Discrete-Element Method Study of the Effect of Ballast Layer Depth on the Performance of Railway Ballast Bed

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Experimental investigation of mechanical behavior and thermal damage of hot dry rock exposing to different cooling conditions

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Carrier gas selection in SIFT-MS: Implications for reagent ions abundance, and ambient VOC detection and quantification

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Explainable Pattern Learning in Exploring Robust Characteristics in Metaheuristic Design

International audienceThe Vehicle Routing Problem (VRP) is a complex optimization problem due to its NP-Hard nature, and it is mostly solved using metaheuristic algorithms. Recent developments in machine learning have demonstrated the potential to improve these approaches by substituting human-crafted designs with data-driven methods. Building on this advance, we examine the role of different characteristics or features in predicting the quality of VRP solutions, identifying several features that consistently serve as strong predictors and could be leveraged in the design of metaheuristic algorithms. We suggest that while feature importance can vary, specific characteristics or features remain reliable predictors across different scenarios

Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance

International audienceTo maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator’s sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal

Physicochemical Properties and Bioreactivity of Sub‐10 μm Geogenic Particles: Comparison of Volcanic Ash and Desert Dust

International audienceExposure to ambient particulate matter (PM) with an aerodynamic diameter of <10 μm (PM 10 ) is a well-established health hazard. There is increasing evidence that geogenic (Earth-derived) particles can induce adverse biological effects upon inhalation, though there is high variability in particle bioreactivity that is associated with particle source and physicochemical properties. In this study, we investigated physicochemical properties and biological reactivity of volcanic ash from the April 2021 eruption of La Soufrière volcano, St. Vincent, and two desert dust samples: a standardized test dust from Arizona and an aeolian Gobi Desert dust sampled in China. We determined particle size, morphology, mineralogy, surface texture and chemistry in sub-10 μm material to investigate associations between particle physicochemical properties and observed bioreactivity. We assessed cellular responses (cytotoxic and pro-inflammatory effects) to acute particle exposures (24 hr) in monocultures at the air-liquid interface using two types of cells of the human airways: BEAS-2B bronchial epithelial cells and A549 alveolar type II epithelial cells. In acellular assays, we also assessed particle oxidative potential and the presence of microorganisms. The results showed that volcanic ash and desert dust exhibit intrinsically different particle morphology, surface textures and chemistry, and variable mineralogical content. We found that Gobi Desert dust is more bioreactive than freshly erupted volcanic ash and Arizona test dust, which is possibly linked to the presence of microorganisms (bacteria) and/or nanoscale elongated silicate minerals (potentially clay such as illite or vermiculite) on particle surfaces

Inter- and intra-site variation of microarthropod communities in urban parks

International audienceUrban parks are one of the most represented green spaces in cities and can support biodiversity. However, most of the studies demonstrating the role of urban biodiversity do not take into account soil biodiversity, which represents at least ¼ of the world's taxonomic diversity. Among these soil organisms, microarthropods are recognised bioindicators because of their role in soil functioning but also because of their high abundance and sensitivity to a variety of environmental and anthropogenic factors. With high heterogeneity due to soil, management or localisation, nothing looks less like a park than another park. In this study, our aim is rather to understand how soil biodiversity is affected by the spatial configuration of the parks. To do this, we studied interand intra-park variation in microarthropod communities using taxonomic and functional approaches based on traits. Our results show communities dominated by Acari, with high abundances of Collembola but low specific richness. Inter-park variations were recorded for the species richness and functional composition of Collembola, Oribatidae abundance, texture and metal levels. That may be linked to the age of the sites in relation with soil physicochemical parameters. Intra-park variations were also observed, particularly for biodiversity indices, which could be explained by the fragmentation of the site and patch size. Despite their small size, soil organisms should be considered in park studies and design (configuration and connectivity). Indeed, understanding the distribution patterns of these organisms is important for improving the role of parks within a functional green and brown urban network

Strong influence of black carbon on aerosol optical properties in central Amazonia during the fire season

International audienceDuring the dry season, the Amazonian atmosphere is strongly impacted by fires, even in remote areas. However, there are still knowledge gaps regarding how each aerosol type affects the aerosol radiative forcing. This work characterizes the chemical composition of submicrometer aerosols and source apportionment of organic aerosols (OAs) and equivalent black carbon (eBC) to study their influence on light scattering and absorption at a remote site in central Amazonia during the dry season (August–December 2013). We applied positive matrix factorization (PMF) and multilinear regression (MLR) models to estimate chemical-dependent mass scattering efficiency (MSE) and extinction efficiency (MEE). Mean PM1 aerosol mass loading was 6.3 ± 3.3 µg m−3, with 77 % of organics, grouped into 3 factors: biomass burning OA (BBOA), isoprene-epoxydiol-derived secondary OA (IEPOX-SOA) and oxygenated OA (OOA). The bulk scattering and absorption coefficients at 637 nm were 17 ± 10 and 3 ± 2 Mm−1, yielding a single scattering albedo of 0.87 ± 0.03. Although eBC represented only 6 % of the PM1 mass loading, MSE was highest for the eBC (13.58–7.62 m2 g−1 at 450–700 nm), followed by BBOA (7.96–3.10 m2 g−1) and ammonium sulfate (AS, 4.79–4.58 m2 g−1). The MEE was dominated by eBC (30.8 %), followed by OOA (19.9 %) and AS (17.6 %). The dominance of eBC over light scattering, in addition to absorption, plays a remarkably important role for this important climate agent, with potentially broad implications for more precise radiative forcing quantification, increasing climate modeling precision and representing deep contributions to Earth's climate system comprehension

An enhanced authentication solution for infrastructureless vehicle environments

International audienceRobust vehicle authentication is essential in order to ensure an effective audit of vehicle-to-vehicle (V2V) communications. However, most existing approaches rely on a centralized infrastructure to access both authorities' certificates andrevocation lists, thus making them ineffective in dynamic and infrastructureless environments. In this paper, we highlight this critical limitation, and propose a method which enables the vehicles to update their local authentication databases independently from infrastructure availability. Our approach aim to allow vehicles to perform V2V authentication using locally stored data, in order to ensure continuity of secure communications even when disconnected from the Infrastructure. We further analyze the probability of successful authentication under two scenarios, which are the first with up-to-date databases, and the second with outdated ones. The analytical results show that the authentication probability decreases to below 75% after 30 hours of disconnection with long-lived certificates, while updates keep it above 90% in highway scenarios, even with short-lived certificates.These findings demonstrate the feasibility of maintaining reliable V2V authentication outside the infrastructure coverage, and point out the necessary improvements for evolving towards secure and auditable V2V communications