Mohd. Arshad

Titanium-based nanocomposite materials: A review of recent advances and perspectives

This article explains recent advances in the synthesis and characterization of novel titanium-based nanocomposite materials. Currently, it is a pressing concern to develop innovative skills for the fabrication of hybrid nanomaterials under varying experimental conditions. This review generally focuses on the adsorption behavior of nanocomposites for the exclusion of organic and inorganic pollutants from industrial effluents and their significant applications in various fields. The assessment of recently published articles on the conjugation of organic polymers with titanium has revealed that these materials may be a new means of managing aquatic pollution. These nanocomposite materials not only create alternative methods for designing novel materials, but also develop innovative industrial applications. In the future, titanium-based hybrid nanomaterials are expected to open new approaches for demonstrating their outstanding applications in diverse fields.

Estimation After Selection From Gamma Populations With Unequal Known Shape Parameters

AbstractLet π1,…,πk be k (≥ 2) independent gamma populations, where the population πi has an unknown scale parameter θi > 0 and known shape parameter νi > 0, i = 1,…, k. We call the population associated with μ[k] = max {μ1,…, μk}, μi = νiθi the best population. For the goal of selecting the best population, Misra and Arshad (2014) proposed a class

UMVU Estimation of Reliability Function and Stress–Strength Reliability from Proportional Reversed Hazard Family Based on Lower Records

{\mathcal D}_{0}

Selecting The Exponential Population Having The Larger Guarantee Time With Unequal Sample Sizes

D0 of selection rules for the case of (possibly) unequal shape parameters. In this article, we consider the problem of estimating the mean μS of the population selected by a fixed selection rule

Study of ZnO and Mg doped ZnO nanoparticles by sol-gel process

{\underline \delta^{\underline a }} \in {\mathcal D}_{0}

Estimation After Selection From Uniform Populations With Unequal Sample Sizes

δ_a_∈D0, under a scale-invariant loss function. We derive the uniformly minimum variance unbiased estimator (UMVUE). Two other natural estimators φN,1 and φN,2, which are respectively the analogs of the UMVUE and the best scale invariant estimators of μi’s for the component problem, are studied. We show that φN,2 is generalized Bayes with respect to a noninformative prior distribution, and is also minimax when k = 2. The UMVUE and the natural estimator φN,1 are shown to be inadmissible, and better estimators are obtained. A numerical study on the performance of various estimators indicates that the natural estimator φN,2 outperforms the other natural estimators.

Histomorphometry of Preterm and Term Human Placentas

SYNOPTIC ABSTRACT In this study, we have obtained the uniformly minimum variance unbiased estimator (UMVUE) of reliability function and stress–strength reliability for one parameter proportional reversed hazard rate family of distribution based on lower record values. Further, the results are reduced for power function distribution, exponentiated Weibull distribution, generalized exponential distribution, generalized Rayleigh (also known as Burr type X) distribution and Topp–Leone distribution. Also, the UMVU estimator and maximum likelihood estimator are compared through simulation.

An investigation of the Plasmodium falciparum malaria epidemic in Kavango and Zambezi regions of Namibia in 2016

Let π1 and π2 be two independent exponential populations, where the population πi has an unknown location parameter (or guarantee time) μi > 0 and known scale parameter σi > 0, i = 1, 2. Let μ[1] ⩽ μ[2] denote the ordered values of μ1 and μ2, and assume that the correct ordering between μ1 and μ2 is not known a priori. Consider the goal of selecting the population associated with μ[2] under the decision theoretic framework. We deal with the problem of finding the minimax selection rule under the 0-1 loss function (which takes the value 0 if correct selection is made and takes the value 1 if correct selection is not made) when (μ1, μ2) is known to lie in a certain subset of the parameter space, called the preference-zone. Based on independent random samples of (possibly) unequal sizes from the two populations, we propose a class of natural selection rules and find the minimax selection rule within this class. We call the minimax selection rule within this class to be the restricted minimax selection rule. This restricted minimax selection rule is shown to be globally minimax and generalized Bayes. A numerical study on the performance of various selection rules indicates that the minimax selection rule outperforms various natural selection rules.

Estimating the parameter of selected uniform population under the squared log error loss function

Nano-crystalline undoped and Mg doped ZnO (Mg-ZnO) nanoparticles with compositional formula MgxZn1-xO (x=0,1,3,5,7,10 and 12 %) were synthesized using sol-gel process. The XRD diffraction peaks match with the pattern of the standard hexagonal structure of ZnO that reveals the formation of hexagonal wurtzite structure in all samples. SEM images demonstrates clearly the formation of spherical ZnO nanoparticles, and change of the morphology of the nanoparticles with the concentration of the magnesium, which is in close agreement with that estimated by Scherer formula based on the XRD pattern. To investigate the doping effect on optical properties, the UV–VIS absorption spectra was obtained and the band gap of the samples calculated.

Estimation of common location parameter of two exponential populations based on records

SYNOPTIC ABSTRACT Consider k (⩾ 2) independent uniform populations π1, …, πk, where πi ≡ U(0, θi), and θi > 0 (i = 1, …, k) is an unknown scale parameter. For selecting the unknown population having the largest scale parameter, we consider a class of selection rules based on the natural estimators of θi, i = 1, …, k. We consider the problem of estimating the scale parameter θS of the selected population, using a fixed selection rule from this class, under the scaled-squared error loss function. We derive the uniformly minimum variance unbiased estimator (UMVUE) of θS. We also consider three natural estimators ϕN, 1, ϕN, 2, and ϕN, 3 of θS which are, respectively, based on the maximum likelihood estimators, UMVUEs, and minimum risk equivariant estimators for component estimation problems. The natural estimator ϕN, 3 is shown to be a generalized Bayes estimator with respect to a non informative prior. Further, we derive a general result for improving a scale-invariant estimator of θS. Using this result, the estimators better than the UMVUE and the natural estimator ϕN, 1 are obtained. It is also shown that a subclass of natural-type estimators, which contains the natural estimator ϕN, 2, is inadmissible for estimating θS under the scaled-squared error loss function. Finally, we provide a simulation study on the performances of various competing estimators of θS.