexistent—AI achieves a 74% accuracy rate, offering significant added value. The primary advantage of utilizing ECGs over genomic data lies in their ability to capture lifetime variations in a patient’s cardiac activity. AI-driven analysis of ECGs enables dynamic risk assessment and personalized adaptation of therapeutic strategies, optimizing patient outcomes. Genomics, on the other hand, enables the personalization of care for each patient. By integrating AI with ECG and genomic data, truly individualized care becomes achievable, surpassing the limitations of the “average patient” model.4855 - C2W - Come To Wallonia - Sources publiques européenne

Methodology for Estimating Indirect Emissions from Scope 3 and Mitigation Proposals Applied in the neighborhood of Benicalap, Valencia (Spain)

peer reviewe

Exhaust gas recirculation cooler fouling morphology: Characterisation, spatiotemporal nature, and system variable-morphology-property correlation

peer reviewedFouling is one of the primary causes of failure in exhaust gas recirculation (EGR) coolers. Morphology may provide a powerful perspective for understanding the mechanisms, behaviours, and properties of fouling. However, a systematic review of fouling morphology is currently lacking. Considering the substantial progress made in morphology-related studies within the industry in recent years, this work reviews the findings on EGR cooler fouling morphology from four aspects: characterisation (scale, object, category, and technique), spatiotemporal nature, variable-morphology correlation, and morphology-property correlation. Furthermore, the current challenges and opportunities in this field are discussed. Based on this, we propose a framework for the morphological characterisation of EGR cooler fouling. It is demonstrated that morphology plays a crucial role in revealing the spatiotemporal characteristics of fouling, the formation and removal mechanisms, and the correlations among system variables, morphology, and properties. Morphology still holds significant potential in four areas: multi-scale and quantitative characterisation, nomenclature and taxonomy, and full lifecycle evolution. The findings provide a morphological perspective for fouling research within the industry and contribute to advancing the science of fouling morphology