Papers by Alexander Hummel
This essay examines and criticizes a set of Kantian objections to Parfit's attempt in Reasons and... more This essay examines and criticizes a set of Kantian objections to Parfit's attempt in Reasons and Persons to connect his theory of personal identity to practical rationality and moral philosophy. Several of Parfit's critics have tried to sever the link he forges between his metaphysical and practical conclusions by invoking the Kantian thought that even if we accept his metaphysical theory of personal identity, we still have good practical grounds for rejecting that theory when deliberating about what to do. The argument between Parfit and his opponents illuminates broader questions about the relationship between our metaphysical beliefs and our practical reasons. RÉSUMÉ : Cet article examine et critique un ensemble d'objections kantiennes à la tentative de Parfit, dans Reasons and Persons , d'ajuster sa théorie de l'identité personnelle à la rationalité pratique et à la philosophie morale. Plusieurs des critiques de Parfit ont essayé de rompre le lien qu'il tisse entre ses conclusions métaphysiques et pratiques en évoquant l'idée kantienne selon laquelle, même si nous acceptons sa théorie métaphysique de l'identité personnelle, il existe cependant de bonnes raisons pratiques de rejeter cette théorie lorsque nous délibérons à propos de ce que nous devons faire. Le débat entre Parfit et ses adversaires nous éclaire sur un questionnement plus large à propos du rapport entre croyance métaphysique et raison pratique.
This study was carried out to analyze the spectral reflectance response of different nitrogen lev... more This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops. Four different nitrogen treatments of 0%, 80%, 100% and 120% BMP (best management practice) were studied. Principal component analysis-loading (PCA-loading) was used to identify the effective wavelengths. Partial least squares (PLS) and multiple linear regression (MLR) models were built to predict different nitrogen values. Vegetation indices (VIs) were calculated and then used to build more prediction models. Both full and selected wavelengths-based models showed similar prediction trends. The overall PLS model obtained the coefficient of determination (R 2) of 0.6535 with a root mean square error (RMSE) of 0.2681 in the prediction set. The selected wavelengths for overall MLR model obtained the R 2 of 0.6735 and RMSE of 0.3457 in the prediction set. The results showed that the wavelengths in visible and near infrared region (350-1000 nm) performed better than the two either spectral regions (1001-1350/1425-1800 nm and 2000-2400 nm). For each data set, the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates. The vogelmann red edge index 2 (VOG 2) performed the best among all VIs. It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.
International Journal of Agricultural and Biological Engineering, 2018
This study was carried out to analyze the spectral reflectance response of different nitrogen lev... more This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops. Four different nitrogen treatments of 0%, 80%, 100% and 120% BMP (best management practice) were studied. Principal component analysis-loading (PCA-loading) was used to identify the effective wavelengths. Partial least squares (PLS) and multiple linear regression (MLR) models were built to predict different nitrogen values. Vegetation indices (VIs) were calculated and then used to build more prediction models. Both full and selected wavelengths-based models showed similar prediction trends. The overall PLS model obtained the coefficient of determination (R 2) of 0.6535 with a root mean square error (RMSE) of 0.2681 in the prediction set. The selected wavelengths for overall MLR model obtained the R 2 of 0.6735 and RMSE of 0.3457 in the prediction set. The results showed that the wavelengths in visible and near infrared region (350-1000 nm) performed better than the two either spectral regions (1001-1350/1425-1800 nm and 2000-2400 nm). For each data set, the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates. The vogelmann red edge index 2 (VOG 2) performed the best among all VIs. It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.
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Papers by Alexander Hummel