Scientific Publications of the University of Toulouse II Le Mirail
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Presenting and integrating new referents into the common ground in dialogue: the case of escape room players’ interactions.
No full textInternational audienc
Pyrrolidinium-based protic ionic liquid electrolytes for high performance RuO<sub>2</sub> micro-supercapacitors
No full textInternational audienceAdvances in smart technologies and Internet of Things (IoT) call for compact and efficient energy storage devices. Among microscale systems, RuO2-based micro-supercapacitors (MSCs) provide high power pulses, but the limited stability window of their aqueous electrolytes constrains their energy density. In this study, we investigated pyrrolidinium-based protic ionic liquids (PILs) with varying alkyl chain lengths and anion types, achieving pseudocapacitive charge storage in RuO2 MSCs with an extended cell voltage up to 1.5 V. Among the tested electrolytes, 1-propyl pyrrolidinium trifluoroacetate [Pyr3H]⁺[TFA]⁻ delivered excellent energy density and cycling performances, while 1-methyl pyrrolidinium tetrafluoroborate [Pyr1H]⁺[BF4]⁻ showed low equivalent series resistance and superior power retention. Experimental findings align with Reactive Force Field (ReaxFF) molecular dynamics simulations, revealing proton exchange and ion diffusion mechanisms. Ionogel-based MSCs demonstrated long-term cycling stability, retaining performance over 5000 charge/discharge cycles. These results underscore the potential of pyrrolidinium-based PILs for on-chip solid-state MSCs powering microelectronics
Risk-controlling Prediction with Distributionally Robust Optimization
No full textInternational audienceConformal prediction is a popular paradigm to quantify the uncertainty of a model's output on a new batch of data. Quite differently, distributionally robust optimization aims at training a model that is robust to uncertainties in the distribution of the training data. In this paper, we examine the links between the two approaches. In particular, we show that we can learn conformal prediction intervals by distributionally robust optimization on a well chosen objective. This further entails to train a model and build conformal prediction intervals all at once, using the same data
Navigating Hybrid Work in Malaysia: Cultural Adaptation, Workplace Transformation and Technological Mediation
No full textInternational audienceHybrid working has emerged as a predominant trend since the pandemic, yet opinions on its effectiveness remain divided among researchers and practitioners. In Malaysia, where hybrid work arrangements are increasingly adopted, the unique socioeconomic and cultural context offers an exceptional opportunity to explore the transformation of workplace regulation and organizational practices. This study examines how hybrid work arrangements reshape traditional regulatory processes and impact employee well-being and performance in Malaysian organizations. Drawing on in-depth interviews with ten professionals across diverse sectors, this explorative research investigates the multifaceted nature of hybrid work within Malaysia’s cultural framework. Employing social regulation theory as the theoretical foundation, the study analyzes how the spatial, temporal, and technological dimensions inherent in hybrid work environments disrupt and reform established regulatory mechanisms. Key findings indicate that hybrid work in Malaysia fosters distinct ‘regulatory microenvironments’, where organizational policies and cultural practices dynamically interact, and that these new arrangements significantly influence employee well-being and performance. Specifically, participants reported increased autonomy alongside new forms of informal monitoring, a heavy reliance on digital communication tools, and creative strategies to blend traditional workplace norms with flexible practices. By extending social regulation theory to accommodate the distinct characteristics of hybrid work, our research contributes to theoretical understandings and provides practical insights. The results highlight the need for culturally informed strategies in designing and managing hybrid work arrangements
BRIDGES Lectures: G2 in action, and a mathematical theory of exceptions
No full textThe BRIDGES meeting in gauge theory, extremal structures, and stability was held in June 2024 at l'Institut d'\'Etudes Scientifiques de Carg\`ese in Corsica, organized by Daniele Faenzi, Eveline Legendre, Eric Loubeau, and Henrique S\'a Earp. The first week was a summer school consisting of four independent but related lecture series by Oscar Garc\’ia-Prada, Spiro Karigiannis, Laurent Manivel, and Ruxandra Moraru. The present document consists of notes for the lecture series by Laurent Manivel on "The geometry of G2 and the other exceptional complex Lie groups". Some assistance in the preparation of these notes by the author was provided by several participants of the summer school. See the Comments field for more information
Early evidence for capacity standardisation in Western Europe. The vessels from Mailhac (Aude, France) 9th-7th centuries BC
