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Objectives: The current study was designed to capture comprehensive imaging of acute ischemic stroke (AIS) using multidetector computed tomography (MDCT). The study also evaluated the accessibility and potential use of computed tomography angiography (CTA) and sought to determine stroke subtypes in patients with AIS. Methods: The presence of ischemic lesions was detected by the MDCT, transesophageal echocardiography (TEE), and non-contrast flat detector computed tomography (FDCT). The study selected the AIS patients with hyperacute stroke for less than 6 hours from July 2021 to August 2022. The CTA of cervical arteries was performed in order to determine stroke subtypes. Results: The MDCT-based detection of ischemic lesions in 110 studied ischemic stroke patients showed 34% had extensive intracranial artery damage, 86% detected a partial territorial infarct, while 52% patients had detected artery-to-artery embolization. These percentages of detected ischemic lesions overlapped in all studied AIS patients. The findings showed significant susceptible plaques and arterial stenosis. Conclusion: The findings of the study clearly indicated that AIS patients admitted within less than 6 hours of a hyperacute stroke showed multiple types of ischemic lesions. The findings also concluded that the MDCT technique is highly useful for the detection of multiple patterns of brain infarction and for the determination of the causative mechanisms in patients with acute ischemic stroke.

One of the most critical stages for developing groundwater resources for drinking water use is assessing the water quality. The use of a Water Quality Index (WQI) is considered an effective method of evaluating water quality. The objective of this research was to evaluate the performance of six multiple artificial intelligence techniques, i.e., linear regression (stepwise), support vector regression SVM (linear and polynomial kernels), Gaussian process regression (GPR), Fit binary tree, and artificial neural network ANN (Bayesian) to predict the WQI in Jizan, Southern Saudi Arabia. A total of 145 groundwater samples were collected from shallow dug wells and boreholes tapping the phreatic aquifer. The WQI was calculated from 11 physicochemical parameters (pH, TDS, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, and TH). The spatial distribution results showed that higher values of Cl− and SO42− were recorded in the places close to the coastline, indicating the occurrence of seawater intrusion and salinisation. Seven wells had a WQI of greater than 300, indicating that the water was unfit for consumption. The results showed that the GPR, linear regression (stepwise), and ANN models performed best during the training and testing stages, with a high correlation of 1.00 and low errors. The stepwise fitting model indicated that pH, K+, and NO3− were the most significant variables, while HCO3− was a non-significant variable for the WQI. The GPR, stepwise regression, and ANN models performed best during the training and testing stages, with a high correlation and low errors. In contrast, the SVM and Fit binary tree models performed the worst in the training and testing phases. Based on subset regression analysis, the optimum input combination for WQI model prediction was determined as these eight input combinations with high R2 (0.975–1.00) and high Adj-R2 (0.974–1.00). The resultant WQI model significantly contributes to sustainable groundwater resource management in arid areas and generates improved prediction precision with fewer input parameters.

Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R2: 0.99, MAPE: 15.99%), outperforming the CNN model (R2: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R2 value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

The water supply in Saudi Arabia is already depleted. Climate change will exacerbate the demand for these resources. This paper examines how climate change affects the water demands of Saudi Arabia’s most important food crops: wheat, clover, vegetables, and dates. To reduce the adverse climate change impacts on these crops’ productivity, as well as their irrigation water requirements (IWR), a number of adaptation techniques were investigated. The study was carried out for the Ar Riyadh region, Saudi Arabia, with a cultivated area of 179,730 ha. In this study, five climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for two Shared Socio-economic Pathways (SSPs), SSP2-4.5 and SSP5-8.5, were used to forecast and investigate the potential impacts of climate change on agricultural water resources in the Al-Riyadh Region of Saudi Arabia. To simulate IWRs under the present and projected climate change scenarios, CROPWAT8.0 was used. The results showed that the maximum increase ratio in 2100 under SSP2-4.5 and SSP5-8.5, respectively, will be 4.46% and 12.11% higher than in the current case (2020). The results showed that the projected maximum temperatures in 2100 will be increased by 4.46% and 12.11%, respectively, under SSP2-4.5 and SSP5-8.5, compared to the current case (2020), supporting past research on the Arabian Peninsula that revealed that both short- and long-term temperature increases are anticipated to be considerable. Under SSP2-4.5 and SSP5-8.5, the projected ETo was found to be increased by 2.18% and 6.35% in 2100, respectively. Given that evapotranspiration closely mirrors the temperature behavior in the study region from June to August, our data suggest that crop and irrigation demand may increase in the mid to long term. The findings indicate that Riyadh, Saudi Arabia’s capital and commercial hub, will require more water to irrigate agricultural land because of the expanding ETo trend. Under SSP2-4.5 and SSP5-8.5, the projected growth irrigation water requirement (GIWR) will be increased by 3.1% and 6.7% in 2100, respectively. Under SSP5-8.5, crop areas of wheat, clover, dates, maize, citrus, tomato, potato, and other vegetables in Ar Riyadh will decrease by 6.56%, 7.17%, 5.90%, 6.43%, 5.47%, 6.99%, 5.21%, and 5.5%, respectively, in 2100. Conversely, under SSP2-4.5, the crop areas will decrease by 3.10%, 3.67%, 2.35%, 3.83%, 2.32%, 4.18%, 1.72%, and 2.38% in 2100, respectively. This research could aid in clarifying the adverse climate change impacts on GIWR in Ar Riyad, as well as improving water resource management planning.

