Teacher-Student Interactions and Science Classroom Learning Environments in India

The research reported in this thesis is an in-depth study of teacher-student interactions and science classroom learning environments in Jammu, India. Jammu city is the winter capital of the state of Jammu and Kashmir, situated at the extreme north of India. This is the first time that any learning environment research has been conducted and reported from this part of the world. The objective of this research was to provide further validation information about two already existing learning environment instruments with Indian students and describe, discuss and analyse information on the associations between student’s perceptions of learning environment and their attitudes and cognitive achievements. Differences in the perceptions of different groups namely gender, religious and cultural were also investigated. The present study commenced with a more positivistic framework, with an aim of providing a large-scale quantitative overview. The Questionnaire on Teacher Interaction (QTI), the What is Happening in this Class? (WIHIC) and an Attitude Scale were administered to 1,021 students from 32 science classes in seven different co-educational private schools in Jammu. The data were analysed to determine the reliability, validity and mean of each scale. Students were interviewed to determine further the reliability of the questionnaires, in addition to providing information that might explain the QTI and WIHIC mean scale scores. As a result of critical reflection, the study moved towards a more interpretative framework, drawing on elements of the constructivist and critical theory paradigms. Multiple research methods were used to member and deepen the researchers understanding of the learning environments in Jammu. An educational critique was used to describe the social and cultural factors that could influence the prevailing learning environments

Very High Energy gamma-ray observations of Mrk 501 using TACTIC imaging gamma-ray telescope during 2005-06

In this paper we report on the Markarian 501 results obtained during our TeV $\gamma$-ray observations from March 11 to May 12, 2005 and February 28 to May 7, 2006 for 112.5 hours with the TACTIC $\gamma$-ray telescope. During 2005 observations for 45.7 hours, the source was found to be in a low state and we have placed an upper limit of 4.62 $\times$ 10$^{-12}$ photons cm$^{-2}$ s$^{-1}$ at 3$\sigma$ level on the integrated TeV $\gamma$-ray flux above 1 TeV from the source direction. However, during the 2006 observations for 66.8h, detailed data analysis revealed the presence of a TeV $\gamma$-ray signal from the source with a statistical significance of 7.5$\sigma$ above $E_{\gamma}\geq$ 1 TeV. The time averaged differential energy spectrum of the source in the energy range 1-11 TeV is found to match well with the power law function of the form ($d\Phi/dE=f_0 E^{-\Gamma}$) with $f_0=(1.66\pm0.52)\times 10^{-11}cm^{-2}s^{-1}TeV^{-1}$ and $\Gamma=2.80\pm0.27$.Comment: 16 pages and 8 Figures Accepted for publication in the Journal of Physics

Artificial Neural Network-based error compensation procedure for low-cost encoders

An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science and Technology (MST

Frequent Promoter Methylation of CDH1, DAPK, RARB, and HIC1 Genes in Carcinoma of Cervix Uteri: Its Relationship to Clinical Outcome

BACKGROUND: Cervical cancer (CC), a leading cause of cancer-related deaths in women worldwide, has been causally linked to genital human papillomavirus (HPV) infection. Although a host of genetic alterations have been identified, molecular basis of CC development is still poorly understood. RESULTS: We examined the role of promoter hypermethylation, an epigenetic alteration that is associated with the silencing tumor suppressor genes in human cancer, by studying 16 gene promoters in 90 CC cases. We found a high frequency of promoter methylation in CDH1, DAPK, RARB, and HIC1 genes. Correlation of promoter methylation with clinical characteristics and other genetic changes revealed the following: a) overall promoter methylation was higher in more advanced stage of the disease, b) promoter methylation of RARB and BRCA1 predicted worse prognosis, and c) the HIC1 promoter methylation was frequently seen in association with microsatellite instability. Promoter methylation was associated with gene silencing in CC cell lines. Treatment with methylation or histone deacetylation-inhibiting agents resulted in profound reactivation of gene expression. CONCLUSIONS: These results may have implications in understanding the underlying epigenetic mechanisms in CC development, provide prognostic indicators, and identify important gene targets for treatment

Comparative performance of some popular ANN algorithms on benchmark and function approximation problems

We report an inter-comparison of some popular algorithms within the artificial neural network domain (viz., Local search algorithms, global search algorithms, higher order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-Bit parity and Two Spiral. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg-Marquardt algorithm yields the lowest RMS error for the N-bit Parity and the Two Spiral problems, Higher Order Neurons algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the Neuro Fuzzy algorithm. The above algorithms were also applied for solving several regression problems such as cos(x) and a few special functions like the Gamma function, the complimentary Error function and the upper tail cumulative $\chi^2$-distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg-Marquardt algorithm yields the best results. Keeping in view the highly non-linear behaviour and the wide dynamic range of these functions, it is suggested that these functions can be also considered as standard benchmark problems for function approximation using artificial neural networks.Comment: 18 pages 5 figures. Accepted in Pramana- Journal of Physic