This paper presents an investigation to the effect of the forming speed on healing voids that inhabit at various size in an ingot. The study was performed by using finite element method with bilinear isotropic material option, circular type voids were considered. The closure index was able to predict the minimum press force necessary to consolidate voids and the reduction. The simulation was carried out, on circular cross-section lead specials containing a central void of different size. At a time with a flat die, different ratio of inside to outside radius was taken with different speed to find the best result of void closure.

Background: changing in lifestyle like displacing place could cause depression which is a common mental disorder that change general health that affect dental caries incidence and severity. The aims of this study were to assess the relation of depression status on prevalence and severity of dental caries among internally displaced people. Material and Method: The sample include 121 internally displaced people aged from 13-17 years. Method for depression measuring is by using Children Depression Inventory (CDI2) questionnaire. Dental caries is measured by using caries experience (DMFs) and caries severity D1-4. Result: the mean value for decayed and missing surfaces were higher in high depression grade as compering with low and medium dep

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge

Despite not being digested, trace elements and/or heavy metals are important for the activity of enzymes, physiological processes, and homeostasis. If certain trace elements are present in excess, they can have harmful effects and pose major health hazards. Objective: The aims to examine the connection between serum zinc, copper levels, and the Cu/Zn ratio, and several anthropometric parameters, including an index of body mass and the waist-hip ratio. In our study, we used atomic absorption spectrometry (AAS) to measure serum levels of copper (Cu) and zinc (Zn) in 60 individuals, 30 patients with kidney cancer and 30 healthy controls. We assessed serum uric acid, creatinine, and urea using the semi-auto analyzer BA-88A (Korea). The results

Sea level rise (SLR) due to climate change is affecting the coastline, causing shoreline changes, the degradation of mangrove forests, and the destruction of coastal resources. This is the cause of a huge amount of mangrove degradation in many parts of the Ganges–Brahmaputra–Meghna delta. A total of 90% of people have been forced to migrate from the island due to extreme weather conditions. In this study, remote sensing (RS) and geographic information system (GIS) techniques were used for LULC change and shoreline shift analyses of Ghoramara Island. LULC classification was carried out using thirty years of Landsat datasets with intervals of ten years (1990 and 2000) and intervals of five years (2005, 2010, 2015, and 2020). The classific