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Eu-doped pollucite CsAlSi2O6 was synthesized by the sol-gel method and heated in an air atmosphere. The crystal structure and the microstructure of the phosphors were investigated by X-ray powder diffraction and SEM images, respectively. The photoluminescence spectra and temperature dependent decay curves were measured. An abnormal reduction phenomenon of Eu(3+) → Eu(2+) was reported when Eu(3+) ions were doped in alkaline metal cation sites in CsAlSi2O6 prepared in an oxidizing atmosphere. The abnormal mechanism was discussed on the basis of the charge compensation model and a rigid three-dimensional framework structure of CsAlSi2O6. The luminescence color centers were investigated by luminescence decay lifetimes and thermal stabilities of Eu(2+) ions. The defect complexes of [(Eu(3+)Cs)(••)-2VCs'] or [(Eu(3+)Cs)(••)-Oi″] induced by the substitution of Eu(3+) on Cs(+) were suggested in the lattices. Eu(2+) ions could be regarded as Eu(3+) ions combining with the released electrons from defects Oi″ or VCs' in close vicinity of Eu(3+) (Eu(3+) + e); the electrons cannot enter the atom track of Eu(2+) presenting luminescence of Eu(2+) ions. The results indicate that several defect traps can be attributed to the abnormal reduction mechanism of Eu(3+) to Eu(2+) ions in a matrix.

No abstract

Psoriasis is a chronic inflammatory skin disease that affects over 3% of the population. Various methods are currently used to evaluate psoriasis severity and to monitor therapeutic response. The PASI system of scoring is widely used for evaluating psoriasis severity. It employs a visual analogue scale to score the thickness, redness (erythema), and scaling of psoriasis lesions. However, PASI scores are subjective and suffer from poor inter- and intra-observer concordance. As an integral part of developing a reliable evaluation method for psoriasis, an algorithm is presented for segmenting scaling in 2-D digital images. The algorithm is believed to be the first to localize scaling directly in 2-D digital images. The scaling segmentation problem is treated as a classification and parameter estimation problem. A Markov random field (MRF) is used to smooth a pixel-wise classification from a support vector machine (SVM) that utilizes a feature space derived from image color and scaling texture. The training sets for the SVM are collected directly from the image being analyzed giving the algorithm more resilience to variations in lighting and skin type. The algorithm is shown to give reliable segmentation results when evaluated with images with different lighting conditions, skin types, and psoriasis types.