Wasit Journal of Engineering Sciences

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.

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.

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.

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.

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.

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.

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.

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 s

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.

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

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.

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.

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.

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.

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].

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.

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

The experimental data is obtained in regional Kuala Lumpur region via three stages. First, develop a statistical model using real-world meteorological data, such as sunlight hours, precipitation/rainfall, and relative humidity, to identify the highest and lowest relative monthly sunshine durations. The solar panel was used to directly measure sun irradiation in June and November 2023, from 9:30 am to 4:30 pm. Checking the statistical model's accuracy is the final step. Research indicated that June had the highest relative sunshine duration (51.7%) and mean daily irradiance (≈619 W/m²), while November had the lowest (46.4%) and mean irradiance (≈560 W/m²). Temperature and day duration vary slightly across annual months, although moisture and cloud cover vary more significantly. The model-estimated decline from June to November (≈10.3%) matches the observed irradiance-based reduction (mean ≈9.4%, range 8.9-9.85%), indicating good agreement within experimental uncertainties. Smoothing the irradiance time series (5-10 min intervals) reduced absolute variability but did not significantly affect the relative decline between months (≈8.7-9.8%), making the comparison robust. Validation metrics and tests (KS and cross-validated regression diagnostics in Methods) confirm the statistical model's reliability for monthly assessments in this tropical urban setting. KL's monthly and seasonal rainfall fluctuations result in a 9-11% variation in solar energy between the driest and wettest months. This variability is significant in system design, financial planning, yield forecasts, reserve capacity planning, and battery size for rooftop and distributed PV. The provided regression model and real-time irradiance measurements agree within experimental error, suggesting that the model may be a helpful tool for monthly-scale PV yield estimate and planning when combined with additional local meteorological inputs and multi-site validation.

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.

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.

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.

This paper provides an extensive comparative study of the thermal management performance of hybrid heat sink systems using phase change materials (PCMs) and air subjected to different levels of heat flux from 2500 - 10000 W/m². Five different configurations were tested: air, foam/air, paraffin/air, organic material OM air, and finned heat sink HS wax hybrids. The air-only baseline was determined not to sufficiently support high heat load applications due to the base temperatures exceeding 840 K. PCM-based systems exhibited considerable thermal buffering capabilities through the use of isothermic plateaus over time. For example, at a low heat flux level of 2500 W/m², the OM and HS wax configurations maintained a temperature of approximately 325 K for nearly 50 minutes. The overall performance of the hybrid systems was quantified by both the liquid fraction and the Thermal Enhancement Ratio (TER). Even though the HS-wax hybrid reaches full liquefaction much faster because of superior thermal distribution through metallic fins, it achieves the maximum amount of latent heat. The results of our analysis regarding the effects of operational temperature on operational time are as follows: At low target temperatures (60˚C - 120˚C), OM wax has the highest latency energy (partial very early) ratio (TER) of three to one. Although when operating under high flow conditions 10000 W/m², the thermal efficiency ratio becomes similar across all of the PCMs. In conclusion, although all the PCMs provide nearly identical thermal efficiencies when subjected to extreme conditions, it is worth noting that total latent heat capacity is likely the most limiting factor to thermal breakthrough, particularly at high flow rates.

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.

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.

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.

