With the rapid digitalization of daily life, reliance on technology has become an essential part for family members, including children. As a result, children are increasingly exposed to different types of online risks, yet research on their cybersecurity awareness, particularly in Saudi Arabia, remains limited. Therefore, this study investigates cybersecurity awareness and online safety practices among Saudi children aged 9–17, alongside their parents’ perceptions and involvement. A cross-sectional survey was conducted with 112 parents and their children, examining parental monitoring, digital literacy, children’s online behaviors, and exposure to online cyber risks. The findings of this study indicate that while most parents are highly educated, their engagement in children’s online safety is moderate, with limited use of monitoring tools. Children demonstrate basic understanding of cybersecurity concepts, particularly regarding passwords, yet they use unsafe practices, such as password reuse and infrequent account sign-out. Both parents and children report concerns about exposure to inappropriate content and cyberbullying, highlighting partial awareness of social threats. The study underscores the need for integrated, family-centered cybersecurity education programs, combining school-based initiatives with parental digital literacy training to foster safer online environments for children.
The rapid growth of Android applications has led to a significant increase in malware threats, making accurate and robust detection mechanisms essential for mobile security. However, challenges such as class imbalance and high-dimensional feature spaces limit the effectiveness of traditional machine learning approaches. This work proposes a robust machine learning pipeline for accurate detection of Android malware by integrating generative data augmentation and deep feature extraction with classical classification models. We employ Conditional Tabular Generative Adversarial Networks (CTGAN) to synthetically balance a permission- and API-based feature dataset (TUANDROMD), developed at Tezpur University from real benign and malicious Android applications. An autoencoder is then utilized to learn compact and discriminative latent representations from the original 241 numerical features, effectively reducing dimensionality and redundancy. The extracted features are used to train multiple machine learning classifiers, including Logistic Regression, Random Forest, and XGBoost, enabling a comparative evaluation of model performance. The models are assessed using accuracy, precision, recall, and F1-score under stratified validation and holdout testing. Four experimental configurations are investigated: (i) baseline classification using raw features, (ii) CTGAN-based data augmentation, (iii) autoencoder-based feature extraction, and (iv) CTGAN-based augmentation followed by autoencoder-driven feature extraction. Experimental results demonstrate that the combined CTGAN and autoencoder pipeline significantly improves minority-class detection while maintaining high overall accuracy. These findings highlight that integrating generative augmentation with learned feature representations is an effective strategy for handling high-dimensional, imbalanced Android malware datasets.
The engagement of students in various learning activities can lead to productive learning. The university education system requires continuous evaluation of quality and suggestions for improvement at several points. All universities follow quality assessment procedures and incorporate them into their mainstream practices. However, many procedures are performed manually, which consumes a significant amount of time for both administration and faculty members. The challenge confronting all universities is to design a quality education framework that effectively minimizes the gap between educational outcomes and current market demand. In this study, we propose an academic quality assessment framework (AQAF) that automates all procedures from the beginning to the showcasing of results by carefully studying the workflows of academic and evaluation processes. Course Learning Outcomes (CLOs) will be mapped to Student Outcomes (SOs); subsequently, these SOs are measured using the scores given by several formative and summative assessment methods. The level of attainment of SOs is achieved in the form of performance indicators. This research aims to examine all academic quality workflows regarding documents and information at various levels, such as department, college, and university, and incorporate all these workflows in the academic quality assessment framework (AQAF). This system serves as both an analytical and quality workflow management tool, achieving approximately 40–60% reduction in processing time compared to manual methods. The system will support computer science (CS) and information technology (IT) program evaluations for the Accreditation Board for Engineering and Technology (ABET) and the National Commission for Academic Accreditation and Assessment (NCAAA) accreditations.
