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Early Detection of Harmful Algal Blooms Using Majority Voting Classifier: A Case Study of Alexandrium Minutum, Pseudo-Nitzschia Australis and Pseudo-Nitzschia Fraudulenta
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Axial Microchannel-based Cellularized Nerve Guidance Conduits for Directed Axonal Regeneration
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Cautious classifier ensembles for set-valued decision-making
No full textInternational audienceClassifiers now demonstrate impressive performances in many domains. However, in some applications wherethe cost of an erroneous decision is high, set-valued predictions may be preferable to classical crisp decisions,being less informative but more reliable. Cautious classifiers aim at producing such imprecise predictionsso as to reduce the risk of making wrong decisions. In this paper, we describe two cautious classificationapproaches rooted in the ensemble learning paradigm, which consist in combining probability intervals.These intervals are aggregated within the framework of belief functions, using two proposed strategies thatcan be regarded as generalizations of classical averaging and voting. Our strategies aim at maximizing thelower expected discounted utility to achieve a good compromise between model accuracy and determinacy.The efficiency and performances of the proposed procedures are illustrated using imprecise decision trees,thus giving birth to cautious variants of the random forest classifier. The performance and properties ofthese variants are illustrated using 15 datasets
Influence of olive leaf extract on the physicochemical properties of yogurts made from cow, sheep, and goat milk
No full textInternational audienceThis study compares the effect of OLE (olive leaf extract) on cow's, sheep's and goat's milk yogurts, in order to better understand its impact according to the type of milk used. The aim is to examine how OLE, as a source of bioactive compounds, influences yogurt properties such as antioxidant activity, and physicochemical characteristics (colour, texture, and viscosity). Each milk was enriched with OLE at 0.5%, 1%, 1.5% and 2% (w/v). The results showed a decrease in pH and an increase in acidity during storage, with no significant buffering effect of OLE for the three milk. OLE-enriched yogurts had a higher total phenol content and antioxidant activity, which increase significantly with OLE concentration. Moreover, the water-holding capacity (WHC) of the yogurts showed a tendency to increase slightly over the storage period of 14 days for cow's and sheep's milk yogurts but decreased slightly for goat's milk yogurt. Sheep's milk yogurts had the highest WHC, followed by goat's and cow's milk yogurts, indicating a better structural stability. OLE reduced brightness (L*) and increased redness (a*) and yellowness (b*) for the three milks. The texture profile showed that increasing OLE concentration increased the firmness of the cow's milk yogurts, whereas the firmness of the sheep's milk yogurts decreased with storage time and OLE concentration. For goat's milk yogurt, firmness remained stable until day 7 but declined by day 14. Consequently, these results demonstrated the potential of OLE as a functional ingredient to enhance the nutritional profile and health benefits of yogurt, in particular through its antioxidant properties
Non parametric observation-driven hidden Markov model
No full textCorrection disponible à l'adresse suivante : https://www.tandfonline.com/doi/full/10.1080/03610926.2025.2466988International audienceHidden Markov models (HMM) are used in different fields to study the dynamics of a process that cannot be directly observed. However, in some cases, the structure of dependencies of a HMM is too simple to describe the dynamics of the hidden process. In particular, in some applications in finance and in ecology, the transition probabilities of the hidden Markov chain can also depend on the current observation. In this work, we are interested in extending the classical HMM to this situation. We refer to the extended model as the observation-driven hidden Markov model (OD-HMM). We present a complete study of the general non parametric OD-HMM with discrete and finite state spaces. We study its identifiability and the consistency of the maximum likelihood estimators. We derive the associated forward-backward equations for the E-step of the EM algorithm. The quality of the procedure is tested on simulated datasets. We illustrate the use of the model on an application focused on the study of annual plant dynamics. This work establishes theoretical and practical foundations for this framework that could be further extended to the parametric context in order to simplify estimation and to hidden semi-Markov models for more realism
