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The lithosphere - asthenosphere system seen by surface waves: New insights from radial anisotropy
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Transgenerational effects of a high temperature impair the resistance of the pest Spodoptera frugiperda to a parasitoid
No full textInternational audienceInsect parasitoids provide a useful ecosystem service to control pest insects. However, climate change could challenge this pest management, as insects are known to be sensitive to temperature. Furthermore, transgenerational effects, which are common in insects, could influence these effects of temperature on host-parasitoid systems. The present study therefore aimed to test the combined effects of developmental and host parental temperatures on a host-parasitoid system, using the fall armyworm (FAW) and the parasitoid Hyposoter didymator. We focused on mean temperatures of 25 and 29 °C, with a daily fluctuation of ±5 °C. The increase in mean temperatures had a significant effect on all the host parameters tested (survival, developmental rate, sex ratio, body mass) and on parasitoid success. Parasitoid success decreased between the developmental temperatures of 25 and 29 °C, and most effects of the increase in developmental temperature on FAW traits were detrimental to the parasitoid. Remarkably, we found transgenerational effects of temperature on the host resistance to parasitoids (the proportion of FAW escaping parasitism), as well as on host traits associated with the probability of parasitoids finding a host (effects on survival and developmental rates) and host quality (body mass, sex ratio). The parental temperature of 29 °C had a detrimental effect on the FAW resistance to parasitoids, but it reinforced the effects of developmental temperature on host traits that have a negative impact on parasitoids. The study shows the high thermal sensitivity of a host-parasitoid system and highlights that thermal transgenerational plasticity should be considered in host-parasitoid interactions
Formal solution for the generalized mutual exclusion contraints problem in a class of timed Petri nets.
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The robust selection problem with information discovery
No full textInternational audienceWe explore a multiple-stage variant of the min-max robust selection problem with budgeted uncertainty that includes queries. First, one queries a subset of items and gets the exact values of their uncertain parameters. Given this information, one can then choose the set of items to be selected, still facing uncertainty on the unobserved parameters. In this paper, we study two specific variants of this problem. The first variant considers objective uncertainty and focuses on selecting a single item. The second variant considers constraint uncertainty instead, which means that some selected items may fail. We show that both problems are NP-hard in general. We also propose polynomial-time algorithms for special cases where the sets of items that can be simultaneously queried are defined by a cardinality or a knapsack constraint. For the problem with constraint uncertainty, we also show how the objective function can be expressed as a linear program, leading to a mixed-integer linear programming reformulation for the general case. We illustrate the performance of this formulation using numerical experiments.</div
Chapter 11 - Organ communication in insects during growth and development
No full textInternational audienceThis chapter aims at summarizing examples of organ communication and formulating possible underlying principles about the cellular and molecular pathways in insect development and growth. Some of these examples of organ communication are explicitly verbalized as a problem of organ communication in the literature, while others are not, and therefore their inclusion here relies on my interpretation. Generally, we distinguish between the embryo that develops and differentiates in a closed system within the egg case and the stages outside the embryo and before metamorphosis that constantly receive information especially nutrients from an open system, the environment
Effect of particle size on bioleaching of chalcopyrite at moderate temperature
No full textInternational audienceSeveral methods have been studied to overcome chalcopyrite passivation during bioleaching at atmospheric pressure. One such method is to reduce particle size to increase the reactive surface area of the mineral. However, little is known about the effect of fine particles on the performance of chalcopyrite bioleaching in terms of bacterial growth and activity. In this study, two size fractions were prepared by sieving a raw chalcopyrite concentrate to produce particles between 20 and 100 µm and particles below 20 µm. These three materials were used in batch bioleaching tests performed in 2 L stirred reactors at 42 • C, with a solid concentration of 10 %w/w and the BRGM-KCC microbial consortium. Redox potential and iron and copper concentrations were monitored over time to characterize the reaction progress. With the coarser materials, the redox potential increased to above 750 mV vs. SHE within a day, whereas particles below 20 µm took an additional day to reach this potential. This lag phase permitted faster copper dissolution, resulting in a yield of 35 % after two days. In contrast, a yield of 10-20 % copper was achieved in 15 days using coarser materials. In contrast to previous assumptions, fine particles did not hinder bacterial growth and activity. Rather, they promoted them by increasing the availability of substrates generated by greater sulfide oxidation. Bioleaching chalcopyrite particles < 20 µm produced higher copper yields thanks not only to their larger reactive surface, but also to the delayed increase in redox potential and subsequent chalcopyrite passivation
