Anbar Journal of Engineering Sciences

Recently, intensive applications of data-driven methods
emerged in marketing decision-making and customer behavior
analysis. Although conventional approaches produced
Association Rules (ARs) from transaction trends, these rules
were frequently overlooked since they were extracted
statically and isolated from lifecycle management systems.
Therefore, this study integrated the association rule extraction
technique with a knowledge management framework to
enhance rule quality and ensure long-term utility. The
proposed approach segmented customers based on purchasing
behavior using recency, frequency, and monetary (RFM)
parameters. Following the extraction of these behavioral
features, the K-Means algorithm clustered customers into
distinct segments, including high-volume, new, and moderate
purchasers. To enhance decision-making accuracy and
eliminate irrelevant patterns concealed within transactions,
machine learning models, specifically XGBoost and Isolation
Forest, were deployed to filter out redundant rules. This
integration of knowledge management enabled continuous
system learning and optimization. Ultimately, the outcomes
demonstrated a substantial positive impact on marketing
decision support, delivering higher-quality, actionable insights
for strategic planning

A computational hydrothermal analysis is conducted in order
to investigate the influence of a time-varying sinusoidal wall
heat flux on both thermal and hydrodynamic performance in a
sinusoidally wavy tube for constant flow. Whereas most of the
earlier works considered only steady thermal boundary
conditions, a temporally varying sinusoidal heat flux is
considered here to interact with the wavy tube geometry. First,
a reference case of a smooth tube is analysed for Re between
100 and 15 000. Then, wavy tubes, with different geometrical
parameters - wave height 0.1D, 0.2D, and 0.4D; wavelength 2D,
4D, and 6D; and tube length 10D, 20D, and 40D - are analysed.
It means the optimal thermal performance, and its Nusselt
number could enhance about 106.4% compared with the
smooth tube for a wavy tube wavelength, wave height, and
length corresponding to 2D, 0.4D, and 20D, respectively. This
optimum configuration is then exposed to a time-varying
sinusoidal heat flux of various frequencies and amplitudes. The
results indicate that the time-averaged Nusselt number is
marginally influenced by the frequency and amplitude of the
imposed heat flux, suggesting the thermal response will be
dominated by the mean heat flux rather than its instantaneous
changes. These results show that oscillatory wall heating does
not affect the overall thermal performance of wavy tubes
within the tested parameter ranges.

The sudden irruption of AI into the world of Law calls
for a deep reflection on the education and training of
lawyers, and specially, on ethics. This Article offers a
new model of AI-driven immersive simulations for the
instruction of legal ethics. Leveraging machine learning
and natural language processing, the platform
generates adaptive, dynamic simulations that emulate
complex ethical situations – like conflict of interest,
algorithmic bias and client confidentiality in an AIimbued
legal world. Such simulations yield timely
feedback which promotes moral-ethical reflection and
professional identity. The paper presents the
possibilities of using it while discussing the evaluation
of its applicability and challenges, which involve
scalability, usability and ethical use. The initiative is to
give law students and lawyers ethical fluency of AIintentionalized
practice of law and so that legal practice
can become equitable and contribute to a better world.

The cycle of an internal combustion engine (ICE) is undergoing
a gradual change as a result of the quest of maximum efficiency,
performance, and zero emissions, that are frequently attained
by means of a series of experiments. Therefore, in this research,
the method of heat release at a constant volume is analyzed
using the MATLAB programming language. This study presents
a numerical simulation of a four-stroke spark-ignition (SI)
engine operating under ideal Otto cycle assumptions with
constant-volume heat addition. The simulation process
included changing compression ratio and inlet pressure of an
internal combustion engine and calculating its pressure, work,
and thermal efficiency. The results show that by raising the
input pressure above the pressure of the atmosphere, the
engine is in a state of overfeeding and practically the work
produced and consequently the nominal average effective
pressure increase significantly. Furthermore, an elevation in
the compression ratio leads to enhancements in thermal
efficiency and nominal mean effective pressure, consequently
resulting in an increase in the maximum pressure and
temperature within the combustion chamber. With ignition
advance from 20 to 80 degrees beyond top dead center, the
amount of work produced decreases by about 50 percent due
to the presence of negative work. This causes special attention
to the ignition start time in internal combustion engines and
precise adjustment of the combustion time.

