Modelling of Ultraviolet and Vissible Emissions in Comets

Comets are the spectacular objects in the night sky since the dawn of mankind. Due to their giant apparitions and enigmatic behavior, followed by coincidental calamities, they were termed as notorious and called as `bad omens'. With a systematic study of these objects modern scienti c community understood that these objects are part of our solar system. Comets are believed to be remnant bodies of at the end of evolution of solar system and possess the material of solar nebula. Hence, these are considered as most pristine objects which can provide the information about the conditions of solar nebula. These are small bodies of our solar system, with a typical size of about a kilometer to a few tens of kilometers orbiting the Sun in highly elliptical orbits. The solid body of a comet is nucleus which is a conglomerated mixture of water ice, dust and some other gases. When the cometary nucleus advances towards the Sun in its orbit the ices sublimates and produces the gaseous envelope around the nucleus which is called coma. The gravity of cometary nucleus is very small and hence can not in uence the motion of gases in the cometary coma. Though the cometary nucleus is a few kilometers in size they can produce a transient, extensive, and expanding atmosphere with size several orders of magnitude larger in space. By ejecting gas and dust into space comets became the most active members of the solar system. The solar radiation and the solar wind in uences the motion of dust and ions and produces dust and ion tails, respectively. Comets have been observed in di erent spectral regions from rocket, ground and space borne optical instruments. The observed emission intensities are used to quantify the chemical abundances of di erent species in the comets. The study of various physical and chemical processes that govern these emissions is essential before estimating chemical abundances in the coma. Cameron band emission of CO molecule has been used to derive CO2 abundance in the comets based on the assumption that photodissociation of CO2 mainly produces these emissions. Similarly, the atomic oxygen visible emissions have been used to probe H2O in the cometary coma. The observed green ([OI] 5577 A) to red-doublet emission ([OI] 6300 and 6364 A) ratio has been used to con rm H2O as the parent species of these emissions. In this thesis a model is developed to understand the photochemistry of these emissions and applied to several comets. The model calculated emission intensities are compared with the observations done by space borne instruments like International Ultraviolet Explorer (IUE) and Hubble Space Telescope (HST) and also by various ground based telescopes.

Diversity of condensed tannin structures affects rumen in vitro methane production in sainfoin (Onobrychis viciifolia) accessions

Sainfoin is a non-bloating temperate forage legume with a moderate-to-high condensed tannin (CT) content. This study investigated whether the diversity of sainfoin accessions in terms of CT structures and contents could be related to rumen in vitro gas and methane (CH4) production and fermentation characteristics. The aim was to identify promising accessions for future investigations. Accessions differed (P < 0·0001) in terms of total gas and CH4 productions. Fermentation kinetics (i.e. parameters describing the shape of the gas production curve and half-time gas production) for CH4 production were influenced by accession (P ≤ 0·038), but not by PEG. Accession, PEG and time affected (P < 0·001) CH4 production, but accession and PEG interaction showed only a tendency (P = 0·08). Increase in CH4 due to PEG addition was not related to CT content. Further analysis of the relationships among multiple traits (nutritional composition, CT structure and CH4 production) using principal component analysis (PCA) based on optimally weighted variables revealed differences among accessions. The first two principal component axes, PC1 (57·6%) and PC2 (18·4%), explained 76·0% of the total variation among accessions. Loading of biplots derived from both PCAs made it possible to establish a relationship between the ratio of prodelphinidin:procyanidin (PD:PC) tannins and CH4 production in some accessions. The PD:PC ratio seems to be an important source of variation that is negatively related to CH4 production. These results suggested that sainfoin accessions collected from across the world exhibited substantial variation in terms of their effects on rumen in vitro CH4 production, revealing some promising accessions for future investigations.

