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Verified Persistent Catenable Deques
No full textInternational audienceThe simple persistent catenable deques invented by Kaplan, Okasaki, and Tarjan (2000) support insertion and extraction at either end and concatenation. They have mutable internal state and rely on a restricted form of mutation; yet they are persistent, that is, they appear to be immutable. Using Iris, we verify that they are correct in sequential and concurrent usage scenarios. Using Iris with time credits, we verify that, provided concurrent accesses are forbidden, every operation has amortized time complexity O(1). This requires repairing a certain mysterious condition in Kaplan, Okasaki, and Tarjan's description
Highlights from the Mech'cheM 2025 conference: New forces in Mechanochemistry, Montpellier, France, June 4-6, 2025
No full textInternational audienceThe International Symposium on Mechanochemistry (Mech'cheM 2025) took place in Montpellier (France) June 4-6, 2025, gathering 145 mechanochemists across the disciplines. Ten years after Mech'cheM 2015, it was an occasion to assess new progress and developments in the field. In this article, we highlight the main features of the plenary lectures and oral communications, illustrating the dynamic current cutting-edge research activities together with significant applications across the field of chemistry
Impact of the physico-chemical properties of commercial pea proteins concentrate and isolate on the quality of meat analogs
No full textInternational audiencePlant proteins extracted from soys and peas, have been identified as potential functional ingredients for plantbased meat analogs producted by high moisture extrusion cooking (HMEC). However, the extraction process (dry for concentrate and wet for isolate) can have an impact on the primary structure of the proteins, affecting their technical and functional properties. This study aims to compare the impact of two fractionation process on pea proteins (structure, fractions, flowing index) and their ability to create a network, which can mimic the fiber structure found in meat, within moisture extrusion. Pea protein concentrates (PPC) obtained by dry fractionation, and pea protein isolate (PPI) by isoelectric precipitation were characterized in terms of functional and flow properties. Results showed that PPI have a higher aggregation, lower solubility and water-holding capacity than PPC, and no thermal event was highlighted by differential scanning calorimetry, indicating proteins denaturation of PPI. Denaturation of proteins before the heating step in extruder barrel contrasts with the current model described in the literature. However, treating with HMEC, PPC is less suitable than PPI where better results in term of flow properties (respectively with flowability indexes of 27.33 and 56.17) and anisotropic structure. In that way, it appears that proteins concentration (55 % and 70 % respectively for PPC and PPI) is the most important parameter for creating a meat like texture in the conditions of the experiment
First order logic and twin-width in tournaments and dense oriented graphs
No full textInternational audienceWe characterise the classes of tournaments with tractable first-order model checking. For every hereditary class of tournaments T , first-order model checking is either fixed parameter tractable or AW[ * ]hard. This dichotomy coincides with the fact that T has either bounded or unbounded twin-width, and that the growth of T is either at most exponential or at least factorial. From the model-theoretic point of view, we show that NIP classes of tournaments coincide with bounded twin-width. Twin-width is also characterised by three infinite families of obstructions: T has bounded twin-width if and only if it excludes at least one tournament from each family. This generalises results of Bonnet et al. on ordered graphs.The key for these results is a polynomial time algorithm that takes as input a tournament T and computes a linear order < on V (T ) such that the twin-width of the birelation (T, <) is at most some function of the twin-width of T . Since approximating twin-width can be done in polynomial time for an ordered structure (T, <), this provides a polynomial time approximation of twin-width for tournaments.Our results extend to oriented graphs with stable sets of bounded size, which may also be augmented by arbitrary binary relations.</div
Cutting date impact on the herbaceous layer in Sahelian rangeland during the wet season
No full textSource Agritrop Cirad (https://agritrop.cirad.fr/615940/)International audienceAnnual herbaceous vegetation is a crucial source of forage for pastoral livestock in the Sahel region. These species grow during the wet season, which coincides with the peak grazing period. Understanding the impact of disturbances on annual herbaceous vegetation is then essential. This study focuses on the temporal aspects of disturbance. In northern Senegal, we established nine different plots and cut them weekly during the wet season and returned to each plot at the end of the season to create a gradient of cutting dates. We measured the plant's phenology, height, dry biomass, and fodder quality. Our results indicated that vegetation growth occurs in three phases: establishment, growth, and flowering. The impact of cutting varied across these phases. Plots cut during the establishment phase exhibited vegetation characteristics similar to those of uncut plots. Plots cut during the growth phase had reduced vegetation height but all individuals completed their growth cycle. Plots cut during the flowering phase had significantly lower biomass at the end of the season and experienced a slight delay in phenological development and increase the quality of the fodder at the end of the season. These findings highlight the importance of cutting timing on vegetation dynamics
Core stability in additively separable hedonic games of low treewidth
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Tensor-based higher-order multivariate singular spectrum analysis and applications to multichannel biomedical signal analysis
