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This paper describes a newly developed instrumented hand manikin fire test system called Pyrohands. This system is designed to measure the thermal protective performance of gloves in controlled flame exposures, including predicted second and third degree burns and the distribution of burns to the hands. This paper also describes a study that demonstrates the utility of the Pyrohands system for comparing the thermal protective performance of gloves made with different materials and constructions. The skin burn protective performance of gloves is of significant interest to firefighters, to soldiers and to others requiring thermal protection against hazardous flame exposures to the hands.

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.

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.

This paper describes a thermal sensor developed for use in the fingers of the PyroHands Fire Test System. The PyroHands Fire Test System measures the thermal protective performance of gloves in laboratory controlled flash fire exposures. The development of the finger sensor presented several challenges; the first was that it required that a small thermal sensor fit into the finger of an anthropometrically designed hand. It was also important to ensure that the thermal sensor accurately measured heat flux incident on the finger. This required showing that the unidirectional heat flux measured by the sensor was unaffected by heat impinging on the sides and back of the finger. An experimental study was conducted in order to investigate the effects of lateral heating on sensor operation. Additional verification of the thermal sensor was provided via the use of computer-aided design models to predict the temperature rise beneath gloves during PyroHands tests.