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Objective: Assessment of staining effect of herbal immune boosters used during COVID-19 pandemic on teeth shade. Material and method: 70 extracted premolars (N=10) were assigned to different solutions which were formulated to mimic the impact of herbal immune boosters on teeth. A baseline reading was performed using in-vitro VITA Easy shade Advance 4.0 and one day later, seven days later, and fourteen days later. The data was entered into SPSS software version 26. Results: A statistically significant difference was noticed in orange, lemon, and curcuma. Conclusion: The consumption of some herbal products has an impact on teeth's shade. Moreover, some materials such as, orange, lemon, and curcuma had the ability to change the teeth shade faster and stronger effect on teeth shade.
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Objective: The purpose of the study was to investigate, assess and explore the impact of herbal products and their effects on oral and dental health in Saudi Arabia. Materials and methods: It is a cross-sectional analytical study conducted in Saudi Arabia. Data were collected from laypeople in Saudi Arabia using a convenient sampling technique distributed electronically. A total of 20 questions were entered, including demographic variables, the usage of herbal products, and possible effects of oral and dental health. Data were analyzed using the SPSS statistical software version 23, and results with a p-value of equal or less than a level of 0.05 were considered significant. Results: 282 females and 103 males have filled the questionnaire in a total of 385. It was observed that about two-thirds of participants 69.9% and 66.8% used lemon and orange. Most of the participants used the herbal immune boosters less than one month 41.8% and 80.5% used these products one time per day. The majority of the participants 94.0% continued dental hygiene practices. Conclusion: COVID-19 pandemic made people try to enhance their immunity by consuming herbal immune boosters' products that may have some consequences on their oral health and dental well-being.
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The recent global health pandemic has shifted the way healthcare is provided. Healthcare providers are overwhelmed with hospitals' beds being occupied and concerned about well being of visiting patients in need of regular checkups. Hence, innovative mobile-based healthcare solutions are needed. This work named Qurra or in Arabic presents a real-time solution that uses pre-trained built-in machine learning (ML)-models on mobile devices for convenient health checkups. The Qurra application operates by sampling data from various mobile sensors and uses the sampled data as input to different machine learning modules to produce a meaningful health diagnosis. The developed modules are the heart rate and cough detection. These modules are the focus of this paper. The approach used in this work relies on externally ML-based trained models that are ported to the application. Then, sensory inputs are tested against these pre-trained models, locally computed and analyzed on the mobile phone. The results are displayed to the user in real-time. This approach of having the models embedded to the mobile phone eliminates the need for internet connectivity. Moreover, the developed system is compared with a third party application in addition to its native model on a desktop computer. Also, the app has fast overall processing time of approximately 2-3.5 seconds.
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The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms- branches of Artificial Intelligence (AI)- can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.
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In March 2020, Saudi Arabia reported that the Coronavirus disease (COVID-19) spread to its territory, originating from China. In this study, a new simulation model estimates and forecasts the number of infected subjects with COVID-19 in the upcoming weeks, based on different parameters, in two major cities in Saudi Arabia, namely Riyadh (the capital) and Jeddah, the second largest city. Unlike most of the recent simulators, our simulator attempts to focus on real data related to Saudi Arabia. Moreover, this paper investigates the parameters that can help to understand and predict the behavior of the biological curve, particularly, in Saudi Arabia. The proposed forecasting model considers several parameters, such as the infection rate, the virus lifetime, the number of infected people, the number of uninfected people, the recovery rate, the death rate, the recovery period, the period of simulation (in days), and the social distancing. The study investigates different scenarios using random test data and real data, where the results show the importance of two parameters on the pandemic spread;the infection rate and the walking distance. Therefore, this work can be used to raise the awareness of public and officials to the seriousness of the current pandemic.