La recherche de naines brunes et étoiles de faible masse dans les associations cinématiques jeunes du voisinage solaire

L’objectif principal de cette thèse est d’identifier les étoiles de faible masse et naines brunes membres d’associations cinématiques jeunes du voisinage solaire. Ces associations sont typiquement âgées de moins de 200 millions d’années et regroupent chacune un ensemble d’étoiles s’étant formées au même moment et dans un même environnement. La majorité de leurs membres d'environ plus de 0.3 fois la masse du Soleil sont déjà connus, cependant les membres moins massifs (et moins brillants) nous échappent encore. Leur identification permettra de lever le voile sur plusieurs questions fondamentales en astrophysique. En particulier, le fait de cibler des objets jeunes, encore chauds et lumineux par leur formation récente, permettra d’atteindre un régime de masses encore peu exploré, jusqu'à seulement quelques fois la masse de Jupiter. Elles nous permettront entre autres de contraindre la fonction de masse initiale et d'explorer la connection entre naines brunes et exoplanètes, étant donné que les moins massives des naines brunes jeunes auront des propriétés physiques très semblables aux exoplanètes géantes gazeuses. Pour mener à bien ce projet, nous avons adapté l'outil statistique BANYAN I pour qu'il soit applicable aux objets de très faibles masses en plus de lui apporter plusieurs améliorations. Nous avons entre autres inclus l'utilisation de deux diagrammes couleur-magnitude permettant de différencier les étoiles de faible masse et naines brunes jeunes à celles plus vieilles, ajouté l'utilisation de probabilités a priori pour rendre les résultats plus réalistes, adapté les modèles spatiaux et cinématiques des associations jeunes en utilisant des ellipsoïdes gaussiennes tridimensionnelles dont l'alignement des axes est libre, effectué une analyse Monte Carlo pour caractériser le taux de faux-positifs et faux-négatifs, puis revu la structure du code informatique pour le rendre plus efficace. Dans un premier temps, nous avons utilisé ce nouvel algorithme, BANYAN II, pour identifier 25 nouvelles candidates membres d'associations jeunes parmi un échantillon de 158 étoiles de faible masse (de types spectraux > M4) et naines brunes jeunes déjà connues. Nous avons ensuite effectué la corrélation croisée de deux catalogues couvrant tout le ciel en lumière proche-infrarouge et contenant ~ 500 millions d’objets célestes pour identifier environ 100 000 candidates naines brunes et étoiles de faible masse du voisinage solaire. À l'aide de l'outil BANYAN II, nous avons alors identifié quelques centaines d'objets appartenant fort probablement à une association jeune parmi cet échantillon et effectué un suivi spectroscopique en lumière proche-infrarouge pour les caractériser. Les travaux présentés ici ont mené à l'identification de 79 candidates naines brunes jeunes ainsi que 150 candidates étoiles de faible masse jeunes, puis un suivi spectroscopique nous a permis de confirmer le jeune âge de 49 de ces naines brunes et 62 de ces étoiles de faible masse. Nous avons ainsi approximativement doublé le nombre de naines brunes jeunes connues, ce qui a ouvert la porte à une caractérisation statistique de leur population. Ces nouvelles naines brunes jeunes représentent un laboratoire idéal pour mieux comprendre l'atmosphère des exoplanètes géantes gazeuses. Nous avons identifié les premiers signes d’une remontée dans la fonction de masse initiale des naines brunes aux très faibles masses dans l'association jeune Tucana-Horologium, ce qui pourrait indiquer que l’éjection d’exoplanètes joue un rôle important dans la composition de leur population. Les résultats du suivi spectroscopique nous ont permis de construire une séquence empirique complète pour les types spectraux M5-L5 à l'âge du champ, à faible (β) et très faible (γ) gravité de surface. Nous avons effectué une comparaison de ces données aux modèles d'évolution et d'atmosphère, puis nous avons construit un ensemble de séquences empiriques de couleur-magnitude et types spectraux-magnitude pour les naines brunes jeunes. Finalement, nous avons découvert deux nouvelles exoplanètes par un suivi en imagerie directe des étoiles jeunes de faible masse identifiées dans ce projet. La future mission GAIA et le suivi spectroscopique complet des candidates présentées dans cette thèse permettront de confirmer leur appartenance aux associations jeunes et de contraindre la fonction de masse initiale dans le régime sous-stellaire.

