Al-Rafidain Journal of Engineering Sciences
Al-Rafidain Journal of Engineering Sciences (RJES) is a double-blind, peer-reviewed, open-access journal founded in 2023. RJES is a semiannual journal published by Al-Rafidain University intended to be interesting and useful for researchers and practitioners in academic and industrial communities. RJES is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering sciences. While it encourages a broad spectrum of contributions in the engineering sciences, its core interest lies in issues concerning material modeling and response. RJES invites research papers that encompass conceptual analysis, design implementation, and performance evaluation. All papers submitted for possible publication at RJES are subject to the peer review process. Submissions are accepted only in the English language. The journal accepts research articles, review articles, case reports, and selected conference articles. The editors reserve the right to reject papers without sending them out for review. The RJES is committed to upholding publication ethics (COPE) and obligates the authors to avoid any plagiarism or illegal citation. The subject areas include the following fields: Engineering and Technology (General) Chemical Engineering Civil and Environmental Engineering Dames and Water Resources Engineering Mechanics, Mechanical Engineering Hydraulic Engineering Electrical and Electronic Engineering Communication Engineering and Information Technology Computer Engineering Mechatronics Engineering and Robotics Architecture and Environmental Design Sustainable Engineering Nano-scale Design Engineering RJES publishes: 1. Origional articles, 2. Review articles. 3. Special issues with selected papers from relevant conferences. © 2023, The Author(s). Published by Al-Rafidain University College. This is an open access journal issued under the license https://creativecommons.org/licenses/by-nc-sa/4.0/ Journal title: Al-Rafidain Journal of Engineering Sciences Journal abbreviation: RJES (Rafidain J. Eng. Sci.) Frequency: 2 issues per year ISSN (Online): 3005-3153 DOI prefix: 10.61268 National Library No. : 2675 (2023) Publisher: Al-Rafidain University Language: English
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
NDVI and NDWI Based Assessment of Land Cover Changes in Samarra, Iraq using Landsat 8 Data
This paper studied the land cover changes in the Samarra urban. Two images from Landsat-8. One image was from July 2013, and the second from July 2020 used for calculate vegetation as well as water bodies. We applied NDVI and NDWI to these images. Iraq is a dry region that is difficult to work in it. The features such as soil and vegetation cover seem almost equal in the scene, this cause conventional methods to generate wrong results. For this paper, we got a Level-2 scene from the USGS website. These scenes are adequate because they are already fixed for the atmosphere; thus, this step is not required. The Maximum Likelihood method is used to classify the scenes. The map tested ,that made with 250 points on Google Earth. The final result was acceptable, with 88.7% accuracy. When comparing the results for 2013 with 2020, it was observed that the water bodies and green areas decreased. The bare surface coverage increased significantly. These changes are mainly attributed to prolonged drought in the region.
Copyright (c) 2026 Al-Rafidain Journal of Engineering Sciences https://creativecommons.org/licenses/by-nc-sa/4.0/
Extended Condition Monitoring of Grid-connected Industrial Induction Motors using SMC Technique
The control method for a vehicle that combines Sliding Mode Control (SMC) and Active Power Control (APC) for three-phase induction motors that encounter unmeasured speed circumstances and unknown disturbances is examined in this work. The primary contribution is the creation of an improved controller with a time-varying gain mechanism that maintains balanced performance and rapid response while managing the peak phenomena often associated with abrupt changes in speed, torque, and loads. Starting from a conservative setting and smoothly transitioning to a high-gain system, the active power control model with the suggested sliding controller is made to adapt to real-time changes and offers a quantitative balance between transient peaks and response precision. A sliding-mode controller with active disturbance compensation is implemented based on accurate assessments of disturbances and reconstructed states from the proposed system. This greatly decreases abrupt vibrations, a persistent problem in classic SMC. The remarkable durability of the Sliding Mode Controller (SMC) and its capacity to manage the intricate nonlinear nature of these motors in a manner that is less complicated than more sophisticated alternatives are what make it unique, particularly in induction motor applications. Since a three-phase induction motor is a highly nonlinear system and its properties, such as internal resistance, fluctuate with temperature and loads, we must examine how it operates in order to comprehend why. The software simulation results of the proposed SMC based on the extended state observer (ESO) model showed THD < 3.5%, with response time < 15 ms, along excellent chattering elimination and high real-time compensation.
A Comprehensive Review of 3D-Printed Shape Memory Polymers: Influence of Infill Design, Printing Parameters, and Applications
Smart materials with shape memory, known as shape memory polymers (SMPs), are able to return to a permanent shape after being deformed, when an external stimulus, such as heat, is applied. Fused deposition modeling (FDM/FFF) is now the primary additive manufacturing process to produce SMP-based complex architectural structures such as those needed for biomedical applications. While a critical and largely unexplored processing parameter for governing porosity, mechanical stiffness, energy absorption, shape fixity ratio (Rf) and shape recovery ratio (Rr), the infill design (pattern geometry/porosity/scaffold design) critically affects all of these factors. This critical review provides an overview of 198 references on: (1) fundamentals and classification of SMPs; (2) FDM processing parameters and its effect on microstructure and thermal stability; (3) quantitative relationships between infill design and mechanical/ shape memory properties; (4) TGA/DSC-based thermal characterization; (5) biomedical and 4D printing applications and (6) challenges and future prospects. Our conclusions show that the highest compressive strength is achieved at 0.1 mm layer thickness, 230°C printing temperature and 20 mm/s printing speed, while 0.2 mm/220°C/30 mm/s yields the best combination of mechanical properties (modulus and strength), energy absorption and cyclic shape memory. With respect to the scaffold design, OK shows the highest Rf (97.92%) while the WV shows the highest Rr (98.11%). Gyroid and honeycomb infill patterns are deemed the best for biomedical applications for their continuous porosity and isotropy.
Improving the Properties of Gypsum Soil Using Cement and Lime Mixtures
Soil is the medium in which the agricultural and non-agricultural activities are carried out and it is the base of food production for human beings. Gypseous soils are one of the most troublesome soil types, and can cause severe damage to building foundations because it's a collapsible soil. This failure happens when water gets to the gypseous soil grains and dissolves the gypsum bonds. Gypsum soils are highly soluble, and are poorly structurally stable and thus pose serious problems for construction and agriculture. These soils typically experience over settlement and loss of supporting capacity when they come into contact with water. Application of lime and cement stabilization has shown potential in solving these issues. The characteristics of gypsiferous soils, the conventional stabilizing materials (limestone and cement), and conventional methods of treatment are reviewed and the effect of these treatments on the behaviour of gypsiferous soils is examined. The physical and chemical characteristics of gypsum soils are briefly discussed, as well as the chemical reactions and equations that occur when cement and lime are added to gypsiferous soil; and selected previous studies that used these stabilizers were presented.
Nonlinear Finite Element Modeling and Experimental Evaluation of Reinforced Foamed Concrete T-Beams
This study focuses on the structural behavior of reinforced foamed concrete T-beams, based on both experimental and theoretical/numerical comparisons by using ABAQUS. The Concrete Damage Plasticity (CDP) model in ABAQUS was employed to model the nonlinear behavior of foamed concrete T-beams, and the steel reinforcement was represented as an embedded truss element. The experimental results were used to validate the numerical results for ultimate load capacity, mid-span deflection, load-deflection response, and crack pattern. A good agreement between the experimental and numerical results was obtained. The average experimental ultimate load was 129.08 kN, and the average numerical ultimate load was 131.78 kN. A parametric study was conducted to simulate the response of foamed concrete T-beams subjected to a uniformly distributed load. Findings showed that distributed loading was successful at increasing load-carrying capacity and minimizing deflection, thereby improving structural performance. Thus, the developed numerical model can be considered well validated for predicting the behavior of fibered-foam reinforced concrete T-beams and for performing parametric studies.
Review on the Rehabilitation and Strengthening of Reinforced Concrete Beam-Column Joints Using Conventional and Advanced Retrofitting Techniques
The beam-column joints in reinforced concrete (RC) moment resisting frame structures are the most critical and vulnerable under seismic loading. Over the past decades, joint failure (diagonal shear cracking, bond loss and brittle collapse) has been recognized as one of the most common causes of casualty and economic loss during earthquake reconnaissance. In this paper, the authors have identified and gathered experimental, analytical and numerical evidence from about 50 published studies, from 1975 to 2024, and synthesized them in this comprehensive review of conventional and advanced retrofitting strategies. The conventional methods discussed are: RC jacketing, steel jacketing and fibre-reinforced polymer (FRP) wrapping with carbon (CFRP), glass (GFRP), aramid (AFRP) and basalt (BFRP) composites. Advanced techniques include near surface mounted (NSM) FRP bars, textile-reinforced concrete (TRC), ultra-high performance fibre-reinforced concrete (UHPFRC) overlays, engineered cementitious composite (ECC/SHCC) and nanotechnology-based systems. Comparative performance parameters such as shear strength, ductility, energy dissipation, stiffness degradation and residual drift are tabulated and discussed in the context of design codes (ACI 318-19, ACI 440.2R-17, EN 1998-3, IS 13920, ASCE 41-17). There are eight schematic figures and five data tables provided. The review pinpoints key areas for further study: full-scale testing, codification of hybrid systems, long-term durability, and assessment of the sustainability of hybrid systems.
