Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been associated with many neurological symptoms but there is a little evidence-based published material on the neurological manifestations of COVID-19. The purpose of this article is to review the spectrum of the various neurological manifestations and underlying associated pathophysiology in COVID-19 patients. Method: We conducted a review of the various case reports and retrospective clinical studies published on the neurological manifestations, associated literature, and related pathophysiology of COVID-19 using PUBMED and subsequent proceedings. A total of 118 articles were thoroughly reviewed in order to highlight the plausible spectrum of neurological manifestations of COVID 19. Every article was either based on descriptive analysis, clinical scenarios, correspondence, and editorials emphasizing the neurological manifestations either directly or indirectly. We then tried to highlight the significant plausible manifestations and complications that could be related to the pandemic. With little known about the dynamics and the presentation spectrum of the virus apart from the respiratory symptoms, this area needs further consideration. Conclusion: The neurological manifestations associated with COVID-19 such as Encephalitis, Meningitis, acute cerebrovascular disease, and Guillain Barré Syndrome (GBS) are of great concern. But in the presence of life-threatening abnormal vitals in severely ill COVID-19 patients, these are not usually underscored. There is a need to diagnose these manifestations at the earliest to limit long term sequelae. Much research is needed to explore the role of SARS-CoV-2 in causing these neurological manifestations by isolating it either from cerebrospinal fluid or brain tissues of the deceased on autopsy. We also recommend exploring the risk factors that lead to the development of these neurological manifestations.

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Modeling of photovoltaic (PV) systems is essential for the designers of solar generation plants to do a yield analysis that accurately predicts the expected power output under changing environmental conditions. This paper presents a comparative analysis of PV module modeling methods based on the single-diode model with series and shunt resistances. Parameter estimation techniques within a modeling method are used to estimate the five unknown parameters in the single diode model. Two sets of estimated parameters were used to plot the I-V characteristics of two PV modules, i.e., SQ80 and KC200GT, for the different sets of modeling equations, which are classified into models 1 to 5 in this study. Each model is based on the different combinations of diode saturation current and photogenerated current plotted under varying irradiance and temperature. Modeling was done using MATLAB/Simulink software, and the results from each model were first verified for correctness against the results produced by their respective authors. Then, a comparison was made among the different models (models 1 to 5) with respect to experimentally measured and datasheet I-V curves. The resultant plots were used to draw conclusions on which combination of parameter estimation technique and modeling method best emulates the manufacturer specified characteristics.

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