French National Centre for Scientific Research

Fe-based materials have received more and more interests in recent years as candidates to fabricate bioresorbable stents due to their appropriate mechanical properties and biocompatibility. However, the low degradation rate of Fe is a serious limitation for such application. To overcome this critical issue, many efforts have been devoted to accelerate the corrosion rate of Fe-based stents, through the structural and surface modification of Fe matrix. As stents are implantable devices, the released corrosion products (Fe 2+ ions) in vessels may alter the metabolism, by generating reactive oxygen species (ROS), which might in turn impact the biosafety of Fe-based stents. These considerations emphasize the importance of combining knowledge in both materials and biological science for the development of efficient and safe Fe-based stents, although there are still only limited numbers of reviews regarding this interdisciplinary field. This review aims to provide a concise overview of the main strategies developed so far to design Fe-based stents with accelerated degradation, highlighting the fundamental mechanisms of corrosion and the methods to study them as well as the reported approaches to accelerate the corrosion rates. These approaches will be divided into four main sections, focusing on (i) increased active surface areas, (ii) tailored microstructures, (iii) creation of galvanic reactions (by alloying, ion implantation or surface coating of noble metals) and (iv) decreased local pH induced by degradable surface organic layers. Recent advances in the evaluation of the in vitro biocompatibility of the final materials and ongoing in vivo tests are also provided.

In this review, we summarize the current state of knowledge on the biosynthesis of carrageenan by exploring both the enzyme activities and their localizations. Genomic data, with the sequencing of the genome of Chondrus crispus and the first transcriptomic study into the life cycle stages of this organism, as well as fine carbohydrate structural determination of matrix glycans, provide leads in the study of carrageenan anabolism. Comparison to related carbohydrate-active enzymes, detailed phylogenies alongside classic histochemical studies and radioactivity assays, help predict the localization of the carrageenan-related enzyme biochemistries. Using these insights, we provide an updated model of carrageenan biosynthesis which contributes to understanding the ancestral pathway of sulfated polysaccharide biosynthesis in eukaryotes.

This paper presents an integrated optimal Energy Management Strategy (EMS) and sizing of a high-speed Flywheel Energy Storage System (FESS) in a battery electric vehicle (EV). The methodology aims at extending the battery cycle life and drive range by relegating fast dynamics of the power demand to the FESS. For the EMS, the battery power and FESS energy are considered as weighted objectives of an optimization problem that are established using Pontryagin's Minimum Principle (PMP). In order to derive the optimal FESS size, the sizing algorithm is targeted to minimize the battery degradation level and increase the FESS energy interaction with the system. The performance of the proposed methodology is assessed using some driving cycles including ECE-15, UDDC, HWFET, and US06. Comparing the proposed battery/FESS to the battery-only topology, the battery SoH and life cycle are improved considerably for different driving cycles.

Accurate soil organic carbon models are key to understand the mechanisms governing carbon sequestration in soil and to help develop targeted management strategies to carbon budget. The accuracy and reliability of soil organic carbon (SOC) models remains strongly limited by incorrect initialization of the conceptual kinetic pools and lack of stringent model evaluation using time-series datasets. Notably, due to legacy effects of management and land use change, the traditional spin-up approach for initial allocation of SOC among kinetic pools can bring substantial uncertainties in predicting the evolution of SOC stocks. The AMG model can fulfill these conditions as it is a parsimonious yet accurate SOC model using widely-available input data. In this study, we first evaluated the performance of AMGv2 before and after optimizing the potential mineralization rate (k 0) of SOC stock following a leave-one-site-out cross-validation based on 24 long-term field experiments (LTEs) in the Southwest of China. Then, we used Rock-Eval® thermal analysis results as input variables in the PARTY SOC machine learning model to estimate the initial stable SOC fraction (C S /C 0) for the 14 LTEs where soil samples were available. The results showed that initializing the C S /C 0 ratio using PARTY SOC combined with the optimized k 0 further improved the accuracy of model simulations (R 2 = 0.87, RMSE = 0.25, d = 0.90). Combining average measured C S /C 0 and k 0 optimization across all 24 LTEs also improved the model predictive capability by 25% compared to using default parameterization, thus suggesting promising avenue for upscaling model applications at the regional level where only a few measurement data on SOC stability can be available. In conclusion, the new version of the AMG model developed in the Tuojiang River Basin context exhibits excellent performance. This result paves the way for further calibration and validation of the AMG model in a wider set of contexts, with the potential to significantly improve confidence in SOC predictions in croplands over regional scales.

