Abstract

 Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

When writing a text, such as a newspaper article, various types of discourse markers are frequently used to group sentences into paragraphs and parts in order to establish a discourse with certain functions, such as coordination, orientation, emphasizing the concepts presented, etc. It should also be noted that this type of mark exists in both written and spoken language. Therefore, it is convenient to dedicate a chapter to these linguistic elements to clarify their use and their classification, which is mainly based on Jose Portolés (2001), as well as the main features, specifically their features (prosodic, morphological, semantic and pragmatic).

Resumen

In this study, reduced graphene oxide (rGO) was synthesized from graphene oxide (GO) via an ascorbic acid-assisted reduction process. GO was synthesized from graphite powder using a modified Hummers technique. The surface morphology, structure, functional groups, and elemental compositions of the produced materials were studied using various methods, such as scanning electron microscopy (SEM)/EDX, X-ray diffraction (XRD), atomic force microscopy (AFM), Fourier transform infrared (FTIR), and UV-Vis. The removal of oxygen-containing functional groups in rGO through reduction resulted in poor sample quality. In addition, FTIR investigations revealed that GO contained more oxygen-containing functional groups than rGO. Typical peaks at 2

Optical losses represent one of the primary obstacles to increasing the efficiency of silicon solar cells. The recommended solution to minimize optical losses is the use of plasmonic metal nanoparticles; however, they act as recombination centers within the solar cell construction, leading to a decrease in performance. The goal of this article is to introduce cobalt/graphene nanoparticles into the solar cell to minimize the optical losses. An ultra-thin film silicon PIN solar cell of dimensions (400 ×400 ×900) nm3 with ring metal contact shape was designed and numerically investigated using COMSOL Multiphysics software version 6.2 by the finite element method (FEM). Core/shell cobalt-graphene (Co/Gr) nanoparticles are periodically int

Background Methotrexate (MTX) is a classical folic acid antagonist widely used in the treatment of malignant and non-malignant disorders. However, its clinical application is often restricted by concomitant adverse effects, including renal damage. Numerous studies have highlighted the role of oxidative stress and inflammation in mediating MTX-related nephrotoxicity. Therefore, the current study aimed to explore the possible renoprotective action of Citronellol (CT), a natural compound with prominent antioxidant and anti-inflammatory activities, against nephrotoxicity induced by MTX. Methods To fulfill our objective, 24 adult male Wistar rats were randomly allocated into four groups: control, MTX, 100 mg/kg CT plus MTX and 200 mg/kg C

Background Methotrexate (MTX) is a classical folic acid antagonist widely used in the treatment of malignant and non-malignant disorders. However, its clinical application is often restricted by concomitant adverse effects, including renal damage. Numerous studies have highlighted the role of oxidative stress and inflammation in mediating MTX-related nephrotoxicity. Therefore, the current study aimed to explore the possible renoprotective action of Citronellol (CT), a natural compound with prominent antioxidant and anti-inflammatory activities, against nephrotoxicity induced by MTX. Methods To fulfill our objective, 24 adult male Wistar rats were randomly allocated into four groups: control, MTX, 100 mg/kg CT plus MTX and 200 mg/kg C