Escherichia coli, as a model microorganism, was treated in phosphate-buffered saline under high hydrostatic pressure between 100 and 300 MPa, and the inactivation dynamics was investigated from the viewpoint of predictive microbiology. Inactivation data were curve fitted by typical predictive models: logistic, Gompertz and Weibull functions. Weibull function described the inactivation curve the...

The clutter is always present in the radar signal, so it is important to generate models that give us the possibility to minimize its effect in the detection of targets in a radar space. In that way, it is focused this paper, where, we try to propose a model that generates discrete time coherent sequences with a Weibull distribution for its modulus and a Uniform distribution for its phase. The ...

The Swedish physicist, Waloddi Weibull, is renowned for formulating a family of probability distributions for modelling the variability of nonnegative, quantitative, random phenomena (see reference 3). The distribution was originally designed to model the breaking strength of steel (Weibull was scientific advisor to Bofors, the Swedish steel company) but, since then, Weibull analysis has evolve...

Bathtub shape is one of the most important behaviors of the hazard rate function that is quite common in lifetime data analysis. Such shapes are actually the combination of three different shapes and, as such, there have been several proposals to model such behavior. One such proposal is to combine at most three different distributions, often the Weibull or some similar model, separately for de...

The purpose of the present study was to test the hypothesis that glazing will affect the Weibull modulus or the mean flexural strength of dental porcelain. Four groups (n = 30) of specimens were prepared from feldspathic body porcelain (Vita VMK 68). The specimens were tested in groups: original glazed condition (control), and after grinding separately with 1,000-grit, 600-grit, and 100-grit si...

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpus and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation (DLDA), which infers posterior samples via a hybrid of stochasticgradient MCMC and autoencoding variational Bayes. The generative network of WHAI has a hi...

By means of Weibull analysis, this study evaluated and compared the flexural strength and shear bond strength of flowable light-cured composites against those of conventional ones. Twenty specimens of each material were prepared for flexural and shear bond strength measurements. Specimens were measured after water storage at 37 degrees C for 24 hours. Three of four flowable composites showed si...

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and autoencoding variational Bayes. The generative network of WHAI has a hierarc...