Avaliação microtomográfica de canais mesiais de molares inferiores preparados com sistemas rotatórios e reciprocantes

O objetivo do presente trabalho foi avaliar, em microtomografia computadorizada (CT), o preparo de canais de molares inferiores com diferentes sistemas acionados a motor. Foram selecionadas 58 raízes mesiais patentes, de diâmetro anatômico correspondente a #10. Para a avaliação em TC, um anel de aço inoxidável foi confeccionado nos moldes do suporte para amostra do microtomógrafo, para que servisse de fôrma para a inclusão das raízes em resina Duralay, a fim de padronizar a posição do espécime no escaneamento inicial e final. Os canais foram preparados com os sistemas Reciproc R25 (n=16); WaveOne Primary File (n=16); Twisted File (n=14), e HyFlex (n=12). Após serem escaneados, foram reconstruídos tridimensionalmente e avaliados quantitativamente quanto à variação de volume (mm3), área de superfície (mm2) e structure model index (SMI). Foi, ainda, realizada a avaliação qualitativa das seções transversais por terço e por quadrante (MV, ML, DV, DL), sendo avaliado o toque de paredes. Os dados paramétricos foram analisados estatisticamente pelos testes ANOVA e t para amostras pareadas (α=5%). Não foi observada diferença estatística nos parâmetros quantitativos avaliados para Reciproc (142,77 76,75; 42,22 19,22; e 14,68 17,69, respectivamente); WaveOne (105,09 64,82; 29,54 19,21; 14,81 9,10, respectivamente); Twisted File (111,83 43,09; 33,31 18,40; 9,16 6,57, respectivamente), e HyFlex (151,74 149,37; 43,08 41,44; 10,80 8,52, respectivamente) (p=0,423). Dentro de cada grupo, foi observada diferença significante entre os resultados pré e pós-operatórios. O teste não paramétrico de Kruskal Wallis foi aplicado para a avaliação relativa ao toque de paredes. Foi observado que o sistema HyFlex apresentou a maior porcentagem de toques (82,3 13,1), seguido por Reciproc (81,3 16,9), Twisted File (78,3 14,4) e, por fim, WaveOne (76,9 21,7) (p>0,05). Em relação aos terços não foi observada diferença significativa (p=0,424). Os resultados da avaliação dos quadrantes intergrupo não demonstraram diferenças, porém indicaram tendência do preparo em direção à parede distal no terço cervical. Ao final, pôde-se concluir que os sistemas testados se equivalem quanto ao preparo de canais mesiais de molares inferiores; porém, nenhuma das técnicas foi capaz de tocar completamente em todas as paredes do canal radicular.

Caracterização mecânica e tribológica de dois glazeadores disponíveis comercialmente e uma composição experimental

