Dijlah Journal of Engineering Sciences

These regulations are becoming more important and will remain significant
in the future. It's essential to use sustainable, eco-friendly materials at every
stage of the oil and gas industry to uphold high standards of sustainable
practices. This study investigates how adding Nano 10 Nano meter ground
hazelnut shells and willow leaf water can improve lubrication and reduce
friction in drilling fluid. The results show that incorporating these
environmentally friendly materials also impacts other properties of the
drilling fluid, including viscosity, density, filtration, and stability. The
findings demonstrate that these materials reduced filtration and density,
while improving stability and viscosity. When compared to conventional
additives like carboxyl methyl cellulose and diesel oil, the additives used in
this
study showed promising efficiency, economic viability, and
environmental benefits. As per the laboratory results, the Model 12
demonstrated superior performance due to its composition of 15g of Ground
Hazelnut Shells, 88ml of diesel, 10g of Ben, 25 gm of Willow Decoction,
and 350ml of water. The results showed that the lubrication coefficient for
Model 12 was 0.89, which is the best, and the stability after 24 hours was
1%, and after 48, 72, and a week, the stability was 1.5, which is the best, as
the lower the value for stability, the better the model, and this is what the
above-mentioned model indicated.

Analyze the possibilities of implementing a parallel algorithm for calculating
the determinant of the Nth order (by modifying it to a suitable form). Design
and implement (in C/C++) a solution based on sending messages between
nodes using the PVM system library. Distribute the load between nodes so
that the calculation time is as small as possible. Find out how the execution
time and calculation acceleration depend on the number of nodes and the
size of the problem (provide a table and graphs). Based on the results,
estimate: the communication latency, for what size the task is (well) scalable
on a given architecture, and what is the maximum size at which the
calculation is still bearable on the available architecture. Discuss the
advantages and/or effectiveness of parallel implementation of individual
algorithms. If the solution requires it, use files for input and output of
matrices where the row of the matrix corresponds to the row of the file and
the column values are separated by spaces or tabs. Unless otherwise stated,
work with real numbers

Due to the increasing complexity of cyber attacks and rapid growth
of the Internet of Things (IOT) network, the study suggests a hybrid
deep learning framework to detect cyber attack in the IOT network.
To properly analyze both structural and temporary patterns in
network traffic, the suggested approach is a mixture of deep nerve
network (DNN) and long short -term memory (LSTM) network.
Several attacks, including distributed Daniel-Off-Services (DDOS),
injection attacks and data leakage, were embedded in large ton-IOT
datasets that were used to measure models. The false alarm rate of
the hybrid model was very low and provided an unprecedented
address accuracy of 100%. Its excellent performance can be replaced
by model capacity to dynamically optimize with changing attack
techniques, learn deep non -linear features through DNN layers, and
analyze complex temporary sequences through LSTM block. With
strong scalability abilities and large data and future threats, these
findings describe an encouraging path towards promoting IOT
security for industrial and mission-mating uses.

Vortex generators (VGs) are used in aviation to delay boundary layer
separation, which is crucial for increasing the critical angle of attack and, as
a result, delaying the aircraft's entry into stall. This is because the vortex
generators (VGs) increase the flow energy within the boundary layer. In
this paper, the impact of punched triangular vortex generators on
convective heat transfer was investigated on a flat plate. Using a wind
tunnel (DUC), four pairs of triangular vortex generators were placed on a
flat plate, with nine thermocouples implanted in it to measure the
temperature change. Three experiments were conducted: the first with a flat
plate without a VG, the second VG without holes, and the last VG with
holes. The results showed that the presence of the holes had a positive
effect on improving heat transfer.

This research aims to study the flexural behavior of hollow composite beams
with simple supports, consisting of a concrete slab connected to an I-beam
steel beam, held together by shear connectors. The experimental work
involved casting eight specimens, divided into four groups based on void
ratio used: 9%, 18%, and 27%. Shear connectors in the form of steel studs
were used in different numbers. Composite beams were tested under
monotonic loading, using two void-free composite beams as reference
specimens. The results showed that the use of voids reduced the ultimate
load to failure of the beams by 8.4, 19.7, and 34.9%, respectively. The use of
13 pairs of steel nails instead of 6 pairs in the hollow composite beams
increased the ultimate load by 2.7%. The failure mode for all specimens was
flexural failure.

