The choice of binary Pseudonoise (PN) sequences with specific properties, having long period high complexity, randomness, minimum cross and auto- correlation which are essential for some communication systems. In this research a nonlinear PN generator is introduced . It consists of a combination of basic components like Linear Feedback Shift Register (LFSR), ?-element which is a type of RxR crossbar switches. The period and complexity of a sequence which are generated by the proposed generator are computed and the randomness properties of these sequences are measured by well-known randomness tests.

The rotation effect upon Morse potential had been studied and the values of the effective potential in potential curves had been calculated for electronic states (X2?+g , B ?u ) K2 molecule. The calculation had been computed for rotational quantum number (J = 5). Also, drawing potential curves for these systems had been done using Herzberg and Gaydon equations. It was found that the values of the dissociation energy which resulting from using Herzberg equation greater than that of Gaydon equation. Besides, it was found that the rotation effect for (X and B) electronic states in Morse potential is very small and in this case may negligible.

Hyperlipidemia is one of the most important factors leading to atherosclerosis and heart disease, therefore, this study conducted to examine the effect of two newly synthesized compounds[3-(5(ethylthio)-1,3,4-thiadiazol-2-yl)-2,3-dihydro-2-(3-nitrophenyl)benzo[1-3-e] thiazin-4-one (I) and 5(4dimethyl amino) benzylidene amino)-1,3,4-thiadiazole-2-thiol(II)] on the activities of creatine kinase(CK) and 3-hydroxy-3-methylglutaryl- CoA reductase (HMGR) in male Wister mice . Also to determine the type of inhibition of these compounds on the above enzymes .The study was carried out on sixty male Wister mice aged seven to eight weeks their weight ranged(180-200 g) . The mice were grouped as: group(1): control group (12 mice).Group(2):consisted

Metal-organic frameworks (MOFs) are a relatively new class of materials of unique porous structures and exceptional properties. Currently, more than 110,000 types of MOFs have been reported among the countless possibilities. In this study, we have synthesised a novel MOF using zirconium chloride as the metal source and 4,4'-dicarboxy-2,2'-biquinoline (bicinchoninic acid disodium salt) as the linker, which reacted in N,N-Dimethylformamide (DMF) solvent. Three preparation methods were employed to prepare five types of the MOF, and they were compared to optimize the synthesis conditions. The resulting MOFs, named Zr-BADS, were characterised using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), microscopy, and

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa

The extract of fig fruit has shown significant medical usefulness in various fields. The entrance of nanotechnology into the field of medicinal and pharmacology has shown remarkable advantages. Plants contain diverse molecules thatcan reduce metals, and provide a safe, eco-friendly approach for synthesizing nanoparticles. Iron oxide nanoparticles (IONPs) have been reported to possess an antimicrobial effect against some strains of bacteria and moulds. We have aimed to synthesize IONPs from fig fruit extract and investigate the influence of fig extract and IONPs in wound healing of mice. UV-Vis spectroscopy, X-ray diffraction (XRD), and field emission scanning electron microscopy were used to characterize the IONPs that were produced

The insulation system of a machine coil includes several layers made of materials with different characteristics. The effective insulation design of machine coils, especially in the machine end winding, depends upon an accurate model of the stress grading system. This paper proposes a modeling approach to predict the transient overvoltage, electric field, and heat generation in machine coils with a stress grading system, considering the variation of physical properties in the insulation layers. A non-uniform line model is used to divide the coil in different segments based on material properties and lengths: overhang, stress grading and slot. The cascaded connection of chain matrices is used to connect segments for the representation of the

This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performan

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing