Journal of Engineering

A hydraulic characteristic analysis was conducted to support the plan to construct the
Cisangkuy River Weir in the Cikalong Hydroelectric Power Plant area, aiming to improve the
fulfilment of raw water needs entering the inlet system of the South Bandung Regional Water
Company. The main problem faced is the seasonal discharge fluctuations and watershed
degradation, which cause the intake discharge during the dry season to only reach 0.09 m3/s,
far below the minimum requirement of 0.7 m3/s. This study uses a research methodology
that includes hydrological data collection, river cross-section surveys, and hydraulic
modeling using HEC HMS and HEC-RAS 1D version 6.3.1 software. The simulation results
show that the construction of a weir height of 1 m and a width of 1.5 m with a sluice gate
system can significantly increase the intake flow rate. In dry conditions, the optimal flow rate
reaches 0.62 m3/s without downstream flow, if the hydroelectric power plant operates and
the gate is opened 0.1 m, the flow inlet conditions will be 0.55 m3/s with a downstream
inflow of 4.69 m3/s. In the rainy season, optimal conditions are achieved at a return period
of 2-5 years by fully opening the two weir gates so that the water surface elevation does not
exceed the peak of the hydroelectric power plant (+885.5 m) with an inflow discharge
between 0.51 and 1.20 m3/s. The integration of the weir with adaptive sluice gates has
proven effective in ensuring the sustainability of raw water supply while mitigating flood
risks.

This research presents a comparative experimental study of machine learning models for
botnet attack detection in Internet of Things network using N-BaIoT dataset. The dataset
consists of benign traffic and malicious traffic generated by Mirai and BASHLITE botnets
family. A total of 115 traffic features are used for classification. The study compares three
representative models of learning, namely Random Forest as a classical machine learning
classifier, XGBoost as an advanced model of ensemble learning and fully connected Artificial
Neural Network as a deep learning classifier. The evaluation of the models was done looking
for performance metrics like accuracy, precision, recall, F1-score etc. As per the experiments
conducted, the developed system achieved the best which is XGBoost which produced an
accuracy rate of 99.12%, a precision of 98.98%, a 99.26% recall, 99.12% F1-score, a 0.88%
false alarm rate, and an AUC which is 0.998. The model also achieved a remarkable inference
time of 0.018 ms per sample, outperforming Random Forest with 0.021 ms and ANN at 0.035
ms. These results indicate that XGBoost provides the most effective balance between
detection accuracy and computational efficiency, making it a suitable candidate for near
real-time IoT botnet detection at gateway or edge-monitoring levels.

Water scarcity and climate variability threaten the sustainable production of strategic
crops such as wheat and maize in arid regions like Iraq. Conventional irrigation methods are
increasingly insufficient to satisfy crop water requirements due to rising evapotranspiration
and reduced effective rainfall. Therefore, Deficit Irrigation (DI), which involves applying
water below full crop water requirements, has become an important strategy for improving
water use efficiency. This paper aimed to evaluate irrigation water requirements and crop
responses to different DI levels in arid conditions, selecting the Al-Raed Research Station in
Iraq as the study area. Climatic data were analyzed using the CROPWAT 8 model to estimate
crop evapotranspiration, effective rainfall, net irrigation requirement, and gross irrigation
requirement at full irrigation and DI levels ranging from 5% to 25% in steps of 5%, with 5%
across four growth stages. The results showed that wheat had relatively low sensitivity to
water deficit, with no yield reduction at DI5% during most growth stages and a maximum
reduction of 2.0% at DI25%, mainly during the mid-season stage. In contrast, maize was
more sensitive to water deficits, particularly during development and mid-season, with yield
reductions of 4.3% and 4.0%, respectively, under DI25%. However, under DI5%, maize
showed only minor yield reduction, not exceeding 0.3%. Overall, DI5% can be recommended
during the initial and development stages for both crops due to their low water demand and
limited sensitivity to water stress during early growth.

The Dwelling is a Fundamental issue related to human beings; throughout time, man has
always had to dwell in a place and build his house. Due to different styles and typologies
appearing all over the world, all containing the character of privacy as the main element of
human life in their dwellings. The present study aims to investigate the temporal shift in
understanding privacy during the 20th and 21st centuries in Baghdadi houses, and what are
the physical elements that are related to the meaning of privacy. The research methodology
analyzed the concepts of privacy to extract the research indicators to be applied to (49)
samples of individual houses in selected neighborhoods in two different periods. The data
was collected from visual surveys and checklist observations that the researchers conducted
with 90 Iraqi residents living in those design types and styles. The results indicated that
privacy resembles a balanced physical treatment interrelated to social considerations
planning, ranging from space designs to the details, which results in organizing the
individual’s interactions with the family and then the family's interaction with others, mainly
the neighbors. Each one of them has its own design and planning requirements. The results
showed increased privacy features and approaching (isolation) in the contemporary
Baghdadi house (after 2003) due to the change in the social nature of neighborhood
members and a decreased sense of security. The research noted that there are planning
indicators associated with an increased sense of privacy, such as the width of the residential
street, the residential nature of the neighborhood, and the nonexistence of a neighbor across
from the house.

