A comparison of eight weakly dispersive Boussinesq-type models for non-breaking long-wave propagation in variable water depth

International audienceWeakly dispersive Boussinesq-type models are extensively used to model long-wave propagation in coastal areas and their interaction with coastal infrastructures. Many equations falling in this category have been formulated during the last decades, but few detailed comparisons between them can be found in the literature. In this work, we investigate theoretically and with computational experiments eight variants of the most popular models used by the coastal engineering community. Both weakly nonlinear and fully nonlinear models are considered, hoping to understand better when the additional complexity of the latter class of models is necessary or justified. We provide an overview and discuss the properties of these models, including the linear dispersion relation in uniform water depth, the second-order nonlinear coupling coefficient, the shoaling gradient, and the sensitivity to wave trough instabilities. The models are then numerically discretised using the same general strategy in a single numerical code, using fourth-order methods for time and space discretisation. Their capacity to simulate coastal wave propagation and their transformation when approaching the shore is assessed on three challenging one-dimensional benchmarks. It appears that fully nonlinear models are more consistent than their weakly nonlinear counterparts, which can occasionally perform better but show different behaviours depending on the case.</div

MsFEM for advection-dominated problems in heterogeneous media: Stabilization via nonconforming variants

International audienceWe study the numerical approximation of advection-diffusion equations with highly oscillatory coefficients and possibly dominant advection terms by means of the Multiscale Finite Element Method. The latter method is a now classical, finite element type method that performs a Galerkin approximation on a problem-dependent basis set, itself pre-computed in an offline stage. The approach is implemented here using basis functions that locally resolve both the diffusion and the advection terms. Variants with additional bubble functions and possibly weak inter-element continuity are proposed. Some theoretical arguments and a comprehensive set of numerical experiments allow to investigate and compare the stability and the accuracy of the approaches. The best approach constructed is shown to be adequate for both the diffusion- and advection-dominated regimes, and does not rely on an auxiliary stabilization parameter that would have to be properly adjusted

Identifying Critical Commuters: A Machine Learning Approach to Flexible Work Hours and Urban Congestion

International audienceCongestion remains one of the most prevalent transport problems in major cities. Recent approaches to managing demand aim to make working hours flexible to reduce congestion during peak periods. However, these approaches must also address synchronization needs at the employer, household, and individual levels. This study presents a framework to identify critical commuters who can adjust their arrival times, benefiting individual motivations, and managing arrival time demands. We explore multiple machine learning approaches to model and predict an individual's ability to shift their workplace arrival times. Ultimately, we choose gradient boosting due to its superior performance. Utilizing this ensemble approach on an employee survey data from Rennes Metropole in France, we analyze the factors that influence individual's flexibility in their arrival times. Key determinants identified include regular school drop-offs, theoretical arrival time contract with employer, and, to a lesser extent, age and income. Our findings demonstrate the need for a bi-level framework that incorporates both arrival time demand management and social justice analyses to ensure effective and equitable outcomes

Recognizing unit multiple interval graphs is hard

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Global climate modelling of Saturn’s atmosphere, Part V: Large-scale vortices

International audienceThis paper presents an analysis of large-scale vortices in the atmospheres of gas giants, focusing on a detailed study conducted using the Saturn-DYNAMICO global climate model (GCM). Large-scale vortices, a prominent feature of gas giant atmospheres, play a critical role in their atmospheric dynamics. By employing three distinct methods-manual detection, machine learning via artificial neural networks (ANN), and dynamical detection using the Automated Eddy-Detection Algorithm (AMEDA)-we characterize the spatial, temporal, and dynamical properties of these vortices within the Saturn-DYNAMICO GCM. Our findings reveal a consistent production of vortices due to well-resolved eddy-to-mean flow interactions, exhibiting size and intensity distributions broadly in agreement with observational data. However, notable differences in vortex location, size, and concentration highlight the model's limitations and suggest areas for further refinement. The analysis underscores the</div

Large-Scale Evacuation with Vehicular Communication: Navigating Through Dark Zones

International audienceIn times of disaster, ensuring the safe evacuation of affected populations is crucial for saving lives and mitigating community risks. This research presents a Dynamic Population Evacuation (DPE) approach, which combines strategic planning and real-time management aided by vehicular communication technology. By addressing the impact of disasters on transportation and communication systems, the DPE framework utilizes dynamic shelter allocation and simulation-based traffic assignment techniques to enhance planning accuracy. It incorporates a trip-based traffic simulator to account for the effects of disasters on transportation networks and a Vehicular Ad Hoc Network (VANET) simulator to manage communication dark zones. To manage dark zones, The proposed framework continuously updates the evacuation plan in real time to manage road blockages and the disabling of VANET resources. Accounting for the temporal evolution of areas with communication dark zones improves evacuation efficiency regarding clearance time. A sensitivity analysis is conducted on the compliance rate of evacuees to instructions provided by the vehicular communication management system. Furthermore, the framework's effectiveness is evaluated by simulating the real test case of the 2018 Camp Fire wildfire in Paradise, California, where roads and communication were severely disrupted. Additionally, by comparing the architectures of cloud computing and fog computing while accounting for blocked roads, the framework shows more than a 12% improvement in network clearance time compared to fog computing and a 19% improvement compared to traditional evacuation planning

Dual structure-aware image filterings for semi-supervised medical image segmentation

International audienceSemi-supervised image segmentation has attracted great attention recently. The keyis how to leverage unlabeled images in the training process. Most methods maintain consistent predictions of the unlabeled images under variations (e.g., adding noise/perturbations, or creating alternative versions) in the image and/or model level. In most image-level variation, medical images often have prior structure information,which has not been well explored. In this paper, we propose novel dual structure-aware image filterings (DSAIF) as the image-level variations for semi-supervised medical image segmentation. Motivated by connected filtering that simplifies image via filtering in structure-aware tree-based image representation, we resort to the dual contrast invariant Max-tree and Min-tree representation. Specifically, we propose a novel connected filtering that removes topologically equivalent nodes (i.e., connected components) having no siblings in the Max/Min-tree. This results in two filtered images preserving topologically critical structure. Applying the proposed DSAIF to mutually supervised networks decreases the consensus of their erroneous predictions on unlabeled images. This helps to alleviate the confirmation bias issue of overfitting to noisy pseudo labels of unlabeled images, and thus effectively improves the segmentation performance. Extensive experimental results on three benchmark datasets demonstrate that the proposed method significantly/consistently outperforms some state-of-the-art methods. Source code is publicly available at https://github.com/GuGuLL123/DSAIF-SEMI

Moral Force: Leaders' Actions and Public Health Compliance in Crisis

Charismatic leaders shape public sentiment but may weaken institutions by prioritizing personal appeal over trust in government. During the COVID-19 pandemic, the Mexican president disregarded stay-at-home guidelines. We assess the impact on social distancing using granular mobility and electoral data. Applying a dynamic difference-in-difference design and leveraging the timing of social distancing announcements and presidential support, we find increased mobility in pro-president areas, leading to 38% more COVID-19 cases and 21% more deaths. Our findings suggest the president's example, rather than partisan differences, drove these effects

Meeting climate target with realistic demand-side policies in the residential sector

International audienceAbstract The EU has established an ambitious policy framework for demand-side mitigation in buildings towards net-zero targets. Here, we conduct a comprehensive quantitative assessment of 384 demand-side policy combinations for residential space heating that complement supply-side decarbonization efforts. We show that the implementation of EU Emissions Trading System 2, even when combined with deep decarbonization of energy supply, falls short of climate targets. Beyond ETS 2, we emphasize the need for ambitious subsidies for heat pumps as a critical component of a successful strategy. Conversely, a large-scale generic ‘Renovation Wave’ modestly contributes to decarbonization, is not a cost-effective strategy at the EU level and requires significant increases in public spending. We advocate for the implementation of a carbon tax, paired with substantial subsidies for heat pumps and targeted incentives for home insulation by country and building. This approach supports the decarbonization of the residential sector, limits the strain on the electricity grid, and alleviates energy poverty