Al-Qadisiyah Journal for Engineering Sciences

Abstract With the advances in technology, sustainable renewable energy implementation in many applications is becoming important, such as in wireless sensors. Vibration energy harvesting can convert environmental mechanical vibration into useful energy. Honeycomb core structure has shown promising results in cantilever dynamic vibration. However, there is a limited study on its structural variation. This research focuses on different beam thicknesses and angle orientations of 3D-printed hexagonal core structures to improve vibrational cantilever energy harvesting. The beam thickness was studied at 4 mm, 6 mm, and 8 mm with a re-oriented honeycomb core at 0° and 30°. The test rig was modelled as a mass-spring system. The model was simulated for modal analysis and power generation with piezoelectric. The natural frequencies and vibration amplitudes were compared between simulation and experimentation with a vibrational shaker machine and a data acquisition device. The results showed that the 30° honeycomb core has better dynamical amplitudes and energy harvesting as compared to the conventional 0° honeycomb core beam. The different beam thicknesses affect the beam stiffness and, therefore, vary the vibrational amplitude and generated power. The 30° core with lower thickness was found to have the highest power generation due to the better distribution of stress along the beam.

Abstract In this search article, an integrated hybrid treatment system combines adsorption activated carbon (AC) and electrocoagulation (EC) to remove lead metal from produced water. The novel used a batch reactor design that facilitates synergistic connections between the two treatment processes. The process was optimized under optimized conditions using Response Surface Methodology (RSM) with a Box-Behnken (BBD. The optimization process conditions include the electrolysis time, pH, activated carbon dose, and current. Lead removal efficiencies reached 88.4\% for activated carbon alone, 95.4\% for electrocoagulation alone, and 99.8\% for the hybrid method at 45 min electrolysis time, pH 3.0, 1 gm of activated carbon dose, and 1 Amp. The results indicate that the combined scheme significantly enhances the lead metal removal rates from the produced water compared to the individual process methods, providing an active solution for the production of clean water. The hybrid process offers an eco-friendly, cost-effective, and sustainable approach to removing metal pollutants from water resources.

Abstract This study investigates the impact of side friction (SF) on traffic movements for selected urban roads in Hilla, focusing on how static and dynamic friction elements influence traffic flow and speed. The primary SF elements observed include parked vehicles and pedestrian movements with more dynamic interactions, such as erratic road crossings. Traffic data collection was conducted on four distinct Hilla streets using elevated video cameras that recorded traffic volume, vehicle speed, and SF events. Data recording occurred on Sundays, Mondays, and Wednesdays, both in the morning and in the evening. An analysis of the recorded data revealed significant variations in traffic behavior due to SF elements. On streets with high commercial activity and educational centers, a notable reduction in average vehicle speed was found to be correlated with periods of high SF activity. During the busiest hours, the speed reduction reached 51\% in comparison of it at periods of low SF activity. This substantial impact underscores the sensitivity of urban traffic flow to side friction factors, particularly during peak hours. Dynamic SF events, such as pedestrians crossing roads erratically, had the highest frequency during evening hours, aligning with the end of business and school activities.

Abstract Recently, software cost estimation has become a more important issue in the software project development cycle, software quality, and decision-making management. In view of the common problem of inaccurate and difficult cost estimation in the software industry, in this article, the proposed bagging method is one of the ensemble learning methods to estimate the cost of software development. Five algorithms were used as the basic models: Random Forest, Decision Tree, AdaBoost, K-Nearest Neighbor, and Gradient Boosting, compiled using the bagging method. The proposed method was applied to a data set (ISBSG). The contribution of the paper suggests a more accurate method compared to previous studies, and applying it to high-quality data, which was prepared to obtain more accurate results when applying the proposed model, which showed its superiority over individual models in estimating the cost of software development. The results showed high accuracy in R² prediction in ratio (97%) and gave a lower error rate (MMRE: Mean Magnitude of Relative Error) compared to previous studies in ratio (0.1). This indicates its accuracy in prediction is closer to the real cost, where the RF model was the basic estimator model in this method, because it surpasses the main models that were used in the proposed method. The KNN model gave the lowest accuracy ratio (73%) among the main models when trained on the ISBSG dataset.

