Journal of Engineering

A Distributed Denial of Service (DDoS) attack is an evil attempt to flood a website or a
network with malicious traffic to force it to slow down or stop working and it is now
extending to other technologies like cloud computing, IoT, and edge computing. This attack
has different types, the attacker can exploit the UDP protocol to flood the victim's devices
with a huge amount of data, or exploit vulnerabilities in network protocols, or may target
the application layer. All of the above attempts to overwhelm all available resources
including memory, CPU, and potentially the entire network aiming to incapacitate the
victim's machine or server. Despite numerous proposed defensive mechanisms, these
mechanisms often fall short as attackers continuously adapt using new automated tools. This
is why we propose a machine learning-based approach for DDoS attack detection in cloud
computing environments. Using machine learning classifiers, Random Forest (RF) and K
Nearest Neighbors (KNN) and compared based on classification performance and
computational efficiency. Experimental results showed that the Random Forest classifier
achieved the best performance by reaching an accuracy of 99.97% with minimal false
positives. Finally, integrate the best selected model into a Flask-based real-time detection
system able to classify generated traffic as either Normal or DDoS Attack.

Manual medication dispensing in pharmacies remains prone to human errors and is time
intensive, particularly under high patient loads, while existing commercial automation
solutions are cost-prohibitive for small to medium-sized pharmacy settings. This paper
presents the design and implementation of a cost-effective smart pharmacy automation
system leveraging a Raspberry Pi 5 platform. The proposed system integrates a web camera
for real-time image acquisition, a stepper motor-driven dispensing mechanism, and an HDMI
display for visual feedback of identified medications. To enable accurate drug recognition,
we employ the YOLOv8 deep learning object detection algorithm. A custom dataset
comprising 1,500 images of ten distinct medication classes—with 150 samples per class,
captured under varying illumination conditions (bright, normal, and low light) and three
viewing angles—was constructed for model training and evaluation. The dataset was
partitioned into 70% training, 20% validation, and 10% testing sets. Experimental results
demonstrate that the YOLOv8-based model achieves exceptional performance, with an
overall precision of 98%, precision and recall values approaching 1.0, and mAP50 and
mAP50-95 scores of approximately 0.99, indicating high reliability for real-time deployment.
The entire system, including both hardware and software, is able to accurately identify and
dispense medications in an automatic manner with minimal delay. This affordable, on-site
alternative to high-cost commercial systems presents an efficient and scalable solution to
help decrease dispensing mistakes and enhance workflow in a pharmacy setting with limited
resources.

Land governance and land-use planning are often treated separately, yet in practice they
meet wherever land rights, institutional decisions and spatial regulations shape actual
places. This study develops a five-dimensional analytical framework for reading the
Governance–Planning Nexus. To build it, the relevant literature was identified, synthesized
and analyzed through a systematic scoping review conducted and reported following the
PRISMA-ScR guidelines. Grounded in institutional planning theory and drawn from four
international references (FAO-VGGT, World Bank-LGAF, UN-Habitat-IGUTP and GLTN), the
framework works through five dimensions: legal, institutional, social, technical and
planning. Five databases (Scopus, Web of Science, ScienceDirect, SpringerLink and Taylor &
Francis Online) were searched for peer-reviewed studies published between 2015 and 2025.
After three-stage screening, 30 studies were retained and coded on a three-level engagement
scheme (0 = absent, 1 = implicit/instrumental, 2 = explicit/analytical). The results show
clear but uneven architecture. The institutional dimension appears in all 30 studies, followed
by planning (29/30), social (27/30) and legal (27/30); the technical dimension is weakest,
present in 19 studies but analytically integrated in only 6. African studies cluster on
institutional and legal questions, European and North American studies emphasize multi
level strategy and community-based planning, and only a few integrate all five in one
explanatory frame. The study's main outcome is a replicable five-dimensional framework
with its operational coding scheme. The evidence shows the central gap lies not in the
absence of governance or planning, but in the partial, uneven integration of their legal, social,
technical and institutional components.

Construction projects are temporary, fragmented, and multi-organizational settings,
performance in which rests not only upon technical and managerial systems but also on the
behavior of individuals and their interactions. Yet construction management literature
remains splintered, with leadership and relationship concepts being treated separately. As
a result, the joint effect of leadership styles and relationship quality on project performance
remains poorly understood. This study critically reviews and integrates previous studies to
better clarify the leadership–relationship–performance interface in construction projects. It
identifies, reviews, and synthesizes studies via major academic databases and some
additional reference tracking to derive themes. The review finds that transformational
leadership emerges as the principal style explored, although transactional, directive,
participative, empowering, and sustainable approaches are also central under certain
project conditions. For the nature of the relationships, trust, communication, collaboration,
and knowledge sharing serve as key relational mechanisms through which leadership
influences performance outcomes. Project performance appears to extend beyond the
narrow triad of time, cost and quality to safety outcomes, effectiveness, preferences of the
workforce, innovations and sustainability outcomes. The study concludes that
understanding the performance of construction projects calls for an integrative view in
which both leadership style and relationship quality interact synergistically across contexts.