Thermo-Mechanical Numerical Simulation in Manufacturing of Aluminum Components: A Comprehensive Review of Turning and Deep Drawing Processes
With their outstanding strength-to-weight ratio, corrosion resistance, and workability, aluminum alloys play a pivotal role in modern lightweight manufacturing. The prediction of the thermo-mechanical response during machining and forming is crucial for optimizing the processes parameters, minimizing the tool wear, reducing the residual stresses and improving the dimensional accuracy. This review is systematic and examines comprehensively the state of the art of the numerical simulation of two of the main methods of producing aluminium products: turning (a subtractive machining process) and deep drawing (an incremental sheet forming process). Topics covered include finite element method (FEM) formulations, constitutive material models such as Johnson–Cook and Zerilli–Armstrong models, contact and friction models, thermal analysis (heat generation and dissipation), prediction of chip morphology, springback and earing defects. The 90 publications that have undergone peer review are critically reviewed and synthesized. Key findings and consolidated data tables are provided to compare various simulation approaches and highlight the greatest research gaps around high speed turning of Al 7xxx alloys, multi-scale modelling of microstructure evolution and machine-learning enhanced FEM. This review is intended as a reference for folks who are engaged in research and practice in the area at the intersection of computational mechanics and aluminum manufacturing.
Review on Hydraulic and Geotechnical Mechanisms of Riverbank Roadway Erosion and Collapse
Riverbank erosion is a critical geomorphological process with significant impacts on river systems and adjacent infrastructure. In many regions, transportation networks are constructed near riverbeds, making them susceptible to bank instability and erosion. Hydraulic forces including flow velocity, turbulence, and shear stress play a major role in determining the rate of riverbank erosion. Concurrently, the geotechnical properties of bank materials, such as cohesion, permeability, and soil structure, influence the riverbank’s resistance to erosion and slope failure. The interplay between hydraulic dynamics and soil mechanical properties results in various bank failure mechanisms, including rotational slip, cantilever failure, and block failure. Additionally, traffic loads and human activities can exacerbate the instability of riverbanks adjacent to transportation infrastructure. This paper reviews previous researches on riverbank erosion, soil characteristics, hydraulic flow conditions, and the instability of roads near rivers. The review aims to identify the key factors controlling riverbank erosion and to highlight existing research gaps, particularly regarding the combined effects of hydraulic loading and traffic-induced stresses. A comprehensive understanding of these mechanisms is essential for developing effective strategies to stabilize riverbanks and safeguard transportation infrastructure.
Traffic Congestion Mitigation on Highways by using the Feedback Strategy
The design of highways in urban areas aims to reduce congestion. When congestion occurs on a highway, it presents a problem that must be solved. The Al-Dawrah Expressway experienced severe congestion, particularly at its entrance, negatively impacting traffic flow. Data was collected via cameras installed along the highway. After calibrating and verifying the data, the ALINE algorithm was used to control traffic flow at highway entrances (Ramp Metering) to congestion mitigation by control of three parameters(flow, occupancy and speed). The results were significant, with four scenarios performed at different time points. The fourth scenario, lasting 10 seconds, showed the highest improvement rate of 20.1%, encompassing increased traffic flow, reduced congestion, decreased critical occupancy and increased speed.
A Review study on foundation above the soil containing voids; challenges and obstacles
Foundations constructed on sandy soils that contain underground voids or cavities often face significant geotechnical challenges. Underground cavities occur as the results of natural causes, manmade causes or sometimes a complex combination of both. Despite considerable research on foundation performance in soluble soils, the effects of cavity geometry specifically, cavity depth and horizontal position relative to foundation centers and load eccentricity on the bearing capacity This paper discusses an extensive survey on the previous investigations the challenges and obstacles on the conducted foundations conducted on soil containing voids footing not fully understood. Various factors were considered effect of void size, depth, void location relative to the foundation. These studies have effectively shown that the presence of voids directly affects soil behavior, leading to reduce shear strength, decrease bearing capacity, and increase settlement, which can cause sudden failures. The greatest impact occured when the voids were located within the critical zone below the foundation, while their effect decreased with increasing depth or horizontal distance.
Phase Change Materials in Cooling Technologies: A Systematic Review of Applications and Studies
Phase Change Materials (PCMs) are substances capable of storing and releasing thermal energy through phase transitions, primarily between solid and liquid states. Due to their high latent heat capacity and nearly constant temperature operation during melting and solidification, PCMs have gained considerable attention for thermal management and energy efficiency applications. This review summarizes recent investigations into PCM-based cooling systems across multiple fields, including building thermal regulation, photovoltaic panels, electronic devices, batteries, and domestic appliances. The integration of PCMs in these systems effectively reduces temperature fluctuations, peak loads, and overall energy consumption, leading to enhanced system performance and environmental benefits. Various organic, inorganic, and eutectic PCMs have been analyzed according to their thermal properties, and selection criteria. Software can simulate the PCMs applications are ANSYS Fluent, EnergyPlus, and Design Builder. The findings highlight that optimized PCM design and integration strategies play a crucial role in sustainable cooling technologies, supporting the global shift toward energy-efficient and low-carbon solutions.
