Dijlah Journal of Engineering Sciences

Analyze the possibilities of implementing a parallel algorithm for calculating
the geometric mean of M numbers. Design and implement (in C/C++) a
solution based on memory sharing between individual "threads" of the
program. Consider using the "PThread" library. Use the file to dump
auxiliary information about the execution of individual threads, with at most
one writing to the file at any time. Determine the speedup when executing on
N processors if consider the PRAM model. Document the design as well as
the implementation in detail. If the solution requires it, use files for the input
and output of matrices (and vectors) where the row of the matrix
corresponds to the row of the file and the values of the columns are
separated by spaces or tabs. Unless otherwise stated, work with real
numbers.

Given the significance about sustainability in the building sector, it is
essential to establish a precise mechanism for evaluating the sustainability
performance of contractors, who are a crucial component in project execution.
Analyzing contractors' sustainability performance indicators is a crucial step
in identifying the contractor most dedicated to sustainability standards. This
study seeks to identify and comprehend the principal sustainability
performance metrics of contractors in Iraq, considering the direct influence of
their projects on community development and welfare. According to previous
research, indicators of sustainability were gathered and categorized into three
categories (environmental, social, and economic), resulting in a total of 127
indicators. A questionnaire was subsequently developed and disseminated to
a selection of engineers and sustainability experts, yielding (104) responses
for study. The statistical software (SPSS-V26) was employed to analyzed the
results and establish priorities. The principle of the relative importance index
was also applied, and indicators with an importance greater than or equal to
0.70 were selected in order To diminish and refine the indicators to 64
essential metrics that underpin the selection process for sustainable
contractors.

Nonlinear differential equations (NDEs) are considered a crucial part
in modeling complex physical, biological, and engineering systems.
Classical numerical methods exhibit limitations in stability,
accuracy, and efficiency. The purpose of this paper is to present a
comprehensive review and analysis of advanced methods for solving
NDEs. In this paper, we focused on recent numerical novel
algorithms and laid the theoretical foundations and practical
performance considerations.

Osseointegration prostheses are a short titanium rod or screw is surgically
inserted into the bone of the amputee's remaining limb as a part of the
osseointegration process. As the bone matures and integrates with the
implant over time, a solid and reliable connection is made. The prosthetic
limb is then joined to the implant, creating a stable and long-lasting
connection. Biomaterials play a critical role in the design of prosthetic limbs,
especially for lower limb amputees. Materials like titanium, ceramics, and
polymers are widely used due to their mechanical strength, corrosion
resistance, and biocompatibility. Titanium, for instance, is favored in boneanchored prostheses because it provides greater stability and comfort
compared to traditional socket-based prosthetics. Although ceramics are
relatively brittle, they are used in low-stress areas of the body due to their
ability to promote bone growth. Technological advancements such as 3D
printing and surface modifications have enhanced the performance of
prosthetic limbs, increasing adaptability and improving patients' quality of
life. Simulation software is also used to investigate mechanical stresses and
are expecting the long-term overall performance of prosthetic implants
before real-life trials. Despite large progress on this field, similarly research
is needed to enhance the capability of prosthetic limbs and produce them
towards natural limb motion.
The paper aims to update current knowledge on lower limb prosthetic
implant design, materials, and integration processes, highlighting
technological advancements and areas requiring further investigation for
improved patient outcomes.

Gypsum soils in Iraq suffer from serious problems related to collapse
and poor stability when exposed to moisture, which over time leads
to subsidence and cracking of structures. This is one of the most
prominent geotechnical challenges facing infrastructure projects in
areas where these soils are common. This research aims to study the
unconfined compressive strength (UCS) of gypsum soils using three
main stabilizing agents: 8% fly ash, 4% silica fume, and 2% nanosilica fume, along with varying ratios of fine particles (10, 20, 30,
and 40%). Soil containing 53% gypsum was collected from a depth
of 0.5–1.0 meters below the natural ground surface, in collaboration
with Tikrit University. To evaluate the mechanical properties of the
soil, an unconfined compression (UCS) test was conducted on both
natural samples and samples treated with various additives. The
results showed that dual treatment using additives with fine particles
represents an effective solution for improving the compressive
strength of gypsum soils. It was found that an optimal ratio of fine
particles achieves the highest strength value. Gypsum soil treated
with nano-silica fume with 10% fine particles recorded the highest
improvement in unconfined compressive strength, reaching
approximately 93%. Soil treated with silica fume with 20% fine
particles achieved an improvement of approximately 82%, while the
improvement reached approximately 73% when using fly ash with
20% fine particles.

