Levan is an exopolysaccharide produced by various microorganisms and has a variety of applications. In this research, the aim was to demonstrate the biological activity of levan which produced from B. phenoliresistens KX139300. These were done via study the antioxidant, anti-inflammatory, anticancer and antileishmanial activities in vitro. The antioxidant levan was shown 80.9% activity at 1250 µg/mL concentration. The efficient anti-inflammatory activity of 88% protein inhibition was noticed with levan concentration at 35 µg/mL. The cytotoxic activity of levan at 2500 µg/mL concentration showed a maximum cytotoxic effect on L20B cell line and promastigotes of Leishmani tropica. Levan has dose-dependent anticancer and antileishman

The present study aimed at examining the factors that affect the choice of A major among a sample of BA fe(male) students at the levels 3-8 in King Abdulaziz University (KAU), in Jeddah, Saudi Arabia. To meet this objective, a descriptive survey method was used together with a questionnaire that consisted of 4 axes to answer the central question: What are the factors affecting the choice of a major at the university? Results have shown that the item that measured the students’ ability to choose the major ranked (First); it was concerned with the effect on the students' choice of his/her major in the university. On the last position and with respect to this effect came the professional tendencies and desires. Results have also shown tha

The inhibitory effect of acetone, ethanol, and aqueous extracts of ten medicinal plants on β-lactamase from Staphylococcus sciuri and Klebsiella pneumoniae was investigated in vitro by starch-iodine agar plate method. The results revealed the success of starch-iodine method for the detection of the inhibition of β-lactamase activity by the various extracts of each individual plant. The acetone extracts of Catharanthus roseus, Eucalyptus camaldulensis, and Schinus terebinthifolius induced an inhibitory effect on β-lactamase from Staphylococcus sciuri. On the other hand, acetone extracts from only Eucalyptus camaldulensis, and Schinus

     Utilizing the Turbo C programming language, the atmospheric earth model is created from sea level to 86 km. This model has been used to determine atmospheric Earth parameters in this study. Analytical derivations of these parameters are made using the balancing forces theory and the hydrostatic equation. The effects of altitude on density, pressure, temperature, gravitational acceleration, sound speed, scale height, and molecular weight are examined. The mass of the atmosphere is equal to about 50% between sea level and 5.5 km. g is equal to 9.65 m/s2 at 50 km altitude, which is 9% lower than 9.8 m/s2 at sea level. However, at 86 km altitude, g is close to 9.51 m/s2, which is close to 15% smaller than 9.8 m/s2.  These resu

In this paper,  a Sokol-Howell prey-predator model involving strong Allee effect is proposed and analyzed. The existence, uniqueness, and boundedness are studied. All the five possible equilibria have been are obtained and their local stability conditions are established. Using Sotomayor's theorem, the conditions of local saddle-node and transcritical and pitchfork bifurcation are derived and drawn. Numerical simulations are performed to clarify the analytical results

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is construct

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

This research utilized natural asphalt (NA) deposits from sulfur springs in western Iraq. Laboratory tests were conducted to evaluate the performance of an asphalt mixture incorporating NA and verify its suitability for local pavement applications. To achieve this, a combination of two types of NA, namely soft SNA and hard HNA, was blended to create a binder known as Type HSNA. The resulting HSNA exhibited a penetration grade that adhered to Iraqi specifications. Various percentages of NA (20%, 40%, 60%, and 80%) were added to petroleum asphalt. The findings revealed enhanced physical properties of HSNA, which also satisfied the requirements outlined in the Iraqi specifications for asphalt cement.

Consequently, HS