Spatial and temporal variations in the incidence of dust storms in Saudi Arabia revealed from in situ observations

This is the final version. Available from the publisher via the DOI in this record.Monthly meteorological data from 27 observation stations provided by the Presidency of Meteorology and Environment (PME) of Saudi Arabia were used to analyze the spatial and temporal distribution of atmospheric dust in Saudi Arabia between 2000 and 2016. These data were used to analyze the effects of environmental forcing on the occurrence of dust storms across Saudi Arabia by considering the relationships between dust storm frequency and temperature, precipitation, and wind variables. We reveal a clear seasonality in the reported incidence of dust storms, with the highest frequency of events during the spring. Our results show significant positive relationships (p < 0.005) between dust storm occurrence and wind speed, wind direction, and precipitation. However, we did not detect a significant relationship with temperature. Our results reveal important spatial patterns, as well as seasonal and inter-annual variations, in the occurrence of dust storms in Saudi Arabia. For instance, the eastern part of the study area experienced an increase in dust storm events over time, especially in the region near Al-Ahsa. Similarly, an increasing trend in dust storms was also observed in the west of the study area near Jeddah. However, the occurrence of dust storm events is decreasing over time in the north, in areas such as Hail and Qaisumah. Overall, the eastern part of Saudi Arabia experiences the highest number of dust storms per year (i.e., 10 to 60 events), followed by the northern region, with the south and the west having fewer dust storm events (i.e., five to 15 events per year). In addition, our results showed that the wind speeds during a dust storm are 15-20 m/s and above, while, on a non-dust day, the wind speeds are approximately 10-15 m/s or lower. Findings of this study provide insight into the relationship between environmental conditions and dust storm occurrence across Saudi Arabia, and a basis for future research into the drivers behind these observed spatio-temporal trends

Modeling and simulation of VERA core physics benchmark using OpenMC code

Detailed analysis of the neutron pathway through matter inside the nuclear reactor core is exceedingly needed for safety and economic considerations. Due to the constant development of high-performance computing technologies, neutronics analysis using computer codes became more effective and efficient to perform sophisticated neutronics calculations. In this work, a commercial pressurized water reactor (PWR) presented by Virtual Environment for Reactor Applications (VERA) Core Physics Benchmark are modeled and simulated using a high-fidelity simulation of OpenMC code in terms of criticality and fuel pin power distribution. Various problems have been selected from VERA benchmark ranging from a simple two-dimension (2D) pin cell problem to a complex three dimension (3D) full core problem. The development of the code capabilities for reactor physics methods has been implemented to investigate the accuracy and performance of the OpenMC code against VERA SCALE codes. The results of OpenMC code exhibit excellent agreement with VERA results with maximum Root Mean Square Error (RMSE) values of less than 0.04% and 1.3% for the criticality eigenvalues and pin power distributions, respectively. This demonstrates the successful utilization of the OpenMC code as a simulation tool for a whole core analysis. Further works are undergoing on the accuracy of OpenMC simulations for the impact of different fuel types and burnup levels and the analysis of the transient behavior and coupled thermal hydraulic feedback

Degree of Satisfaction With Telemedicine Service Among Health Practitioners and Patients in Saudi Arabia

The satisfaction level of patients and physicians with telemedicine services should be comparable to on-site visits if this technology is to be widely adopted. The degree of satisfaction can be used to assess the performance of any healthcare service. Therefore, in this cross-sectional study, we used a five-point Likert scale to evaluate the level of satisfaction with telemedicine by administering survey questionnaires to both patients and physicians online residing across Saudi Arabia. The questionnaires were divided into three parts: consent and summary, demographics, and the survey questions to determine satisfaction level with telemedicine. All statistical analysis was performed using SPSS Version 27. The results showed high satisfaction levels with telemedicine services by both patients and physicians where the age and gender of participants were influential in determining this outcome. Residential areas affected satisfaction levels with telemedicine in physicians but not in patients. Previous experiences with these services influenced the satisfaction of patients with those having previous experience reporting higher satisfaction. However, when the degree of satisfaction between patients and physicians was compared, the results were insignificant showing that the overall perception towards telemedicine usage is similar between healthcare providers and receivers. To conclude, our study showed that the experiences of patients and physicians with telemedicine services are satisfactory; however, certain areas regarding telemedicine usage warrant further improvement

A Multivariate and Geographic-Information-System Approach to Assess Environmental and Health Hazards of Fe, Cr, Zn, Cu, and Pb in Agricultural Soils of Western Saudi Arabia

This study evaluates the environmental and health hazards associated with the presence of Fe, Cr, Zn, Cu, and Pb in agricultural soils from the Makkah region in western Saudi Arabia. Soil samples were collected from 32 farms predominantly cultivating dates and vegetables and analyzed for heavy metals (HMs) using inductively coupled plasma-atomic emission spectrometry (ICP-AES). Multivariate statistical analysis, Geographic Information Systems (GIS), and various contamination indices were employed. The average HM concentrations were arranged in descending order as follows: Fe (35.138 mg/kg), Zn (69.59 mg/kg), Cu (55.13 mg/kg), Cr (47.88 mg/kg), and Pb (6.09 mg/kg). Contamination indices indicated considerable enrichment of Cu and deficient to minimal enrichment for the other HMs, though a few individual samples showed higher enrichment factor (EF) values. Risk assessments revealed a low-level risk associated with HMs in Makkah soils. Multivariate analyses suggested that the HMs primarily originated from natural geological processes, with anthropogenic contributions particularly evident for Cu. Hazard index (HI) values ranged from 0.0003 to 0.0691 for adults and 0.003 to 0.6438 for children, remaining below the threshold of 1.0, which indicates no significant non-carcinogenic risk. Lifetime cancer risk estimates for Pb were below 1 &times; 10&minus;6, while those for Cr ranged from 1 &times; 10&minus;6 to 1 &times; 10&minus;4, indicating tolerable carcinogenic risk levels with a few exceptions for Cr in children. This study is significant as it provides critical baseline data on HM contamination in agricultural soils in the Makkah region, offering insights into natural and anthropogenic contributions to soil pollution. The findings contribute to the broader understanding of environmental risk assessments and serve as a foundation for developing sustainable agricultural practices and targeted mitigation strategies to minimize health risks in regions with similar environmental conditions

Automatic Active Contour Algorithm for Detecting Early Brain Tumors in Comparison with AI Detection

The automatic detection of objects in medical photographs is an essential component of the diagnostic procedure. The issue of early-stage brain tumor detection has progressed significantly with the use of deep learning algorithms (DLA), particularly convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database over several hundred images, which can be time-consuming and require complex computational infrastructure. This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumors in magnetic resonance images. We tested the proposed algorithm using 50 brain images, specifically focusing on glioma tumors, while 2000 images were used for DLA from the BRATS Challenges 2021. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphologic operations for segmentation refinement. We evaluated its performance using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method provided an average of the first six performance metrics of 0.96, which is higher than most classical image segmentation methods and was comparable to the deep learning methods, which have an average performance score of 0.98. These results indicate its ability to detect brain tumors accurately and rapidly. The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumor areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumor identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumor categories and improve computing efficiency to integrate the technology into clinical processes