Electronic states of tetrahydrofuran molecules were studied in the excitation energy range 5.5-10 eV using the technique of electron energy loss spectroscopy in the gas phase. Excitation from the two conformations, C(2) and C(s), of the ground state of the molecule are observed in the measured energy loss spectra. The vertical excitation energies of the (3)(n(o)3s) triplet state from the C(2) and C(s) conformations of the ground state of the molecule are determined to be 6.03 ± 0.02 and 6.25 ± 0.02 eV, respectively. The singlet-triplet energy splitting for the n(o)3s configuration is determined to be 0.31 eV. It is also found that excitation from the C(s) conformation of the ground state has a higher cross section than that from the C(2) conformation.
Values of reflectance and remote sensing reflectance are proportional to the ratio of sea water backscattering to absorption. However, in vertically non-homogeneous waters, this fraction needs to be depth weighted. The usual practice uses normalized vertical transmittance profiles as the weighting function. Recently, it was shown that the correct approach is to use, instead of transmittance, its first derivative. We used both approaches to calculate spectral reflectance and remote sensing reflectance over a submerged bubble cloud and chlorophyll rich layer and compared the results with a radiative transfer Monte Carlo code. We also compared several methods of approximating diffuse attenuation (not measured directly) to estimate the effect on calculating reflectance. Our results show that the traditional method of IOP weighting is inadequate in the presence of bubble clouds and/or chlorophyll rich layers. This is relevant for both "ground truth" studies and inverse methods of remote sensing (including lidar ones) for vertically inhomogeneous ocean sea waters.
Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this study, we used low-resolution, dual-energy, full-body X-ray absorptiometry images to train deep learning models to predict age. In particular, we proposed a preprocessing framework and adapted many partially pretrained convolutional neural network (CNN) models to predict the age of children and young adults. We used a new dataset of 910 multispectral images that were weakly annotated by specialists. The experimental results showed that the proposed preprocessing techniques and the adapted approach to the CNN model achieved a discrepancy between chronological age and predicted age of around 15.56 months for low-resolution whole-body X-rays. Furthermore, we found that the main factor that influenced age prediction scores was spatial features, not multispectral features.
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