Chronique d’une mutation paysagère majeure : le cas des alignements d’arbres le long des canaux de navigationdu sud de la France.

International audienc

How does dispersal shape the genetic patterns of animal populations in European cities? A simulation approach

International audienceContext and objectives: Although urbanization is a major driver of biodiversity erosion, it does not affect all species equally. The neutral genetic structure of populations in a given species is affected by both genetic drift and gene flow processes. In cities, the size of animal populations determines drift and can depend on multiple processes whereas gene flow essentially depends on the ability of species to disperse across urban areas. Considering this, we tested whether variations in dispersal constraints alone could explain the variability of neutral genetic patterns commonly observed in urban areas. Besides, we assessed how the spatial distribution of urban green spaces (UGS) and peri-urban forests acts on these patterns.Methods: We simulated multi-generational genetic processes in virtual populations of animal species occupying either UGS or forest areas (both considered as a virtual species habitat) within and around 325 European cities. We used three dispersal cost scenarios determining the ability of species to cross the least favorable land cover types, while maintaining population sizes constant among scenarios. We then assessed genetic diversity and genetic differentiation patterns for each city and each habitat type across the three cost scenarios.Results: Overall, as dispersal across the least favorable land cover types was more constrained, genetic diversity decreased and genetic differentiation increased. Across scenarios, the scale and strength of the relationship between genetic differentiation and dispersal cost-distances varied substantially, alike previously observed empirical genetic patterns. Forest areas contributed more to habitat connectivity than UGS, due to their larger area and mostly peri-urban location. Hence, population-level genetic diversity was higher in forests than in UGS and genetic differentiation was higher between UGS populations than between forest populations. However, interface habitat patches allowing individuals to move between different habitat types seemed to locally buffer these contrasts by promoting gene flow.Discussion and conclusion: Our results showed that variations in spatial patterns of dispersal, and thus gene flow, could explain the variability of empirically observed genetic patterns in urban contexts. Besides, the largest habitat areas and biodiversity sources are likely to be found in areas surrounding city centers. This should encourage urban planners to pay attention to the areas promoting dispersal movements between urban habitats (e.g., UGS) and peri-urban habitats (e.g.. forests), rather than among urban habitats, when managing urban biodiversity

Introduction to Theoretical and Experimental aspects of Quantum Optimal Control

International audienceQuantum optimal control is a set of methods for designing time-varying electromagnetic fields to perform operations in quantum technologies. This tutorial paper introduces the basic elements of this theory based on the Pontryagin maximum principle, in a physicist-friendly way. An analogy with classical Lagrangian and Hamiltonian mechanics is proposed to present the main results used in this field. Emphasis is placed on the different numerical algorithms to solve a quantum optimal control problem. Several examples ranging from the control of two-level quantum systems to that of Bose-Einstein Condensates (BEC) in a one-dimensional optical lattice are studied in detail, using both analytical and numerical methods. Codes based on shooting method and gradient-based algorithms are provided. The connection between optimal processes and the quantum speed limit is also discussed in two-level quantum systems. In the case of BEC, the experimental implementation of optimal control protocols is described, both for two-level and many-level cases, with the current constraints and limitations of such platforms. This presentation is illustrated by the corresponding experimental results

Energy Management Strategy Based on Optimal System Operation Loss for a Fuel Cell Hybrid Electric Vehicle

International audienceHigh operating cost and deficient longevity of stacks are the twoprimary factors that hinder the widespread commercial usage of fuelcell (FC) technology. In addition, most existing strategiesconcentrate solely on ameliorating system operating efficiency orfuel consumption, without fully considering the impact of otherfactors, such as the degradation of power source performance.Thereby, based on the above research background, this studypresents an energy management strategy based on optimal systemoperation loss (OSOL-EMS), which considers various parameters, suchas power sources' durability, to minimize the operating cost ofelectric vehicles. To accomplish this objective, this studyformulates a life-cycle operating loss evaluation function relatedto the lifetime loss of the power sources and the hydrogenconsumption cost of the FC. Additionally, the voltage loss is alsoutilized to evaluate the operating performance of the FC torestrict its output power fluctuation rate. In addition, this studyalso considers limiting the variation of the battery's state ofcharge (SOC) in order to decrease the equivalent hydrogenconsumption of the system. Moreover, the high-efficiency operationzone for the stack is also divided. Additionally, given that theperformance of FC is related to the working condition, an extendedKalman filter algorithm is used to update the operation parametersof the FC in real-time. The experiment results show that theproposed strategy has approximate global optimization ability andcompared with equivalent hydrogen consumption minimization strategyand state machine control strategy, it can reduce the operatingcost by 19.69% and 28.18%, respectively

