HAL-Université de Bretagne Occidentale
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Method of decoding recursive convolutional codes, and corresponding decoder and computer program product
No full textEvaluating Microplastic Concentrations in the Al Hoceima Marine Protected Area: Implications for Identifying Pollution Hotspots and Formulating Conservation Strategies
No full textInternational audienceGlobal marine ecosystems are significantly endangered by microplastic pollution, leading to comprehensive investigations into its distribution and impacts on the health of ecosystem. This research employs the Alseamar Autonomous Underwater Vehicle (AUV) known as Glider to investigate microplastic concentrations within the Al Hoceima Marine Protected Area (MPA). Our objective is to identify spatial patterns that reveal pollution hotspots and furnish data for targeted conservation efforts and pollution prevention. We aim to identify regions with elevated microplastic concentrations by meticulously analyzing microplastic level graphs, with a specific focus on temporal variations. The results reveal notable patterns, such as increased densities around fishing harbors and near urban centers , potentially linked to anthropogenic activities. Additionally, we observe variations in pollution levels throughout different glider operation cycles, underscoring the importance of understanding the spatio-temporal dynamics of microplastic distribution. Al Hoceima Marine protected areas exhibiting lower microplastic concentrations illustrate the efficacy of such zones in alleviating pollution impacts, thereby underscoring the significance of conservation efforts in safeguarding marine biodiversity and ecosystem resilience. Ultimately, our research enhances our comprehension of the pressures exerted by humans on marine environments and underscores the necessity of proactive conservation measures to shield marine ecosystems from the threats posed by microplastic pollution
An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach
No full textInternational audienc
A Protocol for Comparing Sailboat Routing Algorithms
No full textInternational audienceShip weather routing is crucial today to optimize trajectories, whether for cargo ships, racing sailboats or drones. The well-known Isochrone method provides the fastest route. Multi-Objective Optimization (MOO) approaches can handle extra goals specific to each context. For example, during sailing races, strong winds can induce high speeds but present risks of damage to the vessel and cause exhaustion to the crew due to the maneuvers. In our work, these factors are collectively referred to as stress and more generally, it is interesting to take into account objectives other than time. MOO algorithms produce sets of solutions corresponding to trade-offs between the different objectives, rather than a single solution like time-oriented methods such as the Isochrone algorithm. Furthermore, it is necessary to compare MOO algorithms across all the objectives they address. Currently, the various types of routing algorithms are often evaluated using simple protocols, such as comparisons with existing algorithms. They apply on a reduced number of test cases using a single weather forecast file at the departure time. However, in real conditions, sailors take advantage of the latest grib files to periodically recalculate their trajectory. This paper addresses this weakness in the evaluation and comparison of algorithms. We have developed a protocol based on simulation i.e. the use of series of grib files. Routing algorithms are evaluated using real race data while they are compared using a MOO-specific metric. This method is illustrated on the Retour à la base 2023 race for two MOO algorithms
Epifauna associated with macroalgae in Senegal (Northwest Africa) [résumé]
No full textOne Ocean Science Congress, Nice, FRA, 03-/06/2025 - 06/06/2025The association of epifaunal communities with marine macroalgae has not yet been studied on the Cape Verde Peninsula (Dakar, Senegal). Macroalgae were collected at sea during the warm season (July) from stations around the Cape Verde Peninsula to investigate their association with epifaunal organisms. A predominance of amphipods was noted across the entire epifaunal community. The abundance of this order was higher on the macroalgae Corallina officinalis. The highest epifaunal density was observed on the genus Ulva sp. A significant relationship was observed between the diversity and abundance of epifaunal communities and macroalgal species. However, there was no significant relationship between the biomass of the macroalgae and the biomass of the epifauna. The methodology used in this study could be replicated on a larger spatial and temporal scale. This would provide deeper insights into the communities hosted by macroalgae within the context of the growing blue economy, encouraging future exploitation of macroalgae and addressing biodiversity loss.Keywords: Epifauna, Macroalgae, Dakar, Senegal
GANDALF: Generative ANsatz for DNA damage evALuation and Forecast. A neural network-based regression for estimating early DNA damage across micro-nano scales
No full textInternational audiencePurpose: This study aims to develop a comprehensive simulation framework to connect radiation effects from the microscopic to the nanoscopic scale.Method: The process begins with a Geant4-DNA simulation based on the example ”molecularDNA”, producing a dataset of twelve different types of early DNA damages within an Escherichia coli (E. coli) bacterium, generated by proton irradiation at different kinetic energies, giving a nano-scale view of the particle–matter interaction. Then we pass to the micro-scale with a Geant4 simulation, based on the example ”radiobiology”, providing a microscopic view of proton interactions with matter through the Linear Energy Transfer (LET). Then GANDALF (Generative ANsatz for DNA damage evALuation and Forecast) Machine Learning (ML) toolkit, a Neural Network (NN)-based regression system, is employed to correlate the micro-scale LET data with the nano-scale occurrences of DNA damages in the E. coli bacterium.Results: The trained ML algorithm provides a practical tool to convert LET curves versus depth in a water phantom into DNA damage curves for twelve distinct types of DNA damage. To assess the performance, we evaluated the choice and optimization of the regression system based on its interpolation and extrapolation capabilities, ensuring the model could reliably predict DNA damage under various conditions.Conclusions: Through the synergistic integration of Geant4, Geant4-DNA and ML, the study provides a tool to easily convert the results at the micro-scale of Geant4 to those at the nano-scale of Geant4-DNA without having to deal with the high CPU time requirements of the latter
Derivation of Born/von Kármán difference equations through consistent lattice angular interactions
No full textInternational audienc
Intersection-based slice motion estimation for fetal brain imaging
No full textInternational audienceFetal MRI offers a broad spectrum of applications, including the investigation of fetal brain development and facilitation of early diagnosis. However, image quality is often compromised by motion artifacts arising from both maternal and fetal movement. To mitigate these artifacts, fetal MRI typically employs ultrafast acquisition sequences. This results in the acquisition of three (or more) orthogonal stacks along different spatial axes. Nonetheless, inter-slice motion can still occur. If left uncorrected, such motion can introduce artifacts in the reconstructed 3D volume. Existing motion-correction approaches often rely on a two-step iterative process involving registration followed by reconstruction. They tend to detect and remove a large number of misaligned slices, resulting in poor reconstruction quality. This paper proposes a novel reconstruction-independent method for motion correction. Our approach benefits from the intersection of orthogonal slices and estimates motion for each slice by minimizing the difference between the intensity profiles along their intersections. To address potential misalignments, we present an innovative machine learning-based classifier for identifying misaligned slices. The</div
