Far infrared photoconductive detectors based on multi-wall carbon nanotubes (MWCNTs) were fabricated and their characteristics were tested. MWCNTs films deposited on porous silicon (PSi) nanosurface by dip and drop coating techniques. Two types of deposited methods were used; dip coating sand drop –by-drop methods. As well as two types of detector were fabricated one with aluminum mask and the other without, and their figures of merits were studied. The detectors were illuminated by 2.2 and 2.5 Watt from CO2 of 10.6 􀀀m and tested. The surface morphology for the films is studied using AFM and SEM micrographs. The films show homogeneous distributed for CNTs on the PSi layer. The root mean square (r.m.s.) of the films surface roughness in

The Bartholin gland cyst is a condition that occurs in approximately 2% of women, most of whom are of reproductive age. Although benign pathology, it is associated with significant patient discomfort. This disorder is caused by the obstruction and consequent dilation of the cyst duct. Definitive treatment involves the surgical removal of the entire cyst. Other alternative treatments include Marsupialization, Word catheter, and the use of CO2 laser. CO2 laser can be used either to vaporize or to excise the Bartholin gland cyst. The Objectives: The purpose of the study was to evaluate the efficacy and safety of (10600nm) CO2 laser in the treatment of Bartholin gland cyst. Patients, Materials & Methods: This study was done in laser medi

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

Background: Rehabilitation of the carious tooth to establish tooth structure integrity required cavity design that show a benign stress distribution. The aim of this study was to investigate the influence of the cavity position on the stress values in the reamining tooth structure restored with amalgam or resin composite. Materials and methods: Seven 2-D models of maxillary first premolar include class I cavity design was prepared, one sound tooth (A) 3 composite (B1, B2, and B3) and 3 amalgam (C1, C2, and C3). In design (BI and C1) the cavity position is in the mid distance between bacc-lingual cusp tip, design (B2 and C2) and (B3 and C3) shifted toward the buccal cusp and the lingual cusp for 0.5 mm respectively. One hundred N vertical

This paper presents a computer simulation model of a thermally activated roof (TAR) to cool a room using cool water from a wet cooling tower. Modeling was achieved using a simplified 1-D resistance-capacitance thermal network (RC model) for an infinite slab. Heat transfer from the cooling pipe network was treated as 2-D heat flow. Only a limited number of nodes were required to obtain reliable results. The use of 6^th order RC-thermal model produced a set of ordinary differential equations that were solved using MATLAB - R2012a. The computer program was written to cover all possible initial conditions, material properties, TAR system geometry and hourly solar radiation. The cool water supply was considered time