One of the primary challenges in urban areas of the Kingdom of Saudi Arabia is managing traffic flow and implementing effective urban planning strategies in densely populated cities. Utilizing satellite imagery for vehicle counting presents an effective solution to enhance urban planning and economic monitoring. Based on a review of the literature, the lack of vehicle counting datasets using satellite imagery is a significant gap. To address this, we introduce the Saudi Vehicles Dataset, which comprises 410 images, each 500x500 pixels, with 5,609 manually annotated vehicles. To ensure the dataset's quality and generalization, we benchmark it against two other satellite datasets: Car Synthetic and COWC. Benchmarking was performed using the latest version of the YOLO object detection algorithm, YOLOv12. The results indicate that our dataset yields comparable performance metrics to the other datasets. Specifically, when YOLOv12 was trained on the Saudi Vehicles Dataset, it achieved the high scores for mAP50, recall, precision, and F1-score, with values of 0.977, 0.939, 0.945, and 0.942, respectively. While the best performance was observed on the synthetic dataset, achieving a perfect precision score of 1, the Saudi Vehicles Dataset showed superior performance in terms of recall, precision, and F1-score. The dataset is publicly available for use and can be accessed via this link: https://github.com/Syukrondzeko/saudi-vehicle-dataset
Understanding the stress distribution in front of the crack tip is essential because this region experiences high stress concentration that can control crack initiation and propagation. This study proposes an artificial neural network (ANN) model as a computationally efficient surrogate model for predicting the stress distribution and stress values in front of the crack tip in a simple cracked geometry. The proposed ANN model complements conventional finite element method (FEM) analyses by enabling rapid stress prediction once the model has been trained. The results show that the ANN model can capture the general relationship between the stress intensity factor, radial distance, angular position, and crack-tip stress. However, its predictive performance strongly depends on the magnitude of the stress intensity factor. At low K values, the ANN model shows significant discrepancies in both stress values and contour visualization, and it cannot accurately reproduce the theoretical butterfly-shaped stress distribution. At higher K values, the predicted stress distribution is closer to the analytical solution, and the contour shape is more consistent. These findings indicate that the present ANN model should be regarded as a proof-of-concept surrogate model with limitations, rather than a replacement for analytical LEFM solutions or FEM analysis.
With the increasing adoption of robotic welding systems in industrial manufacturing, understanding the precise control of welding parameters in this difficult position has become essential to ensuring consistent welding quality. This study focuses on evaluating the effects of key Robotic Gas Metal Arc Welding (GMAW) parameters namely current, voltage, and wire feed rate on weld bead appearance, root penetration, and tensile strength when applied to mild steel plates. The research aims to identify the optimal parameter combination capable of maximizing weld quality while minimizing defects, enabling more reliable robotic welding performance in the 3G position. To achieve this, the Taguchi Method was employed to design a robust and systematic experimental framework, allowing efficient parameter variation and analysis. An ABB IRB 1520ID robotic arm integrated with a Fronius TPS 320i robotic GMAW machine was used to ensure repeatability and control throughout the welding trials. A total of nine experimental specimens were produced based on an L9 orthogonal array, with each specimen subjected to visual non-destructive testing (NDT) to assess surface discontinuities and destructive testing (DT) to determine mechanical integrity. Analysis of the experimental data revealed that welding current, voltage, and wire feed rate significantly influence weld penetration and strength in the 3G position. The study identified 110 A of current, 17.9 V of voltage, and a wire feed rate of 2.7 m/min as the optimum parameter settings, achieving a superior tensile strength of 403 N/mm² and eliminating root penetration defects. Furthermore, optimal visual quality was quantified at 80 A, which yielded a 100% defect-free weld bead and root pass. Overall, the findings demonstrate that by establishing an optimized parameter window, this research contributes to improved process reliability, enhanced weld quality, and greater industrial applicability.
This study investigates the effects of climatic variability and long-term module degradation on the technical performance of a 100-kWp grid-connected rooftop photovoltaic system installed on a university campus in a humid tropical climate. A 65-month observational dataset covering January 2020 to May 2025 was combined with 10,000 Monte Carlo iterations and a dynamic feedback model to estimate energy yield, performance ratio (PR), and performance degradation over a 25-year operating period. The simulated annual energy yield ranged from 111 to 129 MWh yr⁻¹ (P10–P90), with a median specific yield of approximately 1,200 kWh kWp⁻¹ yr⁻¹, while the PR remained within 0.75–0.80. A literature-informed two-stage degradation scenario—0.55% yr⁻¹ during Years 1–10 and 1.00% yr⁻¹ during Years 11–25—resulted in an estimated cumulative energy loss of 8.7% relative to the no-degradation baseline. Sobol sensitivity analysis identified the PR residual as the dominant contributor to annual energy-yield variance, followed by irradiance, temperature, and degradation. By preserving irradiance–temperature dependence through paired bootstrap resampling and incorporating time-dependent degradation, the proposed framework provides a more realistic representation of both short-term uncertainty and long-term performance decline. Applications to other humid tropical locations require site-specific inputs and independent validation.
Microplastic contamination (1-5000 µm) in drinking water has been reported worldwide. The ingestion of microplastic-contaminated water poses potential health risks due to the accumulation of microplastics in human blood, placenta, and breast milk. This study aims to design and develop a porous silica-based filter (pore size 50-100 µm) with the capacity to adsorb microplastics from drinking water. The preparation of the modified porous silica material involved two stages: (1) synthesis of porous silica (SiO₂) templates using CaCl₂, and (2) surface modification of silica with 3-aminopropyltriethoxysilane (APTES). The porous structure emerged through chemobrionic self-assembly followed by acid leaching, which produced tubular pores at the micron scale for polyethylene microplastic particle capture. Presence of silica pores, morphologies, and amine functional groups are identified through FTIR and SEM-EDX analysis. The adsorption tests assessed the removal of polyethylene microplastics from electrolyzed drinking water through Nile Red-assisted flow cytometry to measure the extent of microplastic removal and evidence of microplastic removal is observed during 24-hour contact test. Surface analysis following adsorption showed carbon-rich agglomerates which confirmed that polyethylene particles formed bonds with the silica network. Overall, APTES-modified porous silica shows potential as an amine-functional adsorbent to remove microplastics from drinking water treatment systems.
