Mécanismes d'optimisation multi-niveaux pour IP sur satellites de nouvelle génération

L'objectif de cette thèse est de fournir aux réseaux satellitaires géostationnaires des outils d'amélioration de performances dans un contexte contraint, lié au support de communication qui présente une qualité variable. Les défis s'étendent de la couche d'accès au support (l'ordonnancement) à la couche transport (le comportement du TCP étant sensible au délai de propagation dans les réseaux géostationnaires). Nous faisons appel aux mécanismes cross-layer qui mettent en place des interactions entre entités protocolaires non adjacentes du modèle OSI offrant ainsi une adaptation immédiate au changement des conditions dans le réseau. Nous employons à ce but une technique cross-layer informant l'entité protocolaire TCP du débit disponible, permettant d'éviter des conséquences telles que la chute du débit et des pertes liées à la congestion des files d'attente. Nous mettons en oeuvre ce mécanisme au niveau d'un élément sensible du réseau géostationnaire, le proxy TCP. L'intérêt des techniques cross-layer a été mis en évidence au niveau de l'ordonnanceur présent au sein de la gateway d'un réseau DVB-S2/DVB-RCS. Ainsi, ils contribuent à la maximisation de la capacité du système, à l'assurance des contraintes de qualité de service, à l'équité de l'allocation de ressources, etc. Enfin, nous présentons une solution possible d'architecture cross-layer. Nous proposons des caractéristiques demandées pour une architecture cross-layer afin d'assurer l'évolutivité, la modularité et la co-existence avec l'architecture standard existante. ABSTRACT : The main objective of this thesis is to provide tools of improving performance of geostationary satellite networks, operating in a constrained environment mainly due to the variable quality of radio links. There are protocol layers being challenged by such characteristics, such as the medium access protocol and the transport layer protocol (TCP behaviour is impacted by the long propagation delay in geostationary satellite networks). We employ cross-layer mechanisms implementing interaction between non-adjacent protocol layers defined by the OSI architecture aiming at providing a rapid adaptation to changes in network state. We provide a cross-layer technique informing TCP protocol of the available network rate, thus preventing consequences such as TCP rate drop or loss due to buffer overflow. This mechanism is to be implemented in a sensible network element, such as a TCP proxy. Data link layer scheduler can benefit from the advantages of employing cross-layer mechanisms, especially at the gateway of a DVB-S2/DVB-RCS satellite network. The improved scheduler allows an efficient use of network resources and helps insuring quality of services constraints, resource allocation fairness, etc. Finally, we propose a cross-layer architecture along with in-demand characteristics able to offer an inter-operability with the existing architecture, an easy up-grade and design

Multidrug Resistance in Zoonotic Pathogens: Are Medicinal Plants a Therapeuthic Alternative?

Multidrug resistance (MDR) represents a complex phenomenon, caused not only by nondiscriminative antibiotic therapy in both human and animal medicine but also by the transfer of resistance genes between different bacteria. Animals besides different environments embody a niche for the development of resistant microbiomes, representing a serious threat to people not only as contacts but also as consumers/tourists. The epidemiological cycle of MDR bacteria is closed by changes in either their hosts or in their habitats. To prevent further spreading of MDR, natural solutions are investigated as efficacy, including in this category various compounds isolated from medicinal plants (quinones, flavones, flavonoids, and flavonols, tannins, coumarins, terpenoids and essential oils, alkaloids, lectins and polypeptides, etc.). The results of such studies are valuable for the medicine, but could the medicinal plants cover the gap for humans, animals, and the environment? This chapter aims at trying to answer this question

Heavy Metal Pollutome and Microbial Resistome Reciprocal Interaction and Its Impact on Human and Animal Matrices

The chapter aims to reveal the complex relationships between antibiotic resistance in bacteria and heavy metal pollution at the human/animal interface. The antibiotic resistance is a continuously growing threat for both people and animals. Animals could represent a source for zoonotic microbial contamination of humans as subject for consumption and also as contacts (companion, sports, zoo animals, etc.). Antimicrobial treatments in animals, if uncontrolled or injudicious, could raise antibiotic-resistant strains to be transferred to humans where they can cause even more severe diseases. Moreover, the environment has its own microbiome, including some nonpathogenic but antibiotic-resistant species. Human industrial activities are carried out in certain environments, with particular microbiomes and also where animals bearing antibiotic-resistant bacteria are present. Thus, the degree of pollution with heavy metals, as part of the global pollutants to the environment, could impact on the bacteria and their resistome with severe consequences for inhabitants of the area

Electricity Price Evolution and the Disruptive Economic and Geopolitical Context on the Spot Market. A Romanian Case Study

The current context of the electricity markets is marked by the lingering effects of COVID-19 and the conflict in Ukraine that have a significant influence on the European wholesale electricity markets. Both Day-Ahead Market (DAM) and balancing market have been heavily impacted by fluctuating prices. This trend started in October 2021 when the lockdowns were removed and the high request for commodities led to a higher inflation. Then in 2022, the conflict in Ukraine accentuated this evolution and even higher prices were recorded for electricity, gas, oil and other resources. In this paper, we analyze a set of fundamental variables and provide an electricity price forecast on DAM using a multiple regression model. The exogenous variables considered in this paper are the following: power system data (total consumption, total generation and its breakdown: renewables (RES) and Non-RES), economic data (inflation, interest rate), certificate price for CO2 emissions (EU-ETS), level of Danube River and other resources prices (oil, gas). Interesting insights can be extracted from a data set that consists of merged time series collected from January 2019 until August 2022. The results are measured using Mean Absolute Percentage Error (MAPE)