Développement et évaluation d'une stratégie d'atterrissage pour drones semi-autonome sur lignes électriques dans différentes conditions de vent

Au cours des dernières années, le recours aux drones pour l’inspection des lignes électriques à haute tension s’est répandu en raison de leur efficacité, de leur rentabilité et de leur ca- pacité à atteindre des zones autrement inaccessibles. Cependant, faire atterrir en toute sécurité ces drones sur les lignes électriques, notamment dans des conditions venteuses, constitue un défi majeur. Cette recherche présente un modèle de contrôle semi-autonome pour permettre l’atterrissage sur une ligne électrique à l’aide de la plateforme NADILE (un drone conçu spécifiquement pour l’inspection des lignes électriques) et évalue le fonc- tionnement dans différentes conditions de vent. L’analyse de la probabilité de réussite de l’atterrissage en fonction de l’état initial du drone a été effectuée à l’aide de la méthode de Monte Carlo. Les performances du système ont été évaluées pour deux stratégies d’atter- rissage différentes, divers paramètres de contrôle, et quatre niveaux de vent. Les résultats ont montré qu’une stratégie d’atterrissage en deux étapes donne de meilleures chances de réussite de l’atterrissage et fournissent des indications précieuses sur les paramètres de contrôle optimaux et le niveau maximal de vent pour lequel le système est fiable. Une dé- monstration expérimentale de l’atterrissage autonome du système sur une ligne électrique a également été réalisée

Assessing Wind Impact on Semi-Autonomous Drone Landings for In-Contact Power Line Inspection

In recent years, the use of inspection drones has become increasingly popular for high-voltage electric cable inspections due to their efficiency, cost-effectiveness, and ability to access hard-to-reach areas. However, safely landing drones on power lines, especially under windy conditions, remains a significant challenge. This study introduces a semi-autonomous control scheme for landing on an electrical line with the NADILE drone (an experimental drone based on original LineDrone key features for inspection of power lines) and assesses the operating envelope under various wind conditions. A Monte Carlo method is employed to analyze the success probability of landing given initial drone states. The performance of the system is evaluated for two landing strategies, variously controllers parameters and four level of wind intensities. The results show that a two-stage landing strategies offers higher probabilities of landing success and give insight regarding the best controller parameters and the maximum wind level for which the system is robust. Lastly, an experimental demonstration of the system landing autonomously on a power line is presented

Behavioral Economics and Securities Laws: The Theoretical Underpinnings of our Regulatory System and Their Application to Investor Sophistication

This thesis aims at giving an outlook of Canadian securities regulation through the lense of behavioral economics. The author develops it in two times: first, he poses the theoretical underpinnings of the Canadian regulatory system, mostly based on a presumption of investor rationality and the theoretical challenges that have been raised against this presumption. Secondly, he applies these competing theoretical frames to a set of securities rules, i.e. prospectus-exempt distributions and distributions of securitized products. He concludes by presenting an example of behavioral-oriented rules, and discussing why adapting the Canadian rulebook to these theoretical changes might be relevant.LL.M

Genome Scans Reveal Homogenization and Local Adaptations in Populations of the Soybean Cyst Nematode

Determining the adaptive potential of alien invasive species in a new environment is a key concern for risk assessment. As climate change is affecting local climatic conditions, widespread modifications in species distribution are expected. Therefore, the genetic mechanisms underlying local adaptations must be understood in order to predict future species distribution. The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is a major pathogen of soybean that was accidentally introduced in most soybean-producing countries. In this study, we explored patterns of genetic exchange between North American populations of SCN and the effect of isolation by geographical distance. Genotyping-by-sequencing was used to sequence and compare 64 SCN populations from the United States and Canada. At large scale, only a weak correlation was found between genetic distance (Wright's fixation index, FST) and geographic distance, but local effects were strong in recently infested states. Our results also showed a high level of genetic differentiation within some populations, allowing them to adapt to new environments and become established in new soybean-producing areas. Bayesian genome scan methods identified 15 loci under selection for climatic or geographic co-variables. Among these loci, two non-synonymous mutations were detected in SMAD-4 (mothers against decapentaplegic homolog 4) and DOP-3 (dopamine receptor 3). High-impact variants linked to these loci by genetic hitchhiking were also highlighted as putatively involved in local adaptation of SCN populations to new environments. Overall, it appears that strong selective pressure by resistant cultivars is causing a large scale homogenization with virulent populations

Biodiversity Monitoring with Intelligent Sensors: An Integrated Pipeline for Mitigating Animal-Vehicle Collisions

Transports of people and goods contribute to the ongoing 6th mass extinction of species. They impact species viability by reducing the availability of suitable habitat, by limiting connectivity between suitable patches, and by increasing direct mortality due to collisions with vehicles. Not only does it represent a threat for some species conservation capabilities, but animal vehicle collisions (AVC) is also a threat for human safety and security in transport and has a massive cost for transport infrastructure (TI) managers and users. Using the opportunities offered by the increasing number of sensors embedded into TI and the development of their digital twins, we developed a framework aiming at managing AVC by mapping the collision risk between trains and ungulates (roe deer and wild boar) thanks to the deployment of a camera trap network. The proposed framework uses population dynamic simulations to identify collision hotspots and assist with the design of sensors deployment. Once sensors are deployed, the data collected, here photos, are processed through deep learning to detect and identify species at the TI vicinity. Then, the processed data are fed to an abundance model able to map species relative abundance of species around the TI as a proxy of the collision risk. We implement the framework on an actual section of railway in south-western France benefiting from a mitigation and monitoring strategy. The implementation thus highlighted the technical and fundamental requirements to effectively mainstream biodiversity concerns in the TI digital twins. This would contribute to the AVC management in autonomous vehicles thanks to connected TI

Data_Sheet_1_Genome Scans Reveal Homogenization and Local Adaptations in Populations of the Soybean Cyst Nematode.XLSX

<p>Determining the adaptive potential of alien invasive species in a new environment is a key concern for risk assessment. As climate change is affecting local climatic conditions, widespread modifications in species distribution are expected. Therefore, the genetic mechanisms underlying local adaptations must be understood in order to predict future species distribution. The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is a major pathogen of soybean that was accidentally introduced in most soybean-producing countries. In this study, we explored patterns of genetic exchange between North American populations of SCN and the effect of isolation by geographical distance. Genotyping-by-sequencing was used to sequence and compare 64 SCN populations from the United States and Canada. At large scale, only a weak correlation was found between genetic distance (Wright's fixation index, F_ST) and geographic distance, but local effects were strong in recently infested states. Our results also showed a high level of genetic differentiation within some populations, allowing them to adapt to new environments and become established in new soybean-producing areas. Bayesian genome scan methods identified 15 loci under selection for climatic or geographic co-variables. Among these loci, two non-synonymous mutations were detected in SMAD-4 (mothers against decapentaplegic homolog 4) and DOP-3 (dopamine receptor 3). High-impact variants linked to these loci by genetic hitchhiking were also highlighted as putatively involved in local adaptation of SCN populations to new environments. Overall, it appears that strong selective pressure by resistant cultivars is causing a large scale homogenization with virulent populations.</p