Wasit Journal of Engineering Sciences is a peer-reviewed, open access journal published quarterly by the College of Engineering, Wasit University, Iraq. Journal title Wasit Journal of Engineering Sciences Indexing Online ISSN 2663-1970 Print ISSN 2305-6932 Frequency 4issues per year DOI 10.31185prefix Editor in Chief Prof. Dr. Haider TH. Salim ALRikabi Faculty of Electrical Engineering, College of Engineering, Wasit University, Iraq hdhiyab@uowasit.edu.iq Organized by Engineering College – Wasit University, Iraq Citation analysis Scopus | Google Scholar Acceptance rate 40% Review speed 90days Online Submissions Before submission, you have to make sure that your paper is prepared using the WJES paper Template, or Latex Template, has been carefully proofread and polished, and conformed to the author's guidelines. Make a Submission Log in Register Registration and login are required to submit items online and check the current submissions' status.
Green Synthesis and Characterization of Fe₃O₄@SiO₂@AgVOₓ Magnetic Composites for Efficient Removal of Methylene Blue from Aqueous Solution
The growth of Iraq's textile sector has led to an increase in the use of synthetic dyes, which are harmful to the environment because they don't break down naturally. An eco-friendly process was used to create the magnetic composites Fe₃O₄@SiO₂@AgVOₜ, which had silica and silver vanadate coatings. Then, these composites were used to extract synthetic colors from water, such as methylene blue (MB). To create environmentally friendly nanomaterials that are both long-lasting and magnetically recoverable, an alternate green synthesis approach to the current standard could be used. The removal efficiency of the composites in the supported structure was determined to be 91%, while in the core-shell structure it was 87%. Because there were active regions available, the system's efficiency was slightly higher. Productive synthesis, strong magnetism, facile separation, and recyclable manufacturing were all validated by the characterisation procedure. Nuclear magnetic resonance (NMR) and transmission electron microscopy (TEM) images showed that the iron core was uniformly shaped and adequately coated with silica and AgVO3. Findings point to a sustainable application of Fe3O4/SiO2/AgVO3 in wastewater treatment and other environmental cleaning initiatives for the removal of harmful synthetic dyes like Methylene Blue.
Copyright (c) 2026 Alaa Waleed Juma, Nuralhuda Aladdin Jasim, Alaa Kharbat Shadhar, Wisam Fawzi https://creativecommons.org/licenses/by/4.0
Communications Integration of 5G-WSN Towards Environment Monitoring Based on Deep Learning Techniques.
Healthcare monitoring-based systems are experiencing a surge in conventional methods with the integration of cutting-edge technologies such as the Internet of Things (IoT) and deep learning. This paper suggests a Wireless Sensor Network (WSN) with IoT architecture to monitor patients in real-time using the 5G networks. The system can effectively process the data that the wearable sensors capture regarding the heart rate, blood pressure, oxygen levels, and body temperature, and use deep learning models such as CNN-LSTM to process that data and convey the results to the user. It is created to update patients, especially the elderly, about their health promptly and to be able to contact healthcare providers remotely. The experimental results show that the system is effective as the accuracy of the system reaches 0.988% with a Maximum relative error of “heart rate” (2.19), “body temperature” (3.06), “systolic blood pressure” (3.3), “diastolic blood pressure” (3.4), and “SpO2” (1.03). The solution enhances access and offers a strong health monitoring system that is real-time and supports timely intervention and better patient care, particularly in remote locations.
Copyright (c) 2026 MOHAMMED YOUNUS https://creativecommons.org/licenses/by/4.0
Power optimization of Control-plane Signaling in Fog-Assisted Wireless SDN using Hybrid-Hop Transmission
Software-Defined Wireless Sensor Networks (SDN-WISE) provide a centralised control mechanism with flexible programmability for large IoT deployments. However, placing the SDN controller exclusively in the cloud layer introduces excessive energy consumption in the control plane signaling, especially for distant sensor nodes. The problem is exacerbated when sending control packets over long distances using single-hop transmission. To tackle this drawback, this paper presents an energy-aware hybrid-hop architecture in which a Fog-based controller is placed near the edge of the network, and a relay-assisted hybrid-hop mechanism triggered for far-off nodes. Using numerical data to study how much control signaling energy is required for Single-Hop, Relay-based Multi-Hop, and Hybrid-Hop, a Mathematical Energy Model is created to compare the respective aspects. Considering transmission, reception, and processing energy (the latter, which includes the relay node), an analytical threshold distance is obtained, which defines when the sensor node should transition from a direct transmission mechanism to a relay-based forwarding mechanism. MATLAB simulation results indicate that the proposed Hybrid-Hop Fog-based architecture reduces the power consumption for control plane signaling by an estimated (18%-20%) compared to a classical Cloud-based SDN-WISE configuration. These results demonstrate that the application of Fog computing and hybrid-hop signaling can contribute to the energy-efficient design of SDN-WISE IoT networks
Copyright (c) 2026 Duaa Attallah Khudhair, Basim K. J. AL-Shammari, Hasan Fahad Kazal, Ismail Hburi, Emad Badry Mohamed https://creativecommons.org/licenses/by/4.0
A Multi-Stage Intrusion Detection Framework for IoT Networks Using Decision Tree, SVM, and KNN Classifiers
The surge in Internet of Things (IoT) appliances has heightened security threats as they function with minimal processing, memory, and energy. In these conditions, detection systems face the problem of detecting correctly while utilizing less computation. This paper presents a multi-stage intrusion detection framework based on a confidence-based cascade of Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) classifiers. According to the proposed model, DT resolve the 78.9% samples, SVM refine 20.6% samples, and KNN verify only 0.6% samples under highly ambiguous condition. Experiments on N-BaIoT benchmark dataset with 8,000 samples and 115 statistical features show that the framework achieves an accuracy of 94.06%, F1 score of 0.9502, false alarm rate of 0.0656 and per-sample detection time of 0.0363 ms. When standing alone, this SVM reduces latency while maintaining a reasonable level of detection performance. The study gives a meaningful accuracy-latency trade-off for timely intrusion detection in constrained IoT edge-gateway environments.
