Al-Noor Journal of Engineering Management and Computer Science
Al-Noor Journal of Engineering Management and Computer Science (NJEMCS) is an international scholarly refereed research journal which aims to promote the theory and practice of technology, innovation, and engineering management. The journal links engineering, science, and management disciplines. It addresses the issues involved in the planning, development, and implementation of technological capabilities to shape and accomplish the strategic and operational objectives of an organization. It covers not only R&D management, but also the entire spectrum of managerial concerns in technology-based organizations. This includes issues relating to new product development, human resource management, innovation process management, project management, technological fusion, marketing, technological forecasting and strategic planning.
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Development of an RF-Driven Plasma Jet System: From Physical Diagnostics to Biomedical Efficacy
In this paper the experimental study of an argon plasma jet at atmospheric pressure is presented. Herein are reported spectroscopic diagnostics of the plasma for a range of operating conditions with an emphasis on its response to variation in input power as a means of fully determining physical parameters. The basic plasma parameters were measured using Optical Emission Spectroscopy (OES). The variation of the electron temperature (Te) was determined by composing a Boltzmann plot using spectral lines of representing argon atoms. In addition, electron density (ne) was obtained based on the study of the Stark broadening phenomenon for the spectral lines. The experiment showed an unmistakable linear dependence of the electron temperature and electron density both on the multiplier voltage, which was used to increase the input power, thus confirming an intensification of ionizing activity in plasma. The bio-efficacy of the plasma was then assessed against chosen bacterial species. The findings demonstrated that the produced plasma was highly effective in killing bacterial colonies, where increasing power and electron density was correlated with increased bacterial cell death. The paper concludes that obtaining control on power parameters is an important method of customizing plasma characteristics in order to enhance its applicability and perform better in the context of biomedical applications for sterilization/disinfection processes.
Copyright (c) 2026 Al-Noor Journal of Engineering Management and Computer Science https://creativecommons.org/licenses/by-nc-sa/4.0/
Investigation the effect of planar anode area on Characterization of low-pressure argon plasma
This research investigated the effect of both the applied voltage and the anode area on the electrical and plasma properties of a DC glow discharge in low-pressure argon gas. The electrical properties included current-voltage (I-V) curves and Paschen's curves, while the plasma parameters included electron temperature and number density. The plasma was generated between two copper electrodes separated by a constant distance of (4 cm), using anodes of different diameters of (2, 4, 5, and 6 cm). The plasma was characterized by optical emission spectroscopy (OES), which was used to estimate the electron temperature (Te) and number density (ne). The experimental results showed that the discharge operates within an anomalous glow regime. Furthermore, decreasing the anode area led to a higher breakdown voltage required to initiate the discharge, while increasing the anode area resulted in higher spectral emission line intensity, electron temperature, and number density. When the anode diameter changed from (2-6) cm, the electron temperature ranged from (0.389-0.393) eV, while the electron density ranged from (3.26×10¹⁵ - 9.23×10¹⁵) cm⁻³. These results confirm the influential role of anode geometry in the distribution of the electric field and the processes of ionization and excitation, and thus in determining the electrical and plasma characteristics of the discharge.
DC sputtering Panchen’s law Plasma discharges Optical emission Spectroscopy DC glow
Characterization and Preparation of Pure and silver- doped TiO2 particles by RF Magnetron sputtering for Gas sensing and Antibacterial activity
In this study, pure titanium oxide (TiO2) nanoparticles and silver-doped (TiO2:Ag) nanoparticles were produced by radio-frequency magnetic spraying. X-ray diffraction (XRD) studies verified the formation of an anatase phase with metallic silver atoms without changing the base structure, and field-emission scanning electron microscopy (FE-SEM) demonstrated that the nanoparticles aggregated into porous architectures. The optical absorption is enhanced, and the energy gap is lowered from (3.0 to 2.5) eV by the doping of silver. The TiO₂:Ag sample had the maximum sensitivity (14.6%) in sensing applications at low temperature (200°C), compared to the pure TiO₂ sample, which needed 300°C for its highest sensitivity (21.6%). It signifies higher operating efficiency at reduced energy usage. The antibacterial activity of TiO2:Ag sample against Klebsiella pneumoniae was much greater than that of pure TiO2 due to the synergistic action of Ag and TiO2 in enhancing the generation of reactive oxygen species. The flexible TiO2:Ag nanomaterial may therefore represent an attractive candidate for the development of low-temperature, efficient gas-sensing technologies as well as antimicrobial medical devices.
Material-Efficient Ring Foundations on Sandy Soil: Experimental Evaluation of Bearing Capacity Factors
Ring footings have become an increasingly recognized option for a more resource-efficient means of building construction than traditional circular footings. Generally, the ground is the principal load-bearing element in building construction. However, there exists only a limited amount of testing to determine how variations in foundation configuration and embedment depth affect the bearing capacity factors Nγ and Nq associated with ring footings in cohesionless soils. Therefore, this research evaluates the bearing capacity factors (Nγ, Nq) for ring footings of varying radii ratios and embedment depths. To investigate this behavior, experimental study are conducted using a centrifuge device system of ring foundations models with varying ring radii ratios n = 0, 0.2, 0.4, and 0.6. The substantial steel foundations, each with an area of 2200 mm² and a thickness of 50 mm, are situated on and under sandy soil with different relative densities. This study used loose, medium, and dense sand with soil unit weight are 15.2 kN/m³, 16 kN/m³ and 17 kN/m³ respectively. Based on the results obtained, the soil density and ring configuration were found to have a significant influence on the ultimate bearing capacity of ring footings, with the optimal radii ratio producing the best performance being approximately n = 0.4. Ring foundations embedded in the soil exhibit a greater load capacity than foundations placed on the surface. It has been shown that optimally designed ring foundations are capable of producing greater structural performance at a lower material consumptive level. This supports an environmentally sustainable and efficient approach to designing foundations.
