Al-Farabi Journal of Engineering Sciences

The study area is located in the western part of Iraq within the administrative borders of Anbar
Governorate and adjacent to the Iraqi-Jordanian-Syrian borders within the 37 zone and an area of 61600
km². The region is characterized by its rugged desert nature, where altitudes range between (500-900)
meters above sea level, interspersed with some valleys, and the altitudes increase towards the southwest.
Structurally, the study area is located in the stable shelf within the Al-Ma’aniyah-Al-Rutba zone (Al-Rutba
belt) and the Al-Salman-Hatra zone (Akashat-Kubisa belt) .
The study aim to determine the hydrocarbon presence in the Western desert region using remote sensing
technology and to indicate the type of trap, whether it was a structurally, stratigraphic or combined .
The map of the Bougier anomalies showed the values of gravity ranging between (-60 and -10) mg Gal
within the study area, increasing towards the north and north-east and decreasing towards the south and
southwest, and that the axes of the anomalies within the region are towards northwest - southeast. These
anomalies reflect the effect of the movement of the base rocks on the crustal rocks. The residual gravity
showed the presence of a number of positive anomalies within the first study area, whose value is (4) mg
Gal in the eastern part of the study, whose value is (6) mg Gal, and the second, whose value is (4) mg Gal,
in the northern part, in addition to two anomalies whose value is (8) mg Gal located in the western part of
the study. Along the Iraqi-Jordanian border. The magnetic intensity map showed the presence of two
magnetic anomalies with high values, the first located in the southeast of the region and the other in the
northwest of the region, that these two anomalies reflect the presence of two convex high in the region.
On the other hand, two negative anomalies appear in the center and north of the region that reflect the
presence of a concave depression in that part of the study area.

Face recognition is a crucial application of deep learning and computer vision, playing a
significant role in security, authentication, and surveillance systems. Convolutional Neural
Networks (CNNs) have significantly improved the accuracy and efficiency of face recognition
by learning hierarchical feature representations. This paper comprehensively reviews CNN-based
face recognition techniques, including widely used datasets, architectures, and performance
evaluation metrics. The study highlights the advantages of deeper CNN architectures such as
ResNet and GoogleNet while discussing the challenges posed by illumination variations,
occlusions, and pose changes. Furthermore, we examine optimization techniques, such as
pruning and quantization, to enhance computational efficiency. Finally, we outline key research
challenges, including bias mitigation, adversarial robustness, and privacy concerns, to guide
future advancements in this field.

Cybersecurity is a rapidly expanding sector where the reasons for its enormous influence are difficult,
if not impossible, to pin down. Giving hostile threats the green light to operate in any setting is
completely unacceptable and could lead to violent harm. This is especially true for the complex web
of online users, businesses, and information that cyber security firms are struggling to protect.
Everyone whose home, workplace, school, or government is located within range of the internet should
give some consideration to cyber security. The cyber security landscape will be advanced with the use
of Machine Learning. Nowadays, companies collect massive volumes of user data. Any system that is
vital to your company\\'s operations revolves around information. Included in here are the infrastructural
systems that are currently under implementation. Network and cyber security systems in today\\'s high
tech infrastructure collect massive volumes of data and analytics on nearly every crucial component
of mission-critical systems. While human beings continue to play a crucial role in providing
operational oversight and smart insights into modern infrastructure, machine learning and Artificial
Intelligence are rapidly expanding and acquiring tremendous traction in many aspects of modern
systems, whether they are located on premises or in the cyber security estate. opening the path to a
digital future that is both more secure and more robust. The study discusses the challenges and moral
dilemmas that arise when applying Machine Learning to cybersecurity. The importance of a
comprehensive approach that combines contemporary technology with ethical concerns is brought to
light as issues such as adversarial attacks, biassed datasets, and the interpretability of Machine
Learning models are discussed. A more secure and robust digital future is possible through the
combination of human knowledge and artificial intelligence, which provides an effective barrier
against ever-changing cyber threats.