Heat transfer fluids with the use of particles signal a revolutionary approach
in next-generation CSP plants. Despite this transformative potential,
however, pressing issues such as thermal efficiency against hydrodynamic
losses have yet to be resolved. The present study hence experimentally
investigates and analyzes the thermal performance and pressure drop of three
industrially significant particles—silica sand, sintered bauxite, and
engineered ceramic mix—under concentrated irradiance (800–1000 W/m²)
as well as flow velocities (0.5–3.0 m/s). The Nusselt numbers (Nu) and
dynamic pressure drops (ΔP) are measured from a “plug-and-play” solar
simulator facility by controlling mass flow of particle material at 5 g/s and
air velocity; instrument uncertainties were quantified according to GUM
guidelines. Results showed that over the full range tested, bauxite returned
higher Nusselt number at Reynolds number 4500 than silica sand by 63.5%
due predominantly to its high conductivity (0.35 W/m·K) and spectral
absorptivity (0.92). Ceramic mix indicated best hydraulic performance,
giving a 35.2% reduction in the dynamic pressure drop compared with
bauxite at 1.5 m/s because it is more spherical (i.e., sphericity is equal to
0.94). A new relationship Nu = 0.597Re⁰·⁶⁵⁴ψ⁰·²⁰¹ produced results within
±0.23% error of the experimental data and resolved morphology-dependent
heat transfer, which has never been considered by classical models. Tradeoff analysis showed that bauxite maximized efficiency at high irradiance
(78.2% at 1000 W/m²), while ceramic mix achieved Pareto-optimality at
medium flux (71.5% efficiency, 810 Pa ΔP). For commercial deployment,
bauxite is recommended for power towers (>800°C), ceramic mix for
parabolic troughs, and sub-200μm particles should be avoided to control
pressure losses. This work provides the first physics-based selection
framework for particle receivers, advancing toward cost-competitive CSP
with thermal storage

Anemia, a prevalent blood disorder affecting approximately 1.62 billion
people worldwide, represents a significant global health challenge requiring
accurate and efficient diagnostic methods. Traditional manual analysis of
peripheral blood smear (PBS) images for anemia classification is timeconsuming, error-prone, and requires specialized expertise. This paper
presents a novel hybrid approach that integrates the Emperor Penguin
Optimizer (EPO) algorithm for feature selection with Support Vector
Machine (SVM) classifier to achieve efficient anemia classification from
microscopic red blood cell (RBC) images. The proposed methodology
addresses the critica challenge of high-dimensional feature spaces in medical
image analysis by employing EPO's bio-inspired optimization capabilities to
reduce feature dimensionality from 121 extracted features to approximately
64 optimal features. The hybrid EPO-SVM mode demonstrates superior
performance in terms of classification accuracy, reduced computational
complexity, and enhanced diagnostic efficiency. Experimental results show
significant improvements in accuracy (93.2%) while substantially reducing
training time (51.3% reduction) compared to traditional approaches using
full feature sets. The integration of EPO's huddling behavior-inspired
optimization with SVM's robust classification capabilities provides a
promising solution for automated anemia diagnosis, contributing to more
accessible and reliable healthcare diagnostics.

AI-driven decision support systems and voice-assisted health monitoring are
becoming revolutionary technologies in contemporary healthcare
applications. They also show promise as ways to improve accessibility for
elderly and disabled patients and support healthcare systems. Even still,
current approaches frequently do not combine deep learning, speech, and
vision into a single framework. Voice-powered AI systems aim to automate
these processes, allowing physicians to dedicate more time to direct patient
care. This shift not only improves healthcare delivery but also boosts
provider well-being. From a patient perspective, voice assistants increase
accessibility and engagement, especially for elderly and disabled
populations. These systems enable patients to manage appointments, receive
medication reminders, and access health information without navigating
complex interfaces. This research addresses this gap by developing a
multimodal AI-powered assistant capable of interacting with users through
voice commands, real-time image analysis to provide timely medical
information.

