nÃo hà / A periodontite agressiva representa um tipo de doenÃa periodontal inflamatÃria que, embora rara, geralmente acomete indivÃduos em idade precoce e à caracterizada pela destruiÃÃo rÃpida e debilitante do periodonto de suporte. A patogÃnese desta doenÃa vincula-se a um fator etiolÃgico primÃrio, o biofilme dental e tambÃm à resposta inflamatÃria do hospedeiro susceptÃvel à agressÃo bacteriana. O objetivo deste estudo foi avaliar o efeito do tratamento a curto prazo com etoricoxib como adjuvante à terapia de raspagem e alisamento radicular (RAR) sobre os nÃveis de PGE2 e sobre os parÃmetros clÃnicos e radiogrÃficos em pacientes portadores de periodontite agressiva. Os pacientes foram randomicamente alocados para TESTE e CONTROLE (n=10 em cada grupo) e submetidos ao tratamento com etoricoxib (120 mg/dia) ou placebo durante 7 dias. Profundidade de sondagem (PS), nÃvel de inserÃÃo clÃnica (NIC), recessÃo gengival (RG), Ãndice de placa visÃvel (IP), sangramento à sondagem (SS), mensuraÃÃo da distÃncia linear (DL) e anÃlise dos nÃveis de cinza (NC) foram registrados antes e um mÃs apÃs a instituiÃÃo das terapias. A anÃlise visual por vÃdeo e por negatoscÃpio bem como a anÃlise por subtraÃÃo radiogrÃfica digital foram feitas no inÃcio e ao final do perÃodo experimental. A dosagem de PGE2 no fluido crevicular gengival (FCG) foi avaliada por radioimunoensaio no inÃcio, com 7 dias apÃs o inÃcio dos tratamentos e 30 dias apÃs a finalizaÃÃo dos mesmos. Ao final do perÃodo experimental, nÃo foram observadas diferenÃas estatisticamente significantes entre os grupos em relaÃÃo aos parÃmetros clÃnicos, embora ambos os grupos tenham apresentado melhora significativa em todas as variÃveis avaliadas. Houve um decrÃscimo no NIC de 5,54Â0,47 mm para 3,59Â0,53 mm no grupo TESTE e de 5,92Â1,10 para 3,69Â0,80 mm no grupo CONTROLE. Uma significativa reduÃÃo nos nÃveis de PGE2 foi observada apÃs 7 dias de tratamento. A mensuraÃÃo da DL evidenciou diferenÃa entre os grupos. Em conclusÃo, etoricoxib nÃo foi capaz de promover benefÃcio adicional nos parÃmetros clÃnicos, contudo, promoveu reduÃÃo inicial nos nÃveis de PGE2 e discreta melhora na condiÃÃo Ãssea. / Aggressive periodontitis is an inflammatory type of periodontal disease which, although rare, generally affects individuals at an early age and is characterized by the rapid and debilitating destruction of the support periodontium. The pathogenesis of this pathology is related to primary etiologic factor, dental biofilm, associated to the immunoinflammatory response of susceptible host to this aggression. The purpose of this study was to assess the effect of short duration treatment with etoricoxib as adjuvant therapy to scaling and root planing (SRP) on prostaglandin E2 (PGE2) levels and the clinical and radiographic parameters in aggressive periodontitis. Patients were randomly allocated to TEST or CONTROL (N=10 in each group) and submitted to SRP and treatment with 120 mg/day of etoricoxib or placebo during 7 days. Probing depth (PD), clinical attachment level (CAL), gingival recession (GR), visible plaque index (VPI), bleeding on probing (BOP), measurement of the linear distance (LD) and analysis of the gray levels (GL) were recorded before and one month after the therapies. The visual analysis from video and negatoscope and digital subtraction radiographic was made in beginning and in the final experimental period (30 days). The PGE2 dosage in the gingival crevicular fluid (GCF) was measured by radioimmunoassay at the beginning, and 7 and 30 days afterwards. No significant difference was observed between the groups in the clinical parameters at the end of the experimental period, although both groups presented significant improvement in all the variables examined. There was a decrease in CAL from 5.54Â0.47 mm to 3.59Â0.53 mm in the TEST group and from 5.92Â1.10 to 3.69Â0.80 mm in the CONTROL group. A significant reduction in PGE2 was found after 7 days of treatment. The LD measurement was shown to differ between the groups. In conclusion, etoricoxib did not promote additional improvement in the clinical parameters, however it produced an initial reduction in the PGE2 levels in the GCF, which could be related to the discrete improvement in the bone condition.