Biomechanical Evaluation of a Ready-Made TurboMed Ankle Foot Orthosis in a Post-Stroke Patient Using Three-Dimensional Gait Analysis (Motion Capture)
Stroke-related gait impairment is commonly associated with foot drop, reduced walking speed, poor balance, and asymmetrical lower-limb biomechanics. Ankle-foot orthoses (AFOs) are frequently prescribed to improve gait stability and walking efficiency in post-stroke rehabilitation. This study aimed to biomechanically evaluate the effectiveness of a ready-made TurboMed ankle-foot orthosis using three-dimensional gait analysis based on the Helen Hayes marker model. A post-stroke patient underwent motion capture gait assessment under two walking conditions: without an AFO and with a TurboMed AFO. Temporal-spatial and lower-limb kinematic parameters were analyzed and compared. The results demonstrated improvements in walking speed, heel clearance, ankle dorsiflexion, and gait stability during AFO-assisted walking. Walking speed increased from 0.17 m/s to 0.21 m/s, while double-support time decreased from 31.7% to 27.2%, indicating improved balance and confidence during gait. The TurboMed AFO also reduced excessive plantarflexion during swing phase and improved ankle alignment. These findings suggest that ready-made TurboMed AFOs can provide clinically meaningful biomechanical benefits for post-stroke gait rehabilitation.
Shrinkage Mitigation Strategies for Concrete Bridge Decks: A Systematic Review and Bibliometric Analysis
Concrete bridge decks develop early-age shrinkage cracks which create a widespread durability issue that speeds up steel reinforcement corrosion and requires more maintenance expenses and shortens the lifespan of the bridge. The study will conduct an organized evaluation to identify classification methods and assess the success rates of shrinkage control techniques used for concrete bridge decks. The research will identify missing information to direct upcoming studies and operational procedures in the field. Systematic review (PRISMA), which analyzed 154 studies published between 2000 and 2025 that they found in Scopus and Web of Science and various engineering databases. Bibliometric analysis performed to map research trends, geographic distribution, and thematic clusters. The study discovered nine main strategies which were analyzed against each other including shrinkage-reducing admixtures, fiber reinforcement, internal curing with lightweight aggregates, supplementary cementitious materials, expansive agents, optimized mix design, improved curing practices, and combined multi-strategy approaches. Shrinkage-reducing admixtures reduced drying shrinkage by (25-40) %, internal curing by (30-45) %, and UHPC overlays by up to 55%. The combination of shrinkage-reducing admixtures and internal curing methods led to a 52% decrease in results. Fiber reinforcement primarily controlled crack widths rather than total shrinkage strain. The combination of multiple strategic approaches produces better results than using each method independently. The highest performing UHPC solution becomes unaffordable because it requires a major increase in project expenses.
Self-Healing in Asphalt Pavements for Enhanced Road Sustainability: Opportunities and challenges
Self-healing is a characteristic obtained by the asphalt mixture through production or operational conditions, the application of paving, or by modification of the components constituting the asphalt mixture. The asphalt binder is a hydrocarbon material composed of carbon chains that are bonded together to form the asphalt structure. The chemical composition of the asphalt binder can be modified, and the carbon chains enhanced to mitigate the deterioration resulting from the ageing of the asphalt binder and the volatility of its constituents. This is achieved by using an asphalt binder with a softer penetration grade and a high concentration of low molecular weight components, hence reducing volatility-induced ageing. Alternatively, the capability of the asphalt mixture for self-healing can be achieved through the incorporation of components that repair damage and rebuild cracks in the asphalt pavement, such as regeneration agents, steel fibres, or other materials.
Application of Machine Learning in Structural Engineering: A Review of Predictive Models and Design Optimization
In the field of mechanical engineering served as a tool for addressing complex problems in the Structural Engineering due to enabling fact-pushed structural framework evaluation, prediction and optimisation. Different from different literature opinions in concentrating mainly around individual machine learning machine learning paradigm and individual structural application areas, right here we develop a unified and contrasting research for supervised, unsupervised and reinforcement learning methods in the context of structural engineering along with the current developments of physics-informed and hybrid learning frameworks for future Structural Engineering. In the present statistical era, and the advancements made in processing capacity, a mixed framework is examined with the less complex strategy to deal with established engineering solutions towards applying neighbourhood engineering for starting point and detailing the easy way of the examine gadget, and the supervised based totally on memorisation, unsupervised and the reinforcements for each one the paradigms within the Structural Engineering together with structural diagram automatic checks, optimisation, structural overall health tracking, and consequently on. Apart from the statistical efficiency of various gadgets concerning algorithmic intricacy, desirable characteristics, merits and demerits, comparison features related to style discovering of the multi machines also be examined; the content will straight handle with the main limitations of smart systems that prevent a widespread adoption in structural engineering research, the consequences of combination of physics based models with the artificial intelligence demonstrate a terrific prospect for enhancing performance in structural check, monitoring, style procedures, but, we highlight a need of hybrid approach in blending data driven methods along with customary engineering principles, with special reference to their relevance to use in the linear frameworks within the structure study.