In order to build flood barriers, erosion protection, cutoff walls, under dams, waterfront buildings, cofferdams, and excavation support systems, anchored sheet pile walls are used as either temporary or permanent constructions. In this study, experimental work was achieved on a laterally anchored sheet pile wall installed in sandy soil under a cyclic load and seepage condition. Ratio (CLR) of 40% for 100 cycles. Several parameters were considered, including anchor rod length of (40 cm), pile length (20, 30, and 40) cm and angles between batter piles 15. In addition, a linear load of 5 kPa applied on ground surface near sheet pile wall and spacing between anchors of (10, 20, 30) cm were considered. The batter piles were installed at a distance of 40 cm from the sheet pile wall. The test results showed that the increase of length of batter piles from 20 cm to 30 cm and 40 cm leads to give good stability of the sheet pile wall by (12 - 84) % and (12 - 88) %, respectively. Strip footing tilting and total settlement near the sheet pile wall reduced as the length of batter pile. The anchored sheet pile wall system subjected to cyclic load was effective and safer when the length of batter pile 40 cm. The spacing between anchors S/H = 1/3 is the best compared to S/H = 2/3 and 1.The spacing between anchors of S/H = 2/3 gives Stability to sheet pile wall anchored systems approximately (8-78%) and (38-82%) compared to spacing S/H = 2/3 and 1 respectively.

Holy cities are defined by the complex interplay of material components, such as architectural landmarks and urban configurations, with immaterial elements like ritual practices and collective memory. These latter elements represent a city's cultural identity and can be understood as carriers of signs, meanings, and symbols in historical centers that determine a city's overall semiotic character. Yet, despite such richness of these urban environments, the urgent need remains to decode how the notion of cultural identity is semiotically constructed and how urban signs transform into profound cultural symbols. This study fills this gap by juxtaposing theoretical semiotics against the real physical experience of the urban environment. The study applies cultural semiotics as a conceptual framework, mainly referring to Yuri Lotman's view on the city as a cultural urban text wherein meaning is created from the dynamic interaction between structure and content. This is integrated with Kevin Lynch's way of thinking about urban perception, which focuses on the means by which identity, structure, and meaning coalesce through sensory and symbolic cognition to create the image of the city. The research designs a theoretical model for the mechanisms of cultural identity semiotics. This is applied in the analysis of the historical center of the two holy cities of Najaf and Karbala. The study explores how the ideological environment and historical background of the holy cities shape collective perception and transform the urban fabric into a readable text. The paper draws to a conclusion by establishing the comprehensive analytical framework linking urban-environmental components with the connotation of cultural identity. This framework presents the methodology for reading how sacredness is physically and symbolically encoded in the contemporary urban text of holy cities.

This study presents an improved approach for extracting high-purity silicon nanoparticles using an enhanced thermal magnesium-aluminum reduction methodology. It lays the foundation for a systematic understanding and transformation of silicon, converting silica (SiO₂) particles into nanosilicon using an optimized aluminum-magnesium mixture for low-temperature silica reduction (a note of low-impact reduction) through a unique chemical treatment. The study employed a protocol of leaching with 4 N HCl followed by treatment with 2.5NNaOH. This was followed by reduction with an aluminum-magnesium mixture, and then a unique chemical treatment involving synergistic leaching and etching of the silicon nanoparticles, along with revitalization etching using a mixture of hydrofluoric and acetic acids. Using this protocol, high-purity silicon nanoparticles with interconnected porous networks were obtained after the complete removal of residual impurities (including methyl green, silicate residues, and other contaminants). Characterization techniques have confirmed the integrated nature of the structure and form of the extracted silicon. All the products were characterized using a field emission scanning electron microscope (FE-SEM), energy dispersive spectroscopy (EDS/EDX), and x-ray diffraction (XRD). The results showed that nanosilicon production had an average diameter of 49.8625 nm, 30.155, and 58.635 nm when extracted from S1, S2, and S3. Also, the XRD and FT-IR results confirm the high purity of the produced nanosilicon nanoparticles. Despite current challenges associated with producing large quantities of porous silicon nanomaterials using MTR technology, the modified thermochemical method offers a scalable, low-cost, high-throughput approach to manufacturing advanced silicon-based nanomaterials.

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. `

This study examines how campus urban morphology influences the environmental performance of public spaces in hot-arid university settings, using Wasit University in Iraq as a representative case. It responds to a common gap in campus research, where built form, environmental support, and user experience are often treated as separate issues, limiting a more integrated understanding of how outdoor spaces function under harsh climatic conditions.

