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White light interferometry analysis for measuring thin film thickness down to a few nanometers
No full textInternational audienceAbstract We present a practical white-light interferometric method, supported by an open-source Python library optifik for automated spectrum-to-thickness deduction, enabling foam film measurements down to a few nanometers. We describe three typical spectral scenarii encountered in this method: spectra exhibiting numerous interference fringes, spectra with a moderate number of peaks, and spectra with only a few identifiable features, providing illustrative examples for each case. We also discuss the main limitations of the technique, including spectral range constraints, the necessity of knowing the refractive index, and the influence of spectral resolution and signal quality. Finally, we demonstrate the application of the method in a time-resolved study of a TTAB (tetradecyltrimethylammonium bromide) foam film undergoing elongation and thinning. This method can be adapted to measure any thin non-opaque layer. Graphic Abstract
How to Achieve the Best Trade-off Between Robustness and Performance
No full textThe work presents an innovative approach to addressing the trade-off between robustness and performance, known as the optimality criterion. Moreover, the procedure relies on well-established methods that effectively support the search for the best trade-off, namely multi-criteria optimization problem. Thus, by separating the robustness and performance problems, it becomes possible to achieve the best trade-off between these two criteria. In this search for a trade-off, two complementary approaches are combined: robust control for the closed-loop system by minimizing Hinf-norm, and performance optimization in open-loop by minimizing l2-norm. This combination is made possible by the Youla-Kučera parameterization. However, to implement this parameterization in the presence of model errors, a new approach is used: estimating the Youla-Kučera parameterization.</div
GATE 10 Monte Carlo particle transport simulation: II. Architecture and innovations
No full textInternational audienceOver the past years, we have developed GATE version 10, a major re-implementation of the long-standing Geant4-based Monte Carlo application for particle and radiation transport simulation in medical physics. This release introduces many new features and significant improvements, most notably a Python-based user interface replacing the legacy static input files. The new functionality of GATE version 10 is described in the part 1 companion paper (Sarrutet al2025 arXiv:2507.09842). The development brought significant challenges. In this paper, we present the solutions that we have developed to overcome these challenges. In particular, we present a modular design that robustly manages the core components of a simulation: particle sources, geometry, physics processes, and data acquisition. The architecture consists of integrated C++ and Python codes. This framework allows for the precise, time-aware generation of primary particles, a critical requirement for accurately modeling positron emission tomography, radionuclide therapies, or prompt-gamma timing systems. We present how GATE 10 handles complex Geant4 physics settings while exposing a simple interface to the user. Furthermore, we describe the methodological solutions that facilitate the seamless integration of advanced physics models and variance reduction techniques. The architecture supports sophisticated scoring of physical quantities (such as Linear Energy Transfer and Relative Biological Effectiveness) and is designed for multithreaded execution. The new user interface allows researchers to script complex simulation workflows and directly couple external tools, such as artificial intelligence models for source generation or detector response. By detailing these architectural innovations, we demonstrate how GATE 10 provides a more powerful and flexible tool for research and innovation in medical physics. This paper is not intended to be a developer guide. Its purpose is to share with the research community in-depth explanations of our development effort that made the new GATE 10 possible
Atmospheric Specifications for Infrasound Studies: 1. Operational Analyses
No full textInternational audienceOperational analysis products issued by weather services are needed for the Comprehensive Nuclear-Test-Ban Treaty's (CTBT) infrasound monitoring activities. They provide atmospheric specifications as high as 80 km altitude. They are used to feed propagation simulations designed to characterize sources of interest. Acoustic waveguides for long-range infrasound propagation form in the lower and middle atmosphere between approximately 10 and 110 km. Analysis products have biases in the stratosphere and above due to the decreasing amount of operational observations available for data assimilation systems. We investigate differences between two state-of-the-art analysis products, namely that of the Integrated Forecasting System, IFS, from the European Center for Medium-range Weather Forecast and that of the ICOsahedral Nonhydrostatic model, ICON, from the German Weather Service. We compare their differing predictions of acoustic waveguides across the International Monitoring System (IMS) of the CTBT. We demonstrate significant differences in prediction of waveguide strength in the equatorial region, related to different amplitudes of the westerly phase of the Semi-Annual Oscillation. Waveguide occurrence predictions can differ by up to 40% across the IMS, in the meridional