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Is OSSO a Significant Contributor to the Unknown UV Absorber in Venus' Atmosphere?
No full textInternational audienceIt has been proposed that two isomers of the SO dimer (cis-and trans-OSSO) are candidates for the unknown UV absorber in Venus' atmosphere because they have a good spectral match with the absorber, despite the low concentrations predicted by 1D photochemical models. Here OSSO chemistry (production from SO and loss by photolysis, thermal decomposition, and reaction with O and Cl) has been included in the photochemistry scheme of a 3D planetary climate model (PCM-Venus) along with sulfur injection due to meteoric ablation. 1D multiple scattering radiative transfer modeling is then used to predict the resulting top-ofthe-atmosphere reflectance produced by OSSO. The modeled OSSO concentrations are shown to be ∼3 orders of magnitude too low to explain the observed absorbance levels, and the predicted ratio of the OSSO isomers provides an unsatisfactory match to the spectral shape of the unknown absorber
A dataset of annotated ground-based images for the development of contrail detection algorithms
No full textInternational audienceAll economic sectors must understand, measure and mitigate their contributions to climate change. The aviation sector is no exception and has to reduce its CO2 emissions while also addressing its non-CO2 effects which are responsible for a significant radiative impact on climate. The most important of these effects is due to the formation of contrails and their transformation into induced cirrus. Many studies have focused on detecting contrails onto satellite images because, taken together, meteorological geostationary and sun-synchronous satellites provide a good monitoring of the Earth's atmosphere, but unfortunately the spatial resolution and temporal sampling of such satellite images are often insufficient to detect contrails right after their formation and attribute a particular contrail to a given flight. The use of ground-based cameras, especially as part of a network, is therefore complementary to satellite imagery and currently represents an important avenue of research for contrail monitoring. In this article we describe a dataset of annotated ground-based hemispheric sky images that can serve as a basis for the training and validation of contrail detection algorithms, in particular those aiming at segmenting contrails using machine learning methods
Fastest first-passage time statistics for time-dependent particle injection
No full textInternational audienceA common scenario in a variety of biological systems is that multiple particles are searching in parallel for an immobile target located in a bounded domain, and the fastest among them that arrives to the target first triggers a given desirable or detrimental process. The statistics of such extreme events—the first-passage to the target—is well-understood by now through a series of theoretical analyses, but exclusively under the assumption that all N particles start , i.e., all are introduced into the domain instantly, by δ -function-like pulses. However, in many practically important situations this is not the case: to start their search, the particles often have to enter first into a bounded domain, e.g., a cell or its nucleus, penetrating through gated channels or nuclear pores. This entrance process has a random duration so that the particles appear in the domain sequentially and with a time delay. Here we focus on the effect of such an extended-in-time injection of multiple particles on the fastest first-passage time (fFPT) and its statistics. We derive the full probability density function H N ( t ) of the fFPT with an arbitrary time-dependent injection intensity of N particles. Under rather general assumptions on the survival probability of a single particle and on the injection intensity, we derive the large- N asymptotic formula for the mean fFPT, which is quite different from that obtained for the instantaneous δ -pulse injection. The extended injection is also shown to considerably slow down the convergence of H N ( t ) to its large- N limit—the Gumbel distribution—so that the latter may be inapplicable in the most relevant settings with few tens to few thousands of particles. Published by the American Physical Society 202
Atomic Layer Deposition of Spinel Bimetallic Oxides for Enhanced Photoelectrochemical Energy Conversion
No full textSymposia D: Next-Generation Solar Technologies: unconventional materials and sustainable innovations for photovoltaic, photoelectrochemical and photocatalytic systemsInternational audiencePhotoelectrochemical cells (PECs) are attracting growing interest for their ability to generate hydrogen (H2) as a solar fuel by water dissociation. This provides efficient solution for renewable energy production compared with Electrolysis, Steam Methane Reforming, etc. The intermediate PEC technology offers a balance, with moderate complexity and better prospects for robustness and longevity [5]. Among the various electrode materials studied in the literature [1], [2], [3], oxide semiconductors (SCs) are particularly promising for their abundance, low cost, and superior stability compared to other semiconductors. However, they present certain challenges, including limited absorption in the visible range, low electrical conductivity, weak charge transfer kinetics, and carrier transport that can be challenging to improve. Binary oxides emerge as an interesting metal-oxide option with potential applications in photocatalysis [4]. However, achieving precise control over the atomic ratio remains a significant challenge.To address these challenges, Atomic Layer Deposition (ALD) is employed to develop innovative photoelectrodes using bimetallic oxides, such as FexCo3-xO4 with a spinel structure, which offer tunable optical and electrical properties, along with improved thermal stability and catalytic activity [6]. The process starts with the individual processing of Co and Fe oxides followed by their integration into a bimetallic oxide through the ALD supercycle strategy at 200 °C supported by post annealing. Structural characterizations were performed on single metal oxides followed by Raman, UV-VIS, ellipsometry and FTIR measurements. After establishing the relationships between the synthesis parameters, structural properties, and optoelectronic characteristics of these monometallic oxides, a bimetallic oxide was grown successfully. The relationships between the synthesis parameters, structural properties, and optoelectronic characteristics optical absorption of FexCo3-xO4 were thoroughly examined.Following the successful synthesis of the spinel bimetallic oxide FexCo3-xO4, we will integrate them with our previously prepared Nb-TiO₂[7] to form a PN-type photoelectrode (photoanode). Optical and electrical characterizations will be performed to assess the photogeneration and carrier transport properties before photoelectrocatalytic activity measurement