Effect of Geotextile and Tack Coat Type on Asphalt Concrete Performance: A Review
The bonding between asphalt concrete layers in flexible pavement is a crucial parameter affecting the mechanical properties of asphalt pavement, mainly influencing stress distribution, structural framework, and mechanisms of distresses resistance. The current study exhibits a comprehensive review of the combined impact of tack coat materials and geotextile interlayers on the performance properties of asphalt concrete, with highlighting Interlayer Shear Strength (ISS), fracture resistance, and durability. Many tack coat types including conventional emulsions, cutback asphalts, hot asphalt binders, trackless systems, and polymer-modified emulsions were assessed by previous studies. The outcomes indicated that trackless and modified with polymer tack coats enhanced ISS by approximately 20 to 50% as compared to conventional emulsions, while epoxy-modified systems demonstrated superior performance especially at high temperatures. Geotextile interlayers substantially improved pavement properties, raising load-carrying capacity by up to 116%, stiffness by 25–31%, and fatigue life by up to four times (Traffic Benefit Ratio (TBR) approximately equals to 4). In addition, reductions of about 25 to 96% in rutting and 28.6 to 74.8% in strain were reported, based on the location of geotextile in the pavement. Geotextiles also minimized the crack propagation by absorbing around 15% of induced stresses, leading to 2 to 3 times increase in asphalt pavement durability. The contact between tack coat and geotextile in pavement is considered a key design factor. Based on the previous studies, the optimum application rates of tack coat typically ranged from 0.6 to 1.2 L/m², corresponding to around 100 to 125% of the geotextile asphalt retention capacity. Excessive amounts can reduce the ISS because of lubrication effects. Furthermore, saturation of geotextiles can enhance moisture resistance by reducing permeability by up to three times. Despite these results, further research is necessary to develop integrated design methods under differing field conditions.
Reactive Powder Concrete: Composition, Mechanical Properties, and Structural Applications : A Review
This paper reviews the composition, mechanical properties, and structural applications of Reactive Powder Concrete (RPC), an advanced cementitious material that represents a major leap in concrete technology. Unlike conventional concrete, RPC contains no coarse aggregates. Instead, it relies on a carefully engineered mixture of cement, silica fume, fine sand, superplasticizers, and steel fibers to achieve an ultra-dense microstructure. The result is exceptional mechanical performance, with compressive strengths ranging from 170 to 800 MPa, depending on the curing method and mix design. The review of previous studies reveals that heat treatment significantly enhances strength, with curing at 90°C raising compressive strength from 120 MPa to 180 MPa. Adding steel fibers at volume fractions of 1–2% improves splitting tensile strength by up to 39%, flexural strength by 29%, and impact resistance by nearly 150%. Interestingly, some researchers successfully incorporated crushed coarse aggregate into RPC without reducing compressive strength, contradicting early assumptions that coarse aggregate must be eliminated. Structural applications, including beams, T-beams, and bridge decks, demonstrate superior load capacity, ductility, and crack control. However, RPC remains expensive, requires high cement content, and often needs special curing conditions, limiting its widespread use. This review concludes that while RPC is an outstanding material for high-performance applications, further research is needed to reduce costs and validate long-term durability under real-world conditions.
High-Performance Concretes: A State-of-the-Art Review of Material Behavior, rigid pavement under earthquakes
High performance concrete can substitute conventional concrete in rigid pavement systems in earthquake-prone regions due to its enhanced mechanical properties, such high compressive strength, high flexural strength, and durability. An overview of relevant study on HPC material properties, rigid pavement design and performance, the effects of seismic loading on concrete structures in general, as well as specialized materials that include ultra-high-performance concrete (UHPC), engineered cementitious composites (ECC), and fiber-reinforced versions are presented in this review. Engineered cementitious composites (ECC), fiber-reinforced forms, and so forth. Metrics such loss of energy capacity, crack control ability, load transfer ability, and mechanical–environmental durability are highlighted in the review. Additionally, link slab technology, joint design, and additional cementitious materials for enhancing seismic performance and sustainability are among the subjects addressed. In addition to suggesting future directions for research on seismic optimum of nanomaterials-enabled HPC rigid pavement systems, this report discusses cost, standardizing, and performance data over the service life for HPC rigid pavements.