Present research is mainly studying the influence of formation weld
inclusions on the fatigue life to failure using mild steel compact tension
specimen of 6mm thickness with weld position normal to the notch tip where
crack is supposed to initiate and propagate toward the weld line which is
about 3-to 5 mm away from the notch. The weld was conducted using shield
metal arc welding method while the filler weld type used is E6010 Fatigue
tests for all the prepared specimens were implemented under a constant load
amplitude of 6 KN with loading frequency around 80 HZ, Different numbers
of stress ratio were used during tests as explained later on. All tests give a
confirmed results that fatigue life improved as stress ratio decreased while
the number of fatigue cycle to failure were suddenly decrease when the
crack tip hit the spherical weld inclusion which is considered as harmful
defect on the weld joint. Scanning electron machine was massively used to
investigate the fractur surfaces features and to show the shape and position
of the inclusions.

As embedded systems increasingly power energy-sensitive applications,
optimizing software for minimal energy consumption has become a critical
challenge. This study introduces a reinforcement learning (RL)-based
framework that autonomously refactors Python code to enhance energy
efficiency. Targeting legacy codebases, the proposed model leverages Q
learning to detect performance bottlenecks and apply structural code
transformations. Experimental results based on CPU usage and execution
time profiling in a Google Colab environment indicate an average energy
reduction of 27.6% over traditional static optimization methods. The
framework continuously adapts its strategy through learning iterations,
making it both scalable and compatible with modern software engineering
workflows. These findings underscore the model’s potential in advancing
energy-efficient development practices, particularly for IoT and resource
constrained computing environments, while contributing meaningfully to the
intersection of AI-based code analysis and sustainable computing.

This This paper presents a comprehensive review of the seismic behavior of
circular shallow foundations, with a focus on the role of soil-structure
interaction (SSI) under earthquake loading. Both experimental and
numerical studies are examined, including large-scale shaking table tests,
advanced finite element simulations using PLAXIS, and analytical models.
Special attention is given to the effects of dynamic loads, liquefaction,
bearing capacity, settlement, foundation uplift, and nonlinear soil behavior.
Geogrid-reinforced soils and their potential in improving seismic
performance are also discussed. The review highlights recent developments
in SSI modeling, parametric studies on soil and footing properties, and the
influence of ground motion characteristics. Limitations in current modeling
approaches and gaps in experimental data are identified, suggesting
directions for future research. The findings aim to support improved design
methodologies for safer and more resilient foundation systems in seismic
regions.

The present study focused into the utilization of Building Information
Modelling (BIM) in the status of Sustainable Building Project Management
(SBPM). It seeks to study the integration of BIM and sustainable principles
of design might enhance the benefits for project managers to reach the
optimum solution. The goal of present work is to investigate the exploitation
and identify the decisive elements of the BIM in the status of SBPM discuss
to explore the connection methodology between BIM and sustainable
principles of design might improve the avails for project managers.

Using a structured questionnaire sent to medical practitioners at Ibn Al-Haytham Eye
Hospital, this study looks at user needs for a computerized Eye Care Management
System (ECMS). The outcomes underline the requirement of automation in
sophisticated image processing for disease identification, patient management, and
appointment scheduling. Important needs found include easy user interfaces and safe
data storage. These results led to the development of a prototype ECMS containing an
interactive hospital interface with automatic Diabetic Macular Eedema (DME)
identification.
This work shows how user-driven needs could direct the creation of sensible and
quick eye care treatments. The information used in this study came from Ibn Al
Haytham Eye Hospital, therefore guaranteeing the practical relevance and legitimacy
of the study. The created Python code was used for automated analysis and disease
diagnosis to handle this actual data. The Eye Care Management System's user
interface then was developed and put into use depending on the processed data and the
needs found by means of the questionnaire.

