Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast wind energy production. The proposed methodology involves data collection, preprocessing, feature selection, model training, optimization, and evaluation. The performance of the models is assessed using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R-squared) metrics. The results indicate that the proposed SVR-RFR model outperforms individual models, achieving a higher accuracy in forecasting wind energy production.
Enantiomeric resolution and molecular docking studies of meclizine hydrochloride on polysaccharide‐based chiral stationary phase comprising cellulose tris(4‐methylbenzoate) chiral selector (150 × 4.6 mm, 3.0 μm) were presented. The mobile phase used was acetonitrile:10mM ammonium bicarbonate (95:05, v/v). The developed technique was used to perform the enantioselective assay of meclizine hydrochloride in its marketed formulation. The elution order of meclizine hydrochloride enantiomers was determined by docking studies. Target compound was extracted from rabbit plasma using protein precipitation technique, followed by development of bioanalytical chiral separation method using the same matrix. Application of the method to determine pharmacokinetic parameters of meclizine hydrochloride enantiomers was performed using Phoenix WinNonlin 8.1 software. The results demonstrated stereoselective disposition of meclizine hydrochloride enantiomers in rabbits.
Aim: Ultra-fast LC was used to establish a new bioanalytical method for enantiomeric separation of oxomemazine. Methods: The proposed study was carried out using the ultra-fast LC technique with an amylose chiral column. The bioanalytical approach was used in rabbit plasma following US FDA regulations and then extended to oxomemazine enantiomeric separation using metronidazole as the internal standard. Results: The retention times of ( R)-oxomemazine, ( S)-oxomemazine and the internal standard were found to be 9.511, 10.712 and 6.503 min, respectively. Within-run and between-run precision (percent relative standard deviation) was found to be in the range of 0.018–0.102% for ( R)-oxomemazine and 0.028–0.675% for ( S)-oxomemazine, whereas accuracy (%) was found to be in the range of 95.971–99.720% for ( R)-oxomemazine and 97.199–103.921% for ( S)-oxomemazine. Conclusion: The findings revealed that stereospecific distribution of oxomemazine enantiomers does not change significantly.
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