Resistive REBCO Fault Current Limiter (FCL) is a promising solution to the fault current issue in electric grids, which is not fully satisfactorily solved. But the cost of the Superconducting (SC) REBCO conductor should be lowered to facilitate the deployment of this innovative device. What we refer to as REBCO conductor is usually a bare REBCO tape bonded to a stabilizer. The design of the stabilizer is fundamental to optimize the conductor and lower the SFCL cost. The goal is to limit the temperature rise for any fault conditions, notably for any amplitude of the prospective fault current. The specificities of SC tapes, including critical current inhomogeneities along the length, must be considered in the design. Here we study various stabilizer configurations. First the case of “dielectric stabilizer”, electrically insulating is presented. The theoretical performances are interesting in terms of electric field under limitation, but the low thermal diffusivity remains an issue, as well as the implementation. Some experimental investigations are shown. The results are compared to classical metallic stabilizer designs, which are simpler to implement. To enhance the electric field under limitation, a variant of metallic stabilizer is then introduced, using a corrugated structure. A design showing an electric field under limitation of 200 V/m (clearing time of 50 ms) is presented. Further optimizations and higher electric fields seem achievable even if practical implementation remains very challenging.

The biorefining process of lignocellulosic biomass has recently emerged as one of the most profitable biofuel production options. However, pretreatment is required to improve the recalcitrant lignocellulose's enzymatic conversion efficiency. Among biomass pretreatment methods, the steam explosion is an eco-friendly, inexpensive, and effective approach to pretreating biomass, significantly promoting biofuel production efficiency and yield. This review paper critically presents the steam explosion's reaction mechanism and technological characteristics for lignocellulosic biomass pretreatment. Indeed, the principles of steam explosion technology for lignocellulosic biomass pretreatment were scrutinized. Moreover, the impacts of process factors on pretreatment efficiency and sugar recovery for the following biofuel production were also discussed in detail. Finally, the limitations and prospects of steam explosion pretreatment were mentioned. Generally, steam explosion technology applications could bring great potential in pretreating biomass, although deeper studies are needed to deploy this method on industrial scales.

Moisture in the sensing coil of fiber-optic gyroscope (FOG) gives rise to bias drift. To explain and predict this phenomenon, one needs to quantify the strain along the fiber induced by moisture. In this paper, a full theoretical approach to determine the strain field into the sensing coil induced by a moisture loading is proposed. The approach consists in solving an analytical Fickian diffusion model in a semi-infinite medium, to implement a semi-analytical mechanical model of the moisture diffusion effect. The computed strain along the fiber is then compared with the distributed strain along the fiber measured using Rayleigh optical frequency domain reflectometry (Rayleigh-OFDR). Agreement between predicted and measured data demonstrate the validity of the proposed approach. Finally, a step function of $60\%RH$ (relative humidity) is shown to be equivalent to a thermal loading of $1 \ ^{\circ }C/min$ on $\Delta T = 15 \ ^{\circ }C$ and these loadings may induce an estimated bias of $0.1 \ ^{\circ }/h$ .

Recently, PROTEVS GIB20 experiment was performed in the Strait of Gibraltar. Part of this experiment was dedicated to observe the high frequency dynamics near Camarinal Sill, considered as a mixing hotspot in the region. Mooring lines equipped with current profilers and temperature/salinity probes provided data which evidence two dynamical regimes depending on the tidal current intensity; in neap tide floods, local internal hydraulic control is never observed over CS while in spring tide, local internal hydraulic control depicts a tide‐dependent and spatially variable pattern. In spring tide floods, measurements revealed the development of a hydraulic jump over the sill and its advection on the lee side. Cross sill sections with CTD casts and acoustic images confirmed this dynamics and depicted a well developed hydraulic jump on the eastern flank of the sill during spring tide ebbs. The north‐south and temporal variability of the internal hydraulics was analyzed from several zonal sections over Camarinal Sill, the mean topographic feature of the Strait of Gibraltar. We highlighted a complex series of local hydraulic jumps constrained by topography and a significant north‐south variability. The spatiotemporal variability of local hydraulics questions the two dimensional representation of the exchange flow in the Strait of Gibraltar. During neap tide flood, the dynamics of the Mediterranean outflow was investigated from a fixed station. We imaged the development of instabilities at the interface between Atlantic and Mediterranean waters jointly with the generation of much thicker billows deeper. Finally, we discuss our findings in relation to other straits dynamics.

