In this work,  an explicit formula for a class of Bi-Bazilevic univalent functions involving differential operator is given, as well as the determination of upper bounds for the general Taylor-Maclaurin coefficient of a functions belong to this class, are established Faber polynomials are used as a coordinated system to study the geometry of the manifold of coefficients for these functions. Also determining bounds for the first two coefficients of such functions.

         In certain cases, our initial estimates improve some of the coefficient bounds and link them to earlier thoughtful results that are published earlier.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

A total of 96 stool samples were collected from children with bloody diarrhea from two hospitals in Baghdad. All samples were surveyed and examined for the presence of the Escherichia coli O157:H7 and differentiate it from other Non -Sorbitol Fermenting Escherichia coli (NSF E. coli). The Bacterial isolates were identifed by using morphological diagnostic methods, Samples were cultured on liquid enrichment medium, incubated at 37C? for 24 hrs, and then cultured on Cefixime Tellurite -Sorbitol MacConkey Agar (CT- SMAC). 32 non-sorbitol fermenting bacterial isolates were obtained of which 11 were identified as Escherichia coli by using traditional biochemical tests and API20E diagnostic system without differentiation between

Polyaniline Multi walled Carbon nanotubes (PANI/MWCNTs) nanocomposite thin films have been prepared by non-equilibrium atmospheric pressure plasma jet on glass substrate with different weight percentage of MWCNTs 1, 2, 3, 4%. The diameter of the MWCNTs was in the range of 8-55 nm and length - - 55 55 μm. the nanocomposite thin films were characterized by UV-VIS, XRD, FTIR, and SEM. The optical studies show that the energy band gap of PANI/MWCNTs nanocomposites thin films will be different according to the MWCNTs polyaniline concentration. The XRD pattern indicates that the synthesized PANI/MWCNTs nanocomposite is amorphous. FTIR reveals the presence of MWCNTs nanoparticle embedded into polyaniline. SEM surface images show that the MWCNT

A comparative study was done on the adsorption of methyl orange dye (MO) using non-activated and activated corn leaves with hydrochloric acid as an adsorbent material. Scanning electron microscopy (SEM) and Fourier Transform Infrared spectroscopy (FTIR) were utilized to specify the properties of adsorbent material. The effect of several variables (pH, initial dye concentration, temperature, amount of adsorbent and contact time) on the removal efficiency was studied and the results indicated that the adsorption efficiency increases with the increase in the concentration of dye, adsorbent dosage and contact time, while inversely proportional to the increase in pH and temperature for both the treated and untreated corn leaves. The equi

In most manufacturing processes, and in spite of statistical control, several process capability indices refer to non conformance of the true mean (µc ) from the target mean ( µT ), and the variation is also high. In this paper, data have been analyzed and studied for a blow molded plastic product (Zahi Bottle) (ZB). WinQSB software was used to facilitate the statistical process control, and process capability analysis and some of capability indices. The relationship between different process capability indices and the true mean of the process were represented, and then with the standard deviation (σ ), of achievement of process capability value that can reduce the standard deviation value and improve production out of theoretical con

The CuInSe2 (CIS) nanocrystals are synthesized by arrested precipitation from molecular precursors are added to a hot solvent with organic cap- ping ligands to control nanocrystal formation and growth. CIS thin films deposited onto glass substrate by spray - coating, then selenized in Ar- atmosphere to form CIS thin films. PVs were made with power conversion efficiencies of 0.631% as -deposited and 0.846% after selenization, for Mo coated, under AM 1.5 illumination. X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analysis it is evident that CIS have the chalcopyrite structure as the major phase with a preferred orientation along (112) direction and the atomic ratio of Cu : In : Se in the nanocrystals is nearly 1 : 1 : 2

A comparison between the resistance capacity of a single pile excited by two opposite rotary machines embedded in dry and saturated sandy soil was considered experimentally. A small-scale physical model was manufactured to accomplish the experimental work in the laboratory. The physical model consists of: two small motors supplied with eccentric mass 0·012 kg and eccentric distance 20 mm representing the two opposite rotary machines, an aluminum shaft with 20 mm in diameter as the pile, and a steel plate with dimensions of (160 × 160 × 20 mm) as a pile cap. The experimental work was achieved taking the following parameters into consideration, pile embedment depth ratio (L/d; length to diameter) and operating freq

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d