The escalating sophistication and volume of cyber threats necessitate a
paradigm shift from traditional, reactive network security measures towards
proactive, intelligent detection systems. This paper investigates the
application of Machine Learning (ML) techniques for enhancing network
security threat detection. The core research problem addressed is the
inadequacy of conventional signature-based and rule-based systems in
identifying novel, zero-day, and polymorphic attacks effectively. We explore
the fundamental ML paradigms – supervised, unsupervised, and
reinforcement learning – detailing their applicability to various network
security tasks such as intrusion detection, malware analysis, and anomaly
identification. Key aspects of the ML pipeline, including feature selection,
data preprocessing, and robust model evaluation metrics, are discussed. The
paper reviews significant implementations and case studies, highlighting the
performance of different ML algorithms using benchmark datasets like NSL
KDD, CIC-IDS, and UNSW-NB15. Despite promising results demonstrating
ML's capability to improve detection accuracy and reduce false alarms,
significant challenges remain. These include the high dimensionality of
network data, the need for large-scale labeled datasets, the persistent issue of
false positives/negatives, vulnerability to adversarial attacks, data privacy
concerns, computational overhead, and the inherent difficulty in interpreting
complex models (explainability). Future directions point towards the
development of explainable AI (XAI), federated learning for privacy
preservation, advanced reinforcement learning for autonomous response,
hybrid modeling approaches, and strategies to counter adversarial
manipulations. This research concludes that while ML offers powerful tools
for bolstering network defenses, continuous research and development are
crucial to overcome existing limitations and stay ahead of the evolving threat
landscape.

This paper proposes a novel edge-based, predictive-intelligence system
for real-time phishing attack detection and response. By integrating
lightweight AI models with Edge Computing platforms and behavior
monitoring, the system dynamically adapts to emerging threats. We evaluate
its performance in terms of detection accuracy, latency, false-positive rates,
and resource consumption, comparing it to traditional centralized models
(e.g., GRU+WOA [1]). Results demonstrate superior detection speed
(average latency 80 ms vs. 250 ms), higher accuracy (96.2% vs. 89.7%), and
lower false positives (2.1% vs. 5.4%), while operating efficiently on edge
devices.

The rapid growth of IoT introduces significant security challenges,
necessitating effective anomaly detection techniques. This paper implements
a Random Forest Classifier for detecting and classifying anomalies in IoT
network traffic using the RT_IOT2022 dataset. The model achieves 99.8%
accuracy with high precision, recall, and F1-scores across multiple attack
types (e.g., MQTT_Publish, DDoS). Detailed evaluation confirms the
classifier's effectiveness in distinguishing diverse attacks, demonstrating the
viability of machine learning for enhancing IoT security. This work
contributes to developing resilient IoT systems, though future research
should address class imbalance and comparative performance with other
models.

A key challenge in deploying high-speed wireless systems is the presence of
hardware-induced impairments that degrade performance. These
impairments can introduce both in-band and out-of-band interference,
severely affecting system reliability. This study focuses on the nonlinear
distortions
introduced by transmitter components in full-duplex
communication systems, specifically the power amplifier (PA) and the in
phase/quadrature (I/Q) modulator. The nonlinear behavior of these elements
is recognized as a major source of signal degradation, leading to increased
distortion and reduced efficiency. Through comprehensive simulations, the
effects of these impairments are evaluated using critical metrics such as
Peak-to-Average Power Ratio (PAPR) and Error Vector Magnitude (EVM).
To address these issues, the Unscented Kalman Filter (UKF)—a robust
algorithm tailored for nonlinear dynamic systems—is employed to estimate
and compensate for these distortions. The results demonstrate that the UKF
significantly reduces the adverse impact of PA and I/Q modulator
nonlinearities, thereby enhancing transmitter performance and overall signal
integrity.

