HAL Collection UNC (Univ. de la Nouvelle Calédonie)
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7579 research outputs found
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Nantissement de titres, pacte commissoire et désignation d'un expert
No full textInternational audienc
L'information annuelle de la caution malgré la défaillance du débiteur
No full textInternational audienc
Comprendre l’impact du changement climatique sur le métabolome des plantes aromatiques et médicinales tropicales et évaluer le rôle des champignons mycorhiziens dans la régulation des facteurs environnementaux.
No full textNational audienc
Biocultural Community Protocols as Relational Soft Power in the Pacific
No full textInternational audienc
Energy Quantification of Machine learning for Dynamic Spectrum Access in LoRaWAN Device
No full textInternational audienceLow-power wide-area networks (LPWANs), such as LoRaWAN, must balance device lifetime with an increasingly congested sub-GHz spectrum. Machine learning (ML) approaches, such as the Upper Confidence Bound (UCB) multi-armed bandit algorithm, allow for collision-aware channel selection without gateway-level coordination. However, these approaches' computational and memory requirements could adversely affect device autonomy. This study presents an analysis of the power consumption of the UCB approach on an STM32WL55 LoRaWAN system on a chip (SoC). Current traces were captured for payloads ranging from 10 to 50 bytes and transmission powers ranging from 8 to 16 dBm. The standard algorithm was compared to a UCB algorithm. The results show that the UCB algorithm's power consumption is less than 1% per uplink-ACK cycle: 0.60% during transmission, and 0.70% and 0.30% during the Rx1 and Rx2 windows, respectively. This overhead becomes negligible as payload size or transmission power increases. These results confirm that lightweight learning is a practical solution for dynamic spectrum access in large-scale IoT deployments.</div
La dimension environnementale de l'IA
No full textCOMMENT ( R)APPROCHER IA ET ENVIRONNEMENT : THEORIES ET CONCEPTSChap.1 - Pour une approche techno-progressiste des droits fondamentaux durables.Environnementalisme "viridien" et perspective longévitisteDidier COEURNELLE, Vice-Président de l'Association Française Transhumaniste Technoprog, Coprésident de Heales (Healthy Life Extension Society) Chap.2 - Pour une approche techno-progressiste des droits fondamentaux environnementaux ?Marc ROUX, Président de l’Association Française Transhumaniste Chap.3 - De l’écologie scientifique à l’écologie intégrale : apports de l’IA et réflexivitéJean-Pierre LLORED, enseignant-chercheur HDR en histoire des sciences et des techniques, et en philosophie à l’Ecole centrale, Casablanca, Maroc & Anass BOUCHNITA, enseignant-chercheur en mathématiques et informatique à l’Université du Texas (Etats-Unis)LA DESILLUSION FACE AUX REALITESChap.4 - Thinking on Transhumanism and Post-human RightsWoody EVANS, Texas Woman’s University Chap.5 - Le « communisme de luxe » est-il compatible avec l’écologie ? Ou comment les IA vont tout changerMichelle DOBRE (Laboratoire Cerrev) et Aldo HAESLER (1954-2025) (Laboratoire Identités et subjectivités), Université de Caen-NormandieChap.6 - Point de vigilance : est-ce que les modèles chinois et taiwanais prennent en compte les conséquences environnementales dans le crédit social ?Yaoming HSU, Professeur à l’Université Cheng-Chi (politique) de Taiwa
La recherche création sous l’angle de la rencontre et des déstabilisations
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Reliability Assessment of 15 Gridded Rainfall Datasets for the Construction of a Daily High-Resolution Reanalysis across Senegal for Agroclimatic Applications
No full textInternational audienceAbstract This study focuses on developing a new high-resolution gridded rainfall dataset for Senegal, essential for supporting rainfed agriculture, which is sensitive to climate variability. Given the limited number of rain gauges, the research evaluates 15 publicly available gridded rainfall datasets (P datasets) against data from 21 stations of the Senegalese National Meteorological Service (ANACIM) over a 17-yr period (2005–21). The evaluation employs several agroclimatic indices, including the onset and cessation of rain, duration of the rainy season, and extreme events. The findings reveal that the reliability of P datasets varies significantly based on the metrics used. For total rainfall, African Rainfall Climatology, version 2 (ARC2), Climate Hazards Infrared Precipitation with Station (CHIRPS), ERA5, and Rainfall Estimation Algorithm, version 2 (RFEv2) emerged as the most reliable datasets, with ERA5 achieving the highest Kling–Gupta efficiency (KGE) value of 0.81 at daily scale. In terms of agroclimatic parameters, ARC2, CHIRPS, and RFEv2 excelled in accurately representing the start (KGE ≥ 0.45) and end (KGE ≥ 0.39) dates of the rainy season. However, P datasets generally overestimate rainfall events and struggle with identifying dry spells. The newly constructed merged dataset (M dataset) demonstrated over 100% improvement in correlation for daily estimates and significant bias reductions: 99.19% for ARC2, 80% for CHIRPS, and 90.57% for RFEv2. This research provides critical insights for selecting appropriate datasets to enhance climate information for agricultural decision-making in Senegal