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

The Wadi Fatimah area suffers from a lack of freshwater resources, so this study aimed to analyze the groundwater in this region and evaluate its quality for irrigation and drinking purposes. Eleven water quality parameters, including pH, total dissolved solids (TDS), Ca2+, HCO−3, NO−3, F−, Cl−, K+, Mg2+, SO2−4, and Na+, were utilized to evaluate the quality of the water and produce a water quality index (WQI). These parameters were measured at 100 different chosen locations. The spatial distribution map revealed that all parameters are high in the southern part except K+. Using the correlation matrix, a high positive correlation is obtained among TDS, Cl−, Mg2+, and Ca2+ in addition to a high correlation among TDS with Cl− and Na+. From PCA analysis, PCA1, PCA2, and PCA3 represent about 52%, 12%, and 10% of all components along the study area, respectively. PCA1 has low variance than PCA2 and PCA3. The majority of the Southern region’s sites went from having extremely poor to poor water classifications and from poor to unsuitable water. However, the center part possesses exceptionally high-quality groundwater. According to the results from the current study’s water quality index, the presence of nitrate and fluoride in the groundwater samples was primarily responsible for their high WQI values. The statistics showed that a higher percentage of the population had poor drinking water due to direct pollutant release, agricultural effects, and excessive groundwater resource use. The study offers a groundwater quality modeling technique that is both affordable and replicable in other areas.

Coastal environments need continuous environmental risk assessment, especially with increasing coastal development and human activities. The present work evaluates the distribution, contamination, and environmental risk of potentially toxic elements (PTEs) in coastal sediments between Al-Jubail and Al-Khafji cities along the Arabian Gulf, Saudi Arabia, and documents the influence of background references applied in pollution indices. Thirty-two sediment samples were collected for analysis of Ni, Cu, Cr, As, Zn, Pb and Hg using ICP-AES. The ranges of PTEs (mg/kg) were in the following order: Cr (3.00–20.0), Ni (2.00–32.0), Zn (2.00–14.0), As (2.00–4.00), Pb (1.50–5.00), Cu (1.00–5.00), and Hg (0.50–1.00). The coastal sediments show severe enrichment with As and Hg, and no to minor enrichment and a low contamination with Cr, Cu, Cr, Zn, and Pb. Based on sediment quality guidelines, concentrations of Cu, Pb, Zn, As, and Cr do not represent a concern for benthic communities, while Ni and Hg show a risk for benthic communities in four and 17 sampled areas, respectively. Multivariate analysis indicated a geogenic source for Zn, Cr, Cu, Ni, and Pb, mixed natural and anthropogenic sources for As, and an anthropogenic source for Hg, mostly from oil pollution, sewage, and industrial effluents spreading near Al-Jubail industrial city.

Loveringite, a rare member of the crichtonite group with nominal formula (Ca,Ce)(Ti,Fe,Cr,Mg)21O38, was found in the Khamal layered mafic intrusion, the first known locality for this mineral in the Arabian Shield. The Khamal intrusion, a large post-collisional mafic complex, is lithologically zoned, bottom to top, from olivine gabbro through gabbronorite, hornblende gabbro, anorthosite, and diorite to quartz diorite. Loveringite is found near the base of the complex, as an intercumulus phase in olivine gabbro. Most loveringite grains are homogeneous, although a few grains are zoned from cores rich in TiO2, Al2O3, Cr2O3, and CaO towards rims rich in FeO*, ZrO2, V2O3, Y2O3, and rare earth elements (REE). Petrographic relations indicate that loveringite formed after crystallization of cumulus olivine, pyroxenes, and plagioclase. Anhedral and corroded crystals of loveringite are surrounded by reaction rims of Mn-bearing ilmenite and baddeleyite, suggesting that the residual liquid evolved into and subsequently out of the stability field of loveringite. The budget of incompatible elements (Zr, Hf, REE, U, and Th) hosted in loveringite is anomalous for a primitive mafic liquid. Saturation in loveringite is likely the result of early contamination of the primary melt by anatexis of country rock, followed by isolation of evolving liquid in intercumulus space that restricted communication with the overlying magma chamber. The zoned crystals likely reflect diffusive equilibration between residual loveringite grains and their reaction rims of ilmenite.

Objective: To evaluate the influence of changes in the gingival display of the maxillary teeth on smile attractiveness assessed by Saudi Arabian dental professionals and laypeople. Materials and methods: A total of 138 dental professional and 182 laypeople rated the attractiveness of male and female smiles in a computerized survey. Each smile was digitally altered to achieve different levels of gingival exposure. Each photo was accompanied by a visual analog scale (VAS) for rating. Results: Among dental professionals, 61% rated the female photo with a 1-mm low lip line as the most attractive smile (VAS score±SE, 7.3±3.18), while 52.7% of laypeople considered the smile with a 2-mm low lip line as the most attractive (6.7±3.4). Regarding male smile photos, 61.6% of dental professionals found the 1-mm low lip line the most attractive (7.3±3.18). The same rating was given by 48.3% of laypeople (6.1±3.6) (p≤0.009). The least attractive smile photo was the smile showing 4 mm of gingiva for male and female smiles. More than half of the laypeople believed that an attractive smile highly affects social life and communication. Conclusion: The Saudi Arabian population appears to be sensitive to the amount of gingival display. The difference in female smile assessment between dental professionals and laypeople highlights the importance of dentist-patient consensus regarding decisions for esthetic treatments. Clinical Relevance: Esthetic treatment is of a major concern for both dentist and patient. The careful assessment of smile pillars including gingival display must be tailored to each patient.

This study assessed the spatiotemporal variability and pollution grades of heavy metals in water and sediments of Bahr El-Baqar drain, Eastern Nile Delta, Egypt, by integration of geochemical analysis, metal pollution indices, correlation, and multivariate statistical analyses. Twenty samples of water and sediments were collected during 2018 and analyzed for heavy metal concentrations using ICP-OES. Heavy metal contents in the water samples followed the order: Fe > Zn > Al > Pb > Mn > Cu > Ni. The drain sediments were highly contaminated with heavy metals that followed the order: Fe > Al > Mn > V > Zn > Cu > Cr > Ba > Ni > Pb > As. Spatiotemporally, most metals in the drain sediments showed a decreasing trend from upstream (south) to downstream sites (north). Results of principal component analysis (PCA) supported those from the Pearson correlation between investigated heavy metals. In water, Mn, Ni, Pb, Zn, Cu, and Fe showed highly significant correlations. In sediments, Ba, Ni, Zn, Fe, Al, Mn, and V showed strong positive correlations indicating that these metals were derived from similar anthropogenic sources. The calculated metal pollution indices: enrichment factor (EF), contamination factor (CF), pollution load index (PLI), degree of contamination (DC), and index of geo-accumulation (Igeo) indicated high loadings of heavy metals in the drain sediments. EFs revealed low, moderate to significant enrichment, whereas CFs showed low, moderate, and considerable contamination. PLI indicated low, baseline, and progressive contamination, while DC indicated low, moderate, and considerable degree of contamination. Igeo of all investigated metals (except for As; class 1) indicated extremely contaminated sediments (class 7).