Probabilistic neural network to predict cracks in taphole mud used in blast furnaces

One of the major problems facing Blast Furnaces is the occurrence of cracks in taphole mud, as the underlying causes are not easily identifiable. The absence of this knowledge makes it difficult the use of conventional techniques for predictability and mitigation. This paper will address the application of Probabilistic Neural Network using the Matlab software as a means to detect and control such cracks. The most relevant BF operational variables were picked through the statistic tool "Principal Component Analysis - PCA." Based upon the selection of these variables a probabilistic neural network was built. A set of BF operational data, consisting of 30 controlling variables, was divided into 2 groups, one of which for network training, and the other one to validate the neural network. The neural network got 98% of the cases right. The results show the effectiveness of this tool for crack prediction in relation to clay intrinsic properties and as a result of the fluctuation in operational variables.

Modelizzazione spazio-temporale delle dinamiche di uso del suolo ed analisi comparativa di differenti approcci predittivi. Uso integrato di SIG e telerilevamento nello studio dei processi di deforestazione nella regione La Joyanca in Peten, Guatemala

L’uso frequente dei modelli predittivi per l’analisi di sistemi complessi, naturali o artificiali, sta cambiando il tradizionale approccio alle problematiche ambientali e di rischio. Il continuo miglioramento delle capacità di elaborazione dei computer facilita l’utilizzo e la risoluzione di metodi numerici basati su una discretizzazione spazio-temporale che permette una modellizzazione predittiva di sistemi reali complessi, riproducendo l’evoluzione dei loro patterns spaziali ed calcolando il grado di precisione della simulazione. In questa tesi presentiamo una applicazione di differenti metodi predittivi (Geomatico, Reti Neurali, Land Cover Modeler e Dinamica EGO) in un’area test del Petén, Guatemala. Durante gli ultimi decenni questa regione, inclusa nella Riserva di Biosfera Maya, ha conosciuto una rapida crescita demografica ed un’incontrollata pressione sulle sue risorse naturali. L’area test puó essere suddivisa in sotto-regioni caratterizzate da differenti dinamiche di uso del suolo. Comprendere e quantificare queste differenze permette una migliore approssimazione del sistema reale; é inoltre necessario integrare tutti i parametri fisici e socio-economici, per una rappresentazione più completa della complessità dell’impatto antropico. Data l’assenza di informazioni dettagliate sull’area di studio, quasi tutti i dati sono stati ricavati dall’elaborazione di 11 immagini ETM+, TM e SPOT; abbiamo poi realizzato un’analisi multitemporale dei cambi uso del suolo passati e costruito l’input per alimentare i modelli predittivi. I dati del 1998 e 2000 sono stati usati per la fase di calibrazione per simulare i cambiamenti nella copertura terrestre del 2003, scelta come data di riferimento per la validazione dei risultati. Quest’ultima permette di evidenziare le qualità ed i limiti per ogni modello nelle differenti sub-regioni.

An evolutive approach for smile recognition in video sequences

Facial expression recognition is one of the most challenging research areas in the image recognition ¯eld and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human–machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future

Peak-to-Peak Output Current Ripple Analysis in Multiphase and Multilevel Inverters

Analysis of the peak-to-peak output current ripple amplitude for multiphase and multilevel inverters is presented in this PhD thesis. The current ripple is calculated on the basis of the alternating voltage component, and peak-to-peak value is defined by the current slopes and application times of the voltage levels in a switching period. Detailed analytical expressions of peak-to-peak current ripple distribution over a fundamental period are given as function of the modulation index. For all the cases, reference is made to centered and symmetrical switching patterns, generated either by carrier-based or space vector PWM. Starting from the definition and the analysis of the output current ripple in three-phase two-level inverters, the theoretical developments have been extended to the case of multiphase inverters, with emphasis on the five- and seven-phase inverters. The instantaneous current ripple is introduced for a generic balanced multiphase loads consisting of series RL impedance and ac back emf (RLE). Simplified and effective expressions to account for the maximum of the output current ripple have been defined. The peak-to-peak current ripple diagrams are presented and discussed. The analysis of the output current ripple has been extended also to multilevel inverters, specifically three-phase three-level inverters. Also in this case, the current ripple analysis is carried out for a balanced three-phase system consisting of series RL impedance and ac back emf (RLE), representing both motor loads and grid-connected applications. The peak-to-peak current ripple diagrams are presented and discussed. In addition, simulation and experimental results are carried out to prove the validity of the analytical developments in all the cases. The cases with different phase numbers and with different number of levels are compared among them, and some useful conclusions have been pointed out. Furthermore, some application examples are given.