CCHP (combined cooling, heating, and power) systems make
substantial use of renewable energy. Establishing a versatile,
low-carbon, multi-energy complementary energy system is
crucial. However, the performance of the system can be
impacted in a variety of ways by mixing different renewable
energy sources system. This study suggests an ideal planning
strategy to reduce the distribution grid-based CCHP\'s overall
operational and environmental costs. The proposed
optimization problem in this work is formulated as a mixed
integer nonlinear program with interval (MINLP). In order to
offer heat, cool and electricity at the lowest feasible operational
and emission costs, the unit commitment (UC) method is made
to respond to the energy and thermal energy production of the
distribution system. The proposed system consists of multiple
generation units integrated within a CCHP microgrid and is
evaluated based on hourly load demand, renewable energy
inputs, and electricity price profiles to reduce overall operating
and emission costs. The results showed that when the system
was connected to the network, the total cost decreased to 739
$ after it had been 1202.62 $ when disconnected

Time–frequency analysis (TFA) has become an important tool
for studying signals whose frequency content changes over
time, since the classical Fourier transform cannot deal with
such signals well. This paper reviews TFA techniques,
beginning with the initial building blocks, Fourier Transform
(FT), Discrete Fourier Transform (DFT) and Fast Fourier
Transform (FFT), and why they all assume that the signal is not
time-varying. The paper then categorizes TFA techniques into
three broad categories: linear, quadratic, and adaptive datadriven
techniques, all based on the Heisenberg-Gabor
uncertainty rule, indicating that you cannot have sharp time
and sharp frequency at the same time. Linear methods such as
the Short-Time Fourier Transform (STFT) and Wavelet
Transform (WT) give different resolutions at different
frequencies. More sharp images are provided by quadratic
methods such as the Wigner-Ville Distribution (WVD) and the
Cohen’s class, though they suffer from cross-term interference.
Adaptive techniques, such as the Hilbert–Huang Transform
(HHT), Synchrosqueezing Transform (SST), Stockwell
Transform, Empirical Wavelet Transform (EWT), and
Variational Mode Decomposition (VMD), work more effectively
with nonlinear or continually changing signals. The key areas
of application of TFA, including biomedical signals, speech and
audio, mechanical health monitoring, power systems, radar
and communications, and geophysics, and future trends in
hybrid methods and machine learning are indicated

This paper presents a numerical comparative study on a
parabolic trough solar collector (PTSC) with isosceles
triangular, semi-oval, and circular absorber tubes. This study
takes into account volumetric water flow rate through the
absorber tube. The thermal efficiency of the collector and
pressure drop along the absorber tube are determined and
adopted for comparison. The results showed that the
absorber tube shape has a significant influence on the
thermal performance. The thermal efficiency with isosceles
triangular, semi-oval, and circular tubes were 54.6%, 53.8%,
and 42.3%, respectively. This indicates improvement by
29.1% and 27.2% for the isosceles triangular and semi-oval
tubes compared to the circular one. However, the isosceles
triangular tube showed penalty in pressure drop compared
to the other tubes. The thermal efficiency increased with
increasing volumetric flow rate due to the enhancement in
convection heat transfer. However, the influence of changing
water flow rate on the thermal efficiency was comparatively
small. In conclusion, the results of this study can be applied
to enhance the thermal performance of present and new
designed PTSC by changing the cross-section of the absorber
tube

The explosive growth of IoT devices and the diverse traffic
patterns generated by these different devices have made traffic
classification as one of the primary focuses for addressing
challenges in IoT applications. High Quality of Services (QoS)
must also be maintained while handling such different traffic
types at the same time. In particular, machine-to-machine
(machine type communication MTC) communication within
IoT networks has grown significantly, and the competition
between MTC and the existing human type communication
(HTC) has become one of the main challenges that exhausts
specific network resources. This paper has proposed a generic
model to overcome the challenged related to the heterogeneity
of network resources. The proposed system uses QoS
parameters to enhance the IoT traffic flow by using two fuzzy
logic inference system. The system works as a proactive
machine to estimate the QoS condition and manage the traffic
flow between MTC and HTC accordingly. The outcomes of this
machine are maintaining the high level of QoS and dynamically
managing the IoT resources and ranking using an AI approach.