Zero-Trust Architecture for Securing IoT Edge Networks Against Advanced Persistent Threats
The rapid expansion of Internet of Things (IoT) edge networks has introduced significant cybersecurity challenges due to the increasing number of resource-constrained devices operating outside traditional security perimeters. Conventional perimeter-based defenses are inadequate against Advanced Persistent Threats (APTs), which exploit compromised edge devices through stealthy, multi-stage attacks involving reconnaissance, lateral movement, command-and-control communication, and data exfiltration. This study presents Edge-ZTA, a lightweight Zero-Trust Architecture specifically designed for securing Industrial IoT edge environments. The proposed framework integrates three complementary components: dynamic device identity verification based on trusted attestation and behavioral fingerprinting, continuous behavioral monitoring using a Federated Deep Autoencoder for privacy-preserving anomaly detection, and Software-Defined Networking (SDN)-based dynamic micro-segmentation for real-time isolation of compromised devices. A comprehensive hybrid experimental testbed comprising physical edge devices, virtualized nodes, and 500,000 network flow records derived from benchmark cybersecurity datasets was developed to evaluate the proposed architecture under realistic APT scenarios. Experimental results demonstrated a weighted macro-average F1-score of 97.1%, with detection rates of 98.9%, 97.9%, 96.8%, and 95.9% for reconnaissance, lateral movement, command-and-control, and exfiltration attacks, respectively. Furthermore, the decentralized edge-based policy decision mechanism maintained end-to-end latency below 50 ms, while CPU utilization remained below 17%, confirming the framework's suitability for resource-constrained IoT deployments. Scalability experiments involving up to 500 edge nodes further verified stable detection accuracy and predictable latency under heterogeneous operating conditions. These findings demonstrate that Edge-ZTA provides an efficient, privacy-preserving, and scalable cybersecurity framework capable of mitigating sophisticated multi-stage cyberattacks while satisfying the stringent performance requirements of next-generation Industrial IoT infrastructures.
The operational performance of photovoltaic systems on the roofs of fuel supply stations and their impact on the environment is assessed in the Governorate of Babylon, as an example
The aim of this applied study is to investigate the operational efficiency of PV power generation systems on the roof of fuel stations and the impact that they have on the environment in a case study in Babylon Governorate. Solar panels installed on the roofs and canopies of urban fuel stations in Iraq to provide power to these facilities without the need for diesel generators (Off-grid PV) are considered a sustainable solution to replace the conventional diesel generator for power supply. The fuel stations in Babylon Governorate were chosen as a case study because the region is densely populated and at the center of population and the climatic conditions are considered to be vulnerable in the Middle Euphrates region. The efficiency of the solar cells is directly affected by the dust deposition rates. Estimated outputs indicate the system's ability to fully meet operational needs throughout the day at a self-sufficient rate. This means clear economic gains, due to reduced operating and periodic maintenance costs for generators, and substantial conservation of diesel fuel. Moreover, the study presents an important environmental and social aspect, whereby there was a significant reduction in pollutants and toxic gases (including carbon monoxide) and the total removal of noise pollution in the urban environment. This has a direct and positive effect on public health and enhances the quality of life of the community and its surrounding communities.
Real-Time Detection and Mitigation of Prompt Injection Attacks in LLM-Integrated Enterprise Systems
Large language models (LLMs) embedded in enterprise workflows cannot structurally distinguish legitimate instructions from adversarial ones in the same token stream, making prompt injection OWASP's top LLM risk for two consecutive editions a persistent threat across direct and indirect vectors. This paper presents PromptShield-RT, a layered, real-time, model-agnostic framework combining input normalization and provenance tagging, lexical-heuristic pattern matching, a statistical classifier, structural anomaly features, and calibrated risk fusion, with policy-driven mitigation (allow/sanitize/quarantine/block) and an explainable, adaptive-feedback mechanism for SOC workflows. We construct an original evaluation corpus, SynPI-Bench (n = 450, six categories), and a template-disjoint held-out generalization set (n = 31) with novel phrasings, obfuscation encodings, and adversarial hard-negative benign text. Using template-grouped 5-fold cross-validation, the fused pipeline achieves 92.4% accuracy (F1 = 0.930, AUC = 0.990), outperforming heuristic-only (57.0%) and naive-averaged (59.2%) baselines, while a lexical classifier reaches 85.9% with lower precision. We report a pronounced generalization gap on the held-out set (48.4% accuracy, 90% false-positive rate on hard negatives), quantifying a known limitation of surface-lexical defenses. The pipeline achieves sub-millisecond P95 latency (0.266 ms), within typical 50 ms enterprise SLAs. We situate PromptShield-RT relative to structural, architectural, and guardrail-product defenses, arguing for layered, defense-in-depth architectures, with reproducible code provided.
Beyond Segmentation: Bridging the Synthetic-to-Real Gap in Computer Vision A Systematic Review, Taxonomy, and Research Agenda
Background. Synthetic imagery produced by simulators, graphics engines and generative models is now widely used in place of real annotation that is costly, privacy-restricted or unobtainable. Models trained on synthetic data nevertheless underperform when deployed on real data, a discrepancy known as the synthetic-to-real gap. The relevant literature is dispersed across domain adaptation, generative data synthesis, domain randomisation, feature alignment, test-time adaptation and evaluation methodology, and no prior synthesis has treated these strands as a single problem space. Objectives. This review (i) identifies and classifies the sources of the synthetic-to-real gap; (ii) consolidates bridging methods within a common taxonomy in which domain adaptation is one of six families rather than the organising principle of the field; (iii) compares the tasks, datasets, simulators and evaluation protocols used to measure the gap; (iv) appraises the methodological quality and reproducibility of the primary evidence; and (v) proposes an evidence-based research agenda. Methods. The review was conducted in accordance with the PRISMA 2020 statement [1]. Six bibliographic databases and two preprint repositories were searched to 31 December 2025. Records were screened in duplicate; 68 studies met the eligibility criteria and 34 contributed to the quantitative synthesis. Because no established risk-of-bias instrument is applicable to computer-vision transfer studies, RoB 2 [2] was adapted to this setting. Reported gains are presented descriptively in a forest-style display and are not pooled meta-analytically, because the primary studies report no variance estimates. Results. On the canonical GTA5 → Cityscapes benchmark, the median improvement over a source-only baseline was +12.9 mIoU (IQR 9.4 to 18.9; k = 10) for convolutional backbones and +28.2 mIoU (IQR 25.4 to 29.2; k = 3) for transformer backbones. Changing the backbone alone, with the adaptation procedure held fixed, raises source-only performance from 34.3 to 45.6 mIoU [3], indicating that a substantial share of the reported "adaptation" gain is architectural. The strongest current method attains 75.9 mIoU against an oracle of 76.4 [4], so the canonical benchmark is effectively saturated while the deployment failures it was intended to predict remain unaddressed. Appearance and feature-level gaps are comparatively well studied, whereas contextual, annotation and sensor gaps are not. Only 13% of studies reported run-to-run variance, and only 25% released the synthetic assets or generation configurations required to regenerate their data. Conclusions. The synthetic-to-real gap has not been solved; it has been optimised away on a single benchmark. The outstanding challenges are no longer the design of new alignment losses, but the generation of data selected for utility rather than realism, the isolation of architectural from adaptational effects, evaluation in open-world and multi-domain settings, and the release of open simulation pipelines.
Thermal Performance of Steel-Faced Foamed Concrete Sandwich Panels for Building Envelopes under Elevated Temperatures
Enhancing the fire resistance of construction elements is considered one of the primary goals of recent studies. Consequently, since sandwich panels are among the most vulnerable construction elements, greater attention should be paid to improving their fire behavior. This study experimentally investigated the thermal performance and interface behavior of steel-faced foamed concrete sandwich panels subjected to direct flame exposure. The effects of rib spacing (R10 and R15), connector configuration (6 and 9 connectors), and core strength were experimentally evaluated using eight steel-faced foamed concrete sandwich panel specimens. Two target compressive strengths (2.5 MPa (M1) and 4.0 MPa (M2)) foamed concrete mixes were adopted as core materials. The specimens were subjected to direct flame for 25 minutes, and the temperatures of both exposed and unexposed surfaces were recorded throughout the experiment using infrared thermography (IRT). There was a definite thermal gradient between all specimens tested, with the exposed surface always measuring higher temperatures than the unexposed surface. The temperature difference (ΔT) between the two surfaces varied from around 137°C to a maximum of about 586°C during the heating duration, confirming that these sandwich panels can limit heat transfer through the panel thickness and amount for good thermal insulation. Dominant and secondary parameters were identified based on their influence to the thermal behavior, with rib spacing being dominating parameter and connector configuration showing secondary impact attributed to thermal bridging. Thermal exposure also resulted in moisture evaporation, crack and debonding between the steel face sheets and foamed concrete core. It further highlighted the importance of interface integrity to maintain thermal performance under elevated-temperature conditions due to the associated changes in temperature distribution and thermal gradients across the panel thickness observed at these damage mechanisms.
Foamed Concrete, , Shear Connectors, Thermal Performance, Fire Resistance, Bond Resistance
Petrophysical Evaluation of the Jeribe–Euphrates Formation Using Well Log Data, Fauqi Oil Field, Southern Iraq
The work presented in this paper is a case study of the southeastern Iraqi Fauqi Oil Field to evaluate both the petrophysical properties and lithology of the Jeribe–Euphrates Formations. Reservoir characterization was performed using well log data, including Gamma Ray (GR), Density (RHOB), Neutron (NPHI), Sonic (DT), Spontaneous Potential (SP), and resistivity logs. Data processing, correction, and interpretation were conducted using Interactive Petrophysics (IP) software. The Gamma Ray method was used to estimate shale volume (Vsh), while porosity was calculated using various methods, with the neutron–density combination providing reliable results. To determine water saturation (Sw), the Archie equation was applied using commonly adopted parameters for carbonate reservoirs. Lithology was determined from density–neutron cross plots and M–N cross plots, which indicated that the Jeribe–Euphrates Formations are mainly composed of dolomite with minor limestone. The results show variations in reservoir quality, with certain intervals exhibiting favorable petrophysical properties and hydrocarbon potential.
Learning Urban Morphological Transformations Using Pix2Pix GANs: An Applied Study on Barcelona
This study develops an applied urban-generation workflow using the Pix2Pix conditional Generative Adversarial Network (cGAN) for learning and reconstructing urban systems with paired before/after visual data. The first case study was chosen to be Barcelona due to the morphological clarity of the Eixample district. This is because of the presence of a legible grid, the repetition of urban blocks, the consistency in street orientation and the definition of relationships between blocks, streets and open spaces. The urban data was taken from OpenStreetMap and visually checked with Google Earth Pro before being transformed into a standardised black and white learning representation. A red intervention layer was included in the dataset to represent the urban project as the main transformative variable. The data were then sorted as Before-After (AB) image pairs, linking the current urban condition to the desired urban response after the insertion of the new project. The Pix2Pix model was trained in a Google Colab/PyTorch environment with a U-Net generator and a Patch GAN discriminator. The generated outputs were evaluated through visual comparison, morphological interpretation and theoretical analysis informed by Space Syntax, Christopher Alexander’s structural approach and Nikos Salingaros’ principles of urban networks. The results indicate that the model was most stable in regular grid-based sectors, where it was able to maintain street continuity and produce relatively coherent internal subdivisions. However, its performance was reduced in morphologically complex sectors, where visual noise, simplification and weaker internal spatial relationships were observed. The results indicate that generative models can be used to assist in urban design research by providing a means for identifying tendencies in urban transformations. However, their reliability remains linked to dataset clarity, consistent visual representation, and clearly defined spatial relations in the training samples.
Architectural Engineering; Urban Design; Urban Morphology; Artificial Intelligence
Impact of Variable Rainfall on the Stability of Gypsiferous Soils in Iraq. A Comprehensive Review
In Iraq, from 28.6% to 31.7% of its land area that is gypseous which makes up 7.3% – 10% of the world’s total gypsey areas. Also in these dry and semi dry regions whichis recedence to these types of soils, they present a large scale technical issue which is in great part due to their tendency to collapse when they come into contact with water. At the same time climate change has transformed rain patterns in Iraq, out of 81.25% of weather stations at over the 1990 to 2023 period there has been a large scale reduction in precipitation which also has shifted away from the former heavy winter time rainfall to a more even distribution through the year. This review looks at how changing rain patterns play out in terms of the stability of gypsum soils in Iraq, which look at through the lens of geotech engineering, climatology, and soil health literature. In analysis, there are two main ways in which gypseous soils collapse – in the short term have immediate compressibility from flooding, but also a slow progressive issue of plastic deformation which is 2 to 26 times that of just soaking and which even at 5% gypsum content. Climate projections show that precipitation will not be a consistent story geographically – for example in northern Iraq, the coming of a 5.92 to 13.70% less rain by 2100 under the RCP scenarios, to the south there is high interannual variability with what does appear to be a 19.26 mm increase in some of the seasons, and in the central parts of Iraq that very different and complex picture. In each weather zone there’s a different rain pattern that affects gypsey soil stability in unique ways. In the north, decreased land collapse as a positive but also a rise in soil dissolution as a negative, at the same time in the south are seeing more issues with the intensification of the extreme rainfall events. This review present large scale issues with regard to hydro-mechanical response in a variable wetting drying environment, long term leaching rates at which can expect to see changes in climate, and also stability of remediation methods in a changing environmental picture. Out of the emerging sustainable solutions which include bio-enzymatic treatment, nanomaterial incorporation, and use of industrial byproducts some see a 50% or greater reduction in collapse that requires that we validate results in climate altered rainfall conditions. The study results present a framework for incorporating climate projections into analysis of gypsic soil risk which in turn informs adaptive engineering solutions for infrastructure resilience in Iraq as precipitation patterns change.
Developing an Advanced Deep Learning SSD Algorithm Using Computer Vision Approaches to Enhance Vehicle Detection
The development of Computer vision and Deep learning frameworks plays a crucial role in Autonomous driving and real-time intelligent traffic surveillance. The Single Shot Detector (SSD) architecture is well known for its ability to perform inference quickly, and generically configured anchors are problematic because of the severe localization errors they cause. This article proposes an optimized architecture for SSD-based vehicle detection that addresses these challenges in two main steps: (1) using an optimized edge-preserving median filter to remove semantic noise in the image caused by the sensors while preserving the sharpness of the image boundaries and (2) applying custom-scaled anchor box aspect ratios specifically designed to capture the real size of the vehicles on the road. Applied to the standard KITTI Vision Benchmark dataset, the proposed framework's Mean Average Precision (mAP) and recall were 95.90% and 96.60%, respectively. The proposed model is compared with the baseline networks and is shown to significantly outperform them, achieving mAP 89.50% for vanilla SSD300 and 94.10% for YOLOv5. The proposed method reached high reliability with a balanced precision-recall rate that is highly suitable for real-time traffic monitoring systems.
Analyzing Malware Behavior Using Generative Neural Networks
Traditional detection techniques are struggling with ever-evolving malware threats like zero-day attacks, polymorphic malware, and adversarial samples. Current detection systems (signature-based, heuristic-based, conventional machine learning) fail to generalize to unseen/obfuscated malware variants. In an attempt to overcome these constraints, this paper investigates the possibilities of employing Generative Neural Networks (GNNs), in the form of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for the purpose of malware be haviour analysis and detection. We aim to create a novel framework for detecting malware samples that provides some of the best performance in terms of accuracy, precision, and recall while remaining robust to new or unseen malware. This work aims to firstly implement a generative learning-based approach and to measure its adversarial robustness in comparison with the four existing detection techniques. Experimental results show the accuracy, precision, recall of the proposed model is found to be 96.5%, 95.9%, 94.6% with the false positive rate of the model which can be negligible and it is 3.2% which outperforms the traditional machine learning and deep learning models. Our results demonstrate that GNN-based malware detection not only addresses the limitations of conventional approaches in terms of scalability but also provides a more robust and adaptable framework that could be integrated into future real-time threat intelligence and automated defense systems.