Copyright (c) 2026 Ali Mohammed Noori Tarab https://creativecommons.org/licenses/by/4.0
Enhancing the Efficiency of Parabolic Trough Solar Collector Using Mini-Channel Flat Aluminium Tubes
This research aims to use mini-channel flat aluminum tubes with parallel flow in parabolic trough solar collectors. A parabolic trough solar collector was designed, manufactured, and tested for the present study. The length of the solar collector was 0.65 m, the collector aperture width was 0.5 m, the radius of the parabola was 0.25 m, and the rim angle was 90 degrees. In the experimental part, experiments were conducted at flow rates ranging from 0.1 to 0.8 liters per minute through three distinct heating stages to evaluate heat transfer efficiency. The results indicated that the largest amount of heat added to the water was in the case when it passed through the copper tube; the reason for this is the high thermal conductivity of the copper material and also because of the large amount of solar radiation reflected by the reflective surface towards the copper tube. In addition, the two stages in which the water passes through the mini- channel tubes contribute to adding a good amount of heat, helped by their precise design that increases the heat exchange area between the channels walls and the water. Percentages The maximum efficiency achieved by the solar collector was 82% at a flow rate of 0.5 l/min at an angle of 125° towards the southeast in Al-Kut city. Pressure drop analyses revealed a linear relationship with flow rate. This innovative approach highlights the potential for improved solar energy utilization and represents a major breakthrough in the performance of solar thermal systems.
Copyright (c) 2026 Ebtehal Chasseb Jbary, Alaa R. Al-Badri https://creativecommons.org/licenses/by/4.0
Color and COD Removal from Textile Effluent by Advanced Oxidation Processes (ozonation)
Using an ozone reactor, this work looks at how well ozonation removes turbidity, COD, and methylene blue dyes from polluted water. The main goals were to assess ozonation's efficacy in degrading methylene blue dyes, lowering COD, and clearing turbidity, and to find the ideal reaction time for the best removal efficiency. The findings showed that ozonation greatly enhanced the removal of turbidity, color, and COD over time. Ozonation 1 (10%) had the least efficiency in terms of COD removal, with about 30% removal at 180 minutes; the others, Ozonation 2 (20%) and Ozonation 5 (50%) had 55% and 60% removals, respectively. Ozonation 1 removed 50% of the turbidity; ozonation 2 and ozonation 5 showed the greatest efficiency with 75% and 78% elimination at 180 minutes, respectively. Ozonation 1 removed around 50% of the color; Ozonation 2 and Ozonation 5 removed 70% at the same reaction time; thus, the color removal also rose markedly. After 180 minutes, Ozonation 4 (40%) exhibited moderate effectiveness with COD, turbidity, and color removal reaching 70%, 70%, and 75%, respectively. This work shows that higher removal efficiencies for COD, color, and turbidity are significantly influenced by prolonged reaction periods. Particularly for Ozonation 2 and Ozonation 5, the custom-built ozone reactor was successful in optimizing ozone settings, which produced the best overall outcomes in eliminating methylene blue dyes, COD, turbidity, and color.
Copyright (c) 2026 Saja Hamed, Ali Jwaid https://creativecommons.org/licenses/by/4.0
Steel Sheets' Effects on The Structural Behavior of Interior Voided Slab-Column Connections Subjected to Unbalanced Moment: An Experimental Study
This paper investigated the structural performance of a voided slab when strengthened using a steel plate at the interior voided slab-column connections. Two specimens were made with slabs measuring 1100 × 1100 × 130 mm, featuring voids distributed throughout their area. A square column with dimensions of 150×150 mm and a height of 250 mm was extruded from the top face of a slab center. To verify the unbalanced moment, the beam was constructed at 150 mm x 150 mm and extended 150 mm from the column face, with a total length of 300 mm. Four steel sheets were embedded in one specimen to strengthen it by placing two plates in each direction; the other specimen served as the control without a steel plate. These sheets were intersected orthogonally below the column stub. The two specimens had eight square voids with dimensions of 250 mm × 250 mm × 70 mm. They tested under an unbalanced moment until failure. The test findings indicated significant enhancements in the strength, ductility, and toughness of the voided slab with a steel plate compared to the control slab without sheets. However, the addition of steel sheets proved to be an excellent solution for enhancing the structural performance of the voided slab.
Copyright (c) 2026 Athmar M. Alazawiey, Thaar S. AL-Gasham, Sajjad H. Ali https://creativecommons.org/licenses/by/4.0
Analysis and Stepwise Regression of Flexural Load-Capacity of Steel Wire-Reinforced Precast Concrete Sandwich Panels
Precast Sandwich Panel (PSP) is a plate element that is lighted, cheaper, and has significantly better thermal insulation compared to conventional concrete plates, which are typically used as wall or slab units. The positive features of PSP make it a probable solution for many constructional issues in Iraq related to low-cost energy-saving housing, temporary camping, and industrial units, which calls for extensive analysis, experimental, and numerical studies to explore the pros and cons of PSPs as low-cost roofing solution. The aim of this study is to evaluate the degree of influence of the geometry, material, and design parameters on the flexural capacity of PSPs and to introduce a pre-design evaluation tool using multi-variable stepwise regressions. Experimental data including 13 input parameters related to the properties of the outer concrete layers, inner insulation core, and shear connectors between them, in addition to cracking and ultimate loads as outputs were analyzed for 50 experimental steel wire-reinforced sandwich plates. Direct correlations showed that none of the studied parameters has a significant sole impact on the flexural strength of the panels, while stepwise regressions showed that most of these parameters are effective. P-values higher than 5% were recorded for some parameters, which were automatically excluded from the regression models, while the T- and F-values revealed different influence degrees for the included parameters. Finally, two multi-variables stepwise regression models were introduced for cracking and ultimate loads with R2 values of 89.3 and 92.6%, respectively, which reflects their high reliability as pre-design simplified analysis tools.
