Novel derivatives of 1-(´1, ´3, ´4, ´6-tetra benzoyl-β-D-fructofuranosyl)-1H- benzotriazole and 1-(´1, ´3, ´4, ´6-tetra benzoyl-β-D-fructofuranosyl)-1H- benzotriazole carrying Schiff bases moiety were synthesised and fully characterised. The protection of D- fructose using benzoyl chloride was synthesized, followed by nucleophilic addition/elimination between benzotria- zole and chloroacetyl chloride to give 1-(1- chloroacetyl)- 1H-benzotriazole. The next step was condensation reaction of protected fructose and 1-(1-chloroacetyl)-1H- benzotriazole producing a new nucleoside analogue. The novel nucleoside analogues underwent a second conden- sation reaction with different aromatic and aliphatic amines to provide new Schiff b

     The research aimed to identify the extent to which the media offices in the ministries depend on the website of the Iraqi Council of Ministers as a source of information. Such goal includes a set of sub-goals: Knowing the reasons for media offices' reliance on the information provided by the Cabinet's website; and revealing the benefit derived from relying on the Cabinet's website.

The researcher used the survey method to screen and comprehend the extent to which the media offices in the ministries depend on the website of the Iraqi Council of Ministers as a source of information.

The mechanism of comprehensive account of the limits of research wa

This paper presents a study for the influence of magnetohydrodynamic (MHD) on the oscillating flows of fractional Burgers’ fluid. The fractional calculus approach in the constitutive relationship model is introduced and a fractional Burgers’ model is built. The exact solution of the oscillating motions of a fractional Burgers’ fluid due to cosine and sine oscillations of an infinite flat plate are established with the help of integral transforms (Fourier sine and Laplace transforms). The expressions for the velocity field and the resulting shear stress that have been obtained, presented under integral and series form in terms of the generalized Mittag-Leffler function, satisfy all imposed initial and boundary conditions. Finall

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

In this paper the use of a circular array antenna with adaptive system in conjunction with modified Linearly Constrained Minimum Variance Beam forming (LCMVB) algorithm is proposed to meet the requirement of Angle of Arrival (AOA) estimation in 2-D as well as the Signal to Noise Ratio (SNR) of estimated sources (Three Dimensional 3-D estimation), rather than interference cancelation as it is used for. The proposed system was simulated, tested and compared with the modified Multiple Signal Classification (MUSIC) technique for 2-D estimation. The results show the system has exhibited astonishing results for simultaneously estimating 3-D parameters with accuracy approximately equivalent to the MUSIC technique (for estimating elevation and a

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

Quantum key distribution (QKD) provides unconditional security in theory. However, practical QKD systems face challenges in maximizing the secure key rate and extending transmission distances. In this paper, we introduce a comparative study of the BB84 protocol using coincidence detection with two different quantum channels: a free space and underwater quantum channels. A simulated seawater was used as an example for underwater quantum channel. Different single photon detection modules were used on Bob’s side to capture the coincidence counts. Results showed that increasing the mean photon number generally leads to a higher rate of coincidence detection and therefore higher possibility of increasing the secure key rate. The secure key rat

   Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr