Silver nanoparticles synthesized by different species

Introduction and Aim: Klebsiella pneumoniae is a Gram-negative bacterium responsible for a wide range of infections, including respiratory tract infections (RTIs). This research was aimed to study the antibacterial and antibiofilm effect of AgNPs produced by Gram positive and negative bacteria on RTIs associated with K. pneumoniae. Materials and Methods: The biofilm formation of K. pneumoniae was determined by tube method qualitatively from select bacterial species characterized by UV-Visible spectroscopy. The antibacterial susceptibility of the bacteria AgNPs was tested for their antibacterial and antibiofilm activity on a clinical isolate of K. pneumoniae. Results: K. pneumoniae isolated from RTIs were strong biofilm producers. The ant

Introduction and Aim: Klebsiella pneumoniae is a Gram-negative bacterium responsible for a wide range of infections, including respiratory tract infections (RTIs). This research was aimed to study the antibacterial and anti-biofilm effect of AgNPs produced by Gram positive and negative bacteria on RTIs associated with K. pneumoniae.   Materials and Methods: The biofilm formation of K.  pneumoniae was determined by tube method qualitatively from select bacterial species characterized by UV-Visible spectroscopy. The antibacterial susceptibility of the bacteria AgNPs was tested for their antibacterial and antibiofilm activity on a clinical isolate of K. pneumoniae.   Results: K. pneumoniae isolated from RTIs were strong biofilm prod

Copper oxide (CuO) nanoparticles were synthesized through the thermal decomposition of a copper(II) Schiff-base complex. The complex was formed by reacting cupric acetate with a Schiff base in a 2:1 metal-to-ligand ratio. The Schiff base itself was synthesized via the condensation of benzidine and 2-hydroxybenzaldehyde in the presence of glacial acetic acid. This newly synthesized symmetric Schiff base served as the ligand for the Cu(II) metal ion complex. The ligand and its complex were characterized using several spectroscopic methods, including FTIR, UV-vis, 1H-NMR, 13C-NMR, CHNS, and AAS, along with TGA, molar conductivity and magnetic susceptibility measurements. The CuO nanoparticles were produced by thermally decomposing the

This study was carrid out to produce animal gelatin from chicken skin. Gelatin was prepared by the chemical method using HCl 2% and extraction at the temperature degree 70, 80, 90 c° and at the period of time 4, 6, 8 hours, calculated the yield, functional and sensory characteristics were measured at. The result also demonstrated that the produced gelatin have good functional properties in solubility, viscosity, gelling capacity, water absorpation, lipid binding, emulsification. viscosity was higher in gelatin prepared at 70 c° and period of extraction 8 hours and reached 1.0846 cp. Gelatin prepared were featured by highe gelling capacity at 1% for all extraction time periods. The produced gelatin was characterized by good sensory qual

Two oil wells were tested to find the abnormal pressure zones using sonic log technique. We found that well Abu-Jir-3 and Abu-Jir-5 had an abnormal pressure zones from depth 4340 to 4520 feet and 4200 to 4600 feet, respectively. The maximum difference between obtained results and the field measured results did not exceed 2.4%.
In this paper, the formation pressures were expressed in terms of pressure gradient which sometimes reached up to twice the normal pressure gradient.
Drilling and developing such formations were dangerous and expensive.
The plotted figures showed a clear derivation from the normal trend which confirmed the existence of abnormal pressure zones.

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.