Scalable Load Balancing Scheme for Distributed Controllers in Software Defined Data Centers

International audienc

Approche évolutionnaire pour la planification d'itinéraires dans un environnement dynamique

Le problème de planification d'itinéraires dans un environnement dynamique est un problème complexe. Ce problème est d'autant plus difficile que les décisions doivent se prendre dans un temps limité, en se basant sur des informations incertaines et qui évoluent dans le temps. Nous nous sommes intéressés aux méthodes métaheuristiques, et plus particulièrement aux algorithmes génétiques pour résoudre ce problème. Plusieurs approches habituellement proposées pour ce problème sont purement prescriptives et ne garantissent que la convergence vers une solution réalisable au détriment de l'optimalité. De plus, les approches basées sur les algorithmes génétiques souffrent toutes du problème de calibrage des paramètres, une étape importante quand quelqu'un décide de les utiliser. Notre thèse s'inscrit dans le cadre de cette nouvelle direction de recherche et puise sa motivation plus particulièrement dans la volonté de répondre à quelques-unes des problématiques rencontrées dans des applications temps-réel. Nous proposons une approche générique utilisant un processus évolutionnaire à deux niveaux pour régler le problème de calibrage de combinaisons d'opérateurs dans les algorithmes génétiques. Nous avons développé une stratégie d'attente qui exploite des connaissances probabilistes sur les événements futurs afin de produire des solutions robustes et de meilleure qualité en temps réel. De plus, dans le cas des problèmes de transport utilisant des flottes de véhicules (pour la distribution des biens et services), l'approche proposée reflète la réalité de façon plus adéquate en considérant différentes situations observées en pratique comme des temps de voyage variables, et en intégrant des stratégies et mécanismes appropriés pour chacune des situations identifiées. Nous avons présenté une vision plus large du concept de diversion et nous avons introduit une condition d'acceptation d'une diversion dans la planification d'itinéraires en temps réel. Afin de faire face aux pressions temporelles inhérentes à un contexte dynamique, nous avons opté pour des implantations parallèles afin d'accélérer les temps de réponse. Enfin, des tests numériques ont été réalisés à l'aide de simulations utilisant une adaptation des instances de problèmes de Solomon pour le problème de tournées de véhicules avec fenêtres de temps (VRPTW). Dans ce problème, il s'agit d'affecter des requêtes de clients qui arrivent en temps réel à une flotte de véhicules en mouvement. Ce processus implique aussi la construction simultanée d'un ensemble d'itinéraires planifiés qui satisfont la demande, tout en respectant diverses contraintes. Afin de minimiser les biais, le même jeu de données utilisé dans la littérature pour le VRPTW dynamique a été employé pour réaliser notre étude. Les résultats numériques confirment la pertinence des différentes stratégies que nous avons développées et la supériorité de notre approche de planification d'itinéraires en temps réel comparativement à d'autres méthodes proposées dans la littérature

Mixed neuroendocrine-non-neuroendocrine neoplasms of the right colon: a case report

Mixed neuroendocrine-non neuroendocrine neoplasm (MiNENs) is a rare gastrointestinal neoplasm that has been redefined by the World Health Organization (WHO) in 2017 as the association of two types of components, neuroendocrine and non-neuroendocrine, each of them present in at least 30% of the tumour mass. Small case reports and case series have demonstrated the occurrence of this neoplasm in the colon. We here report the case of a 47-year-old man undergoing colonscopy for anemia. This showed impassable polypoidal tumor budding in the right colic flexure. Computerized tomography (CT) scan and magnetic resonance imaging (MRI) showed the presence of liver metastases. As the tumor was hemorrhagic, right hemicolectomy with lymph node dissection was performed. The histological examination showed MiNEN of the ascending colon. The patient received adjuvant chemotherapy

Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application

A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network-based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.This publication was made possible by an NPRP award [NPRP8-910-2-387] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety

Qatar expects more than a million visitors during the 2022 World Cup, which will pose significant challenges. The high number of people will likely cause a rise in road traffic congestion, vehicle crashes, injuries and deaths. To tackle this problem, Naturalistic Driver Behavior can be utilised which will collect and analyze data to estimate the current Qatar traffic system, including traffic data infrastructure, safety planning, and engineering practices and standards. In this paper, an IoT-based solution to facilitate such a study in Qatar is proposed. Different data points from a driver are collected and recorded in an unobtrusive manner, such as trip data, GPS coordinates, compass heading, minimum, average, and maximum speed and his driving behavior, including driver's drowsiness level. Analysis of these data points will help in prediction of crashes and road infrastructure improvements to reduce such events. It will also be used for drivers' risk assessment and to detect extreme road user behaviors. A framework that will help to visualize and manage this data is also proposed, along with a Deep Learning-based application that detects drowsy driving behavior that netted an 82% accuracy.This publication was funded by the NPRP award [NPRP8-910-2-387] from Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

The use of plants in the traditional treatment of diabetes patients: survey in southern Morocco

This study was conducted in public healthcare establishments in Guelmim city in south of Morocco to report medicinal plants used in folk medicine to treat diabetes. Three hundred sixty-two informants were interviewed through semi structured interviews. The inventory includes scientific, popular and common names of the plants, used parts and method of preparation. The survey shows that 24.6% of the patients use these plants. Twenty-seven medicinal plants belonging to seventeen families were inventoried and three species were cited for the first time in the treatment of diabetes in Morocco. Olea europea L., Artemisia herba-alba Asso and Trigonella foenum-graecum L. are the most plant species used to treat diabetes, and the two most cited families are Lamiaceae (5 species) and Apiaceae (4 species). Leaves represented the most utilized part of plants and decoction was the most cited mode of preparation of drugs. The present study highlights the wide variety of herbal remedies used to treat diabetes and the frequency of this use in diabetic patients

Efficacy of vinblastine in central nervous system Langerhans cell histiocytosis: a nationwide retrospective study

<p>Abstract</p> <p>Background</p> <p>Vinblastine (VBL) is the standard treatment for systemic Langerhans cell histiocytosis (LCH), but little is known about its efficacy in central nervous system (CNS) mass lesions.</p> <p>Methods</p> <p>A retrospective chart review was conducted. Twenty patients from the French LCH Study Group register met the inclusion criteria. In brief, they had CNS mass lesions, had been treated with VBL, and were evaluable for radiologic response.</p> <p>Results</p> <p>The median age at diagnosis of LCH was 11.5 years (range: 1-50). Intravenous VBL 6 mg/m²was given in a 6-week induction treatment, followed by a maintenance treatment. The median total duration was 12 months (range: 3-30). Eleven patients received steroids concomitantly. Fifteen patients achieved an objective response; five had a complete response (CR: 25%), ten had a partial response (PR: 50%), four had stable disease (SD: 20%) and one patient progressed (PD: 5%). Of interest, four out of the six patients who received VBL without concomitant steroids achieved an objective response. With a median follow-up of 6.8 years, the 5-year event-free and overall survival was 61% and 84%, respectively. VBL was well-tolerated and there were no patient withdrawals due to adverse events.</p> <p>Conclusion</p> <p>VBL, with or without steroids, could potentially be a useful therapeutic option in LCH with CNS mass lesions, especially for those with inoperable lesions or multiple lesions. Prospective clinical trials are warranted for the evaluation of VBL in this indication.</p