Changes in the terrestrial total water storage (TWS) have been estimated at both global and river basin scales from the Gravity Recovery and Climate Experiment (GRACE) mission and are still being detected from its GRACE Follow-On (GRACE-FO) mission. In this contribution, the sixth release of GRACE-FO (RL06) level-2 products applying DDK5 (decorrelation filter) were used to detect water mass variations for the Nile River Basin (NRB) in Africa and the Mega Aquifer System (MAS) in Asia. The following approach was implemented to detect the mass variation over the NRB and MAS: (1) TWS mass (June 2018–June 2021) was estimated by converting the spherical harmonic coefficients from the decorrelation filter DDK 5 of the GRACE-FO Level-2 RL06 products into equivalent water heights, where the TWS had been re-produced after removing the mean temporal signal (2) Precipitation data from Global Precipitation Climatology Project was used to investigate the pattern of change over the study area. Our findings include: (1) during the GRACE-FO period, the mass variations extracted from the RL06-DDK5 solutions from the three official centers—CSR, JPL, and GFZ—were found to be consistent with each other, (2) The NRB showed substantial temporal TWS variations, given a basin average of about 6 cm in 2019 and about 12 cm in 2020 between September and November and a lower basin average of about −9 cm in 2019 and −6 cm in 2020 in the wet seasons between March and May, while mass variations for the MAS had a relatively weaker temporal TWS magnitude, (3) the observed seasonal signal over the NRB was attributed to the high intensity of the precipitation events over the NRB (AAP: 1000–1800 mm yr−1), whereas the lack of the seasonal TWS signal over the MAS was due to the low intensity of the precipitation events over the MAS (AAP:180–500 mm yr−1).

Every day, mining corporations grow and develop over Egypt’s Eastern Desert in search of gold, silver, and other metals. Mineral resources in Egypt are a powerful tool for increasing national income. In this study, we are interested in mineral exploration, such as gold and porphyry deposits, using aeromagnetic data, applying various processing techniques such as First Vertical Derivative, Total Horizontal Gradient, Analytical Signal, Tilt Derivative, and the center of exploration targeting (CET). The study area is located at the Central Eastern Desert, which includes the Barramiya, Abu Marwa, and Abu Mireiwah regions. The analysis of the delineated structural trends shows that the N–S, NW–SE, and NE–SW are the most effective directions for managing deposits in the investigated area. A minor trend is also shown in the E–W direction and corresponds to the alteration zones reported by geology and prior investigations. Mineralization zones occur most frequently at the contact between ophiolitic serpentinite, sediments, and other rock types. This study intends to add more about the use of the recently developed technology of CET grid analysis for mineral exploration and structural interpretation across the Central Eastern Desert. The CET porphyry analysis map shows locations where several occurrences of porphyry deposits are probable. The newly discovered spots are similar to the area’s usual deposit sites. This study proves that aeromagnetic data are significant in mineral exploration since they are useful for the discovery of the structure and shear zones controlling the mineralization zones.

Water demands have increased even more in recent decades because of the high population density. Surface and groundwater resources are insufficient to meet these demands. As a result, governments have turned to the treatment of sewage water. Sewage water contains multiple types of contamination, creating a major health risk. In the research region, 48 water samples were obtained, including 18 samples of surface water and 30 samples of groundwater. The Canadian Council Water Quality Index (CCWQI) program calculates the water quality index to evaluate the water quality for drinking and human use. The World Health Organization (WHO) and the Egyptian Ministry of Health (EMH) determined regulatory limits for drinking water and each value of the investigated parameter connected with them. According to the findings, 79% of the tested water samples are safe to drink and are excellent for human and wildlife use. Due to infiltration or recharging of groundwater with drainage water, as well as the involvement of dissolution, leaching processes, and anthropogenic activities that damage human health, animals, and some plants, these samples are unfit for drinking and domestic consumption. The heavy metal level of Cd and Pb in the examined water samples was found to be above WHO and EMH acceptable limits. Furthermore, due to oral exposures, the examined water samples may cause complex health concerns such as non-carcinogenic and carcinogenic influences for children over adults due to a reduction in children's immunity. As a result, water treatment should be carried out in the examined region to protect the health of the residents. Validerad;2023;Nivå 2;2023-04-13 (johcin);Funder: Abdullah Alrushaid Chair for Earth Science Remote Sensing Research

Evaluating the suitability of groundwater for human use is essential for water supply and health in arid and semi-arid regions. In this work, a total of 68 groundwater samples were collected from Harrat Khaybar, western Saudi Arabia to evaluate the suitability of groundwater for drinking and irrigation purposes and to document the controlling mechanisms using pollution indices and multivariate statistical methods. The results showed that the average values of the ions Cl–, SO42–, HCO3–, NO3–, Na+, Ca2+, Mg2+, and total dissolved solids (TDS) were greater than the permissible limit for drinking water while the average values of heavy metals (HMs) were less than the permissible limit, with exceeding limits of Cr, Se, As, Zn, and Pb in some individual samples. Piper diagram indicated that 47.10% of the water samples are of Na-K-SO4-Cl type, 23.51% of Ca-Mg-CO3-HCO3 type, 23.51% of Ca-Mg-SO4-Cl type, and 5.88% of Na-K-CO3-HCO3 type. Based on the groundwater quality index (GWQI), 29 of the groundwater samples were categorized as excellent and good water for drinking purposes, while 29 samples fell under poor, very poor water, and unsuitable for drinking. Additionally, results of heavy metal pollution index (HPI) indicated that all water samples fell within the low pollution category, while the metal index (MI) results indicated that 35 samples fell within very pure, pure, and slightly affected categories, while 33 samples fell in the moderately, strongly, and seriously affected categories. Results of electrical conductivity (EC), sodium adsorption ratio (SAR), sodium percentage (%Na), Kelly’s ratio (KR), and magnesium ratio (MR) revealed that 33.82–98.5 % of the water samples are suitable for irrigation depending on the parameter type. Ions exchange reactions and dissolution of carbonates, evaporites, and silicates, as well as industrial and domestic effluents and intensive use of fertilizers and pesticides were the natural and athropogenic factors controlling the groundwater geochemistry in the study area and HM pollution in some wells.

