This research is concerned to investigate the behavior of reinforced concrete (RC) deep beams strengthened with carbon fiber reinforced polymer (CFRP) strips. The experimental part of this research is carried out by testing seven RC deep beams having the same dimensions and steel reinforcement which have been divided into two groups according to the strengthening schemes. Group one was consisted of three deep beams strengthened with vertical U-wrapped CFRP strips. While, Group two was consisted of three deep beams strengthened with inclined CFRP strips oriented by 45^o with the longitudinal axis of the beam. The remaining beam is kept unstrengthening as a reference beam. For each group, the variable considered

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

The main objective of the present research is to conduct a thorough investigation into the impact of construction joints on the structural performance of reinforced concrete deep beams. This study involves a series of experimental tests and the use of advanced numerical analysis techniques to gain a deeper understanding of the behavior of these beams in the presence of construction joints. The experimental component incorporates analysis findings from both previous and current research. Specifically, six reinforced concrete deep beam specimens featuring horizontal and inclined construction joints were utilized as simply being supported with two-point loading. The test findings indicate that the presence of a horizontal construction

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

In this study, the kinematic analysis including the forward and inverse kinematic developed for a 5 degree of freedom robotic arm. The forward kinematic is elaborated using Denavit-Hartenberg (DH) convention. Inverse Kinematic is established using Deep Neural Network (DNN) model with five hidden layer each contain 50 neurons fully connected using ReLu activation. A data of inputs and outputs are created and trained. The inputs are the end-effector position and orientation. The outputs are the joint angles of the manipulator. The data is generated by the forward kinematics, where a set of joint angles that limited by their corresponding ranges are inserted to the forward kinematic equations to result the end-effector positions and or

In search of novel antibacterial agent, a series of new isatin derivatives (3a-d) have been synthesized by condensation isatin (2,3-indolinendione) with piperidine (hexahydropyridine), hydrazine hydrate and Boc-amino acids respectively. Compounds synthesized have been characterized by IR spectroscopy and elemental analysis. In addition, the in vitro antibacterial properties have been tested against E. coli, P. aeruginosa, and Bacillus cereus, S. aureus by employing the well diffusion technique. A majority of the synthesized compounds were showing good antibacterial activity and from comparisons of the compounds, compound 3d has been determined to be the most active compound.

The synthesis of zeolite NaX from locally available kaolin has been studied. The operating conditions for zeolite NaX production from kaolin with good crystallinity were as follows; a gel formation step of metakaolin in alkaline medium in presence of additional silica to crystallize the zeolite was achieved at 60 oC for 1 hr,and with stirring. In ageing step of the reactants at room temperature for 5 days and crystallization step at 87±2 oC for 24 hr. The catalytic activity of catalyst prepared from local kaolin was studied by using cumene cracking as a model for catalytic cracking and compared with standard HY zeolite and HX zeolite catalysts. The activity test was carried out in a laboratory continuous flow unit with fixed bed re

The antibacterial activity of some extracts of A. eupatoria (aqueous and ethanolic) against some pathogenic bacteria (Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli ) and their activity on wound healing in rats , also the presence of some active compounds in both extracts were detected . The results showed that the ethanolic extract was more effective on inhibiting tested bacteria than the aqueous extract . P.aeruginosa was the most resistant bacteria, while highest inhibition zone appeared on E.coli (20 mm) .There was a moderate activity against S.aureus with inhibition zone 15 mm. by using ethanolic extract (10 mg/ml) . The phytochemical analysis for detection of active compounds revealed the presence of Carbohydrate