Dissipative dynamics of fission in the framework of asymptotic expansion of Fokker-Planck equation

The dynamics of fission has been formulated by generalising the asymptotic expansion of the Fokker-Planck equation in terms of the strength of the fluctuations where the diffusion coefficients depend on the stochastic variables explicitly. The prescission neutron multiplicities and the mean kinetic energies of the evaporated neutrons have been calculated and compared with the respective experimental data over a wide range of excitation energy and compound nuclear mass. The mean and variance of the total kinetic energies of the fission fragments have been calculated and compared with the experimental values.Comment: 13 pages, 6 figures, accepted for publication in European Physical Journal

成長の追跡：第二言語として英語を学ぶ日本人のための、協同学習を通して英語の発音区別発音の改善

AbstractDeveloping pronunciation is a difficult skill that many overlook with the hopes that it will correct itself throughout the learning process. For this research, six Japanese university CEFR A2 learners of English were divided into a group of four and two, respectively. They were tasked with enunciating various English sound pairs for the first part of the study, followed by keeping a pronunciation diary about their practices for the second part whilst being exposed to poetry that was specifically designed to test English pronunciation. It was found that the diary was a valuable Assessment for Learning (AfL) tool, and pair work was a more effective method than group work for pronunciation development.概要発音を良くすることは、学習の過程で自然と直ることを期待し、多くの人が見落とす難しい技能である。この研究では、日本の大学生六名の英語のCEFR A2英語学習者をそれぞれ四名と二名のグループに分けた。一番初めの研究では、練習の発音の日誌をつけることが求められた。様々な音の組の英語を明瞭に発音すること、そして次の研究では英語の発音のテスト用に特別に作られた。参加者に色々な英語の発音を厳しく練習させるために、特別な詩をあげた。日誌は学習手段において貴重な評価（Assessment for Learning - AfL）であり、発音を伸ばす事は、グループ学習よりも二人組学習の方がより効果的な方法であるということがわかった

Human Face Recognition and Detection

Human face detection and recognition play important roles in many applications such as video surveillance and face image database management. In our project, we have studied worked on both face recognition and detection techniques and developed algorithms for them. In face recognition the algorithm used is PCA (principal component analysis), MPCA(Multilinear Principal Component Analysis) and LDA(Linear Discriminant Analysis) in which we recognize an unknown test image by comparing it with the known training images stored in the database as well as give information regarding the person recognized. These techniques works well under robust conditions like complex background, different face positions. These algorithms give different rates of accuracy under different conditions as experimentally observed. In face detection, we have developed an algorithm that can detect human faces from an image. We have taken skin colour as a tool for detection. This technique works well for Indian faces which have a specific complexion varying under certain range. We have taken real life examples and simulated the algorithms in MATLAB successfully