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This study examines the attributes of date palm fruits that influence consumer purchasing decisions and measures the attributes' relative importance weights for understanding consumption patterns relative to the cultivation areas. A case study was conducted for a selected date fruit, Khalas, which is cultivated in Saudi Arabia and ranked first in the world in exported dates. Our empirical investigation is based on utilizing a proposed quantitative analysis that integrated the entropy weighting method and binary logit models. With this survey design, 486 questionnaires were collected. Analysis results revealed a ranking list of preferred attributes, with size, mellowness, price, and color being the most valued. However, this ranking list fluctuates when different cultivated types of Khalas dates are available. The results also showed that consumption patterns may change in terms of preference index and shopping location. The paper concludes with a discussion of managerial implications, limitations, and future research directions. [ABSTRACT FROM AUTHOR]

The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (χ2 = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow's milk (OR = 2.92, P = 0.000), birth weight >4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age >25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia. [ABSTRACT FROM AUTHOR]

This paper investigates the online discourses on "sustainability" and explores how environmental sustainability has been constructed within the Saudi online discourse. In doing so, the study focuses on the potential of the Saudi Green Initiative (SGI), along with Green Riyadh and King Salman Park, to promote sustainability awareness in the kingdom. The corpus of the study comprises news articles and Twitter hashtags. In addition, the study uses corpus-based discourse analysis to examine the keyness and intertextuality. The results of the study convey a contextualized national identity while communicating internationally within discursive circles. It is also identified that interdiscursivity is central in the corpus, although the discourse of action especially dominated other smaller discourses, such as consequences/risks, economy, and tourism. The results also indicate the prominent construction of officials, which could be linked to the dynamics of change in the Saudi scene. Finally, the study emphasizes the action-oriented nature of such discourse while drawing attention to some of the challenging issues to long-term advocacy in the country. [ABSTRACT FROM AUTHOR]

Introduction: Physicians promote oral health by screening for oral pathologies, providing emergency help, and referring patients to dentists. The literature consistently reports a robust association between periodontal disease and systemic diseases. However, it is not fully known if physicians in Saudi Arabia are aware of the oral-systemic disease links. Therefore, the study aimed to evaluate physicians' awareness of the oral-systemic disease links and their association with patients' referral to dentists and patients' access to oral care. Methods: This cross-sectional study was conducted (June 2019-January 2020) on physicians working in private and public hospitals in the Eastern province of Saudi Arabia. The physicians (n = 333) responded to a paper-based self-administered and pilot-tested questionnaire which inquired about their awareness of oral-systemic disease links and patient referral to dentists. The reliability of items of oral-systemic disease links was evaluated by calculating Cronbach's alpha (0.84). Results: Most participants (92.8%) agreed/strongly agreed that oral health is associated with systemic health. Half of the sample (50.8%) referred patients to dentists during the last month. Almost 80.5% believed that more patients will be referred to the dentists because of their awareness of the oral-systemic disease links and 84% believed that more patients will access oral care services if patients were aware of the oral-systemic disease links. The participants who referred patients to the dentists during the last month (P = 0.038), believed that more patients will be referred (P = 0.001), and believed that more patients will access oral care (P = 0.003) demonstrated significantly higher oral-systemic disease links awareness score. The adjusted model showed patients' referral to dentist (OR = 1.96.10, P = 0.006) and believing in more patients accessing oral care (OR = 2.05, P = 0.044) were associated with significantly increased awareness of the oral-systemic disease links. Conclusion: Most physicians in the study were aware of the oral-systemic disease links. Increased awareness of the oral-systemic disease links was significantly associated with the referral of patients to dentists and belief in improved patients' access to oral care services. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic spread rapidly worldwide. On September 15, 2021, a total of 546,251 confirmed cases were recorded in Saudi Arabia alone. Saudi Arabia imposed various levels of lockdown and forced the community to implement social distancing. In this paper, we formulate a mathematical model to study the impact of these measures on COVID-19 spread. The model is analyzed qualitatively, producing two equilibrium points. The existence and stability of the COVID-19 free equilibrium and the endemic equilibrium depend on the control reproduction number, Rc. These results are in good agreement with the numerical experiments. Moreover, the model is fitted with actual data from the COVID-19 dashboard of the Saudi Ministry of Health. We divide the timeline from March 12, 2020, to September 23, 2020, into seven phases according to the varied applications of lockdown and social distancing. We then explore several scenarios to investigate the optimal application of these measures and address whether it is possible to rely solely on social distancing without imposing a lockdown. [ABSTRACT FROM AUTHOR]

The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delayed diagnosis of T1D. This paper has utilised Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Random Forest (RF) to model and predict the age at onset of T1D in children in Saudi Arabia (S.A.) which is ranked as the 7th for the highest number of T1D and 5th in the world for the incidence rate of T1D. De-identified data between (2010-2020) from three cities in S.A. were used to model and predict the age at onset of T1D. The best subset model selection criteria, coefficient of determination, and diagnostic tests were deployed to select the most significant variables. The efficacy of models for predicting the age at onset was assessed using multi-prediction accuracy measures. The average age at onset of T1D is 6.2 years and the most common age group for onset is (5-9) years. Most of the children in the sample (68%) are from urban areas of S.A., 75% were delivered after a full term pregnancy length and 31% were delivered through a cesarean section. The models of best fit were the MLR and RF models with R2 = (0.85 and 0.95), the root mean square error = (0.25 and 0.15) and mean absolute error = (0.19 and 0.11) respectively for logarithm of age at onset. This study for the first time has utilised MLR, ANN and RF models to predict the age at onset of T1D in children in S.A. These models can effectively aid health care providers to monitor and create intervention strategies to reduce the impact of T1D in children in S.A. [ABSTRACT FROM AUTHOR]