Epilepsy is a chronic neurological disorder that occurs due to irregular neuronal activity in the central nervous system. The main job of a radiologist is to investigate the structural etiology in epilepsy patients. This study was undertaken to find out the importance of magnetic resonance imaging (MRI) in the screening of intractable epilepsy through a systematic search of literature.A systematic review was performed using the PRISMA guidelines. Peer-reviewed studies on MRI and intractable epilepsy were retrieved from MEDLINE, ScienceDirect and Google Scholar. Moreover, studies cited in the key articles were also screened to increase the sensitivity and specificity of the systematic search.The database search till March 2022 found a total of 112610 articles. Out of them, only 10 highly selected articles were included in the study. The pooled data point out that the rapid development in MRI techniques and the functional MRI (fMRI) has now become more and more critical in the diagnosis and management of patients with epilepsy. In addition, the data also pointed out that MRI-based approaches are also very useful for post-operative epilepsy patients as it gives information about the quality of the surgery. The data collected showed that the MRI is the choice technique for the evaluation of patients with epilepsy.The applicability of MRI in epilepsy diagnosis is highly accessible in all over the globe. The pooled data concluded that the MRI-based surgical approaches are extremely useful for the surgeons to provide three-dimensional imaging with superimposed real-time pointer details that have proved successful for epilepsy patients.

Porphyromonas gingivalis (P. gingivalis) is a cornerstone pathogen in the development and progression of periodontal and peri-implant tissue destruction. It is capable of causing dysbiosis of the microbial biofilm and modulation of the host immune system. Hyaluronic acid (HA) is a naturally occurring glycosaminoglycan found in all living organisms. It is well known and has been used for improving tissue healing. In addition, some studies have suggested that there may be an antimicrobial potential to HA. The aim of this study was to evaluate the effect of hyaluronic acid, azithromycin (AZM), and chlorhexidine (CHX) on the expression of genes (i.e., fimA, mfa1, hagA, rgpA, rgpB, and kgp) related to the virulence and adhesion of P. gingivalis. The study groups were divided into four: (1) HA treated group; (2) AZM treated group; (3) CHX treated group; and (4) untreated group to serve as a negative control. P. gingivalis ATCC 33277 was cultured and then exposed to four different concentrations (100% MIC, 50% MIC, 25% MIC, and 12.5% MIC) of HA, AZM, and CHX for 24 h. The expression levels of the aforementioned genes were measured using quantitative reverse transcription polymerase chain reaction (RT-qPCR). Relative fold-change values were calculated and compared between groups. The fold-change values of all genes combined were 0.46 ± 0.33, 0.31 ± 0.24, and 0.84 ± 0.77 for HA, AZM, and CHX, respectively. HA has downregulated all the genes by mostly a half-fold: 0.35 ± 0.20, 0.47 ± 0.35, 0.44 ± 0.25, 0.67 ± 0.46, 0.48 ± 0.33 and 0.35 ± 0.22 with fimA, mfa1, hagA, rgpA, rgpB and kgp, respectively. The effect of HA was significant on all genes except rgpB compared to the untreated control. Lower concentrations of HA tended to exhibit greater downregulation with 1 mg/mL being the most effective. High molecular weight (1.5 MDa) hyaluronic acid has a potent effect on P. gingivalis by downregulating fimA, mfa1, hagA, rgpA, and kgp. The effect of HA was generally less than that of AZM but greater than that of CHX.

A severe threat to natural resources and human livelihood is groundwater scarcity. Therefore, mapping groundwater potentiality (GWP) is necessary for future resource management. In this article, a framework for conducting ensemble modeling is introduced. This framework will be used to map GWP at the national level under the climate change scenario. Thirteen elements linked to topography, geology, hydrology, and land cover, as well as six climatic indicators based on historical time series data, were used to map the GWP. To provide extremely reliable groundwater potentiality mapping, three traditional standalone machine learning techniques such as logistic model tree (LMT), logistic regression (LR), and artificial neural network (ANN) have been merged with a stacking ensemble framework. Using the empirical and binormal receiver operating characteristic curves, the GWP mapping has been validated (ROC curve). According to research, Bangladesh's major rivers run along the high GWP zones in the country's southern and central regions. Additionally, the validation using the ROC curve demonstrates that the stacking model which had all three MLAs—performed better than other models (AUC: 0.971). The study may have a substantial impact on Bangladesh's national water planning and policy, which will be made using evidence. Additionally, the suggested method might be applied to map GWP on a broader scale in additional nations as well as at the continental level.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

The groundwater quality and heavy metal (HM) contamination were evaluated in palm farms, central Saudi Arabia, using pollution indices, irrigation quality parameters, and multivariate statistical analyses. Thirty groundwater samples were collected in October 2020 for major anions, cations, and HMs analyses and interpretation. The results showed that the average concentrations of total dissolved solids (TDS), Ca+, Na+, K+, Cl−, SO42−, and F− were greater than the permissible limits of the WHO standards for drinking water. The groundwater facies types were Ca–Na–SO4–Cl (23 samples), Ca–Cl–SO4, (4 samples), and Ca–SO4–Cl type (3 samples). The groundwater quality index indicated that 15 groundwater samples were of good quality and 15 were of poor quality, whereas the metal index and heavy metal pollution index indicated that all samples were categorized as slightly affected and with low pollution, respectively. The variation is attributed to the increasing average concentrations of some ions and decreasing HMs. The dissolution/precipitation of silicates, gypsum, and carbonates and soil leaching were the natural factors affecting groundwater chemistry, whereas higher PO43−, NO3−, F−, Pb, and Zn values in some samples may be attributed to human activities from the extensive use of fertilizers and pesticides on the investigated farms.

