Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents. In this study, using Al-Ahsa city in Saudi Arabia’s Eastern Province as a case study, a Bayesian belief network (BBN) model was established by incorporating an expectation–maximization algorithm. The model examines the relationships between indicator variables with a special focus on driving behavior to measure the uncertainty associated with accident outcomes. The BBN was devised to analyze intentional and unintentional driving behaviors that cause different types of accidents and accident severities. The results showed when considering speeding alone, there is a 26% likelihood that collision will occur; this is a 63% increase over the initial estimate. When brake failure was considered in addition to speeding, the likelihood of a collision jumps from 26% to 33%, more than doubling the chance of a collision when compared to the initial value. These findings demonstrated that the BBN model was capable of efficiently investigating the complex linkages between driver behavior and the accident causes that are inherent in road accidents.

Climate change poses a challenge to the security and long-term viability of the global food supply chain. Climate unpredictability and extreme weather events have significant impacts on Saudi Arabia’s vulnerable food system, which is already under stress. The Kingdom of Saudi Arabia faces distinct challenges in comparison to other dry locations across the world. Here, the per capita water demand is high, the population is growing, the water resources are extremely limited, and there is little information on the existing groundwater supplies. Consequently, it is anticipated that there will be formidable obstacles in the future. In order to make data-driven decisions, policymakers should be aware of causal links. The complex concerns pertaining to the Saudi Arabian food system were analyzed and rationally explained in the current study. A causality analysis examined different driving factors, including temperature, greenhouse gas (GHG) emission, population, and gross domestic product (GDP) that cause vulnerabilities in the country’s food system. The results of the long-run causality test show that GDP has a positive causal relationship with the demand for food, which implies that the demand for food will increase in the long run with an increase in GDP. The result also shows that Saudi Arabia’s GDP and population growth are contributing to the increase in their total GHG emissions. Although the Kingdom has made some efforts to combat climate change, there are still plenty of opportunities for it to implement some of the greatest strategies to guarantee the nation’s food security. This study also highlights the development of appropriate policy approaches to diversify its import sources to ensure future food security.

Considering hydro-climatic diversity, integrating dynamic dimensions of water security modeling is vital for ensuring environmental sustainability and its associated full range of climate resilience. Improving climate resiliency depends on the attributing uncertainty mechanism. In this study, a conceptual resilience model is presented with the consideration of input uncertainty. The impact of input uncertainty is analyzed through a multi-model hydrological framework. A multi-model hydrological framework is attributed to a possible scenario to help apply it in a decision-making process. This study attributes water security modeling with the considerations of sustainability and climate resilience using a high-speed computer and Internet system. Then, a subsequent key point of this investigation is accounting for water security modeling to ensure food security and model development scenarios. In this context, a four-dimensional dynamic space that maps sources, resource availability, infrastructure, and vibrant economic options is essential in ensuring a climate-resilient sustainable domain. This information can be disseminated to farmers using a central decision support system to ensure sustainable food production with the application of a digital system.

Formaldehyde (HCHO), a prevalent indoor air pollutant released from furniture and building materials, poses significant health risks due to its carcinogenic nature. In this study, a binary cuprous oxide–titanium dioxide (Cu₂O–TiO₂) composite photocatalyst was synthesized via a hydrothermal method to enable efficient visible-light-driven degradation of gaseous formaldehyde at ambient temperature. The structural, morphological, and optical properties of the as-prepared catalysts were characterized using XRD, SEM, TEM, EDX, and UV-Vis spectroscopy. While pristine Cu₂O exhibited a formaldehyde degradation efficiency of approximately 68% under white light illumination, the incorporation of TiO₂ markedly enhanced the photocatalytic performance. Among the different mass ratios tested, the Cu₂O–TiO₂ (1:1) composite demonstrated the highest activity, achieving 83% degradation of formaldehyde within 240 min under white light. Enhanced performance is attributed to the formation of a heterojunction that reduces the effective bandgap, promotes charge separation, and suppresses electron–hole recombination. Additionally, the generation of carbon dioxide and water as end products confirmed complete mineralization. The catalyst also showed good reusability, retaining over 81% efficiency after five cycles. This work presents a cost-effective, stable, and visible-light-active Cu₂O–TiO₂ heterojunction photocatalyst with strong potential for indoor air purification applications.

Basalt Fiber-Reinforced Concrete (BFRC) is a sustainable building material characterized by high strength, crack resistance, and durability. Because its mechanical properties involve intricate, nonlinear interactions among binder constituents, aggregates, water content, and fiber characteristics, traditional empirical mix design methods are often inefficient. Current literature largely focuses on single-property prediction using individual machine learning (ML) models, with limited integration of evolutionary optimization, Explainable AI (XAI), and practical deployment tools—leaving significant gaps in real-world applicability. To address this, this research presents a novel, integrated evolutionary ML framework for the accurate prediction and multi-objective optimization of BFRC Compressive Strength (CS) and Splitting Tensile Strength (STS). Three regression models—Random Forest, Gradient Boosting Regressor, and XGBoost—were developed and coupled with evolutionary optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The trade-offs between CS and STS were further evaluated through multi-objective optimization using the NSGA-II algorithm and Pareto front analysis. Additionally, SHapley Additive exPlanations (SHAP) were utilized to enhance model interpretability, revealing that cement content, water content, and fiber volume fraction are the predominant contributors to strength. The optimized models achieved high predictive accuracy, yielding $R^2$ values of 0.88 for CS and 0.95 for STS, alongside low prediction errors (Root Mean Square Error < 2.80 MPa and 0.29 MPa; Mean Absolute Error < 2.10 MPa and 0.19 MPa, respectively). Finally, a graphical user interface (GUI) was developed to enable real-time prediction and mix optimization, providing a practical decision-support tool for performance-driven and sustainable BFRC mix design.

Basalt fiber-reinforced concrete (BFRC), reinforced with chopped basalt fibers having lengths ranging from 12 to 30 mm and diameters ranging from 0.013 to 0.020 mm, is a sustainable construction material with enhanced strength and durability; however, its complex nonlinear behavior makes accurate prediction and optimal mix design challenging. This study proposes an integrated machine learning framework combining evolutionary optimization, multi-objective optimization, and explainable artificial intelligence (XAI) for BFRC strength prediction and mix design optimization. The proposed framework further incorporates a graphical user interface (GUI) deployment to enhance practical usability and support engineering decision-making. Random Forest, Gradient Boosting Regressor, and XGBoost models were optimized using Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution, while NSGA-II was employed to identify optimal trade-offs between compressive strength and splitting tensile strength. SHAP analysis was applied to interpret the influence of key mix parameters on strength prediction. The optimized models achieved high prediction accuracy, with R² values of 0.88 for compressive strength and 0.95 for splitting tensile strength, demonstrating the effectiveness of the proposed framework. The developed GUI provides a practical decision-support tool for sustainable and performance-oriented BFRC mix design.