In our article, three iterative methods are performed to solve the nonlinear differential equations that represent the straight and radial fins affected by thermal conductivity. The iterative methods are the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM) to get the approximate solutions. For comparison purposes, the numerical solutions were further achieved by using the fourth Runge-Kutta (RK4) method, Euler method and previous analytical methods that available in the literature. Moreover, the convergence of the proposed methods was discussed and proved. In addition, the maximum error remainder values are also evaluated which indicates that the propo

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

Porosity is important because it reflects the presence of oil reserves. Hence, the number of underground reserves and a direct influence on the essential petrophysical parameters, such as permeability and saturation, are related to connected pores. Also, the selection of perforation interval and recommended drilling additional infill wells. For the estimation two distinct methods are used to obtain the results: the first method is based on conventional equations that utilize porosity logs. In contrast, the second approach relies on statistical methods based on making matrices dependent on rock and fluid composition and solving the equations (matrices) instantaneously. In which records have entered as equations, and the matrix is sol

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

The effect of gamma rays on males and females ofCallasobruchus maculatus and Trogoderma granarium which were irradiated as 1-3 days old adults was investigated. The results revealed that the percent egg hatch for both pests was zero ,and average number of egg (34.2,21.5) for both pests respectively where their males where irradiated with 0.18 kGy and mated with unirradiated females. While the percent of egg hatch and the average number of egg (zero,21,3) respectively when the females where irradiated with 0.18 and 0.15 kGy and mated to unirradiated males for C. maculatus and T. granarium respectively. Furthermore, the results showed that the percent of eggs hatch and average number of egg was ( zero,22.7) for C. maculatus when both sexes i

ABSTRACT Background: This study aimed to study the effect of some acidic drinks (Vinegars and fresh Orange juice) and energy drinks (Red bull) on surface roughness of three types of bulkfill composite materials: Filtek posterior bulkfill (3M), Sonicfill (Kerr) and Filtek p60 (3M). Materials and Methods: Total number of 120 samples are prepared by using a mold of (12mm diameter and 3mm height), which were divided into three groups forty samples for each group: Group A: Filtek bulkfill posterior composite (3M), Group B: Sonicfill composite (Kerr), Group C: Filtek P60 (3 M) which then divided into four sub- groups (n=10) (1) samples were kept in distilled water as a control group (2) samples were immersed in Redbull (3) samples were immersed

The major aim of this research is study the effect of the type of lightweight aggregate (Porcelinite and Thermostone), type and ratio of the pozzolanic material(SF and HRM) and the use of different ratios of w/cm ratio(0.32 and 0.35) on the properties of SCLWC in the fresh and hardened state. SF and HRM are used in three percentage 5%,10%, and 15% as a partial replacement by weight of
cement for all types of SCLWC. The requirements of self-compatibility for SCC are fulfilled by using the high performance superplasticizer (G51) at 1.2liter per 100 kg of cement. The values of air dry density and compressive strength at age of 28 days within the limits of structural lightweight concrete. The air dry density and compressive strength at a