Société Nationale des Chemins de Fer Français (France)

Return to performance (RTPerf) after anterior cruciate ligament (ACL) surgery remains difficult to predict in elite alpine skiers, as general criteria may not fully apply. This study aimed to identify determinants and the time needed to achieve RTPerf at the same competitive level. A prospective cohort of 71 elite alpine skiers (mean age: 23.6 ± 5.2 years; height: 173.0 ± 7.8 cm; weight: 72.0 ± 12.1 kg) who underwent ACL surgery was analysed. Demographic and contextual variables, including sex, age, world ranking and type of injury, were collected. Isokinetic knee muscle strength was assessed six months postsurgery. RTPerf (YES/NO) and time to return were evaluated 2 years postoperatively using the International Ski and Snowboard Federation points system. Associations with categorical and quantitative variables were assessed using χ2 and t- or Mann-Whitney U tests. Variables with p < 0.2 were included in multivariable logistic regression. Predictors of return time were analysed using Cox regression and receiving operating characteristic (ROC) curves. Statistical significance was set at p < 0.05. Seventy-nine percent of skiers returned to preinjury performance within 2 years. Primary ACL injury (vs. revision) (odds ratio [OR]: 6.6; 95% confidence interval [CI]: 1.85-23.6; p = 0.004) and isolated injury (vs. complex) (OR: 5.35; 95% CI: 1.39-20.48; p = 0.014) were significant predictors. Average return time was 348 ± 51.6 days. Greater relative knee extensor strength was associated with earlier return. Limb symmetry in knee extension at 60°/s predicted return within 1 year, while the hamstring-to-quadriceps functional ratio at 90°/s predicted later return (area under the curve [AUC]: 0.78; p < 0.01). Primary and isolated ACL injuries were linked to higher RTPerf rates, while greater knee extensor isokinetic strength was associated with shorter time to RTPerf in elite alpine skiers. Level II.

The SuperRail project is the world’s first installation of a high-temperature superconducting (HTS) cable system for commercial operation in a railway electric grid. The aim is to increase traffic capacity and achieve national low-carbon emission targets by strengthening the railway grid. The project involves the development, manufacture, installation, and operation of an HTS DC cable system at Paris’ Montparnasse railways station. Due to limited available rights-of-way, conventional cable technologies are not feasible, making HTS cable technology the only viable solution for supplying power to the railway tracks in such a dense urban area. Two HTS cables are installed in parallel. Each one can carry 2.25 MW and support fault conditions of 67 kA for 100 ms due to their direct connection to the transmissions grid. In order to validate the HTS cable system design and the accessories performances, a complete 35-m HTS cable type test loop was installed and tested at the SNCF Railway Test Agency. This paper shows the results, including dielectric performance, current carrying capacity and fault current tests. The HTS cable system successfully passed all the tests, and the design has been fully qualified.

Abstract Introduction Degraded peatlands contribute up to 2 gigatons CO 2 ‐equivalent annually (approximately 5% of anthropogenic greenhouse gas emissions). As restoration efforts increase, the effectiveness of combined hydrological and vegetation restoration strategies remains insufficiently understood. Objectives This study investigates the short‐term impacts of rewetting, topsoil removal, and Sphagnum transplantation on carbon fluxes and greenhouse gas carbon balance in La Guette peatland in France, a disturbed temperate peatland invaded by Molinia caerulea . Methods Carbon dioxide and methane fluxes, vegetation, and environmental variables were monitored between 2014 and 2017 at La Guette peatland, across Control (unchanged), Bare (top‐soil removed), and Sphagnum (top‐soil removed + Sphagnum transplants) plots within rewetted and non‐rewetted subsites. Empirical models for ecosystem respiration, gross primary production, and methane fluxes were used to estimate annual carbon budgets, considering only ecosystem–atmosphere exchanges (excluding the carbon physically lost through topsoil stripping). Results Rewetting elevated the water table by approximately 7 cm and slightly reduced carbon emissions (by approximately 124 gCO 2 ‐eq m −2 yr −1 ) when implemented alone. Control plots showed the highest emissions. Top‐soil removal lowered emissions, and when coupled with Sphagnum reintroduction in rewetted areas, achieved the greatest greenhouse gas mitigation. Methane emissions increased post‐rewetting, but this increase remained outweighed by reductions in carbon dioxide emissions. One Sphagnum ‐restored plot approached carbon neutrality within the third year. Conclusions Although restored plots remained net carbon sources, combined hydrological and vegetation interventions significantly limited carbon losses and CO 2 ‐equivalent emissions compared to degraded conditions. Restoration thus prevents further carbon stock deterioration in disturbed peatlands while reducing climate impacts.