To detect groundwater-bearing potential zones in Wadi Fatimah, western Saudi Arabia, geophysical data from three profiles of two-dimensional electrical resistivity tomography (ERT) and remote sensing data were gathered, integrated, and evaluated. The DEM and slope maps indicate that Wadi Fatimah has a high potential to store great amounts of groundwater. The maximum elevations range from 0 to 933 m, with an average elevation of 466 m AMSL. The amount of surface water that infiltrates into the ground is affected by the slope. Rainwater can be collected in low-sloped areas and percolate into the subsurface, replenishing groundwater. In the study area, the slope ranged from 0° to 38°. The slopes of Wadi Fatimah ranged from 0° to 9.1°, with highlands having slopes ranging from 9.1° to 38°. Wadi Fatimah has a high stream density. Furthermore, because it is unconfined, the groundwater-bearing zone reaches the ground surface and recharges continuously during the rainy season. The drainage density is 0.433 km/km2, which is considered normal for coarse drainage. Lithology, infiltration capacity, and topographic relief all have an impact on drainage texture. Because of the basement rocks’ low slope, a coarse drainage texture of 0.059 was calculated, indicating additional groundwater recharge from precipitation. Moreover, based on the 2D inversion results of the ERT data, Wadi Fatimah’s unconfined aquifer has a high potential for groundwater. This aquifer is distinguished by a zone of low resistivity (less than 100 Ω.m) and a depth of up to 50 m below the ground surface. This aquifer is underlain by the weathered/fractured and/or fresh basement rocks. Wadi Fatimah basin is recharged by rainfall creating a promising or strategic area for groundwater supply for future planning and urbanization projects in surrounding areas.

Jazan province on Saudi Arabia’s southwesterly Red Sea coast is facing significant challenges in water management related to its arid climate, restricted water resources, and increasing population. A total of 180 groundwater samples were collected and tested for important hydro-chemical parameters used to determine its adaptability for irrigation. The principal components analysis (PCA) was applied to evaluate the consistency/cluster overlapping, agglomeration in the datasets, and to identify the sources of variation between the 11 major ion concentrations (pH, K+, Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, NO3−, TDS, and TH). The EC values ranged from excellent to unsuitable, with 10% being excellent to good, 43% permissible, and 47% improper for irrigation. The SAR classification determined that 91.67% of groundwater samples were good to excellent for irrigation, indicating that they are suitable for irrigation with no sodium-related adverse effects. Magnesium hazard values showed that 1.67% of the samples are unsuitable for irrigation, while the remaining 98.33% are suitable. Chloro-alkaline indices signify that most groundwater samples show positive ratios indicating that ion exchange is dominant in the aquifer. The Gibb’s diagram reflects that evaporation, seawater interaction, and water–rock interaction are the foremost processes impacting groundwater quality, besides other regional environmental variables. A strong positive correlation was declared between TDS and Na+, Mg2+, Ca2+, Cl−, SO42− in addition to TH with Mg2+, Ca2+, Cl−, SO42−, TDS, and also Cl− with Na+, Ca2+, Mg2+ were major connections, with correlation coefficients over 0.8 and p < 0.0001. The extracted factor analysis observed that TH, Ca2+, TDS, Cl−, and Mg2+ have high positive factor loading in Factor 1, with around 52% of the total variance. This confirms the roles of evaporation and ion exchange as the major processes that mostly affect groundwater quality, along with very little human impact. The spatial distribution maps of the various water quality indices showed that the majority of unsuitable groundwater samples were falling along the coast where there is overcrowding and a variety of anthropogenic activities and the possible impact of seawater intrusion. The results of the hierarchical cluster analysis agreed with the correlations mentioned in the factor analysis and correlation matrix. As a result, incorporating physicochemical variables into the PCA to assess groundwater quality is a practical and adaptable approach with exceptional abilities and new perspectives. According to the study’s findings, incorporating different techniques to assess groundwater quality is beneficial in understanding the factors that control groundwater quality and can assist officials in effectively controlling groundwater quality and also enhancing the water resources in the study area.

Background and Objectives: Numerous studies have indicated that antibiotics may adversely affect testicular and sperm function. As an alternative to penicillin, vancomycin is a glycopeptide antibiotic developed to treat resistant strains of Staphylococcus aureus. A few studies have suggested that vancomycin could cause testicular toxicity and apoptosis. Vancomycin, however, has not been investigated in terms of its mechanism of causing testicular toxicity. Materials and Methods: An experiment was conducted to investigate the effects of resveratrol (20 mg/kg, oral gavage) against vancomycin (200 mg/kg, i.p.) on the testicular function of Wistar rats for one week (7 days). There were three subgroups of animals. First, saline (i.p.) was administered to the control group. Then, in the second group, vancomycin was administered. Finally, vancomycin and resveratrol were administered in combination in the third group. Results: After seven days of vancomycin treatment, testosterone levels, sperm counts, and sperm motility were significantly reduced, but resveratrol attenuated the effects of vancomycin and restored the testosterone levels, sperm counts, and sperm motility to normal. In the presence of resveratrol, the vancomycin effects were attenuated, and the luteinizing hormone and follicular hormone levels were normalized after seven days of treatment with vancomycin. Histologically, vancomycin administration for seven days caused damage to testicular tissues and reduced the thickness of the basal lamina. However, the resveratrol administration with vancomycin prevented vancomycin’s toxic effects on testicular tissue. Conclusion: Resveratrol showed potential protective effects against vancomycin-induced testicular toxicity in Wistar rats.

