This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different

The current study involves placing 135 boreholes drilled to a depth of 10 m below the existing ground level. Three standard penetration tests (SPT) are performed at depths of 1.5, 6, and 9.5 m for each borehole. To produce thematic maps with coordinates and depths for the bearing capacity variation of the soil, a numerical analysis was conducted using MATLAB software. Despite several-order interpolation polynomials being used to estimate the bearing capacity of soil, the first-order polynomial was the best among the other trials due to its simplicity and fast calculations. Additionally, the root mean squared error (RMSE) was almost the same for the all of the tried models. The results of the study can be summarized by the production

Abstract

This study aims at investigating the effectiveness of the cooperative inquiry strategy in developing the achievement and development of mathematical communication skills of elementary school students. To achieve this aim, the experimental method was utilized. The study sample consisted of an experimental group (n = 34) and a control group (n = 31), which were randomly selected from primary schools. A Teacher's Guide for teaching a unit entitled "Statistics and Graphic Representations" for the sixth elementary graders in the first semester were devised. A (30) multiple-choice item achievement test distributed at the levels of (recall, comprehension, and application), as well as an (18) item mathematical c

Twelve pends were selected and distributed on three verticals transects paths on the Tigers river in Al Rasheed county.Passing through land covers, that classified and covers the whole region. Based on the 8 Landsat of the year 2015. It was oriental classified by using Erdas 10.2 . The pedons were distributed on the area of each varicty of these classes. the series of soil according of the transect series (DW74,MMg,DMu6 , Df96) respectively were represented P₁ , P₂ , P₃ , P₄  .

The second transits series(DM97,MM5,DM96,DF115) respectively were  represented P₅ , P₆ , P₇ , P₈  .The third  transits series(DM46,MMg,MF12,MM11) re

The best optimum temperature for the isolate was 30○C while the pH for the maximum mineral removal was 6. The best primary mineral removal was 100mg/L, while the maximum removal for all minerals was obtained after 8 hrs, and the maximum removal efficiency was obtained after 24 hrs. The results have proved that the best aeration for maximum removal was obtained at rotation speed of 150 rpm/ minute. Inoculums of 5ml/ 100ml which contained 106 cell/ ml showed maximum removal for the isolate.

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

The objective of this research was to estimate the dose distribution delivered by radioactive gold nanoparticles (198 AuNPs or 199 AuNPs) to the tumor inside the human prostate as well as to normal tissues surrounding the tumor using the Monte-Carlo N-Particle code (MCNP-6.1. 1 code). Background Radioactive gold nanoparticles are emerging as promising agents for cancer therapy and are being investigated to treat prostate cancer in animals. In order to use them as a new therapeutic modality to treat human prostate cancer, accurate radiation dosimetry simulations are required to estimate the energy deposition in the tumor and surrounding tissue and to establish the course of therapy for the patient. Materials and methods A simple geometrical