This review presents a comprehensive study of recent advances in
the fracture behavior of functionally graded porous materials
(FGPMs), with a specific focus on application, fracture
mechanism, and mechanical characteristics. The mechanism of
mechanical fracture involves two stages: the first is the formation
of a crack or defect in the material, and the second is the growth of
the crack under loads until the material separates and total failure
occurs. The most important characteristic of functionally graded
porous materials is their very good strength-to-weight ratio; they
also possess sound and thermal insulation and impact absorption.
The study is based exclusively on data gleaned from published
literature, database resources, and previous research reports. The
components of functionally graded materials (FGMs), from
production techniques to stress analysis and their performance,
have already been the subject of a sizable body of work because
they have huge potential to be useful in a wide variety of important
structural applications, especially when thermo-mechanical
loading is applied. The current study concentrates on developments
in structural composites derived from FGMs, particularly crack
formation and fracture behavior. The review showed that finite
element analysis (FEA) is used to simulate the mechanical behavior
of functionally graded porous materials (FGPMs), enabling
accurate prediction of crack growth, the effect of porosity ratio, and
improved structural design to resist crack growth under external
loads. Such a review provides useful information on the main
issues that have been faced previously and lays the groundwork for
future solutions to evaluate the structural integrity of this type of
material when cracks are present

This review examines the application of artificial intelligence
(AI), specifically deep learning (DL) and convolutional neural
networks (CNNs), in the detection, classification, and diagnosis
of dental caries across diverse imaging modalities. Dental
caries remains one of the most prevalent chronic diseases
worldwide, and early detection is critical for timely treatment
and prevention of complications.
Methods: A systematic literature search was conducted across
PubMed, Scopus, Web of Science, and IEEE Xplore for studies
published between 2018 and 2025. Only studies utilizing
machine learning and deep learning algorithms for caries
detection were included. Radiographic images, intraoral
photographs, and cone beam computed tomography (CBCT)
were considered suitable sources. Findings: AI-based caries
detection systems have demonstrated sensitivity ranging from
73% to 96%, specificity from 84% to 97%, and accuracy from
86% to 99%, depending on the imaging modality and model
architecture. CNN models such as ResNet, VGG, U-Net,
EfficientNet, and YOLO variants have shown considerable
promise across multiple clinical scenarios.
Conclusions: AI technologies represent an innovative
advancement in dental caries diagnosis, offering greater
objectivity and reduced observer variability. However,
challenges including dataset standardization, model
generalizability, and regulatory frameworks require further
attention to facilitate clinical integration.

the thermo-hydraulic performance of compact heat
exchangers remains a critical challenge for improving energy
efficiency and reducing operational costs. In this study, a
numerical analysis investigation was conducted to evaluate the
effects of corrugation phase shift angles (ϕ) on flow structure,
heat transfer behavior, and pressure drop characteristics in
corrugated plate heat exchanger. Four phase shift angles (0°,
15°, 30°, and 45°) were investigated within the range of
Reynolds numbers of 200–1000. It is found that there exists a
reduction in heat transfer efficiency with increasing phase shift
angles. The wavy channel configuration with ϕ = 0ᵒ at
Reynolds number 1000 showed respectively 3.82, 1.15, 1.26,
and 1.37 times higher than those of the straight channels, wavy
channels with ϕ = 15ᵒ, 30ᵒ, and 45ᵒ. In addition, an increase in
phase shifts results in a reduction of the Fanning friction factor,
indicating a reduction in pressure drops attributed to
smoother flow and weaker vortex excitation near the channel
crests. This behaviour demonstrates a clear thermo-hydraulic
trade-off between heat transfer enhancement and flow
resistance. The performance evaluation criterion (PEC) values
for ϕ= 0°, 15°, 30°, and 45° were 2.55, 2.31, 2.05, and 2.01,
respectively. Likewise, increasing the phase angle reduces the
pressure drop, resulting in a reduction in the cost of pumping
power.

Solar cells are a crucial renewable resource, yet photovoltaic (PV)
devices suffer performance degradation as temperatures rise
(typically 0.4–0.5% per °C, reaching up to 15% under extreme heat),
especially in demanding environments like deserts and space. At
higher temperatures, carrier recombination becomes more
pronounced, the bandgap slightly decreases, and material degradation
accelerates, thereby reducing the device efficiency and long-term
stability. Therefore, understanding and managing thermal effects is
essential for optimizing performance and extending the lifespan of
solar cells.This review discusses the latest advancements (2017–2025)
in mitigating thermal impacts across various photovoltaic
technologies, including thin-film solar cells, single- and multi-junction
III–V cells, traditional silicon cells, and emerging perovskite and
tandem structures. To address these challenges, diverse strategies
have been explored, such as bandgap engineering, doping
optimization, and absorber thickness control, alongside thermal
management approaches like phase-change materials, cooling
systems, enhanced encapsulation, and nanostructured
coatings.Perovskite solar cells can exhibit improved thermal tolerance
under optimized conditions compared to traditional silicon-based
devices, attributed to their tunable bandgap and low temperature
coefficient; they can be further adapted through material and interface
engineering. Recent studies demonstrate that optimally engineered
perovskite cells maintain high efficiency at elevated temperatures,
making them suitable for high-temperature and demanding
environmental applications. However, their long-term stability
remains limited in hot conditions despite these enhancements. This
review highlights deficiencies in existing research and proposes
strategies for advancing the development of solar cells that are both
highly efficient and thermally