Abstract In this study, the effects of ground granulated blast furnace slag (GGBFS) stabilization of subgrade soil on flexible pavement layer thicknesses and initial construction costs were investigated. For this purpose, Proctor compaction tests were conducted on bentonite (BT) soil samples prepared by adding GGBFS at rates of 5%, 10%, 15%, and 20% by weight. Following these tests, unconfined compressive strength specimens were prepared and tested after curing periods of 3 and 7 days. The results indicated that the highest compressive strength was obtained in mixtures containing 15% GGBFS. In addition, untreated soil and soil samples stabilized with 15% GGBFS were subjected to the California Bearing Ratio (CBR) test after 3 and 7 days of curing. It was determined that the CBR values of the samples containing 15% GGBFS increased by 2.09 and 2.85 times, respectively. Using the CBR values obtained, pavement layer thicknesses were calculated based on the AASHTO 1993 flexible pavement design guidelines, and cost analyses were performed. The results showed that stabilization with 15% GGBFS reduced the initial construction costs by 23.03%.

Abstract In situations where reliable crash data is unavailable, Surrogate Safety Measures (SSMs) become valuable tools for traffic safety research. This study focuses on assessing traffic safety at signalized intersections, specifically examining how traffic operations such as stopped delay time and Level of Service affect hourly traffic conflicts using surrogate measures. Three four-leg signalized junctions in Karbala City were selected. Data on traffic volume and conflicts were collected using video cameras managed by the Karbala Traffic Police. The VISSIM software was utilized for traffic operation analysis, which then served as input for the Surrogate Safety Assessment Model (SSAM) software. The results show that simulation-based hourly traffic conflicts increase as stopped delay time increases, with a correlation coefficient between them ranging from R² = (0.9127-0.7533). Specifically, stopped delay values ranged from 117 seconds at the Sayed Jawda intersection (considered high-risk) to 79 seconds at the Al-Sofaraa intersection (considered low-risk). Rear-end conflicts accounted for the highest percentage of total conflicts, reaching 63%, 58%, and 66% at the respective intersections. This high percentage is attributed to rear-end conflicts predominantly occurring within the same approach in the traffic queue. This research provides valuable insights into evaluating safety at signalized intersections in urban areas and serves as an additional reference for future road safety studies. This study differs from previous research by emphasizing the correlation between stopped delay time and the frequency of hourly traffic conflicts using SSAM software. This study provides a more nuanced understanding of the relationship between operational characteristics and safety at signalized intersections.

Abstract As global energy consumption increases, there is an increasing desire to investigate novel nanotechnology-based approaches for enhancing oil recovery. Integrating nanostructures has shown an augmentation in the rate of crude oil recovery. This study used Iraqi crude oil obtained from well-known fields in Basra, such as Majnoon, Zubair1, North Rumaila, Nahran Omar, and West Qurna1, which feature unique attributes. Titanate nanoparticles (TiNP) and titanate nanotubes (TiNT) were employed as additives to enhance various properties of Iraqi heavy crude oil, including viscosity, API, specific gravity, surface tension, and interfacial tension. These additives were used either as enhancers or for advanced treatment purposes. The study investigated the impact of nanoparticle concentrations on crude oil efficiency in various fields. TiNT showed significant performance in reducing the viscosity of Majnoon crude oil by 37%, outperforming other fields. The addition of 0.1wt% of TiNT reduced surface tension by 15.5%, 14.5%, 15.1%, 18.6%, and 15.7% in Zubair1, North Rumaila, Nahran Omar, and West Qurna1 fields. However, TiNP showed more efficacy in decreasing specific gravity by 4.3% in Majnoon crude oil. The inclusion of titanate nanostructures resulted in API values of 23.5%, 19.5%, 17%, 22.4%, and 21.2% with TiNP and 15.15%, 14.8, 15.4%, 15.6%, and 8.2% with TiNT at a concentration of 0.1wt%.

Abstract A B S T R A C T
Diagnosing attacks in wireless sensor networks (WSN) is a crucial concern in cyber security that impacts diagnosis models' accuracy because of data imbalance and outliers existence. The present paper aims to design and perform a novel strategy for efficient attack diagnosis in WSN that applies DL models’ integration (DNN and CNN+LSTM) and Bayesian optimization. The present study checks concerns on data analysis such as various attack complexities and instances’ imbalance. Such concerns have been mentioned by applying techniques such as MinMax scaling, data balancing with ADASYN, polynomial feature engineering, and outlier elimination with DBSCAN. The outcomes of the experiments illustrate that the DNN model obtained an F1-Score of 85.71%, and appeared an accuracy of 99.14% which is an important development across conventional techniques. Such results illustrate that was presented technique could develop WSN security and possesses high capability for various attacks’ kinds’ diagnosis.


© 2024 University of Al-Qadisiyah. All rights reserved.

Abstract Understanding the near-field distribution of antennas has become more significant since it directly effects radiation performance, energy absorption, and safety in current wireless applications, especially when antennas operate close to the human body. This research explores a Meander Line Antenna (MLA)’s electric field patterns within in-body contexts, emphasizing propagation loss, energy conservation, and electric field deterioration in different physiological tissues. This investigation examines the process of signal weakening in embedded antenna use by assessing the variations in electric field strengths among Muscular tissue, skin, and fatty layers at diverse conductivity levels. By utilizing results from FEKO simulations, the research demonstrates the absorption losses associated with the electric field distribution across tissue surfaces. The findings indicate these tissue components exhibiting higher electrical conductance, such as muscle and skin, experience faster degradation when subjected to electric fields compared to fat, which possess lower dielectric constant. These results provide crucial insights into effectively optimizing MLA structure for medical purposes, ensuring reliable wireless signal transfer in physiological environments.

Abstract The stability analysis of Marangoni magneto-convection (MMC) is investigated in a two-layer system consisting of an electrically conductive fluid-saturated porous layer overlain by an identical fluid layer, incorporating variable heat sources and a uniform magnetic field. The upper fluid surface is free, allowing surface-tension-driven convection, while the lower porous boundary is rigid. Two thermal boundary conditions are examined: (i) adiabatic–adiabatic (A–A) and (ii) adiabatic–isothermal (A–I). The governing equations are solved analytically using an exact method to obtain the thermal Marangoni number, an eigenvalue, as a function of depth ratio, Darcy number, Chandrasekhar number, internal Rayleigh numbers, wave number, and thermal diffusivity ratio. Graphical results show that the onset of MMC can be either advanced or delayed by appropriate choices of depth ratio and thermal boundary conditions. The novelty of the present work lies in deriving closed-form expressions for a composite fluid–porous system with simultaneous consideration of variable internal heat sources and magnetic effects under dual thermal boundary conditions, and demonstrating how depth ratio and thermal boundary conditions can be strategically tuned to either advance or delay the onset of instability.

Abstract Creating sustainable environments has become an urgent goal for most communities all over the world. This is because creating a sustainable environment would lead to improving a community's life socially, environmentally, and economically. To do so, urban sustainability indicators need to be incorporated into the urban design process, beginning with the architecture teaching syllabus. This study aims to find out how students’ projects in the urban design course could consider sustainability indicators when developing built environments, including campuses, and how architecture students’ projects can consider the role of physical character in achieving sustainability when developing campuses. Therefore, students' projects designated for developing the main campus of the University of Mosul were the case study of this research. Explorative graphical analysis was adopted as the study method. Findings highlighted key characteristics of sustainable urban design that are incorporated into these projects and the way to do so.

Abstract This work creates a simulation-based scheme to optimize Proton Exchange Membrane Fuel Cells (PEMFCs) in the maritime sector, focusing on the interim functions of thermal, water, and hydrogen pathways in defining efficiency and sustainability. Findings indicate that although peak efficiencies are close to 0.90, dependable operation is limited to 0.84 -0.87, better than the hydration limit of 0.82 and worse than the thermal instability threshold of 0.88. The best hydrogen usage is between 70 -82 because lower percentages were tiger and higher percentages were almost 2 times higher rates of degradation when 0.006 V/1000h (harbour) and 0.012 V/1000h (sprint) were used in respectively. Mode comparison proves cruise operation at 91% net efficiency, 13% auxiliary demand, and 27% performance 27 % Excellent, 36% Good, 3% Fail, and sprinting is 86% efficiency and 16% auxiliaries with 27% Fail/Poor results. This study outsmarts the other studies by being the first to establish quantitative safe operation envelopes of maritime PEMFCs and provide a workable blueprint of sustainable deployment by integrating radar, multi-panel, and 3D threshold-based analysis.