Global warming potential and societal-governmental impacts of the hydrogen ecosystem in the transportation sector

International audienceThe environmental and societal challenges of our contemporary society are leading us to reconsider our approaches to vehicle design. The aim of this article is to provide the reader with the essential knowledge needed to responsibly design a vehicle equipped with a hydrogen fuel cell system.Two pivotal aspects of hydrogen-electric powertrain eco-design are examined. First, the global warming potential is assessed for both PEMFC systems and Type IV hydrogen tanks, accounting for material extraction, production, and end-of-life considerations. The usage phase was omitted from the study in order to facilitate data adaptation for each type of use. PEMFC exhibits a global warming potential of about 29.2 kgCO$_{2eq}$/kW, while the tank records 12.4 kgCO$_{2eq}$/kWh, with transportation factors considered. Secondly, the societal and governmental impacts are scrutinized, with the carbon-intensive hydrogen tank emerging as having the most significant societal and governmental risks. In fact, on a scale of 1–5, with 5 representing the highest level of risk, the PEMFC system has a societal impact and governance risk of 2.98. The Type IV tank has a societal impact and governance risk of 3.31.Although uncertainties persist regarding the results presented in this study, the values obtained provide an overview of the societal and governmental impacts of the hydrogen ecosystem in the transportation sector. The next step will be to compare, for the same usage, which solution between hydrogen-electric and 100% battery is more respectful of humans and the environment

Enhancing Meditation Techniques and Insights Using Feature Analysis of Electroencephalography (EEG)

International audienceThrough a Bluetooth connection between the Muse 2 device and themeditation app, leveraging IoT capabilities. The methodologyencompasses data collection, preprocessing, feature extraction, andmodel training, all while utilizing Internet of Things (IoT)functionalities. The Muse 2 device records EEG data from multipleelectrodes, which is then processed and analyzed within a mobilemeditation platform. Preprocessing steps involve eliminatingredundant columns, handling missing data, normalizing, andfiltering, making use of IoT-enabled techniques. Feature extractionis carried out on EEG signals, utilizing statistical measures suchas mean, standard deviation, and entropy. Three different models,including Support Vector Machine (SVM), Random Forest, andMulti-Layer Perceptron (MLP), are trained using the preprocesseddata, incorporating Internet of Things (IoT) based methodologies.Model performance is assessed using metrics like accuracy,precision, recall, and F1-score, highlighting the effectiveness ofIoT-driven techniques. Notably, the MLP and Random Forest modelsdemonstrate remarkable accuracy and precision, underlining thepotential of this IoT-integrated approach. Specifically, the threemodels achieved high accuracies, with Random Forest leading at0.999, followed by SVM at 0.959 and MLP at 0.99. This study notonly contributes to the field of brain-computer interfaces andassistive technologies but also showcases a viable method toseamlessly integrate the Muse 2 device into meditation practices,promoting self-awareness and mindfulness with the added power ofIoT technology

Wildlife ecotoxicology of plant protection products: knowns and unknowns about the impacts of currently used pesticides on terrestrial vertebrate biodiversity

International audienceAgricultural practices are a major cause of the current loss of biodiversity. Among postwar agricultural intensification practices, the use of plant protection products (PPPs) might be one of the prominent drivers of the loss of wildlife diversity in agroecosystems. A collective scientific assessment was performed upon the request of the French Ministries responsible for the Environment, for Agriculture and for Research to review the impacts of PPPs on biodiversity and ecosystem services based on the scientific literature. While the effects of legacy banned PPPs on ecosystems and the underlying mechanisms are well documented, the impacts of current use pesticides (CUPs) on biodiversity have rarely been reviewed. Here, we provide an overview of the available knowledge related to the impacts of PPPs, including biopesticides, on terrestrial vertebrates (i.e. herptiles, birds including raptors, bats and small and large mammals). We focused essentially on CUPs and on endpoints at the subindividual, individual, population and community levels, which ultimately linked with effects on biodiversity. We address both direct toxic effects and indirect effects related to ecological processes and review the existing knowledge about wildlife exposure to PPPs. The effects of PPPs on ecological functions and ecosystem services are discussed, as are the aggravating or mitigating factors. Finally, a synthesis of knowns and unknowns is provided, and we identify priorities to fill gaps in knowledge and perspectives for research and wildlife conservation