Self-sufficient ideographies are rare because they are stifled by the issue of standardization. Similar issues arise with abstract art or drawings created by young children or great apes. We propose that mathematical indices and artificial intelligence can help us decode ideography, and if not to understand its meaning, at least to know that meaning exists.

Explaining the outcome of programs has become one of the main concerns in AI research. In constraint programming, a user may want the system to explain why a given variable assignment is not feasible or how it came to the conclusion that the problem does not have any solution. One solution to the latter is to return to the user a sequence of simple reasoning steps that lead to inconsistency. Arc consistency is a well-known form of reasoning that can be understood by a human. We consider explanations as sequences of propagation steps of a constraint on a variable (i.e. the ubiquitous revise function in arc-consistency algorithms) that lead to inconsistency. We characterize several cases for which providing a shortest such explanation is easy: For instance when constraints are binary and variables have maximum degree two. However, these polynomial cases are tight. For instance, providing a shortest explanation is NP-hard when constraints are binary and the maximum degree is three, even if the number of variables is bounded. It remains NP-hard on trees, despite the fact that arc consistency is a decision procedure on trees. The problem is not even FPT-approximable unless the FPT \(\ne \) W[2] hypothesis is false.

The present study investigates the physicochemical and biological properties of a novel inorganic-organic hybrid material called (2R,5S)-2,5-dimethylpiperazine-1,4-diium dinitrate (RSDPN). This material was synthesized under mild conditions and crystallized to the monoclinic system with space group P 2 1 / c . The organic portion of the structure forms bifurcated N–H⋯O and weak C–H⋯O hydrogen bonds with the nitrate anions, resulting in wavy layers parallel to the (100) plane. The integration of organic and inorganic elements in the RSDPN compound is evident through infrared absorption spectroscopy. In order to comprehensively examine the structural, electrical, and biological properties, a DFT approach was employed. Various analysis techniques such as Hirshfeld surfaces analysis (HS), Atoms-In-Molecules (AIM), Reduced Density Gradient (RDG), and Electron Localized Function (ELF) were utilized to visualize and quantify the intermolecular interactions and types of hydrogen bonds that contribute to the stability and cohesion of the structure. The title compound exhibits remarkable stability and strong electrophilic activity, both of which are common characteristics in physiologically active compounds, as indicated by frontier orbital analysis. Thermal examination revealed a two-stage breakdown process where the substance ignites, producing volatile fumes and a dark carbonaceous residue. Molecular docking analysis suggests that RSDPN inhibitors hold potential for the treatment of Parkinson’s, Schizophrenia, and Alzheimer’s disease. Overall, this study provides a detailed experimental and theoretical investigation of the RSDPN compound, shedding light on its physicochemical and biological properties, and highlighting its potential applications in the field of therapeutic intervention for neurodegenerative disorders.

Mars harbors ice deposits in several forms, on the surface and in the subsurface, which exchange with each other on various timescales. We seek to study the pore ice evolution over millennial time scales and how it contributes to and affects the Polar cap's evolution. We calculate the evolution of SubSurface Ice (SSI) pore filling by coupling two models, the Mars LMD Global Climate Model, which calculates the atmospheric and surface evolution on an annual timescale, and the dynamical version of the Mars Subsurface Ice Model, which calculates the evolution of the SSI on a millennial timescale. The SSI latitudinal boundary fluctuates over more than 25° in one obliquity cycle, overall extending equatorward of latitude ±35° at high obliquity, and receding to about ±60° at low obliquity. In locations where the SSI is stable continuously over orbital cycles, the simulations predict layering caused by a sublimation front at the SSI top boundary. Between 5 and 2.5 Myr ago, the subsurface lost at least ∼95 m of polar equivalent layer ice. The SSI flux routinely reaches ∼1 mm/Mars year. In addition to the direct contribution to the growth of the North Polar Layered Deposits (NPLD), the SSI causes variations in the NPLD accumulation rate due to the changes in the SSI distribution that affect the seasonal energy budget. These variations are comparable to the change in rate due to variations in orbital elements. When running paleo‐climate simulations, particularly to reconstruct the NPLD profile, changes in the SSI distribution should be considered.

