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Although Saudi Arabia has experienced very high rates of urbanization, little interest has been given to investigating national and provincial trends in urbanization in space and time. Night-time lights satellite sensor data are considered as a suitable source of imagery for mapping urban areas across large regions. This study uses night-time lights data to analyze the spatial and temporal patterns and dynamics of urban growth in Saudi Arabia between 1992 and 2013 at the national and provincial levels. A hybrid method was applied to ensure the continuity and consistency of the Defense Meteorological Satellite Program (DMSP) Operational Line-Scan System (OLS) of stable night-time (SNT) data through time. As a result of spatial variation in the character of urban areas across Saudi Arabia, different thresholds were used to derive urban areas from the imagery. The extracted urban morphology was assessed using socio-economic data and finer resolution imagery, and accuracy assessment revealed excellent agreement. Based on the rigorous stepwise calibration analysis undertaken here, urban areas in Saudi Arabia were found to have increased three-fold between 1992 and 2013, with most of the increase concentrated in three provinces (Makkah, Riyadh and Eastern). In addition, significant variation was observed in urbanization at the provincial level. The observed high rates of urban growth are aligned with the prosperity and socio-economic development of Saudi Arabia over the last 40 years. The research shows that DMSP-OLS SNT data can provide a valuable source of information for mapping the space–time dynamics of urban growth across very large areas. Such data are required by urban and regional planners, as well as policy makers, for characterizing urban growth patterns, interpreting the drivers of such dynamics and for forecasting future growth, as well as achieving sustainable development management.

In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spatial simulation and prediction of desert urban patterns has received little attention, including in Oman. While most urban settlements in Oman are located in desert environments, research exploring and monitoring this type of urban growth is rare in the scientific literature. This research focuses on analysing and predicting land use–land cover (LULC) changes across the desert city of Ibri in Oman. A methodology was employed involving integrating the multilayer perceptron (MLP) and Markov chain (MC) techniques to forecast spatiotemporal LULC dynamics and map urban growth patterns. The inputs were three Landsat images from 2010 and 2020, and a series of covariate layers based on transforms of elevation, slope, population settlements, urban centres, and points of interest that proxy the driving forces of change. The findings indicated that the observed LULC changes were predominantly rapid across the city during 2010 to 2020, transforming desert, bare land, and vegetation into built-up areas. The forecast showed that area of land conversion from desert to urban would be 5666 ha during the next two decades and 7751 ha by 2050. Similarly, vacant land is expected to contribute large areas to urban expansion (2370 ha by 2040, and 3266 ha by 2050), although desert cities confront numerous environmental challenges, including water scarcity, shrinking vegetation cover, and being converted into residential land. Massive urban expansion has consequences for biodiversity and natural ecosystems—particularly in green areas, which are expected to decline by approximately 107 ha by 2040 (i.e., 10%) and 166 ha by 2050. The outcomes of this research provide fundamental guidance for decision-makers and planners in Oman and elsewhere to effectively monitor and manage desert urban dynamics and sustainable desert cities.

The year 2020 witnessed the arrival of the global COVID-19 pandemic, which became the most devastating public health disaster in the last decade. Understanding the underlying spatial variations of the consequences of the pandemic, particularly mortality, is crucial for plans and policies. Nevertheless, few studies have been conducted on the key determinants of COVID-19 mortality and how these might vary geographically across developing nations. Therefore, this research aims to address these gaps by adopting the Geographically Weighted Poisson Regression (GWPR) model to investigate spatial heterogeneity of COVID-19 mortality in Oman. The findings indicated that local GWPR performed better than global Ordinary Least Square (OLS) model, and the relationship between risk factors and mortality cases varied geographically at a subnational scale. The local parameter estimates of the model revealed that elderly populations, respiratory diseases, and population density were significant in predicting mortality cases. The elderly population variable was the most influential regressor, followed by respiratory diseases. The formulated policy recommendations will provide decision-makers and practitioners with key factors related to pandemic mortality so that future interventions and preventive measures can mitigate high fatality risks.

Dwelling unit (population) data at finer zones are important for different applications. The census data are not available every year and published at coarse zones. While the remotely sensed data have addressed this limitation, analysis of spatial non-stationarity in the relationship between dwelling and detailed urban remote-sensing covariates for dwelling estimates is almost novel. Riyadh, Saudi Arabia, is chosen as a case study. The remote-sensing variables have been derived from QuickBird data using an object-based image analysis. To analyse the spatial non-stationarity, ordinary least squares (OLS) and geographically weighted regression (GWR) approaches are applied. The OLS models suggest that utilizing three-residential classes provides more accurate results than built area and residential built area. The GWR models are more accurate than the OLS model. This research proves that the GWR approach cannot completely handle the spatial non-stationarity problem without efficient explanatory variables. For example, the GWR model that uses three-residential classes is the best model to estimate dwelling compared with the GWR model that uses residential built area. This article confirms that the application of the OLS approach with efficient explanatory variables can successfully account the spatial non-stationarity and the results are relatively comparable with the GWR model.

