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Fast Estimation of mmWave Power-Angular Spectrum Using Leaky-Wave Antennas
No full textInternational audienceLeaky-wave antennas (LWAs) provide compact andcost-effective solutions for direction-of-arrival (DoA) estimationdue to their frequency-dependent beam-scanning characteristics.However, in coherent multipath environments, the source covariancematrix becomes rank-deficient, which limits the performanceof conventional subspace-based methods. Interpolationbasedspatial smoothing (SSP) can restore the rank and recoverDoAs, but accurate power estimation remains difficult, hinderingthe acquisition of the power angular spectrum (PAS). In thiswork, we propose an on-grid sparse Bayesian learning (SBL)approach that operates directly on the fast frequency-scanningresponses of meandered waveguide-based LWAs. The methodjointly estimates DoAs and their corresponding powers withoutrequiring rank-restoration techniques. Simulation results demonstratethat the SBL-based approach provides robust and accuratePAS estimation, even in the presence of fully coherent sources
Enabling Free Growth of Thin Films by Magnetron Sputtering for Blooming Microstructures
No full textInternational audienceAbstract Magnetron sputtering is a versatile and scalable technique that enables the precise tuning of key properties, including crystalline phase, stoichiometry, and phase ratios. Despite its widespread use in producing dense non‐porous coatings on flat substrates, its potential for developing porous electroactive materials remains underexplored. Here the use of rough substrates, such as carbon nanotubes and nanosheets, revealing a dramatic change in the growth mechanism of this model systems, nickel nitride, and chromium nitride is investigated. The nanometric roughness of these carbon nanomaterials leads to distinct effects, including strong shadowing of deposited species and non‐competitive growth. This results in enhanced porous thin films with open microstructures and high available surface areas. It is then apply the experimental findings, confirmed by Monte Carlo simulations, to other thin films to demonstrate this approach extends to multiple nanorough substrate/thin film systems, thereby paving the way for designing new materials with enhanced energy conversion and storage performance
Better understanding of the maleinization of oils using model fatty compounds: Insights from NMR, SEC, and HRMS analyses
No full textInternational audienceThe chemical modification of vegetable oils through maleinization reaction offers a promising route to sustainable bio-based materials for diverse industrial applications. However, the underlying mechanisms of the maleinization reaction such as the ene, Diels-Alder, and radical pathways, remain insufficiently understood, limiting process optimization and broader adoption. In this study, we employed a model compound approach using methyl oleate (C18:1), methyl linoleate (C18:2), and their corresponding triglycerides to systematically investigate the maleinization reaction with maleic anhydride (MA) under controlled conditions. Advanced analytical techniques, including nuclear magnetic resonance (NMR) spectroscopy, size-exclusion chromatography (SEC), and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were performed to elucidate reaction mechanisms, quantify product distributions (when possible), and assess structural modifications. Our results demonstrated that the ene reaction is the predominant pathway for C18:1 substrates while radical mechanisms contribute to minor products. The Diels-Alder cycloaddition is negligible under the studied conditions. The extent of maleinization increases with MA equivalents, as confirmed by quantitative 13C NMR and FT-ICR MS analyses while SEC reveals some polymerization
In vivo OCT angiography of mouse ovary
No full textInternational audienceThe ovary undergoes cyclic development for follicle formation and regression, a hormonally regulated process that is key to pregnancy. Angiogenesis and vascular remodeling play critical roles in this process, but a detailed and dynamic follicle-vascular relationship remains to be elucidated. Simultaneous imaging of the ovarian vascular network and follicular structure is essential for studying this relationship, and studies have largely relied on fluorescence imaging with optically cleared ovary samples from the mouse model. The lack of high-resolution in vivo imaging to probe the mouse ovary is a major hurdle for studying the dynamic interaction between vasculature and follicles under physiological conditions. Here, we addressed this technical hurdle by establishing in vivo angiography of the mouse ovary with optical coherence tomography and an intravital window. This work opens the possibility to study the dynamic ovarian follicle-vascular relationship in vivo in the mouse model
