The value of b0 images obtained from diffusion-weighted echo planar sequences for the detection of intracranial hemorrhage compared with GRE sequence

AbstractPurposeOur aim was to evaluate the clinical utility of b0EPI images obtained from diffusion sequence for the detection of the intracranial hemorrhagic lesions, especially acute intracerebral bleeds thereby shorten the scan time particularly in the critical acute cases of stroke.Materials and methodsAmong all consecutive MR brain studies performed in our department last year, we retrospectively selected those who followed the following criteria: (1) clinically suspected or radiographically confirmed acute infarction or hemorrhage. (2) MRI imaging including DWI and T2∗ images. Sensitivity of hemorrhage detection, conspicuity of lesions, and diagnostic certainty were compared between the b0EPI and GRE sequences.ResultsThere were 77 hemorrhagic lesions with a variety of pathogeneses in various locations. 76/77 (98.7%) of these lesions were hemorrhagic (hypointense) on the GRE sequences, whereas 61 (79.2%) were characterized as hemorrhagic on b0EPI images, and 16 (20.8%) were not detected. The overall difference in hemorrhage conspicuity/diagnostic certainty between GRE and b0EPI sequences was statistically significant (P<.05).Conclusionb0EPI sequence, although shorter in acquisition time, was inferior to GRE imaging in the detection of acute and chronic intracerebral hemorrhage

Comparison between physical properties of ring-spun yarn and compact yarns spun from different pneumatic compacting systems

A comparative study pertaining to physical and mechanical properties of ring-spun yarn vis-à-vis compact yarns spun using three different compacting systems has been reported. Rieter (K-44), Toyota (RX-240) and Suessen (Fiomax) spinning machines have been used and the condensing process of the fibres in the yarn cross-section as per these compact spinning systems is accomplished pneumatically. Thus, a yarn of linear density 5.9 tex (100 Ne) is spun on the spinning systems using Egyptian cotton of the type Giza 86. One way Anova together with least significant difference are employed to feature the means of the properties of spun yarns and a significant difference among them is observed. According to the performed statistical analysis, there is a significant difference between ring - spun yarn properties and each of the pnuematic compact spun yarns. These compact-spun yarns are also found to differ significantly in terms of their physical and mechanical properties; however, they are all found superior to the ring-spun yarn

A New Inverse Rayleigh Distribution with Applications of COVID-19 Data: Properties, Estimation Methods and Censored Sample

This paper aims at modelling the COVID-19 spread in the United Kingdom and the United States of America, by specifying an optimal statistical univariate model. A new lifetime distribution with three-parameters is introduced by a combination of inverse Rayleigh distribution and odd Weibull family of distributions to formulate the odd Weibull inverse Rayleigh (OWIR) distribution. Some of the mathematical properties of the OWIR distribution are discussed as linear representation, quantile, moments, function of moment production, hazard rate, stress-strength reliability, and order statistics. Maximum likelihood, maximum product spacing, and Bayesian estimation method are applied to estimate the unknown parameters of OWIR distribution. The parameters of the OWIR distribution are estimated under the progressive type-II censoring scheme with random removal. A numerical result of a Monte Carlo simulation is obtained to assess the use of estimation methods

A New Inverse Rayleigh Distribution with Applications of COVID-19 Data: Properties, Estimation Methods and Censored Sample

This paper aims at modelling the COVID-19 spread in the United Kingdom and the United States of America, by specifying an optimal statistical univariate model. A new lifetime distribution with three-parameters is introduced by a combination of inverse Rayleigh distribution and odd Weibull family of distributions to formulate the odd Weibull inverse Rayleigh (OWIR) distribution. Some of the mathematical properties of the OWIR distribution are discussed as linear representation, quantile, moments, function of moment production, hazard rate, stress-strength reliability, and order statistics. Maximum likelihood, maximum product spacing, and Bayesian estimation method are applied to estimate the unknown parameters of OWIR distribution. The parameters of the OWIR distribution are estimated under the progressive type-II censoring scheme with random removal. A numerical result of a Monte Carlo simulation is obtained to assess the use of estimation methods

A new generalization of the Pareto distribution and its applications

This paper introduces a new generalization of the Pareto distribution using the Marshall Olkin generator and the method of alpha power transformation. This new model has several desirable properties appropriate for modelling right skewed data. The Authors demonstrate how the hazard rate function and moments are obtained. Moreover, an estimation for the new model parameters is provided, through the application of the maximum likelihood and maximum product spacings methods, as well as the Bayesian estimation. Approximate confidence intervals are obtained by means of an asymptotic property of the maximum likelihood and maximum product spacings methods, while the Bayes credible intervals are found by using the Monte Carlo Markov Chain method under different loss functions. A simulation analysis is conducted to compare the estimation methods. Finally, the application of the proposed new distribution to three real-data examples is presented and its goodness-of-fit is demonstrated. In addition, comparisons to other models are made in order to prove the efficiency of the distribution in question

The Exponentiated Generalized Alpha Power Family of Distribution: Properties and Applications

In this paper, we introduce the exponentiated generalized alpha power family of distributions to extend the several other distributions. We used the new family to discuss the exponentiated generalized alpha power exponential (EGAPEx) distribution. Some statistical properties of the EGAPEx distribution are obtained. The model parameters are obtained by the maximum likelihood estimation (MLE), maximum product spacing (MPS) and Bayesian estimation methods. A Monte Carlo Simulation is performed to compare between different methods. We illustrate the performance of the proposed new family of distributions by means of two real data sets and the data sets show the new family of distributions is more appropriate as compared to the exponentiated generalized exponential, alpha power generalized exponential, alpha power exponential, generalized exponential and exponential distributions

Bayesian Estimation of A one Parameter Akshaya Distribution with Progressively Type II Censord Data

A progressively type-II right censored sample has been examined in this paper for the inference about parameters for the one-parameter Akshaya distribution. As point estimates for the parameter, the maximum likelihood estimate (MLE), and Bayesian estimate are obtained. The asymptotic distribution of MLE is obtained. Also, the approximate confidence intervals (ACIs) and bootstraps confidence intervals for unknown parameter are obtained. Further, for symmetric loss functions such as squared error loss function, Bayesian estimates are obtained. Gibbs within Metropolis–Hasting samplers use the Monte Carlo chain (MCMC) technique to get the estimate of the unknown parameter from Bayes algorithm is used and the relevant credible interval (CRI) is obtained. Finally, the proposed methods are applied a real data set

Accelerated Life Testing for Bivariate Distributions based on Progressive Censored Samples with Random Removal

The significance of the statistical inference problem in reliability theory for the bivariate models under accelerated life testing (ALT) is enormous. In practice, independent variables are assumed for the sake of convenience, which contradicts the nature of the problem. The constant stress accelerated life testing (CS-ALT) for the bivariate model based on copula function is introduced in this study. The copula is the method that describes the dependence structure between variables. The model parameters are evaluated using maximum likelihood and Bayesian estimation methods, taking into account that units fail due to only two dependent variables under continuous stress ALTs and a Type-II progressive censoring scheme. For the bivariate model, random removal has been referred to as binomial removal. The Bayesian estimation has been created using symmetric and asymmetric loss functions. The asymptotic confidence intervals are generated using approximated confidence intervals. Interval Bayesian estimators have been employed with credible confidence intervals. The set of simulated data is evaluated for demonstrative reasons, taking into account two and numerous stress levels. Different Monto Carlo simulations are built to compare estimating approaches