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.

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati

Pultruded materials made of Fiber-Reinforced Polymer (FRP) come in a broad range of shapes, such as bars, I-sections, C-sections, etc. FRP materials are starting to compete with steel as structural materials owing to their great resistance, low self-weight, and cheap maintenance costs, especially in corrosive conditions. This study aims to evaluate the effectiveness of a novel concrete Composite Column (CC) using Encased I-Section (EIS) as a reinforcement in contrast to traditional steel bars by using Glass Fiber-Reinforced Polymer (GFRP) as I-section (CC-EIS) to evaluate the effectiveness of the hybrid columns which have been built by combining GFRP profiles with concrete columns. To achieve the aims of this study, nine circular co

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

Aim of the study: Using surface roughness and tensile bond strength tests, the objective of this investigation was to ascertain the impact of laser surface modification on the binding strength of injectable thermoplastic acrylic denture base material to acrylic-based soft-liner material. Materials and methods: Acrylic base soft liner material was bonded to injectable thermoplastic acrylic resin (Deflex). Forty specimens were created (20 disc, 20 dumbbells) 10 of each specimen type as control specimens, and 10 were treated with nano pulse Nd: YAG laser. The data were analyzed using the Kruskal-Wallis test and unpaired t-test (a=.05) and the roughness test was performed utilizing a double column universal test machine. Results: Compar