Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10⁻⁵ and 1.743 × 10⁻⁵, respectively.

The new coronavirus disease (COVID-19) has caused more than 1.8 million deaths, with a fatality rate of 2.5% in more than 200 countries as of January 4, 2021. Analysis of COVID-19 clinical features can help predict disease severity and risk of mortality, early identification of high-risk patients, and provide knowledge to inform clinical interventions. The purpose of this study is to investigate the clinical characteristics and possible predictors associated with mortality in patients with COVID-19 admitted to King Fahad (KFH), Ohood, and Miqat hospitals in Madina, Saudi Arabia. This retrospective observational study to investigate the clinical characteristic and possible predictors associated with mortality for those 119 mild, moderate, or critically ill patients confirmed by laboratory results to have COVID-19 who were admitted to three hospitals in Madina, Saudi Arabia, from March 25, 2020, to July 30, 2020. Data were collected from December 1, 2020, to December 14, 2020. Of the 119 patients included in the study, the mean age was 54.2 (±15.7) years, with 78.2% survivors and 21.8% non-survivors. The demographic analysis indicated that the likelihood of mortality for patients in the older age group (i.e., ≥65 years) was five times higher than those in the younger age group (OR = 5.34, 95% CI 1.71–16.68, p = 0.004). The results also indicated those patients who admitted to the intensive care unit (ICU) was approximately seven times higher odds of mortality compare with those who were not admitted (OR = 6.48, 95% CI 2.52–16.63, p < 0.001). In addition, six laboratory parameters were positively associated with the odds of mortality: white blood cell count (OR = 1.11, 95% CI 1.02–1.21, p = 0.018), neutrophil (OR = 1.11, 95% CI 1.02–1.22, p = 0.020), creatine kinase myocardial band (OR = 1.02, 95% CI 1.00–1.03, p = 0.030), C-reactive protein (OR = 1.01, 95% CI 1.00–1.01, p = 0.002), urea (OR = 1.06, 95% CI 1.01–1.11, p = 0.026), and lactate dehydrogenase (OR = 1.00, 95% CI 1.00–1.01, p = 0.020). In this cohort, COVID-19 patients within the older age group (≥65 years) admitted to the ICU with increased C-reactive protein levels in particular, were associated with increased odds of mortality. Further clinical observations are warranted to support these findings and enhance the mapping and control of this pandemic.

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (${\mathrm{R}}^2$), adjusted R² (${\mathrm{R}}_{\mathrm{adj}}^2$), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, ${\mathrm{R}}^2$ = 99.88%, ${\mathrm{R}}_{\mathrm{adj}}^2$ = 99.98%, and $\mathrm{AIC}$ = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic.

A modified monopole patch antenna for microwave-based hemorrhagic or ischemic stroke recognition is presented in this article. The designed antenna is fabricated on a cost-effective FR-4 lossy material with a 0.02 loss tangent and 4.4 dielectric constant. Its overall dimensions are 0.32 λ × 0.28 λ × 0.007 λ, where λ is the lower bandwidth 1.3 GHz frequency wavelength. An inset feeding approach is utilized to feed the antenna to reduce the input impedance (z = voltage/current). A total bandwidth (below −10 dB) of 2.4 GHz (1.3–3.7 GHz) is achieved with an effective peak gain of over 6 dBi and an efficiency of over 90%. A time-domain analysis confirms that the antenna produces minimal signal distortion. Simulated and experimental findings share a lot of similarities. Brain tissue is penetrated by the antenna to a satisfactory degree, while still exhibiting a safe specific absorption rate (SAR). The maximum SAR value measured for the head model is constrained to be equal to or below 0.1409 W/kg over the entire usable frequency band. Evaluation of theoretical and experimental evidence indicates the intended antenna is appropriate for Microwave Imaging (MWI) applications.