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 study was conducted at the College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad. The aim of this study was to isolate and diagnose fungi from fish feedstuff samples, and also detection of aflatoxin B1 and ochratoxin A in fish muscles and feedstuffs. Randomly, the samples were collected from some fish farms from Baghdad, Babil, Wasit, Anbar, and Salah al-Din provinces. This study included the collection of 35 feedstuff samples and 70 fish muscle samples, and each of the two fish samples fed on one sample of the feedstuff. The results showed the presence of several genera of different fungi including Aspergillus spp, Mucor spp., Penicillium spp., Yeast spp., Fusarium spp., Rhizopus spp., Scopiolariopsis spp., Ep

loaded reinforced concrete circular short columns. An experimental investigation into the behavior
of 24 short reinforced concrete columns with and without steel fibers was carried out. The columns
had a circular section (200 mm diameter and 900 mm long). Test variables include concrete
strength, spacing of spiral reinforcement, and inclusion of steel fibers. The axial stress and axial
strains were obtained and used to evaluate the effects of the presence of steel fibers. It was found
that the addition of steel fibers slightly improves the load carrying capacity of the tested columns
whereas it significantly enhances the ductility of these specimens. Test results also indicated that for
the same confinement parameter

The effect of short range correlations on the inelastic longitudinal
Coulomb form factors for different states of J   4 , T  1with
excitation energies 3.553,7.114, 8.960 and 10.310 MeV in 18O is
analyzed. This effect (which depends on the correlation parameter )
is inserted into the ground state charge density distribution through
the Jastrow type correlation function. The single particle harmonic
oscillator wave function is used with an oscillator size parameter b.
The parameters  and b are considered as free parameters, adjusted
for each excited state separately so as to reproduce the experimental
root mean square charge radius of 18O. The model space of 18O does
not contribute to the tra

          This study aims to identify the concepts of financial crisis and its reasons of creation , also explain the effects of the accounting disclosure and the International Accounting Standards in current financial crisis, In addition to,  indicate the role of accounting in the reform of the financial system from the impact of financial crisis.      

       The  methodology of this study orientied to two main aspects, the first is an identifying approach through exploring the opinion of financial experts, the second aspect is based on an analytical approach to satisfy the requirements  of experts to get there opi

This work used the deposition method to synthesize nickel oxide nanoparticles. The materials mainly used in this study were nickel sulfate hexahydrate (as a precursor) and NaOH (as a precipitant). The properties of the nanopowder were characterized by XRD, FE-SEM, EDX, and VSM. The obtained results confirmed the presence of nickel oxide nanoparticles with a face-centered cubic (FCC) structure with a lattice constant (a=4.17834 Å). Scherer and Williamson-Hall equations were used to calculate the crystallite size of about (30.5-35.5) nm. The FE-SEM images showed that the particle shape had a ball-like appearance with a uniform and homogeneous distribution and confirmed that the particles were within the nanoscale. The presence of oxygen a

The aim of this work is studying the binary system ??'??? Ni?)with two ratios (y=36,80) by using casting method for preparing the samples.Magnetic and Mechanical properties have been studidt different httrea^nttem^rature.All the alloys were found a ferromagnetic behavior and sensitive to the heat treatment. Best properties were found at the heat treatment 1100 C°.A significant different results were found above 1100C° for lower magnetic and mechanical values. This is possibly due to the change on the degree of magnetic moment orders, in which most of the moments are started to remove from coupled ferromagnetically.?

Off-nucleus isotropic magnetic shielding (σiso(r)) and multi-points nucleus independent chemical shift (NICS(0-2 Å)) index were utilized to find the impacts of the isomerization of gas-phase furfuraldehyde (FD) on bonding and aromaticity of FD. Multidimensional (1D to 3D) grids of ghost atoms (bqs) were used as local magnetic probes to evaluate σiso(r) through gauge-including atomic orbitals (GIAO) at density functional theory (DFT) and B3LYP functional/6-311+G(d,p) basis set level of theory. 1D σiso(r) responses along each bond of FD were examined. Also, a σiso(r) 2D-scan was performed to obtain σiso(r) behavior at vertical heights of 0–1 Å above the FD plane in its cis, transition state (TS) and trans forms. New techniques fo

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D