Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte

This research aims to examine the relationship between hydrothermal alteration and mineralization (ore mineralogy) in the study area and geological structures in the deformation mechanism. The hydrothermal alteration was determined based on petrographic analysis, and ore mineralogy which was determined based on the ore microscopic analysis. The deformation mechanism is determined by paleo stress analysis using win-tensor, and the direction of principal stress on joints/veins and faults is calculated by the right-dihedron method. Hydrothermal alteration includes silicification, argillic, propylitic, and phyllic alterations; and ore mineralogy consists of stibnite, cinnabar, pyrite, chalcopyrite, sphalerite, covellite, hematite, and a

Background. Neutrophils play an important role in maintaining periodontal status in conditions of healthy homeostasis. They achieve their surveillance function by continuously migrating to the gingival sulcus and eradicating periodontal pathogens. In addition, neutrophils are considered an integral element in the pathogenesis of periodontal diseases. Among several neutrophil subsets, low‐density neutrophils (LDN) have recently received attention and are linked with cancer, immunological, inflammatory, and infectious diseases. However, the presence, phenotypes, and potential role of LDN in the pathogenesis of periodontitis have not yet been investigated. Objectives. To investiga

In this work, electrochemical process was presented to polymerized eugenol on Gr.2 and Gr.5 titanium alloys before and after treated by Micro Arc Oxidation (MAO), where Gr.2 is commercial pure titanium and Gr.5 is Ti-6Al-4V dental alloys. The deposited layers were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The adhesion strength of polymeric thin-film was estimation by using pull-off adhesion test and the result was the adhesion strength of PE was (1.23 MPa) on Gr.2 before MAO and increase to (1.98 MPa) on Gr.2 after MAO treatment. The corrosion behavior of Gr.2 and Gr.5 alloy in artificial saliva environment at

The population has been trying to use clean energy instead of combustion. The choice was to use liquefied petroleum gas (LPG) for domestic use, especially for cooking due to its advantages as a light gas, a lower cost, and clean energy. Residential complexes are supplied with liquefied petroleum gas for each housing unit, transported by pipes from LPG tanks to the equipment. This research aims to simulate the design and performance design of the LPG system in the building that is applied to a residential complex in Baghdad taken as a study case with eight buildings. The building has 11 floors, and each floor has four apartments. The design in this study has been done in two parts, part one is the design of an LPG system for one building, an

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

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

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol