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/
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.
Copyright (c) 2026 Al-Rafidain Journal of Engineering Sciences https://creativecommons.org/licenses/by-nc-sa/4.0/
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.
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.
Fifth Annual Scientific Research Conference (BCHS)
Proceedings of the Fifth Annual Scientific Research Conference Baghdad College High School/Adhamiya
Stabilizing expansive clay using nano-silica and recycled brick under sulfate exposure
Expansive clay soils present significant durability challenges when exposed to sulfate-rich environments due to volumetric instability and mineralogical transformations. Conventional stabilizers such as cement and lime often experience long-term degradation under sulfate attack, primarily through ettringite and thaumasite formation. This systematic review evaluates recent experimental investigations (2022-2025) focusing on the durability performance of expansive clays stabilized using nano-silica (NS) and recycled brick powder (RBP), with particular emphasis on hybrid systems. A structured screening methodology yielded 37 eligible peer-reviewed studies. The review synthesizes mechanical indicators including unconfined compressive strength (UCS), swelling reduction and residual strength under sulfate exposure, alongside microstructural evidence from SEM and XRD analyses. Findings indicate that hybrid NS-RBP systems refine pore structure, promote low Ca/Si ratio C-S-H formation, and significantly reduce sulfate ion diffusion. Compared to cement only system, hybrid binders demonstrate superior strength retention (typically 78-92% under high sulfate concentrations) and enhanced durability stability. Sustainability considerations further support hybrid stabilization due to reduced embodied carbon relative to conventional cement treatment. Despite promising laboratory results, long-term field validation remains limited. The study concludes that Ns-RBP hybrid stabilization represents a technically viable and environmentally responsible solution for sulfate-prone expansive soils.
Punching Shear Behaviour of Reinforced Concrete Flat Slabs with Openings: Mechanisms, Failures, and Strengthening Strategies: A Review
Reinforced concrete flat slabs are widely used for their architectural flexibility and construction efficiency, but they are vulnerable to punching shear failure—a sudden collapse at slab-column connections. Openings for stairs, elevators, and utility ducts worsen this vulnerability by removing concrete and reinforcement from critical regions. This review synthesized studies from 2007 to 2025 to evaluate punching shear behavior in slabs with openings and the effectiveness of strengthening techniques. Findings showed that openings adjacent to columns reduced punching shear capacity by 29% to 65%, with circular openings causing less damage than rectangular ones. Openings beyond four times the slab thickness had minimal effect. Strengthening methods proved highly effective. Externally bonded CFRP sheets restored or enhanced capacity by 22% to 77%. ECC applied around columns increased punching capacity by 34% to 38% and transformed failure from brittle to ductile. Super elastic Shape Memory Alloy (SMA) stirrups improved energy absorption by over 100%. Sustainable alternatives like geopolymer concrete with CFRP strengthening achieved up to 77% capacity improvement, while basalt-FRP bars reached 89% of steel-reinforced slab strength. Traditional design codes like ACI 318 were overly conservative, whereas Artificial Neural Network models provided more accurate predictions with only 9% average error. This review confirms that while openings severely weaken flat slabs, modern strengthening techniques effectively restore performance.
Improving The Engineering Properties of Gypseous Soils by Adding Polymers: A Review
gypseous soils are distributed in many regions in the world. These soils have a considerable potential for collapse because of the presence of Gypsum, Gypseous soils was one of the most complicated salty soils that challenged the geotechnical engineers. Also, Buildings constructed on gypsum soil will experience unanticipated distortions that will eventually lead to disastrous of collapse. A number of stabilizers and methods have been used to stabilize the soil and render it to be more suitable for construction purposes. This paper gives an overview of some previous researches on the behavior of this soil when it stabilized with polymers. These studies reveal that treating the soils with polymers in liquid, powder or Fiber form improves the soil properties. These include a decrease in the collapse potential, an increase in durability, and improved permeability. Moreover, polymer stabilization is more economical and environmentally friendly than conventional techniques.
Steel Fiber-Reinforced Concrete: From Material Design to Structural Performance—A Review
Steel Fiber Reinforced Concrete (SFRC) has been developed as a high-performance material that can overcome the limitations of conventional concrete, especially its low tensile strength. This review aims to provide a critical evaluation of SFRC by correlating its material design properties with its mechanical properties. Emphasis has been given to the fiber properties, especially its geometry, aspect ratio, dosage rate, and distribution. The fiber properties are important at both the micro-level and the macro-level. The fiber-matrix bond, especially at the interfacial transition zone (ITZ), plays a crucial role. The crack-bridging mechanism has been identified as the main contributor to the improved tensile strength, bending strength, toughness, and post-crack load capacity. SFRC has also shown promising results in terms of compressive strength, impact resistance, fatigue resistance, and durability. SFRC has shown promising results at the structural level, especially in beams, slabs, pavements, and seismic-resistant structures. SFRC can provide ductility and increase the life of structures, thereby reducing the amount of conventional reinforcement used. However, there are some limitations, especially with regard to the standardization of designs and the durability of SFRC.
Functionally Graded Materials beyond Conventional Design: Multiscale Modeling, Advanced Manufacturing, and Emerging Engineering Applications
Functionally graded materials (FGMs) are a new generation of superior materials that default compositions, microstructure, and functions with smooth changes in volume within a material. The paper is a thorough review of the history of FGMs since the 1980s when it was first developed in Japan, until now where it has seen a wide variety of uses in aerospace, biomedical and energy industries. The theoretical considerations of the design of FEM are systematically discussed in the paper the composition variability, optimization of the gradient, and mechanical and thermal behavior analysis. A special emphasis is laid on the functional graded additive manufacturing (FGAM) technology, which focuses on the optimization of procedures of the techniques, i.e. selective laser melting, fused deposition modeling, and the new multi-material additive manufacturing technology. Significant applications to engineering examined by the review are lightweight aerospace components, which have better thermal management, orthopedic implants, which have better integration and reduced stress shielding, and energy systems, which include new thermal barrier coating. In spite of the significant advances, the extensive industrial implementation of FGMs are still burdened by the challenges such as the excessive costs of production, the technical restrictions connected with defect control and standardization of quality products, and the inexistence of the regulatory frameworks. The paper ends with the definition of the future research directions, which include the use of artificial intelligence and machine learning in predictive design, the introduction of nanocomposite-enhanced FGMs, and convergence with smart technologies to monitor and adapt to real-time. These developments make FGMs become a key facilitator of sustainable, high-performance engineering solutions to the next-generation applications.
Research Trends and Performance Evolution of Sustainable Self-Compacting Concrete: A Systematic and Bibliometric Review
Sustainable self-compacting concrete is an important material in the modern engineering construction sector due to its distinctive mechanical properties, ease of shaping, and significant contribution to reducing environmental impact. This paper provides a comprehensive systematic and bibliometric review of publications on this topic between 2010 and 2026. The systematic PRISMA-based approach was taken to define, filter, and examine the pertinent journal articles that are registered in Scopus. Bibliometric mapping techniques were used to gauge global research trends, publication development, dominant countries, and theme development. The results obtained indicate that the number of publications has increased considerably in the past 10 years, with the use of supplementary cementitious materials, recycled aggregates, and low-carbon binders as the main contributors. The most popular research topics are mechanical and durability performance, whereas the new research topics include rheology modification, fiber reinforcement, and life-cycle assessment. The review shows that there are crucial knowledge gaps concerning long-term sustainability, large-scale system application, and comprehensive sustainability assessment. The research has provided a knowledge map structure for research and practice, and has identified future research areas to improve high-performance sustainable self-compacting technologies.
Engineering Performance of Sulfate-Bearing Soils in Iraq and Their Impact on Concrete Foundations: Mechanisms, Evaluation Methods, and Mitigation Approaches – A Critical Review
In Iraq, which has a very wide distribution of sulfate-bearing soils, especially in the central and southern regions, these present a continuous issue for the concrete base’s durability and service life. Also, we see that in areas with elevated sulfate levels in the soil and groundwater, together with high temperatures, salinity, and the wetting and drying cycles, we get enhanced chemical and physical degradation. Into the concrete go the sulfate ions, which in turn react with the hydration products, mainly tricalcium aluminate and calcium hydroxide, to produce expansive compounds such as ettringite and gypsum. These reactions produce internal stresses, cracking, and long-term structural integrity issues. This review brings together available geological, geotechnical, and materials research to look at the spatial distribution and classification of sulfate soils in Iraq, the chemical and mechanical processes which underpin soil – concrete interaction, and what those issues mean for shallow, raft, and pile foundation systems. In this study, both traditional and advanced evaluation techniques, which included chemical sulfate analysis, expansion testing, durability indices, and field-based non-destructive methods. Also, we went in-depth into mitigation approaches, which covered sulfate-resistant cements, supplementary cementitious materials, optimized mix design, soil stabilization, and protective barrier systems. This review also reports that present-day Iraqi engineering practices fall short in some areas, which in large part is a result of the low use of durability-based design frameworks and also the lack of in depth long term monitoring.
Impact of Void Geometry, Size, and Location on the Structural Behavior of RC Hollow Beams: A Review
Reinforced concrete (RC) hollow beams have been increasingly adopted in structural applications due to their potential for reducing self-weight, material consumption, and environmental impact without significantly compromising structural performance. This review investigates the influence of void geometry, size, and location on the structural behaviour of RC hollow beams. Previous experimental and numerical studies were comprehensively examined to evaluate their effects on load-carrying capacity, cracking behaviour, deflection, ductility, and failure modes. The findings indicated that the presence of voids generally resulted in a reduction in first cracking and ultimate load; however, this reduction was often disproportionately small compared to the achieved concrete volume savings. Circular voids were found to be more effective than square or rectangular voids in preserving strength and enhancing ductility. The location of the void played a crucial role, as placing hollows near the neutral axis or within the tension zone minimized adverse effects on structural performance. Increasing void size led to higher deflections and reduced stiffness, particularly when the void area exceeded approximately 10% of the gross cross-sectional area. The incorporation of additional materials such as steel fibers, steel or aluminium inserts, and fiber-reinforced polymer strengthening systems was shown to significantly improve strength, ductility, and failure behaviour. Numerical simulations using finite element software demonstrated strong agreement with experimental results, confirming their reliability for predicting hollow beam behaviour. Overall, RC hollow beams were demonstrated to be an efficient and sustainable structural solution when void characteristics were properly optimized.
Impact of Printing Process Parameters on the Tensile and Flexural Strength of Filament Material
This research evaluates the mechanical properties of three polymer-based filament materials used in Fused Deposition Modeling (FDM): Polylactic Acid Plus (PLA+), Polyethylene Terephthalate Glycol (PETG), and Carbon Fiber-reinforced Polylactic Acid (PLA-CF). The study focuses on maximizing structural integrity and minimizing porosity by employing a 95% solid 3D Honeycomb infill density. Through standardized ASTM D638 tensile testing and ASTM D790 three-point bending tests, the research identifies PLA-CF as the most rigid material, achieving a flexural modulus of 5643.06 MPa. In contrast, PETG and PLA+ demonstrate superior peak tensile forces and higher ductility, making them better suited for applications requiring toughness. The findings indicate that optimized wall loops and high-density infill patterns significantly mitigate catastrophic failure under load. Comparative analysis with recent literature (2024–2026) corroborates the dominance of infill density in determining mechanical performance, while also highlighting critical research gaps in PLA+ specific parameter optimization and the interaction between wall loops and structural strength. This comprehensive assessment provides essential guidance for material selection in load-bearing engineering applications, such as prosthetic components.
Physical Layer Security Enhancement for Cooperative NOAM Systems
NOMA (Non-Orthogonal Multiple Access) has been recognized as new multiple access technology for wireless communication networks of the fifth generation and beyond, owing to its ability to improve spectral efficiency, support massive connectivity, and provide user fairness. However, it has been observed that the superposition-based transmission principle of NOMA is highly susceptible to eavesdropping attacks, especially in an open wireless network where all wireless communications are inherently broadcast in nature. Hence, ensuring secure wireless communications for NOMA systems has now become an important area of research. This research aims to improve the secrecy level of wireless communications for NOMA systems by employing Physical Layer Security (PLS) techniques. Specifically, two prominent PLS techniques, Artificial Noise (AN) and Cooperative Jamming (CJ), have been employed to provide security against passive eavesdropping attacks. The cooperative NOMA system architecture is considered, and the secrecy enhancement is analyzed for two possible scenarios when the eavesdropper is close to the relay node and when the eavesdropper is close to the base station. The secrecy performance of the proposed system is analyzed through various parameters, namely, Secrecy Outage Probability (SOP), Secrecy Capacity (SC), Secrecy Rate (SR), and Secrecy Energy Efficiency (SEE) in terms of the Signal to Noise Ratio (SNR). The numerical results for the secrecy metrics are obtained through MATLAB-based simulations, whereas the system-level model is developed using the Simulink platform to verify the suggested system's functionality in the presence of AN and CJ for the legitimate NOMA users and the eavesdropper. The results show that the secrecy performance is significantly enhanced with the introduction of AN and CJ, and the system shows robust performance in terms of energy efficiency. This paper provides significant insights into the design of the NOMA system from the secrecy perspective and validates the potential of PLS techniques for the design of the future wireless system.
A Review of Machine Learning Classifiers for Feature-Based Image Forgery Detection
The increasing use of digital image manipulation and the generation of synthetic images has led to serious concerns regarding the authenticity and trustworthiness of images. The recent development of advanced generative models like StyleGAN, StyleGAN2, and diffusion models has the potential to generate very realistic synthetic images that are very difficult to distinguish from real images, thus leading to serious challenges in digital image forensics. This paper presents a comprehensive review of image forgery detection techniques with a primary focus on machine learning-based techniques. Various image forgery techniques, including traditional image forgery and AI-created synthetic images, are presented to understand their nature and challenges in image forgery detection. The paper presents a review of machine learning-based methods, popular datasets, and evaluation metrics for image forgery detection. Furthermore, the paper presents the challenges of existing methods, particularly their poor generalization capability for diffusion-based forgeries and the lack of representative benchmarks, and concludes with open challenges and future research avenues for developing generalized machine learning methods for authentic detection of image forgeries.
Improving a Flower Pollination Algorithm to Demonstrate Data Upgrading to Deep Data
Most computational tasks if not all depends highly on the data formula, as well as the techniques used to achieve these tasks like clustering, classification and regression. Therefor most recent research focus on using data preprocessing as an initial stage. In this research a framework is designed and built that depends on two approaches, the first one is transforming the data into a new formula called deep data through seven structural steps instead of data preprocessing in order to get a data formula that is more robust, reliable and general form. The second approach resides in using the improved flower pollination algorithm (IFPA) relying on local and global search that guaranties the dual behavior to achieve exploitation and exploration behaviors which make the algorithm more flexible in dealing with different tasks like clustering ,classification and regression. The proposed framework is based on obtaining a data formula that is deep data as new solution for processing unstructured and unbalance data in different types textual, numerical and descriptive data types which gave the algorithm an explicit flexibility to manage various tasks. The results shows obvious superiority of the proposed framework in comparison to traditional methods without using Deep data, improved FPA or both, also the proposed framework proved robustness in dealing with clustering, classification and regression as the experimental results showed
A Hybrid Activated Sludge-Moving Bed Biofilm Reactor System for the Treatment of Petroleum Refinery Effluents
A novel hybrid biological treatment system was developed to enhance the efficacy of effluent from petroleum refineries, as demonstrated in this experimental study. In the proposed pilot system, activated sludge is mixed with a sand filtering unit and a moving bed biofilm reactor (MBBR). There was a mix of attached and suspended microbes in the hybrid reactor, which consisted of an aeration tank containing activated sludge and polyurethane biofilm carriers. Shahid Tondgooyan Oil Refining Company's dissolved air flotation (DAF) discharged water was used to collect the influent wastewater. Researchers looked into how treatment efficacy was correlated with carrier mixing intensity and hydraulic retention time (HRT). Quantities of total dissolved solids (TDS), dissolved oxygen (DO), pH, total suspended solids (TSS), turbidity (TU), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), and ammonia (NH3) were among the water quality metrics examined. The results demonstrated that expanding the HRT length to 6 to 9 hours and increasing the carrier mixing intensity to a moderate range improved the system's carrier mixing intensity. The COD, NH 3, TSS, BOD 5, TU, and TDS removal efficiencies were approximately 98.6, 97.1, 82.4, 99.2, 98.7, 15.1 and 99.4 percent, respectively, when working circumstances were good. In contrast, increasing the mixing intensity to even higher levels reduced treatment efficiency slightly, probably due to biofilm dissociation and decreased microbial stability. Following a 9-hour HRT and a medium carrier mixing regime, the optimal operating conditions were identified. The major reason for this was the inclusion of the sand filtering unit, which greatly improved the effectiveness of turbidity removal. It appears that the hybrid system of sludge-MBBR-sand filter might be utilized to treat effluent from petroleum refineries, as it outperformed conventional biological treatment methods in eliminating a variety of contaminants.
Neural Networks for Real-Time Fault Diagnosis and Control Reconfiguration in Mechatronic Systems
Mechatronic systems, which combine mechanical, electrical, and artificial intelligence technologies, are exposed to a variety of unexpected faults that reduce system efficiency. These problems can lead to system failure and deterioration if not identified in a timely manner. This study seeks to provide modified and contemporary models and processes for effectively diagnosing and detecting unexpected faults in real time, as well as for identifying and diagnosing problems in mechatronic systems under dynamic and changing operating conditions. The study proposes a model for an intelligent technique based on artificial neural networks. A hybrid technique combining Long Short-Term Memory (LSTM) networks and Feed-forward Neural Networks (FNN) was proposed. This method captures the temporal dynamics of faults while accurately classifying the type of fault. A real dataset was used to train the proposed hybrid technique, which simulates the representation of electrical faults (such as voltage fluctuations) as well as software faults (such as errors in local sensor units). MATLAB software was used to simulate the hybrid technique model, and the software simulation achieved a response time of less than 15 milliseconds and a problem diagnosis accuracy of up to 98%. By modeling fault data, this technique finds wide applications, especially in industrial robot motors, self-driving cars, and drones. The hybrid technique is unique because it combines neural network models with intelligent control methods, significantly enhancing the applications of mechatronics systems and problem diagnostics.
Comparative review of Soil Reinforcement Types in Mechanically Stabilized Earth walls
Mechanically Stabilized Earth (MSE) wall is one of the most common retaining wall systems used for roads and bridges because it is structurally efficient, cost-effective, and easy to implement. Soil resistance is enhanced by integrating it with reinforcement elements to increase load-bearing capacity and prevent collapse, resulting in a cohesive composite system. MSE walls are reinforced with different types of soil reinforcement, the most prevalent being metallic, i.e., steel strips; or geosynthetic, i.e., geogrid and geotextiles. These different types of reinforcement vary in tensile strength, interaction with soil, durability, corrosion resistance, and cost. These advantages have encouraged choosing the right reinforcement types to achieve both internal and external wall stability. The purpose of this work is to analyze the comparison of various soil reinforcement materials for MSE walls in respect to structural behavior, geotechnical efficiency, economic factors, and operational lifespan. Such studies work to determine the most appropriate reinforcement system that is suitable for infrastructure projects according to site conditions and design requirements
Reliability and safety evaluation of selected road facilities: A review Paper
Increasing traffic demand, rapid urbanization, and infrastructure limitations have intensified the need for integrated approaches to evaluate road performance. Traditional assessment methods often treat operational efficiency and safety independently, overlooking the influence of uncertainty in demand and capacity. This paper presents a comprehensive review of reliability concepts in transportation systems, with particular emphasis on capacity reliability and its relationship to road safety performance. The review synthesizes developments in connectivity reliability, travel-time reliability, and capacity reliability, highlighting methodological differences and practical applications. Capacity estimation approaches, demand forecasting models, the Level of Service of Safety (LOSS), and Crash Modification Factors (CMFs) are critically examined to establish a unified perspective for performance evaluation. The study identifies gaps in linking probabilistic capacity analysis with safety performance assessment and emphasizes the importance of integrating demand–capacity variability into safety evaluation frameworks. The paper provides a structured foundation for developing reliability-based decision-support tools aimed at enhancing both operational stability and road safety, particularly in urban networks facing fluctuating traffic conditions
A Review Study on the Use of Phase Change Materials in Heat Exchangers: An Overview of Hybrid Techniques for Thermal Performance Enhancement
