Thus, it can be concluded that NS formulation of EFV can provide improved oral bioavailability due to enhanced solubility, dissolution velocity, permeability and hence absorption.

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In maintenance planning of rail track, it is imperative to assess the potential and frequency of rail defects. Although this problem has been mainly studied in the literature by either data‐driven or mechanic‐based models, in the present study a new method is proposed to account for the strengths of both approaches in a single model. The envisaged model incorporates fatigue crack growth model, through Finite Element Modeling (FEM), into Approximate Bayesian Computation (ABC) framework. The method is applied to the prediction of rail defect frequency for transverse defects obtained from a US Class I Railroad. The results of the proposed model show that inducing the mechanics of rail defects into a data‐driven model outperforms the traditional pure data‐driven models by over 20%. The outcome of this study, along with necessary future developments to broaden the scope of applicability of the method, will benefit railroad existing practice in capital and maintenance planning.

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This paper develops a Bayesian framework to explore the impact of different factors and to predict the risk of recurrence of rail defects, based upon datasets collected from a US Class I railroad between 2011 and 2016. To this end, this study constructs a parametric Weibull baseline hazard function and a proportional hazard (PH) model under a Gaussian frailty approach. The analysis is performed using Markov chain Monte Carlo simulation methods and the fit of the model is checked using a Cox–Snell residual plot. The results of the model show that the recurrence of a defect is correlated with different factors such as the type of rail defect, the location of the defect, train speed limit, the number of geometry defects in the last three years, and the weight of the rail. First, unlike the ordinary PH model in which the occurrence times of rail defects at the same location are assumed to be independent, a PH model under frailty induces the correlation between times to the recurrence of rail defects for the same segment, which is essential in the case of recurrent events. Second, considering Gaussian frailties is useful for exploring the influence of unobserved covariates in the model. Third, integrating a Bayesian framework for the parameters of the Weibull baseline hazard function as well as other parameters provides greater flexibility to the model. Fourth, the findings are useful for responsive maintenance planning, capital planning, and even preventive maintenance planning.

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Cancer is becoming a global threat as its treatment accounts for many challenges. Hence, newer inventions prioritize the requirement of developing novel anticancer agents. In this context, kinases have been exclusively investigated and developed as a promising and novel class of drug targets for anticancer regimen. Indole derivatives have been found to be most effective for targeting multiple kinases, such as PIM, CDK, TK, AKT, SRC, PI3K, PKD, GSK, etc., to inhibit cell proliferation for cancer. Recently, a group of researchers have proposed their research outcomes related to this moiety, such as Zhang et al. described some potent PI3K inhibitors by substitution at the 4th position of the indole ring. Kassis et al. enumerated several potent CDK5 inhibitors by substituting the 2nd and 6th positions of the indole ring. In the present review, we have taken the initiative to summarize structure-activity relationship (SAR) studies of indole derivatives as kinase inhibitors for the development of potential inhibitors.