Multi-antenna non-line-of-sight identification techniques for target localization in mobile ad-hoc networks

Target localization has a wide range of military and civilian applications in wireless mobile networks. Examples include battle-field surveillance, emergency 911 (E911), traffc alert, habitat monitoring, resource allocation, routing, and disaster mitigation. Basic localization techniques include time-of-arrival (TOA), direction-of-arrival (DOA) and received-signal strength (RSS) estimation. Techniques that are proposed based on TOA and DOA are very sensitive to the availability of Line-of-sight (LOS) which is the direct path between the transmitter and the receiver. If LOS is not available, TOA and DOA estimation errors create a large localization error. In order to reduce NLOS localization error, NLOS identifcation, mitigation, and localization techniques have been proposed. This research investigates NLOS identifcation for multiple antennas radio systems. The techniques proposed in the literature mainly use one antenna element to enable NLOS identifcation. When a single antenna is utilized, limited features of the wireless channel can be exploited to identify NLOS situations. However, in DOA-based wireless localization systems, multiple antenna elements are available. In addition, multiple antenna technology has been adopted in many widely used wireless systems such as wireless LAN 802.11n and WiMAX 802.16e which are good candidates for localization based services. In this work, the potential of spatial channel information for high performance NLOS identifcation is investigated. Considering narrowband multiple antenna wireless systems, two xvNLOS identifcation techniques are proposed. Here, the implementation of spatial correlation of channel coeffcients across antenna elements as a metric for NLOS identifcation is proposed. In order to obtain the spatial correlation, a new multi-input multi-output (MIMO) channel model based on rough surface theory is proposed. This model can be used to compute the spatial correlation between the antenna pair separated by any distance. In addition, a new NLOS identifcation technique that exploits the statistics of phase difference across two antenna elements is proposed. This technique assumes the phases received across two antenna elements are uncorrelated. This assumption is validated based on the well-known circular and elliptic scattering models. Next, it is proved that the channel Rician K-factor is a function of the phase difference variance. Exploiting Rician K-factor, techniques to identify NLOS scenarios are proposed. Considering wideband multiple antenna wireless systems which use MIMO-orthogonal frequency division multiplexing (OFDM) signaling, space-time-frequency channel correlation is exploited to attain NLOS identifcation in time-varying, frequency-selective and spaceselective radio channels. Novel NLOS identi?cation measures based on space, time and frequency channel correlation are proposed and their performances are evaluated. These measures represent a better NLOS identifcation performance compared to those that only use space, time or frequency.

Multi-Mode Tensor Representation of Motion Data

In this paper, we investigate how a multilinear model can be used to represent human motion data. Based on technical modes (referring to degrees of freedom and number of frames) and natural modes that typically appear in the context of a motion capture session (referring to actor, style, and repetition), the motion data is encoded in form of a high-order tensor. This tensor is then reduced by using N-mode singular value decomposition. Our experiments show that the reduced model approximates the original motion better then previously introduced PCA-based approaches. Furthermore, we discuss how the tensor representation may be used as a valuable tool for the synthesis of new motions.

