The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.

This paper presents a novel observer-based robust fault predictive control (OBRFPC) approach for a wind turbine time-delay system subject to constraints, actuator/sensor faults, and external disturbances. The proposed approach is based on an augmented state-space representation that contains state-space variables and estimation errors. The proposed augmented representation is then used to synthesize a robust predictive controller. In addition, an observer is developed and used to estimate both state variables and actuator/sensor faults. To ensure that the proposed approach has disturbance rejection capabilities, the disturbance estimates were merged with the prediction model. In addition, the disturbance rejection capabilities and fault tolerance were insured by formulating the control process as an optimization problem subject to constraints in terms of linear matrix inequalities (LMIs). As a result, the controller gains are acquired by solving an LMI problem to guarantee input-to-state stability in the presence of sensor and actuator faults. A simulation example is conducted on a nonlinear wind turbine (1 MW) model with 3 blades, a horizontal axis, and upwind variable speed subject to actuator/sensor faults in the pitch system. The results demonstrate the ability of the proposed method in dealing with nonlinear systems subject to external disturbances and keeping the control performance acceptable in the presence of actuator/sensor faults.

The design, micro-fabrication, and characterization of a resistance temperature detector (RTD) based micro sensor for minimally invasive breathing analysis and monitoring is presented. Experimental results demonstrate that the change in air temperature while inhaling and exhaling can be transduced into a time varying electrical signal, which is subsequently used to determine the breathing frequency (respiratory rate). The RTD is placed into a Wheatstone bridge to simultaneously reduce the sensor’s output noise and improve overall system accuracy. The proposed design could potentially aid health care providers in the determination of respiratory rates, which is of critical importance during the current COVID-19 pandemic.

In this paper, an observer-based robust fault-tolerant predictive control (ORFTPC) strategy is proposed for Linear Parameter-Varying (LPV) systems subject to input constraints and sensor failures. The main objective of this work is to establish a real observer based on a virtual observer to be used to estimate both states and sensor failures of the system. The proposed virtual observer is employed to improve the observation precision and reduce the impacts of the sensor faults and the external disturbances in the LPV systems. In addition, a real observer is proposed to overcome the virtual observer margins and to ensure that all states and sensor faults of the system are properly estimated, without the need for any fault isolation modules. The proposed solution demonstrates that, using both observers, a robust fault-tolerant predictive control is established via the Lyapunov function. Moreover, sufficient stability conditions are derived using the Lyapunov approach for the convergence of the proposed robust controller. Furthermore, the proposed approach simultaneously computes the gains of the real observer and the controller from a linear matrix inequality (LMI), which is deduced from the estimation errors. Finally, the performance of the proposed approach is investigated by a simulation example of a quarter-vehicle model, and the simulation results under a sensor fault illustrate the robustness and performance of the proposed method.

Metal oxides are commonly used in optoelectronic devices due to their transparency and excellent electrical conductivity. Based on its physical properties, each metal oxide serves as the foundation for a unique device. In this study, we opt to determine and assess the physical properties of MoO3 metal oxide. Accordingly, the optical and electronic parameters of MoO3 are evaluated using DFT (Density Functional Theory), and PBE and HSE06 functionals were mainly used in the calculation. It was found that the band structure of MoO3 calculated using PBE and HSE06 exhibited indirect semiconductor properties with the same line quality. Its band gap was 3.027 eV in HSE06 and 2.12 eV in PBE. Electrons and holes had effective masses and mobilities of 0.06673, −0.10084, 3811.11 cm2V−1s−1 and 1630.39 cm2V−1s−1, respectively. In addition, the simulation determined the dependence of the real and imaginary components of the complex refractive index and permittivity of MoO3 on the wavelength of light, and a value of 58 corresponds to the relative permittivity. MoO3 has a refractive index of between 1.5 and 3 in the visible spectrum, which can therefore be used as an anti-reflection layer for solar cells made from silicon. In addition, based on the semiconducting properties of MoO3, it was estimated that it could serve as an emitter layer for a solar cell containing silicon. In this work, we calculated the photoelectric parameters of the MoO3/Si heterojunction solar cell using Sentaurus TCAD (Technology Computing Aided Design). According to the obtained results, the efficiency of the MoO3/Si solar cell with a MoO3 layer thickness of 100 nm and a Si layer thickness of 9 nm is 8.8%, which is 1.24% greater than the efficiency of a homojunction silicon-based solar cell of the same size. The greatest short-circuit current for a MoO3/Si heterojunction solar cell was observed at a MoO3 layer thickness of 60 nm, which was determined by studying the dependency of the heterojunction short-circuit current on the thickness of the MoO3 layer.