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Effets combinés des ondes RF et des nanomatériaux : une éventualité environnementale ?
No full textNational audienceLes champs électromagnétiques sont largement employés pour des applications civiles et militaires. D'un point de vue économique et sociétal, ces champs radiofréquences (RF) prennent une place croissante dans notre vie et notre environnement quotidien depuis plusieurs décennies, que ce soit pour du chauffage rapide, les télécommunications sans fil, la géolocalisation (GPS), ainsi que pour des radars divers (anti-collision, domotique, signaux vitaux…), ou encore le transfert d’énergie à distance notamment. Même si la réglementation actuelle implique des conditions d’exposition aux ondes RF, il est primordial de connaître et d'être sûr des possibles effets de ces ondes sur la santé humaine ainsi qu’à l’échelle cellulaire, qu'ils soient bénéfiques ou néfastes, et ce en fonction des conditions d'exposition et dans un environnement réaliste. Jusqu’ici la très grande majorité des études sont menées en ne considérant que les ondes RF seules. Or l’environnement humain est complexe et peut impliquer des agents polluants chimiques, physiques, comme les pesticides, les nanomatériaux ou encore les rayonnements lumineux ou ionisants utilisés pour des analyses médicales et lors de thérapies. A ce jour, peu de littérature sur d'éventuels effets combinés des ondes RF avec d'autres facteurs de stress environnementaux existe et se limite majoritairement à des co-expositions aux ondes RF avec des produits chimiques de type médicamenteux. Pourtant la forte interaction des ondes électromagnétiques avec les nanomatériaux est connue et utilisée pour des applications cliniques, notamment en oncologie (thérapie hyperthermique du cancer). Cette présentation aborde grâce au développement de systèmes d’exposition in vitro et en champ proche l’évaluation de l’association d’ondes RF avec d’autres agents polluants dont des nanomatériaux
Comparative impact of proton versus photon irradiation on triple‐negative breast cancer: Role of VEGFC in tumour aggressiveness
No full textThis work was performed using the microscopy (PICMI)and mouse facilities of IRCANLETTER TO THE JOURNAL 7 of 7fund from the Canceropôle PACA, ANR, INCA, theH2020 TheraLymph Grant project ID: 874708, the LigueNationale contre le Cancer (Equipe Labellisée 2019), Fon-dation ARC de la Recherche contre le Cancer ProgrammeLabellisé 2022 and program ARCAGEING2023020006332and postdoc grant ARCPOST-DOC202107000408International audienc
Online Stochastic Matching: A Polytope Perspective
No full textStochastic dynamic matching problems have recently gained attention in the stochastic-modeling community due to their diverse applications, such as supply-chain management and kidney exchange programs. In this paper, we study a matching problem where items of different classes arrive according to independent Poisson processes. Unmatched items are stored in a queue, and compatibility between items is represented by a simple graph, where items can be matched if their classes are connected.We analyze matching policies in terms of stability, delay, and long-term matching rate optimization. Our approach relies on the conservation equation, which ensures a balance between arrivals and departures in any stable system. Our main contributions are as follows.We establish a link between the existence of stable policies, the dimensionality of the solution set of the conservation equation, and the compatibility graph's structure.We describe the convex polytope formed by non-negative solutions to the conservation equation, and we design policies that can achieve or closely approximate the vertices of this polytope.Lastly, we discuss potential extensions of our results beyond the main assumptions of this paper
Robustness of GaAs and GaN LNAs in X-and Kuband: performances and strategies of protection under jamming or destructive signal
No full textInternational audienceModern Radio communications or radar receivers can be subject to unintentional (jamming) or intentional (jamming or destruction) aggressions. Low Noise Amplifiers (LNA) are generally provided with an upstream protection system to prevent such effects, at the cost of a significant degradation of the noise factor (around 1dB of typical degradation in X-and Ku-band). This paper compares LNAs from two low noise technologies GaAs and GaN, on their electrical performances (parameters [S] and P1dB) and in high frequency noise before (during) and after the application of RF step stress. Different protection strategies are used depending on the respective bandgap potential of GaAs and GaN technologies regarding the RF power to be supported. The advantages and weaknesses of these technologies are identified and discussed at the LNA level and its integration with possibly a limiter
TorchGDM: A GPU-accelerated Python toolkit for multi-scale electromagnetic scattering with automatic differentiation
No full textInternational audienceWe present “torchGDM”, a numerical framework for nano-optical simulations based on the Green’s Dyadic Method (GDM). This toolkit combines a hybrid approach, allowing for both fully discretized nano-structures and structures approximated by sets of effective electric and magnetic dipoles. It supports simulations in three dimensions and for infinitely long, two-dimensional structures. This capability is particularly suited for multi-scale modeling, enabling accurate near-field calculations within or around a discretized structure embedded in a complex environment of scatterers represented by effective models. Importantly, torchGDM is entirely implemented in PyTorch, a well-optimized and GPU-enabled automatic differentiation framework. This allows for the efficient calculation of exact derivatives of any simulated observable with respect to various inputs, including positions, wavelengths or permittivity, but also intermediate parameters like Green’s tensor components, which can be interesting for physics informed deep learning applications. We anticipate that this toolkit will be valuable for applications merging nano-photonics and machine learning, as well as for solving nano-photonic optimization and inverse problems, such as the global design and characterization of metasurfaces, where optical interactions between structures are critical