A class of Lyapunov functions for discrete-time Lurie systems with monotonic non-linearities is proposed. The Lyapunov functions are composed of quadratic terms on the states and of the system's non-linearities as well as Lurie-Postnikov type integral terms. Crucially, positive definiteness of the matrix in the generalised quadratic form and positivity of the scaling terms of the Lurie-Postnikov integrals are relaxed in the stability conditions. Furthermore, they are used for regional stability analysis and performance assessment. Numerical examples show that the proposed Lyapunov function structure matches or outperforms existing ones for these systems.

Modular Multilevel Converters (MMC) are now expected to fulfill AC and DC grid stability functions while at the same time they must ensure their own operation. In this paper, we propose a new MIMO control structure to coordinate the AC/DC power exchange and MMC internal energy control using a hierarchical MPC. This controller is tested on an AC/DC system where the MMC works in grid-forming mode on the AC side and controls the DC-grid voltage at the same time. This MPC solution is implemented in a real-time controller to operate a physical mockup of a 6kVA MMC which contains 60 submodules and interfaces a 400V DC grid and a 120V (line-to-ground) AC grid. The MPC solution shows similar tracking performance with the state-of-the-art dual PI control solution based on two PI controllers working in parallel, and exhibits a better disturbance rejection capacity than this dual PI.

It is well known that ionizing radiation impact dielectric and mechanical properties of polymers, as a result of chain scission, crosslinking, and oxidation steps. The electrical conductivity is generally substantially increased after irradiation. We analyze such effects in XLPE after gamma-irradiation. Current measurements reveal a substantial increase in conductivity after irradiation at doses up to 200 kGy. Current-field characteristics change after irradiation and appear indicative of an ionic-type conduction. Space charge measurements confirm the mechanism with obvious heterocharge build-up after irradiation.

Classification neural networks fail to detect inputs that do not fall inside the classes they have been trained for. Runtime monitoring techniques on the neuron activation pattern can be used to detect such inputs. We present an approach for monitoring classification systems via data abstraction. Data abstraction relies on the notion of box with a resolution. Box-based abstraction consists in representing a set of values by its minimal and maximal values in each dimension. We augment boxes with a notion of resolution and define their clustering coverage, which is intuitively a quantitative metric that indicates the abstraction quality. This allows studying the effect of different clustering parameters on the constructed boxes and estimating an interval of sub-optimal parameters. Moreover, we automatically construct monitors that leverage both the correct and incorrect behaviors of a system. This allows checking the size of the monitor abstractions and analysing the separability of the network. Monitors are obtained by combining the sub-monitors of each class of the system placed at some selected layers. Our experiments demonstrate the effectiveness of our clustering coverage estimation and show how to assess the effectiveness and precision of monitors according to the selected clustering parameter and monitored layers.

The diagnosis of unruptured intracranial aneurysms from time-of-flight Magnetic Resonance Angiography (TOF-MRA) images is a challenging clinical problem that is extremely difficult to automate. We propose to go beyond the mere detection of each aneurysm and also estimate its size and the orientation of its main axis for an immediate visualization in appropriate reformatted cut planes. To address this issue, and inspired by the idea behind YOLO architecture, a novel one-stage deep learning approach is described to simultaneously estimate the localization, size and orientation of each aneurysm in 3D images. It combines fast and approximate annotation, data sampling and generation to tackle the class imbalance problem, and a cosine similarity loss to optimize the orientation. We evaluate our approach on two large datasets containing 416 patients with 317 aneurysms using a 5-fold cross-validation scheme. Our method achieves a median localization error of 0.48 mm and a median 3D orientation error of 12.27 \(^\circ \)C, demonstrating an accurate localization of aneurysms and an orientation estimation that comply with clinical practice. Further evaluation is performed in a more classical detection setting to compare with state-of-the-art nnDetecton and nnUNet methods. Competitive performance is reported with an average precision of 76.60%, a sensitivity score of 82.93%, and 0.44 false positives per case. Code and annotations are publicly available at https://gitlab.inria.fr/yassis/DeepAnePose.

We consider the field-road system, a model for fast diffusion channels in population dynamics, consisting of two parabolic equations posed on sets of different dimensions and coupled through exchange terms on “the road”. We propose a finite volume scheme for this model, the analysis of which requires an unconventional discrete Poincaré-Wirtinger inequality, and establish some numerical analysis results.