SQP-Based Cable-Tension Allocation for Multi-Drone Load Transport
No full textInternational audienceMulti-Agent Aerial Load Transport Systems (MAATS) offer greater payload capacity and fault tolerance than single-drone solutions. However, they have an underdetermined tension allocation problem that leads to uneven energy distribution, cable slack, or collisions between drones and cables. This paper presents a real-time optimization layer that improves a hierarchical load-position-attitude controller by incorporating a Sequential Quadratic Programming (SQP) algorithm. The SQP formulation minimizes the sum of squared cable tensions while imposing a cable-alignment penalty that discourages small inter-cable angles, thereby preventing tether convergence without altering the reference trajectory. We tested the method under nominal conditions by running numerical simulations of four quadrotors. Experimental evaluation shows that the SQP routine runs in a few milliseconds on standard hardware, indicating that it can operate in real-time. A sensitivity analysis confirms that the gain of the cable-alignment penalty can be tuned online, enabling a controllable trade-off between safety margin and energy consumption without compromising stability. This framework provides a scalable path to safe and energy-balanced cooperative load transport in practical deployments
Toward Secure Content-Centric Approaches for 5G-Based IoT: Advances and Emerging Trends
No full textInternational audienceThe convergence of the Internet of Things (IoT) and 5G technologies is transforming modern communication systems by enabling massive connectivity, low latency, and high-speed data transmission. In this evolving landscape, Content-Centric Networking (CCN) is emerging as a promising alternative to traditional Internet Protocol (IP)-based architectures. CCN offers advantages such as in-network caching, scalability, and efficient content dissemination, all of which are particularly well-suited to the constraints of the IoT. However, deploying content-centric approaches in 5G-based IoT environments introduces significant security challenges. Key concerns include content authentication, data integrity, privacy protection, and resilience against attacks such as spoofing and cache poisoning. Such issues are exacerbated by the distributed, mobile, and heterogeneous nature of IoT and 5G systems. In this survey, we review and classify existing security solutions for content-centric architectures in IoT-5G scenarios. We highlight current trends, identify limitations in existing approaches, and outline future research directions with a focus on lightweight and adaptive security mechanisms
No More Purification: A Straightforward and Green Process for the Production of Melamine–Vanillylamine-Based Benzoxazine-Rich Resins for Access to Various Composite Materials
No full textInternational audienceA rapid microwave-assisted process minimizing waste was set up to produce bio-based benzoxazine-like monomers produced from vanillylamine and melamine. Without excessive purification, different viscous liquid precursors had a remarkable ability to form four strong and transparent different solid cross-linked thermosets, displaying lower curing temperatures under 130 °C. The long and strong adhesive performance of the cured materials was observed using glass slides or aluminum surfaces and they could become a good alternative to adhesive epoxy resin for metal surfaces. At the higher temperatures, these solids could act as efficient flame-retardants proven by thermogravimetric measurements. The best candidates gave a limiting oxidation index value of 41.9. In order to improve the intrinsic surface hydrophobicity of the phenolic resins, slight amounts of silica and iron oxide nanoparticles were dispersed in the polymer matrix, and finally mechanical resistance was pointed out. The most promising of our melamine-based resin was loaded with aluminum pigment to furnish a silver-colored paste ready for being cured to afford a robust solid, which does not undergo contraction or deformation
Une formalisation en Rocq des ordres monomiaux et gradués
