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https://www.selleckchem.com/products/S31-201.html The mentors were found especially helpful in the decision-making situations and in other conflicts that may arise with career development. Additionally, the mentees felt that mentoring contributed to the development of leadership skills required for the job market and assist in personal development. This paper concludes that participation of young medical physicists in a mentoring group program is beneficial to their career and therefore should be encouraged.The estimation of soil phosphorus is essential for agricultural activity. The laboratory chemical analysis techniques are expensive and labor-intensive. In the last decade, near-infrared spectroscopy has been become used as an alternative for soil attributes analysis. It is a rapid technique, and inexpensive relatively. However, this technique requires a calibration step using different machine learning and chemometrics tools. This study aims to develop predictive models for total soil phosphorus and extractable phosphorus by the Olson method (P-Olson) using three regression methods, namely partial least squares (PLS), regression support vector machine (RSVM) and backward propagation neural network (BPNN), combined with a proposed variable selection algorithm (PARtest) and a genetic algorithm PLS (GA-PAS). Also, it aims to investigate the effect of the texture on the accuracy of the prediction. The results show that PARtest combined with PBNN outperform the other used algorithms with an R2t = 0.86, RMSEt = 1104 mg kg-1, and RPD = 3.23 for the TP. For P-Olson the RSVM coupled with GA-PLS outperforms all other methods with an R2t = 0.77, RMSEt = 20.09 mg kg-1, and RPD = 1.90. The use of hierarchical ascendant clustering (HAC) helps to reduce the heterogeneity of soil and helps to increase the quality of prediction. The obtained results show that the models for clayey and loamy soils yielded an excellent prediction quality with an R2t = 0.88, RMSEt = 857.33 mg kg-1,

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