The effects of scaling a cavity with respect to a fixed incoming boundary layer thickness on its flow dynamics and control was studied experimentally. Three cavity models with constant length-to-depth ratio of 5 and length-to-width ratio of 2 and with corresponding linear dimensions in the ratio 0.5 : 1 : 2 were tested at freestream Mach number 0.71. Additionally, the 0.5 and 1 scale models were tested at freestream Mach number of 0.85. The experiments involved timeaveraged pressure measurements, unsteady pressure measurements, and PIV measurements. Time-averaged pressure measurements made at the floor were used to study the ’flow-type’ of the cavities. Unsteady pressure measurements were used to study the acoustic characteristics of the cavity. The cavity length-to-boundary layer thickness ratios tested were 10, 20 and 40. The Cp distribution on the clean cavities indicated a change in the cavity flowtype with change in the cavity scale. Varying the L/δ from 10 to 40 changed the cavity flow-type from open to transitional. Analysis of the frequency spectra of the cavity revealed an increase in tonal amplitudes and OASPL with increasing L/δ . The PIV measurements indicated that this could be caused by an increase in energy exchange between the freestream and the cavity. The velocity magnitudes inside the cavities were found to increase with increase in L/δ . A comparative study of different passive control methods on the largest cavity showed that leading-edge spoilers were superior in cavity tone suppression. Of these, the effectiveness of a sawtooth spoiler on the three cavities of different scales was tested. The results showed that while the spoiler was effective in eliminating tones and suppression of noise for the smaller cavities, it was unable to eliminate the tones completely for the largest cavity. To find the correct method for scaling the spoilers with the cavity dimensions, different spoiler heights were tested on the three cavities. The results showed that the cavity noise suppression for a given cavity attains saturation level at a particular spoiler height, called the critical spoiler height. An increase in spoiler height beyond the critical spoiler height was found to have no effect on the noise suppression. It is also found that this critical spoiler height can be scaled with the length of the cavity (for given L/D, M and spoiler profile) irrespective of the boundary layer thickness.

Judging goodness of fit in multidimensional scaling requires a comprehensive set of diagnostic tools instead of relying on stress rules of thumb. This article elaborates on corresponding strategies and gives practical guidelines for researchers to obtain a clear picture of the goodness of fit of a solution. Special emphasis will be placed on the use of permutation tests. The second part of the article focuses on goodness-of-fit assessment of an important variant of multidimensional scaling called unfolding, which can be applied to a broad range of psychological data settings. Two real-life data sets are presented in order to walk the reader through the entire set of diagnostic measures, tests, and plots. R code is provided as supplementary information that makes the whole goodness-of-fit assessment workflow, as presented in this article, fully reproducible.

Weigh scaling is a method of estimating the total volume of timber harvested from a given region. The implementation of statistical sampling techniques in weigh scaling is described, along with related issues. A review of ratio estimators, along with variance estimators of the classical ratio estimator is conducted. The estimation of the variance of the estimated total volume is considered using jackknife- and bootstrap-based variance estimators. Weighted versions of the jackknife and bootstrap variance estimators are derived using influence functions and Fisher Information matrices. Empirical studies of analytic and resampling-based variance estimators are conducted, with particular emphasis on small sample properties and on robustness with respect to both the homoscedastic variance and zero-intercept population characteristics. With a squared error loss function, the resampling-based variance estimators are shown to perform very well at all sample sizes in finite populations with normally distributed errors. These estimators are found to have small negative biases for small sample sizes and to be robust with respect to heteroscedasticity. / Science, Faculty of / Statistics, Department of / Graduate

abstract: Universal biology is an important astrobiological concept, specifically for the search for life beyond Earth. Over 1.2 million species have been identified on Earth, yet all life partakes in certain processes, such as homeostasis and replication. Furthermore, several aspects of biochemistry on Earth are thought to be universal, such as the use of organic macromolecules like proteins and nucleic acids. The presence of many biochemical features in empirical data, however, has never been thoroughly investigated. Moreover, the ability to generalize universal features of Earth biology to other worlds suffers from the epistemic problem of induction. Systems biology approaches offer means to quantify abstract patterns in living systems which can more readily be extended beyond Earth’s familiar planetary context. In particular, scaling laws, which characterize how a system responds to changes in size, have met with prior success in investigating universal biology. This thesis rigorously tests the hypothesis that biochemistry is universal across life on Earth. The study collects enzyme data for annotated archaeal, bacterial, and eukaryotic genomes, in addition to metagenomes. This approach allows one to quantitatively define a biochemical system and sample across known biochemical diversity, while simultaneously exploring enzyme class scaling at both the level of both individual organisms and ecosystems. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Joint Genome Institute’s Integrated Microbial Genomes and Microbiomes (JGI IMG/M) database, this thesis performs the largest comparative analysis of microbial enzyme content and biochemistry to date. In doing so, this thesis quantitatively explores the distribution of enzyme classes on Earth and adds constraints to notions of universal biochemistry on Earth. / Dissertation/Thesis / Masters Thesis Geological Sciences 2020

No description available.

