Large-scale streamflow regionalization in ungauged West African catchments: How do classical and deep learning approaches compare?

International audienceIn West Africa, limited access to hydrometric data remains a major challenge for advancing surface water research and improving water management. Since the early 1980s, many gauging stations have been decommissioned, leaving gaps in reliable streamflow records across numerous catchments. Parameter regionalization of hydrological models is commonly employed to enable runoff prediction in ungauged catchments. This study represents an assessment of rainfall-runoff model regionalization across West Africa. We used an unprecedented dataset of 189 near-natural catchments to compare two contrasting approaches: (i) a benchmark conceptual modeling framework using the GR4J model, regionalized with three parameter-transfer techniques (spatial proximity, physiographic similarity, and Random Forest), and (ii) a data-driven framework based on Long Short-Term Memory (LSTM) neural networks. Using a leave-one-out resampling approach, regionalization approaches were evaluated using different performance metrics: (i) the Kling-Gupta Efficiency (KGE), calculated between simulated and observed streamflows, (ii) the relative bias (rBias) on several hydrological signatures computed with observed or simulated discharge and (iii) the difference between observed and simulated flood quantiles. Results show that the conceptual modeling approach with traditional parameter-transfer techniques consistently underperforms compared to the LSTM, failing to reproduce key hydrological signatures. In contrast, the LSTM model showed better generalization performance, accurately simulating streamflow with a median KGE of 0.67 and reliably capturing hydrological signatures and flood quantiles across West Africa’s diverse climates and landscapes with lower biases. These findings highlight the potential of data-driven approaches to enhance hydrological prediction in data-scarce regions, supporting more effective flood risk management and water resource planning

Le droit constitutionnel à l’épreuve du premier quinquennat d’Emmanuel Macron

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Hemophagocytic lymphohistiocytosis with granulomatosis revealing a cat scratch disease in an immunocompetent adult: a case report and mini review

International audienceWe report a case of hemophagocytic lymphohistiocytosis [HLH] triggered by Bartonella henselae, a gram-negative bacterium classically associated with cat-scratch disease. The patient was a 22year-old immunocompetent male whose condition was initially misdiagnosed as tuberculosis and ultimately identified through clinical metagenomic sequencing. We also present the first focused review of Bartonella henselae-associated HLH, identifying only eight previously reported cases, the majority occurring in immunocompromised individuals. In addition, we analyse 16 cases in which infectious granulomatous diseases-including tularemia, leprosy, brucellosis, yersiniosis, coxiellosis, and actinomycosis-were initially misdiagnosed as tuberculosis. Finally, we examine the underlying factors contributing to these diagnostic errors, emphasizing the importance of epidemiological context and microbiological evidence in distinguishing Bartonella henselae infection and other granulomatous diseases from tuberculosis.</div

Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing

International audienceRuntime monitoring is essential to ensure the safety of ML applications in safetycritical domains. However, current research is fragmented, with independent methods emerging from different communities. In this paper, we propose a unified framework categorising runtime monitoring approaches into three distinct types: Operational Design Domain (ODD) monitoring, which ensures compliance with expected operating conditions; Out-of-Distribution (OOD) monitoring, which rejects inputs that deviate from the training data; and Out-of-Model-Scope (OMS) monitoring, which detects anomalous model behaviour based its internal states or outputs. We demonstrate the benefits of this categorization with a dedicated experiment on an aeronautical safety-critical application: runway detection during landing. This framework facilitates design of monitoring activities, with complementary categories of monitors, and enables evaluation and comparison of different monitors using common, safety-oriented metrics

Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter

International audienceMulti-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability. Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response

Next-Gen IoT localization: When quantum-SSA-Markov hybridization meets energy efficiency for robust, accurate, and sustainable positioning in smart environments

International audienceThe Internet of Things (IoT) is transforming communication and data exchange across sectors such as healthcare and transportation, where efficient and precise localization and high-resolution sensing are critical requirements for applications ranging from asset tracking and autonomous navigation. Existing localization techniques often suffer from accuracy limitations, excessive energy consumption, and latency issues due to the dynamic and resource-constrained environments in which IoT devices operate. These limitations are exacerbated by the dense deployment of IoT devices, multipath signal propagation, and environmental interference, which can compromise the reliability of location-based services. This paper presents a novel hybrid model that integrates quantum mechanics for state representation, Markov processes for predictive modeling, and the Salp Swarm Algorithm (SSA) for optimizing nodal positions and speeds. Our Quantum-SSA-Markov model significantly enhances localization accuracy and efficiency in IoTbased wireless sensor networks (WSNs), outperforming existing techniques in energy consumption, latency, signal-to-noise ratio (SNR), and data flow rate. Experimental results confirm that this innovative approach provides a scalable and reliable solution for next-generation IoT systems, particularly in scenarios that require robust real-time localization