O objetivo desse estudo, in vitro, foi avaliar através de testes mecânicos e tribológicos, a aplicação de dois glazeadores disponíveis comercialmente e uma composição experimental como material de cobertura em restaurações de resina composta com relação à rugosidade superficial, à dureza e à resistência ao desgaste. Foram confeccionados 24 corpos de prova (CP) do compósito Z350XT (3M/ESPE) e divididos em 4 grupos. O grupo controle (GC) não recebeu selamento, o grupo Biscover LV (GB) recebeu aplicação do Biscover LV (Bisco), o grupo Natural Glaze (GN) recebeu aplicação do Natural Glaze (Nova DFL) e o grupo Experimental (GE) recebeu aplicação de um glazeador experimental contendo nanopartículas (1% em peso). Posteriormente, os CP foram submetidos à análise da rugosidade superficial utilizando um perfilômetro e avaliação da dureza através de um nanoindentador, que fornece também o módulo elástico do material. Em seguida, os CP foram submetidos ao teste de desgaste linear alternado, durante 15.000 ciclos, com carga de 5N, em água destilada. A profundidade máxima de desgaste foi avaliada através de um perfilômetro. A análise dos dados relativos à rugosidade superficial (m) foi realizada utilizando ANOVA/Duncan (p-valor = 0,000). As médias e desvio padrão foram: GC-0,12(0,01); GB-0,06(0,01); GN-0,13(0,02); GE-0,13(0,01). A análise da dureza (GPa) e módulo elástico (GPa) foram avaliados aplicando o teste não-paramétrico de Kruskal-Wallis. As médias e desvio padrão para dureza foram: GC-1,10(0,24); GB-0,31(0,004); GN-0,08(0,004); GE-0,12(0,008) para carga de 1,25mN; GC-1,08(0,139); GB-0,32(0,004); GN-0,08(0,003); GE-0,13(0,006) para carga de 2,5mN; GC-1,10(0,101); GB-0,33(0,003); GN-0,09(0,002); GE-0,13(0,056) para carga de 5,0mN. As médias e desvio padrão para módulo elástico foram: GC-17,71(1,666); GB-5,44(0,084); GN-3,484(0,114); GE-4,55(0,178) para carga de 1,25mN; GC-17,5(1,449); GB-5,18(0,065); GN-3,38(0,078); GE-4,55(0,12) para carga de 2,5mN; GC-17,69(1,793); GB-5,04(0,041); GN-3,63(0,066); GE-4,85(0,104) para carga de 5,0mN. A análise dos dados relativos à profundidade de desgaste (m) foi realizada utilizando ANOVA/Dunnett (p-valor = 0,000). As médias e desvio padrão foram: GC-12,51(0,89); GB-0,59(0,07); GN-1,41(0,12); GE-1,84(0,18). A partir dos resultados apresentados pode-se concluir que apenas o Biscover LV foi capaz de reduzir a rugosidade superficial da resina composta testada. Os demais, Natural Glaze e Experimental, não alteraram a rugosidade superficial e foram estatisticamente semelhantes entre si e com o grupo controle. Todos os glazeadores testados reduziram a dureza e o módulo elástico da resina composta quando comparados com o grupo controle, diferindo entre si, apresentando uma ordem crescente de dureza e módulo elástico (Natural Glaze < Experimental < Biscover < Controle). Todos os glazeadores testados foram capazes de reduzir o desgaste da resina composta, quando comparados com o grupo controle, diferindo entre si, apresentado uma ordem crescente de desgaste (Biscover < Natural Glaze < Experimental < Controle).

Field applications of the second-generation Environmental Sample Processor (ESP) for remote detection of harmful algae: 2006-2007

We assess the application of the second-generation Environmental Sample Processor (ESP) for the detection of harmful algal bloom (HAB) species in field and laboratory settings using two molecular probe techniques: a sandwich hybridization assay (SHA) and fluorescent in situ hybridization (FISH). During spring 2006, the first time this new instrument was deployed, the ESP successfully automated application of DNA probe arrays for various HAB species and other planktonic taxa, but non-specific background binding on the SHA probe array support made results interpretation problematic. Following 2006, the DNA array support membrane that we were using was replaced with a different membrane, and the SHA chemistry was adjusted. The sensitivity and dynamic range of these modifications were assessed using 96-well plate and ESP array SHA formats for several HAB species found commonly in Monterey Bay over a range of concentrations; responses were significantly correlated (p < 0.01). Modified arrays were deployed in 2007. Compared to 2006, probe arrays showed improved signal:noise, and remote detection of various HAB species was demonstrated. We confirmed that the ESP and affiliated assays can detect HAB populations at levels below those posing human health concerns, and results can be related to prevailing environmental conditions in near real-time.