Generation and pharmacological manipulation of 3D-spheroid cultures derived from zebrafish adult neural stem cells in a droplet-based microfluidic platform
No full textInternational audienceNeural stem cells (NSCs) generate neurons and glia in the adult vertebrate brain, crucial for tissue maintenance and plasticity. They balance neurogenesis with self-renewal, regulated through transitions between quiescence, activation, and lineage progression. The molecular and cellular mechanisms behind these processes remain incompletely understood. Here we describe a protocol to isolate and expand NSCs from the adult zebrafish pallium, a major NSC niche. We present the procedures to propagate primary cultures of NSCs, followed by the generation of 3Dspheres and their regulation in a droplet microfluidic platform. We then detail the procedure to analyze adult NSC fate within the 3D-spheroids following drug treatment. We show that 7 µL droplets are sufficient to allow the formation of size-controlled 3D-spheroids, in which NSCs sustain self-renewal and are able to balance quiescence and activation. We outline potential applications, including investigation of factors involved in adult NSC activation and monitoring of their soluble environment, for which a confined culture system is advantageous
Uncertainty Quantification as a Complementary Latent Health Indicator for Remaining Useful Life Prediction on Turbofan Engines
No full textInternational audienceHealth Indicators (HIs) are essential for predicting system failures in predictive maintenance. While methods like RaPP (Reconstruction along Projected Pathways) improve traditional HI approaches by leveraging autoencoder latent spaces, their performance can be hindered by both aleatoric and epistemic uncertainties. In this paper, we propose a novel framework that integrates uncertainty quantification into autoencoder-based latent spaces, enhancing RaPP-generated HIs. We demonstrate that separating aleatoric uncertainty from epistemic uncertainty and cross combining HI information is the driver of accuracy improvements in Remaining Useful Life (RUL) prediction. Our method employs both standard and variational autoencoders to construct these HIs, which are then used to train a machine learning model for RUL prediction. Benchmarked on the NASA C-MAPSS turbofan dataset, our approach outperforms traditional HI-based methods and end-to-end RUL prediction models and is competitive with RUL estimation methods. These results underscore the importance of uncertainty quantification in health assessment and showcase its significant impact on predictive performance when incorporated into the HI construction process
A high-order matrix-free adaptive solver for the shallow water equations with irregular bathymetry
No full textWe present the first step in the development of an Adaptive Mesh Refinement (AMR) solver for coastal engineering applications, based on a high-order Discontinuous Galerkin (DG) method as implemented in the deal.II library. This environment provides efficient and native parallelization techniques and automatically handles non-conforming meshes to implement both static and dynamic AMR approaches. The proposed method is automatically well-balanced, allows the use of realistic bathymetry data without any regularity assumption, and includes a consistent conservative discretization for transported chemical species. Numerical experiments on idealized benchmarks validate the proposed approach, while results obtained on realistic bathymetries and complex domains show its potential for accurate and efficient adaptive simulations of coastal flows
Shell models on recurrent sequences: Fibonacci, Padovan, and other series
No full textInternational audienceA new class of shell models is proposed, where the shell variables are defined on a recurrent sequence of integer wave-numbers such as the Fibonacci or the Padovan series, or their variations including a sequence made of square roots of Fibonacci numbers rounded to the nearest integer. Considering the simplest model, which involves only local interactions, the interaction coefficients can be generalized in such a way that the inviscid invariants, such as energy and helicity, can be conserved even though there is no exact self-similarity. It is shown that these models basically have identical features with standard shell models, and produce the same power law spectra, similar spectral fluxes and analogous deviation from self-similar scaling of the structure functions implying comparable levels of turbulent intermittency. Such a formulation potentially opens up the possibility of using shell models, or their generalizations along with discretized regular grids, such as those found in direct numerical simulations, either as diagnostic tools, or subgrid models. It also allows to develop models where the wave-number shells can be interpreted as sparsely decimated sets of wave-numbers over an initially regular grid. In addition to conventional shell models with local interactions that result in forward cascade, a particular helical shell model with long range interactions is considered on a similarly recurrent sequence of wave numbers, corresponding to the Fibonacci series, and found to result in the usual inverse cascade
Easing Optimization Paths: a Circuit Perspective
No full textAccepted at ICASSP 2025International audienceGradient descent is the method of choice for training large artificial intelligence systems. As these systems become larger, a better understanding of the mechanisms behind gradient training would allow us to alleviate compute costs and help steer these systems away from harmful behaviors. To that end, we suggest utilizing the circuit perspective brought forward by mechanistic interpretability. After laying out our intuition, we illustrate how it enables us to design a curriculum for efficient learning in a controlled setting. The code is available at \url{https://github.com/facebookresearch/pal}
The Muon Collider
No full textInternational audienceMuons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose an R&D project that can make the muon collider a reality