Spatial Analysis and Performance Evaluation of Hospital Wastewater Treatment Plants Using GIS-Based Kernel Density Estimation and Buffer Analysis: Basrah Governorate
Higher Hospital wastewater (HWW) poses a significant and under-regulated environmental threat in developing regions. This study aimed to evaluate the performance of hospital wastewater treatment plant (HWWTs) in Basrah governorate, Iraq, using a spatially explicit approach to address the critical gap in site-specific environmental assessment. A geospatial database was developed for seven HWWTs, and their pollutant removal efficiencies were quantified. Performance variability was analyzed using Kernel density estimation (KDE) to identify pollution hotspots and multi-distance buffer analysis(500-1500m) was employed to assess environmental risk zones. Results indicated inconsistent treatment performance, with acceptable removal for organic load (BOD/COD) but critically low efficiency for nutrients particularly phosphate (PO43-), which ranged from 20.4% to 64.1%. the spatial analysis revealed district clusters of low-performing facilities, identifying specific hospitals as significant hotspots for effluent discharge. The buffer analysis confirmed a cumulative environmental risk, with potential impacts extending to the Shatt Al-Arab River, the region’s primary water source. The study concludes that the current treatment infrastructure is inadequate for the complex HWW matrix, especially concerning nutrient management. The finding validates GIS-Based spatial analysis as powerful evidence-based tool for environmental authorities to prioritize regulatory enforcement and implement site-specific management strategies in Basrah governorate.
Enhanced Classification of UML Class Diagram Components Using Deep Learning for IT and Engineering Solutions
UML class diagrams are key to object-oriented software development, capturing structural descriptions of software systems. But manual interpretation and analysis of UML diagrams is time-consuming, prone to errors and not scalable, especially during software maintenance and reverse engineering. In this paper, a deep learning model is presented to automate recognition of UML class diagram components by using a Convolutional Neural Network (CNN). The proposed approach does not use text-based features of UML class components and instead uses only visual features to classify UML class components into four classes: Entity, Service, Controller, and Utility. A dataset of real UML class diagrams was labeled for supervised learning. Our model outperformed baseline methods, with a classification accuracy of 92.3% on the test set and generalised well to different diagram types. This paper makes the following contributions: (1) a lightweight CNN-based model for UML component classification, (2) a benchmark labeled dataset, and (3) comparison of visual-only learning with traditional techniques in UML component classification. This research advances the field of Software Engineering, especially Computer-Aided Software Engineering (CASE), by facilitating smart automation in software modeling and documentation tools. The paper presents a feasible and effective roadmap to intelligent software modeling tools and automated documentation systems.
General Concept and Structural Behavior of Reinforced Concrete Wide Beams: A Review
This paper presents a comprehensive review of experimental and numerical studies on the structural behavior of reinforced concrete wide beams, defined by a width-to-effective depth ratio (b/d) of 2 or greater. The synthesis of findings from investigations over the past two decades reveals that wide beams exhibit fundamentally different responses to loading compared to conventional narrow beams, particularly in shear distribution, crack propagation, and failure mechanisms. Key findings indicate that reinforced concrete wide beams demonstrate an inherent extra shear resistance of 30-40% beyond first cracking, a beneficial characteristic underestimated by current design codes including ACI 318 and Eurocode 2. The review confirms that properly designed web reinforcement, including stirrups, hybrid steel-FRP configurations, and polypropylene fibers, substantially enhances shear strength, ductility, and crack control, with fibers capable of completely substituting minimum stirrups. Additionally, horizontal openings introduce stress concentrations requiring special detailing. The findings emphasize the critical need for revised code provisions that account for width effects and modern reinforcement strategies in wide beam applications.
Optimization of Production Throughput and Quality Control Metrics: A Case Study in Home Appliance Assembly
The given research paper is a rather detailed quantitative study of the daily and monthly production rates and defects in the production of home appliances in the General Company of Light Industry, Iraq, in the first quarter of 2026. Three different product lines have been studied in terms of the 10-foot vertical display unit (January), 2-door display unit (February), and 3-door display unit (March). The methods of statistical process control (SPC) such as p-control chart and Pareto analysis were used to describe the defects occurrences, out-of-control conditions and the root causes. Total production volumes were 1,250, 360, and 250 units, with overall efficiencies of 96.72%, 94.17%, and 90.80% respectively. The overall rate of rejection in all three months was 4.07% (75 rejected out of 1,860 total units). The most common types of defects were injection mold (March), shortages in the quantity of the foam (all months), and external structural damage (January and March). Ishikawa analysis was applied to trace the causes systematically to materials, machine conditions, human factors, and environmental disturbances such as lack of power. Recommendations that combine Six Sigma DMAIC approach, preventive maintenance regimes and corrective process controls to operational excellence are provided.
