The main purpose of this paper, is to characterize new admissible classes of linear operator in terms of seven-parameter Mittag-Leffler function, and discuss sufficient conditions in order to achieve certain third-order differential subordination and superordination results. In addition, some linked sandwich theorems involving these classes had been obtained.  

In this study, a double frequency Q-switching Nd:YAG laser beam (1064 nm and λ= 532 nm, repetition rate 6 Hz and the pulse duration 10ns) have been used, to deposit TiO2 pure and nanocomposites thin films with noble metal (Ag) at various concentration ratios of (0, 10, 20, 30, 40 and 50 wt.%) on glass and p-Si wafer (111) substrates using Pulse Laser Deposition (PLD) technique. Many growth parameters have been considered to specify the optimum condition, namely substrate temperature (300˚C), oxygen pressure (2.8×10-4 mbar), laser energy (700) mJ and the number of laser shots was 400 pulses with thickness of about 170 nm. The surface morphology of the thin films has been studied by using atomic force microscopes (AFM). The Root Mean Sq

This research presents the possibility of using banana peel (arising from agricultural production waste) as biosorbent for removal of copper from simulated aqueous solution. Batch sorption experiments were performed as a function of pH, sorbent dose, and contact time. The optimal pH value of Copper (II) removal by banana peel was 6. The amount of sorbed metal ions was calculated as 52.632 mg/g. Sorption kinetic data were tested using pseudo-first order, and pseudo-second order models. Kinetic studies showed that the sorption followed a pseudo second order reaction due to the high correlation coefficient and the agreement between the experimental and calculated values of q_e. Thermodynamic parameters such as enthalpy change (ΔH

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Light isotopes, especially closed shell nuclei, have significance in thermonuclear reactions of the Carbon-Nitrogen-Oxygen (CNO) cycle in stars. In this research, 12C(p, γ) 13N and 14N(p, γ) 15O reactions have been calculated by means of Matlab codes to find the reaction rate across a temperature range of 0.006 to 10 GK using non-resonant parts, as well as the astrophysical S- factor S(E) at low energies. It was concluded that the high binding energy of 12C and 14N nuclei make the reaction less probable thus enabling other competitive processes to develop, which enhances the probability of other competitive proton reactions in the CNO cycle.

Research aims to develop a novel technique for segmental beam fabrication using plain concrete blocks and externally bonded Carbon Fiber Reinforced Polymers Laminates (CFRP) as a main flexural reinforcement. Six beams designed an experimentally tested under two-point loadings. Several parameters included in the fabrication of segmental beam studied such as; bonding length of carbon fiber reinforced polymers, the surface-to-surface condition of concrete segments, interface condition of the bonding surface, and thickness of epoxy resin layers. Test results of the segmental beams specimens compared with that gained from testing reinforced concrete beam have similar dimensions for validations. The results show the effectiven

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

Sphingolipids (SLs) are major structural constituents of eukaryotes, including the kinetoplastid parasite Leishmania. SLs are important for cellular trafficking and signaling and participate in different cell functions, such as, differentiation and cell death (apoptosis). In this study we have investigated the viability of Leishmania major wild type (W.T) and L. major knockout LmLCB2, one of two subunits of serine palmitoyl transferase (SPT) after treatment with myriocin (potent inhibitor of SPT) in order to detect the survival and proliferation of the parasites in vitro. This is to focus on the de novo sphingolipids biosynthesis pathway in both Leishmania wild type which can synthesize SPT and knockout Leishmania which genetically ablated

A new modified differential evolution algorithm DE-BEA, is proposed to improve the reliability of the standard DE/current-to-rand/1/bin by implementing a new mutation scheme inspired by the bacterial evolutionary algorithm (BEA). The crossover and the selection schemes of the DE method are also modified to fit the new DE-BEA mechanism. The new scheme diversifies the population by applying to all the individuals a segment based scheme that generates multiple copies (clones) from each individual one-by-one and applies the BEA segment-wise mechanism. These new steps are embedded in the DE/current-to-rand/bin scheme. The performance of the new algorithm has been compared with several DE variants over eighteen benchmark functions including sever

     This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s