The palm and date sector is one of the most important sectors in Saudi Arabia. The total number of fertile palm trees in Saudi Arabia is about 31 million. In the production of pitted dates, date molasses, date paste, and date confectionery, a considerable number of date kernels are usually discarded as waste. This study reports experimental investigations conducted to evaluate the potential of waste date kernel ash (DKA), obtained by the calcination of date pits at 800 °C, as a partial cement replacement in concrete. DKA has low silica oxide and does not qualify as a pozzolanic material. The effect of DKA partially replacing the cement and acting as a filler material in concrete was investigated, and its properties were compared with two pozzolanic materials, fly ash (FA) and natural pozzolan (NP). Twelve concrete mixes in which cement was replaced with different proportions of calcined DKA (5%, 10%, 15%, 20%, and 30%), NP (10%, 20%, and 30%), and FA (10%, 20%, and 30%) were investigated in the experimental program. The properties of DKA, FA, and NP concrete mixes were evaluated in fresh and hardened states, including the heat of hydration, mechanical characteristics, and thermal properties. The results show that replacing cement with 5% date kernel ash increases the compressive strength by 0.42%, 3.2%, and 2.5% at 3, 7, and 28 days, respectively, while the 28-day compressive strength decreases by 2.4%, 5.4%, 16.3%, and 26.69% when the cement is replaced with 10%, 15%, 20%, and 30% DKA, respectively. Date kernel ash concrete mixes with 10%, 20%, and 30% replacement levels demonstrated higher compressive and tensile strengths and lower thermal conductivity, density, and workability when compared to natural pozzolan and fly ash. DKA is a promising partial cement replacement material; nevertheless, additional research is required to assess the durability of DKA in concrete.

This research demonstrates the results of an investigation into the California bearing ratio (CBR) of granular soils from Qassim region, Saudi Arabia, using multilinear regression (MLR), pure quadratic (PQ) models, and gene expression programming (GEP) methods utilized to develop mathematical models for estimating the CBR based on basic soil index properties. In this study, samples were collected from different borrowing pits in the Qassim area. Forty‐three samples of soil were taken and transferred to a laboratory for examination. Seven multilinear regressions and seven PQ models were investigated, while four GEP models were made. The selection of each model variable depends on soil indices, grouping into grain size distribution, Atterberg limits, and compaction parameters. The results of this analysis showed that the PQ model had a higher accuracy [coefficient of determination (R²) = 0.89, root mean square error (RMSE) = 16.006, uncertainty (U95) = 16.17, and reliability = 57%] than the multilinear regression model, which has a lower accuracy model [R² = 0.811, RMSE = 20.791, U95 = 15.569, and reliability = 51%]. The best GEP model yields [R² = 0.776, RMSE = 22.552, U95 = 15.787, and reliability = 53%]. Furthermore, sensitivity analysis was conducted to distinguish the influences of different input variables on CBR; it was found that fines percentage (F₂₀₀), maximum dry density (MDD), and optimum moisture content (OMC) are the most influential variables.

Academic institutions are back to normal, but what about students? What were the cultural and lifestyle changes and impacts due to the remote mode of learning? Classes in Saudi universities in the pre-pandemic time were predominantly 100% face to face. During the COVID-19 pandemic, the education sector moved to 100% online and then went through hybrid mode (a mixture of face to face and remote) until the return to the old normal, i.e., 100% in person. Students in the post-pandemic period are wondering “Why are we coming to campus? What is the value in this teaching and learning experience where we have to commute daily?” This research addresses students’ concerns and questions, and it addresses the following questions: to what extent do they prefer learning remotely? In person? Hybrid? Observations and discussions preceded the design of the research instruments. A questionnaire survey that consists of closed-ended and open-ended questions was distributed and received responses from 74 engineering students, at the Qassim University, who were in the last three semesters of their studies and who went through the three modes of learning. To complement the quantitative research and to achieve a better understanding of matters that were revealed through the survey, the results of the analysis were shared through three focus group sessions with students and faculty members. The findings from this mixed methods approach revealed that the hybrid mode of learning is gaining significant preference among students and partial support from the teaching staff. On the other hand, unfavorable perceptions were expressed towards both going back to the old normal or the transition to a completely remote mode of learning.

This research assesses the economic impact of Autonomous Vehicles (AVs) on the logistics network of the China-Pakistan Economic Corridor (CPEC), a vital part of China’s Belt & Road Initiative (BRI). Using a scenario simulation, researchers evaluated the effects of AV adoption on logistics costs, the environment, and traffic efficiency. Widespread adoption of fully AV is estimated to reduce logistics costs by 57%, CO2 emissions by 50%, and traffic congestion by 30%, aligning with global economic and environmental goals. The findings offer decision-makers at national and local levels valuable insights into necessary infrastructure investments, regulations, and policies to support AV integration. The study also addresses issues such as labour displacement and public acceptance, highlighting mechanisms to achieve equitable economic and environmental outcomes. This feedback provides a framework for policymakers and stakeholders to refine future transport logistics and resource allocation across cross-border corridors.

Traffic congestion at intersection is one of the significant socioeconomic concerns worldwide. To tackle this challenge, researchers and practitioners are researching and executing different plans to control and manage long queues and delays. The general department of traffic in Saudi Arabia has implemented a new signal timing pattern in a number of signalized intersections that were designed with an additional flashing green phase complemented with law enforcement cameras (SAHER) to improve the capacity and safety of signalized intersections. This research aims to evaluate the impact of flashing green intervals on driver behavior and traffic efficiency of five signalized urban intersections equipped with SAHER in the Al-Qassim region, Saudi Arabia. Analyses for the current situation (base scenario) and proposed scenarios (without SAHER) are performed and validated using the microsimulation model (VISSIM) with field collected data at the selected intersections. The results showed that, despite fewer improvements in vehicle delays, the intersections without SAHER and flashing green intervals yield shorter queue lengths than the intersections with SAHER and flashing green intervals. Further, it was also revealed that drivers tend to stop early and start late in the case of SAHER due to fear of red light fines, thus not utilizing the full green split and yellow time. Analysis for the average vehicle delay and queue lengths is also conducted to assess the efficacy of implemented green light flashing with SAHER on driver behavior and operational efficiency of the selected intersections.