Nowadays, the ideas of integrating the concepts of the environment and saving it are being famous. These ideas are widely seen in many fields of study, and language education is one of them. Thus, the identity of English Language teachers (ELT) is a step toward transferring this concept in EFL materials in ELT departments. The EFL teacher's identity takes different meanings. Sometimes, it only means the teacher who teaches the English language, and other times, it means, the cultural and social aspects that the teacher and students interact during the study course. These cultural and social aspects represent the environment in teacher’s identity. This study aims to explore the environmental identity within EFL teacher identity. The sam

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

     Equilibrium and rate of mixing of free flowing solid materials are found using gas fluidized bed. The solid materials were sand (size 0.7 mm), sugar (size0.7 mm) and 15% cast iron used as a tracer. The fluidizing gas was air with velocity ranged from 0.45-0.65 m/s while the mixing time was up to 10 minutes. The mixing index for each experiment was calculated by averaging the results of 10 samples taken from different radial and axial positions in fluidized QVF column 150 mm ID and 900 mm height.

     The experimental results were used in solving a mathematical model of mixing rate and mixing index at an equilibrium proposed by Rose. The results show that mixing index increases with inc

The aim of this work is to study the influence of the type of fiber glass –mat on fatigue behavior of composite material which is manufactured from polyester and E-glass (woven roving, chopped strand mat (CSM)) as a laminate with a constant fiber volume fraction (VF) of 33%. The results showed that the laminates reinforced with E-glass (woven roving) [0/90, ±45.0/90] and [0/90, CSM, 0/90] have lower fatigue strength than the laminates reinforced with E-glass [0/90]₃,[CSM]₃ and [CSM, 0/90, CSM]  although they had different tensile strength; the best laminate was [0/90]₃ .

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

Studies on the flexural behavior of post-tensioned beams subjected to strand damage and strengthened with near-surface mounted (NSM) technique using carbon fiber-reinforced polymer (CFRP) are limited and fail to examine the effect of CFRP laminates on strand strain and strengthening efficiency systematically. Furthermore, a design approach for UPC structures in existing design guidelines for FRP strengthening techniques is lacking. Hence, the behavior of post-tensioned beams strengthened with NSM-CFRP laminates after partial strand damage is investigated in this study. The testing program consists of seven post-tensioned beams strengthened by NSM-CFRP laminates with three partial strand damage ratios (14.3% symmetrical damage, 14.3%

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel.  Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Sig