The Atoms in Molecules (AIM) analysis for triosmium cluster, which contains trihydridede, carbon, carbonyl and 2-methylbenzothiazolide ligands, [Os₃(µ-H)₃(µ³-ɳ²-CC₇H₃(2-CH₃)NS)(CO)₈] is reported. Bonding features in this cluster has been analyzed based on QTAIM ("Quantum Theory of Atoms in Molecules") in this work. The topological indices derived from electron density of relevant interactions in triosmium compound have been studied. The major interesting point of the AIM analyses is that the core of part (Os₃H₃) reveals the absence of any critical points and bond paths connecting any pairs of O

This research seeks to attempt to reveal the intention of Al-Hafiz Al-Mazi from his market to hadiths from Imam Ahmad’s Musnad in his book “Masterpiece of Supervision in Knowing the Parties”, and researchers know that this book was classified by Al-Mazi to collect the parties to the six books: Sahih Al-Bukhari, Sahih Muslim, Sunan Abi Dawud, Jamea Al-Tirmidhi and Sunan Al-Nasa’i and Sunan Ibn Majah, in addition to some of the books ’authors’ books, and arranging them according to the Musnad, and it is not Musnad Ahmad from his condition, even if it is close to the arrangement of Mazi’s work, as he was a follower of that to the original owner Abu al-Qasim bin Asaker in his book “Supervising Knowledge of the Parties” in M

Abstract

These experiments seek to investigate the effects of the fixed variations to the basic box plot on subjects' judgments of the box lengths. The study consists of two experiments, were constructed as an extension to the experiments carried out previously by Hussin, M.M. (1989, 2006).  Subjects were asked to judge what percentage the shorter represented of the longer length in pairs of box lengths and give an estimate of percentage, one being a standard plot and the other being of a different box length and also varying with respect to other elements such as, box width or whisker length. When he (1989) suggested in the future research points (1, 2), the changing length of the st

            Environmentally friendly copper oxide nanoparticles (CuO NPs) were prepared with a green synthesis route via Anchusa strigosa L.  Flowers extract. These nanoparticles were further characterized by FTIR, XRD and SEM techniques. Removing of Gongo red from water was applied successfully by using synthesized CuO NPs which used as an adsorbent material. It was validated that the CuO NPs eliminate Congo red by means of adsorption, and the best efficiency of adsorption was gained at pH (3). The maximum adsorption capacity of CuO NPs for Congo red was observed at (35) mg/g. The equilibrium information for adsorption have been outfitted to the Langmuir, Freundlich, Temkin and Halsey adsorption isot

In this paper, we have extracted Silica from rice husk ash (RHA) by sodium hydroxide to produce sodium silicate. 3-(chloropropyl)triethoxysilane (CPTES) functionalized with sodium silicate via a sol-gel method in one pot synthesis to prepare RHACCl. Chloro group in compound RHACCl replacement in iodo group to prepere RHACI. The FT-IR clearly showed absorption band of C-I at 580 cm^-1. Functionalized silica RHACI has high surface area (410 m²/g) and average pore diameter (3.8 nm) within mesoporous range. X-ray diffraction pattern showed that functionalized silica RHACI has amorphous phase .Thermogravemitric analysis (TGA) showed two decomposition stages and SEM morphology of RHACI showed that the particles have irregu

The pancreatic ductal adenocarcinoma (PDAC), which represents over 90% of pancreatic cancer cases,
has the highest proliferative and metastatic rate in comparison to other pancreatic cancer compartments. This
study is designed to determine whether small nucleolar RNA, H/ACA box 64 (snoRNA64) is associated with
pancreatic cancer initiation and progression. Gene expression data from the Gene Expression Omnibus (GEO)
repository have shown that snoRNA64 expression is reduced in primary and metastatic pancreatic cancer as
compared to normal tissues based on statistical analysis of the in Silico analysis. Using qPCR techniques,
pancreatic cancer cell lines include PK-1, PK-8, PK-4, and Mia PaCa-2 with differ

المحاسبة السحابية افق جديد لتنظيم العمل المحاسبي

 Abstract   

Lightweight materials is used in the sheet metal hydroforming process,  because it can be adapted to the manufacturing of complex structural components into a single body with high structural stiffness. Sheet hydroforming has been successfully developed in industry such as in the manufacturing of the components of automotive.The aim of this study is to simulate the experimental results ( such as the amount of pressure required to hydroforming process, stresses, and strains distribution)  with results  of finite element analyses (FEA)  (ANSYS 11)  for aluminum alloy (AA5652) sheets with  thickness (1.2mm) before heat treatm

In this work, 332 Al alloy was prepared and reinforced with (0.5% and 1%) nano-Al₂O₃ particles. The prepared unreinforced and reinforced 332 Al alloy with nano-Al₂O₃ were solution heat treated (T6) at 510 ̊C and aged at 225 ̊C with different times (1, 3, and 5 h). Hardness test was performed on all the prepared alloys. All prepared alloys were dry slided under different applied loads (5, 10, 15, and 20 N) against steel counterface surface using pin on disk apparatus. The results showed that refinement effect was observed after addition of nano-Al₂O₃ particles and a change in silicon morphology after performing the solution heat treatment. The results also showed that har

The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:

Model: T = T₀(-1.36729 + 0.037101∙10^λpH + 0.048928t + 0.00741387∙alk)

The developed model will aid the predictiv