Acteurs, Ressources et Territoires dans le Développement

In the environmental field, the “functional” divisions in space that mark out areas for management are often presented as natural. The European Union thus frequently builds its macroregional strategies on natural characteristics by attaching them to mountain ranges, seas and of course river basins. The river basin and its European counterpart under the Water Framework Directive (WFD) enacted in 2000, the river basin district, do not escape this rule. This chapter examines the issues surrounding the construction and adoption of the natural spatial references – the catchment basin, the river basin district, and the body of water – in the implementation process of the WFD. Surface water is split into four categories: waterways, lakes and reservoirs, transitional waters and coastal waters.

Multimodal Deep Learning enhances decision-making by integrating diverse information sources, such as texts, images, audio, and videos. To develop trustworthy multimodal approaches, it is essential to understand how uncertainty impacts these models. We propose LUMA, a unique multimodal dataset, featuring audio, image, and textual data from 50 classes, specifically designed for learning from uncertain data. It extends the well-known CIFAR 10/100 dataset with audio samples extracted from three audio corpora, and text data generated using the Gemma-7B Large Language Model (LLM). The LUMA dataset enables the controlled injection of varying types and degrees of uncertainty to achieve and tailor specific experiments and benchmarking initiatives. LUMA is also available as a Python package including the functions for generating multiple variants of the dataset with controlling the diversity of the data, the amount of noise for each modality, and adding out-of-distribution samples. A baseline pre-trained model is also provided alongside three uncertainty quantification methods: Monte-Carlo Dropout, Deep Ensemble, and Reliable Conflictive Multi-View Learning. This comprehensive dataset and its tools are intended to promote and support the development, evaluation, and benchmarking of trustworthy and robust multimodal deep learning approaches. We anticipate that the LUMA dataset will help the research community to design more trustworthy and robust machine learning approaches for safety critical applications. The code and instructions for downloading and processing the dataset can be found at: https://github.com/bezirganyan/LUMA.

Mosquitoes are vectors of many disease-causing pathogens, including malaria, dengue, chikungunya, and yellow fever. According to the World Health Organization, these vector-borne diseases account for several hundred thousand deaths annually. They also cause zoonoses, such as Rift Valley fever and West Nile fever. In this context, the development of operational tools to support surveillance and control strategies is essential—not only in countries of the Global South, where mosquito-borne diseases are most prevalent in tropical and subtropical regions, but also in the countries of the North, where the establishment of invasive species such as the tiger mosquito is increasing the risk of disease emergence. To address these challenges, Earth observation imagery offers valuable potential: the spatial distribution and seasonal dynamics of mosquito populations are closely linked to climatic factors (such as temperatures, rainfall, and humidity) and environmental variables (such as the presence of water bodies and vegetation), many of which can be monitored through satellite data. Numerous recent studies have led to the development of innovative methods that combine remote sensing with spatial modelling to predict the spatial and temporal dynamics of vector mosquitoes and associated diseases. Moving beyond proof-of-concept, some of these approaches have given rise to operational tools and processing chains that are now actively used by public health authorities and vector control agencies. This book, intended for students, researchers, and public health professionals, offers a synthesis of current research and operational tools in the field.

Abstract Fire is often assumed to be a key driver in shaping tropical vegetation structure and composition in grass-dominated ecosystems, while forests experience infrequent but impactful fires that influence ecosystem resilience. Although short-term interactions between fire and vegetation are well-documented, long-term dynamics remain underexplored. This study examines fire regimes, vegetation dynamics, and its biodiversity over the past 17 000 years in southwest Tanzania, using sedimentary charcoal and pollen. Major ecological transformations of vegetation and fire regimes were recorded, with vegetation changes consistently preceded shifts in fire regimes. Increased grass pollen correlated with more frequent or larger fires, while high tree cover in Miombo woodland reduced fire activity. Interestingly, pollen evenness was a precursor to major ecological transformations as peaks preceded changes in ecosystem states. Changes in precipitation and moisture seems the major top–down drivers of these changes in vegetation and fire. Fire regimes were indirectly controlled by water availability, and vegetation exhibited resilience to fire at centennial timescales before reaching ecosystem shifts at 12 400 and 1700 cal BP. Our results emphasize the critical role of tree and grass cover in shaping fire regimes and highlight the interplay between climate, vegetation structure, and fire in East African ecosystems.