Assessment of PEG on Polymeric Particles Surface, a Key Step in Drug Carrier Translation

Injectable drug nanocarriers have greatly benefited in their clinical development from the addition of a superficial hydrophilic corona to improve their cargo pharmacokinetics. The most studied and used polymer for this purpose is poly(ethylene glycol), PEG. However, in spite of its wide use for over two decades now, there is no general consensus on the optimum PEG chain coverage-density and size required to escape from the mononuclear phagocyte system and to extend the circulation time. Moreover, cellular uptake and active targeting may have conflicting requirements in terms of surface properties of the nanocarriers which complicates even more the optimization process. These persistent issues can be largely attributed to the lack of straightforward characterization techniques to assess the coverage-density, the conformation or the thickness of a PEG layer grafted or adsorbed on a particulate drug carrier and is certainly one of the main reasons why so few clinical applications involving PEG coated particle-based drug delivery systems are under clinical trial so far. The objective of this review is to provide the reader with a brief description of the most relevant techniques used to assess qualitatively or quantitatively PEG chain coverage-density, conformation and layer thickness on polymeric nanoparticles. Emphasis has been made on polymeric particle (solid core) either made of copolymers containing PEG chains or modified after particle formation. Advantages and limitations of each technique are presented as well as methods to calculate PEG coverage-density and to investigate PEG chains conformation on the NP surface.

Doctrine of Estoppel

The object of every law is to render justice. But sometimes the strict implementation of low may result in injustice. Under such circumstances equity will step in to prevent the injustice. Estoppel is one such concept evolved by equity for rendering justice even deviating from strict legal principles. This study is an analysis of the essence of the principle of estoppel, its scope, circumstances and application. The related principles known as estoppel by record, estoppel by deed, estoppel by representation, promissory estoppel, estoppel against public authority is also considered. Estoppel, originated from the sense of justice, equity and good consciousness has since developed through various judicial pronouncements. Further section 115 of the Evidence Act has statutorily recognized and laid down the principles of estoppel. But Section 115 of the Evidence Act or any other statute does not cover the modern development of estoppel in the form of promissory estoppel.

Aircraft down-range profiles formed from simulated and compact-range radar backscatter

Compact-range radar backscatter measurements are taken of aircraft scale models. In addition, computer software is used to predict the RCS of the aircraft. Synthetic down-range profiles formed from the two sources of backscatter data are compared and visualized in an innovative manner. Similar discrimination rates between the two aircraft are obtained on data from both source

Down-regulation of cerebellar 5-HT2C receptors in pilocarpine-induced epilepsy in rats: Therapeutic role of Bacopa monnieri extract

Epilepsy is a syndrome of episodic brain dysfunction characterized by recurrent unpredictable, spontaneous seizures. Cerebellar dysfunction is a recognized complication of temporal lobe epilepsy and it is associated with seizure generation, motor deficits and memory impairment. Serotonin is known to exert a modulatory action on cerebellar function through 5HT2C receptors. 5-HT2C receptors are novel targets for developing anticonvulsant drugs. In the present study, we investigated the changes in the 5-HT2C receptors binding and gene expression in the cerebellum of control, epileptic and Bacopa monnieri treated epileptic rats. There was a significant down regulation of the 5-HT content (pb0.001), 5-HT2C gene expression (pb0.001) and 5-HT2C receptor binding (pb0.001) with an increased affinity (pb0.001). Carbamazepine and B. monnieri treatments to epileptic rats reversed the down regulated 5-HT content (pb0.01), 5-HT2C receptor binding (pb0.001) and gene expression (pb0.01) to near control level. Also, the Rotarod test confirms the motor dysfunction and recovery by B. monnieri treatment. These data suggest the neuroprotective role of B. monnieri through the upregulation of 5-HT2C receptor in epileptic rats. This has clinical significance in the management of epilepsy

Design of a microstip fed step slot antenna for UWB communication

The design and performance of a stepped slot printed monopole antenna in the ultrawideband is presented in this article. Multiple resonances generated by the stepped slot geometry are matched in the ultrawideband using a modified microstrip feed. The impedance bandwidth (SWR < 2) of the antenna is from 3 to 11 GHz. Radiation patterns are stable and omnidirectional with appreciable gain throughout the band. Performance of the antenna is also analyzed in the time domain, which reveals good pulse handling capabilities. Compact geometry of the antenna allows easy commercial deployment.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Enhanced electron-electron correlations in nanometric epitaxial SrRuO3 epitaxial films

Epitaxial and fully strained SrRuO3 thin films have been grown on SrTiO3(100). At initial stages the growth mode is three-dimensional- (3D-)like, leading to a finger-shaped structure aligned with the substrate steps and that eventually evolves into a 2D step-flow growth. We study the impact that the defect structure associated with this unique growth mode transition has on the electronic properties of the films. Detailed analysis of the transport properties of nanometric films reveals that microstructural disorder promotes a shortening of the carrier mean free path. Remarkably enough, at low temperatures, this results in a reinforcement of quantum corrections to the conductivity as predicted by recent models of disordered, strongly correlated electronic systems. This finding may provide a simple explanation for the commonly observed¿in conducting oxides-resistivity minima at low temperature. Simultaneously, the ferromagnetic transition occurring at about 140 K, becomes broader as film thickness decreases down to nanometric range. The relevance of these results for the understanding of the electronic properties of disordered electronic systems and for the technological applications of SrRuO3¿and other ferromagnetic and metallic oxides¿is stressed.