The Internet of Things' growth (IoT) has also led to an increase in damaging
attacks that seriously jeopardize unprotected IoT equipment. IoT
technologies therefore raise a number of security and privacy concerns.
Anomalies in IoT systems may enable an attacker to infiltrate a system,
often resulting in unforeseen interruptions or sensor malfunctions.
Consequently, it is indispensable to monitor the unforeseen occurrences
resulting from sensor inputs.
This study evaluates the efficacy of an
ensemble mechanism knowledge model against a conventional learning
method for detecting attacks and anomalies inside an IoT smart home
environment, using a categorical dataset of mainSimulationAccessTraces
traffic data.
To achieve optimal anomaly detection outcomes, we use the
Random Forest methodology to consolidate the most effective models
subsequent to training each model using the training dataset. This paper
elucidates many metrics and evaluation models while also providing a
complete explanation of the training strategy.
We also compare our
findings to those of comparable model versions.
difficulties and future projects.

This research explores the complex challenges and emerging
opportunities associated with integrating renewable energy sources
into conventional electricity grids. As nations intensify efforts to
reduce carbon emissions and embrace sustainability, the role of
renewable sources—particularly solar and wind—has become more
critical than ever. However, these sources introduce significant
variability and uncertainty into the grid, necessitating new
approaches in system design, operation, and regulation. The study
provides a comprehensive analysis of technical, economic, and
institutional barriers to integration, including limitations in grid
flexibility, inadequate storage infrastructure, fragmented sectoral
coordination, and market structures that fail to incentivize
innovation.
To address these issues, the paper investigates advanced
technological solutions such as smart grids, battery storage systems,
predictive control algorithms, and sector coupling strategies. These
innovations are evaluated not only for their technical potential but
also for their applicability in real-world contexts—particularly in
developing countries. Iraq is presented as a national case study,
where infrastructure constraints and regulatory gaps coexist with
enormous solar energy potential. The research proposes policy
reforms, pilot projects, and capacity-building initiatives as pathways
toward a more resilient and diversified energy future. Lastly, the
results put into context that a successful energy transition involves an
overarching framework that situates technological progress in
coordination with regulatory adaptability and stakeholder
participation.

This research deals with the topic of risk management through the
application of binomial theory and risk matrix to analyze the relationship
between the importance of risks and their impact on hospital construction
projects, with the aim of developing an effective mechanism to prioritize
dealing with risks and ensuring the success of the project. To achieve this
goal, a questionnaire based on the five-point Likert scale was prepared, and
the stability coefficient (Cronbach's alpha) was 0.891, confirming the
reliability of the tool. The questionnaire was distributed to engineers and
specialists in the implementation of hospital projects, and the data were
analyzed using accurate statistical tools. The results showed that
cybersecurity risks came in first place with a score of 18.11, followed by
financing risks, power outages, and unexpected costs. The data also showed
that most of the risks fall into the high-risk category, which requires
continuous monitoring and immediate interventions. The study concluded
that the Risk Matrix is a practical and flexible tool for managing complex
risks in health projects. The study recommends that information security
should be given top priority from the early stages of the project, to protect
digital infrastructure and ensure business continuity.

Distortion is a serious undesirable phenomenon in the electrical
and communication systems. It is produced in the transmitting channel and
distorts the input signal with addition of noise to it. Because of its adverse
impacts on the system, it is inevitable to eliminate or at least reduce its
harmful effects to the minimum level by a simple and feasible technique. In
this paper, we adopt the strategy of Recursive Least Square Lattice (RLSL)
algorithm for adaptive transversal equalization to remove the effect of any
type of distortion. The ingenuity of this research includes studying the
convergence speed of the algorithm, degree of sensitivity to eigenvalue
spread, and the regression coefficients for the equalizer for various channels.
MATLAB software is used to perform the simulation work of this algorithm.
The results indicate that the mean square errors have been reduced
significantly throughout using this algorithm.