Pretreatment Tables Predicting Pathologic Stage of Locally Advanced Prostate Cancer

BACKGROUND Pretreatment tables for the prediction of pathologic stage have been published and validated for localized prostate cancer (PCa). No such tables are available for locally advanced (cT3a) PCa. OBJECTIVE To construct tables predicting pathologic outcome after radical prostatectomy (RP) for patients with cT3a PCa with the aim to help guide treatment decisions in clinical practice. DESIGN, SETTING, AND PARTICIPANTS This was a multicenter retrospective cohort study including 759 consecutive patients with cT3a PCa treated with RP between 1987 and 2010. INTERVENTION Retropubic RP and pelvic lymphadenectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Patients were divided into pretreatment prostate-specific antigen (PSA) and biopsy Gleason score (GS) subgroups. These parameters were used to construct tables predicting pathologic outcome and the presence of positive lymph nodes (LNs) after RP for cT3a PCa using ordinal logistic regression. RESULTS AND LIMITATIONS In the model predicting pathologic outcome, the main effects of biopsy GS and pretreatment PSA were significant. A higher GS and/or higher PSA level was associated with a more unfavorable pathologic outcome. The validation procedure, using a repeated split-sample method, showed good predictive ability. Regression analysis also showed an increasing probability of positive LNs with increasing PSA levels and/or higher GS. Limitations of the study are the retrospective design and the long study period. CONCLUSIONS These novel tables predict pathologic stage after RP for patients with cT3a PCa based on pretreatment PSA level and biopsy GS. They can be used to guide decision making in men with locally advanced PCa. PATIENT SUMMARY Our study might provide physicians with a useful tool to predict pathologic stage in locally advanced prostate cancer that might help select patients who may need multimodal treatment.

Stratification of high-risk prostate cancer into prognostic categories: a European multi-institutional study

BACKGROUND High-risk prostate cancer (PCa) is an extremely heterogeneous disease. A clear definition of prognostic subgroups is mandatory. OBJECTIVE To develop a pretreatment prognostic model for PCa-specific survival (PCSS) in high-risk PCa based on combinations of unfavorable risk factors. DESIGN, SETTING, AND PARTICIPANTS We conducted a retrospective multicenter cohort study including 1360 consecutive patients with high-risk PCa treated at eight European high-volume centers. INTERVENTION Retropubic radical prostatectomy with pelvic lymphadenectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Two Cox multivariable regression models were constructed to predict PCSS as a function of dichotomization of clinical stage (< cT3 vs cT3-4), Gleason score (GS) (2-7 vs 8-10), and prostate-specific antigen (PSA; ≤ 20 ng/ml vs > 20 ng/ml). The first "extended" model includes all seven possible combinations; the second "simplified" model includes three subgroups: a good prognosis subgroup (one single high-risk factor); an intermediate prognosis subgroup (PSA >20 ng/ml and stage cT3-4); and a poor prognosis subgroup (GS 8-10 in combination with at least one other high-risk factor). The predictive accuracy of the models was summarized and compared. Survival estimates and clinical and pathologic outcomes were compared between the three subgroups. RESULTS AND LIMITATIONS The simplified model yielded an R(2) of 33% with a 5-yr area under the curve (AUC) of 0.70 with no significant loss of predictive accuracy compared with the extended model (R(2): 34%; AUC: 0.71). The 5- and 10-yr PCSS rates were 98.7% and 95.4%, 96.5% and 88.3%, 88.8% and 79.7%, for the good, intermediate, and poor prognosis subgroups, respectively (p = 0.0003). Overall survival, clinical progression-free survival, and histopathologic outcomes significantly worsened in a stepwise fashion from the good to the poor prognosis subgroups. Limitations of the study are the retrospective design and the long study period. CONCLUSIONS This study presents an intuitive and easy-to-use stratification of high-risk PCa into three prognostic subgroups. The model is useful for counseling and decision making in the pretreatment setting.

