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France’s reaction to Russia’s invasion of Ukraine was initially conciliatory. However, the current French President’ statements, Macron, gradually changed, suggesting a potential shift in France's stance. The French president recently suggested troops could be sent to Ukraine to fight the Russians, a move that could significantly impact the course of the war. Our paper aims to address this possibility. How feasible would this be from a military perspective? Second, are there any other relevant political actors that will support it? Third, why has Paris changed its position to such a radical stance? We start from the theoretical design of realist constructivism. Postcolonial theories and the concept of locked-in path dependence supported this research. The methods follow the path of historical synthesis, discourse analysis of key political actors, and text analysis of defense white papers. Quantitative military variables are used to understand France’s and the EU’s military-industrial complex capabilities and assess its potential. The collapse of France’s neocolonial empire in Africa and Russia’s growing influence in the region partially explains Macron’s discourse change. This is correlated with the need to follow a more independent European security policy and the worsening military situation in Ukraine. [ABSTRACT FROM AUTHOR]

In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France. [ABSTRACT FROM AUTHOR]

• The 80 km/h speed limit is associated with a significant reduction in fatalities. • Estimated benefits are between 300 and 350 lives saved. • Estimated effects vary between counties, illustrating the potential impact depending on the local context. • The dynamics of the measure evolve over time. Introduction : Speeding is considered to be a major contributor to road fatalities and injuries worldwide. Inappropriate speeding behavior is associated with a high casualty burden. It could be responsible for at least 30% of road accidents. Method : In 2018, the French authorities decided to introduce a new speed limit. They lowered the speed limit to 80 km/h on the unseparated interurban network. The aim was to reduce the number of fatalities and injuries and to implement some measures in line with international commitments. This paper uses different econometric models applied to time series for different groups of counties. Results : The results show a significant positive contribution of the new speed limit. The estimated number of lives saved is between 300 and 350. The overall reduction in the number of fatalities is 10%. The results also show a differentiated impact according to the local context and the different dynamics at play. Conclusions and Practical Applications : The results of this paper are in line with the scientific literature on speed limit reductions. They represent a validation of a debated public decision, while at the same time consolidating the body of knowledge on the subject, helping the decision-maker to adopt an appropriate measure to improve road safety performance. [ABSTRACT FROM AUTHOR]

To reduce total test time and increase the efficiency of statistical analysis of a life-testing experiment adaptive progressive Type-II censoring scheme has been proposed. This paper addresses the statistical inference of the unknown parameters, reliability, and hazard rate functions of logistic exponential distribution under adaptive progressive Type-II censored samples. Maximum likelihood estimates (MLEs) and maximum product spacing estimates (MPSEs) for the model parameters, reliability, and hazard rate functions can not be obtained explicitly, hence these are derived numerically using the Newton–Raphson method. Bayes estimates for the unknown parameters and reliability and hazard rate functions are computed under squared error loss function (SELF) and linear exponential loss function (LLF). It has been observed that the Bayes estimates are not in explicit forms, hence an approximation method such as Markov Chain Monte Carlo (MCMC) method is employed. Further, asymptotic confidence intervals (ACIs) and highest posterior density (HPD) credible intervals for the unknown parameters, reliability, and hazard rate functions are constructed. Besides, point and interval Bayesian predictions have been derived for future samples. A Monte Carlo simulation study has been carried out to compare the performance of the proposed estimates. Furthermore, three different optimality criteria have been considered to obtain the optimal censoring plan. Two real-life data sets, one from electronic industry and other one from COVID-19 data set containing the daily death rate from France are re-analyzed to demonstrate the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from Aerosol Chemical Speciation Monitor (ACSM) and multi-wavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA program. The datasets contain the mass concentrations of major chemical species within PM1, namely organic aerosols (OA), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl-), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both the guidance from the French reference laboratory for air quality monitoring and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM1 at each site (43–60 %), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO3 is the second most important contributor to PM1 (15–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO4 (8–14 %) and eBC (5–11 %) complement the major fine aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively. Such chemically-speciated multi-year datasets have significant value for the scientific community, offering opportunities for future research, including source apportionment studies, trend analyses, and epidemiological investigations. They are also vital for evaluating and validating regional air quality models. In this regard, a comparison with the CHIMERE Chemical Transport Model shows high correlations between simulations and measurements, albeit underestimating OA concentrations by 46–76 %. Regional discrepancies in NO3 concentration levels emphasize the importance of these datasets in validating air quality models and tailoring air pollution mitigation strategies. [ABSTRACT FROM AUTHOR]