This paper discusses the limitations of initial value problems in relation to
the stability, uniqueness, and accuracy of boundary value problems
involving fractional polynomial differential equations. The objective is to
establish a robust framework for addressing complex boundary value
problems through wavelet-based numerical techniques. The wavelet
collocation method uses Taylor wavelets to figure out mixed partial
derivatives while also taking into account the boundary conditions. The
method tries to find solutions to problems with boundary values that are
related to the Benjamin Ona Mahony equation. We look at the single
solutions for two-dimensional and three-dimensional corner domains, as
well as smooth domains with localized forcing terms that come from
polynomial exponential Laplacian expressions. The study demonstrates that,
unlike initial value problems, the geometry of the domain and the magnitude
of the eigenvalues influence the stability of boundary value problems and
Green's functions. The wavelet collocation method provides a highly
accurate numerical solution for challenging domains in physics and
engineering by effectively capturing pronounced peaks and managing the
boundary layers. This is not the same as how things are in problems with
initial values.

Ordinary differential equations (ODEs) are used to model dynamic systems
in many different fields of science and engineering. However, traditional
multistep approaches like Adams–Bashforth and Adams–Moulton can fail
when stiffness, nonlinearity, or great dimensionality. This research offers a
framework that combines hybrid predictor–corrector methods with adaptive
step sizing to increase accuracy and speed. We give Thorough stability and
error analyses—including absolute stability regions and—are carried out
using comparative benchmarks on representative standard test problems
against conventional one-step and spectral solvers. Convergence
characteristics. Applications range from stiff chemical kinetics, epidemic
dynamics, climate inspired models, and nonlinear oscillators, therefore
showing robustness under different situations. Numerical experiments show
faster convergence, improved stability at larger time steps, and lower
computational cost—reductions up to 40%—relative to standard approaches.
The framework scales to contemporary large simulations, making hybrid
multistep strategies an alternative to common solvers. We conclude with
directions for further gains, including coupling machine learning with
multistep integrators to drive dynamic step-size selection and predict error,
stiffness, and stability margins. Overall, the results point to a practical,
innovative path for advancing ODE solvers in sophisticated scientific
computing where reliability, speed, and scalability are critical.

Modern signal processing makes great use of wavelet transforms since they
provide a flexible way to examine nonstationary signals both across time and
frequency. This study looks at the optimization and use of several wavelet
transforms include dual tree complex wavelet, stationary wavelet transforms
(SWT), discrete wavelet transform (DWT), and continuous wavelet
transform (CWT). For jobs including denoising, compression, feature
extraction, and time frequency analysis, transform (DTCWT) is used. The
paper looks at the tradeoffs between how fast it works, how good it is, and
how reliable it is when choosing the right transform for various kinds of
signals, including pictures, ECG, and speech. Important issues covered are
the retention of high frequency components, edge integrity, and look into
sophisticated approaches like synchro squeezed wavelet transforms (SSWT)
for better frequency tracking and wavelet Galerkin feature stability across
signal types. The study also examines techniques for resolving partial
differential equations. Through performance evaluation using parameters
such Signations Ratio (SNR), Peak Signations Ratio (PSNR), and Structural
Similarity Index (SSIM), the research gives fresh perspective on the
optimum methods for using wavelet transforms to address practical signal
processing challenges.

This investigation centers on tactics for overseeing eigenvalues, merging
both established and current methodologies. In various fields like
engineering and science, eigenvalues serve an essential purpose, impacting
crucial functions such as structural evaluations and quantum studies.
Approaches like the Power Method and QR method run into obstacles,
mainly when managing large or sparse matrices. The primary eigenvalue is
disclosed through the power method, but the QR method does compute
eigenvalues and may find larger matrices to be problematic. This paper
delves into iterative techniques that enhance efficiency for extensive systems
through Lanczos and Arnoldi, all while reducing size. Additionally, it will
examine strategies aimed at accelerating operational processes, highlighting
parallel execution and proactive conditioning, to enhance the effectiveness
of iterative methods. In the near past, the landscape of quantum computing
has transformed, alongside the progress of quantum systems that incorporate
machine learning to handle eigenvector complications. An elaborate
investigation into progressive eigenvalue calculation methods could aid
professionals in attaining their targets with superior dexterity and capability.