Machine Learning Based Environmental Impact Prediction of Construction Products Using EPD Data
No full textInternational audienceThe construction sector significantly contributes to global greenhouse gas emissions, creating an urgent need for efficient environmental impact assessment methods. This study evaluates machine learning (ML) models to rapidly predict the Global Warming Potential (GWP) of construction products using standardized Environmental Product Declaration (EPD) data from the ÖKOBAUDAT database. Five regression models - Artificial Neural Network (ANN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) were trained, optimized, and compared. Data preprocessing involved careful feature selection through correlation analysis, management of missing values, and outlier removal. Model performance was assessed using the coefficient of determination R2, root mean square error (RMSE), and mean absolute error (MAE). Results demonstrated that ensemble tree-based models (RF and XGB) performed best achieving R2 ~ 0.90 on held-out dataset and retaining R2 ~ 0.81–0.83 on an independent external EPD validation set with mean error of almost 10% of a typical product footprint. These findings demonstrate ML’s capability to effectively approximate product life-cycle impacts, providing rapid estimates for early-stage sustainability decision-making. However, detailed Life Cycle Assessments (LCA) remain essential for critical decisions
Fast Estimation of mmWave Power-Angular Spectrum Using Leaky-Wave Antennas
No full textInternational audienceLeaky-wave antennas (LWAs) provide compact andcost-effective solutions for direction-of-arrival (DoA) estimationdue to their frequency-dependent beam-scanning characteristics.However, in coherent multipath environments, the source covariancematrix becomes rank-deficient, which limits the performanceof conventional subspace-based methods. Interpolationbasedspatial smoothing (SSP) can restore the rank and recoverDoAs, but accurate power estimation remains difficult, hinderingthe acquisition of the power angular spectrum (PAS). In thiswork, we propose an on-grid sparse Bayesian learning (SBL)approach that operates directly on the fast frequency-scanningresponses of meandered waveguide-based LWAs. The methodjointly estimates DoAs and their corresponding powers withoutrequiring rank-restoration techniques. Simulation results demonstratethat the SBL-based approach provides robust and accuratePAS estimation, even in the presence of fully coherent sources
Identifiability and Observability Analysis for Epidemiological Models: Insights on the SIRS Model
No full textInternational audienceThe problems of observability and identifiability have been of great interest as previous steps to estimating parameters and initial conditions of dynamical systems to which some known data (observations) are associated. While most works focus on linear and polynomial/rational systems of ODEs, general nonlinear systems, including non-analytic systems have received far less attention and, to the best of our knowledge, no unified constructive methodology has been proposed to assess and guarantee parameter and state recoverability in this setting. Some symbolic tools provide automated analyses for rational or nonlinear, analytical systems, offering qualitative identifiability and observability verdicts that are sometimes incomplete. In this work, we introduce a family of efficient and fully constructive procedures that can enable explicit recovery of the unknown parameters and/or initial conditions, whenever possible, for a large class, not necessarily rational or analytic, nonlinear ODE systems. Each procedure is tailored to different observational scenarios and based on the resolution of linear systems. As a case study, we apply these procedures to several epidemic models, with a detailed focus on the SIRS model, demonstrating its joint observability-identifiability when only a portion of the infected individuals is measured, a scenario that has not been studied before. In contrast, for the same observations, the SIR model is observable and identifiable, but not jointly observable-identifiable. This distinction allows us to introduce a novel approach to discriminating between different epidemiological models (SIR vs. SIRS) from short-time data. For these two models, we illustrate the theoretical results through some numerical experiments, validating the approach and highlighting its practical applicability to real-world scenarios
k-scale: k-Anonymizing Millions of Trajectories
No full textInternational audienceTrajectory datasets collected by network operators and service providers offer detailed information about individual mobility and have wide application in business and research. However, managing such data raises privacy risks, as the unique movement patterns of individuals pose significant re-identification risks and make common countermeasures like pseudonymization ineffective. The privacy-preserving data publishing (PPDP) of trajectory datasets that maintains post-anonymization accuracy and truthfulness is an open problem -especially for large datasets with millions of records like those gathered by major actors in the telco ecosystem. We close this gap with k-scale, a framework that implements k-anonymity in massive mobile user trajectory datasets, removing uniqueness while safeguarding accuracy at the record level. Not only k-scale is the first model capable of scaling k-anonymization to a dataset of one million trajectories, but it does so while also outperforming state-of-the-art methods for trajectory data publishing in terms of preserved data quality, which we prove in real-world massive datasets and applications