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The presence of extreme events gives rise to outrageous results regarding population parameters and their estimates using traditional moments. Traditional moments are usually influenced by extreme observations. In this paper, we propose some new calibration estimators under the L-Moments scheme for variance which is one of the most important population parameters. Some suitable calibration constraints under double stratified random sampling are also defined for these estimators. Our proposed estimators based on L-Moments are relatively more robust in presence of extreme values. The empirical efficiency of proposed estimators is also calculated through simulation. Covid-19 pandemic data from January 22, 2020, to August 23, 2020, is considered for simulation study.

Abusive supervision (". .. subordinates' perceptions of the extent to which their supervisors engage in the sustained display of hostile verbal and nonverbal behaviors, excluding physical contact"; Tepper, 2000, p. 178) is a type of workplace mistreatment that has gained momentum in research and practice. Indeed, "the boss" may be one of the major factors that can lead employees to experience stress in their jobs (Michie, 2002) and literature shows far-reaching consequences of abusive supervision for employee attitudes and behavior (Martinko, Harvey, Brees, & Mackey, 2013). In addition to affecting employee well-being, abusive supervision affects employees' discretionary behavior. Abusive supervision predicts reductions in positive discretionary behaviors (e.g., Xu, Huang, Lam, & Miao, 2012) such as organizational citizenship behavior (OCB). These desirable OCBs can entail behaviors such as helping colleagues (individual-directed OCB; OCBI) or attending nonmandatory organizational meetings (organization-directed OCB; OCBO). Abusive supervision also relates positively to negative discretionary behaviors (Mitchell & Ambrose, 2007), such as counterproductive work behaviors (CWB). CWBs can entail behaviors such as ridiculing or embarrassing coworkers (individual-directed CWB) or leaving work early and taking longer breaks (organization-directed CWB). Recent research suggests that, when employees have better resources to cope with abuse, they may show less of the detrimental behavioral effects (e.g.

Many authors defined the modified version of the mean estimator by using two auxiliary variables. These proposed estimators highly depend on the calculated regression coefficients. In the presence of outliers, these estimators do not give satisfactory results. In this study, we improve the suggested estimators using several robust regression techniques while obtaining the regression coefficients. We compared the efficiencies between the suggested estimators and the estimators presented in the literature. We used two numerical examples and a simulation study to support these theoretical results. Empirical results show that the modified ratio estimator performs well in the presence of outliers when adopting robust regression techniques.

Traditional ordinary least square (OLS) regression is commonly utilized to develop regression-ratiotype estimators with traditional measures of location. Abid et al. [1] extended this idea and developed regression-ratio-type estimators with traditional and non-traditional measures of location. In this article, the quantile regression with traditional and non-traditional measures of location is utilized and a class of ratio type mean estimators are proposed. The theoretical mean square error (MSE) expressions are also derived. The work is also extended for two phase sampling (partial information). The pertinence of the proposed and existing group of estimators is shown by considering real data collections originating from different sources. The discoveries are empowering and prevalent execution of the proposed group of estimators is witnessed and documented throughout the article.

Hundreds of image encryption algorithms have been developed for the security and integrity of images through the combination of DNA computing and chaotic maps. This combination of the two instruments is not sufficient enough to thwart the potential threats from the cryptanalysis community as the literature review suggests. To inject more robustness and security stuff, a novel image encryption scheme has been written in this research by fusing the chaotic system, DNA computing and castle -a chess piece. As the plain image is input, its pixels are shifted to the scrambled image at the randomly chosen pixel addresses. This scrambling has been realized through the routine called Image Scrambler using Castle (ISUC). Castle randomly moves on the hypothetical large chessboard. Pixels taken from the plain image are shifted to the addresses of the scrambled image, where castle lands in each iteration. After the plain image is scrambled, it is DNA encoded. Two mask images are also DNA encoded. Then to throw the diffusion effects in the cipher, DNA Addition and DNA XOR operations between the DNA encoded pixels data and the DNA encoded mask images have been conducted. Next, the pixels data are converted back into their decimal equivalents. Four dimensional chaotic system has been used to get the chaotic vectors. The hash codes given by the SHA-256 function have been used in the cipher to introduce the plaintext sensitivity in its design. We got an information entropy of 7.9974. Simulation carried out through the machine, and the thorough security analyses demonstrate the good security effects, defiance to the varied attacks from the cryptanalysis community, and the bright prospects for some real world application of the proposed image cipher.

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