Often used in engineering, scientific computing, and optimization, the study
investigates numerical approaches for managing big sparse linear systems.
Regarding memory Many of whose entries are zeros, sparse matrices will
benefit greatly in large systems using both computational speed and use.
Resolving such systems, however, creates difficulties in memory usage and
computational complexity, and numerical stability. The essay contrasts
direct and iterative methods along with their respective advantages and
disadvantages. Direct techniques such LU decomposition yield right results,
for larger systems, however, they are rather hard. Iterative methods such as
the Conjugate Gradient (CG) method become useful for badly conditioned
system (when combined with preconditioning), and can be very beneficial
when convergence is accelerated. This article describes the method of
solving large sparse linear systems that exist in the real-world using
parallelization and preconditioning in combination with iterative methods.

This study investigates the Structural behavior of hollow rubberized
ferrocement beams reinforced solely with Glass Fiber Reinforced Polymer
(GFRP) bars. The experimental program involved six full-scale beams tested
under two-point bending to evaluate the influence of reinforcement quantity
and mesh layers on structural performance. Results indicated that increasing
the number of GFRP bars and mesh layers enhanced the first cracking load
by 147.1% and the ultimate capacity by 72.6%, while simultaneously
reducing ultimate deflection by 27.3%. Beams reinforced with additional
mesh layers also exhibited improved crack resistance and stiffness. All the
Beams reinforced with GFRP showed a brittle failure behavior as was
expected because of the inherent behavior of GFRP. Beam weightReduced
the weight of hollow-core beam to approximately 40% of that of the solid
beam form, which showed the application of the hollow-core beam in light
structures. Altogether, these results presented a hope and also a limitation of
the GFRP Reinforcement to the Engineering structures and inspection other
work is needed for enhancing the ductility and serviceability of such GFRP
reinforcement in structural application.

Effective mitigation of wind power intermittency requires cutting-edge
energy storage solutions since it poses great problems for grid stability and
dependable electricity supply. This study examines the crucial function of
energy storage systems in resolving wind power variation by thorough
examination of grid integration methods, hybrid storage configurations, and
battery technology. For their ability to smooth wind power variations, the
research assesses Battery Energy Storage Systems (BESS), Compressed Air
Energy Storage (CAES), and lithium-ion technologies. Using HOMER and
MATLAB optimization techniques, including Particle Swarm Optimization
and Robust Model Predictive Control, the best energy storage configurations
are found for several wind farm situations. Results show that BESS performs
better in voltage stabilization and frequency regulation while keeping grid
stability measures within permissible levels. Economic study shows that
combining lithium-ion batteries into 30 MW wind farms has a positive Net
Present Value, which means they are good for business. These results show
that energy storage is very important for making wind power more popular
because it helps to solve the technical and economic problems that come
with wind power varying. This supports the change to renewable energy,
which is good for the environment.

Though their use creates great operational advantages as well as technical
and moral problems. Although these systems try to lower human personnel
hazards, quicken response times, and, in addition to improving mission
efficiency, they also pose serious issues of accountability for deadly
decisions, compliance with international humanitarian law (IHL), and the
potential algorithmic bias undermining civil life. Incorporating technology
dangers such cyber security vulnerabilities, system dependability in conflict
zones, and the adaptability of artificial intelligence in fast combat adds still
further complication. circumstances. Study suggests a new Ethical Technical
Governance Framework (ETGF) emphasizing embedded ethical principles,
Ongoing human observation and independent audits ensure ethical and legal
application of artificial intelligence. By blending defense aims with legal and
moral responsibilities, this research offers pragmatic direction for legislators,
military groups, and AI developers, therefore extending the discussion.
regarding ethical artificial intelligence in military environments.

Cardiovascular diseases, particularly Myocardial Infarction (MI), are
still a primary cause of international mortality. The last COVID-19
pandemic has further complicated the cardiac health landscape, with
the virus known to induce cardiac injuries such as Advanced heart
block (AHB) and Myocardial Injury (HMI). The Electrocardiogram
(ECG) is a primary, non-invasive diagnostic tool for these
conditions. This paper presents a comprehensive comparative
analysis of three state-of-the-art deep learning architectures—
ResNet-50, YOLOv8, and Meta CLIP—for the automated
classification of cardiac conditions from ECG signals. We collected
a dataset comprising ECG traces from several sources with MI,
AHB, HMI, COVID-19, and normal rhythm. Each model was
trained and validated on this dataset. Our experimental results
demonstrate exceptional performance across all architectures.
ResNet-50 and YOLOv8 achieved a training accuracy of 0.99 and
0.97, respectively, with training losses of 0.0982 and 0.181. whereas
the Meta CLIP model achieved a training loss of 0.106 and an
accuracy of 0.99, with commensurate high validation accuracy. The
findings suggest that deep learning models, originally designed for
computer vision, can be effectively adapted for robust and accurate
ECG analysis, paving the way for enhanced clinical decision-support
systems

Heat transfer plays a critical role in energy, and environmental
systems, where modelling is important to monitor the performance
and efficiency of the system. With the rise of complex physical
processes, partial differential equations were the corner stone to
describe them in detail. In this paper, we will highlight the role of
PDEs for numerous applications of heat transfer, including
conduction, convection, radiation, and phase-change processes. Out
paper examines recent case studies from many engineering domains
such as solar thermal systems, phase-change solar collectors,
industrial drying ovens, and electrical transformer cooling systems.
The paper concludes by emphasizing future directions in physicsinformed machine learning, fractional modelling, and digital twin
integration for real-time thermal management.

This study modified zinc oxide nanoparticles using rice bran. Rice bran is
crucial for zinc oxide nanoparticle production. Finally, XRD and SEM
investigations verified nanoparticle production quality. Photos showed the
synthesized organic zinc oxide nano-particles' surface uniformity.
Afterwards, medium and light crud oils were treated with customized
nanoparticles at varying weight percentages. At pressures from 10 to 300
bar, injection was done at 30-150 C. Adding rice bran to the adhesion force
of light or medium crud oil molecules modified organic zinc oxide
nanoparticles. Both the light and medium crud oil samples showed a rise in
thermal conductivity from 0.19 to 2.45 W/m C and 1.61 to 6.45 W/m C,
respectively, as the working temperature was raised. Findings showed that
asphaltene precipitation % fell as crud oil API grew. Nano-light and medium
crud oil had more asphaltene precipitation than basic light and medium crud
oil. Nanoparticles improved reservoir crud oil recovery. Compared to normal
light crud oil, light crud oil nanoparticles reduce asphaltene precipitation by
28.3%. Compared to simple medium crud oil, this is an 8.1% increase.

With the significant increase and growing expansion in the use of smart
devices within homes, home wireless networks have become more
vulnerable to direct cyber-attacks such as unauthorized access attempts,
identity theft, and brute force attacks against Wi-Fi protocols in general.
Although standard and traditional security technologies such as
WPA2/WPA3 encryption and MAC filtering provide a basic level of
protection, they still suffer from security issues in the form of vulnerabilities
that can be exploited and local log manipulation. To overcome these
challenges, this research proposes a blockchain-based system to prevent
intrusions into home wireless networks. The system relies primarily on realtime monitoring of traffic through the router to detect suspicious access
attempts, using a special distributed ledger (blockchain) to store lists of
trusted devices and security logs in a tamper-proof and forgery-proof
manner. This system prevents unauthorized devices from joining the
network and builds reliable audit logs of security events. This work
highlights the potential for integrating blockchain technology with simple,
low-cost security solutions to enhance the security of wireless networks in
everyday environments.

Revision Total knee arthroplasty (rTKA) implies significant surgical
difficulty and removal of cement is still considered one of the most
technically-challenging elements. Retention of cement is associated with
peri-prosthetic infection (PJI) which may be responsible in cases of bone
wasting away and loosening of the implant. Modern methods of cement
removal in rTKA are discussed with a critical approach, namely nonpowered manual equipment; powered equipment such as high-speed burrs,
flexible reamers, ultrasonic based equipment, endoscopy-assisted surgery,
and surgical procedures such as cortical windows and bone episiotomies.
The comparisons between the benefits and limitations of both of the methods
are based on efficacy, bone preservation, the invasiveness of the method, and
the possibility of complications. The following synthesis provides an insight
that personalized intervention that considers patient anatomy, characteristics
of cement and fixation of implants is vital to good results. The recent
innovations in ultrasonic and endoscopic technologies present an effective
way forward when it is safer and more effective in extracting cement during
the future rTKA surgeries.

The release of pharmaceutical contaminants, especially antibiotics, into the
ecosystem is a major environmental concern. This study evaluates the
effectiveness of the adsorption and desorption process for ciprofloxacin
and rifampicin, two common antibiotics due to their known adverse
environmental effects. The adsorption process was conducted using a
moving column filled with immobilized Chlorella Sorokiniana algae. The
study aimed to understand the effect of flow rate on column efficiency and
the formation of the breakthrough curve under different operating
conditions. The column was set up with a diameter of 4 cm and a length of
90 cm, and the drug solution was pumped at various flow rates (1, 5, and
10 ml/min). Samples were collected from the column outlet at specific
time intervals, and the drug concentration was analyzed using a UV-Vis
device. The results showed that increasing the flow rate decreased the time
required to reach the drug's breakthrough point, with a steeper curve and
faster column saturation. After adsorption was complete, the drug removal
process was performed using a suitable solvent (5% nitric acid) to ensure
drug recovery and analyze the process performance. The results showed an
increase in the removal rate during the first 60 minutes, followed by a
decrease with time. Mathematical models (Yoon-Nelson, Adams-Bohart,
and Thomas), were applied to simulate the adsorption curves and
determine the column capacity and removal efficiency. They also helped
estimate the effect of flow rate on column dynamics. The models indicated
that the column can remove a high percentage of the drug at low flow
rates. This study provides a qualitative improvement in the design of
moving columns for use in treating wastewater containing
pharmaceuticals. The study provides realistic operational data from which
precise, detailed adsorption processes can be designed by determining
optimal flow rates and other operating conditions to achieve high
efficiency. The results can be generalized to other pharmaceuticals.
Therefore, it can be considered a practical framework for applying
adsorption and dissolution processes in industrial and laboratory settings,
leading to the development of management strategies for pharmaceuticalcontaminated water to minimize its environmental impacts.

Geographers and surveyors attempted to determine each's precise shape to
be accurately shown on a flat surface. Geographers and surveyors tried to
determine each's precise shape to be accurately shown on a flat surface. The
Earth's shape was known as "spheroid" or "flattened" until it was
demonstrated that it was the consequence of an ellipsoidal shape rotating
about its semi-major axis. This search aims to investigate the current
topographic map system (UTM 6-degree zone) and contrast it with the
proposed system (UTM 3-degree zone). The case will be discussed and
searched in the following headings: the contrast between zone 3° and zone
6°. As part of our research, we rely on the accompanying index (1/1000000).
The Ellipsoidal Transverse Mercator is the foundation for the UTM
projection, a worldwide coordinate system adopted by the U.S. Army in
1947. From the 180th meridian eastward, it splits the Earth between 84°N
and 80°S into 60 zones, each 6° broad in longitude and numbered 1 through
60. The research demonstrates that using a 3° UTM zone significantly
reduces projection distortion, as seen by the smaller error margins between
scale-corrected and raw lengths. The results underline the effectiveness of
smaller zones in precision-critical geodetic work and confirm the
significance of zone width selection in geospatial analysis. This research
shows that azimuthal distortions can affect the accuracy of geodetic readings
even within a 6° UTM zone. Even though these distortions are minor, they
must be corrected for high-accuracy applications like exact engineering
layouts, geodetic control networks, and cadastral surveys. Applying the scale
factor to azimuth values makes directional data more reliable and consistent
with the geodetic datum.

Modern communication systems, such as fifth-generation (5G) networks,
require antennas with a simple structure, lightweight, and high gain to
achieve efficient performance. However, designing compact and effective
antennas capable of working in the millimeter-wave (mmWave) band with
multiband capability remains a challenge. This paper presents the design
and simulation of two ultra-compact rectangular microstrip patch antennas
with feedline transmission, fabricated on a compact Rogers RT5880 (lossy)
substrate with a small size (1.52 mm×2 mm). The first design (RMSPA-I) is
a traditional rectangular patch antenna without a slot, while the second
(RMSPA-II) is incorporates an H-shaped slot on the patch plane and a slot
on the ground plane to improve performance. Both designs achieve
multiband operation, with RMSPA-I resonating at (2.3GHz and 95.3GHz),
and RMSPA-II resonating at (2.3GHz, 73.5GHz, and 88.5GHz). The
proposed designs demonstrate improved gain, VSWR, and return loss
compared to existing works, while maintaining ultra-compact dimensions
suitable for modern mmWave communication systems, including 5G and
beyond.

In this investigation bending is applied on pure copper pipe with a weld joint
made by oxyacetylene welding method to access the integrity and soundness
of the weld joint under this type of mechanical testing, the chemical
composition inspection of pipes metals is confirmed that is a pure copper
(99.96% Cu). Pure copper is known to be weld able and can produce a
weldment with good mechanical properties. High achievement weld joint
resistance under bending without any evidence of surface defects and
internal discontinuities that was confirmed by applying a liquid penetrant
examination for the weld surfaces and also using x-ray test to detect internal
defects. All tests’ processes were confirmed that, all weld rejoin is free from
all possible discontinuities after performing bend testing

As the globe faces serious climate and energy concerns, Artificial
Intelligence has shown to be a transformative force in the
advancement of renewable energy technologies. This paper examines
the existing and prospective applications of artificial intelligence in
renewable energy. This work focuses on the transformative role of
Artificial Intelligence (AI) in making renewable energy systems
more efficient, reliable, and scalable. AI technology - from machine
learning and deep learning to learning to learning - optimization of
energy production, forecasts demand, future maintenance and
management of decentralized energy networks. Looking ahead,
emerging fields like quantum machine learning and AI-powered
augmented reality offer exciting possibilities, with the potential to
fundamentally reshape energy infrastructures. The survey highlights
major innovations across wind and solar power, energy storage, and
smart grids, emphasizing how AI helps address persistent challenges
such as intermittency and variability. Equally important are the
supporting technologies—big data, the Internet of Things (IoT), and
real-time analytics—that drive the development of more advanced
AI models. We also explore how AI is shaping energy policy and
market modeling, paving the way for broader renewable energy
adoption. Real-world applications bring these ideas to life. For
instance, Google's collaboration with DeepMind has enhanced wind
power generation using wiser forecasting, while Australia's National
Electricity Market has looked to AI in order to enhance grid stability.
These cases demonstrate that AI’s role in renewable energy is not
just theoretical—it is already delivering measurable results and
redefining what’s possible in the global energy landscape. In order to
optimize AI's potential for advancing sustainable energy and
combating climate change, this study identifies the obstacles
preventing its implementation in renewable energy systems and
makes suggestions for enhancing current technology.

With the arrival of technological time, connection on the
Internet has become inevitable.
Many networks can be a challenging question when it comes to
protests as well as security issues. Virtual private networks (VPN)
have been used to provide safe channels for connection, but are
surrounded by delay and scalability. On the other hand, softwaredefined wide area networks (SD-WAN) have been an alternative
with customized routing, dynamic bandwidth distribution and
underlying safety facilities. We analyze VPN and SD-WAN
solutions when it comes to safety and performance in this article.
Our findings indicate that although VPN is heavily encrypted, it will
not avoid heavy load tests. The SD-WAN solution is still better in
terms of performance and uses a new security mechanism, and that's
why an appropriate replacement