Historias del ecologismo, influencias y trayectorias en España (1950-2024)

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Autonomic cardiac regulation to slow-paced respiration in seated and supine positions

International audiencePurpose: Respiratory modulation and positional control are the main two regulators of cardiac autonomic activity. Although both slow-paced breathing and supine position promote parasympathetic regulation, their interaction remains poorly documented. Here, the objective of this work is to study the interaction between these two autonomic controls. Methods: Twenty healthy volunteers (12 males, 8 females), age of 25.9 ± 3.9 years were included in this study. They were randomly subjected to 6 different slow and controlled breathing at 4.5, 5, 5.5, 6, 6.5, and 7 min/cycles for 3 min in supine or seated position after a 3 min baseline recording in spontaneous breathing. ECG was continuously monitored and RR intervals (RRI), total power (Ptot), the standard deviation of normal R-R intervals (SDNN), high frequency power (HF), the root mean square of successive R-R interval differences (RMSSD), and low frequency power (LF) were calculated to study autonomic regulation. Results: We observed (1) a similar increase in parasympathetic (RMSSD and LF) and overall autonomic (RRI, Ptot, and SDNN) activities in slow-paced breathing conditions, whatever the respiratory rate in comparison with control spontaneous breathing; (2) these autonomic parameters increased in sitting position, but in parasympathetic (RMSSD and LF) and overall autonomic (Ptot, and SDNN) activities interacted with respiratory control and were higher in seated slow-paced breathing. Conclusion: These results showed that (1) whatever the slow-paced breathing frequency, slow breathing favours parasympathetic control and slow heart rhythm; and (2) seated position favors autonomic cardiovascular interaction between respiratory modulation and positional control

Species introduction woes: How human‐mediated crop transport can influence community dynamics through mutualist displacement

International audienceAgriculturally relevant crop species are universally associated with complex communities consisting of antagonists, commensals and mutualists. When the domesticated species are introduced into new habitats, some community associates can be left behind, and the consequences of such losses can be difficult to study. The cultivated fig Ficus carica and its community of mutualists and antagonists (wasps, nematodes and fungi) provide an excellent system to examine community shifts in this context. Cultivated F. carica can suffer significant losses due to a disease caused by Fusarium fungi. Through demographic surveys of F. carica figs in the USA and their wild European relatives as well as phylogenomic analysis of Fusarium species associated with fig and crop hosts, our research examines how species introductions can influence disease prevalence and severity. We found significant negative correlations between the presence of nematodes and fungi in wild F. carica figs in Europe. We observed similar negative correlations across wild Neotropical figs despite profound environmental differences. These observations suggest that nematodes are key components of fig communities and may directly mitigate or out‐compete pathogenic fungus. Following introduction from its endemic Mediterranean habitat to North America, F. carica crops lost an association with one endemic nematode. Consequently, we found F. carica figs in the USA to have significantly more fungal infestation than their European counterparts. Furthermore, transplantation through North America has introduced multiple novel fungal species into the F. carica system, which likely causes more severe disease for the US fig industry. Synthesis and applications : Similar negative effects associated with community disruption may be a common consequence of the introduction of many domesticated species. While it is difficult in many biological communities to successfully identify important associates and the effects of their potential losses during introduction, the biology of the fig community allows us to examine this context with greater clarity. We argue that fungal diseases in F. carica in the USA could be managed with the re‐introduction of nematodes. Further, nearly all crop species suffer losses due to a Fusarium pathogen, and nematodes may represent an unexpected and cost‐effective method of disease management beyond figs

Optimization of the representation of results in interval arithmetic

Interval arithmetic enables rigorous bounding of rounding errors, but standard representations require storing two floating-point numbers per interval, which increases memory costs and data transfer in large-scale computations. We propose a compressed interval representation inspired by the FP-ANR format, encoding both the center and the radius within a single floating-point word while preserving the strict inclusion of the original interval. We present an efficient algorithm to convert center-radius intervals into this format with minimal over-approximation. Its applications to the interval matrix product and the interval Newton method demonstrate the practical benefits of this representation. Additionally, the proposed approach enables the integration of mixed-precision computations, paving the way for scalable and memory-efficient interval arithmetic in scientific computing