Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations

Knowledge and perceptions regarding Coronavirus (COVID-19) among pediatric dentists during lockdown period

Aim: To assess the knowledge and perceptions of COVID-19 among pediatric dentists based on their dependent source of information. Methods: A descriptive-analytical cross-sectional survey using a self-administered questionnaire with 23 questions was sent via Google forms to pediatric dentists. All participants were divided into three groups [postgraduate residents (PGs), private practitioners (PP), and faculty (F)]. The comparison of knowledge and perception scores was made based on occupation, source of information, and descriptive statistics used for the analysis using SPSS 21.0 (IBM, Armonk, NY, USA). Results: A total of 291 pediatric dentists completed the survey, and the majority of them were females (65%). Overall, good mean scores were obtained for knowledge (9.2 ± 1.07) and perceptions (5.6 ± 1.5). The majority of the participants used health authorities (45%) to obtain updates on COVID-19, while social media (35.1%) and both (19.6%) accounted for the next two. A statistically significant difference (p < 0.05) was found among different pediatric dentists groups for relying on the source of information. Conclusion: Overall good pediatric dentists showed sufficient knowledge regarding COVID-19. The pediatric dentists’ age, occupation, and source of information influenced knowledge regarding COVID-19, whereas perceptions were influenced by age and gender of the participants. Health authorities successfully educated pediatric dentists than the social medi

Dynamical System and Parameter Identification for Power Systems

The complexity of dynamical analysis has been growing to suffice the understanding and modeling of dynamical systems. Besides its nonlinearity and high-dimensionality, the dynamics of power systems contain uncertainty that complicates its analysis. Recently, dynamical modeling has been categorized into three types: white-box, black-box, and gray-box. White-box modeling has the accessibility of all system components. Black-box modeling has the observability of the system measurements without knowing the actual system. Gray-box modeling has the observability of the system measurements with the reachability to some of the system components. The scope of this dissertation focuses on black-box and gray-box models to achieve practical system and parameter identification of power system applications. Dynamic Mode Decomposition (DMD) is a black-box method that has been proposed by the fluid community. It is a free-equation model identification technique and it has proven its practicality in various fields including brain modeling, fluid experiments, video separation, flows around a train, and financial trading strategies. Our work reviews the DMD algorithm and implements it for mode identification and signal reconstruction in three power system-related applications: RLC circuit dynamics, phasor measurement unit (PMU) measurements of an unknown system, and AC voltage waveform polluted by harmonics. In the first two applications, we compare DMD with Eigensystem Realization Algorithm (ERA) and present that the two methods have the same accuracy level. The last application shows that DMD can also work as fast Fourier transformation (FFT), which can identify harmonics and their magnitudes in the analyzed system. The standard DMD is unable to identify real-world measurement data captured by phasor measurement units (PMUs) because they are noisy. In our research, we enhance DMD performance by data stacking that increases the rank of the data matrix. Correspondingly, DMD accurately identifies the system eigenvalues and eigenvectors. The eigensystem components reveal the details of the dynamics and reconstruct the signals in the time-evolving format. While data stacking raises the computation cost, we further implement a randomization technique for DMD to radically reduce the size of the data matrix. The randomized DMD (rDMD) has high accuracy and efficiency. Our work shows that the identified mode shapes (eigenvectors) of the DMD/rDMD can recognize the oscillation mode type whether it is local or interarea. PMU data from three real-world oscillation events are used for demonstration. Also, we compare both DMD and rDMD with the classical identification methods including Prony, Matrix Pencil, and Eigensystem Realization Algorithm. The second part of this dissertation focuses on gray-box dynamical modeling for parameter identification. The two classical parameter identification methods are the prediction error method (PEM) and the similarity matrix technique. These methods are nonlinear and require a good initial guess of parameters that must be in the domain of convergence. Recently, two new methods have been developed by the system identification community. These methods start from the two conventional methods, make computing improvement by taking into consideration the low-rank characteristic of data, and formulate the estimation problems as rank-constraint optimization problems. Furthermore, the rank-constraint optimization problems are converted to difference of convex programming (DCP) problems and solved by convex iteration. The new convexification technique leads to more accurate parameter estimation. Our work presents the four methods and implements the problem formulations and solving algorithms for synchronous generator and inverter-based resource (IBR) dynamic model parameter estimation