Copyright (c) 2026 Khaldoon Shamran, Omar Mahdi, Sallal Rashid Abid, Mustafa Özakça https://creativecommons.org/licenses/by/4.0
Impact of Operational Parameters Using Fine Powder Brick for Nitrate Removal in Continuous Systems
<a href="https://ejuow.uowasit.edu.iq/index.php/ejuow/This research looks at how to constantly remove nitrate from water using fine powder brick, which is a cheap and eco-friendly material. The paper addresses the growing concern of nitrate contamination in water sources, particularly from agricultural runoff and industrial discharge, which generates significant health risks such as methemoglobinemia and environmental harm. The main goal of the study is to see how well fine powder brick works under different conditions, like starting nitrate levels (7–21 mg/L), flow rates (4–16 L/hr), and the depth of the adsorbent bed (6–18 cm). Monitoring the effluent-to-influent concentration ratio (Co/Ce) over time showed breakthrough behavior. The results indicated that nitrate removal worked better at slower flow rates, lower starting concentrations, and shallower bed depths because there was more time for the nitrate to come into contact with the adsorbent and a larger area for it to stick to. The study emphasized the potential of recycling construction waste as an adsorbent for nitrate removal in continuous flow systems and provided helpful suggestions for optimizing fixed-bed column operations in real water treatment applications.">This research looks at how to constantly remove nitrate from water using fine powder brick, which is a cheap and eco-friendly material. The paper addresses the growing concern of nitrate contamination in water sources, particularly from agricultural runoff and industrial discharge, which generates significant health risks such as methemoglobinemia and environmental harm. The main goal of the study is to see how well fine powder brick works under different conditions, like starting nitrate levels (7–21 mg/L), flow rates (4–16 L/hr), and the depth of the adsorbent bed (6–18 cm). Monitoring the effluent-to-influent concentration ratio (Co/Ce) over time showed breakthrough behavior. The results indicated that nitrate removal worked better at slower flow rates, lower starting concentrations, and shallower bed depths because there was more time for the nitrate to come into contact with the adsorbent and a larger area for it to stick to. The study emphasized the potential of recycling construction waste as an adsorbent for nitrate removal in continuous flow systems and provided helpful suggestions for optimizing fixed-bed column operations in real water treatment applications.
Copyright (c) 2026 Taktum Jwad Kadhim, Ali Jwaid https://creativecommons.org/licenses/by/4.0
Statistical Analysis of Ultimate Deflection and Load Assessment for One–Way Voided Slabs under Static Loads
In this study, the ultimate load and deflection formulas for one-way voided slabs subjected to static point loads were determined through statistical analysis. A comprehensive empirical database of a number of void geometries and design parameters was compiled from the published literature. The experimental data were statistically analyzed by the stepwise regression technique in order to create prediction equations for ultimate load and deflection. This leads us to obtain appropriate equations for engineers to accurately predict the one-way voided slab's structural response with a determination coefficient (R²) of 0.99 at the ultimate deflection formula and 0.87 at the ultimate load formula. Furthermore, the fitted line plot method was also used to provide several equations to be compared with the equations of stepwise regression. The comparison revealed that the stepwise regression equations provide the most accurate prediction of ultimate load and ultimate deflection. Further, the Pearson coefficient of correlation was also determined and revealed that the association between the variables and ultimate load and deflection varied from moderate to weak or revealed no significant association.
Copyright (c) 2026 Fatima Hussein Awad, Hussain A. Jabir https://creativecommons.org/licenses/by/4.0
Intelligent Diagnosis of Bearing Faults Using Feature Selection Based on Minimum Redundancy Maximum Relevance and Neural Network Optimization with Particle Swarm Method
Rolling element bearings play a crucial supporting role in rotating machines in many manufacturing processes. The failure of rolling bearings can result in catastrophic damage and human casualties, and their condition has a direct impact on the safe functioning of rotating machinery. Numerous bearing detection techniques have been designed to prevent such outcomes and avoid unplanned downtime. Techniques that have no dependence on the manual selection of attributes or the classifier parameters are still required. In order to meet this requirement, the current method incorporates the following algorithms: Max Relevance Min Redundancy (mRMR) for automated feature ranking, Artificial Neural Network (ANN) for bearing condition categorization, and Particle Swarm Optimization (PSO) for tuning the ANN hyperparameters, including the hidden layer size, the number of neurons in each layer, and the learning rate. The performance of three training algorithms was compared, and the Scaled Conjugate Gradient algorithm was selected based on its high results. The mRMR utilizes the statistical characteristics extracted from the time domain to organize bearing categories according to their relevance, and the PSO adjusts the ANN classifier's hyperparameters to improve its performance. The outcomes demonstrate that this approach successfully ranks the features, adjusts the hyperparameters, and improves classification performance with high accuracy.
Copyright (c) 2026 Mohammed Mutar Alaidi, Hussein Al-Bugharbee https://creativecommons.org/licenses/by/4.0
Performance Comparison of DFT and RDFT Systems Based on Optical-OFDM in PAPR Reduction
This study investigates an advanced Visible Light Communication (VLC) system employing Real Discrete Fourier Transform (RDFT)-based DC-biased Optical OFDM (DCO-OFDM), focusing specifically on the reduction of Peak-to-Average Power Ratio (PAPR) via the Walsh-Hadamard Transform (WHT). This study aims to reduce the substantial computational complexity and power inefficiency of traditional DFT-based DCO-OFDM systems, while preserving comparable spectral efficiency and Bit Error Rate (BER) performance. The RDFT is a superior mathematical approach compared to the DFT as it eliminates the need for costly Hermitian symmetry calculations. This reduces the quantity of real multiplications and real additions by 50%. We simulated a system with N=256 in MATLAB over an AWGN channel utilizing 16-PSK and 16-QAM modulation techniques. The results demonstrate that DCO-OFDM with RDFT shows a BER performance akin to that of DFT-based systems. At an SNR of approximately 33.5 dB, 16-PSK has a BER of 10⁻⁴. At an SNR of approximately 29.5 dB, 16-QAM exhibits an equivalent BER. The application of WHT pre-coding reduces PAPR; for example, RDFT-WHT demonstrates a decrease of around 0.5 dB at a CCDF of 10⁻⁴ in comparison to the exclusive use of RDFT. Upon examining complexity, it is evident that both addition and multiplication have been simplified. The findings indicate that the RDFT-WHT-based system is the optimal choice for future optical wireless communication systems due to its user-friendliness, energy efficiency, and superior performance compared to alternative systems.
Copyright (c) 2026 Ameen Salih Jasim, Mounir T. Hamood https://creativecommons.org/licenses/by/4.0
An Urban Influence Radius Framework for Environmental-Spatial Evaluation in Medium-Sized Cities
Medium-sized cities are densifying in ways that concentrate high-occupancy projects inside compact cores. Conventional plot-based controls such as floor area ratio, setbacks, and coverage regulate quantity rather than spatial distribution, and therefore cannot reveal off-site environmental and pedestrian effects. This paper formalizes an Urban Influence Radius and applies it to a dense hospital site in central Kut, Iraq. The Urban Influence Radius is the minimum radius R inside which a project’s massing measurably alters shading, airflow, thermal comfort, and pedestrian performance. We adopt a practical baseline R = max(2H, 1.5W, 60 m), rounded to the nearest 5 m, where H is the main building height and W is the adjacent street width. Using Rhino, Grasshopper, and Ladybug Tools, we compute key performance indicators on-site and within the Urban Influence Radius. Holding FAR near 5.0, the baseline high-coverage massing performs poorly within the radius, while a lower-coverage alternative improves summer UTCI, winter solar access, wind renewal, and walkability without reducing program area. A sensitivity analysis over R = 40-80 m and coverage = 50-80 percent confirms robustness. The results support adding Urban Influence Radius screening and threshold-driven iteration to approvals and motivate an Enhanced Environmental FAR that reports FAR together with KPI compliance. The workflow is replicable with standard tools and SI units.
Copyright (c) 2026 Haider Majid Hasan, Hussam Sachit Oweid, Ayam Sh. Altameemi, Noor Dheyaa Joodi https://creativecommons.org/licenses/by/4.0
Adaptive and Self-Healing Protection Schemes for Renewable-Rich Microgrids Using Deep Reinforcement Learning
Integrating distributed renewable energy sources into microgrids creates new protection challenges such as bidirectional power flows, reduced fault currents, and topology changes that limit traditional relaying methods, this paper proposes a novel integrated protection framework using reinforcement learning (RL) to enable adaptive fault response and autonomous self-healing, the core innovation employs deep RL agents for two key tasks: fast and accurate fault classification and localization using raw voltage/current waveforms, and optimal service restoration via cooperative multi-agent RL that maximizes load recovery while respecting operational constraints. Experimental results show 99.9% fault classification accuracy under ideal conditions and 96.7% reliability at 20 dB noise, outperforming CNN-based methods by 12.4%, the framework reduces average outage duration from 8.7 minutes to 2.31 seconds, improving resilience and reliability for microgrids in the renewable energy era.
Copyright (c) 2026 Hussein Naeem Qasim https://creativecommons.org/licenses/by/4.0
Investigating the Current Flood Discharge Capacity of a Reach of the Tigris River From the Downstream of the Amarah Barrage to Alqurna River
Our study, which involved creating a hydraulic model using the HEC-RAS program, calculating the current flood capacity of the Tigris River, and determining the Manning's coefficient for the 150-kilometre section of the Tigris River, was conducted with meticulous attention to detail and thoroughness. This reach ends at the Alqurna River, which begins at the Amarah Barrage station. In April 2020, the Qal'at Saleh gauging station flow ranged between 88 and 115 m³/s, and this data set was used to calibrate the HEC-RAS model, Version 6.7. With a least-squares root difference of only 0.095 between the estimated and measured water levels, the values of Manning's n coefficient for the main channel and floodplain are equal to 0.03 and 0.035. According to the 2019 Iraqi Water Resources and Land Strategy, the extracted data show that the Tigris River may discharge up to 400 cubic meters per second in the region between the Al-Amarah Barrage and the Qurna River. The river can safely handle this flow without creating floods or adverse effects on its banks or adjacent areas, according to hydraulic models and scenarios studied for several specific cross-sections along this portion of the river.
Copyright (c) 2026 Ranea Emad Kadhem; Nazar H. Muhamed; Clare B. Harris https://creativecommons.org/licenses/by/4.0
Voltage Stability Enhancement in Power Systems Using PSO-Based Tuning of AVR and PSS Parameters
<a href="This study introduces a coordinated tuning method for Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS) parameters based on Particle Swarm Optimization (PSO), applied to the IEEE-39 Bus New England test system. The aim is to strengthen both transient and small-signal stability when the system is exposed to severe disturbances. The evaluation was carried out through time-domain simulations in MATLAB/Simulink, considering three operating cases: without a stabilizer, with a conventional PSS, and with a PSO-tuned PSS. The proposed optimization yielded clear improvements by lowering overshoot, enhancing damping ratios, and reducing the settling time to less than two seconds. Eigenvalue analysis further supported these findings by showing that the dominant modes shifted toward the left half-plane, indicating more secure stability margins. In comparison with conventional approaches reported in earlier studies, the PSO-based tuning demonstrated greater effectiveness and robustness for large-scale interconnected power networks.">This study introduces a coordinated tuning method for Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS) parameters based on Particle Swarm Optimization (PSO), applied to the IEEE-39 Bus New England test system. The aim is to strengthen both transient and small-signal stability when the system is exposed to severe disturbances. The evaluation was carried out through time-domain simulations in MATLAB/Simulink, considering three operating cases: without a stabilizer, with a conventional PSS, and with a PSO-tuned PSS. The proposed optimization yielded clear improvements by lowering overshoot, enhancing damping ratios, and reducing the settling time to less than two seconds. Eigenvalue analysis further supported these findings by showing that the dominant modes shifted toward the left half-plane, indicating more secure stability margins. In comparison with conventional approaches reported in earlier studies, the PSO-based tuning demonstrated greater effectiveness and robustness for large-scale interconnected power networks.