A reliable and cost-effective monitoring system for real-time
monitoring of electrical parameters is essential in the
transition towards intelligent smart grids, for example, to
improve the efficiency of the system, load management, and
detection of potential issues in advance. The paper present the
design and experimentally a WSN-based monitoring system for
home load monitoring. The monitoring system uses voltage
and current sensors with Arduino Uno for voltage and current
collecting and uses ZigBee modules based on IEEE 802.15.4 for
wireless communication. Experimental results show precise
voltage (220–230 V) measurement and current (0–10 A)
reading with high fidelity to the reference equipment. The
system achieved stable wireless transmission with low latency,
as well as real-time data logging, visualization, and analysis.
The results indicate that the proposed architecture is a real
time montring, scalable and low-cost solution for distributed
smart grids monitoring and for the future integration of
intelligent energy management systems.

This work discusses the design and verification process of an
advanced tool called Stability Analyzer for effective control
system stability analysis using MATLAB software. It helps in
automating the computation of several key parameters, such as
closed loop poles, gain margin, and phase margin, and allows
the visualization of the system\'s performance through the use
of Bode, Nyquist, and poles-zeros plots. One major
achievement of this project is adding the element of graphical
user interface, which allows for interactive use even by those
without coding knowledge. For increased accessibility, the
application is released using MATLAB runtime, making
MATLAB installation unnecessary. To prove the precision and
effectiveness of this tool, comparison tests have been
conducted, demonstrating exact agreement between the
results and those obtained using Control System toolbox
provided by MATLAB. Also, the pilot test involving engineering
students reveals increased analysis efficiency, simplicity, and
understanding compared to the conventional and script-based
techniques.

Solar energy is a sustainable energy resource worldwide, but
the amount of energy captured by solar panels is usually
minimal because the panels are installed at a specific angle of
inclination to capture the highest level of sun radiation during
the day. The best performance of photovoltaic systems can be
achieved if the panel remains perpendicular to the direction of
the sun\'s radiation. To obtain the maximum output power of
the panels, scientists have found that this is achieved by making
them as perpendicular to the solar radiation falling on them as
possible and this is done by solar trackers known as \"solar
trackers\", which track The Sun\'s rays throughout the day. This
study aims to discover the best orientation of photovoltaic
panels by analyzing several sun tracking technologies. Several
types of solar tracking technology are discussed, such as the
degrees of motion of single- axis and dual-axis solar tracking
systems, active and passive solar tracking, open/ closed loop
are also discussed. This study included a summary of the
performance of tracking technologies and the benefits and
disadvantages of existing solar tracking systems. The results
indicate that dual-axis solar tracking systems significantly
enhance the energy efficiency and overall performance of
photovoltaic systems, making them a viable solution for
optimizing the use of solar energy

Brain tumor classification from MRI is essential for efficient
diagnosis and treatment. In this study, the authors proposed a
hybrid deep learning model based on CNN features and GLCM
texture-mapped image descriptors for brain tumor multi-class
classification. The proposed framework follows a three-stage
pipeline: deep feature extraction using a pre-trained ResNet-
18 network, texture feature fusion via Gray-level Cooccurrence
Matrix (GLCM) descriptors, and final classification
through a fully connected softmax layer. Additional classifiers
including an ECOC based SVM, were evaluated solely as
baseline comparators to contextualise the performance of the
proposed architecture. The dataset has 7200 MRI images
categorized as glioma, meningioma, pituitary tumor, and no
tumor. The data is split into balanced training/ testing sets of
6400/ 1600 scans. Experiments on the dataset show that this
hybrid model achieves an accuracy of 96.8%, around 4.5%
better than conventional CNN-only methods. The model gets
precision of 96.5%, recall, and the F score at 96.2% and 96.3%
respectively. Class-wise analysis revealed that the method
performed relatively well across the pituitary (AUC = 0.994)
and no-tumor (AUC = 0.985) categories, while the glioma class
was the hardest to predict, with the highest AUC of 0.888, thus
the class appeared to be heterogeneous in nature. The GLCM
features improved robustness by summarizing subtle changes
in texture features that were not fully captured by deep
features in isolation. The further addition of Grad-CAM added
a layer of visual interpretability by providing heat maps
indicating relevant regions of the tumor, thereby improving the
reliability of the model in practice.