This research aims to evaluate and compare classical (frequentist)
confidence intervals and Bayesian confidence intervals in estimating the
mean of the Poisson distribution (λ).
The study relied on a systematic computer simulation approach to compare
the main classical methods (such as Garwood, modified Wald, Begaud) and
Bayesian methods (such as Jeffreys and HPD). The simulations were
conducted by generating 10,000 Poisson samples for various λ levels (0.1
20) and sample sizes (n)
.)
100
5
(
Custom Python algorithms were used: SciPy to calculate inverse
distributions, NumPy for statistical simulation, and Matplotlib to visualize
the results.
The evaluation criteria were applied: actual coverage (% coverage), expected
interval length (E(L)), and non-coverage equilibrium.
And The important results that research reached is following: Bayesian
superiority in small samples: Bayesian-Jeffreys intervals achieved coverage
closer to the 95% confidence level when n<30 (coverage: 92–94% versus
85–90% for classical methods).
Narrow Bayesian intervals: Bayesian HPD intervals were 15–30% shorter
than classical methods when λ<5.
Performance of classical methods: Garwood\'s (exact) method demonstrated
overconservatism (coverage up to 98%), increasing the interval length by
40% when n=10.

Since the depletion of fossil fuels, the world has become increasingly reliant
on renewable energy sources. With each passing year, reliance on renewable
energy sources increases significantly. As a result, complex and hybrid
power generation systems are being designed and developed to meet the
energy needs and ensure energy security in any country. Continuous
technological improvements and efforts to provide uninterrupted power to
end users depend heavily on an efficient and fault-tolerant operation and
maintenance system. Therefore, innovative algorithms and techniques using
artificial intelligence have been introduced to reduce equipment and plant
downtime. Efforts are underway to develop robust diagnostic maintenance
systems that can identify faults before they occur. To achieve this goal, AI
techniques and tools are being used in power systems to increase the overall
efficiency of these diagnostic maintenance systems. This research provides
an overview of the frameworks for using AI techniques in power system
operations, focusing on predictive maintenance, which contributes to
reducing downtime and improving the quality and reliability of operational
processes. Research discusses the most important artificial intelligence
technologies used in energy systems and reflects the ability of these devices
to identify errors in errors and detect weak and deficient places before
failure. It also reveals the benefits and benefits that these technologies
receive and need to get global support to automate the energy sector with the
aim of gaining stability and efficiency.

The rise of the smart era has turbocharged the growth of connected
devices—and with it, the demand for better power sources. Lithium-ion
batteries are widely used today, but their performance and flexibility are still
constrained by inherent material limits and the complexity of new
technologies. As we move deeper into Industry 4.0, driven by information
technology and AI, disruptive materials and methods are opening the door to
new batteries with stronger electrochemical performance and greater
reliability. Just as important, “intelligence” is increasingly being built into
how batteries are designed and managed.
This review clarifies what we mean by “smart batteries,” proposes a three
generation typology based on intelligence and functionality, and lay out how
they work and where they can be applied. We also offer practical
recommendations to address real-world development challenges and support
the long-term sustainability of battery technologies.

Because stingless bees forage in a unique way, an optimization algorithm
needs to account for this behavior. Stingless bees, whether they forage in
groups or alone, have a particular way of doing so. Within bee groups,
foraging behavior differs from group to group. Based on the unique
behaviors of stingless bees, we created an optimization algorithm in this
study. The proposed stingless bee algorithm was then put to the test in order
to tackle the challenge of wireless network routing optimization while
accounting for residual energy. Accurate calculations of the performance of
the stingless bee algorithm were performed by selecting variables for some
of the nodes we worked on. used in this work—5, 10, 15, 20, and 25,When
there are more nodes, there are more possible solutions. The most crucial
elements of the designed stingless bee algorithm are the reduction and early
termination procedures. The algorithm differs from other bee-swarm-based
algorithms due to these two principles.

This study delves into the impact of influent salinity on the efficacy of
aerobic granular sludge (AGS) in wastewater treatment. It emphasizes the
influence of salinity on vital biological processes like organic elimination,
denitrification, nitrification, and phosphate removal while assessing the
applicability of AGS technology under varying salinity conditions. The
research further examines how salinity shapes microbial diversity and
structure within the AGS framework. Utilizing three separate sequential
batch reactors with distinct NaCl concentrations (2000, 5000, and 10000)
mg/l, the research analyzed aspects such as sludge properties, settling speed,
sludge volume index, and the efficiency of COD and NH4-N removal.
Findings underscored that increased salinity levels from, (2000 to 10000)
mg/l adversely affected AGS operations, resulting clear drop in removal
efficiencies: from 94% to 68% for COD, from 91% to 52% for NH4, from
80% to 20% for TN and caused reducing in settling speeds, granular
deterioration,. Moreover, the microbial composition within the AGS was
notably influenced by escalated salinity concentrations. This study offers
crucial perspectives on the potential challenges of employing AGS
technology for saline wastewater treatment and underscores the importance
of additional investigations in this domain.

