Tip vortices are one of the most critical phenomena facing rotary wings such as propellers and wind turbine blades and lead to changes in the aerodynamic parameters of blades. The winglet (WL) device is considered one of the most significant passive flow control devices. It is used to diminish the strength of vortices at the blade tip, enhance the aerodynamic characteristics of turbine rotor blades, and thereby increase the overall turbine efficiency. The main objective of this research is to improve the aerodynamic characteristics of wind turbines by adding a winglet at the blade tip. An optimum turbine blade profile was taken to build the turbine rotor geometry. The turbine has three blades with a radius of 0.36 m, and the NACA4418 airfoil blade sections were used to build the blade profile. The computational domain was created by ANSYS software, and the model was validated for spalart-allmaras and k-ω SST turbulence models with experimental measurements. The computational model was solved for blade shapes without and with tip winglets. Various winglet height lengths per blade radius (WHLR) of 0.008, 0.02, 0.04, 0.05, 0.06, 0.07, and 0.08 were studied for a 90-degree cant-angle and a constant design tip speed ratio of 4.92. Generally, the results illustrate that the performance characteristics of the turbine rotor were improved by using the tip winglet. The lift-to-drag ratio coefficient (C_L/C_D) and power coefficient (C_p) are increasing with increasing WHLR until they reach the highest improvement value, and then they start to decrease gradually. The optimum WHLR is about 0.042, with a percentage improvement in the lift-to-drag ratio (C_L/C_D) and power coefficient (C_p) related to the blade without winglet of about 11.6% and 6.9%, respectively, and an increase in the thrust force of 14.8%. This is mainly caused by decreasing the vortex strength near the tip region and improving the characteristics of stall behaviors.

Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learning (ML) model, to categorize and predict work zone-related crashes and to interpret the various contributing factors. The issue of data imbalance was also addressed by utilizing work zone crash data from the state of New Jersey, comprising data collected over the course of two years (2017 and 2018) and applying data augmentation strategies such synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, and SVM-SMOTE. The EBM model was trained using augmented data and Bayesian optimization for hyperparameter tuning. The performance of the EBM model was evaluated and compared to black-box ML models such as combined kernel and tree boosting (KTBoost, python 3.7.1 and KTboost package version 0.2.2), light gradient boosting machine (LightGBM version 3.2.1), and extreme gradient boosting (XGBoost version 1.7.6). The EBM model, using borderline-SMOTE-treated data, demonstrated greater efficacy with respect to precision (81.37%), recall (82.53%), geometric mean (75.39%), and Matthews correlation coefficient (0.43). The EBM model also allows for an in-depth evaluation of single and pairwise factor interactions in predicting work zone-related crash severity. It examines both global and local perspectives, and assists in assessing the influence of various factors.

The formation of cracks in concrete structures occurs due to a multitude of causes ranging from shrinkage to external loading and environmental exposure. This phenomenon can significantly affect the lifecycle of concrete structures. Self-healing concrete (SHC) is considered a promoted innovation capable of overcoming this inevitable occurrence. In accordance with current SHC development processes, this paper utilizes the numerical simulation approach to test the performance of reinforced SHC beam specimens modeled using the commercial software ABAQUS 6.14 (Vélizy-Villacoublay, France). This paper aims to contribute to the scarce literature on SHC models by utilizing the overlooked dicyclopentadiene (DCPD) agent and ambiguous variability of crystalline admixtures. The SHC is introduced to the beam models at various depths and analyzed using load against displacement curves compared with a reference model of ordinary concrete. The effects of SHC on the mechanical properties of structural elements were determined. The results show a distinct improvement of the load-carrying capacity of SHC beams, indicating an efficient contribution of SHC in structural applications.

The study investigated the viability of implementing a demand-responsive transportation (DRT) system within the premises of Al-Baha University to facilitate the punctual arrival of students to their classes and ensure their convenient departure from the campus. This study aims to demonstrate and elucidate how data mining enhances the efficiency of DRT systems. The case study investigates the potential improvements in DRT operations through the utilization of data mining techniques. The research employs descriptive and prescriptive techniques for mining trip planning data. The database maintained by the Deanship of Admission and Registration et al.-Baha University in Al-Baha City, Saudi Arabia, has identified 50 designated locations for student pick-up. The research establishes a comprehensive database that classifies pick-up points as spatial vectors, incorporating longitude, latitude, and counts of students. Cluster analysis and multidimensional scaling techniques are employed to minimize travel times and enhance operational efficiency by consolidating pick-up points. A completely adaptable DRT system prioritizes user satisfaction over operational efficiency, facilitating superior flexible services with fewer buses. The University Transport Department can implement Fixed Route systems in regions with significant population density and substantial demand. Conversely, in areas with a lower population, where Fixed Route systems may be less efficient, the department can opt for DRT systems. Additionally, the findings demonstrate the potential cost-saving benefits of implementing a hybrid system that integrates the university and off-campus student housing. The results indicate that providing fully flexible DRT services to students’ harms efficiency and user experience. A comparative analysis between the Fixed route system and the DRT system reveals that the latter has the potential to offer enhanced service to students, utilizing an equivalent number of vehicles while reducing the number of rides required, all while maintaining a higher degree of flexibility in service provision. According to the recommendation of this study, it is advisable to implement a hybrid fixed route and DRT system. However, to assess the feasibility of such a system in a university setting in the Kingdom of Saudi Arabia (KSA), it is necessary to conduct a comprehensive viability analysis to identify any potential challenges or concerns.

Fine particulate matter (PM_2.5) poses major public health and environmental threats due to its capacity to enter deep respiratory passages and degrade urban air quality. In the Kingdom of Saudi Arabia (KSA), cities such as Riyadh, Dammam, and Jeddah show an elevated level of PM_2.5 due to rapid urban growth, dense traffic activity, and wide industrial operations. This study proposes a hybrid Variational Mode Decomposition–Bidirectional Gated Recurrent Unit (VMD–BiGRU) framework for multi-horizon PM_2.5 forecasts based on daily data from January 2022 to September 2024. The daily PM_2.5 series was split through VMD into Intrinsic Mode Functions (IMFs) that represent multi-scale temporal patterns. A seven-day ahead forecast was carried out, and model performance was compared with VMD–GRU, VMD–LSTM, and VMD–TCN. For Riyadh, RMSE values for t + 1, t + 2, and t + 3 were 9.25, 12.26, and 16.05 µg/m³, with R² above 0.90 up to the third day. For Dammam, RMSE values for the same horizons were 4.46, 7.24, and 11.34 µg/m³, and R² remained above 0.90 up to the fourth day. For Jeddah, the corresponding values were 3.97, 6.09, and 9.36 µg/m³, and R² remained above 0.90 up to the fourth day. The hybrid VMD–BiGRU model achieved higher accuracy for short horizons (t + 1 to t + 3). The study establishes a basis that aids short-term PM_2.5 prediction and improves air quality assessment across major urban centers in KSA.

Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The “significance and effects” of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the “length-lanes” significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors.