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The study focuses on the development of a new probability distribution with applications to extreme values. The distribution is proposed by incorporating an additional parameter into the inverse Pareto distribution using the α-Power Transformation. Various properties of the new distribution are derived. The paper also explores the estimation of the parameters by the Maximum Likelihood Estimation (MLE) technique. Simulations are performed to evaluate the performance of the MLEs. In addition, two real data sets with extreme values are used to evaluate the efficacy of the proposed model. It is concluded that the proposed model performs well in the case of extreme values compared to the existing distributions.

Recently, the Covid-19 pandemic has caused tremendous trauma over the world, leading to psychological and behavioral harm in addition to social and economic instabilities. Even though the pandemic's statistical analysis is still in progress, it is essential to fit Covid-19 data using statistical models to prevent further harm. In order to model Covid-19 data, the study suggests a novel family of distributions called the exponentiated log-logistic family. The basic Pareto distribution is transformed as a special case, and certain properties of the proposed distribution are discussed. To estimate the model parameters, the maximum likelihood estimation approach is used. Moreover, a simulation study is conducted to ensure the consistency of parameter estimates. Three real-world datasets relevant to the Covid-19 pandemic are examined to demonstrate the applicability of the suggested approach. The proposed model is shown to be more flexible and provides an improved fit to describe the Covid-19 data when compared to various alternative forms of Pareto distribution. [ABSTRACT FROM AUTHOR]

In this paper, the geometric distribution parameter is estimated under a type-I censoring scheme by means of the Bayesian estimation approach. The Beta and Kumaraswamy informative priors, as well as five loss functions are used for this purpose. Expressions of Bayes estimators and Bayes risks are derived under the Squared Error Loss Function (SELF), the Quadratic Loss Function (QLF), the Precautionary Loss Function (PLF), the Simple Asymmetric Precautionary Loss Function (SAPLF), and the DeGroot Loss Function (DLF) using the two aforementioned priors. The prior densities are obtained through prior predictive distributions. Simulation studies are carried out to make comparisons using Bayes risks. Finally, a real-life data example is used to verify the model's efficiency. [ABSTRACT FROM AUTHOR]

For modern high‐tech flexible energy storage devices, it becomes important to synthesize micro‐/nanostructures as per the required shape and morphology with superior physical and electro‐active characteristics. This work shares the fabrication and characterization of ZnSn(OH)6 (Zinc hydroxystannate [ZHS]) prepared by facile microwave‐assisted technique and furthermore converted into flexible sheets by employing lignocelluloses (LC) known as natural fibers, collected from Carica papaya leaf petiole as a substrate to provide the flexible matrix. X‐ray diffraction measurements confirm the successful crystalline structure of ZHS. Scanning electron microscopy and transmission electron microscopy showed the solid spherical structure of ZHS microspheres. Fourier transform infrared spectrometry and Raman spectroscopy confirmed the composite formation of ZHS and LC‐based composite sheets (ZHS/LC sheets). Electrochemical measurements that is, cyclic voltammetry (CV), Galvanostatic charge/discharge, and electrochemical impedance (EIS) spectroscopy revealed the electroactive behavior of ZHS/LC paper sheets as working electrode for energy storage applications. CV measurements revealed the specific capacitance of 100 F/g and EIS measurements confirmed the decrease in the resistance of LC fiber after the growth of ZHS microspheres. Presented flexible ZHS based paper sheets will be highly feasible for the modern bendable/flexible/disposable energy storage applications. [ABSTRACT FROM AUTHOR]

Background. Lack of infrastructure for disposal of effluents in industries leads to severe pollution of natural resources in developing countries. These pollutants accompanied by solid waste are equally hazardous to biological growth. Natural attenuation of these pollutants was evidenced that involved degradation by native microbial communities. The current study encompasses the isolation of pesticide-degrading bacteria from the vicinity of pesticide manufacturing industries. Methods. The isolation and identification of biodegrading microbes was done. An enrichment culture technique was used to isolate the selected pesticide-degrading bacteria from industrial waste. Results. Around 20 different strains were isolated, among which six isolates showed sig- nificant pesticide biodegrading activity. After 16S rRNA analysis, two isolated bacteria were identified as Acinetobacter baumannii (5B) and Acidothiobacillus ferroxidans, and the remaining four were identified as different strains of Pseudomonas aeruginosa (1A, 2B, 3C, 4D). Phylogenetic analysis confirmed their evolution from a common ancestor. All strains showed distinctive degradation ability up to 36 hours. The Pseudomonas aeruginosa strains 1A and 4D showed highest degradation percentage of about 80% for DDT, and P. aeruginosa strain 3C showed highest degradation percentage, i.e., 78% for aldrin whilst in the case of malathion, A. baumannii and A. ferroxidans have shown considerable degradation percentages of 53% and 54%, respectively. Overall, the degradation trend showed that all the selected strains can utilize the given pesticides as sole carbon energy sources even at a concentration of 50 mg/mL. Conclusion. This study provided strong evidence for utilizing these strains to remove persistent residual pesticide; thus, it gives potential for soil treatment and restoration. [ABSTRACT FROM AUTHOR]

