DIYALA JOURNAL OF ENGINEERING SCIENCES

This research delves into the evaluation of Deep learning signal constellation identification (DL-SCI) algorithms in underwater acoustic communications using Orthogonal Frequency Division Multiplexing (OFDM). It distinctly examines at how effective the recurrent neural networks (RNNs), particularly, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) algorithms in predicting the signal constellation when applied to different underwater acoustic channels characteristics. Unlike manual feature selection in machine learning (ML), in this paper, DL-SCI exploits the labelled OFDM signals at the transmitter to detect and decode them at the receiver. In order to measure their effectiveness performance metrics, Bit Error Rate (BER) and parameters derived from the confusion matrix such as accuracy and precision are used. The study highlights the importance of utilizing zero cyclic prefix techniques which can exploit the inherent bandwidth limitation effectively.Furthermore, when examining complexity, it is observed that both GRU and LSTM algorithms require less floating-point operations (FLOPS) compared to traditional methods such as Minimum Mean Square Error (MMSE) and Least Squares (LS). Interestingly GRU shows performance in terms of complexity when compared to LSTM. Moreover, GRU outperforms LSTM by achieving a 4 dB improvement for long subcarriers. These results emphasize the effectiveness of learning techniques in enhancing performance and efficiency in acoustic communications

In the current network security framework, Intrusion Detection Systems (IDSs) happen
to be among the major players in ensuring that the network activity is being monitored
round the clock for any intrusions which may occur. The rising degree of cyber threats’
intricacy enforces the constant development of IDS methodologies to maintain
effectiveness in detecting and reversing the emergence of any extra risks. Therefore, to
settle the matter featured by, this research studies try to incorporate the most powerful
metaheuristic algorithms, Lion Optimization Algorithm (LOA) and Grey Wolf
Optimizer (GWO) in particular, to develop better detection accuracy and efficiency. The
core obstacle recognized in this article is the fact that many systems of IDS send out
false alarms and their mechanisms of detection of the true anomalies need to be
improved immensely. In a nutshell, the change would unveil a fresh way of using LOA
and GWO using them to promote the enhancement of internet defences systems in realtime. These schemes can discover previously unknown weaknesses or stealthy attacks.
The core of this undertaking would consist in the conception and implementing of a
Hybrid Network Intrusion Detection System, which will be created by blending the Lion
Optimization Feature Selection (LOFS) and GWO smelters, denoted as LOFSGWO.
Critically, the main purpose is to incorporate the GWO as a tool in the operations to cut
down the dangerous parameters favourable towards an intrusion mechanism in the
framework of a Hybrid CNN-LSTM Deep Learning system. Model tests reveal over
99.26% accuracy of low negative samples into out of a box that are served as testing as
well as NSL-KDD dataset, which are similar to the simulation of WUSTL-EOM 2020
system. The obtained outcomes verify the relevance and efficiency of the suggested
strategy, which may be used in the resolution of the issues faced in a network security
today.

Water crisis, drought, and desertification are prominent environmental issues facing many countries and threaten their sustainable development. Therthar depression basin in Iraq was selected as a study area. A new approach was adapted in producing a bathymetric map by merging contours derived from both topographic maps and digital elevation models using ArcGIS applications. Water storage volumes were estimated from 2017 to the end of 2023 using the trapezoidal rule. The results demonstrate that the maximum water storage volume was 80% of the total storage capacity in 2019. In 2021, this percent began to decrease significantly as a result of climatic changes until reached a dead storage about 40% in 2023. Large surface area of Therthar depression leads to increased evaporation and infiltration rates. The water consumption exceeded the incoming water storage by 5.292 billion cubic meters (bcm) in May and 13.127 bcm in October, between 2017 and 2023. The comparison between the estimated and actual live storage volumes resulted a root mean square error of 0.534 m and a coefficient of determination (R2) equals 0.871. However, to award off flood risks in rainy years, the study proposes increasing Therthar depression design elevation to 67 m rather than 65 m in proportion to Therthar arranger gate level.

Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring.

