Denture bases are fabricated routinely using Poly(methyl methacrylate) (PMMA) acrylic resin. Yet, it is commonly known for its major drawbacks such as insufficient strength and ductility. The purpose of this study was to improve the performance of PMMA acrylic resin as a denture base material by reinforcement with surface treated lithium disilicate glass ceramic powder. The ceramic powder was prepared by grinding and sieving IPS e.max CAD MT blocks. Then, the powder was surface treated with an organosilane coupling agent (TMSPM) and added to PMMA in amount of 1%, 3%, 5% and 7% by weight. Characterizations of the powder was done by particle size analysis, XRD and FTIR. Transverse strength, Impact strength, Shore D hardness and surface roughn

Solid dispersion (SD) formulation has attracted much attention due to its potential in enhancing dissolution performances of poorly soluble active pharmaceutical ingredients (API). Recently, a review on dissolution performances of SDs classifies the improvement into 3 categories, where 82 % of the studies showed improved bioavailability, 8 % showed reduced bioavailability and 10 % revealed similar bioavailability as compared to pure APIs. This indicates the inconsistent degrees of dissolution improvement of poorly soluble APIs in SD. Although a few factors related to the choice of carriers have been suggested to contribute to the dissolution improvement, however, the underlying factor determining the discrepancy in the degree of dissolution

Uropathogenic E. coli (UPEC) is problematic and still the leading cause of urinary tract infections worldwide. It is developed resistance against most antibiotics. The investigation, surveillance system, and efficient strategy will facilitate selecting an appropriate treatment that could control the bacterial distribution. The present study aims to investigate the epidemiology and associated risk factors of uropathogenic E. coli and to study their antibiotic resistance patterns. 1585 midstream urine specimens were collected from symptomatic urinary tract infections (UTI) patients (225 males and 1360 females) admitted to Zakho emergency hospital, Zakho, Kurdistan Region, Iraq from January 2016 until the end of December 2

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

The synthesized ligand [4-chloro-5-(N-(5,5-dimethyl-3-oxocyclohex-1-en-1-yl)sulfamoyl)-2-((furan-2-ylmethyl)amino)benzoic acid] (H2L1) was identified utilizing Fourier transform infrared spectroscopy (FT-IR), 1 H, 13 C – NMR, (C.H.N), Mass spectra, UVVis methods based on spectroscopy. To detect mixed ligand complexes, analytical and spectroscopic approaches such as micro-analysis, conductance, UV-Visible, magnetic susceptibility, and FT-IR spectra were utilized. Its mixed ligand complexes [M(L1)(Q)Cl2] [ where M= Co(II), Ni(II) , and Cd(II)] and complexes [Pd(L1)(Q)] and [Pt(L1)(Q)Cl2]; [H2L1] =β-enaminone ligand =L1 and Q= 8-Hydroxyquinoline = L2]. The results showed that the complexes were synthesised utilizing the molar ratio M: L1

Abstract

Anaerobic digestion process of organic materials is biochemical decomposition process done by two types of digestion bacteria in the absence of oxygen resulting in the biogas production, which is produced as a waste product of digestion. The first type of bacteria is known as acidogenic which converts organic waste to fatty acids. The second type of bacteria is called methane creators or methanogenic which transforms the fatty acids to biogas (CH₄ and CO₂). The considerable amounts of biodegradable constitutes such as carbohydrates, lipids and proteins present in the microalgae biomass make it a suitable substrate for the anaerobic digestion or even c

One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued input and a vector-valued output. A scalar-valued input signal must somehow be converted into a suitable vector-valued signal. This conversion is called preprocessing. Preprocessing is a mapping process which is done by a prefilter. A postfilter just does the opposite.

The most obvious way to get two input rows from a given signal is to repeat the signal. Two rows go into the multifilter bank. This procedure is called “Repeated Row” which introduces oversampling of the data by a factor of 2.

 For data compression, where one is trying to find compact transform representations for a