Low Voltage Ride-through in DFIG Wind Generators by Controlling the Rotor Current without Crowbars

Among all the different types of electric wind generators, those that are based on doubly fed induction generators, or DFIG technology, are the most vulnerable to grid faults such as voltage sags. This paper proposes a new control strategy for this type of wind generator, that allows these devices to withstand the effects of a voltage sag while following the new requirements imposed by grid operators. This new control strategy makes the use of complementary devices such as crowbars unnecessary, as it greatly reduces the value of currents originated by the fault. This ensures less costly designs for the rotor systems as well as a more economic sizing of the necessary power electronics. The strategy described here uses an electric generator model based on space-phasor theory that provides a direct control over the position of the rotor magnetic flux. Controlling the rotor magnetic flux has a direct influence on the rest of the electrical variables enabling the machine to evolve to a desired work point during the transient imposed by the grid disturbance. Simulation studies have been carried out, as well as test bench trials, in order to prove the viability and functionality of the proposed control strategy.

Debonding identification in FRP-plated RC structures using PZT sensors

The application of the Electro-Mechanical Impedance (EMI) method for damage detection in Structural Health Monitoring has noticeable increased in recent years. EMI method utilizes piezoelectric transducers for directly measuring the mechanical properties of the host structure, obtaining the so called impedance measurement, highly influenced by the variations of dynamic parameters of the structure. These measurements usually contain a large number of frequency points, as well as a high number of dimensions, since each frequency range swept can be considered as an independent variable. That makes this kind of data hard to handle, increasing the computational costs and being substantially time-consuming. In that sense, the Principal Component Analysis (PCA)-based data compression has been employed in this work, in order to enhance the analysis capability of the raw data. Furthermore, a Support Vector Machine (SVM), which has been widespread used in machine learning and pattern recognition fields, has been applied in this study in order to model any possible existing pattern in the PCAcompress data, using for that just the first two Principal Components. Different known non-damaged and damaged measurements of an experimental tested beam were used as training input data for the SVM algorithm, using as test input data the same amount of cases measured in beams with unknown structural health conditions. Thus, the purpose of this work is to demonstrate how, with a few impedance measurements of a beam as raw data, its healthy status can be determined based on pattern recognition procedures.

Global color estimation of special-effect coatings from measurements by commercially available portable multiangle spectrophotometers

Colors of special-effect coatings have strong dependence on illumination/viewing geometry and an appealing appearance. An open question is to ask about the minimum number of measurement geometries required to completely characterize their observed color shift. A recently published principal components analysis (PCA)-based procedure to estimate the color of special-effect coatings at any geometry from measurements at a reduced set of geometries was tested in this work by using the measurement geometries of commercial portable multiangle spectrophotometers X-Rite MA98, Datacolor FX10, and BYK-mac as reduced sets. The performance of the proposed PCA procedure for the color-shift estimation for these commercial geometries has been examined for 15 special-effect coatings. Our results suggest that for rendering the color appearance of 3D objects covered with special-effect coatings, the color accuracy obtained with this procedure may be sufficient. This is the case especially if geometries of X-Rite MA98 or Datacolor FX10 are used.

Caractérisation de l’activité Nur77 et de ses complexes en essais BRET par complémentation

