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Ferroelectric control of spin and orbital Rashba effects at the Ni/HfO2 interface
No full textInternational audienceWe predict the giant ferroelectric control of interfacial properties of Ni/HfO2, namely, (i) the magnetocrystalline anisotropy and (ii) the inverse spin and orbital Rashba effects. The reversible control of magnetic properties using electric gating is a promising route to low-energy consumption magnetic devices, including memories and logic gates. Synthetic multiferroics, composed of a ferroelectric in proximity to a magnet, stand out as a promising platform for such devices. Using a combination of simulations and transport calculations, we demonstrate that reversing the electric polarization modulates the interface magnetocrystalline anisotropy from in-plane to out-of-plane. This modulation compares favorably with recent reports obtained upon electromigration induced by ionic gating. In addition, we find that the current-driven spin and orbital densities at the interface can be modulated by about 50% and 30%, respectively. This giant modulation of the spin-charge and orbit-charge conversion efficiencies opens appealing avenues for voltage-controlled spin- and orbitronics devices
Macroscopic limit of a Fokker-Planck model of swarming rigid bodies
No full textInternational audienceWe consider self-propelled rigid-bodies interacting through local body-attitude alignment modelled by stochastic differential equations. We derive a hydrodynamic model of this system at large spatio-temporal scales and particle numbers in any dimension n ≥ 3. This goal was already achieved in dimension n = 3, or in any dimension n ≥ 3 for a different system involving jump processes. However, the present work corresponds to huge conceptual and technical gaps compared with earlier ones. The key difficulty is to determine an auxiliary but essential object, the generalized collision invariant. We achieve this aim by using the geometrical structure of the rotation group, namely, its maximal torus, Cartan subalgebra and Weyl group as well as other concepts of representation theory and Weyl's integration formula. The resulting hydrodynamic model appears as a hyperbolic system whose coefficients depend on the generalized collision invariant
Experimental investigation of the axial and in-plane magnetic fields of a square coil using a smartphone
No full textInternational audienceWe present an experimental analysis of the magnetic field generated by a square coil. In contrast to the usual circular coil, an analytical expression is easily derived over the whole space. This allows quantitative comparisons not only along the coil axis but also in the coil plane, where we demonstrate a smooth transition from the infinite-wire model to the dipole model. Unlike the 1D Hall probes usually found in teaching laboratories, we use a smartphone whose 3D sensor allows precise alignment with respect to the coil's symmetry elements
A bibliometric literature review of integrated data and model based diagnosis approaches for the industry 4.0
No full textInternational audienceThe increasing presence of Cyber-Physical Systems and the Internet of Things has accelerated the digital transformation of industrial environments, commonly known as Industry 4.0. In this context, Artificial Intelligence techniques are increasingly used to support automatic diagnostic tasks. This paper presents a systematic literature review of hybrid diagnostic systems that combine Model-Based Diagnosis (MBD), which relies on physical models to detect abnormal behavior, and Data-Based Diagnosis (DBD), which uses machine learning to identify faults from data. The review has two objectives: (i) to examine how MBD and DBD methods have been combined to improve diagnostic performance, and (ii) to identify integration opportunities through existing machine learning frameworks to support reusable and adaptive solutions. A bibliometric analysis was conducted following a simplified PRISMA 2020 methodology. From over 1,300 records, 75 articles were selected and analyzed. Most hybrid systems adopt a serial architecture where DBD classifiers analyze residuals from MBD for fault detection and isolation. The most common applications are found in smart manufacturing and energy systems, and as for the most used machine learning techniques, there are those of deep learning and ensemble methods. Challenges remain regarding real-time scalability, interpretability, and standardization. This review provides a structured foundation for designing explainable, efficient, and reusable diagnostic solutions for Industry 4.0
Numerical analysis of the homogeneous Landau equation: approximation, error estimates and simulation
No full textWe construct a numerical solution to the spatially homogeneous Landau equation with Coulomb potential on a domain D_L with N retained Fourier modes. By deriving an explicit error estimate in terms of L and N , we demonstrate that for any prescribed error tolerance and fixed time interval [0, T ], there exist choices of DL and N satisfying explicit conditions such that the error between the numerical and exact solutions is below the tolerance. Specifically, the estimate shows that sufficiently large L and N (depending on initial data parameters and T ) can reduce the error to any desired level. Numerical simulations based on this construction are also presented. The results in particular demonstrate the mathematical validity of the spectral method proposed in the referenced literature
Health and environmental benefits of the design of a novel hybrid food mixing meat and mushroom
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Versatile Sol–Gel and Inkjet Printing Route for Low-Cost Fabrication of Mesoporous SiO 2 -Based Conductive Bridge Memristors