and zonal directions. We quantify biases with respect to LiDAR wind and temperature observations at three sites. We demonstrate biases of up to 40% and 60% in terms of waveguide occurrence prediction for ICON and IFS, respectively, using the Institute for Atmospheric Physics's LiDAR simultaneous wind and temperature observations. We stress the added-value for more high-resolution measurements in the tropical region where operational products strongly disagree at altitudes that matter for infrasound propagation.Plain Language Summary Weather forecast products are issued daily and feed different operational activities relying on knowledge of the atmospheric state. Among those, the monitoring of the atmosphere with infrasound technology has been put in place to monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). To localize and characterize acoustic sources of interest, meteorological conditions from the surface to high altitudes (120 km) are needed. Indeed winds and temperatures up to ∼120 km height can affect the way acoustic waves propagate through atmospheric layers across large distances (up to 1,000 s of km) before being detected at infrasound stations of the International Monitoring System (IMS). Two state-of-the-art meteorological products are compared with respect to their prediction of acoustic waveguides. They are respectively issued by the European Center for Medium-range Weather Forecast (ECMWF) and by the German Weather Service (DWD). We find notable difference across the IMS and more specifically in equatorial regions in the upper stratosphere where the Semi-Annual Oscillation's amplitude differs between products. We also compare the products with ground-based LiDAR observations, allow scanning of the vertical atmospheric structure at three sites to identify notable differences. This further supports the importance of using measurements to help improve and validate these high-resolution models
Influence of printing orientation of Inconel 718 specimens on LEFM parameters analyzed via DVC
No full textInternational audienceLaser powder bed fusion (LPBF) is an additive manufacturing technique that enables for the production of metallic parts with complex geometries, and the possibility of locally adapting mesostructures. This study aims at determining the influence of hatch angle and build orientation on Linear Elastic Fracture Mechanics (LEFM) parameters of specimens obtained by LPBF. In the present work, the mechanical response of mini CT specimens made of Inconel 718 alloy obtained by LPBF were studied when subjected to in-situ tensile loading. The LEFM parameters of the different specimens were extracted from displacement fields measured with Digital Volume Correlation (DVC) using Williams' fields. The crack front and Stress Intensity Factors (SIF) profiles of the different experiments were analyzed. The extracted LEFM parameters for the different specimens displayed significant differences in Young's Modulus and fracture toughness due to variations in build orientation
QP-Based Control of an Underactuated Aerial Manipulator under Constraints
No full textThis paper presents a constraint-aware control framework for underactuated aerial manipulators, enabling accurate end-effector trajectory tracking while explicitly accounting for safety and feasibility constraints. The control problem is formulated as a quadratic program that computes dynamically consistent generalized accelerations subject to underactuation, actuator bounds, and system constraints. To enhance robustness against disturbances, modeling uncertainties, and steady-state errors, a passivity-based integral action is incorporated at the torque level without compromising feasibility. The effectiveness of the proposed approach is demonstrated through high-fidelity physics-based simulations, which include parameter perturbations, viscous joint friction, and realistic sensing and state-estimation effects. This demonstrates accurate tracking, smooth control inputs, and reliable constraint satisfaction under realistic operating conditions
7-year trend of timely hepatitis B birth dose vaccination coverage in The Gambia: a retrospective, population-based analysis
No full textInternational audienceBackgroundAccording to WHO and UNICEF, Africa has the lowest coverage (18%) of timely (within the first 24 h) hepatitis B birth dose (HepB-BD) vaccination worldwide. To eliminate hepatitis B by 2030, 90% vaccination coverage is required. Experiences from The Gambia, the first African country to adopt HepB-BD vaccination in 1990, could guide HepB-BD implementation and scale-up in Africa. We aimed to assess the trend of, and barriers to, timely HepB-BD vaccination coverage over a 7-year period in The Gambia.MethodsIn this retrospective analysis, 2015–21 data were extracted from population-based Health and Demographic Surveillance Systems in three rural areas (Basse, Bansang, and Farafenni) in The Gambia. Fluctuation tests and Bayesian analysis using Markov chain Monte Carlo methods assessed the rate of timely (within days 0–1 of birth) and delayed HepB-BD vaccination coverage, change points (abrupt variation between two stable periods) in the average coverage of timely HepB-BD vaccination, and the factors associated with delayed HepB-BD vaccination during the first 7 years following the WHO recommendations on hepatitis B elimination.FindingsBetween Jan 1, 2015, and Dec 31, 2021, 4560 of 71 088 livebirths (6·4%, 95% CI 6·2–6·6) received a timely HepB-BD. Timely HepB-BD vaccination coverage increased from 1·7% (95% CI 1·3–2·0) in the first half of 2015 (ie, January to June) to 22·4% (21·3–23·6) in the