Simultaneous Removal of Nutrients from Water Using Advanced Sustainable Technologies: A Comprehensive Systematic Review (2005-2024)
Nitrates (NO3-) and phosphates (PO43-) are released in high amounts of industrial waste and runoffs, causing the eutrophication process, which can impair aquatic life. The present paper is a systematic and comprehensive review of the most recent removal technologies, consisting of physical, chemical, biological, and nanotechnology technologies, and concentrates on newly published articles (since 2005). One of the studies deals with the complicated mechanisms of reaction, like ion exchange, electrostatic attraction, and the formation of a surface complex. The experiments showed that using a biochar-based adsorption system and nano-modified adsorbents is the most sustainable and economically applicable method, as the efficiencies of removal reach over 90% within the most favorable working environment. The review concludes that water treatment lies in the emergence of "Hybrid Systems" combining high efficiency with qualitative nutrient recovery in conformity with the concept of the circular economy and the resource-constrained areas.
PPO-Based Neural Architecture Search for Efficient and Hardware-Aware Windows Malware Detection in Cybersecurity
The rapid growth of malicious software targeting Windows operating systems has created significant challenges for modern cybersecurity systems. Traditional malware detection approaches often rely on manually designed models or signature-based techniques, which struggle to identify newly emerging and highly obfuscated malware variants. Although recent deep learning methods have improved detection accuracy, many of these models depend on fixed neural architectures and require extensive manual tuning, limiting their adaptability and efficiency. To address these challenges, this study proposes a reinforcement learning–driven Intelligent Agent framework for automated neural architecture search in Windows malware detection. The proposed system employs a reinforcement learning agent based on Proximal Policy Optimization to explore a simplified architecture search space and automatically construct an effective deep learning model. The approach was evaluated using the EMBER Dataset, a widely used benchmark dataset for malware classification. Experimental results demonstrate that the architecture discovered by the proposed Intelligent Agent achieves strong detection performance, reaching an accuracy of 97.3 while also outperforming several baseline approaches in terms of Precision, Recall, F1-Score, and AUC metrics. These findings indicate that integrating reinforcement learning with neural architecture search can significantly enhance malware detection performance while reducing the need for manual model design. In addition to achieving high detection accuracy, the proposed approach considers computational efficiency by enabling the discovery of hardware-aware neural architectures suitable for real-world deployment. The proposed framework highlights the potential of intelligent agent–based systems in developing adaptive and efficient cybersecurity solutions capable of addressing evolving malware threats.
A Review of Origami–Inspired Structure According to Pattern, Manufacturing and Application
Abstract: Corrugations can be one of several methods to enhance the properties of thin-walled mechanical structures in terms of handling surface area. It is considered a solution to many application problems for devices and equipment and to solve engineering problems. This paper highlights the most famous origami shapes, manufacturing methods, and applications. The shapes of origami are dependent on the degree of freedom. This means that the shapes in this paper discussed the five most common types and the cell units they consist of. These units are repeated over the whole sheet. All shapes were drawn up with all their details or details of units that they were formed from. Also, it showed the single-cell unit that makes up each of the five most common patterns in engineering applications. The five most common patterns were waterbomb, Miura-Ori, Yoshimura, Kresling, and Resch. Owing to the ability of energy absorption and mechanical features, Miura-Ori was deeply discussed. For manufacturing processes, the most known methods were highlighted, which gave a hint for manufacturing differences in origami. In addition, the applications of origami in seven fields were presented in detail. The seven fields were automotive, aeroplan, aerospace, buildings and structures, biomedical engineering, map and floor plan, and architecture. Applications of origami in engineering have been extensively reviewed in fields such as the architecture field in the five specific disciplines. The five specific disciplines were building facades and adaptive shading systems, transformable vaults, bridge with load bearing capacity, transformable canopies, and sawtooth roofs. The objectives of the study are to make readers recognize the shapes of origami, to show the most important methods of manufacturing processes for origami, and to provide the applications in major origami-based engineering fields. In conclusion, this paper provides a significant reference for those interested in this matter or in the field of origami to obtain a wide and concise view of patterns, manufacturing processes, and applications of origami structures.
Pressure Drop Analysis Using Deep Learning Techniques to Enhance Micro-channel Cooling Performance in Energy Conversion Systems
The high power energy conversion systems are quickly increasing in power, which requires a great deal of efficiency in thermal management, and micro-channel heat sinks have found considerable use in this area. But it is computationally expensive and time consuming to determine the pressure drop in these complicated micro-geometries accurately by using the conventional experimental or computational fluid dynamics methodology. In a bid to overcome this critical issue, this work suggests a new deep learning model, which is referred to as the Channel-Inspired Neural Network (CoINN), which is specially designed to identify pressure drop in the variety of micro-channel arrangements. A large dataset was created and described that was based on physics, and it included different operational fluid dynamics and geometric parameters. CoINN model was trained and strictly benchmarked against known machine learning baseline models, such as Gradient Boosted Regression Trees, shallow Artificial Neural Network, and Support Vector machine. The CoINN framework has been shown by quantitative analysis to be absolutely superior with a Coefficient of Determination of 0.9998 and a Mean Absolute Error of only 0.859 kPa which means that CoINN would compare with the traditional algorithms that would have trouble with the non-linearities inherent with the fluid data. Moreover, systematic sensitivity analysis confirmed the physical interpretability of the deep learning model and accurately found the two key physical mechanisms governing the pressure drop due to frictional inlet velocity and hydraulic diameter. The suggested framework offers a very robust, predictive surrogate model, which is accurate and computationally fast, and it opens the way to faster design and optimization of advanced micro-scale cooling technologies.