Regenerative braking in conjunction with friction braking represents one of the most common braking strategies employed in electric and hybrid vehicles. As the friction brake accounts for about 5 to 85% of total braking in these “blended scenarios”, this warrants for more research into the development of new lightweight, wear resistant, and sustainable friction materials and also for reviewing the existing testing procedures.This research primarily focuses on generation of new knowledge related to development of environmentally friendly, lightweight friction material for vehicles with regenerative braking systems. A sample formulation was initially developed from knowledge gained by literature review and analysis of existing commercially available brake linings. Design of experiments based on Taguchi method and other statistical analysis tools were used to optimize the sample formulation. The density, porosity, shore D hardness, thermal stability, damping capacity of these pads were measured and compared with commercially available friction material. Pads were tested against commercially available rotors (ASTM A48 C30 gray cast iron) coated with ceramic material. Friction performance tests were completed using Universal Mechanical Tester (Tribolab by Bruker) and the scaled-down ISO SAE J2522 procedure. Friction surfaces of pads and rotors were analyzed in order to understand their surface chemistry and morphology, as well as their impact on performance of the tested friction material. The final optimized pad based on design of experiments (DOE 9) showed high and stable friction levels in high temperature sections (Section 9, Section 12.2 and Section 14 in ISO/SAE J2522 testing procedure) and excellent recovery capabilities at lower temperatures (Section 10, Section 13 and Section 15). This pad material was compatible with coated rotors, had low wear, and meets all the industry specifications and standards. SEM and EDX analysis showed that the pads developed stable friction layer on the friction surfaces and this capacity to develop friction material contributed towards overall friction stability of the material.

A new parallel solver based on SPIKE-TA algorithm has been developed using OpenMP API for solving diagonally-dominant banded linear systems on shared memory architectures. The results of the numerical experiments carried out for different test cases demonstrate high-performance and scalability on current multi-core platforms and highlight the time savings that SPIKE-TA OpenMP offers in comparison to the LAPACK BLAS-threaded LU model. By exploiting algorithmic parallelism in addition to threaded implementation, we obtain greater speed-ups in contrast to the threaded versions of sequential algorithms. For non-diagonally dominant systems, we implement the SPIKE-RL scheme and a new Spike-calling-Spike (SCS) scheme using OpenMP. The timing results for solving the non-diagonally dominant systems using SPIKE-RL show extremely good scaling in comparison to LAPACK and modified banded-primitive library.

Cloud computing and containerization has been on the rise in recent years and have become important areas of research and development in the field of computer science. One of the challenges in distributed and cloud computing is to predict the resource utilization of the nodes that run the applications and services. This is especially relevant for container-based platforms such as Kubernetes. Predicting the resource utilization of a Kubernetes cluster can help optimize the performance, reliability, and cost-effectiveness of the platform. This thesis focuses on how well different resources in a cluster can be predicted using machine learning techniques. The approach consists of 3 main steps: data collection, data extraction and pre-processing, and data analysis. The data collection step involves stressing the system with a load-generator called Locust and collecting data from Locust and collecting data from Kubernetes with the use of Prometheus. The data pre-processing and extraction step involves extracting relevant data and transforming it into a suitable format for the machine learning models. The final step involves applying different machine learning models to the data and evaluating their accuracy. The results of this thesis illustrate that machine learning can work well for predicting resources in a cluster based on how stressed the system is and that the best performing machine learning model tested was Support Vector Machine with a polynomial kernel. / Cloud computing och containerisering har ökat de senaste åren och har blivit viktiga områden för forskning och utveckling inom datavetenskap. En av utmaningarna inom distribuerad och cloud computing är att förutsäga resursutnyttjandet av de noder som kör applikationerna och tjänsterna. Detta är särskilt relevant för containerbaserade plattformar som Kubernetes. Att förutsäga resursutnyttjandet av ett Kubernetes-kluster kan hjälpa med att optimera plattformens prestanda, tillförlitlighet och kostnadseffektivitet. Denna avhandling fokuserar på hur väl olika resurser i ett kluster kan förutsägas med hjälp av maskininlärningstekniker. Tillvägagångssättet består av 3 huvudsteg: datainsamling, dataextraktion och för-processering, samt dataanalys. Datainsamlingssteget innebär att stressa systemet med en load-generator som heter Locust och samla in data från Locust och även samla in data från Kubernetes med hjälp av Prometheus. Steget för för-processering och extrahering av data innefattar att extrahera relevant data och omvandla den till ett lämpligt format för maskininlärningsmodellerna. Det sista steget innefattar att tillämpa olika maskininlärningsmodeller på data och utvärdera deras noggrannhet. Resultaten av denna avhandling demonstrerar att maskininlärning kan fungera bra för att förutsäga resurser i ett kluster baserat på hur stressat systemet är och att den bäst presterande maskininlärningsmodellen som testades var Support Vector Machine med en polynom-kernel.

No description available.