second half of 2021 (ie, July to December; p<0·0001). Delayed HepB-BD administration was associated with being born on Friday (odds ratio [OR] 3·51 [95% CI 3·03–4·08]; p<0·0001) or Saturday (5·93 [4·96–7·13]; p<0·0001) compared with Tuesday; being born in Basse (2·03 [95% CI 1·83–2·25]; p<0·0001) or Farafenni (1·84 [1·63–2·08]; p<0·0001); and being born during the rainy season (1·16 [1·08–1·25]; p<0·0001). Average timely HepB-BD vaccination coverage significantly decreased from 10·1% (95% CI 9·5–10·6) pre-COVID-19 pandemic to 5·4% (4·5–6·3) during the first COVID-19 wave (p<0·0001). After adjusting for all other factors, being born during the first COVID-19 wave was associated with delayed HepB-BD vaccination (OR 1·41 [1·22–1·64]; p<0·0001).Interpretation30 years after the adoption of HepB-BD in The Gambia, the rate of vaccination coverage remains low and was significantly affected by the COVID-19 pandemic, highlighting the challenges for its implementation
AI-metaverse at work: trading value for risk in organizational knowledge systems
No full textFNEGE 4, ABS 2International audienceArtificial Intelligence (AI) and metaverse technologies are transforming organisational knowledge ecosystems by facilitating immersive, intelligent, and interactive digital work environments. Utilising the theory of consumption value and perceived risk theory, this research formulates and experimentally evaluates two structural models to investigate the impact of AI–metaverse features on knowledge engagement as well as on knowledge application performance. SmartPLS4 was used to look at survey data from 279 professionals who worked in IT, manufacturing, finance, healthcare, education, and retail. The findings indicate that AI–metaverse learning value, cognitive immersion, and enjoyment substantially improve knowledge engagement and subsequent application performance. Conversely, techno-overload surprisingly has a positive effect, implying adaptive behaviour in digitally saturated contexts. On the other hand, information overload, data-surveillance fear, and perceived security vulnerability act as positional non-inhibitors of knowledge engagement. These results enhance theory of consumption value and perceived risk theory by illustrating that cognitive–affective appraisal mechanisms collaboratively influence user engagement in organisational metaverse systems. The paper makes a unique contribution by being one of the first to show how value-driven and risk-based AI-metaverse traits work together to affect organisational knowledge outcomes. This helps us understand both the theory and practice of responsible metaverse-enabled work design
New directions for interconnector research: drawing from social sciences and humanities perspectives to explore the Celtic Interconnector
No full textInternational audienceThe current discourse on interconnectors primarily centers on the technical and economic aspects necessary for delivering a stable grid infrastructure powered by renewable sources and for integrating energy markets. This article, therefore, explores opportunities to broaden definitions of energy grid interconnectivity beyond the techno-economic sphere. It considers multidisciplinary perspectives and presents novel exploratory viewpoints from the social sciences and humanities. It examines ideas of interconnection by drawing on the Celtic Interconnector, an Irish-French initiative, to explore the cultural, historical, political, and geographical dimensions of interconnectivity. Insights are derived from two workshops with academics in Ireland and France, encouraging a more contextual understanding of energy interconnections beyond their physical and economic dimensions. The article builds on these insights to set out an agenda for future research and reflect on frames of reference for describing, analysing, and engaging with emerging interconnector processes and the multiple stakeholders involved
Reconstruction of temperature and heat flux from Thermal Large Eddy Simulations using Deep Learning
No full textIt is paramount to understand the coupling between velocity and temperature for many flows found in nature, or industrial applications, like in solar receivers, found inside concentrated solar power towers. Thermal-Large Eddy Simulation (T-LES) is a set of turbulence and thermal modeling equations for turbulent flows with an important thermal component. T-LES modeling can capture the largest scales of turbulence and heat flux fluctuations at a reasonable cost compared to DNS. However, fluctuations at small scales are not captured. This study develops and compares three different methods to reconstruct the small scales of temperature fluctuations by inverting the numerical T-LES filter. This filter is never fully known. First, a supervised learning method only applicable a priori. Second, a weakly supervised learning method, requiring the two point correlation of the temperature. This method is applicable a posteriori. A third method is proposed based on the Adaptive Local Deconvolution Method applied in a constrained learning framework with a single filter. The results of the first method highlight the ability of the neural network to generalize the filter inversion from a small number of training examples, on both the temperature fields and on the reconstructed heat flux. The results of the second method show the capability of the network to reconstruct plausible raw data and to fit the statistics of the Direct Numerical Simulation (DNS) on temperature fields. The third method exhibits satisfactory results while involving only a few parameters to learn on temperature and heat flux.</div