Copyright (c) 2026 Ali Mushref Abdullah https://creativecommons.org/licenses/by/4.0
Recent Developments in Managed Pressure Drilling System
<a href="As oil and gas exploration expands into challenging terrains such as deep formations, ultra-deep waters, and unconventional reservoirs, the complexity of drilling operations has surged. This expansion introduces heightened risks, including surge pressures, well leaks, formation collapse, and stuck pipe incidents. To address these challenges, there is an urgent need to advance managed pressure drilling (MPD) technology and equipment with enhanced automation and intelligent control systems. Such advancements aim to transition from semi-automated to fully automated and intelligent drilling processes, enabling precise prediction and management of complex subsurface conditions. By doing so, drilling risks can be mitigated more effectively and efficiently. This study explores the development of MPD technology and intelligent equipment internationally. It focuses on key aspects such as intelligent control systems, real-time data acquisition and processing, and advanced technologies like complex machine learning (ML) algorithms and decision-making software. The research highlights how these innovations contribute to safer and more efficient drilling operations. Preliminary field tests have demonstrated the technical feasibility and advantages of intelligent MPD systems; however, further validation and optimization are required in practical applications. Looking ahead, integrating MPD technology with cutting-edge intelligent systems is expected to revolutionize pressure control in drilling operations. This integration will support the exploration and development of complex oil and gas resources, ultimately fostering technological independence and self-sufficiency in oil and gas engineering sector">As oil and gas exploration expands into challenging terrains such as deep formations, ultra-deep waters, and unconventional reservoirs, the complexity of drilling operations has surged. This expansion introduces heightened risks, including surge pressures, well leaks, formation collapse, and stuck pipe incidents. To address these challenges, there is an urgent need to advance managed pressure drilling (MPD) technology and equipment with enhanced automation and intelligent control systems. Such advancements aim to transition from semi-automated to fully automated and intelligent drilling processes, enabling precise prediction and management of complex subsurface conditions. By doing so, drilling risks can be mitigated more effectively and efficiently. This study explores the development of MPD technology and intelligent equipment internationally. It focuses on key aspects such as intelligent control systems, real-time data acquisition and processing, and advanced technologies like complex machine learning (ML) algorithms and decision-making software. The research highlights how these innovations contribute to safer and more efficient drilling operations. Preliminary field tests have demonstrated the technical feasibility and advantages of intelligent MPD systems; however, further validation and optimization are required in practical applications. Looking ahead, integrating MPD technology with cutting-edge intelligent systems is expected to revolutionize pressure control in drilling operations. This integration will support the exploration and development of complex oil and gas resources, ultimately fostering technological independence and self-sufficiency in oil and gas engineering sector
Copyright (c) 2026 Rettag Ahlgrebawi, Mustafa Abd-Almaged, Saif Isam Jalal Jalal, Yousif Al-Ghanimi https://creativecommons.org/licenses/by/4.0
Removal of Nickel Ion From synthesis wastewater Using Nanoparticles Under Ultrasonic Waves
One of the most significant pollutants released into the environment by industrial processes is heavy metals. The adsorption technique has emerged as a leading strategy for metal ion removal in the last several years. The mass transfer process can be enhanced with the help of ultrasound. This study examined the effectiveness of high frequency ultrasonic waves in increasing the absorption of nickel (II) in aqueous solutions containing Fe3O4 nanoparticles. A higher initial amount of adsorbent was associated with a higher removal efficiency of nickel (II). When comparing the effectiveness of using shakers and an ultrasonic test vessel, the latter achieved a maximum efficiency of 84.3% at 60 minutes of contact time, 8 g of adsorbent at pH=5, while the former performed better. On the other hand, while using a shaker and 100 minutes of contact time with 10 g of adsorbent and pH=9, the greatest efficiency was 54.79%. Looking at how contact time affected the adsorption rate, we found that it was fastest at the beginning of the contact period. Increasing the contact duration and equilibrium time resulted in an increase in the amount of nickel (II) elimination from 20 minutes to 60 minutes when using either a shaker or a test vessel. But when using a shaker, the nickel (II) removal rate spiked again between the 60-minute and 80-minute contact times, reaching a peak of 79.54% in the latter. Actually, cavitacin and high-frequency ultrasonic waves can produce microcurrents that remove a large amount of nickel (II) from water in a very short amount of time. A modest quantity of adsorbent was able to achieve a rapid increase in absorption rate when ultrasonic waves were introduced into the test vessel.