The COVID-19 pandemic has introduced a pressing environmental challenge with the widespread use of
disposable face masks (DFMs). Millions of face masks (FMs) are discarded, increasing the risk of
environmental pollution through microplastic accumulation. This study presents a recycling method for
converting DFMs into fibers for addition to concrete. Before the addition, the DFMs underwent a five-day
sunlight disinfection process to ensure COVID-19 negativity. The study investigated the effect of
recycled face mask fibers (RFMFs) on concrete strength. 270 concrete samples, consisting of various
volume contents (0%, 0.15%, 0.30%, and 0.45%) of RFMFs as an additional volume of concrete, were
subjected to compression, splitting tensile, and flexural strength tests. The RFMFs, measuring 20 mm, 30
mm, and 40 mm in length and 5 mm in width, were utilized in the concrete. The specimens were cured
for different durations (3, 7, and 28 days). The results indicated a significant enhancement in the concrete
strength with the addition of RFMFs after 3 days of curing. An optimum increase of 17.47% in
compressive strength was observed at 0.15% volume content and 20 mm length of RFMF. Moreover, an
18.52% increase in splitting tensile strength was achieved at 0.15% volume content and 40 mm length of
RFMF. Additionally, at 0.45% volume content and 30 mm length of RFMF, a significant 36.62% increase
in flexural strength was recorded. This study presents an innovative approach to strengthening concrete at
an early age, providing a practical solution to mitigate the environmental impact and waste management
challenges

Wellbore stability analysis has proven to be a useful tool for reducing drilling risks and
expenditures associated with unwanted drilling events such as stopped pipe, loss circulation, hole
collapse, kick, and so on. Wellbore stability issues typically arise when drilling in reservoirs of
hydrocarbons such as shale, fractured carbonate formations, and high-pressure, high-temperature
formations with a narrow safety mud weight. These issues can have a substantial impact on drilling
time, expenses, and the overall drilling operation. The aim of this study is to predict wellbore stability
in Halfaya oil field (Saadi reservoir) using 1D mechanical earth model built with well logs. The
workflow includes the calculating the pore pressure, vertical stress, rock strength, rock elastic
parameters,and horizontal stresses Mogi-Coulomb failure criteria determined the deformation of the
well. 1DMEM can also be conduct a comprehensive geomechanical wellbore stability analysis for the
Saadi formation.The results showed that the failure occurred in the well is mud loss and kick on the
left boundary and the breakout and breakdown type were not observed. Also, the magnitude of the
utilized mud weight (10.25 ppg) is the most safe to avoid a lot of problem of WBS. Mud loss and kick
did not significantly affect the stability of the well. Mud loss and kick are the wellbore instability issues
appeared in this study along all formations, but mud loss is increased in the top of Hartha, Tanuma and
KhasibA and Khasib B. and the formation of Saadi B has a little of mud loss effect the borehole stable
that means the drilling operation will continue without problems. Finally, we concluded that the well
HF-55 is rather stable. The Modified Lade failure criteria doesn\'t neglect the intermediate principal
stress effect on the predicted failure. The regime of stress in all formations are normal fault regime due
to (SV> SH>Sh).

This effort focuses on the failure of drilling pipes due to various drilling problems. These problems include buckling,
fatigue, collapse, and explosion. The issue of torsion was identified, along with the indicators of torsion occurrence
and its causes. The research also discusses the risks resulting from torsion and how to treat it, as well as reduce its
occurrence. Additionally, the study delves into the effect of torque on the twisting of drill pipes and calculates the
angle of torsion using a mathematical relationship. The research also explores the impact of formations on distortion
and identifies those with a high percentage of distortion. Furthermore, it delves into the effect of Revolution per
Minutes (RPM) and Weight on Bit (WOB) on drill pipe, buckling and the relationship between them. On other
hands, the study covers dents, their types, and the calculation of the strength of wall loss caused by dents using a
mathematical equation. options to prevent or reduce buckling are discussed. Finally, it was concluded that the torque
must be properly managed by adjusting the drilling parameters, such as the weight on the drill bit and the rotation
speed, so that the pipe does not become twisted, and also so that the shear stress generated by drilling is not applied.
The torque does not exceed the ultimate shear stress of the pipe. It is crucial to monitor the drill shaft for small
cracks to prevent potential explosions and serious issues over time.