Dans le système nerveux central, la dopamine joue un rôle crucial dans de nombreuses fonctions physiologiques telles que : l’apprentissage, le mouvement volontaire, la motivation, la cognition et la production hormonale. Il a été aussi démontré que le système de signalisation dopaminergique est altéré dans plusieurs maladies neurologiques et psychiatriques comme la maladie de Parkinson et la schizophrénie. Des études, effectuées dans le laboratoire du Dr.Daniel Lévesque (laboratoire d’accueil), ont montré que les récepteurs nucléaires Nur77 (NR4A1, NGFI-B) et RXRγ (retinoid X receptors γ) sont impliqués dans la régulation des effets de la dopamine dans le système nerveux central. De plus, ces données suggèrent que le complexe Nur77 et RXR joueraient un rôle crucial dans l’effet des médicaments antipsychotiques et antiparkinsoniens. Toutefois, très peu de médicaments ciblant Nur77 ont été identifiés à ce jour et les médicaments agissant sur RXRγ restent mal caractérisés. En outre, les analyses actuellement disponibles ne peuvent pas résumer la complexité des activités des NRs et génèrent des mesures indirectes des activités des drogues. Afin de mieux comprendre comment est régulée l’interaction Nur77/RXRγ dans ces processus, mon projet a été de mettre au point un essai BRET (Bioluminescence Resonance Energy Transfer) et PCA-BRET (Protein Complementation Assay-BRET) basé sur le recrutement d'un motif mimant un co-activateur fusionné avec la YFP. Nos différents essais ont été validés par courbes dose-réponse en utilisant différents composés RXR . Les EC50 (concentration efficace médiane, qui permet de mesurer l'efficacité d'un composé) obtenues étaient très semblables aux valeurs précédemment rapportées dans la littérature. Nous avons aussi pu identifier un composé le SR11237 (BMS649) qui semble posséder une sélectivité pour le complexe Nur77/RXRγ par rapport aux complexes Nurr1/RXRγ et RXRγ /RXRγ. Nos résultats indiquent que ces essais de BRET peuvent être utilisés pour évaluer la sélectivité de nouveaux composés pour les complexes Nur77/RXRγ, Nurr1/RXRγ et RXRγ /RXRγ. Un autre aspect de mon projet de doctorat a été de mettre en évidence par BRET l’importance de la SUMOylation dans la régulation de l'activité de Nur77 dans sa forme monomèrique, homodimèrique et hétérodimèrique. Nous avons ainsi identifié que Nur77 recrute principalement SUMO2 sur sa lysine 577. Il est intéressant de noté que le recrutement de la SUMO2 à Nur77 est potentialisé en présence de la SUMO E3 Ligase PIASγ. Aussi, la perte de la SUMOylation sur la lysine 577 entraîne l'incapacité de Nur77 de recruter divers motifs de co-activation mais pas pour ses formes homo- et hétérodimèrique. Cependant, la présence de PIASγ ne potentialise pas le recrutement du co-activateur, suggérant que cette SUMO E3 Ligase est seulement impliqué dans le processus de recrutement de la SUMO mais pas dans celui du co-activateur. Nous avons ainsi déterminé une nouvelle modification post-traductionnelle sur Nur77 régulant spécifiquement son activité monomérique Ces projets pourraient donc apporter de nouvelles données cruciales pour l’amélioration du traitement de la maladie de Parkinson ou de la schizophrénie, ainsi que d'obtenir une meilleure compréhension sur les mécanismes permettant la régulation de la fonction de Nur77

Neuropathological heterogeneity in frontotemporal lobar degeneration with TDP-43 proteinopathy: a quantitative study of 94 cases using principal components analysis

Studies suggest that frontotemporal lobar degeneration with transactive response (TAR) DNA-binding protein of 43kDa (TDP-43) proteinopathy (FTLD-TDP) is heterogeneous with division into four or five subtypes. To determine the degree of heterogeneity and the validity of the subtypes, we studied neuropathological variation within the frontal and temporal lobes of 94 cases of FTLD-TDP using quantitative estimates of density and principal components analysis (PCA). A PCA based on the density of TDP-43 immunoreactive neuronal cytoplasmic inclusions (NCI), oligodendroglial inclusions (GI), neuronal intranuclear inclusions (NII), and dystrophic neurites (DN), surviving neurons, enlarged neurons (EN), and vacuolation suggested that cases were not segregated into distinct subtypes. Variation in the density of the vacuoles was the greatest source of variation between cases. A PCA based on TDP-43 pathology alone suggested that cases of FTLD-TDP with progranulin (GRN) mutation segregated to some degree. The pathological phenotype of all four subtypes overlapped but subtypes 1 and 4 were the most distinctive. Cases with coexisting motor neuron disease (MND) or hippocampal sclerosis (HS) also appeared to segregate to some extent. We suggest: 1) pathological variation in FTLD-TDP is best described as a ‘continuum’ without clearly distinct subtypes, 2) vacuolation was the single greatest source of variation and reflects the ‘stage’ of the disease, and 3) within the FTLD-TDP ‘continuum’ cases with GRN mutation and with coexisting MND or HS may have a more distinctive pathology.