No full textInternational audienceThis paper presents an original study of the metrological characteristics of a series of vessels discovered in the necropolis of Le Moulin (Mailhac, southern France) and dated to the Late Bronze Age and the beginning of the Early Iron Age. A metrological study of the capacities of these artefacts is presented, based on a protocol of 3D modelling from 2D drawings to calculate the internal volumes of the vessels, and a series of mathematical and statistical analyses. The results make it possible to identify one of the earliest evidence for metrological practices based on capacity in Western Europe
A Hamilton-Jacobi approach for the evolutionary dynamics of a model with gene transfer: characterizing monomorphic dynamics for non-concave fitness functions
No full textWe study the asymptotic behavior of an integro-dierential equation describing the evolutionary adaptation of a population structured by a phenotypic trait. The model takes into account mutation, selection, horizontal gene transfer and competition. Previous works, based on the numerical studies or theoretical study of the corresponding stationary problem, have shown that the dynamics of the solutions are rich and we may expect several qualitative outcomes. In this article, we characterize the dynamics of the solution in two regimes: 1) a situation where the solution concentrates around a dominant trait, evolving gradually to a trait determined by a balance between selection and horizontal gene transfer; 2) a situation where the solution concentrates around a dominant trait which evolves gradually to a maladapted trait such that the population becomes extinct (a situation known as the evolutionary suicide). Our analysis is based on an approach involving Hamilton-Jacobi equations with constraint. Previously, the solutions to such equations were characterized for globally concave growth rates. Here, we extend this approach to situations where the growth rate is not globally concave
Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method: DeepONet-based deflation for Conjugate Gradient Method
No full textWe propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient (PCG) method for solving parametric large-scale linear systems of equations. Unlike traditional deflation techniques that rely on eigenvector approximations or recycled Krylov subspaces, we generate the deflation subspaces using operator learning, specifically the Deep Operator Network (DeepONet). To this aim, we introduce two complementary approaches for assembling the deflation operators. The first approach approximates near-null space vectors of the discrete PDE operator using the basis functions learned by the DeepONet. The second approach directly leverages solutions predicted by the DeepONet. To further enhance convergence, we also propose several strategies for prescribing the sparsity pattern of the deflation operator. A comprehensive set of numerical experiments encompassing steady-state, time-dependent, scalar, and vector-valued problems posed on both structured and unstructured geometries is presented and demonstrates the effectiveness of the proposed DeepONet-based deflated PCG method, as well as its generalization across a wide range of model parameters and problem resolutions.</div
Directional light scattering in Mie-resonant Si particles with ultra-thin plasmonic shells
No full textInternational audienceWe present the synthesis and characterization of Au-decorated Si core-shells as candidate meta-atoms. We found a damped magnetic dipole (MD) for smaller Si cores (100 – 130 nm) and an enhanced MD for larger cores (150 – 200 nm). Continuous plasmonic shells of ~12 nm are needed to significantly improve forward scattering.Subwavelength-sized Si particles interact strongly with visible (vis) and near-infrared (NIR) light to produce strong electric and magnetic resonances. These can combine to produce interesting optical effects, such as pure forward scattering. A requirement for this to occur efficiently is that the electric dipole (ED) and magnetic dipole (MD) modes must be of similar amplitude and phase. At wavelengths where this occurs, the particles act analogously to the forward-propagating point sources of light used in Huygens’ constructions. This directional scattered light has a range of potential applications in the creation of metamaterials.We have investigated dielectric@metal core-shell architectures comprised of both resonant cores and resonant shells as candidate particles in which the spectral overlap of the electric and magnetic dipoles might be controlled to create strong directional scattering. There are currently two reports of the synthesis and characterization of Au shells around Si cores, both thicker than desirable. [1,2] Chaâbani et al. presented Si@Au particles, with non-uniform shells composed of Au particle diameters between 10 and 25 nm, which presented enhanced electric field and Fano-resonances due to the coupling of the Mie modes of the Si core and the localized surface plasmon resonance (LSPR) of the Au shell. [1] Sugimoto et al. similarly presented Si@Au