Information sharing via social networking systems (SNS) is a common practice among academics, as well as others, that brings substantial benefits. At the same time, privacy concerns are widespread among SNS users, which may tend to inhibit their maximising the benefit from using the systems. This paper investigates the proposition that SNS user attitudes and behaviour are affected by privacy concerns, and that the effects are subject to significant cultural factors. A broad assessment of the literature provides the context for the study. Working in the context of Saudi Arabia, we apply a mixed-methods approach beginning with in-depth interviews, exposing in detail a range of views and concerns about privacy and SNS use, also allowing us to identify three key factors that bear on SNS usage and users' concerns. Analysis of these factors in the light of the "theory of reasoned action" derives a structural model predicting several hypotheses relating the factors and users' attitudes and behaviour. We assess the model through development of a questionnaire, administered to a large pool of academic participants, that allows us to examine how the model responds in general, and via multigroup partial least squares analyses, differentially to gender and to culturally distinct (Arab vs. non-Arab) constituents of the participant group. Results show good support for the hypotheses and clear gender and culture effects. Picking up issues from the interviews, discussion focuses on users' views about SNS providers' privacy policies and their inadequacy regarding culturally specific ethical concerns. We argue that these views may reflect different regulatory environments in combination with other cultural factors. [ABSTRACT FROM AUTHOR]

Goal 17 of the sustainable development goals (SDGs) attracted attention to the importance of partnerships between governments, the private sector, and nonprofit organizations (NPOs) for sustainable development. This paper aims to analyze the processes of establishing and operating the partnerships between NPOs and other actors in terms of governance. The best practices for partnership governance were examined according to the partnering life cycle framework. A simple random sample of 184 NPOs in six regions of Saudi Arabia was selected for data collection. These organizations were analyzed according to their governance practices in 937 partnerships established during 2016–2018. The findings showed that the organizations had strongly implemented the phases of building and scoping and managing and maintaining, while their governance practices regarding phases of reviewing and revising and sustaining outcomes ranged between moderate and low levels. The results also revealed significant differences between the overall implementation of the partnering life cycle practices and the NPO's year of establishment. It was concluded that analyzing the current situation of implementing the best practices of partnership governance is useful to explore the efficiency and effectiveness of partnerships between NPOs and other actors, as well as the existing policy gaps, so as to create and implement sustainable-oriented partnerships. [ABSTRACT FROM AUTHOR]

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers. [ABSTRACT FROM AUTHOR]

Background: BMI is a feasible and recommended measure for overweight and obesity screening in children and adolescents. The study aimed to determine how often physicians correctly identified obesity/ overweight status in children and adolescents by using BMI percentile charts. Methods: This retrospective cross-sectional study reviewed the paper medical records of children and adolescents (6–14 years) who visited family medicine and pediatric outpatient clinics (Jan-June 2012) in a medical city in Riyadh. Investigators calculated BMI percentiles (using height, weight, age and gender data retrieved from the records) in order to identify patient weight status. Physician documentation of obesity/overweight diagnoses in patient problem lists were cross checked against their BMI percentile to assess the accuracy of physicians’ identification of weight status. The recommended management plan for identified patients was also recorded. Results: A total of 481 charts were reviewed, 213 (44%) children were seen by family medicine physicians and 268 (56%) by pediatricians. The sample was equally distributed by gender. Height was undocumented for 13% (71) of visiting patients. Eighteen percent of patients (86) were classified as overweight (35)/obese (51) according to age and sex adjusted BMI percentile. Physicians’ correctly identified and documented weight status in 20% of overweight/obese patients: 17 out of 86 subjects. Weight status identification was higher among pediatricians—25% as compared to family medicine physicians—10% [p = 0.08]. Dietary referral was the most common management plan for the identified children. Physicians were more likely to identify obese children {≥95th} compared to overweight {≥85th - 95th} children. Subjects whose BMI for age classified them into the highest BMI percentile category {≥95th} were more likely to be correctly identified (29%) compared to those classified within {≥85th - 95th} category—6% [p = 0.007]. Conclusion: Physician identification of obesity/ overweight status for children and adolescents was low, irrespective of their specialty, and despite the condition being prevalent in the sample. Future research that concentrates on interventions that may improve documentation of obesity/overweight diagnoses and parameters needed for BMI indices would be beneficial. [ABSTRACT FROM AUTHOR]

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data. [ABSTRACT FROM AUTHOR]

This paper develops an agent-based computational model of violent political revolutions in which a subjugated population of citizens and an armed revolutionary organisation attempt to overthrow a central authority and its loyal forces. The model replicates several patterns of rebellion consistent with major historical revolutions, and provides an explanation for the multiplicity of outcomes that can arise from an uprising. The relevance of the heterogeneity of scenarios predicted by the model can be understood by considering the recent experience of the Arab Spring involving several rebellions that arose in an apparently similar way, but resulted in completely different political outcomes: the successful revolution in Tunisia, the failed protests in Saudi Arabia and Bahrain, and civil war in Syria and Libya. [ABSTRACT FROM AUTHOR]