Applying Software Engineering for Robust Early Fault Detection in Industrial Systems Using Heterogeneous Ensemble Learning Models
Software engineering plays a critical role in developing robust and scalable systems to monitor industrial systems to predictive maintenance. Industrial systems should have early fault detection and prevent equipment failures and downtime. Single classifiers do not however work well in identifying subtle and rare defects especially in imbalance data set. In this paper, I am proposing a heterogeneous ensemble learning model consisting of Random Forest, Gradient Boosting and Support Vector Machine (SVM) with a stacking method in order to enhance the detection accuracy. Data pre-processing methods were used to improve model performance such as feature scaling and class balancing. The experimental results demonstrate that the proposed ensemble had the accuracy of 0.95, precision of 0.93, recall of 0.91, and F1-score equal to 0.92 with the original dataset (1000 samples). As the number of samples grew to 5000, the accuracy, precision, recall and F1-score improved to 0.96, 0.94 and 0.93 respectively. This paper presents a heterogeneous stacking ensemble structure that is scalable, which incorporates multiple classifiers in a modular software design, and is capable of providing robust early fault detection during small and large data sets. The analysis of the importance of features showed that Temperature and Vibration are the most important indicators of early faults. The suggested solution is very robust and reliable in fault detection, it has low false alarms and it offers a good predictive maintenance solution to industrial and IoT enabled systems.
Lightweight Concrete: Types, Properties, and Applications,A Comprehensive Analytical Review
Lightweight Concrete (LWC) has come to be regarded as one of the most strategically important construction materials in contemporary construction engineering, offering a balance of reduced self-weight with structural, thermal, and acoustic performance. In this paper, a comprehensive, analytically focused review of the major types of LWC, namely Lightweight Aggregate Concrete (LWAC), Foamed Concrete, Autoclaved Aerated Concrete (AAC), No-Fines Concrete, and High-Strength Lightweight Concrete (HSLWC), as well as the newly developed type of Geopolymer Lightweight Concrete, is presented. Instead of merely presenting a descriptive classification of these types of LWC, the review is carried out from a critical analytical perspective, examining the mechanisms of performance trade-offs, the Interfacial Transition Zone (ITZ) as the key factor in determining mechanical response, and the efficacy of supplementary cementitious materials, nanomaterials, and fibers in overcoming the drawbacks of these materials. Five sets of analytical tables comparing the performance of these types of LWC, drawing on over 75 peer-reviewed publications, are presented. The review is concluded by presenting a forward-looking perspective on the newly developed areas of self-healing LWC, digital twin monitoring, and geopolymer-based LWC.
Dimensionality Reduction in Data Mining Application: A Systematic Review
The rapid growth of high-dimensional data introduces redundancy and noise that hinder data mining and machine learning, a challenge commonly known as the curse of dimensionality. Dimensionality Reduction (DR) techniques, including feature selection and feature extraction, alleviate these issues by reducing computational complexity while maintaining informative structures and enhancing predictive accuracy. This review systematically examines classical DR methods such as Principal Component Analysis (PCA) and its variants, alongside recent advances in nonlinear and manifold learning approaches, including Isomap and Locally Linear Embedding (LLE). Emerging probabilistic, soft computing, automated pipeline, and semi-supervised DR techniques are also discussed. Applications across bioinformatics, recommender systems, trajectory mining, and disease diagnosis are reviewed, with comparative analyses highlighting the strengths and limitations of each approach.
Several Studies Proceed For the HTPS: Challenges and Optimization
The rapid expansion of oil production has increased the demand for effective separation systems, with oil separators playing a vital role as the first stage in oil production due to their fast response. The horizontal three-phase separator (HTPS) is the most commonly used type, valued for its effectiveness and low maintenance requirements. HTPS is an essential component in oil and gas production systems, efficiently separating crude oil into oil, water, and gas phases. This paper examines the limitations of current research and the challenges associated with a comprehensive review of HTPS components and optimization methods. It outlines the design fundamentals, internal configurations, and operational challenges, while also analysing mitigation techniques. This study provides a comparison between different optimization approaches in the oil recovery (OR) separator. This study contributes to ongoing research and serves as a practical guide for enhancing the design, efficiency, and reliability of TPSHs in diverse operational environments.