The primary objective of this study is to investigate the effects of thermal curing and fly ash
incorporation on the mechanical and physical properties of reactive powder concrete (RPC)
to develop a more sustainable RPC mixture with partial replacement of ordinary Portland
cement (OPC) by fly ash. Four mixes were prepared: a reference mix (R) and mixes with fly
ash replacement levels of 10%, 20%, and 30% (RF10, RF20, and RF30). Each mixture was
cast and tested in three specimens for each age of testing. The specimens were thermally
cured at 70°C for 5 hours per day for a total of 3 days, followed by water curing until the
testing age. The experiments showed that adding 20% fly ash improved the overall
performance of RPC. The RF20 mix showed the best compressive strength, with increases of
3.39%, 4.47%, and 4.86% at 7, 28, and 56 days, respectively, relative to a reference mix.
Furthermore, flexural strength and splitting tensile strength increased by 2.39% and 1.69%,
respectively, at 28days. The RF20 mixture had the highest dry density (2683 kg/m3), the
lowest water absorption (0.114%), and the lowest porosity (1.75%). However, a 30%
replacement of cement with fly ash reduced mechanical properties. Results indicated that
20% fly ash inclusion under thermal curing conditions could effectively improve RPC
performance and promote sustainability by reducing cement use.

The seasonal variation in the level of the river influences the quantity of water that these
dams can contain. In this study, the effect of changing the water level of the river on the
stability and performance of a filtration system at a small earth dam is evaluated for a period
of 10 years. The article discusses the influence of alternate droughts and heavy rain periods
on dam water storage and highlights that hydraulic conductivity is not constant, as is usually
considered in leakage studies. Specifically, the study focuses on the Al-Wand Earth Dam. The
study analyzed the daily water pressure data from 2015 to 2025 using GeoStudio (SEEP/W).
The research was carried out in four steps: data gathering, computer modelling, seasonal
decomposition, and validation calculations. This process showed considerable differences
between the theoretical assumption of continuous permeability and the actual situation in
the field. Thus, a hydraulic analysis was carried out and divided into two seasonal models:
winter/filling and summer/low water level. The results showed remarkable long-term
accuracy with cumulative Nash-Sutcliffe efficiency values of 98 % for the winter model and
95 % for the summer model. Importantly, the study observed that the effective permeability
at summer low water levels reduced by nearly 88% relative to the design values, a
substantial departure from the safety limits.

This study aims to investigate the effect of different curing methods on the strength of
reactive powder concrete (RPC) and to evaluate the use of brick powder as a partial
replacement of cement. The use of brick powder is intended to reduce cement content and
utilize construction waste more sustainably. In this work, waste clay bricks collected from
construction sites were crushed into fine powder and used at replacement levels of 5% and
10%. The mixtures were subjected to different curing methods, including standard curing
(C.S), coating curing, and warm water curing, followed by standard curing. The warm curing
included immersion in water at (35 ± 2) °C for one day (W1S), two days (W2S), and three
days (W3S), followed by standard curing until testing. The results showed that the
compressive strength of the control mix under standard curing reached 92.5 MPa at 28 days.
The strength increased by 11.24%, 21.72%, 25.08%, and 29.83% for (C.S), (W1S), (W2S),
and W3S, respectively . In addition, the mixture with 10% brick powder showed better
performance, with increases of 30.94%, 31.89%, and 32.77% in compressive strength,
29.03%, 29.60%, and 30.50% in flexural strength, and 27.80%, 28.89%, and 29.99% in
tensile strength at 7, 28, and 90 days, respectively, compared to the control mix. Overall, the
results suggest that brick powder can be used as a sustainable material in RPC, especially
when combined with suitable curing methods.

Phytoremediation is considered an attractive and green method of treating urban
wastewater, especially in developing countries, due to its low operating cost and sustainable
environmental option. This research aimed to evaluate an advanced method of
phytoremediation using natural media composed of walnut shells and gravel with peace lily
plants (S. Cochlearispathum) for treating real wastewater with methyl orange dye. A Lab
scale of three tanks, the first one is wastewater only as a control containment (WW), the
second tank includes substrate only (WS), and the third one includes substrate with the plant
of S. Cochlearispathum (WSP). The experiment was operated with a batch system. The WSP
tank recorded the highest treatment efficiency, where the percentage removal was found to
be 61.60% for TSS, 65.70% for color, 82.35% for nitrate, and 67.90% for phosphate. Overall,
walnut shells as a substrate with phytoremediation were found to significantly improve the
treatment of wastewater, which can be a feasible, economical, and sustainable technique for
developing regions.