Veterinarians are a pivotal force in addressing animal health and welfare surveillance, with a critical role in improving public health security and increasing the profits of livestock farmers. Yet, the veterinary profession is adversely affected by personnel shortages, particularly in rural areas. Since the health of people, animals and their shared environment are interconnected in a One Health perspective, a set of policies are required to ensure public health by attraction and retention of veterinarians in rural areas. In France, a tutored internship programme, financially subsiding students and mentors to execute a training period in remote rural areas, was promoted to better integrate and retain veterinary students ending their veterinary training. This paper aims to evaluate how veterinarians' tutored internships influences students' choices for rural practice, using three different statistical methods derived from causal inference theory. Using survey data for the period 2016–2020, we show that: (i) the average effect of the tutored internship on veterinarians' work in food animal sector is not significant; and that (ii) the tutored internship leads veterinarians with a low share of work in the food animal sector to have a rural practise after they graduated between 13 and 20% greater than those who did not participate in the tutored internship. [ABSTRACT FROM AUTHOR]

Background: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers. Methods: We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods. Results: A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information. Conclusions: This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain. [ABSTRACT FROM AUTHOR]

Solar Photovoltaic (SPV) technology advancements are primarily aimed at decarbonizing and enhancing the resiliency of the energy grid. Incorporating SPV is one of the ways to achieve the goal of energy efficiency. Because of the nonlinearity, modeling of SPV is a very difficult process. Identification of variables in a lumped electric circuit model is required for accurate modeling of the SPV system. This paper presents a new state-of-the-art control technique based on human artefacts dubbed Drone Squadron Optimization for estimating 15 parameters of a three-diode equivalent model solar PV system. The suggested method simulates a nonlinear relationship between the P–V and I–V performance curves, lowering the difference between experimental and calculated data. To evaluate the adaptive performance in every climatic state, two different test cases with commercial PV cells, RTC France and photo watt-201, are used. The proposed method provides a more accurate parameter estimate. To validate the recommended approach's performance, the data are compared to the results of the most recent and powerful methodologies in the literature. For the RTC and PWP Photo Watt Cell, the DSO technique has the lowest Root Mean Square Error (RMSE) of 6.7776 × 10–4 and 0.002310324 × 10–4, respectively. [ABSTRACT FROM AUTHOR]

Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km 2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68 –2.38 gC m -2 , R2=0.87 –0.77; biomass: RMSE =11.34 g m -2 , R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m -2 , - 39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes. [ABSTRACT FROM AUTHOR]