Copyright (c) 2026 Nuralhuda Aladdin Jasim, Walaa Abdulkhaleq Zghair, Manal Abdulsattar Muhammed, Ahmed Muhammad Dakhil, Kamaludin Mohamad Yusof https://creativecommons.org/licenses/by/4.0
Morphological Transformations in Baghdad's Historic Center (Old Rusafa): A Structural-Spatial Reading Integrated with the Urban Injection Strategy
Unplanned urban growth constitutes one of the major challenges affecting historic centers in traditional cities, as it leads to fragmentation of the urban fabric and a weakening of morphological cohesion. This issue is clearly observable in the historic center of Baghdad particularly in Rusafa and Karkh—which represents one of the city’s principal urban nuclei [1,5]. Over time, the pathway network has undergone significant morphological transformations, shifting from organic routes oriented perpendicularly to the Tigris River, to pathways parallel to it, and ultimately to a grid-like pattern resulting from modern planning interventions and bridge construction connecting both riverbanks [1,5]. Nevertheless, a knowledge gap persists concerning the origins of modern spatial orientations and the extent to which they remain rooted in historical pathway structures [1]. This study addresses this gap by examining the evolutionary development of the pathway network in the historic center of Baghdad and testing two hypotheses. The first posits that pathway orientations shifted gradually—from organic, to river-parallel, to grid patterns—under the influence of modern planning. The second suggests that many modern streets do not represent a rupture from traditional pathways but instead derive from inherited morphological roots detectable through spatial analysis [1,2]. The research uses Space Syntax methodology to analyze integration, connectivity, and choice across four developmental stages, based on the concept of morphological genotype in urban structural studies [2,3]. The results show that many modern streets, though grid-like, are structurally connected to older routes that retain spatial importance. Integration values highlight a lasting "background structure" that shapes the contemporary network [2,5]. The study concludes that unplanned urban expansion altered but did not erase the historic spatial framework, supporting the development of revitalization strategies through the urban injection approach to preserve Baghdad's historic identity [4,5].
Copyright (c) 2026 Hawraa Oleiwi Shakeer, Sabeeh Lafta Farhan, Abdalhakim Almakkas https://creativecommons.org/licenses/by/4.0
The effect of the islands on the nature of the flow of the Tigris River Using HEC-RAS
Erosion and sediment deposition are key factors that affect the hydraulic behavior of rivers and the stability of hydraulic structures. This study investigates these processes downstream of the Al-Kut Barrage, where mid-Channel Islands influence the natural flow pattern of the Tigris River. A one-dimensional hydrodynamic model was developed using HEC-RAS to simulate flow conditions and assess the impact of island removal on erosion–deposition under different discharge scenarios. The model was calibrated and validated using field data from the study reach, showing a strong agreement between observed and simulated results (r = 0.998) with a Manning’s roughness coefficient of 0.03. The results show that the existing islands have a minor effect on flow capacity and flood wave attenuation. After modifying the cross-sections and removing the islands virtually, the discharge decreased by 3.59 m³/s (around 0.34%), indicating a slight improvement in wave dissipation and delay. Although the effect was limited, the study highlights the need for continuous monitoring and maintenance of the Tigris River channel to preserve its hydraulic performance and reduce sedimentation impacts downstream of the Kut Barrage.
Copyright (c) 2026 Zainab S.faris, Ali N.Hilo, Tabarak W.Mahdi, Clare B. Harris https://creativecommons.org/licenses/by/4.0
The Impact of Green Space Design On The Academic Performance Of Students At Wasit University
The need to study the impact of green spaces on academic performance within the university campus is crucial, as they are a fundamental element in providing a comfortable and stimulating learning environment that fosters creativity and positive interaction at Wasit University. Despite the presence of some green spaces, their role in improving the educational environment and enhancing students' academic achievement has not been adequately studied. This issue is particularly important given the increasing number of students and the growing need for an attractive and inspiring university environment, especially in light of the challenges of climate change and urban pressures that may affect the sustainability of these spaces. This research will examine several colleges as case studies, namely the Colleges of Engineering, Fine Arts, Education, Arts, and Administration and Economics. These colleges were selected due to their varying spatial distribution within Wasit University, as well as the differing distribution of green spaces around their respective buildings. Furthermore, they represent diverse educational environments encompassing both scientific and humanities disciplines, providing an ideal opportunity to study the relationship between the design characteristics of green spaces and the level of academic achievement across different study patterns. The research problem lies in questioning the efficiency of the design and distribution of green spaces in these colleges, and whether they meet the standards that contribute to supporting the educational experience and enhancing academic performance. The study also aims to identify any shortcomings or deficiencies in these spaces compared to optimal design standards, and to provide practical recommendations for improving them in accordance with the needs of the students and the nature of their educational environment. Based on this premise, this study aims to measure and evaluate the impact of green space design in five selected colleges on students' academic performance, focusing on design aspects and spatial distribution, and analyzing the educational implications of this green environment within the campus of Wasit University. Furthermore, this research aims to analyze the relationship between green spaces at Wasit University and the academic performance of its students, by observing the impact of these spaces on motivating learning, supporting mental health, and enhancing a sense of belonging to the university campus. The research also seeks to provide practical recommendations that can contribute to improving the design and management of these spaces in a way that serves the educational process and enhances the quality of university life.
Copyright (c) 2026 Haider Majid Hasan, Hussain A. Saifan, Hadi Ebadi https://creativecommons.org/licenses/by/4.0
Implementing Adaptive Cruise Control -Based Model Predictive Control using Turtlebot3 Robot
Adaptive cruise control (ACC) systems have been developed to enhance the performance of traditional cruise control (CC) systems. ACC efficiently reduces congestion and traffic accidents, decreases drivers’ workloads, and improves economic efficiency. This paper first validates and verifies a dual-level controller based on model predictive control (MPC) through an experiment that results in formulating an adaptive cruise control algorithm that improves robustness and safety. Second, two control methodologies, the MPC and conventional proportional integral derivative (PID) control, are tested and compared. The tests are realized using the TurtleBot3 robot as the host vehicle and a toy car as the target vehicle. Thus, it demonstrates a clear relationship between theoretical and practical system functionality while questioning the controller’s performance with real hardware. The novelty of this work lies in the integration of a dual-level architecture with real hardware validation, including a Light Detection and Ranging (LiDAR) data-filtering method that improves real-time responsiveness. Quantitative results from experimental scenarios show that the MPC controller achieves a 32% reduction in distance tracking error and improved robustness compared with the conventional PID controller. The numerical findings and experiments have proven the advantages and effectiveness of the MPC method over the traditional PID method when adaptability to perturbations or aggressive scenarios is essential.