Nitrogen dioxide (NO₂) is a very critical atmospheric pollutant, which has greatly influenced environmental sustainability and public health. The measurements collected for NO₂ concentrations are valuable to improve air quality management and mitigation strategies. We propose an AI-oriented sustainable environment management system that predicts NO₂ in urban and industrial areas by implementing machine learning (ML) techniques. The model is developed to forecast the NO₂ concentrations based on the recorded past data on air quality and meteorological parameters (temperature, humidity, and wind speed) traffic flow data. For time-series forecasting and feature analysis advanced ML algorithms such as LSTM, Random Forest, and XGboost have been used. The results of this study indicate that these models are highly accurate by employing actual datasets in their evaluation. Further, the use of ML to predict provides early intervention for policymakers, enforces data-based environmental policies, has an overall favorable impact in decreasing pollution. Thus, the present study enhances the understanding of smart city applicability, air quality sentinel, as well as sustainable environmental management by applying AI for enhanced pollution detection.
The rapid growth of Android devices has led to a significant increase in malware targeting mobile platforms, posing serious risks to user privacy and system security. Existing machine learning-based detection approaches often suffer from feature redundancy, limited automation, and reduced effectiveness in identifying previously unseen threats. This paper proposes a hybrid machine learning framework for real-time pre-installation Android malware detection using static analysis. The system integrates automated APK analysis using the SISIK tool, Genetic Algorithm (GA)-based feature selection for dimensionality reduction, and classification using Support Vector Machine (SVM) and Random Forest (RF). In addition, Maximum Mean Discrepancy (MMD) is incorporated to capture distributional differences between benign and malicious applications. Experimental evaluation on publicly available Android malware datasets demonstrates that the proposed approach achieves an accuracy of approximately 95%, outperforming baseline models. The results highlight the effectiveness of combining feature optimization and distribution-aware analysis for improved detection performance. The main contributions of this work include an automated analysis pipeline, an adaptive feature selection mechanism, and a hybrid detection framework capable of identifying both known and unknown malware patterns in a pre-installation setting.
This study uses the application of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology to significantly reduce the defect rate of Conical Lighting Poles (CLPs). A high CLP defect rate directly impacted profitability and customer satisfaction, prompting the need for a structured improvement initiative. Utilizing the DMAIC framework, the problem and its impact were defined. The measure phase involved data collection to quantify the current defect levels. In the analyze phase, a cause-and-effect analysis was conducted to identify root causes, with Pareto charts highlighting the most significant contributors to defects. Furthermore, a brainstorming session with experts from manufacturing, quality control, and maintenance departments facilitated the development of targeted solutions in the improve phase. The control phase implemented measures to sustain the improvements. The results successfully demonstrate the efficacy of the DMAIC methodology, leading to a substantial improvement in Pp and Ppk, which increased from 3.23 to 6.22. In addition, the process achieved statistical control with no out-of-control points, indicating a more stable and capable manufacturing process. This research not only reduced defects but also enhanced overall operational efficiency and product quality.
Manufacturing operations management continuously strives for competitiveness and adaptability. While Lean 4.0 primarily leveraged digitalization for efficiency gains, this paper addresses the critical research gap concerning the lack of a prescriptive, normative conceptual model capable of structurally aligning Lean principles with the three core tenets of Industry 5.0: human-centric, resilience and sustainability. A theory-building approach utilizing systematic conceptual analysis and the axiomatic design methodology was employed. This methodology maps conventional Lean principles against the Industry 5.0 pillars, enabling the derivation of a novel, multidimensional framework: the Lean 5.0 parameter integration matrix. This matrix formalizes design using the axiomatic design independence axiom, providing a mathematical basis to decouple conflicting efficiency and human-centric goals. The paper details the operational mechanisms for key Lean 5.0 tools that utilize advanced technologies like explainable artificial intelligence and collaborative robotics to achieve normative Industry 5.0 outcomes. The framework\'s effectiveness is validated through an empirical case study in an additive manufacturing environment. The quantitative results demonstrate that implementing the Lean 5.0 parameter integration matrix successfully resolved a critical design contradiction, leading to a 77% reduction in mean time to recovery and a 74% reduction in setup error rate, all while maintaining or slightly improving overall operational efficiency. The Lean 5.0 parameter integration matrix provides the essential structural framework for the next generation of human-centric, resilient and sustainability manufacturing.