Spelling errors or domain-specific expressions are common in clinical texts,
especially in gynecology, which impairs the performance of Natural Language
Processing (NLP) tools applied in medical use cases. In this study, we customized it
to correct spelling errors based on typical gynecological medical texts used therein.
The module relies on a rule-based methodology, using a structured dataset that
includes gynecological symptoms, diagnostic tests, chronic diseases, and
medications, along with additional words and characters that express the patient's
condition.
The system relies not only on general spelling rules but also on medical expressions
and keywords to improve correction performance. It used sample sentences
generated with intentional misspellings, mimicking the type of input expected from
physicians. The system proved effective at finding misspelled clinical terms and
words from its internal dataset. However, it did not modify any terms that were
missing or irrelevant to the dataset, except in a few cases where they were very close
to known terms.
These results confirm that combining rule-based methods with medical domain
knowledge improves correction accuracy and prepares clinical text for reliable NLP
analysis.

Today, the limitations associated with fossil fuel consumption and the
growing concerns over environmental pollution have led to an increased
reliance on distributed generation resources. At the same time, the role of
responsive loads in enhancing network management has gained significant
importance. The integration of these elements, alongside energy storage
systems, forms what is known as a Virtual Power Plant (VPP). A VPP
aggregates distributed generation units, responsive loads, and storage
facilities, all coordinated and managed through a central energy management
system. The primary objective of this thesis is to develop an effective
management framework for the operation of a VPP within the electricity
market environment, with the goal of maximizing the profitability of all
participating components. In this study, the uncertainties associated with
wind turbine output power and fluctuations in energy and reserve market
prices were explicitly considered as stochastic variables .The simulation
results demonstrate that the proposed probabilistic optimization model
enables the VPP to effectively manage these uncertainties. In such a way, the
model assures the variability of wind power and the instability of market
prices is minimally deficient, and thus there occurs a dramatic hinge in the
general profitability of the aggregated system as preferred by the factors of
the model.

Prosthetic feet play a critical role in restoring mobility and improving
quality of life for individuals with lower-limb amputations. These prosthetic
feet are not as versatile as normal feet, but they do enhance the patient's
performance. When it comes to prosthetic feet, the choice of a device is
determined by how well it fits the features of the human foot. Prosthetic feet
must fulfil different specifications in which they depend on material
qualities, foot design, and manufacturing method. This review explores the
wide diversity of prosthetic feet currently available, with a focus on the
materials used and the production techniques employed in their fabrication.,
By bringing these elements together, the review offers a clear picture of how
design choices, material selection, and fabrication methods work hand-in
hand to improve prosthetic function and user experience. The goal is to
support future innovation by connecting practical engineering insights with
real-world impact.

Intrusion Detection System is a security system that serves as a shield to the infrastructure in place. Over the years, IDS technology has expanded significantly in order to meet the demands of new computer crime. Starting from the middle of the eighties to the current century, efforts have been made to improve its ability to identify attacks without compromising the performance of the network. This paper proposes a new method for a misuse IDS model based on RNA encoding and Frequent Pattern Mining for improving pattern extraction and classification, where Frequent Pattern Mining can deal with large amounts of data, therefore fast extractions of patterns and can detect intrusion in real-time. In this work, the characteristic patterns related to the malicious activity are identified using the CICIDS2017 dataset containing different types of network attacks. The proposed system comprises four stages: Data Selection and Preprocessing, RNA encoding, Frequent Pattern Mining and lastly classification. The experiments showed that the proposed IDS model has high accuracy and lower computational complexity as well as better scalability to be applied to real-time network intrusion detection.