Copyright (c) 2026 Farah Mahdi Ali, Nizar Hadi Abbas https://creativecommons.org/licenses/by/4.0
A Hybrid Approach for Improving Machine Learning Models Using Federated Learning and Ensemble Techniques
To have robust software systems, software reliability prediction plays a major role. A hybrid and advanced design to enhance the quality and performance of machine learning models are proposed in this research; it consists of different steps of data preprocessing, feature engineering, model refinement with the assistance of federated learning, hyperparameter optimization, and the hybrid learning methods. One initially works with the data with the help of algorithms like SMOTE and SMOTEENN to address the issue of uneven data, and then the elements are resolved and adjusted in an innovative manner. The ensemble learning and hyperparameter optimization are then used to optimize the models. Specifically, data privacy in the distributed models is maintained using the federated learning approach. Lastly, generated models are tested on complex data with the help of deep neural nets (DNN) and their performance is measured with the means of precision, recall, prediction accuracy, and F1 score. The resulting methods, which are mainly useful in assisting to enhance the efficiency and prediction accuracy of machine learning models, apply in complex problems.
Copyright (c) 2026 Yahia Eed Ali https://creativecommons.org/licenses/by/4.0
The Impact of Building Façade Colors on Sense of Place in a Residential Area
Building façade color plays a significant role in shaping urban culture, city image, and residential experiences. Yet, in many urban planning practices, the significance of building façade color is often overlooked or treated as a secondary consideration, despite its potential to enhance or detract from a sense of place. This study investigates building façade color, isolating it as an independent variable, examining hue, saturation, and brightness as main factors influencing the dimensions of sense of place, namely: place identity, place attachment, and place dependence. Multiple digitally manipulated building façade color photographs were prepared according to the HSB color model, with data gathered through a semantic differential questionnaire supported by multiple-choice and dichotomous questions. the results indicate that the existing façade color, white/neutral, forms the strongest place identity and attachment. Among the simulated color variations, participants reported a stronger sense of place for cool blue and the warm yellow façades. Color characteristics also influenced emotional evaluations, where a more positive emotional response was recorded with low-to-mid saturation (25%-50%) while brightness preferences varied by façade hue rather than exhibiting a uniform optimal range. The study was conducted in the Royal Empire Apartments complex in Erbil, providing a real residential context for examining these effects. These findings highlight the important role played by building façade color in shaping a sense of place, emphasizing the need color design to balance carefully against contextual fit, perceptual comfort, and user preference.
Copyright (c) 2026 Avan Kareem Hamad, Hamid Turki Maliki https://creativecommons.org/licenses/by/4.0
Experimental Temperature Measurements on a Full-Scale Reinforced Concrete Box-Segment during the Construction Stage
A full-scale square-hollow reinforced concrete segment was constructed in an open environment to evaluate the effect of the heat produced from cement hydration and the environmental cycles of cooling and heating during the first 48 hours after concrete casting. Ten thermocouples were installed along the centreline of four walls, where the wall thickness was 500 mm, while two more thermocouples were installed at the exterior and interior wall surfaces. Three environmental sensors were also installed at the site to measure air temperature, solar radiation, and wind speed. The results showed that minor differences were recorded between the eight thermocouples during the first 24 hours, while the southwest thermocouple recorded temperatures that were slightly higher during the day and slightly lower during the night compared to the core thermocouples, revealing some sensitivity to environmental thermal loads. The comparisons of the first 48 hours' temperatures with those after 72 hours showed that the hydration heat increased the concrete temperature by approximately 26 to 32 oC during the first 24 hours. Additionally, the effect of hydration heat became minimal after 30 hours and diminished after 36 hours, which declares the start of the life-span state of full-dependency on solar radiation and air temperature.
Copyright (c) 2026 Sallal R. Abid, Noora K. Jebur, Mustafa Özakça https://creativecommons.org/licenses/by/4.0
Effective Connectivity Patterns During Bilateral and Mirror-Induced Unilateral Hand Movements: An EEG Source-Level Study
This study explores the effective connectivity patterns of underlying two visuomotor processing tasks: bilateral hand movement and unilateral hand movement with mirror-box. Electroencephalography and electromyography data were recorded using 44 and 2 electrodes respectively from 20 healthy participants while they performed the two tasks. Data were first preprocessed though epoching, band-pass filtering (1-45Hz), artifact rejection, Independent Component Analysis, and source localization. Then a connectivity pattern estimators known as direct Directed Transfer Function were utilized to calculate the directed information flow amongst brain signal sources. The results identified distinct task- and frequency-dependent effective connectivity across visual, parietal, and sensorimotor regions. Such that, Beta-band coupling dominated during bilateral hand movements, while alpha-band connectivity decreased during mirror-box tasks. These findings support utilizing source-localized Electroencephalography to investigate dynamic neural interaction to guide and to monitor rehabilitation therapies.
Copyright (c) 2026 Salim Mohammed Hussein Al-Wasity https://creativecommons.org/licenses/by/4.0
Engineering Perspectives on Rehabilitation Robotics: An Integrative Review of Control Strategies, Optimization Methods, and IoT-Enabled Platforms
Rehabilitation robotics has developed into an interdisciplinary field which uses mechanical design and control theory and optimization techniques together with information technologies to create better recovery results for people who suffer from motor disabilities. The present review assesses rehabilitation robotics research through engineering application studies which use more than 120 peer-reviewed articles published between 2014 and 2024. The discussion covers four main areas which include control strategies that start from basic PID methods and extend to sophisticated adaptive and intelligent control systems. The study utilizes bio-inspired and metaheuristic optimization methods to enhance system functionality and develop control paths. The system uses cloud and IoT technology to deliver remote medical monitoring services and perform data analysis and create rehabilitation systems which can grow in capacity. The study investigates new human-robot interaction methods which include voice-activated control systems. Existing reviews often address these aspects in isolation, but this work presents an integrated taxonomy and highlights cross-domain synergies that drive innovation in rehabilitation systems. The analysis reveals two research gaps which include researchers who study multimodal biosignal integration with real-time adaptive control and researchers who need to create affordable modular systems for use in resource-limited environments. The findings of this review present engineering-based evidence which shows the requirements for building intelligent accessible and sustainable rehabilitation robots of the future.