Solar photovoltaic (PV) technology is becoming more important in
modern energy strategies, because of their scalability and
affordability. However, they also have shortcomings such as voltage
fluctuations, harmonic distortion, and reduced inertia, and instability
due to weather. These technical challenges can be assessed using
spectral analysis methods such as Fourier and harmonic
decomposition, which helps solve these problems. This paper
summarizes recent research, linking solar energy sources with
electrical sources using advanced power electronics, intelligent
inverters, hybrid storage systems. Findings suggest that we can
detect flicker, and small-signal instabilities with spectral methods,
and that integrating them with hybrid systems increases the
reliability and sustainability of the grids.

Severe mechanical vibrations in the Power Take-Off (PTO) shafts of
agricultural implements represent a significant engineering
challenge, leading to reduced operational lifespan, increased
maintenance costs, and degraded performance accuracy, this research
addresses this problem by designing and developing an Active
Vibration Control (AVC) system, the proposed solution is based on
the systematic integration of piezoelectric actuators, known for their
rapid response and wide bandwidth, with an H-infinity (H∞) robust
control strategy, which is capable of guaranteeing performance and
stability even in the presence of model uncertainties and external
disturbances, a dynamic mathematical model of the shaft was
developed using the Finite Element Method (FEM), and all stages of
controller design and simulation were executed within the opensource Python environment, leveraging its specialized scientific
libraries. Comprehensive numerical simulation results demonstrated
that the proposed closed-loop system is capable of achieving a
significant reduction in vibration amplitude across a wide range of
operating conditions compared to the open-loop system, also
robustness tests proved the controller's ability to maintain its high
performance under variations in system parameters, thereby
demonstrating the viability and effectiveness of the proposed
approach as an advanced and reliable solution for the problem of
vibrations in agricultural mechanization

This study explores the feasibility of using renewable energy
sources—specifically solar and wind power—to operate fuel
stations, either partially or fully, as an alternative to conventional
fossil fuels. The research is driven by growing environmental
concerns, escalating energy costs, and the global pursuit of
sustainability and lower carbon emissions." The research, within its
theoretical framework, addresses the definition of renewable energy
sources, their types, and their generation mechanisms, highlighting
the techniques for linking these sources to fuel station operating
systems. A number of applied models of stations in various countries
that have relied partially or fully on renewable energy were also
analyzed, evaluating the economic, technical, and environmental
feasibility of this transition. The research concluded that relying on
renewable energy in this field is not only a possible option, but a
future necessity, due to its benefits, including reduced long-term
operating costs, reduced environmental footprint of stations, and
enhanced energy independence. It also demonstrated that technical
challenges can be overcome through the use of smart storage and
load management technologies, along with governmental and
legislative support. The research concludes with practical
recommendations for adopting these solutions, proposing a general
framework for implementing renewable energy projects in the fuel
station sector in environments with high solar radiation, such as Iraq.

The objectives of this paper is to avoid the difficulty of analysis and the
high amount of time that is required by conventional translation methods
and reduce the validation loss by simply using training data when
augmenting data. In addition, To make deep learning easier for researchers
including the processing optimization unit and framework design And
Utilize a wide variety of performance measurements, analyze how well the
model are doing their jobs. The power leverage of transformers model,
specifically BERTs (Bidirectional Encoders Representation from
Transformer), for the task of translating text from German to English. The
method used in this thesis involves pre-training BERT on extensive German
text corpora, followed by fine-tuning with a focus on translation-specific
data. We extend the investigation to incorporate the Marian Transformer, a
dedicated neural machine translation architecture. The noteworthy outcome
of our research is the achievement of a validation loss of 0.11 with the
Marian Transformer. This remarkably low loss metric reflects the model's
proficiency in producing highly accurate translations during validation. It
underscores the effectiveness of our approach in capturing linguistic
nuances and context while facilitating smooth cross-lingual communication.
The results of this work is by provide 0.11 train and test loss which is better
than the approaches in the related works. The importance of validation loss
as a reliable indicator of translation quality. This research contributes to
advancing the state-of-the-art in machine translation and opens avenues for
improved multilingual communication.