The quality of drinking water is of critical importance to the health of people, especially in
river-based supply systems facing the growing environmental uncertainty. Nevertheless,
most of the tests are based on coarse indices like the Water Quality Index (WQI) that might
fail to capture short-term surpluses and incidental degradation. This paper will provide a
combined assessment of drinking water quality in the Al-Doura Water Treatment Plant,
Baghdad, Iraq, which receives a direct supply of raw water from the Tigris River. WQI was
used to test monthly monitoring parameters of physicochemical key parameters in raw and
treated water under mean- and maximum-based exceedance analysis in relation to the
World Health Organization standards from January to December 2024. The WQI of treated
water (mean = 61.31) is moderate. Mean-based exceedance analysis proves that in the case
of average operating conditions, all analyzed parameters are generally compliant with the
guideline values. However, extreme turbidity changes in raw water have been described as
one of the consistent operational stressors. The maximum-based exceedance analysis
further revealed that calcium levels in treated water exceeded the WHO permissible limit by
up to 28%, highlighting short-term risks and treatment constraints that remain hidden
within aggregated indices, a phenomenon known as the eclipsing effect. By exposing these
masked exceedances, the maximum-based approach offers a more reliable early-warning
tool than WQI alone for safeguarding public health in river-dependent supply systems.

In this work, we present a comprehensive review on how to tackle the challenging tasks of
navigation of mobile robots in cyber-physical environments which are prone to failures. The
main contribution of this paper is to present a system partitioning criterion, i.e. how to divide
the control authority of the different subsystems in order to prevent a single failure from
triggering a chain of system failures. To this end, we analyze the effects of three types of
environmental degradation, i.e. environmental disturbances, structural instability and
cyber-physical disturbances, on the different layers of a typical robot, i.e. perception, SLAM,
planning and control. We then review all the existing methods for navigation, i.e.
probabilistic methods for uncertainty estimation, learning methods and hybrid systems for
various layers and provide a review of the ways to test them. We also provide a number of
observations such as the high navigation performance of multi-layered hybrid learning
systems which can reach up to 99% while single networks fail to reach even 48% and the
high performance of embedded perception which reaches up to 93% when the onboard
perception fails. We also present a modeling method for such systems by combining an
adaptive learning representation with a model-based representation which can be used for
recursive feasibility and safety verification. Finally, we present the open challenge of cross
layer verification for such complex systems.

Manning's roughness coefficient (n) is crucial for reliable hydraulic analysis of natural and
artificial open channels. This paper presents a state-of-the-art work on principles of flow
resistance mechanisms of natural channels and n estimation approaches. Furthermore, it
reviews classical empirically driven approaches, such as Strickler, Cowan, Meyer–Peter and
Müller, Limerinos, and Henderson, as well as tabulated, photographic, and storage-based
techniques. The latter is a consideration in terms of formulations that factor into vegetation
effects, bed material characteristics, bedform-induced resistance, and composite channel
roughness. Newer advances in entropy theory, statistical approaches, and data-driven
methods are also introduced. The study describes both the advantages and disadvantages of
specific methods and shows that no way to predict Manning’s n can be applied universally
under different flow conditions and channel types. Instead, the precise estimation has to be
based on empirical field observations, analytical construction, and engineering judgment.
The observations support the use of an integrated and hybrid approach and can be
supplemented with high-resolution measurements and standardized databases for the
purpose of minimizing prediction uncertainty for the roughness estimation. This review
contributes to enhancing the knowledge and application base of researchers and
practitioners in river hydraulics, channel design, and flood modeling.