Using Ant Colony Optimization Algorithm (ACO) to determine the optimal parameters
Our proposed system Ant Colony Optimization (ACO) is a meta- heuristic introduced by Dorigo et al. [9] which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. In this paper we analyse the parameter settings of the ACO algorithm. These determine the behavior of each ant and are critical for fast convergence to near optimal solutions of a given problem instance. We classify TSP instances using three measures of complexity and uniformity. We describe experimental work that attempts to correlate ‘types’ of TSP problems with parameter settings for fast convergence. We found these optimal parameter settings to be highly problem specific and dependent on the required accuracy of the solution. This inspired us to explore techniques for automatically learning the optimal parameters for a given TSP instance. We devised and implemented a hybrid ACO algorithm, similar to the one independently developed in [16], which used a genetic algorithm in the early stages to ‘breed’ a population of ants possessing near optimal behavioural parameter settings for a given problem. This hybrid algorithm converges rapidly for a wide range of problems when given a population of ants with diverse behavioural parameter settings.
Development of an Integrated CNC Technology Assessment Framework (ICTAF) for Smart CNC Technology Evaluation in Modern Manufacturing
Computer Numerical Control (CNC) technology has become a fundamental component of modern manufacturing systems due to its capability to provide high accuracy, enhanced productivity, and flexible production performance. The continuous evolution of CNC systems has been driven by advances in motion control strategies, servo drive technologies, precision machining methods, sensing systems, and intelligent manufacturing solutions. This study develops an integrated decision-support framework for evaluating smart CNC technologies by considering multiple technical and industrial factors. A comprehensive analysis of recent CNC advancements is conducted to identify the key assessment dimensions influencing technology evaluation, including performance capability, economic feasibility, risk and reliability, sustainability considerations, and industrial readiness. Based on these requirements, the Integrated CNC Technology Assessment Framework (ICTAF) is proposed as a systematic methodology for supporting technology evaluation and adoption decisions in smart manufacturing environments. The proposed framework integrates technological characteristics with strategic assessment criteria to address the limitations of fragmented evaluation approaches commonly applied to CNC technology selection and implementation. The study demonstrates that effective evaluation of modern CNC systems requires a multidisciplinary perspective combining mechanical performance, control capabilities, digital integration, operational requirements, and sustainability considerations. The developed framework provides a structured approach for assessing smart CNC technologies and supports manufacturers in making informed decisions toward reliable, efficient, and sustainable manufacturing systems.
Learning Generalizable Visual Representations for AI-Generated Image Detection
Detectors of AI generated images often perform extremely well when learning from generators they have never encountered information from, and fail when learning from generators they have not learned from. Detectors of AI generated images typically have an accuracy rate of almost 100% when they are trained on generators they have not seen information from, but perform poorly when they are trained on generators they have not seen information from, or when they are trained on a visual subject matter they have not seen information from. This paper poses the question of which visual representations actually convey transferable evidence of synthetic image formation. Using a controlled corpus of 217,100 images spanning twelve generators from five architectural families (style-based GANs, latent diffusion, pixel-space diffusion, autoregressive transformers and closed commercial systems) and five semantic domains, in which real and generated images are matched on prompt, resolution, aspect ratio, format and compression and de-duplicated by perceptual hashing and embedding similarity, we benchmark nine representation families under a common parameter budget. We then propose three-stream detector, GVR-Det, which incorporates a semantic-structural, a local-forensic, and a spectral encoder, all of which are connected by a gate, and which is trained by class-conditional generator and domain-invariance, cross-generator contrastive alignment and transformation consistency. In addition to accuracy we quantify generator leakage, domain leakage, class-conditional maximum mean discrepancy and centred kernel alignment, under leave-one-generator-out, leave-one-family-out, leave-one-domain-out and joint protocols. The best transferring representation is not the best distributing representation: there is significant domain information in the global semantic features, while the residual and spectral features are more stable across the generator families, but more vulnerable to compression. The gated, invariance-regularised model is the best joint out-of-distribution behaviour and its generator leakage only drops from 0.914 to 0.402; it degrades the least when JPEG re-encoded, rescaled and blurred.
AI, image processing, deep learning, machine learning
Quantum Band Structure Engineering of InGaN/GaN Heterostructure Quantum Well under External Magnetic Fields
Using a two-dimensional Schrödinger–Poisson model at T = 300 K, the quantum band structure of InGaN/GaN quantum wells under the influence of magnetic fields was investigated. According to the findings, the ground-level energy levels decreased from roughly 0.35 eV to 0.08 eV when the indium content was increased from 10% to 20% or the well thickness was increased from 2 to 4 nm. The upper levels also decreased in a comparable manner. When a magnetic field is applied up to 10 Tesla, the Landau shift is up to 0.01-0.02 eV. The electron density distribution was mainly just inside the quantum well, and so the Poisson potential has some effect on energy levels. This finding is also supported by our previous studies of InGaN/GaN quantum wells. A distinct relationship between material characteristic, quantum structure and spectral shifts.
Hybrid Digital Image Watermarking Using Singular Value Decomposition of the Cover Image and Discrete Wavelet Transform of the Watermark
This paper proposes a hybrid digital image watermarking scheme that integrates Singular Value Decomposition (SVD) applied to the cover image with a Discrete Wavelet Transform (DWT) preprocessing stage applied to the watermark. The cover image is factorized via SVD into left and right orthogonal matrices and a diagonal singular value matrix, while the watermark is decomposed using the Haar wavelet into four frequency subbands. The low-frequency approximation subband of the watermark is then embedded additively and exclusively into the K most dominant diagonal elements of the singular value matrix, ensuring that the most perceptually significant singular values serve as the primary carriers of the embedded watermark, while the remaining singular values retain their original magnitudes. The watermarked image is subsequently synthesized through inverse SVD reconstruction and the concealed watermark is retrieved via a semi-blind extraction process that exploits the stored orthogonal SVD matrices and the DWT details subbands as a compact secret key. Comprehensive experimental evaluations performed in MATLAB using widely adopted grayscale benchmark images verify that the proposed algorithm attains satisfactory imperceptibility and resilience against a diverse set of signal processing distortions, encompassing JPEG compression, additive noise, spatial filtering and intensity transformations. Furthermore, quantitative comparisons with the existing state- of- the- art watermarking schemes substantiate that the proposed approach successfully reconciles the competing demands of imperceptibility and robustness, establishing its viability as an effective solution for digital image copyright protection and ownership verification.
Explainable Artificial Intelligence for Cyberattack Detection in Industrial IoT Environments
The growth of Industrial Internet of Things (IIoT) has greatly increased the attack surface against critical infrastructure such as manufacturing, energy and transportation systems. While machine learning (ML) intrusion detection systems (IDS) are accurate, they are opaque and thus difficult for operators to trust or comply with regulations. So In this paper, an Explainable AI (XAI) enhanced framework for cyberattack detection in IIoT networks is proposed. We perform ensemble learning models: Random Forest (RF) and Extreme Gradient Boosting (XGBoost) and apply SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) as post-hoc explainability techniques for two publicly available benchmark datasets: Edge-IIoTset and CICIoT2023. Experimental results shows that XGBoost model is capable of 98.9% detection accuracy and 99.2% F1 score in 14 attack categories. SHAP selects the most discriminative traffic features for each attack class, and LIME generates instance-level explanations of the decisions to be made by a non-expert operator. Finally, by closing the transparency gap in the security of the IIoT, the proposed XAI-IDS framework will allow for threat intelligence that is both actionable and human-readable, while no compromising the detection performance. The results serve as a repeatable reference point for future studies in industrial cybersecurity that can be trusted.
Department Computer Engineering – Software, Islamic Azad University
Secure Integration of IoT Devices into Enterprise NetworksUsing VLAN Segmentation and Access Control Policies
The explosion in number of IoT devices in enterprise network has presented an interesting security problem. The smart cameras, environment monitors, printers, access control units, building controllers, and other devices are commonly placed near corporate devices but are generally less protected by more vulnerable firmware, less monitored and have different patch schedules. This paper provides a secure integration framework for IoT devices based on VLAN segmentation and access control policies. This framework, referred to here as SIVAC-IoT, classifies devices into role-based VLANs, assigns least-privilege access policy to each VLAN, and controls communications between VLANs using Layer-3 ACLs, firewall policies, and logging controls. The paper presents a structured enterprise-network implementation framework supported by clear topology design, policy engineering, validation procedures, and measurable security outcomes suitable for professional deployment planning. The evaluation criteria include the reduction of lateral movement, prevention of unauthorized flows, broadcast-domain control, policy overhead, and maintainability. The experimental results show that moving from a traditional flat enterprise design to a VLAN-based architecture with clearly defined access control significantly reduces unnecessary connectivity between IoT devices and corporate resources while maintaining a modest latency overhead. Most importantly, secure integration of IoT into enterprise networks requires intentional design rather than simply connecting devices to switch ports.
Design and Development of a Multidimensional Software System for Arabic Text Rendering Quality Assessment in Computer Vision Applications with Efficiency Optimization under Green AI Principles
Optical Character Recognition (OCR) face challenges when trying to identify Arabic text due to the intricacies presented in the ligatures, cursive styles, and complexity of the text (due to a combination of the diacritics and ligatures). In this paper, we propose an Arabic text rendering evaluation that maintains the structure of Arabic text rendering and applies the Multidimensional Quality Assessment (MQA) model of “Green Arabic” Quality Assurance (GreenArabicSQA) within the Computer Vision Pipeline (CVP). The model is presented along with the numerous operational and structural attributes (dimensions) of the rendering’s design and structure. This is inclusive of sharpness, contrast, sense, and entropy, while also including computational latency as a penalty dimension. A dominant design pertaining to the rendering quality was constructed in order to identify the design’s trade-off between the rendering dimensional quality and rendering design efficiency. The results of the dynamic “Trusted/Reject” decision are presented. This showed reduced dimensions of rendering quality and deep machine learning model. This filtering mechanism also brought improvement to other dimensions within the Arabic Quality Assurance systems. The proposed solutions are in harmony with the Green AI principles of Arabic Quality Assurance (QA) systems as they are more sustainable and scalable solutions.
