The combination of carbon nanotubes (CNT) and conducting polymers offers an attractive route for the production of novel compounds that can be used in a variety of applications such as sensors, actuators, and molecular scale electronic devices. In this work, functionalized multiwall carbon nanotubes (f-MWCNTs) were added in different load ratios (3 wt%, 5 wt% and 10 wt%) to thiophen (PTh) polymer to procedure PTh/CNTs nanocomposite and deposited on porous silicon substrate by electropolarization. Photoconductive detectors were fabricated using PTh/f-MWCNTs matrix to work in the near region and middle IR regions. These detectors were illuminated by semiconductor laser diode wavelength of 808(nm) and Nd-YAG laser of wavelength 1064 (n

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

The Ground Penetrating Radar (GPR) is frequently used in pavement engineering
for road pavement inspection. The main objective of this work is to validate
nondestructive, quick and powerful measurements using GPR for assessment of subgrade
and asphalt /concrete conditions. In the present study, two different antennas
(250, 500 MHz) were used. The case studies are presented was carried in University
of Baghdad over about 100m of paved road. After data acquisition and radar grams
collection, they have been processed using RadExplorer V1.4 software
implementing different filters with the most effective ones (time zero adjustment and
DC removal) in addition to other interpretation tool parameters.
The interpretatio

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

The present study discusses the names of the Day of Resurrection in Uri Rubin’s translations of the Holy Qur’an. To identify the translation difficulties and complications and to find out the Hebrew equivalence, the study, through the application of a comparative analytical approach, aims at analyzing the linguistic structures of Day of Resurrection names cited in the Holy Qur’an. Heavenly religions are rich with distinct and unique terms that distinguish and reflect their specificity of other religions; something requires a great accuracy in the translation of such terminology. By investigating selected examples of the names of the Day of Resurrection in Arabic and Hebrew, the present study sheds light on the morphological and semant