No full textInternational audienceThe growing demand for cost-effective nonvolatile memory has driven research into emerging resistive random-access memory (ReRAM) technologies, including conductive bridge random-access memories (CBRAMs). In this work, we investigate the electrical performance of CBRAMs based on mesoporous solgel SiO 2 electrolytes, fabricated using a cost-effective process combining sol-gel deposition, evaporation-induced self-assembly, and inkjet printing. By varying key fabrication parameters, namely, porosity and electrolyte thickness, we analyze their impact on switching behavior, retention, and endurance. Our findings reveal that mesoporous SiO 2 significantly enhances CBRAM performance, offering well-controlled ionic pathways for filament formation and dissolution while keeping the deposition process flexible in terms of deposition parameters. Among the tested configurations, the memory cells based on a moderately porous (20%) and thick (280 nm) SiO 2 layer demonstrate the best stability, with minimal SET/ RESET voltage variability, strong retention over 28 h, and reliable endurance over 1000 cycles. In contrast, highly porous electrolyte layers lead to greater variability in resistive states due to unstable filament dissolution. These results highlight the potential of mesoporous SiO 2 -based CBRAMs for next-generation memory applications, particularly in flexible electronics and neuromorphic computing. The study provides key insights into optimizing memory design through electrolyte engineering and scalable, low-cost manufacturing processes.</div
Recommendation of RILEM TC 283-CAM: performance-based assessment of alkali-activated concrete durability using the 10 V rapid chloride permeability test
No full textInternational audienceThe major barriers to the widespread adoption of alkali-activated materials by the construction industry include concerns about durability and their exclusion from current standards. The chemical reactions characterizing alkali-activated binder systems differ drastically from the conventional hydration process of Portland cement. Thus, the mechanisms by which concrete achieves potential durability are different between the two types of binders. RILEM Technical Committee (TC) 283-CAM (Chloride transport in Alkali-activated Materials) aimed to address key questions related to chloride transport in alkali-activated binders and concretes, with a view toward drafting recommendations for the appropriate selection and application of testing methods, and this document represents a key output of that TC. The standard ASTM C1202 Rapid Chloride Permeability Test (RCPT) method fails to measure the charge passed through most alkali-activated concretes due to samples overheating when applying the specified 60 V potential difference. A modified RCPT using a 10 V potential difference was used in the interlaboratory testing campaign of TC 283-CAM. The 10 V-RCPT method described in this Recommendation allowed the successful completion of tests for all alkali-activated concretes considered. Various precursors were investigated including fly ash, GGBS, calcined clay and ferronickel slag. 10 V-RCPT results are validated against ASTM C1556 bulk diffusion test results. Performance-based specifications are proposed
Moment-SOS hierarchies for arrow-type polynomial matrix inequalities with applications to structural optimization
No full textThe Arrow Decomposition (AD) technique, initially introduced in [Mathematical Programming 190(1-2) (2021), pp 105-134], demonstrated superior scalability over the classical chordal decomposition in the context of Linear Matrix Inequalities (LMIs) if the matrix in question satisfied suitable assumptions. The primary objective of this paper is to extend the AD method to address Polynomial Optimization Problems (POPs) involving large-scale Polynomial Matrix Inequalities (PMIs), with the solution framework relying on moment-sum of square (mSOS) hierarchies. As a first step, we revisit the LMI case and weaken the conditions necessary for the key AD theorem presented in [Mathematical Programming 190(1-2) (2021), pp 105-134]. This modification allows the method to be applied to a broader range of problems. Next, we propose a practical procedure that reduces the number of additional variables, drawing on physical interpretations often found in structural optimization applications. For the PMI case, we explore two distinct approaches to combine the AD technique with mSOS hierarchies. One approach involves applying AD to the original POP before implementing the mSOS relaxation. The other approach applies AD directly to the mSOS relaxations of the POP. We establish convergence guarantees for both approaches and prove that theoretical properties extend to the polynomial case. Finally, we illustrate the significant computational advantages offered by the application of AD, particularly in the context of structural optimization problems
Tests of Artificial Neural Network-Based Diabatization Approaches on Simple 1D Models
No full textInternational audienceRecently, a novel diabatization scheme has been proposed [Shu, Y.; Truhlar, D. G. J. Chem. Theory Comput. 2020, 16, 6456–6464] using artificial neural networks. Most importantly, the method almost exclusively requires the knowledge of adiabatic energies, which are routinely obtained from ab initio calculations. However, many questions related to the favorable performance of the method remain unanswered. In the present paper, some of these questions are considered for selected one-dimensional models with one configurational variable. In particular, various activation functions are tested, including nonlinear ones in the output layer, the effect of the regularization term in the loss function is analyzed, and computationally cheap extensions of training sets are proposed. Significant improvements of the performance of the original method have been achieved