Groundwater plays a key role in providing access to drinking water, especially in semi-arid regions where surface water is scarce or absent for much of the year. In the semi-arid region of southern Madagascar, approximately 2,000,000 people face one of the highest poverty rates in the world, making them particularly vulnerable to climatic hazards. As a result, describing and predicting groundwater dynamics is essential to understand and anticipate drought-related humanitarian crises. How to estimate groundwater recharge in a such poorly documented area?Our work consisted of comparing two complementary approaches for estimating groundwater recharge. First, the Groundwater Resource Observatory for Southwestern Madagascar was established in 2014 in difficult logistical settings to monitor piezometric level from 16 boreholes located in various hydrogeological systems. This observatory provides long-term piezometric time series at an hourly time step, which were used to calculate recharge following the Water Table Fluctuation (WTF) Method.Second, a spatial hydrology approach was developed to estimate potential recharge using precipitation and evapotranspiration global products based on remote sensing data. The two approaches were compared, revealing the potential and limits of both. Based on these results, we compare our findings with health outcomes, offering new avenues for research.

This study addresses the critical need for accurate reference evapotranspiration (ET0) estimation in data-scarce environments such as West Africa, where rapid population growth and climate change intensify water resource challenges. Traditionally, computing ET0 with the FAO-56 Penman-Monteith method requires multiple meteorological inputs—such as solar radiation, humidity, and wind speed—collected from costly, fully instrumented weather stations. However, the availability and maintenance of such equipment can be prohibitive in remote regions. To overcome these constraints, we explore the potential of machine learning (ML), specifically XGBoost (XGB) models, trained on NASA Power climate reanalysis datasets. Our approach relies on a limited subset of easily measured in situ variables—daily minimum and maximum temperatures and rainfall—to estimate ET0. Departing from standard ML practices that depend on short-term, site-specific data, we leverage the extensive historical depth and broad spatial coverage of reanalysis products. We trained and validated twenty locally adapted XGB models using measurements from twenty diverse West African weather stations. Our results show that certain XGB model configurations, notably those incorporating temperature and rainfall data, can approximate ET0 estimates from the FAO-56 Penman-Monteith equation with median RMSE values frequently below 1 mm/day—levels comparable to commonly employed empirical formulas. This finding demonstrates that minimal on-site instrumentation, combined with ML and reanalysis data, can effectively support irrigation scheduling and enhance water-use efficiency under varying agro-ecological conditions. To foster broader implementation, we have released our XGB-ET0 code under the GPLv3 licence (https://github.com/SARRA-cropmodels/RF-ET0), enabling researchers and practitioners to locally train and deploy these models, thereby improving ET0 estimation and sustainable agricultural water management in resource-limited settings.

Lors de la mise en place de la prévention sida en 1994 au Cap d’Agde Naturiste, la cohabitation naturisme familial en journée, libertinage le soir et la nuit est bouleversée par l’extension du nombre de voyeurs, l’arrivée massives de libertin…es(toutes couches sociales confondues) et les transformations des sexualités.L’article décrit les situations topographiques, sexuelles et sociales, les mises en scène des corps, et les réponses administratives et policières à ce dérèglement.Dans cette capitale européenne de l’échangisme, comme partout en France, le commerce libertin, tourisme compris, quitte l’artisanat, le bricolage et le « fait-main » pour se capitaliser et s’industrialiser.À l’opposé d’un « naturisme naturaliste » qui fleure bon les valeurs familialistes réactionnaires, se développe une offre de sexualités dites « non-conformistes » qui se propose de satisfaire couples, hommes seuls et les quelques rares femmes seules qui affluent au fur et à mesure que la grande presse en parle. Le tout baigné par la domination masculine hétéronormative.

En parallèle à la déforestation, l’Amazonie brésilienne connaît une croissance démographique et une urbanisation sans précédent : sa population a décuplé depuis les années 1960, et en 2010, 71,5 % de la population (13,1 millions de personnes) de l’Amazonie légale vivait déjà en milieu urbain (IBGE, 2010). Les villes amazoniennes ont accueilli plus de 3 millions d’habitants entre 2000 et 2010 (+ 30,3 %) [Tritsch et Le Tourneau, 2016].Si cette évolution est par

Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety-critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation. We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research.