Large-scale solar energy production is still a great deal of obstruction due to the unpredictability of solar power. The intermittent, chaotic, and random quality of solar energy supply has to be dealt with by some comprehensive solar forecasting technologies. Despite forecasting for the long-term, it becomes much more essential to predict short-term forecasts in minutes or even seconds prior. Because key factors such as sudden movement of the clouds, instantaneous deviation of temperature in ambiance, the increased proportion of relative humidity and uncertainty in the wind velocities, haziness, and rains cause the undesired up and down ramping rates, thereby affecting the solar power generation to a greater extent. This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. Three layered systems have been suggested, consisting of an input layer, hidden layer, and output layer feed-forward in conjunction with back propagation. A prior 5-min te output forecast fed to the input layer to reduce the error has been introduced to have a more precise forecast. Weather remains the most vital input for the ANN type of modeling. The forecasting errors might enhance considerably, thereby affecting the solar power supply relatively due to the variations in the solar irradiations and temperature on any forecasting day. Prior approximation of stellar radiations exhibits a small amount of qualm depending upon climatic conditions such as temperature, shading conditions, soiling effects, relative humidity, etc. All these environmental factors incorporate uncertainty regarding the prediction of the output parameter. In such a case, the approximation of PV output could be much more suitable than direct solar radiation. This paper uses Gradient Descent (GD) and Levenberg Maquarndt Artificial Neural Network (LM-ANN) techniques to apply to data obtained and recorded milliseconds from a 100 W solar panel. The essential purpose of this paper is to establish a time perspective with the greatest deal for the output forecast of small solar power utilities. It has been observed that 5 ms to 12 h time perspective gives the best short- to medium-term prediction for April. A case study has been done in the Peer Panjal region. The data collected for four months with various parameters have been applied randomly as input data using GD and LM type of artificial neural network compared to actual solar energy data. The proposed ANN based algorithm has been used for unswerving petite term forecasting. The model output has been presented in root mean square error and mean absolute percentage error. The results exhibit a improved concurrence between the forecasted and real models. The forecasting of solar energy and load variations assists in fulfilling the cost-effective aspects.
In this paper, a mathematical model for assessing the impact of COVID-19 on tuberculosis disease is proposed and analysed. There are pieces of evidence that patients with Tuberculosis (TB) have more chances of developing the SARS-CoV-2 infection. The mathematical model is qualitatively and quantitatively analysed by using the theory of stability analysis. The dynamic system shows endemic equilibrium point which is stable when R 0 < 1 and unstable when R 0 > 1 . The global stability of the endemic point is analysed by constructing the Lyapunov function. The dynamic stability also exhibits bifurcation behaviour. The optimal control theory is used to find an optimal solution to the problem in the mathematical model. The sensitivity analysis is performed to clarify the effective parameters which affect the reproduction number the most. Numerical simulation is carried out to assess the effect of various biological parameters in the dynamic of both tuberculosis and COVID-19 classes. Our simulation results show that the COVID-19 and TB infections can be mitigated by controlling the transmission rate γ .
Purpose: To standardize a nomenclature system for defining clinical phenotypes, and outcome measures for reporting clinical and research data in patients with ocular tuberculosis (OTB).Methods: Uveitis experts initially administered and further deliberated the survey in an open meeting to determine and propose the preferred nomenclature for terms related to the OTB, terms describing the clinical phenotypes and treatment and reporting outcomes.Results: The group of experts reached a consensus on terming uveitis attributable to tuberculosis (TB) as tubercular uveitis. The working group introduced a SUN-compatible nomenclature that also defines disease "remission" and "cure", both of which are relevant for reporting treatment outcomes.Conclusion: A consensus nomenclature system has been adopted by a large group of international uveitis experts for OTB. The working group recommends the use of standardized nomenclature to prevent ambiguity in communication and to achieve the goal of spreading awareness of this blinding uveitis entity.
An international, expert led consensus initiative was set up by the Collaborative Ocular Tuberculosis Study (COTS) group to develop systematic, evidence, and experience-based recommendations for the treatment of ocular TB using a modified Delphi technique process. In the first round of Delphi, the group identified clinical scenarios pertinent to ocular TB based on five clinical phenotypes (anterior uveitis, intermediate uveitis, choroiditis, retinal vasculitis, and panuveitis). Using an interactive online questionnaires, guided by background knowledge from published literature, 486 consensus statements for initiating ATT were generated and deliberated amongst 81 global uveitis experts. The median score of five was considered reaching consensus for initiating ATT. The median score of four was tabled for deliberation through Delphi round 2 in a face-to-face meeting. This report describes the methodology adopted and followed through the consensus process, which help elucidate the guidelines for initiating ATT in patients with choroidal TB.