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Due to the limited clarification of the combined effect of geogrid layers and a 0.4B skirt on
square footings over weak dry sand, this paper summarizes the bearing capacity of a square
shallow footing with dimensions of 10 × 10 cm under central loading, with the footing resting
on weak dry sand with a relative density of 30%. The experimental program compared a
footing without a skirt and the same footing supported by a skirt with a length of 0.4B, where
B represents the footing width. The soil was studied in five cases: unreinforced soil, one
geogrid layer, two geogrid layers, three geogrid layers, and four geogrid layers. A skirt length
of 0.4B was adopted to evaluate its effect on bearing capacity and settlement. The results
showed that the footing supported by a 0.4B skirt without soil reinforcement increased the
bearing capacity and reduced the settlement. Adding geogrid layers further improved the
footing response; however, the improvement became limited after the third geogrid layer.
Therefore, the use of three geogrid layers can be considered the most effective arrangement.
The highest bearing capacity ratio (BCR) was obtained when three geogrid layers were used
with a 0.4B skirt length, reaching about 3.50 times the reference case, which corresponds to
an improvement of approximately 250%. For settlement, the settlement reduction factor
(SRF) reached 0.790 for the skirted footing case. Therefore, the combined use of geogrid
layers and a 0.4B skirt length provided a noticeable improvement in bearing capacity and
settlement behavior compared with the untreated unskirted footing.

So far, Iraqi research on urban planning has offered little empirical treatment of the
relationship between the quality of sustainable urban design and residents’ decisions to
migrate under climatic stress. Local studies on internal migration have focused on economic
and security drivers. While reports on displacement illustrate the size of mass migrations,
they do not examine urban design determinants. This gap is the research problem of the
present study. The study hypothesizes that deficiencies in sustainable urban design
features–principally the absence of green cover, shading, and natural ventilation–are a
decisive, quantifiable driver of climate-induced displacement in Iraqi cities, independent of
the primacy conventionally given to economic migration determinants. To test this
hypothesis, the study adopts a descriptive-analytical, mixed-methods design integrating GIS
based spatial analysis of multi-temporal satellite imagery (2003–2024), a household survey
(n = 450; 150 households in each of Baghdad, Samarra, and Najaf), and interviews with local
planning officials and experts. The analysis demonstrates that, statistically significantly,
deficient design features are associated with migration intentions of residents. The urban
design variables account for the greatest share of variance in decision-making regarding
displacement across the three cities, excluding income and housing-tenure status. The study
calls for a Green Building Code to be a prerequisite for construction, to ensure at least a
minimal greening with each new building and a National Sustainable Urban Planning
Authority to strengthen the climate resilience of cities and curb climate-related
displacement.

Classifying environmental sounds is considered a challenging task because of the complex
nature of acoustic signals. This classification involves identifying the acoustic flow
associated with different sounds in the environment. Several factors affect it, such as the
distances between the source and the destination, voice interference, and the huge number
of sound sources. These problems have been addressed in this work, where a hybrid deep
learning technique is proposed to improve the classification of sounds, focusing on
fundamental audio features which are accurate in these tasks. The proposed hybrid
classification framework combines a Support Vector Machine (SVM) and a Convolutional
Neural Network (CNN). The SVM model uses feature extraction based on the orthogonal
transformation (DCT), and the CNN model uses machine learning to represent data from
Log-Mel spectral diagrams. Noisy sound signals are generated using the TIMIT and NOISEX
92 datasets by combining the clean signal with various types of environmental sounds,
resulting in 17 categories, including the clean signal category. A soft voting strategy is then
used to enhance overall decision-making. It calculates the weighted average probability for
each class across all models and selects the class that has the highest average probability as
the final output with more robust and reliable classification. The experimental results prove
that the proposed model achieves high performance compared to current ways and
individual models, reaching an accuracy of 99.28% with improvements in F1 score, accuracy
and recall. The results obtained prove the efficiency of integrating ML and DL methods for
classifying environmental sounds.

This study investigates high-energy proton–oxygen collisions (√sNN=0.056) using the
Optical Glauber Model and accounts for how different nuclear density distributions affect
the properties of the collision both geometrically and dynamically. The Glauber Model is a
very common technique in heavy-ion physics that treats nuclei as continuous matter
distributions; however, the effect on proton–nucleus systems of adopting different nuclear
density distributions remains insufficiently studied, especially the Woods–Saxon and Sharp
Sphere distributions. In this regard, oxygen nuclei were selected because their low mass
makes them an important reference system that has rarely been considered for comparison
in the past. This work provides a systematic comparison of these two density distributions
by computing several observables such as the thickness and overlap functions, the number
of participating nucleons, and the number of binary collisions. It is observed from the results
that the Woods–Saxon distribution produces a relatively smoother and wider overlap
geometry along with a larger Npart, while the Sharp-Sphere distribution provides steeper
behaviour to all observables as the collision impact parameter increases. In both cases, the
Npart and the Ncoll decrease with a higher collision impact parameter, owing to a shrinking
nuclear overlap region. These results demonstrate a significant sensitivity of the collision
geometry on the presumed method of specifying the nuclear density distribution, as well as
emphasize that precise models of nuclear structure are essential to describing properties of
the initial state in high-energy p-18O interactions.