Unknown area exploration for robots with energy constraints using a modified Butterfly Optimization Algorithm

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with wellknown metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants

Contribution à l’Optimisation d’un Comportement Collectif pour un Groupe de Robots Autonomes

This thesis studies the domain of collective robotics, and more particularly the optimization problems of multirobot systems in the context of exploration, path planning and coordination. It includes two contributions. The first one is the use of the Butterfly Optimization Algorithm (BOA) to solve the Unknown Area Exploration problem with energy constraints in dynamic environments. This algorithm was never used for solving robotics problems before, as far as we know. We proposed a new version of this algorithm called xBOA based on the crossover operator to improve the diversity of the candidate solutions and speed up the convergence of the algorithm. The second contribution is the development of a new simulation framework for benchmarking dynamic incremental problems in robotics such as exploration tasks. The framework is made in such a manner to be generic to quickly compare different metaheuristics with minimum modifications, and to adapt easily to single and multi-robot scenarios. Also, it provides researchers with tools to automate their experiments and generate visuals, which will allow them to focus on more important tasks such as modeling new algorithms. We conducted a series of experiments that showed promising results and allowed us to validate our approach and model.Cette thèse étudie le domaine de la robotique collective, et plus particulièrement les problèmes d'optimisation des systèmes multirobots dans le cadre de l'exploration, de la planification de trajectoires et de la coordination. Elle inclut deux contributions. La première est l'utilisation de l'algorithme d'optimisation des papillon (BOA : Butterfly Optimization Algorithm) pour résoudre le problème d'exploration de zone inconnue avec des contraintes d'énergie dans des environnements dynamiques. A notre connaissance, cet algorithme n'a jamais été utilisé pour résoudre des problèmes de robotique auparavant. Nous avons également proposé une nouvelle version de cet algorithme appelée xBOA basée sur l'opérateur de croisement pour améliorer la diversité des solutions candidates et accélérer la convergence de l'algorithme.La deuxième contribution présenté dans cette thèse est le développement d'une nouvelle plateforme de simulation pour l'analyse comparative de problèmes incrémentaux en robotique tels que les tâches d'exploration. La plateforme est conçue de manière à être générique pour comparer rapidement différentes métaheuristiques avec un minimum de modifications, et pour s'adapter facilement aux scénarios mono et multirobots. De plus, elle offre aux chercheurs des outils pour automatiser leurs expériences et générer des visuels, ce qui leur permettra de se concentrer sur des tâches plus importantes telles que la modélisation de nouveaux algorithmes.Nous avons mené une série d'expériences qui ont montré des résultats prometteurs et nous ont permis de valider notre approche et notre modélisation

From the fine mapping to the cloning of the D gene involved in quantitative traits in peach fruit quality

International audienc

The Silene latifolia genome and its giant Y chromosome

Abstract In some species, the Y is a tiny chromosome but the dioecious plant Silene latifolia has a giant ∼550 Mb Y chromosome, which has remained unsequenced so far. Here we used a hybrid approach to obtain a high-quality male S. latifolia genome. Using mutants for sexual phenotype, we identified candidate sex-determining genes on the Y. Comparative analysis of the sex chromosomes with outgroups showed the Y is surprisingly rearranged and degenerated for a ∼11 MY-old system. Recombination suppression between X and Y extended in a stepwise process, and triggered a massive accumulation of repeats on the Y, as well as in the non-recombining pericentromeric region of the X, leading to giant sex chromosomes. One-Sentence Summary This work uncovers the structure, function, and evolution of one of the largest giant Y chromosomes, that of the model plant Silene latifolia , which is almost 10 times larger than the human Y, despite similar genome sizes

Flow cytometry-based diagnostic approach for inborn errors of immunity: experience from Algeria

PurposeIn this study, we retrospectively reviewed the use of flow cytometry (FCM) in the diagnosis of inborn errors of immunity (IEIs) at a single center in Algeria. Sharing insights into our practical experience, we present FCM based diagnostic approaches adapted to different clinical scenarios.MethodsBetween May 2017 and February 2024, pediatric and adult patients presenting with clinical features suggestive of immunodeficiency were subjected to FCM evaluation, including lymphocyte subset analysis, detection of specific surface or intracellular proteins, and functional analysis of immune cells.ResultsOver a nearly seven-year period, our laboratory diagnosed a total of 670 patients (372 (55.5%) males and 298 (44.5%) females), distributed into 70 different IEIs belonging to 9 different categories of the International Union of Immunological Societies classification. FCM was used to diagnose and categorize IEI in 514 patients (76.7%). It provided direct diagnostic insights for IEIs such as severe combined immunodeficiency, Omenn syndrome, MHC class II deficiency, familial hemophagocytic lymphohistiocytosis, and CD55 deficiency. For certain IEIs, including hyper-IgE syndrome, STAT1-gain of function, autoimmune lymphoproliferative syndrome, and activated PI3K delta syndrome, FCM offered suggestive evidence, necessitating subsequent genetic testing for confirmation. Protein expression and functional assays played a crucial role in establishing definitive diagnoses for various disorders. To setup such diagnostic assays at high and reproducible quality, high level of expertise is required; in house reference values need to be determined and the parallel testing of healthy controls is highly recommended.ConclusionFlow cytometry has emerged as a highly valuable and cost-effective tool for diagnosing and studying most IEIs, particularly in low-income countries where access to genetic testing can be limited. FCM analysis could provide direct diagnostic insights for most common IEIs, offer clues to the underlying genetic defects, and/or aid in narrowing the list of putative genes to be analyzed