The Effect of Auxiliary Electrical Energy Engineering Friction Stir Welding Technology (EAFSW): A Review
Friction Stir Welding (FSW) has been recognized as a reliable and versatile solid-state joining process capable of providing high-quality joints without any melt-related distortions. Despite the above-mentioned advantages, traditional FSW has certain drawbacks related to the large values of process forces and torque, the fast wear of the tool, and the limited ability to be used with high-strength metals and hard-to-weld materials (steels, titanium alloys). Various modifications of FSW utilizing auxiliary energy sources have been proposed for overcoming these deficiencies, but Electrically Assisted Friction Stir Welding (EAFSW) has gained much popularity during the last twenty years [1], [12]. In EAFSW, an electric current is fed into the rotating tool and/or the work piece, thus creating the localized Joule heat and sometimes a thermal electro plastic effect resulting in the softening of material ahead and around the tool and reducing the flow stresses of the welding materials. Consequently, the application of an electric current allows lowering the axial force, traverse force, and torque, extending the life of the tool, increasing the speed of welding, and expanding the spectrum of materials that can be welded together [2],[5]. The current paper reviews the results obtained in the field of EAFSW and focuses on the physical principles and mechanisms behind EAFSW, electrode design and circuit configuration, effects of electrical parameters on forces and torque, microstructure and mechanical properties of aluminum, magnesium, titanium, steel and dissimilar materials and composites joints made using EAFSW, numerical simulations and comparisons with other auxiliary-energy-assisted FSW processes. Finally, principal problems associated with current distribution, electrode wear, arcing, safety and cost factors, as well as the necessity of electro-thermo-mechanical modeling will be highlighted in this review.
Electric assisted friction stir welding (EAFSW)
The extensive dependence on conventional fossil-fuel-based energy systems has been widely recognized as a major driver of anthropogenic climate change, primarily through the continuous release of greenhouse gas emissions. In this context, Organic Rankine Cycle (ORC) technology has emerged as a promising and technically viable solution for mitigating environmental impacts by enabling the efficient conversion of low- and medium-grade thermal energy, including industrial waste heat and renewable heat sources, into useful power. This review presents a comprehensive and updated assessment of Organic Rankine Cycle systems, with particular emphasis on recent developments in cycle configurations, working-fluid selection, and performance enhancement strategies. Unlike earlier reviews that predominantly focused on conventional ORC architectures operating under subcritical or supercritical conditions, the present study extends the discussion to advanced configurations, including recuperative, regenerative, reheated, two-stage, hybrid, and polygeneration ORC systems. The reviewed literature indicates that recuperative and regenerative configurations generally provide superior thermodynamic performance compared with simple and reheated single-stage cycles, mainly due to their enhanced internal heat recovery capability. Furthermore, two-stage ORC arrangements have demonstrated considerable potential for improving thermal efficiency and net power output, with reported enhancements reaching approximately 20% and 44%, respectively, relative to conventional single-stage systems. Hybrid ORC-based configurations, particularly those integrated with Brayton cycles or other complementary thermodynamic systems, exhibit even higher performance potential; however, their practical implementation is often constrained by the requirement for high operating temperatures. Overall, most experimentally and commercially investigated ORC systems operate within the small-scale power range, typically from about 1 kW to several tens of kilowatts, and commonly employ microturbines and plate heat exchangers as key system components.
Optimization of Thermal Annealing Conditions to Tailor the Physical Properties of Sol-Gel Derived TiO₂ Nano-films for Optoelectronic Applications
In this work, titanium dioxide (TiO2) thin films were prepared using Sol-Gel technique combined with Spin Coating. Thermal treatment of the films was conducted at different annealing temperatures ranging from (525°C) to (700°C) to study the effect of temperature on the structural, morphological, optical, and electrical properties. (XRD) results showed formation of the tetragonal crystalline phase in (TiO2) thin films with main crystalline peaks appearing at the (101), (200), and (211). As for (FESEM) results showed that the membranes possess a homogeneous nanostructure with a noticeable increase in grain size at certain annealing temperatures due to grain coalescence. EDX analysis confirmed presence of only titanium and oxygen elements, indicating success in the preparation process. The absorption and transmittance spectra showed that thin films have absorption in the ultraviolet region with good transmittance in the visible region. The optical band gap results indicated gradual decrease in band gap values with increasing annealing temperature. Hall effect results showed that the thin films possess (n-type), with increase in conductivity and charge carrier concentration with higher annealing temperatures, while resistivity decreased, indicating improved charge transport within thin films at higher temperatures.
Modeling and Optimization of Ultrasonic Vibration-Assisted Machining Devices: A Review
It is currently becoming popularly accepted that ultrasonic vibration-assisted machining (UVAM) represents a promising option for machining difficult-to-machine materials like titanium alloys, advanced ceramics, and fiber-reinforced composites. The problems associated with these materials include high cutting forces, tool wear, and poor surface integrity. This is because ultrasonic vibration-assisted machining entails the application of high frequency and low amplitude vibrations on the tool or workpiece through the acoustic horn which acts as the main medium that transfers vibrational energy generated by the piezoelectric transducer to the cutting area. This research will summarize 34 papers which were published between 2000 and 2025 on the topic of UVAM simulation and optimization. Some of the aspects that will be considered include the use of Webster's horn equation and other mathematical modeling approaches in determining the ideal horn design and also the use of finite element analysis (FEA) in ANSYS Workbench in terms of modal analysis, harmonic analysis, and stress analysis. In addition, some of the advanced optimization techniques such as Response Surface Methodology, Genetic Algorithm, and Artificial Neural Network will also be discussed. Some of the applications that will be covered include the manufacturing industry, biomedical engineering, food processing, welding, and additive manufacturing among others. There will be simulative results that indicate significant reductions in cutting forces and improvement in surface roughness compared to conventional machining processes. Some of the reports about reduction in cutting forces are between approximately 20% and 50% while for surface roughness, there is up to 4 times improvement compared to traditional machining. However, thermal drifts and alignment issues remain problems to contend with hence future work must focus on multi-physics simulations.