Electro-Mechanical Characterization and Modeling of a Broadband Piezoelectric Microgenerator Based on Lithium Niobate

International audienceVibration energy harvesting based on piezoelectric transducers is an attractive choice to replace single-use batteries in powering Wireless Sensor Nodes (WSNs). As of today, their widespread application is hindered due to low operational bandwidth and the conventional use of lead-based materials. The Restriction of Hazardous Substances legislation (RoHS) implemented in the European Union restricts the use of lead-based piezoelectric materials in future electronic devices. This paper investigates lithium niobate (LiNbO3) as a lead-free material for a high-performance broadband Piezoelectric Energy Harvester (PEH). A single-clamped, cantilever beam-based piezoelectric microgenerator with a mechanical footprint of 1 cm2, working at a low resonant frequency of 200 Hz, with a high piezoelectric coupling coefficient and broad bandwidth, was designed and microfabricated, and its performance was evaluated. The PEH device, with an acceleration of 1 g delivers a maximum output RMS power of nearly 35 μW/cm2 and a peak voltage of 6 V for an optimal load resistance at resonance. Thanks to a high squared piezoelectric electro-mechanical coupling coefficient (k2), the device offers a broadband operating frequency range above 10% of the central frequency. The Mason electro-mechanical equivalent circuit was derived, and a SPICE model of the device was compared with experimental results. Finally, the output voltage of the harvester was rectified to provide a DC output stored on a capacitor, and it was regulated and used to power an IoT node at an acceleration of as low as 0.5 g

φ-FEM-FNO: a new approach to train a Neural Operator as a fast PDE solver for variable geometries

In this paper, we propose a way to solve partial differential equations (PDEs) by combining machine learning techniques and a new finite element method called φ-FEM. For that, we use the Fourier Neural Operator (FNO), a learning mapping operator. The purpose of this paper is to provide numerical evidence to show the effectiveness of this technique. We will focus here on the resolution of the Poisson equation with non-homogeneous Dirichlet boundary conditions. The key idea of our method is to address the challenging scenario of varying domains, where each problem is solved on a different geometry. The considered domains are defined by level-set functions due to the use of the φ-FEM approach. We will first recall the idea of φ-FEM and of the Fourier Neural Operator. Then, we will explain how to combine these two methods. We will finally illustrate the efficiency of this combination with some numerical results on two test cases, showing in particular that our method is faster than learning-based and finite element solvers for a fixed accuracy

French Society for Biological Psychiatry and Neuropsychopharmacology (AFPBN) guidelines for the management of patients with partially responsive depression and treatment-resistant depression: Update 2024

International audienceIntroduction: The purpose of this update is to add newly approved nomenclatures and treatments as well as treatments yet to be approved in major depressive disorder, thus expanding the discussions on the integration of resistance factors into the clinical approach.Methods: Unlike the first consensus guidelines based on the RAND/UCLA Appropriateness Method, the French Association for Biological Psychiatry and Neuropsychopharmacology (AFPBN) developed an update of these guidelines for the management of partially responsive depression (PRD) and treatment-resistant depression (TRD). The expert guidelines combine scientific evidence and expert clinicians' opinions to produce recommendations for PRD and TRD.Results: The recommendations addressed three areas judged as essential for updating the previous 2019 AFPBN guidelines for the management of patients with TRD: (1) the identification of risk factors associated with TRD, (2) the therapeutic management of patients with PRD and TRD, and (3) the indications, the modalities of use and the monitoring of recent glutamate receptor modulating agents (esketamine and ketamine).Conclusion: These consensus-based guidelines make it possible to build bridges between the available empirical literature and clinical practice, with a highlight on the 'real world' of the clinical practice, supported by a pragmatic approach centred on the experience of specialised prescribers in TRD