Convolvulus arvensis is a species of bindweed that is rhizomatous and is in the morning glory family (Convolvulaceae) native to Europe and Asia. The plant is naturally grown in Iraq. The plant was reported to be used in traditional medicine from as early as 1730s.

The Aerial parts of Convolvulus arvensis were macerated in 80% ethanol for 6 days. The concentrated extract was partitioned with n-hexane, chloroform, ethyl acetate- and n-butanol successively. The n-hexane and ethyl acetate, fractions were examined for the presence of phytochemicals by thin l

Globalisation and rapid environmental change have created many challenges for public and private organisations across Iraq as a developing country, particularly in the higher education sector. This includes, for example, decreases in government funding; increased demand for higher education; a need for economic transformation, and related competitiveness of organizations. Such challenges require exceptional leaders and strategic planning in order to take action to improve. In Iraq, the higher education sector is still one of the main foundations in progressing the knowledge economy. Studies into leadership style, strategic planning processes, and the importance of leadership and organisational culture to an organisation’s success have bee

In this research, damping properties for composite materials were evaluated using logarithmic decrement method to study the effect of reinforcements on the damping ratio of the epoxy matrix. Three stages of composites were prepared in this research. The first stage included preparing binary blends of epoxy (EP) and different weight percentages of polysulfide rubber (PSR) (0%, 2.5%, 5%, 7.5% and 10%). It was found that the weight percentage 5% of polysulfide was the best percentage, which gives the best mechanical properties for the blend matrix. The advantage of this blend matrix is that; it mediates between the brittle properties of epoxy and the flexible properties of a blend matrix with the highest percentage of PSR. The second stage

Background This study establishes a mathematically consistent and computational framework for the simultaneous identification of two time-dependent coefficients in a one-dimensional second-order parabolic partial differential equation. The considered problem is governed by nonlocal initial, boundary, and integral overdetermination conditions. Methods The direct problem is solved using the Crank-Nicolson finite difference method (FDM), which ensures unconditional stability and second-order accuracy in both spatial and temporal discretizations. The corresponding inverse problem is reformulated as a nonlinear regularized least-squares optimization problem and efficiently solved used the MATLAB subroutine

The two body model of (Core+n) within the radial wave functions of the cosh potential has been used to investigate the ground state features such as the proton, neutron and matter densities, the root mean square (RMS) nuclear proton, neutron, charge and mass radii of unstable neutron-rich 14B, 15C, 19C and 22N nuclei. The calculated results show that the two body model with the radial wave functions of the cosh potential succeeds in reproducing neutron halo in these nuclei.

This paper presents an experimental and theoretical analysis to investigate the two-phase flow boiling heat transfer coefficient and pressure drop of the refrigerant R-134a in the evaporator test section of the refrigeration system under different operating conditions. The test conditions considered are, for heat flux (13.7-36.6) kW/m², mass flux (52-105) kg/m².s, vapor quality (0.2-1) and saturation temperature (-15 to -3.7) ˚C. Experiments were carried out using a test rig for a 310W capacity refrigeration system, which is designed and constructed in the current work. Investigating of the experimental results has revealed that, the enhancement in local heat trans

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m