Concrete columns reinforced with glass fiber-reinforced polymer (GFRP) bars have been greatly interesting recently. The distinct properties of GFRP bars, such as high tensile strength and low modulus of elasticity compared to steel bars, as well as the linear stress-strain behavior, make the study of GFRP-reinforced concrete (FRP-RC) columns important. This paper investigates the structural behavior of column specimens reinforced by fully and partially GFRP bars subjected to concentric and eccentrically applied Compressive loads. 12 columns were reinforced by (36%, 64%, and 100%) of the GFRP bars ratio, and the control specimen was reinforced by conventional steel rebars; all specimens were tested under different eccentric ratios (e/h) 0, 0.66, and 1. The failure mode, the relation between the axial load and the average axial displacement, and a comparison between the experimental results and the theoretical interaction diagram for columns were presented and discussed. The results show that most of the failure in specimens occurs as a compressive failure, and it fails in the weakest region by crushing concrete, as well as kinking in GFRP bars. Using GFRP bars significantly increases the axial displacement values compared to the steel rebars in longitudinal reinforcement and decreases the failure load for specimens with an increase in the ratio of GFRP bars. The average axial displacement value for columns specimens tested under eccentric load at e/h equal to 0.66 and 1 decreases by 75% and 94.4% compared with the control specimen.

The rise in demand of concrete products has led to overexploitation of river sand the main fine aggregate in concrete resulting in major environmental degradation. As a result, researchers have focused their efforts on developing eco-friendly concrete using alternative renewable materials like volcanic tuff and other natural pozzolana types. This study therefore, aims at investigating the use of Kenyan, Kitengela volcanic tuff as a partial replacement of river sand in self-compacting concrete, and determining the effects it will have on the rheological and mechanical properties of the self-compacting concrete. The study involved partially replacing river sand with volcanic tuff in percentages of 0%, 2.5%, 5%, 7.5% and 10% and carrying out rheological tests (V-funnel test, L-box test, T-500 test and J-ring test) on fresh concrete and mechanical tests (compressive strength and tensile strength tests) on hardened self-compacting concrete on days 7, 14, and 28 to determine the effects of volcanic tuff on properties of both fresh and hardened self-compacting concrete. There was a general decrease in rheological properties (flow and passing abilities) of self-compacting concrete with increase in volcanic tuff percentage replacement from 0 % to 10%, with least flow and passing abilities recorded at 10% replacement. Similarly, increase in volcanic tuff percentage replacement led to decrease in both compressive and tensile strength of self-compacting concrete with lowest values recorded at 10% volcanic tuff replacement.

In modern communities, lots of offenders are prone to recidivism, hence, there is a requirement to inhibit such criminals, especially from impending socioeconomically disadvantaged and high-crime areas that experience elevated levels of criminal activity, involving drug-related offenses, violence, theft, and other forms of anti-social behavior. Consequently, surveillance cameras have been installed in relevant institutions, and further personnel have been provided to monitor videos using various surveillance apparatus. However, relying solely on monitoring with the naked eye and manual video processing falls short of accurately evaluating the footage acquired via such cameras. To handle the issues of conventional systems, there is a need for a system that is able to classify acquired images while supporting surveillance personnel actively. Therefore, in this paper, a deep-learning approach is developed to build a crime detection system. This developed approach includes various layers necessary to perform feature extraction and classification processes and make the system capable of efficiently and accurately detecting crime activities from surveillance video frames. Besides the proposed crime activity detection system, two deep-learning approaches (EfficientNet-B7, and MobileNet-V2) are trained and assessed on the popular UCF Crime and DCSASS datasets. Generally, the proposed detection system encompasses dataset preparation and pre-processing, splitting the pre-processed crime activity image dataset, and implementing the proposed deep learning approach and other pre-trained approaches.

The development of an environmentally friendly CO2 absorbent with significant energy utilization which can be an alternative to CO2 capture by an ethanolamine solution is presently an obligatory issue. In this work, binary and ternary deep eutectic solvents (DESs) were prepared according to their CO2 absorption/desorption performances. A series of DESs comprise different hydrogen bonding donor (HBD)- acceptor (HBA) pairs as CO2 capturing solvents; HBAs include choline chloride (ChCl) and tetrabutylammonium bromide (TBAB), and selected amines are represented as HBDs and include monoethanolamine (MEA), diethanolamine (DEA), and triethanolamine (TEA). Binary DESs synthesized of ChCl/MEA, ChCl/DEA, ChCl/TEA, TBAB/MEA, TBAB/DEA, and TBAB/TEA in a CO2 absorption cell at a mole ratio of 1:4. While selected amidines were represented as super-based (SB) and included 1,5-Diazabicyclo [4.3.0] non-5-ene (DBN) and 1,8-Diazabicyclo [5.4.0] undec-7-ene (DBU). The ternary DESs were prepared by adding DBN or DBU to the binary DESs system in a (1:4:3) molar ratio. CO2 absorption experiments were attained at atmospheric pressure and a temperature of 30 °C using 15 vol.% CO2 with 85 vol.% N2. On the other hand, the regeneration process for DESs was conducted at 90 oC. Binary DES synthesized from ChCl/MEA gives a higher absorption rate of CO2 of 0.0177 mole/kg. min, CO2 absorption loading of 2.9092 mole CO2/kg solvent, cyclic loading of 2.0001 mole CO2/kg solvent, and a regeneration efficiency of 68.75%. The synthesis DESs showed a better performance compare with a common Ionic liquids.