Using integrated Interferometric Synthetic Aperture Radar (InSAR) datasets (Envisat: 2003–2009; Sentinel-1: 2014–2018), local gravity surveys, and passive seismic data, we investigated the environmental hazards associated with the rise of the Miocene Jazan salt diapir (JZD; ~ 2 km2) within Jazan city, Saudi Arabia, and identified areas at risk in its immediate surroundings. Our findings include (1) the JZD outcrop and its northern, southern and western bordering areas have been undergoing substantial uplift (up to 4.7 mm/yr), whereas the sabkhas to the east are witnessing subsidence (up to − 7.5 mm/yr); (2) a low Bouguer anomaly (7.5 mGal) was observed over the JZD relative to its surroundings (8.5–12 mGal) with the steepest gradient along its eastern side; (3) strong and clear horizontal/vertical (H/V) spectral ratio peak and high frequency (5–10 Hz) over the JZD outcrop and areas proximal to its western margin, but areas to the east have a weak H/V peak and low frequency (1.5-3 Hz); (4) drilling confirmed presence of a shallow (4 m) salt bedrock layer west of the JZD and the absence of this layer to its east (up to depths of 60 m); (5) uplift patterns along the diapir margins are indicative of near-vertical contact along the JZD eastern margin and less steep contacts along the remaining margins; and (6) additional near-surface diapirs could potentially be identified in the vicinity of the JZD using our integrated approach.

Background: Long-term insertion of the tracheostomy tube may lead patients to have complications such as tracheal stenosis, leakage, sepsis or aspiration. Aim of the work: Our study aimed at applying standards for decannulation of the tracheostomy tube depending on the expertise from prior studies. Methods: The study included 102 patients who underwent tracheostomy during the study period. Results: Surgical tracheostomy was done in operating room in 67% of patients and in intensive care unit by percutaneous dilatational technique in 33% of patients. Decannulation was successful in 97.7% of patients and discharged from the hospital within 24-48 hours. Decannulation failed in 2.3% of patients. Conclusion: The decision to perform elective decannulation of the tracheostomy tube can be challenging. However, we have demonstrated that decannulation based on objective criteria is effective in improving clinical judgment and weaning safety, with a low failure rate. We did not encounter any mortality or significant adverse events.

Available water resources in the Middle East, as one of the most water-scarce regions of the world, have undergone extra pressure due to climatic change, population growth, and economic development during the past decades. The objective of this study is to detect the trends and quantify the changes in aridity with respect to precipitation and potential evapotranspiration in 20 countries of the Middle East and the adjacent area. A pixel-wise trend analysis was conducted on precipitation, potential evapotranspiration, and the aridity index for 71 years from 1948 to 2018. Results showed a statistically significant (|Z| > 1.96) increase up to 106% in aridity (a downward trend in the aridity index) from December to September in most parts of the region. Aridity in October and November had a downward tendency in most parts of the study area. At an annual time scale, 62.5% of the detected trends in aridity were found to be upward (up to 96% increase) due to the combined effects of the decrease in precipitation and the increase in potential evapotranspiration. Annual aridity was found to be downward in 37.5% of the detected trends (up to 61% decrease). The highest and the lowest trends in aridity were found in the north of Sudan (96%) and eastern Arabia (− 61%).

Aim: To evaluate the overall job satisfaction among periodontists, highlighting the significance of intrinsic and extrinsic factors and their satisfaction level with patients, facilities, and workplace when working in the Kingdom of Saudi Arabia. Materials and Methods: An interview-type questionnaire-based cross-sectional study was formulated and consisted of total 38 questions in 8 domains. Contact details of registered periodontists were taken and a weblink containing details of the questionnaire using the tool SurveyMonkey was sent to periodontist from the office of Saudi Dental Society. A single investigator was done to minimize bias evaluated with all the responses. Statistical Package for the Social Sciences software version 21 (SPSS Inc., Chicago, IL, USA) was used for tabulation of descriptive analysis. Standard deviation, percentages and means were calculated. Results: Out of the total 75 emails sent to the periodontists, 65 (86%) responses were received. 63 (96.9%) of the respondents were Saudi nationals, whereas the rest 2 (3.1%) were non‑Saudis. 40 (61.5%) respondents were males, whereas 25 (38.5%) were females. Majority of the respondents were dentists 43 (66.2%). 32.3% reported better professional growth as the main reason for choosing periodontal profession. However, a vast majority of the respondents, 56.9% stated that working as a periodontist was their first choice. 49.3% of the respondents affirmed that they were pleased with the working environment as it was conductive and professional. Approximately, 73.9% of the periodontist were of the view that they had good relations with their patients. Conclusion: Job satisfaction among periodontists working in the Kingdom of Saudi Arabia was adequate as majority of them displayed a positive attitude toward working environment, their profession, peers, and intrinsic and extrinsic factors related to periodontal profession. Clinical Significance: Job satisfaction plays a vital role in delivering standard quality health care. Dissatisfaction in job may lead to stress, anxiety, and/or depression leading to compromised health care facilities and patient satisfaction.

Eight time-domain electromagnetic profiles were carried out in Harrat Ithnayn, northwestern Saudi Arabia, to detect potential groundwater-bearing aquifers in the area. The collected data were processed and analyzed, with the results inferred or extrapolated between one-dimensional vertical soundings to obtain two-dimensional resistivity sections of the subsurface geological setting. We found that the resistivity values change laterally and vertically throughout the area. High resistivity values (greater than 100 Ohm-m) occupy most of the sections down to depths of approximately 100–150 m, whereas the low-resistivity regions of the section appear to be shallower than 100 m toward the west in the northern part of Harrat Ithnayn. The high-resistivity regions correspond to the Harrat basalts and are underlain by low resistivity (down to a few Ohm-m) in the saturated zones. There are also localized regions of low resistivity at depths of approximately 20–30 m, indicating shallow local groundwater aquifers. Aquifer thickness varies from approximately 50 to more than 100 m. In the southern part, the resistivity sections indicate that the main aquifer is at a depth of 150–200 m (possibly near 170 m) below the basalt. A saturated zone or shallow aquifer approximately 20–30 m thick also exists at a depth of approximately 15 m. In both the shallow and deep aquifers, the resistivity values indicate that there is good quality fresh water.

Thirty groundwater samples were taken from Assiut’s northern outskirts. The physicochemical properties of these samples were investigated. For the evaluation of water–rock interaction, the saturation index (SI), chloro-alkaline indices (CAI1 and CAI2), Gibbs ratios for cations and anions (GC and GA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) were used. (1) With the exception of five samples that were supersaturated, the rest of the groundwater samples were under-saturated with carbonate mineral dissolution (calcite, dolomite, and aragonite) and evaporite mineral dissolution (anhydrite, gypsum, halite, and sylvite). (2) The presence of ion exchange between alkali elements (Na+ + K+) and earth alkaline elements (Ca2+ + Mg2+) is explained by the positive values of the chloro-alkaline indices. (3) In four groundwater samples, negative chloro-alkaline indices suggest reverse ion exchange and an increasing alkali element content. (4) The Gibbs diagram demonstrates that, with the exception of four samples that reflect saline groundwater, the majority of the groundwater samples are freshwater recharging from the fresh surface water in the research area. (5) Groundwater types include Ca-HCO3 (11%), Mg-HCO3 (48%), Na-HCO3 (23%), Mg-Cl (13%), and Na-Cl (3%). (6) The alkali, earth alkaline elements, and sulphate are the key controls on groundwater salinity, according to PCA and HCA. (7) The somewhat saline groundwater in the research area’s western desert margins should not be used since it poses a health danger to people.

Saudi Arabia is seeking fresh groundwater resources to face the increase in anthropogenic activities. The groundwater storage variations and occurrence were investigated and the surface and subsurface structures influencing the groundwater resources in the research area were defined using a combined study of Gravity Recovery and Climate Experiment, aeromagnetic data, and electrical resistivity data with other relevant datasets. Results are: The groundwater storage fluctuation is calculated at −0.34 ± 0.01 mm/yr during the period 04/2002-12/2021. The area is receiving an average annual rainfall rate of 117.6 mm during the period 2002 to 2019. Three structural trends, defined in the directions of NS, NNW, and NNE are cutting the sedimentary cover and the basement rocks. The sedimentary cover ranges from 0 to 1.2 km thick. Vertical electrical sounding results indicate three main geoelectric layers: the surface geoelectrical layer of higher resistivity values (428-9626 Ω. m) is made up of unconsolidated Quaternary sediments; the water-bearing layer of saturated sands with a resistivity range between 5.1 and 153 Ω. m and with depths vary from 1 to 94 m, and highly fractured basement rocks with resistivity values ranging from 813 to 6030 Ω. m. The integrated results are useful in providing a comprehensive image of the study area’s surface and subsurface structures, as well as groundwater potential in the southwestern part of Saudi Arabia. Our integrated approach provides a reproducible model for assessing groundwater potential in arid and semiarid areas.

Soils along the Egyptian coast are vulnerable to environmental degradation and soil salinity problems. The main objective of this study is to identify and rapidly predict salt affected soils using remote sensing data and multivariate statistical analysis. To achieve this objective, the Operational Land Imager 8 (OLI) of Landsat imagery acquired in March 2022 was processed through the Maximum Likelihood classifier to assess Landscape features and to produce Normalized difference salinity index (NDSI) and normalized difference vegetation index (NDVI). Water and soil samples were collected from 13 field sites as ground truth data and to investigate representative physical and chemical properties. Linear regression model was used to predict soil salinity while soil parameters were mapped using Inverse Distance Weight (IDW) in ArcGIS 10.5. In order to explore the soil salinity content using VNIR-SWIR spectra, this work investigated the potential of Partial least squares regression (PLS regression) and SVM (Support vector machine). For simulating salinity in the investigated area, a total number of 65 different sites were identified considering that almost 75 % (50 sites) were used to develop the model and 25 % (15 sites) for validation of the established model. The results indicated that EC levels of water samples are not suitable for irrigation (3 mS/cm). Majority of the collected soil samples represent saline-alkaline soils. NDSI ranged from -0.83 to 0.57 with mean of -0.25. Based on the variance of components, 90 % of data were obtained from the first three PCA. The PLS model's R

Human activity has led to a rise in the demand for water, prompting Saudi Arabia to search for alternative groundwater supplies. Wadi As-Sirhan is one area that has experienced extensive agricultural growth and the severe over-exploitation of its groundwater resources. The groundwater drawn from the wadi should be continuously monitored to determine the best management options for groundwater resources and economic growth. The most recent Gravity Recovery and Climate Experiment (GRACE) mission and outputs of land surface models were combined to estimate the depletion rate of the groundwater of the Wadi As-Sirhan drainage basin in the northern region of Saudi Arabia throughout the period of April 2002–December 2021. The findings are: (1) the average GRACE-derived terrestrial water storage variation (ΔTWS) was calculated at −13.82 ± 0.24 mm/yr; (2) the soil moisture storage variation was averaged at +0.008 ± 0.004 mm/yr; (3) the GRACE-derived groundwater depletion rate was estimated at −13.81 ± 0.24 mm/yr; (4) the annual precipitation data over the Wadi As-Sirhan was averaged at 60 mm/yr; (5) The wadi has a minimal recharge rate of +2.31 ± 0.24 mm/yr, which may partially compensate for a portion of the groundwater withdrawal; (6) the sediment thickness shows an increase from 0 m at the southern igneous and volcanic rocks to more than 3000 m close to the Saudi–Jordanian border; (7) The wadi’s eastern, southern, and western portions are the sources of its tributaries, which ultimately drain into its northwestern portion; (8) change detection from the Landsat photos reveals considerable agricultural expansions over recent decades. The integrated method is useful for analyzing changes to groundwater resources in large groundwater reservoirs and developing environmentally appropriate management programs for these resources.