Pain assessment is critical for gaining valuable insights into a patient\'s health status and predicting recovery outcomes. The subjective nature of pain and the influence of individual, psychological, and social factors make assessment difficult. Pain assessment is primarily based on self-reporting or expert observation; however, both methods have inherent limitations. Self-reporting may lack reliability and feasibility for specific patients. In contrast, expert observation is inherently subjective and requires experienced personnel, making it impractical in the context of rising inpatient numbers and overburdened healthcare providers. As a result, This research study proposes “HAIEN,” an intelligence system designed to autonomously detect and categorize pain levels in inpatients using facial expression analysis. The “HAIEN” application aims to provide a valid and reliable pain assessment, allowing healthcare providers to make informed treatment decisions and ensure ongoing patient care. Two classifier models, kNN and SVM, were trained on the UNBC-McMaster Shoulder Pain Database. The two classifiers used three feature extraction methods: VGG16, EfficientNetB3, and InceptionV3. The findings show that these models successfully captured facial movements and correctly identified pain. Using Artificial Intelligence technology in the “HAIEN” application improves the pain assessment process and patient health outcomes.
Power dissipation stands as a crucial challenge in VLSI design, especially for high-speed counters used in frequency synthesizers, PLLs, and digital converters. Conventional binary counters suffer from large fan-out and propagation delays, while traditional LFSR counters operate with only (2^m – 1) states, requiring additional circuitry for full counting sequences. This work proposes a novel LFSR counter with a state extension technique that achieves 2^m states without degrading the counting rate. The architecture combines a low-order LFSR sub-counter and a high-order synchronous binary counter, optimized with clock gating to reduce unnecessary switching activity. Implemented using Verilog HDL in Xilinx ISE/Vivado, the proposed design demonstrates significant improvements: power reduced from 249 mW to 114 mW (54% savings) while maintaining constant delay performance. Compared to conventional binary and LFSR counters, the design achieves superior trade-offs in power, speed, and area, making it highly suitable for advanced high-speed VLSI applications.
This paper discusses the optimization of the Capacitated Warehouse Location Problem (CWLP) under uncertain demand and supply. We propose an optimization framework that addresses the CWLP by considering blood distribution to identify optimal blood center locations. The objective is to meet all blood orders at the lowest possible cost, subject to warehouse capacity constraints. This study applies the framework to cities in the Kingdom of Saudi Arabia with high demand for blood delivery. We utilized census data from selected cities, with a representative sample of each city designated as a customer base. A novel mixed-integer linear programming (MILP) model was developed and solved using Python to determine the minimum total transportation and fixed costs for blood center construction. The proposed warehouse locations are presented on a map, showing each city connected to its optimal blood center warehouse.
Most developed and developing countries are becoming increasingly aware of the limited energy resources, which is why they develop strategies and establish stringent processes to address this problem. In this study, a thermodynamic parametric analysis was conducted for a real case of a gas turbine power plant located in India. The investigation yields energy engineering recommendations for the real case study, ultimately reducing fuel consumption while enhancing overall power plant performance. The elaborated model examines all relevant compartments in the gas turbine cycle from the perspectives of energy, exergy, and exergy destruction. The results revealed that the combustion chamber accounted for the highest exergy destruction, amounting to 82.28%. The compressor followed this at 8.10% and the turbine at 6.10%. The overall energy and Exergy Efficiencies of the system were determined to be 28.8% and 27.17%, respectively. The exergy efficiencies of the air compressor, combustion chamber, and gas turbine are 97.2%, 50.3%, and 93.3%, respectively. The exergy destruction efficiencies of the air compressor, combustion chamber, and gas turbine are 8.1%, 82.28%, and 6.10%, respectively. As the temperature increases, more exergy is lost, leading to lower efficiency and reduced net power output. Therefore, optimising the design of the combustion chamber is essential to counteract the negative effects of hot weather. The insights gained from this study can be used to improve the design and operation of gas turbine plants in hot climates.