The occurrence of dam failures and the consequent floods pose substantial risks to submerged regions, and their unpredictability increases the hazard of their impact. This research simulates the flood event scenario due to the dam failure of the Hemrem dam in Diyala Governorate, Iraq. HEC-RAC model jas been used to modeling the Hemren dam failure. The generated flood waves are routed downstream until the confluence of the Diyala and Tigris rivers, covering a large study area of 5,378.5 km². The configuration of the model's geometry is formulated using the Digital Elevation Model (DEM) that encompasses the geographical extent of the Diyala River study area, from Hemren Dam to the confluence with the Tigris River south of Baghdad. The length of this reach is approximately 210 km. Based on the inundation maps, the region's geography is classified as having catastrophic constraints due to the water depth and flow rate. The results demonstrate how to predict the magnitude of floods and highlight the severity if Hemren Dam were to fail. This underscores the need for effective risk management. Furthermore, the maps created by this study can be utilized to develop long-term flood control plans.

Visually impaired individuals often face significant challenges in navigating their environments due to limited access to visual information. To address this issue, a portable, cost-effective assistive tool is proposed to operate on a low-power embedded system such as the Jetson Nano. The novelty of this research lies in developing an efficient, lightweight video captioning model within constrained resources to ensure its compatibility with embedded platforms. This research aims to enhance the autonomy and accessibility of visually impaired people by providing audio descriptions of their surroundings through the processing of live-streaming videos. The proposed system utilizes two distinct lightweight deep learning modules: an object detection module based on the state-of-the-art YOLOv7 model, and a video captioning module that utilizes both the Video Swin Transformer and 2D-CNN for feature extraction, along with the Transformer network for caption generation. The goal of the object detection module is for providing real-time multiple object identification in the surrounding environment of the blind while the video captioning module is to provide detailed descriptions of the entire visual scenes and activities including objects, actions, and relationships between them. The user interacts via a headphone with the proposed system using a specific audio command to trigger the corresponding module even object detection or video captioning and receiving an audio description output for the visual contents. The system demonstrates satisfactory results, achieving inference speeds between 0.11 to 1.1 seconds for object detection and 0.91 to 1.85 seconds for video captioning, evaluated through both quantitative metrics and subjective assessments.

The advent of additive manufacturing (AM) technologies revolutionized design and production processes across various industries. In renewable energy, AM enabled new possibilities for optimizing solar e-hub (solar energy harvesting module) configurations, enhancing efficiency and performance. This study examined critical considerations such as material selection, durability, and cost-effectiveness in solar hub development. This fast-prototyping technique was controlled by computer-aided design (CAD) software like CREO Parametric 7.0 and Creality Slicer 4.8. Experimental results indicated that PLA (Polylactic acid) materials exhibited superior strength, with an impact energy of 4.8 Joules. The deformation study revealed that the maximum load of 22 MPa aligned with the ultimate tensile strength of PLA, and a hardness test result of 83.1 HRF featured its exemplary hardness properties. These findings advanced the understanding of using AM to investigate mechanical behaviours of PLA materials and optimize solar e-hub configurations for portable device applications, promoting sustainable energy solutions and the adoption of renewable energy technologies. In addition, the successful implementation of this approach will enable the renewable energy sectors to minimize the carbon foot-prints towards helping the global net-zero emissions by aligning the circular economy approach.

The underwater environment is harsh and challenging for human life, prompting companies and researchers to develop advanced technologies for exploration. Building on previous work that applied a CNN-based method for underwater object classification, this paper focuses on the design and implementation of an interactive embedded system for a compact Remotely Operated Vehicle (ROV) with specific dimensions and weight. The primary goal is to capture real-time underwater video using remote control communication via Ethernet. The ROV is powered by five brushless motors controlled by a smart PID controller. Precise pulse-width modulation signals enhance stability during movements along three axes, enabling high-resolution video capture. The system utilizes the Raspberry Pi 3's computing power for motion control, positioning, temperature monitoring, and video acquisition. Experimental results demonstrate the system's capability to process 42 frames per second. A user-friendly graphical interface allows for remote ROV control across various operating systems. With a depth rating of 100 meters and speed of 0.148 m/s. This ROV surpasses human divers' limitations and holds significant potential for applications in surveillance, operations, maintenance, and measurement tasks underwater. The proposed ROV contributes to the advancement of underwater exploration technology by combining high performance with cost-effectiveness.