Giant step bunching from self-organized coalescence of SrRuO3 islands

Step bunching develops in the epitaxy of SrRuO3 on vicinal SrTiO3(001) substrates. We have investigated the formation mechanisms and we show here that step bunching forms by lateral coalescence of wedgelike three-dimensional islands that are nucleated at substrate steps. After coalescence, wedgelike islands become wider and straighter with growth, forming a self-organized network of parallel step bunches with altitudes exceeding 30 unit cells, separated by atomically flat terraces. The formation mechanism of step bunching in SrRuO3, from nucleated islands, radically differs from one-dimensional models used to describe bunching in semiconducting materials. These results illustrate that growth phenomena of complex oxides can be dramatically different to those in semiconducting or metallic systems.

Theoretical Studies on Air Pollution Meteorology in South India

Man's inadvertent interference with the environment by way of indiscreL¢ industrflflization has led to the deteriorating air quality in the recent times. The search is on to find the remedies to confine the air pollution levels with in their thershold limits. Theoretical studies play A crucial role in the control and for abatment of air pollution. Improper siting of industry is one of the most common reasons for the increased levels of air pollution in urban environments. A proper and effective ecological planning is an essential first step for any region in order to reduce the effects of air pollution. By means of theoretical models one can obtain the pollutant distribution in any urban area, provided the necessary data are available with the help of which the sites for new industries could be suggested, given the emission inventory. Studies on air pollution meteorology serve and aid the planners to initate remedial actions to bring down the levels of pollution and also to out—line the control strategy. In the present thesis some theoretical studies on air pollution meteorology over South India are made. The thesis is divided into six chapters

Loop bifurcation and magnetization rotation in exchange-biased Ni/FeF2

Exchange-biased Ni/FeF2 films have been investigated using vector coil vibrating-sample magnetometry as a function of the cooling field strength HFC . In films with epitaxial FeF2 , a loop bifurcation develops with increasing HFC as it divides into two sub-loops shifted oppositely from zero field by the same amount. The positively biased sub-loop grows in size with HFC until only a single positively shifted loop is found. Throughout this process, the negative and positive (sub)loop shifts maintain the same discrete value. This is in sharp contrast to films with twinned FeF2 where the exchange field gradually changes with increasing HFC . The transverse magnetization shows clear correlations with the longitudinal subloops. Interestingly, over 85% of the Ni reverses its magnetization by rotation, either in one step or through two successive rotations. These results are due to the single-crystal nature of the antiferromagnetic FeF2 , which breaks down into two opposite regions of large domains.

RNS based programmable multi-mode decimation filter for WCDMA and WiMAX

The recent trends envisage multi-standard architectures as a promising solution for the future wireless transceivers to attain higher system capacities and data rates. The computationally intensive decimation filter plays an important role in channel selection for multi-mode systems. An efficient reconfigurable implementation is a key to achieve low power consumption. To this end, this paper presents a dual-mode Residue Number System (RNS) based decimation filter which can be programmed for WCDMA and 802.16e standards. Decimation is done using multistage, multirate finite impulse response (FIR) filters. These FIR filters implemented in RNS domain offers high speed because of its carry free operation on smaller residues in parallel channels. Also, the FIR filters exhibit programmability to a selected standard by reconfiguring the hardware architecture. The total area is increased only by 24% to include WiMAX compared to a single mode WCDMA transceiver. In each mode, the unused parts of the overall architecture is powered down and bypassed to attain power saving. The performance of the proposed decimation filter in terms of critical path delay and area are tabulated.

Influence Of Additives On The Drain Down Characteristics Of Stone Matrix Asphalt Mixtures

In Kerala highways, where traditional dense graded mixtures are used for the surface courses, major distress is due to moisture induced damages. Development of stabilized Stone Matrix Asphalt (SMA) mixtures for improved pavement performance has been the focus of research all over the world for the past few decades. Many successful attempts are made to stabilize SMA mixtures with synthetic fibres and polymers. India, being an agricultural economy produces fairly huge quantity of natural fibres such as coconut, sisal, banana, sugar cane, jute etc.. Now- a -days the disposal of waste plastics is a major concern for an eco- friendly sustainable environment. This paper focuses on the influence of additives like coir, sisal, banana fibres (natural fibres), waste plastics (waste material) and polypropylene (polymer) on the drain down characteristics of SMA mixtures. A preliminary investigation is conducted to characterize the materials used in this study. Drain down sensitivity tests are conducted to study the bleeding phenomena and drain down of SMA mixtures. Based on the drain down characteristics of the various stabilized mixtures it is inferred that the optimum fibre content is 0.3% by weight of mixture for all fibre mixtures irrespective of the type of fibre. For waste plastics and polypropylene stabilized SMA mixtures, the optimum additive contents are respectively 7% and 5% by weight of mixture. Due to the absorptive nature of fibres, fibre stabilizers are found to be more effective in reducing the drain down of the SMA mixture. The drain values for the waste plastics mix is within the required specification range. The coir fibre additive is the best among the fibres investigated. Sisal and banana fibre mixtures showed almost the same characteristics on stabilization.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems