This paper investigates the short‐term response of the Saudi stock market (Tadawul) to the COVID‐19 outbreak. Event study methodology applied to data derived from the 21 industry groups that constitute the Saudi stock market to calculate abnormal returns for the trading days after the announcement of the COVID‐19 in both China and Saudi Arabia. The results indicate that the estimated CARs for the industry groups and their sum on the event day were not statistically significant. Furthermore, the formal announcement of the first case of the COVID‐19 in China had a negative but not significant impact on the Saudi stock market. In contrast, in the first 9‐days event window, the announcement of the first confirmed case in Saudi Arabia had a negative and significant effect. Moreover, the most negatively affected industry groups were banks, consumer services, capital goods, transportation and commercial services, whereas telecommunication services and food and beverage were positively affected at the event window (+1, +9). In general, the Saudi stock market's response had become weaker in the event windows come after (+1, +9), and different industry groups were found to have different responses to the COVID‐19 outbreak.

In the process of building a linear regression model, the essential part is to identify influential observations. Various influence measures involving Cook's distance and DFFITS are designed to detect the linear regression's influential observations using the Least Squares (LS). The existence of influential observations in the data is complicated by the presence of severe collinearity and affects the efficiency of the detection measures. This paper proposes new diagnostic methods based on the Liu type estimator (LTE) defined by Liu [1]. The Cook's distance and DFFITS for the LTE are introduced. Moreover, approximate formulas for Cook's distance and DFFITS are also proposed for LTE. Two real data sets with a high level of multicollinearity among the explanatory variables as well as the simulation study are used to illustrate and evaluate performance of the methodologies presented in this paper.

In this paper, we propose three stochastic restricted biased estimators for the linear regression model. These new estimators generalize the least squares estimator, mixed estimator, and biased estimator. We derive the necessary and sufficient conditions for the superiority of the proposed estimators over existing ones, as well as their relative superiority among each other, using the mean squared error matrix as a criterion. A simulation study is conducted to validate the theoretical findings, and two real-world examples are provided to demonstrate the practical advantages of the proposed estimators.

Contents IJPAM: Volume 85, No. 4 (2013)

Effective water resource planning is essential for Saudi Arabia, where limited freshwater availability is challenged by rapid population growth, economic development, and climate variability. This study introduces a copula-based modeling framework for forecasting water demand across the country’s urban, industrial, and agricultural sectors. Copulas, compared to traditional models, effectively capture nonlinear and asymmetric relationships among essential variables, including population, temperature, GDP, and sectoral water consumption. Multivariate copula models (Gaussian, Clayton, Gumbel, Frank, t-Copula, and Vine structures) were fitted and evaluated using historical data from 2008 to 2024, obtained from national authorities, including the Ministry of Environment, Water, and Agriculture, the General Authority for Statistics, and the National Center for Meteorology. The 4D normal copula was developed as the most efficient method across all sectors, with MAPE values of 6.37% for urban, 17.51% for industrial, and 23.20% for agricultural consumption. Scenario-based forecasts, which include baseline, high-growth, and sustainability-focused trajectories, indicate that the sustainability scenario yields the best results, resulting in significant demand reductions (21.7% urban, 20.4% industrial, and 8.2% agricultural) with minimal climate impact (+0.4 °C) and the lowest risk levels. The study demonstrates the successful decoupling of water demand from population and economic growth through proper policy interventions, with conditional risk analysis offering actionable early warning capabilities for proactive management. These findings provide a valuable foundation for enhancing national water strategy planning in Saudi Arabia under Vision 2030 and contribute to methodological improvements applicable to water-scarce regions internationally.

The arachidonate 5-lipoxygenase activating protein (ALOX5AP) regulates leukotrienes (LTs) synthesis. LTs are involved in inflammation which is implicated in cardiovascular diseases (CVDs) and stroke. Variations in ALOX5AP gene are associated with CVDs, stroke and others because of their possible effects on ALOX5AP stability and function. In this study we investigated with molecular dynamics (MD) simulation the structural impacts of L12F, A56V, G75R, and G87R variants on ALOX5AP. We employed an array of bioinformatics techniques, including SIFT, PolyPhen-2, PANTHER, SNPs&GO, PhD-SNP, i-Mutant, MuPro, MutPred, ConSurf, and GROMACS. Results showed that the L12F variant increased structural compactness, as indicated by diminished solvent accessibility, a reduced radius of gyration, and a decrease in hydrogen bonding capacity. The A56V variant destabilized the ALOX5AP, demonstrating elevated root mean square deviation (RMSD), augmented solvent-accessible surface area, and diminished ALOX5AP compactness. The G75R and G87R variants exhibited mild effects on ALOX5AP wildtype. However, simulation trajectory snapshots results indicated G75R and G87R variants induce instability leading to structural perturbations of ALOX5AP probably due to the charge of arginine introduced by the G75R and G87R mutation. The G75R and G87R variants potentially influence ALOX5AP dynamics, stability, and function. These results require further verification in future case-control and protein functional studies.