No full textEven if binary relations and orders are a common formalization topic, we need to formalize specific orders (namely monomial and graded) in the process of formalizing in Rocq the finite element method. This article is therefore definitions, operators, and proofs of properties about relations and orders, thus providing a comprehensive Rocq library. We especially focus on monomial orders, that are total orders compatible with the monoid operation. More than its definition and proved properties, we define several of them, among them the lexicographic and grevlex orders. For the sake of genericity, we formalize the grading of an order, a high-level operator that transforms a binary relation into another one, and we prove that grading an order preserves many of its properties, such as the monomial order property. This leads us to the definition and properties of four different graded orders, with very factorized proofs. We therefore provide a comprehensive and user-friendly library in Rocq about orders, including monomial and graded orders, that contains more than 700 lemmas.Même si les relations binaires et les ordres sont un sujet de formalisation courant, nous avons besoin de formaliser des ordres spécifiques (à savoir monomial et gradué) dans le processus de la formalisation en Rocq de la méthode des éléments finis. Cet article traite donc des définitions, des opérateurs et des preuves des propriétés des relations et des ordres, fournissant ainsi une bibliothèque Rocq complète. Nous nous concentrons tout particulièrement sur les ordres monomiaux, qui sont des ordres totaux compatibles avec l'opération de monoïde. Au-delà de la définition et des propriétés prouvées, nous en définissons plusieurs, dont les ordres lexicographique et grevlex. Par souci de généricité, nous formalisons la gradation d'un ordre, un opérateur de haut niveau qui transforme une relation binaire en une autre, et nous prouvons que la gradation d'un ordre conserve bon nombre de ses propriétés, telle que celle d'ordre monomial. Cela nous mène à la définition et aux propriétés de quatre ordres gradués différents, avec des preuves très factorisées. Nous mettons donc à disposition une bibliothèque Rocq sur les ordres complète et facile à utiliser, incluant les ordres monomiaux et gradués, qui contient plus de 700 lemmes
Prédiction de la porosité dans des réservoirs à hydrogène haute pression en matériaux composites par IA Cognitive Floue Augmentée en lien avec les paramètres du procédé de mise en œuvre
No full textInternational audienceHigh-pressure hydrogen storage vessels of type IV (HPV) are essential for industrial applications and hydrogenvehicles. Made from composite materials, particularly carbon fibers, these vessels are lightweight and fatigue-resistant.However, internal defects such as porosities can compromise their integrity. While some porosity is tolerated bycertification standards, excessive presence can lead to mechanical failures. This study aims to develop a predictivemodel for the number of porosities based on manufacturing process parameters. To overcome the limitations of physicalapproaches, we use the Xtractis General Reasoning Artificial Intelligence (GRAI), which enables the automaticdiscovery of robust and intelligible models. Out of 58 potential predictors, 15 were selected to create a predictivemodel. The results show high performance with a 7.94% RMSE and a correlation of 0.824 on an external test set. Theintelligibility of models generated by Xtractis is a major advantage compared to other nonlinear techniques like BoostedTree, which lack transparency. The Xtractis approach offers new perspectives for improving the quality management ofvessels.Les réservoirs d'hydrogène haute pression de type IV (HPV) sont essentiels pour les applications industrielles et lesvéhicules à hydrogène. Fabriqués à partir de matériaux composites, notamment des fibres de carbone, ces réservoirssont légers et résistants à la fatigue. Toutefois, des défauts internes comme les porosités peuvent compromettre leurintégrité. Bien que certaines porosités soient tolérées, une présence excessive peut entraîner des défaillancesmécaniques. Cette étude vise à développer un modèle prédictif du nombre de porosités en fonction des paramètres duprocédé de fabrication. Pour surmonter les limites des approches physiques, nous utilisons l’Intelligence ArtificielleRaisonnée Générale (IARG) Xtractis, qui permet la découverte automatique de modèles robustes et intelligibles. Àpartir de 58 prédicteurs potentiels, 15 ont été sélectionnés pour créer un modèle prédictif. Les résultats montrent uneperformance réelle élevée avec un RMSE de 7,94 % et une corrélation de 0,824 sur un jeu de test externe.L’intelligibilité des modèles générés par Xtractis est un avantage majeur par rapport à d’autres techniques non-linéairescomme Boosted Tree, qui manquent de transparence. L’approche Xtractis offre ainsi de nouvelles perspectives pouraméliorer la gestion de la qualité des réservoirs