Abstract This study proposes a conceptual urban policy framework for planning and designing urban streets that promotes people-oriented development. We utilized a scoping review and content analysis to map the key factors, indicators, methodological approaches, and tools within research on people-oriented development. The search was conducted across the Scopus and Google Scholar databases to ensure broader coverage of relevant peer-reviewed literature published between 2013 and 2023, resulting in 210 manuscripts. After three rounds of inclusion and exclusion criteria, we ended with 83 manuscripts. We also conducted a content analysis of a selection of 24 manuscripts for deep qualitative analysis of insights about people-oriented development. The findings highlight a strong relationship between people-oriented street design and improved community satisfaction, social interaction, and overall well-being. The review identified twenty-four methodological tools, which were synthesized into a five-step action plan: pre-assessment, data collection, analysis, evaluation, and the development of a priority map. This framework offers urban planners and policymakers an evidence-based pathway for implementing integrated, people-focused street design strategies. The study’s findings strengthen the conceptual understanding of people-oriented development. The insight gained from the scoping review supports the creation of more inclusive, livable, and human-centered urban streets.

Abstract Recently, a proliferation of techniques capable of replicating sounds has emerged, encompassing manipulated recordings, synthesized audio, and deepfake technologies. These advanced methods for generating artificial sounds have inadvertently given rise to a multitude of issues, prominently including the dissemination of misinformation, propaganda, and significant reputational damage. This article addresses this critical problem by proposing the application of a Convolutional Neural Network (CNN) model designed to predict the authenticity of a given sound. For feature extraction from the audio, Mel Frequency Cepstral Coefficients (MFCC) are utilized. To rigorously assess the robustness of the proposed model, the intricate combinations of these extracted features were further analyzed using derivatives of the MFCC features. The Fake-or-Real (FoR) dataset was used to train and test the proposed model. The Accuracy, F1-score, Precision, Recall, and Loss are used as metrics to evaluate the model's performance. The model performed well, with an accuracy of 99.64%. The proposed model was comprehensively evaluated by systematically varying the extracted features through different derivative orders, where the accuracy decreased to 94.34%. The experimental results demonstrate the effectiveness of the model in accurately detecting fabricated audio.

Abstract Distance measuring equipment (DME), which gauges the distance between an aircraft and a ground station, is an essential navigational tool in aviation. However, because of instrument constraints, multipath interference, and ambient conditions, DME measurements are frequently noisy and prone to errors. This study introduces a framework that integrates machine learning (ML), sensor fusion, neural networks (NNs), and real-time processing with the aim at enhancing the accuracy and reliability of distance estimation, with particular emphasis on regression models. To boost robustness, the suggested system uses sensor fusion to combine DME data with inputs from additional sensors, such as GPS and Inertial Navigation Systems (INS). The intricate correlations between sensor inputs and actual distance are modelled by NNs because of their ability to produce precise predictions even in the presence of noise, and they provide a very accurate distance calculation with minimal latency. ML based regression models further improve system reliability by detecting and correcting anomalies in the sensor data. When tested in MATLAB and compared with standalone DME measurements, the proposed system shows higher accuracy of distance estimation. In addition, the real-time sensor fusion ensures precise and timely outputs for essential aviation applications. Using this method not only improves the DME system but also provides a scalable and flexible solution for different navigation and positioning systems in dynamic scenarios. The system is measured based on significant metrics including mean squared error (MSE), peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR).

Abstract The authors sincerely apologize for this oversight and for any inconvenience it may have caused. To address this matter, the authors wish to revise the Funding
Source section as follows:
Funding source:
This study was supported by Faculty of Architecture and Planning, Thammasat University Research Fund, Contract No. TDS 16/2566.