In this paper, we produced a new family of distribution called Gull Alpha Power Family of distributions (GAPF). A Special case of GAPF is derived by considering the Weibull distribution as a baseline distribution called Gull Alpha Power Weibull distribution (GAPW). The suitability of the proposed distribution derives from its ability to model both the monotonic and non-monotonic hazard rate functions which are a common practice in survival analysis and reliability engineering. Various statistical properties were derived in addition to their special cases. The unknown parameters of the model are estimated using the maximum likelihood method. Moreover, the usefulness of the proposed distribution is supported by using two real lifetime data sets as well as simulated data. [ABSTRACT FROM AUTHOR]

Gene-mapping studies, regularly, rely on examination for Mendelian transmission of marker alleles in a pedigree as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is, usually, necessary to resolve or remove the genotyping errors prior to consideration. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output. We scrutinize via carefully designed simulations that how well CIDR’s data cleaning rules work in practice. We found that genotype errors in siblings are detected more often than in parents for less polymorphic SNPs and vice versa for more polymorphic SNPs. Through computer simulations, we conclude that some of the CIDR’s rules work poorly in some circumstances, and we suggest a set of modified data cleaning rules that may work better than CIDR’s rules. [ABSTRACT FROM AUTHOR]

This study explores the distribution of order statistics of the Alpha Power Pareto (APP) distribution. Alpha Power Pareto is a more flexible distribution proposed by adding an extra parameter in the well-known Pareto distribution. This paper focuses on the derivation of single and product moment of the APP order statistics. Additionally, a recurrence link for single moments of order statistics is established. Moreover, analytical formulas of Rényi and q-entropy for APP order statistics are obtained. [ABSTRACT FROM AUTHOR]

In this paper we have proposed a class of transformed efficient estimators of finite population mean by modifying Mohanty and Sahoo (1995) transformation. The approximate bias and MSE of the proposed class of estimators have been found. Conditions under which the proposed class of transformed estimators performs good, have been obtained. Examples are given based on real life data to illustrate the results. [ABSTRACT FROM AUTHOR]

In order to design Sustainable Land Management (SLM) plans or interventions it is important to understand the land use change and subsequent impacts. The basic aim of this study was to assess the impact of land use land cover (LULC) change on soil physico-chemical properties and nutrients loss under different land use/land cover changes within Miandam watershed of Swat valley, Pakistan. The study area was divided into three major types on the basis LULC type (Dense forest, Moderate forest and Agriculture land). A total of 81 soil samples were collected from the study area (27 from each land use) through stratified random sampling and were transferred to the laboratory. The prepared soil samples were then tested for various physico-chemical properties such as pH, electrical conductivity, contents of organic matter, organic carbon, sand, silt, clay, nitrogen, phosphorus, potassium, iron, copper and zinc. Results showed that LULC significantly affected all soil parameters except electrical conductivity and potassium. For all the nutrients assessed, the enrichment ratio was greater than 1 which showed that the most fertile layer of the soil in the cropped areas was transported through soil erosion. In this study, time-series satellite images were used to determine the spatiotemporal changes in the LULC of Miandam valley of Swat, Pakistan, and the possible effect of LULC on soil properties was reported. The information generated on the soil properties as indicators of soil health could be used to inform the stakeholders about the effect of LULC change in the study area. Multi-temporal image acquired by Landsat sensors for the year 2018 was used for quantifying changes in the study area. The Landsat images for the year 2018 were classified into six land cover/land use classes. The increase in agricultural activities caused an increase in the land farming areas which led to an increase in the rate of deforestation in the valley. Thus, the present study results reveal that the use of the soil for agriculture instead of keeping them naturally or in other words changes in the land use land cover affects the soil physico-chemical properties and the overall nutrients availability. [ABSTRACT FROM AUTHOR]

During the past couple of years, statistical distributions have been widely used in applied areas such as reliability engineering, medical, and financial sciences. In this context, we come across a diverse range of statistical distributions for modeling heavy tailed data sets. Well-known distributions are log-normal, log- t , various versions of Pareto, log-logistic, Weibull, gamma, exponential, Rayleigh and its variants, and generalized beta of the second kind distributions, among others. In this paper, we try to supplement the distribution theory literature by incorporating a new model, called a new extended Weibull distribution. The proposed distribution is very flexible and exhibits desirable properties. Maximum likelihood estimators of the model parameters are obtained, and a Monte Carlo simulation study is conducted to assess the behavior of these estimators. Finally, we provide a comparative study of the newly proposed and some other existing methods via analyzing three real data sets from different disciplines such as reliability engineering, medical, and financial sciences. It has been observed that the proposed method outclasses well-known distributions on the basis of model selection criteria. [ABSTRACT FROM AUTHOR]

In regression analysis least square (LS) estimator fails because of its sensitivity to unusual observations present in the data. Robust estimation provides alternative estimates which are insensitive and efficient, when the data are not normally distributed or polluted with distant observations usually called outliers. To cope with the problem of outliers is more challenging in nonlinear regression (NLRM) than linear regression. In this study, least trimmed absolute (LTA) estimator which is a robust and high breakdown estimator is adopted for nonlinear regression model fitting. Bias and mean square error is used to check the efficiency of the proposed estimator. The performance of the estimator is compared with LS and existing robust M-estimators using simulated data sets and real world problems. It has been observed that LTA is efficient in case of contaminated data sets as compared to LS and M estimator. Furthermore, it has been concluded that in case of 40% contamination LTA outperforms LS and M estimator, while in case of 20% outliers LTA and M estimators perform equally well. In regard to clean data sets, LS, LTA and M estimator performs equally well. Conclusion has been made on the basis of simulated data sets as well as real data sets. [ABSTRACT FROM AUTHOR]