Nanoscale thermal radiation including sub-wavelength emitters; application to energy-conversion devices
No full textInternational audienc
In-situ measurement of AlAs growth rate by magnification inferred curvature method
No full textInternational audienceDespite the development of numerous in-situ monitoring tools applied to MBE, the direct absolutemeasurement of growth rates remains challenging [1]. The calibration of group-III element fluxes, andconsequently the associated growth rate, is performed through various methods (such as RHEEDoscillations, equivalent pressure measurement, optical methods, quartz crystal microbalance, etc.) [2].However, these direct in-situ measurements are often constraining since valid only under specificconditions and/or involve indirect measurements that require the knowledge of additional parameters.In this study, we applied the magnified inferred curvature method (MIC) [3] to directly measurethe growth rate of slightly strained AlAs layers on GaAs substrate, and thus enabling to calibrate in-situ the Al effusion cells, and simultaneously controlling directly the growth rate and composition ofAlxGa1−xAs layers independently of their thickness, and the growth conditions. From MICmeasurements, we were able to deduce the elastic parameters at growth temperature, enabling us toachieve an universal calibration of the atomic flux for the aluminum cell. This calibration is alsoapplicable to other group-III element cells, provided they induce strain relatively to the substrate, andis fully and directly transferable from one epitaxy chamber to another.This method, when applied to the MBE growth of complex III-V epitaxial structures [4], such asBragg mirrors [5,6] and VCSELs, proves to be highly advantageous, significantly reducing the numberof calibration runs needed to achieve the desired device characteristics
Thermite Combustion: Current Trends in Modeling and Future Perspectives
No full textInternational audienceAluminum-based reactive composites, such as thermites, represent a unique class of energetic materials characterized by their high energy densities, tunability in combustion properties and safety. Prepared using techniques such as mechanical mixing, milling, and physical vapor deposition, these materials are promising for achieving energetic functions beyond the capabilities of traditional energetic materials. Applications include thermal plugging, smart initiation, pyro-fusing in civilian devices where the use of explosives is not feasible. Unfortunately, engineers and researchers face the lack of predictive combustion models to optimize the thermite materials to a given application. The reason is the insufficient knowledge and quantification of reaction and combustion mechanisms and the key variables governing them. That is why, over the past decades, several approaches and models ranging from atomic-scale modeling to macroscopic simulations using computational fluid dynamics, were developed and are reviewed in this article. These methods provided insights into key reaction pathways, ignition mechanisms, and flame propagation dynamics. Despite these advancements, substantial gaps remain, particularly in capturing multiphase flow dynamics and suboxides condensation/nucleation process during the combustion at high temperature. Boundary-resolved transient direct numerical simulation approach and particle-resolved numerical techniques will allow acquiring knowledge in gasparticle and particle-particle interaction. Recent breakthroughs in machine learning will further accelerate the design and optimization of thermites by enabling the establishment of predictive quantitative structure-property relationships in complement of heavy detailed physical models. This review highlights foundational theoretical developments for thermite materials, and emphasize the need for interdisciplinary efforts particularly between fluid dynamicists and condensed matter physicists to realize the full potential of these versatile energetic materials
Défi du GDR GPL ADaptation DYnamique et ConTinue ADDYCT
No full textL'adaptabilité est un enjeu majeur des systèmes complexes dans des environnements dynamiques. Ces environnements regroupent les architectures distribuées composées de systèmes "component-based" et les infrastructures déployées sur des plateformes hétérogènes à différentes échelles : Cloud, Fog, Edge, ou IoT. Tous ces systèmes doivent être capables d'ajuster leur configuration de manière autonome pour répondre à des évènements exogènes et/ou endogènes.Les systèmes logiciels doivent être considérés dès leur conception comme des systèmes durables en termes de temporalité (Système temps long, Cycle de vie, Couplage), de scalabilité (Granularité, Interfaces, Gestion massive de données) et d'hétérogénéité (Intégration, Interopérabilité).Ces challenges sont d'autant plus importants lorsque la taille du système est grande et couplée avec des artefacts matériels (IoT, CPS, Jumeaux numériques, Cloud...) L'objectif de ce défi est de modéliser, analyser et d'implémenter des moyens et des politiques d'adaptation pour des systèmes logiciels complexes (distribués, componentisés etc.).L'approche adoptée repose sur les boucles de contrôle MAPE-K, un modèle d'auto-adaptation autonome capable de s'ajuster dynamiquement à un environnement permettant de répondre à la nécessité d'intégrer les données collectées et leurs modèles de traitement, le système opérant et sa connexion avec son environnement, l'évolution dynamique et la nécessité de maintenir une représentation fidèle du comportement attendu