The objective of the paper is to validate SolaRes (Solar Resource estimate) in clear-sky conditions, and to examine the aerosol influence on the differences between observation and estimate. SolaRes has the ambition to fulfil both research and industrial applications exploiting downwelling solar radiation at surface level. Consistently with solar resource applications, we show the capacity of SolaRes to reproduce the angular behaviour of the angular field, by validating not only global horizontal irradiance (GHI), but also direct normal irradiance (DNI), diffuse horizontal irradiance DifHI), global and diffuse irradiance in tilted plane (GTI, DifTI), and even the circumsolar contributions. Computations are made with the SMART-G radiative transfer code, taking spectral aerosol optical thickness (AOT) data sets as input, which are delivered by the Aerosol Robotic network (AERONET) and the Copernicus Atmospheric Monitoring Service (CAMS). A mixture of two aerosol models is required to compute aerosol optical properties. Measurements for validation are made at two sites in Northern France. Clear-sky is identified by two methods to show its influence: 1) a method reproducing the AOT variability conditions, and 2) a stricter method eliminating some residual cloud influence but also conditions with largest AOT. SolaRes is validated according to comparison scores found in the literature, with the (relative) root mean square difference (RMSD) in GHI as low as 1%, and the mean bias difference (MBD) which could be 0%. Angular behaviour is reproduced with satisfying scores. The circumsolar contribution improves MBD in DNI and DifHI, by 1% and 4% respectively, as well as RMSD by ~0.5%. MBD in DNI is around -1% and RMSD around 2%, and MBD in DifHI is 2% and RMSD around 9%. RMSD and MBD in both DNI and DifHI are larger than in GHI because they are more sensitive to the aerosol and surface properties. DifTI measured in a vertical plane facing South is reproduced with a RMSD of 8%, similar to DifHI. It is suggested a strong influence of reflection by not only ground surface but also surrounding buildings, increasing the albedo from 0.13 to 0.35. The sensitivity study on the aerosol parameterisation shows that the spectral AOT contains enough information for best quality in DNI retrieval. The choice of aerosol models in the parameterisation has an influence in RMSD smaller than 0.7%. Complementary information on angular scattering and aerosol absorption has a significant influence in GHI by reducing RMSD by ~0.5%, and MBD by ~0.8%. The uncertainty on the data source has a significant influence. The CAMS data set increases the RMSD in DNI by 5%, but has has less influence in GHI, by increasing RMSD by ~1% and MBD by ~0.4%. RMSD in GHI still remains slightly smaller than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In a context of massive renovation of residential buildings, stakeholders need decision-support models based on knowledge of the current building stock and accurate simulation of energy demand. This paper presents a new strategy for reducing energy consumption in the building sector, a key factor in combating climate change and promoting sustainability. We introduce an approach to (1) plan retrofits at community level, with a building resolution, for different years of an optimization period and (2) assist local authorities in selecting effective measures to improve the environmental performance of their building stock. The focus is on creating trajectory retrofit plans creation for a building stock with three main retrofit options: improving insulation, heating systems and hot water systems. We adapt a complex but linear approach, a type of problem-solving structure known as a multidimensional multiple-choice knapsack problem, which manages to handle a large number of possible retrofit combinations without becoming unwieldy. The planning process is streamlined as a single-objective optimization task that aims to reduce the total cost of retrofits by reducing their net present value. The efficiency of the model is demonstrated by simulating retrofit scenarios for 4,000 buildings in a French region to prove its ability to tackle large problems. France's targets for decarbonizing the residential sector are taken into account, with a target of reducing GHG emissions by a factor of 10 and a building stock consuming 80kWhEP/m2/year. The results show that these plans are feasible, but that they will require 50% of all buildings to undergo major renovation with abatement costs of around €200/tGES. Our practical application to an actual community demonstrates the model's ability to identify appropriate retrofitting measures and compile building data. • Development of an energy optimization framework integrating a linear simulation model. • Multi-stage optimization generalizing the knapsack problem for building renovation dynamics. • Bridging the gap in energy retrofit strategies by comparing decision-maker and model proposals. [ABSTRACT FROM AUTHOR]

This document is a response to a letter submitted by Tassi et al. regarding a recent paper on the uptake of pre-exposure prophylaxis (PrEP) among men who have sex with men (MSM) in France. The authors address concerns raised by Tassi et al. about the use of unadjusted regional data on new HIV infections and the limitations of self-reported survey-based data. They acknowledge the limitations of their approach and caution against interpreting the results as potential underestimations. The authors also discuss the potential for future research using clinical and insurance data to improve data quality. [Extracted from the article]

There is a pressing need for a fast and efficient satellite remote sensing tool to estimate coastal bathymetry for any coastline in the world. To date, satellite methods for deriving bathymetry have mainly focused on linking the radiometric response to a known water depth, as with SPOT, Landsat and Sentinel. Here, wave properties (static and dynamic) are approximated using the small time delay between the different color bands of Sentinel-2 to then calculate a depth using wave linear dispersion theory. In this paper, we present a spatial correlation method within the S2Shores (Satellites to Shores) Python toolbox: a processing chain/toolbox of coastal observations using methods applied to optical satellites. The resulting individual bathymetries are finally qualified according to the standards of the International Hydrographic Organization, anticipating their operational use. • S2Shores spatial-correlation method for optical satellite coastal observations. • Bathymetry qualified with the International Hydrographic Organization standards. • Wave characteristics from the time delay between spectral bands of Sentinel-2. [ABSTRACT FROM AUTHOR]