Copyright (c) 2026 Noor Sabah Mohammed Ali, Muna Hadi Saleh, Nizar Hadi Abbas, Hisham A. Shehadeh https://creativecommons.org/licenses/by/4.0
Energy Conversion Performance of Horizontal-Axis Wind Turbines under Variations in Blade Geometry
This paper presents a three-dimensional numerical investigation of the aerodynamic behavior of a horizontal-axis wind turbine (HAWT) blade based on the experimental NREL Phase VI model. The objective is to evaluate how selective geometric modifications affect energy conversion efficiency under different wind conditions. The reference blade geometry was reconstructed from experimental data, and a computational model was developed in ANSYS Fluent by solving the steady Reynolds-averaged Navier–Stokes (RANS) equations coupled with the k–ω SST turbulence model. The baseline model was validated against measured aerodynamic torque at a free-stream wind speed of 7 m/s, showing deviations below 10%. After validation, seven alternative blade designs were created by adjusting chord length and twist angle distributions along the span. Their aerodynamic performance was assessed at wind speeds of 5, 7, 9, and 11 m/s in terms of torque, power, and power coefficient (Cp). The results show that the impact of geometric changes depends strongly on wind speed and blade region. Root modifications mainly improved starting torque at low wind speeds, whereas mid-span changes increased power and Cp at moderate speeds. Conversely, adjustments aimed at reducing downwind aerodynamic stresses enhanced power at higher wind speeds. Among all configurations, simultaneous modifications of chord and twist produced the most balanced and stable performance over a wider operating range. Overall, the findings demonstrate that relatively small geometric changes can improve wind turbine performance. The proposed numerical framework offers a method for design and optimization of HAWT blades.
Copyright (c) 2026 Hamid Irhayyim Abdulhussein Imari https://creativecommons.org/licenses/by/4.0
A Comprehensive Review of GIS-Based Spatial and Spatiotemporal Approaches in Road Traffic Accident Analysis
A bunch of factors may cause Road Traffic Accidents (RTAs), which adversely impact daily lives of people and cause significant economic, human, and social losses worldwide. These factors can be related to the roadway characteristics, driver behavior, and traffic demand. It is crucial to comprehend the spatial and temporal patterns of RTAs for an effective road safety planning and policy formulation. Geographic Information Systems (GIS) is a powerful tool to investigate RTA datasets throughout participating spatiotemporal and attribute information within a combined analytical framework. Thus, this study presents an inclusive review of GIS-based spatiotemporal approaches applied for RTA dataset. Some of frequently GIS approaches that have been utilized by literature such as Kernel Density Estimation (KDE), spatial autocorrelation measures, hotspot detection metrics, and network-based studies are reviewed in this study. By highlighting their limitations and assets, this study examines the resources of dataset that are utilized in RTA studies, such as datasets of hospital, police, and emerging GPS- and sensor-based. The review of this study highlights the main analytical challenges, like inadequate data quality, sensitivity to geographical scale, adjustable issues of area unit, temporal resolution limitations, and adaptability of limited model over domains. Besides, the scarcity of dataset in developing nations, artificial intelligence and machine learning integration, spatiotemporal modeling, and gaps in research, all of which are discussed in this research. The review of this study is an excellent tool for transportation planners, researchers, lawmakers for safety improvement by GIS-driven accident analysis for more predictive with combining prior research.
Copyright (c) 2026 Muqdad Al Hamami, Salam Ridha Aletba, Norhidayah Abdul Hassan, Hashim Al-Sumaiday https://creativecommons.org/licenses/by/4.0
Artificial Intelligence–Driven Smart Home Energy Management Systems: Deep Learning–Based Load Forecasting, Optimization Strategies, and Open Research Challenges
Traditional residential buildings are progressively evolving into smart, adaptive, and energy-efficient environments through the combination of Artificial Intelligence (AI) and smart home technologies. In light of this development, this review provides a complete overview of recent research on AI-driven energy management in household settings. A total of 45 peer-reviewed articles available between 2019 and 2025 are systematically analyzed and categorized according to the adopted AI methods, including Machine Learning (ML), Deep Learning (DL), and Deep Reinforcement Learning (DRL), as well as hybrid frameworks. The reviewed studies primarily address energy optimization at the HVAC, lighting, and appliance level, with an emphasis on control architectures, deployment strategies, and algorithmic design aspects. Reported finding indicate that Multi-Agent Deep Reinforcement Learning (MADRL). systems can achieve electricity cost reductions , while maintaining satisfactory thermal comfort conditions. However, the literature also reveals determined challenges, particularly in relative to data privacy concerns, computational complexity, scalability across various buildings, and real-time control viability. In this paper, the researchers present a hybrid intelligent architecture that combines Proximal Policy Optimization (PPO), Long Short-Term Memory (LSTM) networks, and Graph Neural Networks (GNN) for the heating, cooling, and lighting modules of smart homes to control the usage of electrical energy. In this review, several approaches in the AI domain are compared and contrasted in order to point out the respective strength, weakness, and future of each and every one of them in the context of occupant comfort, efficiency, and sustainability in Buildings. The outcomes of this study aim to support researchers, system designers, and decision-makers in advancing robust and scalable AI-enabled smart home energy solutions. `
Copyright (c) 2026 Roaa A. Mohammed, Faisal Theyab Abed, Jamal Saad A. Omer, Rania Ragab Hussein https://creativecommons.org/licenses/by/4.0