Background: The prevalence of both obesity & diabetes are increasing all over the world & more in women.  They have a negative impact not only on morbidity & mortality but also on quality of life.

Objectives: To assess the HRQoL with a specific comparison between obese & normal weight among wo

In this paper, the ability of using corn leaves as low-cost natural biowaste adsorbent material for the removal of Indigo Carmen (IC) dye was studied. Batch mode system was used to study several parameters such as, contact time (4 days), concentration of dye (10-50) ppm, adsorbent dosage (0.05-0.25) gram, pH (2-12) and temperature (30-60) ^oC. The corn leaf was characterized by Fourier-transform infrared spectroscopy device before and after the adsorption process of the IC dye and scanning electron microscope device was used to find the morphology of the adsorbent material. The experimental data was imputing with several isotherms where it fits with Freundlich (R² = 0.9

Poly urea formaldehyde –Bentonite (PUF-Bentonite) composite was tested as new adsorbent
for removal of mefenamic acid (MA) from simulated wastewater in batch adsorption
procedure. Developed a method for preparing poly urea formaldehyde gel in basic media by
using condensation polymerization. Adsorption experiments were carried out as a function of
water pH, temperature, contact time, adsorbent dose and initial MA concentration .Effect of
sharing surface with other analgesic pharmaceuticals at different pH also studied. The
adsorption of MA was found to be strongly dependent to pH. The Freundlich isotherm model
showed a good fit to the equilibrium adsorption data. From Dubinin–Radushkevich model the
mean free

This paper presents a study to investigate the behavior of post-tensioned segmental concrete beams that exposed to high-temperature. The experimental program included fabricating and testing twelve simply supported beams that divided into three groups depending on the number of precasting concrete segments. All specimens were prepared with an identical length of 3150 mm and differed in the number of the incorporated segments of the beam (9, 7, or 5 segments). To simulate the genuine fire disasters, nine out of twelve beams were exposed to a high-temperature flame for one hour. Based on the standard fire curve (ASTM – E119), the temperatures of 300◦C (572◦F), 500◦C (932◦F), and 700◦C (1292◦F) were adopted. Consequently,

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

the study including isolation and identification of candida spp causing UTIs from patintes coming to al-yarmouk hospital

KE Sharquie, R Hayani, J Al-Rawi, A Noaimi, SH Radhy, CLINICAL AND EXPERIMENTAL RHEUMATOLOGY, 2010

The haplotype association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease.Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls.It starts with inferring haplotypes from genotypes followed by a haplotype co-classification and marginal screening for disease-associated haplotypes.Unfortunately,phasing uncertainty may have a strong effects on the haplotype co-classification and therefore on the accuracy of predicting risk haplotypes.Here,to address the issue,we propose an alternative approach:In Stage 1,we select potential risk genotypes inste