Abstract In this article, we develop a risk assessment methodology for analyzing the resilience of railway assets to climate change. The methodology relies on climate projections, asset data, and hazard modelling to identify areas of highest risk and vulnerability. The latest climate projections in France are used to identify potential changes in extreme weather events for different time horizons and different climate change scenarios. The results of this approach enable to make informed decisions about the most effective ways to reduce the potential impacts of climate change on railway assets.

Discrete Element Method (DEM) simulations of granular media are computationally intensive, particularly during initialization phases dominated by large displacements and kinetic energy. This paper presents a novel generative pipeline based on 3D diffusion models that directly synthesizes arbitrarily large granular assemblies in mechanically realistic configurations. The approach employs a two-stage pipeline. First, an unconditional diffusion model generates independent 3D voxel grids representing granular media; second, a 3D inpainting model, adapted from 2D techniques using masked inputs and repainting strategies, seamlessly stitches these grids together. The inpainting model uses the outputs of the unconditional diffusion model to learn from the context of adjacent generations and creates new regions that blend smoothly into the context region. Both models are trained on binarized 3D occupancy grids derived from a database of small-scale DEM simulations, scaling linearly with the number of output voxels. Simulations that spanned over days can now run in hours, practically enabling simulations containing more than 200k ballast particles. The pipeline remains fully compatible with existing DEM workflows as it post-processes the diffusion generated voxel grids into DEM compatible particle meshes. Being mechanically consistent on key granulometry metrics with the original DEM simulations, the pipeline is also compatible with many other applications in the field of granular media, with capability of generating both convex and non-convex particles. Showcased on two examples (railway ballast and lunar regolith), the pipeline reimagines the way initialization of granular media simulations is performed, enabling scales of generation previously unattainable with traditional DEM simulations.

This paper proposes a data-centric method for identifying geometric defects in railway tracks using acceleration data collected from high-speed trains. Unlike many existing drive-by monitoring approaches that rely on classical supervised learning models requiring extensive labeled data from every line, a novel framework based on unsupervised domain adaptation (UDA) is proposed. This framework transfers the geometric defects diagnosis model learned from one line (source domain) to a new line (target domain) without necessitating labeled data from the latter. Given the variability in operational conditions, a detection model trained on one known scenario cannot be directly applied to another. Thus, the framework learns features sensitive to geometric defects and invariant to different tracks using the progressive distribution alignment based on label correction (PDALC) algorithm. Input data comprises labeled time-domain features extracted from acceleration data of the source line and unlabeled data from the target line. Output predictions are health status (target domain labels) for each track zone of the target line. The framework is evaluated using a comprehensive dataset of field measurements from a high-speed train traversing four different lines of France’s high-speed rail network, representing four distinct domains. Comparative results across 12 cross-domain recognition tasks reveal that the UDA framework based on PDALC outperforms four other UDA algorithms: transfer component analysis, maximum mean and covariance discrepancy, learning via low-rank and sparse representation, and geodesic flow kernel algorithm. Compared to the basic method (a classical supervised learning model without UDA), the proposed framework achieves a 12% increase in defect detection accuracy. Furthermore, the paper investigates the impact of different sensor layouts, tuning parameters of PDALC, classification algorithms, and the number of features on the accuracy of the approach.