This research aims to improve gas turbine performance and suppress compressor rotating stall and surge. The effect of water spray at the compressor inlet on the stable operating range and performance of the gas turbine was examined. In gas turbines, combustion is a complex phenomenon that involves a variety of physical and chemical processes, changes in flow rate, turbulence intensity, and operating pressure and temperature variations. These phenomena cause inappropriate behaviors, like abrupt acceleration, stopping, and explosions, in continuous combustion processes within the combustion chamber. The blow-by phenomenon in aviation engines is greatly impacted by problems with combustion instability and flame continuity, especially at high altitudes. These engines must operate at low flame temperatures and in lean, difficult-to-ignite mixture conditions due to emissions regulations. This increases the opportunities and potential for extinguishing fires, contributing to the development of blow-off conditions. There isn\'t a comprehensive theory for how gas turbines burn, so this study must rely on empirical correlations and basic models.
The current study explores the effects of alkaline activator composition and curing regimes on the properties of geopolymer mortars synthesized primarily from pumice dust. Three sodium silicate-to-sodium hydroxide (SS/SH) ratios (2.5, 2.0, and 1.5) and two NaOH molarities (10M and 12M) were the variables to design six mixtures. Specimens were treated under three curing regimes: C1 (80 °C for 2 days), C2 (80 °C for 3 days), and C3 (hybrid regime: 80 °C for 2 days followed by 160 °C for 1 day). The six mixtures were evaluated based on flowability, compressive strength, dry density, water absorption, and visual efflorescence. Results revealed that flowability increased with the decrease in the SS/SH ratio and molarity. The increase in the SS/SH ratio increased compressive strength. The highest compressive strength was recorded in curing (C1) at SS/SH of 2.5 and the molarity of 10, indicating that higher molarity does not always lead to higher compressive strength. Prolonged curing duration decreased compressive strength. Hybrid curing (C3) caused 12M mixes to have the highest strength, unlike the curing regime (C1), where 10M mixes were the highest. Moreover, mixes with SS/SH of 1.5 and 2 at 12M achieved their highest strength values in the hybrid regime (C3). The decrease in the SS/SH ratio and molarity decreased the density and increased the absorption. The extended curing regime (C2) or the hybrid regime (C3) increased density and reduced absorption, but did not necessarily increase compressive strength in most cases. The decrease in the SS/SH ratio increased efflorescence, but the extension of curing duration mitigated it. The highest recorded compressive strength of 37.2 MPa was achieved at 10 M and SS/SH = 2.5 under curing 1 (C1), accompanied by a water absorption of 12.7%.
Carbon fiber reinforced polymer (CFRP) laminated structures are extensively utilized in critical fields such as transportation, aviation, and aerospace, owing to their superior mechanical properties. However, in practical service, these structures are susceptible to internal defects induced by environmental factors, such as matrix cracking and interlayer delamination, which significantly degrade their mechanical performance. Consequently, the effective detection of structural damage in composite materials has become a pressing issue that demands immediate attention. This study investigates cantilever bending experiments performed on CFRP laminates with pre-embedded damage, aiming to accurately identify localized structural damage. An fsFBG sensor, composed of six serially connected gratings, was mounted along the centerline of the CFRP laminate surface to capture strain response data from multiple measurement points. Through systematic analysis and comparison of the collected strain data, this research explores the influence of diverse damage locations and types within CFRP laminated specimens on surface strain patterns and confirms the linear strain response characteristics of the fsFBG sensor. The results indicate that both matrix cracking and interlayer delamination damage cause a significant increase in strain near the fixed end of the cantilever beam. Specifically, matrix cracking within the 0° layer and delamination between the [0/45] layers exhibit the most pronounced effects on the strain response. Further curve fitting of strain data for different damage types shows that positive and negative coefficients can effectively distinguish the specific characteristics of interlayer delamination and matrix crack damage. This finding provides a theoretical basis for reverse identification of damage locations. Moreover, the study demonstrates the effectiveness of fsFBG sensors in detecting the location and type of internal damage within CFRP laminates, offering novel references for structural health monitoring and damage assessment in relevant fields.
The increasing digitization of critical energy infrastructure has amplified the need for integrated frameworks that ensure data reliability and operational readiness during emergencies. This paper proposes a novel Cyber-Physical Data Assurance Framework that unifies data governance, real-time analytics, and emergency coordination across digital and physical systems. The framework is architected into four functional layers—Data, Governance, Analytics, and Interface—each designed to preserve data integrity, enhance situational awareness, and synchronize field operations with control systems. Using systems engineering methodology, the framework was validated through simulations of high-risk scenarios including pipeline ruptures, SCADA cyber intrusions, and industrial fire events. Evaluation results demonstrated a 14.3% increase in data availability, a 21.7% improvement in coordination accuracy, and a 40.4% reduction in response latency relative to legacy systems. The model’s alignment with standards such as NIST SP 800-53, ISO/IEC 27001, and ISA/IEC 62443 reinforces its operational feasibility and compliance posture. This research offers a scalable, standards-compliant solution that bridges the gap between IT governance and emergency response readiness in complex, high-stakes energy environments.
Many current and upcoming technologies are based on electronic gadgets controlled by an electronic clip. Because of their continuous operation, the efficiency of most electronic devices declines due to ineffective cooling methods. Researchers conducted several studies using Minichannels and Microchannels to dissipate heat from continuously operating electronic devices. The use of Minichannels and Microchannels to dissipate heat enhances the performance of electronic devices. The present study presents a numerical investigation using the Finite Element Method for circular mini-channels with hydraulic diameters of 167 µm to 2.5 mm and 200 mm long. Air is forced to pass through the channels, which are drilled into Aluminium piece of dimensions 200mm long, 120 wide, and 20 mm thick. The number of channels is 5, 9, and 11, with the same gap between each channel. The average air velocity through the channels varies from 0.5m/s to 1.0 m/s with a step of 0.5. The numerical results show that as the number of channels increases, the pressure drops across them, and the heat transfer rate increases for the entire range of airflow rates through the channels. Compared to the airflow rates through the channels, the heat transfer coefficient is significantly affected by a number of channels. However, the number of channels and the airflow rates through the channels more or less equally affect the friction factor.
Micro-thermoelectric generators (μTEGs) are emerging as promising power sources for low-energy devices, including Internet of Things (IoT) nodes, medical implants, and wearable electronics. Their compactness, reliability, and maintenance-free operation make them highly attractive, since μTEGs exploit solid-state thermoelectric conversion without moving parts and directly harvest waste heat. This study develops a comprehensive modeling framework to analyze the impact of critical design parameters on μTEG performance. Particular emphasis is placed on leg length, as scaling thermoelectric legs to the microscale reduces thermal resistance and enhances power density. Simulation results demonstrate that reducing leg length initially improves both output power and efficiency, though performance declines when parasitic effects dominate at excessively small scales. Additional parameters, including hot-side temperature, leg cross-sectional area, and ceramic plate thickness, are also systematically investigated. The hot-side temperature strongly governs output voltage and conversion efficiency, while leg area influences the trade off between electrical resistance and heat conduction. Similarly, ceramic plate thickness affects thermal spreading resistance, which significantly alters overall device efficiency. These findings provide useful design guidelines for optimizing μTEG structures. By tailoring microscale geometries and carefully managing coupled thermal–electrical pathways, compact and efficient μTEGs can be realized for future self-powered energy application.
This paper presents an analytical assessment of the trends and future forecasts for non-oil GDP in Saudi Arabia. As Saudi Arabia shifts its focus towards diversifying its economy and reducing reliance on oil, understanding the trajectory of non-oil GDP has become increasingly important. Utilizing a comprehensive dataset from 2010 to 2023 sourced from the General Authority for Statistics, we apply advanced time-series forecasting techniques to model and predict the future performance of non-oil GDP. Our findings suggest that the non-oil sector will continue to experience growth, driven by ongoing diversification efforts and strategic investments across various non-oil sectors. The results provide valuable insights for policymakers, highlighting the critical role of non-oil industries in shaping Saudi Arabia’s economic future. This study contributes to the understanding of the economic transition in the Kingdom and offers a foundation for future research and policy decisions aimed at fostering sustainable economic growth and achieving the goals of Vision 2030.