An integrated system has been developed to remotely track health status using Wireless
Medical Sensor Networks (WMSNs) for continuous patient monitoring. Physiological
information is collected in remote locations and then sent to cloud-based repositories, where
it is further analyzed, allowing real-time telemedical diagnostics and therapeutics conducted
by urban specialists in cooperation with local healthcare providers. Sensors provide
isolation and constant surveillance of patients infected with infectious diseases in their
homes. This system employs multi-stage processing, beginning with the collection of
baseline data, including heart rate, blood oxygen saturation, and temperature. This data then
undergoes several processes to improve its quality, such as collecting raw measurements,
reducing noise, and compensating for missing values. Next, each patient's risk score is
assessed, and an abnormality score is calculated. To eliminate false alarms, an alarm system
with a confirmation mechanism is activated in high-risk situations. Finally, the transmission
rate is adjusted based on the risk level using adaptive sampling/event-based transmission
to ensure a rapid response. The aim of this approach is to reduce power consumption,
increase network efficiency, and accelerate transmission during emergencies. The data
transmission rates are applied according to risk stratification. Such records undergo
descriptive and inferential statistical analysis, and visualizations are created using Python
based data analytics libraries. It is centered on early disease diagnosis, faster treatment
adjustments by physicians, cost reduction, and reducing unnecessary encounters in
hospitals.

Facial expression recognition (FER) is crucial in expressing a human's emotional state.
Emotions and expressions on the human face are information that computers and deep
learning can recognize. FER is a current research topic due to the recent advancement and
use of human-computer interaction systems. The recognition of facial expressions is
challenging for current deep learning models due to the variable brightness, background,
pose, etc. of the face images. This work presents an improved learning method based on a
feature fusion convolutional neural network (CNN) to recognize seven facial expressions.
First, we trained three different base model structures and then fused the features of the pre
trained base models 1 and 2 from the final fully connected layers to obtain a fusion network.
Second, the fusion network was trained and used with the third pre-trained base model for
performance evaluation. We applied the max-score fusion and mean-score fusion techniques
between the fusion network and the third pre-trained base model to predict the output class.
Our results indicated that the proposed method outperforms the base models in all metrics
and achieves a classification accuracy of 69.03% on the facial expression dataset.

This study investigates the mechanical, physical, and microstructural performance of
structural lightweight self-compacting concrete (SLWSCC) using waste glass as a partial
substitute for natural sand and micro-steel fibers as reinforcement under high-temperature
conditions. The research will focus on optimizing the sustainability, fire resistance, and
structural performance of lightweight concrete and reducing the effects of waste disposal.
Four mixes were made, a control mix (REF) and three mixes, which included 20%, 30%, and
40 % of waste glass, which weighed 30%of sand, reinforced by 1 % of micro-steel fibers.
Slump flow, V-funnel, and L-box tests were used to determine fresh properties based on
EFNARC guidelines to verify self-compacting behavior. Measurement of hardened
properties, before and after exposure to 200°C, 400°C, 600°C, and 800°C, was done to assess
compressive strength, splitting tensile strength, density, and water absorption. The
specimens were heated in a gas furnace, followed by foam cooling. Scanning Electron
Microscopy (SEM) analysis of the microstructure revealed information on the integrity of
the matrix and the development of pores and fiber-matrix bonding at high temperatures.
The resultant of waste glass enhanced the strength and density of values dramatically in
ambient conditions because of the micro-filling effect. The G30+MS mix was the most
effective one, with a higher compressive strength of 9.2% (52.2 MPa) than the control mix;
it retained 35% of its strength at 800°C, which was higher than that of the reference mix.
Micro-steel fibers limited the propagation of cracks and avoided spalling, retaining the
structural integrity of the composite after exposure to fire.

Protecting medical images in medical applications is considered a delicate task and needs
to be carefully managed. Generally, the demand is to protect the data of the patient without
changing the content of the actual diagnostic data. Zero-watermarking (ZWM) technique
provides an elegant solution as it logically links copyright information to essential image
properties instead of hiding it in the image pixels themselves. However, there is a trade-off
in performance; this is because the existing methods fail to handle image rotation attacks.
For example, a small tilt of the image can render the watermark ineffective due to a drop in
the feature extraction process. In this paper, the proposed method tackles the
aforementioned challenge using Dual-Tree Complex Wavelet Transform (DT-CWT) to
generate stable, shift-invariant features from the low-frequency components. Then, the
extracted features are processed with Improved Differential Entropy (IDE) to resist common
attacks. In addition, the most important part is the blind geometric correction system, where
it automatically detects and corrects rotation or reflection by analyzing statistical moments
and image distortion, which is performed without the need for the comparison of original
image. Finally, the security is enhanced using Arnold scrambling and logistic mapping before
mapping the watermark to the extracted features. The developed method is resilient to
noise, filtering, JPEG compression, and crucially, geometric attacks. The results show that the
Normalized Correlation (NC) scores above 0.99 under different attacks including heavy
rotation, solving a long-standing vulnerability in ZWM research. For medical image
protection, this developed method is considered reliable, secure, and practical for
telemedicine applications.