In this review paper, phase change materials (PCM) usage in the heat exchangers are explored with attention paid to hybrid enhancement methods to enhance the thermal performance and energy stores. PCMs have been well known to have a high latent heat capacitance as well as a capability to hold and release thermal energy at approximately steady temperatures, which qualifies them as application in solar thermal systems, energy management in buildings, waste heat recovery, and thermal management in industries. Nonetheless, their widespread application in heat exchangers is hampered by a thermal conductivity, which is similarly low preventing thermal conductivity, lowering the speed of melting and solidification and reducing the total efficiency of heat conduction. In order to overcome this shortcoming, the recent past has seen studies on various improvement measures such as the use of extended surfaces and novel fin geometries, nanoparticles-enhanced PCMs, metal foams and device optimum shell-and-tube designs. Hybrid methods involving fins and nano-enhanced PCMs have proved to have high synergistic gains of melting, uniformity of temperatures, and efficacy of the system. Computational fluid dynamics (CFD) models have also been used to predict thermal behavior, optimization of geometry and validate experimental results. In spite of the significant advancement, issues including long-term stability, supercooling, material compatibility, manufacturing complexity and cost are still obstructions to large-scale adoption. Overall, there is high potential of the use of hybrid PCM-based heat exchanger as a next generation thermal energy storage system, especially in the renewable energy system, as long as material durability, standardization and economic viability are refined.
Numerical Assessment of Passive Cooling Strategies for Wind Turbine Power Electronics and Nacelle Components under Extreme Environmental Conditions
The increasing dimensions and complexity of the modern wind turbine type have added to the problem of thermal control more so of the power electronics and other constituents of the nacelle which must carry out their duties in a very hostile environment. The generators, converters, insulated gate bipolar transistors (IGBTs) and gearboxes may be less efficient when generating high power, cause materials to wear out faster and shorten the life of a single component. A numerical analysis of the passive cooling of both the power electronics and nacelle systems of a wind turbine in a severe climatic condition of extreme hot and cold temperature are provided in this paper. Various passive techniques that reduce thermal loads have undergone testing such as phase change materials (PCMs) and high thermal mass material, reflective coating, refined ventilation design, fin and thermosiphon system. The computational fluid dynamics (CFD), Finite Element analysis (FEA) and multi physics simulations are advanced mathematical models that are used to analyze the airflow patterns, heat and structural-thermal distributions in the nacelle. Whenever experimental and field data is available, it can be used to validate the model and increase its predictive capacity. Temperature differential, heat dissipation rate, thermal stability, durability and energy efficiency are performance measures that have been used to compare passive and active cooling methodologies. It has been found out that enhanced passive cooling setups can cut down the internal temperature extremes, enhance thermal comfort, and cut down operational energy expenses, especially in off-shore or distant facilities. The results signify the value of holistic building planning and multi-purpose optimization towards the improvement of passive cooling. The study is useful in developing resilient, low-cost, and sustainable thermal management of the next generation wind turbines to be deployed under severe environmental conditions.
A Comprehensive Review of Real-Time Deepfake Detection Using Light Distribution and Illumination Consistency Analysis
The recent fast breakthroughs in deep learning and generative models have made it possible to synthesize highly faked deepfakes realistically. The classical approaches that depend on the static visual artifacts, spatial irregularities, or frequency domain anomalies are hard to be done in a real-time system. This paper delivers an in-depth overview of recent real-time deepfake detection methodologies that take advantage of the physical behavior of light, such as illumination distribution, analysis of corneal reflection or effects due to vibration. By taking advantage of the inherent interaction between light and real facial material, these methods add actively physical probing signals that not neural nets growing existing generative models can emulate nimbly. Experimental results in the literature suggest that light-based techniques and physical-response models yield high temporal consistency, robustness and accuracy, usually at the cost of traditional data-driven methods for live video with efficiency. However, there are still some problems un-solved, e.g. illumination variations, hardware limitation of the devices and the artifacts introduced by JPEG compression etc., and few well-annotated databases for illumination-based recognition have been proposed in this area. To overcome these limitations, hybrid (i.e. physical-light cue oriented) architectures that mix the best of both worlds and merge state-of-the-art deep learning model (e.g., YOLO-based ones) are employed for building robust, explainable and real-time BASED systems in upcoming research.