Fintech and Entrepreneurship: An Assessment Model to Evaluate Policy Instruments for Fintech Adoption by Small and Medium Enterprises (SMEs)

Small and medium enterprises (SMEs) are the engines that drive economic development. They are the backbone of the middle class as they provide social stability, innovation, inclusive growth, and poverty alleviation. SMEs contribute significantly to job creation, employment, tax provision and the Gross Domestic Product (GDP). However, they face inferior conditions and challenges when it comes to their financing compared to that of large enterprises, as well as having a high expectancy of failing. Because of these limitations, SMEs tend to grow slowly since building up higher credit is difficult for them, and in addition to this, they lack access to broad financing channels. Hence, Fintech solutions offer promising potential for improving SMEs\u27 access to finance through extending them more accessible and available services, more efficient credit risk assessments and reduced transaction costs. These tools can offer a valuable opportunity for ventures that are too small in size, and involve a great deal of risk, or serve a social purpose. While researchers and practitioners have been promoting Fintech as a potential financial safeguard for SMEs\u27 needs, evidence shows an inadequate adoption rate of SMEs to such solutions. Therefore, this research aims to provide a comprehensive examination for Fintech policy instruments and analyze their effectiveness on increasing the adoption of Fintech by SMEs through evaluating the essential policy targets impacting the adoption of Fintech and assessing their weights and priorities in the context of SMEs. The research was built upon an inclusive hierarchical decision model and a comprehensive literature review. Experts\u27 insights were utilized to identify the most important factors influencing Fintech adoption and policy effectiveness. The Hierarchical Decision Modeling (HDM) methodology was used to identify the relative importance of those factors proposing a policy evaluation tool to assess the effectiveness of policy instruments on increasing Fintech adoption. To test the practicality and value the research model adds to the research objective, a case study of the policy instrument effectiveness, the Saudi Arabian regulatory sandbox, was conducted. This research presents the identification of seventeen distinct policy targets that fall within four main perspectives along with their relative weights, as it also integrates the desirability curves methodology that measures the importance of each perspective and criterion. The case study was introduced to illustrate how the model could be used to identify the policy instrument\u27s actual performance in terms of influencing SMEs adoption of Fintech, identify the instrument\u27s strengths and weaknesses, and offer recommendations and guiding principles on how to improve the detected weaknesses to increase the policy instrument\u27s effectiveness in increasing the adoption of Fintech by SMEs

The Legal Procedures of Saudi Arbitration Regulations 1983 And 1985

In the light of Saudi Arabia's Shari'ah based legal system, the aspects of the procedures of the arbitration regulations that were first implemented in the 1980's are discussed

CBCT in Pediatric Dentistry: Awareness and Knowledge of Its Correct Use in Saudi Arabia

CBCT (Cone-beam computed tomography) is used for diagnosis, planning of treatment, and research. However, there are doubts and opinions regarding the use of CBCT for children and in pediatric dentistry. The knowledge about using this technique for pediatric patients is not clear to the dentists and some dental situations are still debated, therefore this study was done to understand the awareness and knowledge among dental practitioners and students across Saudi Arabia. A cross-sectional and descriptive survey was done on 464 dental practitioners and students, and 21 questions were put forward to assess the knowledge and awareness. All questions were then critically analyzed individually and descriptively concluded with appropriate references. Our study revealed that still very few dental practitioners are aware about the latest advances and use of this technique in pediatric dentistry, and more awareness needs to be created

Perceptions and Preventive Practices Regarding COVID-19 Pandemic Outbreak and Oral Health Care Perceptions during the Lockdown: A Cross-Sectional Survey from Saudi Arabia

Aims: The study aimed to evaluate perceptions and preventive practices regarding the COVID-19 pandemic and oral health care perceptions during the lockdown in the Saudi Arabian population. Materials and Method: This cross-sectional study was performed by collecting the data from individuals belonging to various parts of the Saudi Arabian Population through an online self-reported questionnaire. The questionnaire had two main parts: first comprised of demographic data include the region of residence, gender, nationality, age, the number of family members, monthly income of the family, and the second was further divided into three sections of perception (P), practice (PRA) and oral health care practice (D) questions. All these (P, PRA, and D) were analyzed by comparing all of the demographic characteristics. Statistical analysis was performed using SPSS IBM (version 21.0), and statistical significance was set at a 5% level. Results: Overall, 2013 participants (54% males and 46% females) contributed to the Saudi Arabia study. Only 5% of non-Saudis live in Saudi Arabia were participated in the study, while the majority of participants were of 21–40 years age group (45%), 59% of having more than five family members, and 60% of them had ≤10 K Suadi riyal monthly income respectively. The majority of the participants were from Riyadh (33.7%) and Asir (25.1%) in the study. Overall, 89.5% of the participants were aware of the COVID-19 global pandemic. The majority of the participants (55%) from Saudi Arabia utilized the Ministry of Health website, a source of information regarding COVID-19. However, 56.5% of the participants had COVID-19 related perception, and 74.3% followed an appropriate preventive practice. Approximately 60% had good oral health practice. The study participants showed mixed opinions on perceptions regarding COVID-19, preventive practice, and oral health practices. Conclusion: The present study suggested that the Saudi Arabian population has good attention to COVID-19, but preventive practice and oral health perception need better awareness to control this novel virus spread. The Ministry of Health website utilized as a significant source of information among the Saudi Arabian population regarding COVID-19

Optimal Power Flow Analysis With Renewable Energy Resource Uncertainty Using Dwarf Mongoose Optimizer: Case of ADRAR Isolated Electrical Network

Over the last twin decades, significant advancements have occurred in global electricity grids due to the widespread adoption of renewable energy resources (RES). While these sources play an essential role in total generation cost reduction, transmission power loss minimization, and reduction of environmental hazards related to traditional power plants. Still, however, the optimal planning and operation of the power system in the presence RES is considered a primary challenge due to the their stochastic natural and intermittency. One of the most complex and motivating issues in a power system is optimal power flow (OPF), a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to stochastic OPF problems while keeping system stability. To tackle this challenge, an effective optimization algorithm which mimics on the foraging behavior of dwarf mongooses&#x2019; in the nature is introduced. The objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources. To show the robustness and efficacy of the recommended optimizer, case studies on the customized IEEE 30-bus system and a realistic power system DZA 26-bus (isolated grid) are undertaken. Numerical findings show that the proposed DMOA beats all previous published-results and performs better over a variety of objective functions while finding high-quality optimally viable solutions. The obtained results demonstrate that the DMOA realized exceptional performance for both the test networks, with total generation cost minimized values of 780.982 ${\$}$ /h and 8283.942 US ${\$}$ /h, respectively. These results highlight the precision and robustness of DMOA in effectively addressing various instances of the OPF problem Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study

Sunscreen Use among a Population of Saudi University Students

Introduction. Sunscreen is an important method of sun protection. Many studies were conducted worldwide on the use of sunscreen but only few done in Saudi Arabia. The aim of our study is to assess the prevalence, practices, and factors associated with sunscreen use among Saudi university students. Materials and Methods. A cross-sectional study was performed at King Saud bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. A questionnaire on the use of sunscreen was created in English. Quota sampling technique was used since the sample was divided according to gender and college year. Results. A total of 1,011 students were enrolled. Approximately half were males (n = 510). Half of the students used sunscreen (n = 515, 51%). Female gender, high family income, previous history of sunburn, tanning bed use, and use of other sun protection methods were factors independently associated with sunscreen use. The main reasons for using sunscreen were prevention of sunburns, dark spots, skin cancer, and overall skin darkening. Eighty percent of participants used other methods of sun protection. Sunscreen with a sun protection factor (SPF) > 30 was used in 59% of students. However, the majority did not know if the sunscreen they use provided broad-spectrum coverage or not. Only 35% of students apply sunscreen in both sunny and cloudy days. Most students apply sunscreen less than 10 minutes before going out and do not repeat the application throughout the day. More than 90% of students seem to apply insufficient amount of sunscreen. Conclusion. Almost half of the population in the study use sunscreen. We have identified several areas of improper use of sunscreen. Increasing the awareness of effective sunscreen use in our community might be needed