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This work proposes a semi-analytical new hybrid approach, so-called differential -transform method (D TM), to evaluate the behavior of n-space dimensional fractional-nonlinear hyperbolic-like wave equations, where time-fractional derivative is considered in Caputo format. The D TM is the hybrid method in which projected differential transform is implemented after imposing the recently introduced integral transform, i.e., so-called transform [W. Zhao and S. Maitama, J. Appl. Anal. Comput. 10, 1223 (2020)]. The efficiency and applicability of the proposed D TM had been tested by considering three different test examples of the Caputo time-fractional nonlinear hyperbolic-like wave equations in terms of absolute error norms, and the different order D TM solutions are compared with exact solution behaviors and the existing results, for the large time level τ ∈ [ 0 , 1 0 ]. In addition, the convergence analysis of D TM is studied theoretically and verified it numerically as well as graphically, which confirms that the numerical experiments via D TM for distinct fractional orders support the theoretical findings excellently, and the presented D TM results converge to their exact solution behavior, very fast. The evaluated series approximations are expressed in the compact form of Mittag-Leffler functions. [ABSTRACT FROM AUTHOR]

It is widely recognized that many cancer therapies are effective only for a subset of patients. However clinical studies are most often powered to detect an overall treatment effect. To address this issue, classification methods are increasingly being used to predict a subset of patients which respond differently to treatment. This study begins with a brief history of classification methods with an emphasis on applications involving melanoma. Nonparametric methods suitable for predicting subsets of patients responding differently to treatment are then reviewed. Each method has different ways of incorporating continuous, categorical, clinical and high-throughput covariates. More recent methods have built-in dimension reduction methods for high throughput data. Pre-validation is one method of assessing the added value of high-throughput data to clinical covariates. The way in which treatment interactions are incorporated is important if the goal is to predict a subset of patients which respond differently to treatment. For nonparametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions. It is hoped that this study will stimulate more development of nonparametric methods to predict subsets of patients responding differently to treatment. [ABSTRACT FROM AUTHOR]

Objective: To investigate the social determinants of cervical cancer screening and report the locations vulnerable to poor utilization of cervical cancer screening services., Study Design: An ecological study with the data derived from fourth round of the National Family Health Survey conducted in India in the period 2015-2016., Methods: The study focused on the percentage of women who have never undergone cervical cancer screening across 639 districts in India. Moran's I statistic was used to investigate the overall clustering of location. The Getis-Ord Gi* statistic was used for the detection of significant local clusters. Spatial error, spatial lag, spatial Durbin and spatial Durbin error models were compared, and the model with best fit was reported. ArcGIS, GeoDa and R software were used for the analysis., Results: The existence of spatial autocorrelation (Moran's I = 0.61) necessitates the consideration of spatial component while studying the screening data. A significant clustering of districts with poor screening has been observed in the North-Central and North-Eastern regions of India. The geographic arrangement of the percentage of women who have undergone cervical cancer screening was associated with the percentage of women with poor wealth index (P < 0.001), not using a modern method of contraception (P < 0.001), residing in rural areas (P = 0.033) and never heard of sexually transmitted infection (P = 0.014). The range of percentage of women getting cervix screened for cancer was 0.5-68.4%, presenting the heterogeneity among the population elements., Conclusion: A higher risk of poor cervical cancer screening is observed in the districts where most of the women have poor wealth index, reside in urban area, have never heard of sexually transmitted infection and do not use a modern method of contraception., (Copyright © 2019 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)