Vibration and stress analysis in the presence of structural uncertainty

At medium to high frequencies the dynamic response of a built-up engineering system, such as an automobile, can be sensitive to small random manufacturing imperfections. Ideally the statistics of the system response in the presence of these uncertainties should be computed at the design stage, but in practice this is an extremely difficult task. In this paper a brief review of the methods available for the analysis of systems with uncertainty is presented, and attention is then focused on two particular "non- parametric" methods: statistical energy analysis (SEA), and the hybrid method. The main governing equations are presented, and a number of example applications are considered, ranging from academic benchmark studies to industrial design studies. © 2009 IOP Publishing Ltd.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

The infinite HMM for unsupervised PoS tagging

We extend previous work on fully unsupervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we address the problem of choosing the number of hidden states in unsupervised Markov models for PoS tagging. We experiment with two non-parametric priors, the Dirichlet and Pitman-Yor processes, on the Wall Street Journal dataset using a parallelized implementation of an iHMM inference algorithm. We evaluate the results with a variety of clustering evaluation metrics and achieve equivalent or better performances than previously reported. Building on this promising result we evaluate the output of the unsupervised PoS tagger as a direct replacement for the output of a fully supervised PoS tagger for the task of shallow parsing and compare the two evaluations. © 2009 ACL and AFNLP.

Development of soy protein fortified fish sticks from Tilapia

The objective of this study was to develop soy protein fortified fish sticks from Tilapia. Two preliminary studies were conducted to select the best fish-soy protein-spice mixture combination with four treatments to develop breaded fish sticks. Developed products were organoleptically assessed using 30 untrained panellists with 7-point hedonic scale. The product developed with new combination was compared with market product. Sixty percent of Tilapia fish mince, 12% of Defatted Textured Soy protein (DTSP), 1.6% of salt and 26.4% of ice water (<5°C) and Spice mixture containing 3g of garlic, 2g of pepper 2g of onion and 1.6g of cinnamon were selected as the best formula to manufacture the product. There was no significant difference when compared with market samples in relation to the organoleptic attributes. Proximate composition of the product was 25.76% of crude protein, 2.38% of crude fat, 60.35% of moisture and2.75% of ash. Products were packaged in Poly Vinyl Chloride clear package (12 gauge) and were stored at -1°C and changes in moisture content, peroxide value, pH value and microbiological parameters were assessed during five weeks of storage. Organoleptic acceptability was not changed significantly in all parameters tested (p>0.05). Total aerobic count and yeast and mould count were in acceptable ranges in frozen storage for 5 weeks. Data were analyzed using AN OVA and Friedman non-parametric test.

State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.

Robust filtering and smoothing with gaussian processes

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail. © 2011 IEEE.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.

Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.

First principles interatomic potential for tungsten based on Gaussian process regression

An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However, the computational complexity associated with modern schemes explicitly based on quantum mechanics limits their applications to systems of a few hundreds of atoms at most. This thesis investigates the application of the Gaussian Approximation Potential (GAP) scheme to atomistic modelling of tungsten - a bcc transition metal which exhibits a brittle-to-ductile transition and whose plasticity behaviour is controlled by the properties of $\frac{1}{2} \langle 111 \rangle$ screw dislocations. We apply Gaussian process regression to interpolate the quantum-mechanical (QM) potential energy surface from a set of points in atomic configuration space. Our training data is based on QM information that is computed directly using density functional theory (DFT). To perform the fitting, we represent atomic environments using a set of rotationally, permutationally and reflection invariant parameters which act as the independent variables in our equations of non-parametric, non-linear regression. We develop a protocol for generating GAP models capable of describing lattice defects in metals by building a series of interatomic potentials for tungsten. We then demonstrate that a GAP potential based on a Smooth Overlap of Atomic Positions (SOAP) covariance function provides a description of the $\frac{1}{2} \langle 111 \rangle$ screw dislocation that is in agreement with the DFT model. We use this potential to simulate the mobility of $\frac{1}{2} \langle 111 \rangle$ screw dislocations by computing the Peierls barrier and model dislocation-vacancy interactions to QM accuracy in a system containing more than 100,000 atoms.

基于测量数据反演并联机床非线性位姿误差模型

利用最小二乘技术识别模型参数 ,将非线性问题作线性化处理 ,提出了一种基于测量数据反演非线性误差模型的建模方法。结合算例 ,指出了此类模型设计应注意的问题。五轴并联机床约束机构误差模型仿真结果表明 ,由此得到的误差模型精度高。利用所得模型对机床位姿进行补偿 ,即可提高机床沿该位姿方向的定位精度