No full textInternational audienceSingular spectrum analysis (SSA) is a nonparametric spectral estimation method that decomposes time series signals into interpretable components. With the rise of big time series, the demand for effective and scalable SSA techniques has become increasingly urgent. In this paper, we propose a novel multiway extension of SSA, called higher-order multivariate SSA (HO-MSSA), specifically designed for multivariate and multichannel time series signal analysis via tensor decomposition. HO-MSSA utilizes time-delay embedding and tensor singular value decomposition to transform multichannel time series signals into trajectory tensors, which are then decomposed into elementary components in the Fourier domain, rather than the time domain as in traditional SSA methods. These components are grouped into disjoint subsets using spectral clustering, enabling the reconstruction of the underlying source signals. Experimental results demonstrate that HO-MSSA outperforms state-of-the-art SSA methods in various biomedical applications, including electromyography (EMG), electrocardiography (ECG), and electroencephalogram (EEG) signals
Evaluating risk-based hazard corridors in air traffic controller decisions during space launch failures
No full textInternational audienceThe increasing frequency and diversity of space launch activities challenge the safety and reliability of current air traffic management systems. In this study, we present a risk-based hazard corridor methodology for managing air traffic during space launch failures. Our method combines a debris propagation model with a hazard corridor construction approach that estimates the risk posed by debris to aircraft. We evaluated the constructed risk-based hazard corridors using high-fidelity human-in-the-loop simulations. In our experiments, air traffic controllers managed two strategies of hazard corridors. The dynamic hazard corridor updated the boundary in real-time while the static hazard corridor remained fixed by consolidating the dynamic boundaries over the entire activation period until the last piece of debris fell. The results show that controllers maintained safety separation across all scenarios, although their real-time workload increased significantly during hazard corridor activation. Overall, the controllers’ perceived workload and situation awareness remained stable, implying that the task demands were acceptable for all the experimental runs. Efficiency measure results indicate that the dynamic hazard corridor can reduce extra flight distance and delays, thus minimizing operational disruption caused by space launch failures. We also found that more experienced controllers tend to choose more cautious and conservative rerouting strategies. These findings offer practical guidance for improving resilience in air and space management integration. Furthermore, our study provides a basis for modeling air traffic controller behavior under emergency conditions in a way that is more in line with the real world patterns
Polymer Vesicle Microreactors Produced using Permeable Polymer Blocks: Circumventing Complex Functionality to Impart Membrane Permeability
No full textInternational audienceThe use of giant vesicles as microreactors presents a novel approach to control biochemical reactions in confined spaces, offering advantages such as compartmentalization, tunable permeability, and potential for biomimetic applications. These constructs can serve as versatile platforms for catalysis, drug delivery, and synthetic biology by providing confined environments that mimic natural cellular compartments. We have successfully produced microvesicles (also referred to as giant vesicles) by means of the simple double emulsification method using five amphiphilic block copolymers comprising poly(ethylene oxide) (PEO) as hydrophilic segment and five disparate hydrophobic blocks: poly(caprolactone) (PCL), poly(methyl methacrylate) (PMMA), poly(lactic acid) (PLA), poly [2-(diisopropylamino)ethyl methacrylate] (PDPA), and poly[2-(heptamethyleneimino)ethyl methacrylate] (PHIA). The last two blocks are pH-responsive (PDPA, PHIA), while the first ones are not (PCL, PMMA, PLA). The resulting vesicles have average size ranging from 2.9 to 9.3 µm, with the pHresponsive vesicles exhibiting larger diameters, likely due to partial protonation of the hydrophobic blocks. The formation of the giant vesicles was confirmed via optical and fluorescence microscopy using Nile red as a hydrophobic marker. The ability of the vesicles to encapsulate larger molecules was demonstrated by loading Alexa-labeled bovine serum albumin (BSA-Alexa). In the step further, the potential of these vesicles as microreactors was explored by encapsulating horseradish peroxidase enzyme (HRP) and evaluating the catalytic oxidation of o-dianisidine in the presence of hydrogen peroxide (H₂O₂), a reaction catalyzed by the HRP enzyme. The experimental evidences highlight that the pH-responsive vesicles are permeable to the reactants, as evidenced by colored product formation, whereas the permeability of the nonresponsive assemblies is reported to be negligible. Truly, the non-responsive vesicles exhibited particularly low permeability, even at the pH where the catalytic activity of the enzyme is optimized. These findings highlight the potential of pH-responsive vesicles for controlled molecular transport and catalytic applications, paving the way for their use in biocatalysis as microreactors
Life-cycle performance prediction and interpretation for coastal and marine prestressed concrete beams using active learning-enhanced Bayesian neural networks
No full textInternational audienceIn marine environments, prestressed concrete (PC) structures suffer from chloride-induced deterioration, impacting their serviceability and safety. Traditional deterministic and semi-probabilistic methods inadequately address the deteriorating mechanisms and uncertainties in environmental, material, and structural parameters, hindering accurate structural performance predictions. This study introduces an active learning-enhanced Bayesian Neural Network (BNN) framework for predicting the life-cycle performance and reliability of PC beams in coastal environments. The BNN is trained on a dataset generated via Latin Hypercube Sampling from a comprehensive model ensuring representative input. The active learning component strategically selects the most informative points, enhancing modeling accuracy and efficiency. The Guangdong-Hong Kong-Macao Greater Bay Area is chosen for a case study of PC hollow beams. A life-cycle prediction model for PC structures was developed, considering pitting effects on the geometry, mechanical, and bond properties of prestressing bars, etc. Finally, time-dependent reliability analysis is performed using the surrogate model and Monte Carlo simulation. Results indicate that the BNN achieves high accuracy with active learning. SHAP analysis identifies key factors affecting the behaviors of PC beams, highlighting the importance of material properties and environmental conditions. Also, reliability analysis emphasizes the impact of two-dimensional transport on structural reliability