Ultrasonic vibration assisted machining (UVAM)
Attention-Based Deep Learning Pipeline for AI-Created Image Recognition
The advent of generative AI models that can generate some of the most realistic images including DALL·E, Midjourney, and Stable Diffusion has posed a significant threat to digital forensics and media authenticity verifications. Conventional methods of forgery detection, which focus on splicing or copy-move manipulations, are not very efficient in the case of AI-generated images, which do not contain any explicit tampering traces. This paper presents Attention-Based Deep Learning Pipeline of AI-Created Image Recognition, intended to resolve the increasing challenge of separating AI-synthesized and authentic imagery. The proposed architecture incorporates three integrated branches, including low-level statistical feature extraction, high-level semantic representation learning, and attention-based feature refinement mechanism. The features that are extracted are combined and fed through a fully connected classifier to generate final predictions. The Synthbuster dataset that consists of images of several generative models, including DALL·E 2, DALL·E 3, Firefly, Glide, and versions of Stable Diffusion, were experimentally evaluated. The suggested framework score had an average AUC of 97.4%, as well as high accuracy, precision, recall, and F1-score. These findings support the robustness and generalization ability of the proposed model in detecting AI-generated images in a variety of generators and conditions.
An Integrated MobileNet and XGBoost architecture to Categories Quail based on colors Using a Novel Dataset Images
Precise separation of quail phenotypes based on feather color (black, brown, gold, and white) is essential in selection and genetic improvement programs because of its link with productive qualities. Quail are a significant economic pillar in the poultry industry. By creating a novel hybrid architecture that combines the MobileNet network and the XGBoost algorithm, this research offers a digital alternative to overcome the drawbacks of conventional, laborious, and visually biased human review. The approach makes use of MobileNet's deep visual feature extraction capabilities, and the XGBoost algorithm's better statistical abilities in handling nonlinear relationships are used to handle the final classification jobs.. The researcher's own independent database, which included 1219 photos depicting the four color patterns, was used to train and test the model.,he model's total statistical stability and lack of bias were mathematically demonstrated by the system's 96% overall accuracy and perfectly balanced standard indices of 0.96 for precision, recall, and F1 score. The geometric superiority of the suggested architecture was shown by experimental findings on a different test set (243 photos). The Confusion Matrix of the suggested hybrid approach (MobileNet + XGBoost), which precisely documents the statistical distribution of accurate predictions and validates the model's superior ability to critically distinguish between phenotypic patterns without overlap, is revealed by a thorough analysis of the results by category
Computer Science - Artificial Intelligence - Data Mining (Educational Data Mining)
Beyond the Laboratory Illusion: A PRISMA-Guided Systematic Review of Arabic Sign Language Processing (2015–2025)
Arabic Sign Language (ArSL) processing research has expanded rapidly over the last 10 years. However, a gap exists between innovative algorithms introduced in research and their implementation in society. To help close this gap, the current research offers a systematic review of ArSL processing for the years 2015-2025. Within the PRISMA framework, we provided a multi-dimensional analysis of 33 empirical papers and mapped the main research obstacles, as well as the computational methods and model architectures. This analysis showed a significant imbalance in data availability (32.3%) and a lack of ability to generalize to new domains or tasks (29.0%). The reported accuracy in these studies can be considered "illusions" because those results were likely achieved in laboratory environments. Overall, the available literature described three main eras: Foundation & Exploration, Deep & Dynamic Shift, and Generalization & Generation. Architecturally, the intricacy of spatiotemporal dynamics has called for 'architectural hybridization' (25.0%), along with operational bifurcation toward edge-efficient lightweight models. Furthermore, a distinct paradigm shift is occurring, abandoning intrusive sensors for non-intrusive natural vision, accompanied by a shift from visual decoding of a solitary nature to interactive linguistic generation [1]. The review finally states that future progress in ArSL processing will not only rely on enhancing the algorithms; an immediate shift is required toward data-driven technologies, and embracing the self-supervised paradigm to overcome the annotation bottleneck, and the co-design approach with the Deaf community to ensure the technological deployment is fair and useful.
Keywords: Arabic Sign Language Processing; Systematic Review; Computational Tasks; Model Architectures; Architectural Hybr
Machine-Learning Prediction of Shear Capacity of RC Corbels Under Combined Vertical and Horizontal Loading
The shear capacity of reinforced concrete corbel (RCC) remains difficult to predict because these members are disturbed (D-) regions governed by strut-and-tie action rather than by conventional theory, and because their strength is further modified when a horizontal tension force acts together with the vertical load. This study develops and compares data-driven models for the shear strength of RCCs using a compiled database of 53 experimental specimens under combined vertical shear and horizontal force, described by twelve geometric, material, and reinforcement parameters. Five algorithms: multiple linear regression (MLR), random forest (RF), gradient-boosted regression trees (GBRT), support vector regression (SVR), and artificial neural network (ANN) were trained and evaluated under a cross-validation protocol to obtain out of sample predictions. Support vector regression achieved the highest accuracy (R2 = 0.926, RMSE = 59.5 kN, mean absolute percentage error = 15.7%), then followed by gradient boosting (R2 = 0.913) and the neural network (R2 = 0.904), whereas the linear model and the random forest were less accurate. Taylor and radar diagrams confirmed the same ranking across multiple criteria. Feature-importance analysis identified the corbel width, the concrete strength, the shear-span-to-depth ratio, and the applied horizontal force as the dominant predictors, in agreement with established corbel mechanics. The results indicate that the shear strength capacity of RCCs can be predicted with lower scatter than a semi-empirical formulation adopted by standard codes, and therefore provide an interpretable basis for design capacity estimation.