Electron paramagnetic resonance evidence for Jahn-Teller glasses

Single crystal E.P.R. studies of copper as a dopant in lithium potassium sulphate, lithium ammonium sulphate and lithium sodium sulphate have been carried out from room temperature down to 77K. The three Jahn-Teller (JT) systems behave very similarly to one another. The room temperature dynamic JT spectra with giso = 2.19 ± 0.01 and Aiso = ±(33 ± 4)× 10-4 cm-1 transform around 247 K to spectra characterized by randomly frozen-in axial strains with g = 2.4307 ± 0.0005, g = 2.083 ± 0.001, A = ±(116 ± 2) × 10-4 cm-1 and A = ±(14 ± 4) ×10-4 cm-1. We proposed that the low temperature phase (below 247 K) of each of these systems provides an example of a Jahn-Teller glass

Mixing of quasiparticle excitations and gamma-vibrations in transitional nuclei

Evidence of strong coupling of quasiparticle excitations with gamma-vibration is shown to occur in transitional nuclei. High-spin band structures in [166,168,170,172]Er are studied by employing the recently developed multi-quasiparticle triaxial projected shell model approach. It is demonstrated that a low-lying K=3 band observed in these nuclei, the nature of which has remained unresolved, originates from the angular-momentum projection of triaxially deformed two-quasiparticle (qp) configurations. Further, it is predicted that the structure of this band depends critically on the shell filling: in [166]Er the lowest K=3 2-qp band is formed from proton configuration, in [168]Er the K=3 neutron and proton 2-qp bands are almost degenerate, and for [170]Er and [172]Er the neutron K=3 2-qp band becomes favored and can cross the gamma-vibrational band at high rotational frequencies. We consider that these are few examples in even-even nuclei, where the three basic modes of rotational, vibrational, and quasi-particle excitations co-exist close to the yrast line.Comment: 7 pages, 6 figure

Artificial Neural Network based Cancer Cell Classification

This paper addresses the system which achieves auto-segmentation and cell characterization for prediction of percentage of carcinoma (cancerous) cells in the given image with high accuracy. The system has been designed and developed for analysis of medical pathological images based on hybridization of syntactic and statistical approaches, using Artificial Neural Network as a classifier tool (ANN) [2]. This system performs segmentation and classification as is done in human vision system [1] [9] [10] [12], which recognize objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features and segmentation with Artificial Neural Network (ANN) classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semi-supervised approach in which supervision is involved only at the level of defining structure of Artificial Neural Network; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. Finally, algorithm was applied to selected pathological images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated [18] [21]. Keywords: Grey scale images, Histogram equalization, Gausian filtering, Haris corner detector, Threshold, Seed point, Region growing segmentation, Tamura texture feature extraction, Artificial Neural Network(ANN), Artificial Neuron, Synapses, Weights, Activation function, Learning function, Classification matrix