This study focuses on assessing the effect of short- and long-term nonlinear deformation (plasticity and creep) on the thermo-hydro-mechanical (THM) responses of Callovo-Oxfordian (COx) claystone, considered as the host formation for the geological radioactive waste disposal in France, if it is licensed. The temperature increase due to heat emitted from the waste package induces a pore pressure rise in low permeability porous rocks such as the COx claystone, due to the difference in the thermal expansion coefficients between water and the argillaceous skeleton. This phenomenon has been evidenced by laboratory and in situ heating experiments. Recently, the key THM phenomena taking place around the high-level radioactive waste (HLW) repository based on the French concept, have been investigated using a thermoporoelasticity approach. In this paper, certain nonlinearities observed in the mechanical behaviour of the COx claystone have been considered in the evaluation of these THM processes. Three numerical simulations (thermoporoelastic, thermoporoelastoplastic and thermoporoelastoviscoplastic) are performed using Comsol Multiphysics code, in which the time-dependent behaviour of the COx is described by a power law whereas the elastoplastic behaviour is modelled by a strain hardening with a Hoek-Brown criterion and a non-associative flow rule. The role of elastoplastic and viscoplastic parts of deformations on the temperature, pore pressure and stresses time evolution in the far-field, is highlighted. • THM processes around a repository for HLW (high-level activity) radioactive waste based on the French concept. • Role of mechanical nonlinearities on the thermal pressurisation and their impacts on pore pressure and stress changes in the far field. • Competition between the thermal pressurisation and the creep mechanism on the variations of the pore pressure and the host rock integrity is highlighted. [ABSTRACT FROM AUTHOR]

• Half of parental transfer crowded out by the means-tested scholarship. • IV estimate using nonlinear schedule slightly higher (0.5) than OLS (0.4). • Larger estimated crowding-out effect under IV is driven by non-intact families. • Predictions of the altruistic model (full eviction) not validated. • Means-testing necessary to prevent a windfall effect for wealthy parents. This paper investigates the extent to which means-tested scholarships received by higher education students crowd out parental financial support at the intensive margin. We estimate a private transfer function using survey data collected in France in 2014 on a sample of students aged 18–24 who receive public scholarships. Introducing the amount of public transfer as an exogenous covariate, we find that one additional euro of scholarship is associated with a decrease in parental transfers of 0.40 euro. Using an instrumental variable strategy that exploits the non-linear schedule of the scholarship amount, we find a larger effect with a decrease of about 0.50. Our results suggest that a substantial part of the scholarship benefits low-income parents by reducing the amount of money they give to their student children. [Display omitted] [ABSTRACT FROM AUTHOR]

Proponents of short food supply chains (SFSC) have lauded their environmental benefits. Nevertheless, most studies on SFSCs have focused on their climate impact, while the synthetic pesticide use by farmers participating in SFSCs has received little research attention. In this study, we investigate the effect of farmers' involvement in different SFSC channels on synthetic pesticide use and crop yields. This study relies on data obtained from the 2020 French agricultural census and a 2018 French national survey on the phytosanitary practices of representative market gardeners. This paper uses a multinomial endogenous treatment effect model in order to account for endogeneity. We demonstrate that the effect of SFSC participation on farmers' synthetic pesticide use varies depending on the type of SFSC channel employed. Farmers who sell part of their vegetable crops through direct-to-consumer (DTC) channels use significantly fewer synthetic pesticides than those who only sell their crops through long food supply chains (LFSC). However, there is no evidence that farmers involved in direct-to-retailer (DTR) channels use significantly fewer synthetic pesticides. In addition, we have not found any evidence that SFSC participation decreases crop yields. [ABSTRACT FROM AUTHOR]