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively.

Leukonychia refers to the whitening of the nail plate. We identified 22 cases reported in the literature. This study presents two cases of idiopathic leukonychia from Saudi Arabia, which, to our knowledge, are the first reported cases in the region. The first case involves a 26-year-old male with a decade-long history of white discoloration affecting all fingernails. He exhibited low vitamin B12 and elevated thyroid-stimulating hormone (TSH) levels. The second case involves a 23-year-old female with asymptomatic white discoloration on five fingernails and a positive family history of leukonychia in her younger brother. This case series contributes to the limited knowledge of true leukonychia and emphasizes the importance of recognizing its benign nature and distinguishing it from other nail disorders.

The COVID-19 pandemic greatly impacted socioeconomic life globally. Nighttime-lights (NTLs) data are mainly related to anthropogenic phenomena and thus have the ability to monitor changes in socioeconomic activity. However, the overglow effect is a source of uncertainty and affects the applicability of NTL data for accurately monitoring socioeconomic changes. This research integrates the NTL and fine bare-land-cover data to construct a novel index named the Bare Adjusted NTL Index (BANTLI) to lessen the overglow uncertainty. BANTLI was used to measure the post-pandemic resumption of religious rituals and socioeconomic activity in Makkah and Madinah at different spatial levels. The results demonstrate that BANTLI significantly eliminates the overglow effect. In addition, BANTLI brightness recovered during the post-pandemic periods, but it has remained below the level of the pre-pandemic period. Moreover, not all wards and rings are affected equally: wards and rings that are near the city center experienced the most explicit reduction of BANTLI brightness compared with the suburbs. The Hajj pilgrimage period witnessed a larger decrease in BANTLI brightness than the pandemic period in Makkah. The findings indicate that (i) BANTLI successfully mitigates the overglow effect in the NTL data, and (ii) the cultural context is important to understand the impact of COVID-19.

In vitro fertilisation (IVF) is the most commonly used assisted reproductive technology employed to treat infertility. It includes intricate treatment processes such as egg retrieval, fertilisation, ovulation induction, embryo transfer, and implantation. The lower success rate of IVF was projected owing to the poor quality of embryos; thus, many embryos are frequently moved, raising difficulties for children and mothers, as well as increasing the cost of healthcare. A precise assessment of embryo grade, size, and developmental stage is significant in an embryo transfer program. Completely automatic embryo assessments, in which quality grade is assigned without user intervention from an image-analysis perspective, are complex owing to the intricacy of embryo morphology. Conversely, developments in deep learning (DL) have enabled precise, intention-based classification of images across non-medical and medical domains without the need for labour-intensive feature engineering. This article proposes an ensemble deep learning-enabled embryo evaluation system using advanced feature engineering of biomedical images (EDLEVS-AFEBI) model in IVF procedures. This paper’s primary aim is to propose an automated embryo grading method using advanced techniques to improve selection for successful implantation and pregnancy outcomes. At first, the image pre-processing phase uses an adaptive Gaussian bilateral filter (AGBF) to enhance image quality by removing noise. The improved DenseNet model is employed for the feature extraction process to recognise and isolate the most relevant information from raw data. Finally, ensemble learning models such as the temporal convolutional network (TCN), the Elman neural network (ENN), and the conditional variational autoencoder (CVAE) are utilised for embryo classification. The comparison analysis of the EDLEVS-AFEBI methodology showed an accuracy of 99.39% compared with other models on the microscopic image dataset.

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

A promising future for oncology treatment has been brought about by the emergence of a novel approach utilizing oncolytic viruses in cancer immunotherapy. Oncolytic viruses are viruses that have been exploited genetically to assault malignant cells and activate a robust immune response. Several techniques have been developed to endow viruses with an oncolytic activity through genetic engineering. For instance, redirection capsid modification, stimulation of anti-neoplastic immune response, and genetically arming viruses with cytokines such as IL-12. Oncolytic viral clinical outcomes are sought after, particularly in more advanced cancers. The effectiveness and safety profile of the oncolytic virus in clinical studies with or without the combination of standard treatment (chemotherapy, radiotherapy, or primary excision) has been assessed using response evaluation criteria in solid tumors (RECIST). This review will comprehensively outline the most recent clinical applications and provide the results from various phases of clinical trials in a variety of cancers in the latest published literature.