Background: Acute Headache is one of the most frequent health problems. This study analyzed incidental brain-associated pathologies using Magnetic Resonance Imaging (MRI) findings in subjects suffering from acute headaches. Methods: A retrospective study was conducted between January 2018 and December 2021 to assess brain MRI in adult patients suffering from acute headaches. One hundred patients (61 females and 39 males) aged 12–80 years were recruited. MRI scanning was performed, and incidental brain pathologies were analyzed. Results: MRI findings incidentally showed brain-related abnormalities in 71% of acute headache subjects. Out of them, 59.1% were females. The most frequent abnormality was white matter changes observed in 22% of subjects, followed by 20% with paranasal sinus pathologies, including acute/chronic sinusitis, retention cysts, and paranasal lymphoma. Optic abnormalities were observed in 9% of subjects, whereas cysts were observed in 9% of patients, including retention and arachnoid cysts. Other highly prevalent neurological abnormalities detected were intracranial hypertension, microvascular issues, lesions such as meningiomas, orbital lymphoma, low-grade tumor, ganglioglioma, and anaplastic oligodendrogliomas. Conclusions: Incidental findings of brain MRI in subjects suffering from acute headaches showed various neurological abnormalities, such as intracranial hypertension, optic disease, and paranasal sinus pathologies. Importantly, metastasis and cerebral atrophy were also incidentally detected in studied subjects. The data also concluded that abnormalities were more frequent in females. These findings indicate that acute Headache is a neurological condition that might be linked to other severe neurological disorders.

Two-dimensional (2D) electrical resistivity tomography (ERT) survey has been conducted in the Harrat Khaybar volcanic field, northeast of Al Madinah Al Munawwarah, Saudi Arabia, to assess the presence of groundwater resources. For this purpose, eleven ERT profiles were generated at six sites oriented north–south and east–west at various lengths between 590 m and 890 m. The ERT data were collected using the Syscal Pro multinode resistivity imaging system with a dipole–dipole array and 5 m of electrode separation. Then, these data have been processed using RES2DINV software to get 2D geoelectric cross-sections. The interpreted 2D geoelectric cross-sections clarified that the Harrat Khaybar occupied by sheets of lava flows underlain by alluvial deposits of gravelly sand and clay. These deposits rest on a Precambrian crystalline rock configuration that has the potential to provide groundwater to the region. Promising water-bearing formations were detected within sub-basaltic alluvial sediments and basaltic flows as well. Moreover, the reported water table in the area has 47 m depth in average with thickness varies between 6 and 200 m. These results correlate well with those of previous hydrogeological studies conducted in Harrat Khaybar. These results are very important for the residents in Harat Khaybar, where enables them to benefit from the groundwater resources in the area for domestic and agricultural purposes.

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

In the framework of the Saudi Vision 2030, which intends to develop the coastal areas along the Red Sea to establish some of the world’s most attractive tourism destinations, we conducted this study to offer decision makers with one case study illustrating the role of geoscience while planning for such mega projects. The coastal strip between the cities of Ummlujj and Al-Wajh was selected for the present study due to its suitability for construction of such resort project due to the wide coast reaches (10 km), low slope ranges from 0.1 to 5 with slope of the plateau less than 15°, and a general slope trend of the area toward the W, NW, and SW with a subordinated trend to E-NE. Environmental assessment of the study area reflects sedimentomorphic geodiversity relevant to three different depositional environments. These are the colluvium, coastal marine, and aeolian geodiversity. The sedimentomorphic geodiversity of the colluvium environment is associated with the major wadis in the area. It comprises mangroves, mudflats, nebkhas, and alluvial terraces. The sedimentomorphic geodiversity of the coastal marine environment comprises coral reef terraces of two levels at 1 m and 3–5 m ASL, coastal lagoons, marine sands, and coastal sabkhas. The sedimentomorphic geodiversity of the aeolian environment comprises sand dunes and sand sheets 9–20 km away from the coast. Assessment of environmental hazards that may threaten the area includes the vulnerability of the northern part of the area to flash floods at Wadi Al-Hamd basin with an approximate area of 41,897 km2. This allows alluvial fan, tidal flat, and mangrove growing areas. Another hazard factor is the fast growing dune movement in a high-energy aeolian environment eastward of the area shows a mean annual rate of 15.7 m. A sedimentomorphic map is developed based on the obtained results with further recommendations to consider the sedimentomorphic elements while planning coastal zones.

The Nubian Sandstone Aquifer System (NSAS) is made up of three major sub-basins: Kufra, Dakhla, and the N. Sudan Platform. It is one of the world’s largest groundwater systems. The aquifer’s hydrologic setting, connectivity of its sub-basins, and groundwater flow across these sub-basins are currently unclear. To address these issues, we used a combined approach that included: (1) a regionally calibrated groundwater flow model that mimics early (>10,000 years) steady-state conditions under wet climatic periods and later (36 Cl,81 Kr) and isotopic (18 O,2 H) data. The NSAS was recharged on a regional scale in previous wet climatic periods; however, in dry periods, its outcrops are still receiving local modest recharge. A progressive increase in36 Cl groundwater ages was found along groundwater flow directions and along structures that are sub-parallel to the flow direction. The NE–SW Pelusium mega shear zone is a preferential groundwater flow conduit from the Kufra to the Dakhla sub-basin. The south-to-north groundwater flow is hampered by the Uweinat–Aswan basement uplift. The findings provide useful information about the best ways to use the NSAS.

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

The groundwater quality and heavy metal contamination were evaluated in thirty palm farms, central Saudi Arabia using pollution indices, irrigation quality parameters, and multivariate statistical analyses. The results showed that the average values of TDS, Ca + , Na + , K + , Cl − , SO 4 2− , and F − were greater than the permissible limits of the WHO standards for drinking water. The groundwater facies types were Ca–Na–SO 4 –Cl (23 samples), Ca–Cl–SO 4 , (4 samples), and Ca–SO 4 –Cl type (3 samples). The groundwater quality index indicated that 15 groundwater samples were of good quality and 15 were of poor quality, whereas the metal index and heavy metal pollution index indicated that all samples were categorized as slightly affected and with low pollution, respectively. The variation is attributed to the increasing average values of some ions and decreasing HMs. The dissolution/precipitation of silicates, gypsum, and carbonates, and soil leaching were the natural factors affecting groundwater chemistry, whereas higher PO 4 3− , NO 3 − , F − , Pb, and Zn values in some samples may be attributed to human activities from the extensive use of fertilizers and pesticides on the investigated farms.