article "Romantisme", Dictionnaire des contes, sous la direction de Jean-Loïc Le Quellec, CNRS éditions, à paraître
No full textExclusive four pion photoproduction in ultraperipheral Pb-Pb collisions at TeV
No full textInternational audienceThe intense photon fluxes from relativistic nuclei provide an opportunity to study photonuclear interactions in ultraperipheral collisions. The measurement of coherently photoproduced final states in ultraperipheral Pb-Pb collisions at TeV is presented for the first time. The cross section, d/d, times the branching ratio () is found to be mb in the rapidity interval . The invariant mass distribution is not well described with a single Breit-Wigner resonance. The production of two interfering resonances, and , provides a good description of the data. The values of the masses () and widths () of the resonances extracted from the fit are MeV/, MeV/, MeV/ and MeV/, respectively. The measured cross sections times the branching ratios are compared to recent theoretical predictions
Clinical outcomes of addictive disorders six months after ADHD Diagnosis: Insights from the START study
No full textInternational audienceIntroductionAttention-Deficit/Hyperactivity Disorder (ADHD) is overrepresented in patients with addictive disorders but remains underdiagnosed. This comorbidity complicates clinical presentations and worsens prognosis. We aimed to evaluate addictive disorder outcomes six months after ADHD diagnosis in patients undergoing addiction treatment and to identify factors associated with a favorable outcome. Secondary objectives explored patient characteristics and therapeutic strategies.MethodThe START study (Study on the Treatment of ADHD and addiction comorbidity: a ReTrospective analysis of medical records) was an observational, retrospective analysis of electronic medical records from patients recently diagnosed with ADHD using the DIVA. Included patients had either substance use disorders (SUD) or behavioral addictions (eating, sex, gambling, gaming/screen use, shopping, and physical exercise), with diagnoses established by the referring physician according to clinical judgment, and attended at least two consultations six months apart. Descriptive analyses were conducted for the total sample and by clinical outcome, followed by multivariate logistic regression to identify predictors of improvement.ResultsTobacco and cannabis use disorders were the most common (61.3 % each). Psychiatric comorbidities were frequent (lifetime 84.6 %, current 34.1 %). Methylphenidate initiation was delayed in 29 % of cases, primarily due to current psychiatric comorbidities or ongoing addiction. Improvement in addictive disorders was observed in 61.3 % of patients. Favorable outcomes were associated with older age (OR = 1.13, 95 % CI [1.04–1.23], p = 0.006) and living as a couple (OR = 3.84, 95 % CI [1.10–13.42], p = 0.035), whereas poorer outcomes were associated with the ADHD combined presentation (reference group), compared with the predominantly inattentive and hyperactive/impulsive presentations (OR = 0.16, 95 % CI [0.03–0.90], p = 0.037).ConclusionThe study highlights socio-demographic and clinical predictors of outcome, advancing our understanding of ADHD-addiction comorbidity. Findings warrant confirmation in prospective longitudinal research
A Scaled Poisson Bayesian Model for Viral Epidemic Monitoring
No full textInternational audienceMonitoring an ongoing epidemic requires accurate, trustworthy and easy to use tools, capable of handling low quality data. Extending existing epidemiological models quantifying the propagation intensity via a time-varying reproduction number, this work proposes a scaled Poisson model, accounting for large intrinsic variability in infection counts. The associated scaled likelihood is plugged into a Bayesian model with a quasi-noninformative prior. A carefully designed Markov Chain Monte Carlo algorithm yields a point estimate and credibility intervals of the reproduction number. The accuracy and robustness to model misspecification and to scale parameter selection of the proposed estimator is demonstrated through intensive numerical experiments on COVID-19 case counts in different countries and during various phases of the pandemic