To address this gap, the study adopts a mixed-method approach that combines GIS-based morphological analysis with a user perception survey involving 131 participants. The spatial analysis focuses on the relationship between built and open space structure, pedestrian network connectivity (γ = 0.42), network redundancy (α = 0.18), global integration (Rn), and shaded path continuity (SPCI = 0.28) in order to evaluate the level of environmental support provided by the pedestrian system. Survey data were analyzed using descriptive statistics, reliability testing (Cronbach’s α = 0.832), and Spearman correlation analysis.

The findings reveal a noticeable gap between the spatial availability of open areas and their actual environmental usability. Although open spaces account for nearly 66% of the total campus area, their fragmented layout and weak integration within the movement network reduce their functional and environmental effectiveness. Environmental conditions were found to play a major role in route selection (M = 3.878), while perceptions of nighttime safety (M = 2.702) and support for informal study activities (M = 2.893) were relatively weak.

The correlation analysis further shows that psychological comfort is strongly linked to several environmental qualities, particularly cleanliness and maintenance (ρ = 0.612), greenery (ρ = 0.461), natural lighting and ventilation (ρ = 0.447), and pedestrian continuity (ρ = 0.421), all at a high level of statistical significance (p < 0.001). Overall, the results suggest that environmental performance in hot-arid campuses depends less on the quantity of open space and more on the continuity, integration, and climatic responsiveness of the public-space network.

The study contributes to the field by proposing a context-sensitive morphology-continuity framework that connects spatial configuration with user perception, offering a more climate-responsive basis for evaluating and improving campus public spaces.

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.

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.

Wellbore pressures from production wells that use heavy oil beam pumping systems have low volumetric efficiencies due to problems such as excessive viscous friction, insufficient pump fillage and the inability to adapt variable speed pump drive systems to continually changing wellbore conditions while maintaining the mechanical stress/production trade-off that must be met with a fixed-speed operation. A new approach that uses a closed-loop variable speed drive (VSD) controller with a variable speed strategy allows for real-time adjustments to pumping speed based on real-time pump fillage that was inferred from surface dynamometer cards thus eliminating the requirement to use downhole sensors for this application. The combined results of a coupled dynamic simulation and field validation showed that the use of the variable speed controller reduced peak polished rod load by 22.9% and friction work per stroke by 25.0%, while the pump fillage was stabilized to between 85-95% elimination of fluid pound and volumetric efficiency increased from 78.0% to 91.2%. Additionally, the energy efficiency increased by 30% relative to the constant-speed operation providing the best scenario. The proposed controller represents the first controller to optimize both friction and volumetric efficiency for heavy oil without additional downhole instrumentation thus resolving the fundamental mechanical stress/production trade-off. Retrofitting existing units with a fillage-based VSD represents a low-cost upgrade with rapid payback due to the energy savings and reduction in rod failures leading to increased production

The evaluation of Streetscapes has seen increasing development in the use of artificial intelligence and deep learning techniques to analyze Streetscape in a more objective and continuous manner. Many studies still rely on human assessments as a primary reference to verify the accuracy of intelligent models, with limited research addressing the impact of residents’ spatial background and local expertise on urban evaluation outcomes. This study aims to compare the assessments of local experts and external experts in evaluating urban landscape quality based on imageability and enclosure indicators, and to analyze the extent to which each group aligns with the results of a deep learning model. The research relied on a set of urban images evaluated by urban design experts, which were then compared with the outputs of a model based on convolutional neural networks. The results showed a clear difference between the two groups, with external experts achieving higher agreement rates with the model, reaching 82.26% for the Imageability indicator and 88.71% for the containment index, compared to local experts, whose agreement rates were 69.11% and 62.60%, respectively. The results indicate that spatial familiarity and direct experience with a place may influence urban perception and produce a certain type of perceptual bias, while external evaluations tend to show a greater degree of neutrality and consistency with AI-based analysis.

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.