particles with a rough ~25 nm Au shell. [2] In both of these reports of Si@Au core-shell particles, the experimental data could not be accurately fit by simulations due to non-spherical cores and inhomogeneous shells. Ultrathin and homogeneous coatings have not yet been achieved around spherical Si particles.In this study, we present a two-step aqueous approach to prepare Si@Au core-shell particles with controllable shell thickness below 10 nm. The Au nanoparticle (AuNP) density around the Si particles can be increased by performing a second functionalization/deposition step. We studied the electromagnetic response of the particles using single-particle scatter spectroscopy and electron energy loss spectroscopy (EELS). Our results were compared with reference Si spheres and SiO2@Au core-shell particles, to allow us to establish the contribution from the Au decoration to the optical response of the hybrid particles. To further elucidate the nature of the electromagnetic response of the particles, these observations were supported by T-matrix simulations which replicated our experimental findings, and showed the importance of controlling the shell/core dimensions and the need for a continuous shell to maximize forward scattering. We found that continuous plasmonic shells of ~12 nm thickness are needed to significantly improve forward scattering intensity.References1.Chaâbani, W, J Proust, S Ouellet, A Movsesyan, J Béal, R Bachelot, T Xu, A L Baudrion, PA Adam, D Boudreau, A Chehaidar, and J Plain, “Si@Au core–shell nanostructures: Toward a new platform for controlling optical properties at the nanoscale.” J Phys Chem C, Vol. 125, 20606. 2021. DOI: 10.1021/acs.jpcc.1c061822.Sugimoto, H., T Hinamoto, Y Kazuoka, A Assadillayev, S Raza, and M Fujii. “Mode hybridization in silicon core–gold shell nanosphere.” Small Vol. 18, 2204890, 2022. DOI: 10.1002/smll.20220489
Analyse des Systèmes Interconnectés avec Dynamiques Nonlinéaires et Hybrides
No full textNational audienceThe complexity of modern control systems can often be attributed to two elements: firstly, such systems involve logic-based decision making which results in dynamics at different time scales, and secondly, these systems comprise several subsystems which play an important role in shaping the properties of the integrated system. Following this viewpoint, the thesis addresses the analysis techniques for interconnection of systems described by switching, nonsmooth, or more generally, hybrid dynamics in both deterministic and stochastic framework.Starting from some earlier work, we first present the classical cascade configuration for time-dependent switched systems where the stability conditions are formulated for a certain class of switching signals using multiple Lyapunov functions, and the notion of input-to-state stability. As a generalization, and using the tools from nonsmoth analysis, we study the feedback interconnections of Filippov differential inclusions (for state-dependent switched systems) with application to observer-based control, and anti-maximal monotone differential inclusions (for projected systems, complementarity systems, and sweeping processes) with application to analyzing certain optimization algorithms. Moving forward, and in the spirit of studying a broader class of interconnections, we study graph-coupled nonlinear systems where the exchange of information between agents is described by switching, but jointly-connected, graphs. The analysis of such systems is carried out by developing singular perturbation theory for hybrid systems, where we propose a novel decomposition of hybrid systems resulting in a continuous-time quasi-steady-state system and a purely discrete-time boundary layer system with constrained switching. We provide conditions for asymptotic practical stability, which in the setting of graph-based interconnections, translate to checking some properties of the graphs and the stability of reduced-order subsystems.In the final part of the thesis, we step away from the deterministic framework and study interconnections in stochastic setting that appear in the design of certain filtering algorithms. The first such class of interconnections is seen in ensemble filters (for systems described by stochastic differential equations and discrete observations) where we propose algorithms for computing the approximation of the posterior distribution of the state conditioned upon the measurements by simulating particles resulting from continuous-discrete McKean-Vlasov type differential equations. We then develop appropriate tools for analyzing the interconnection of particles coupled to each other via the empirical mean and empirical covariance. Another class of interconnections is seen in studying filtering algorithms with unknown parameters (such as noise covariances), where we use Bayesian inference algorithms and the optimal estimate is described by a probabilisitic weighted sum of the conditional posteriors. Under certain assumptions on system dynamics, we study asymptotic convergence for such algorithms towards the optimal solution determined by complete information of the parameters