Metacognition as a Predictor of Conceptual Change

Metacognitive ability - proficiency in analyzing ones own thought processes - is related to the ability to correctly gauge one\u27s mastery of a task (Kruger, 1999; Dunning, 2003). It may also be tied to the ability to make radical conceptual changes learning new information incongruous with prior beliefs. We hypothesize performance on an expanded version of the Cognitive Reflection Test (Frederick, 2005), a battery of questions designed to measure metacognitive ability, would be a predictor of the extent to which undergraduate college students (N=103) improved their understanding of evolution after a semester of college level biology, particularly in the Darwinian principles behind natural selection such as inheritance, variation, and superfecundity. The benefits should be most pronounced in subjects displaying greater metacognitive ability whose prior knowledge incorporated flawed beliefs such as LeMarckianism or needs-based evolution. If metacognitive ability is indeed predictive of learning, it would suggest conceptual change is facilitated by a disposition to think about one\u27s own concepts. It would also suggest that the quality of education in fields such as biology may be improved by fostering and encouraging more reflective thinking

Periodic gamma-ray emissions from Geminga at or = 10(12) eV

Analysis of data from an atmospheric Cerenkov telescope indicated the periodic emission of gamma rays of energy 10 to the 12th power eV, at 60.25 second period, from 2CG 195+4. The gamma ray flux at 99% confidence level is estimated to be 9.5 x 10 to 12 photons/sq cm/s

Search for TeV γ\gamma -rays from H1426+428 during 2004-07 with the TACTIC telescope

The BL Lac object H1426+428 ($z\equiv 0.129$) is an established source of TeV $\gamma$-rays and detections of these photons from this object also have important implications for estimating the Extragalactic Background Light (EBL) in addition to the understanding of the particle acceleration and $\gamma$-ray production mechanisms in the AGN jets. We have observed this source for about 244h in 2004, 2006 and 2007 with the TACTIC $\gamma$-ray telescope located at Mt. Abu, India. Detailed analysis of these data do not indicate the presence of any statistically significant TeV $\gamma$-ray signal from the source direction. Accordingly, we have placed an upper limit of $\leq1.18\times10^{-12}$ $photons$ $cm^{-2}$ $s^{-1}$ on the integrated $\gamma$-ray flux at 3$\sigma$ significance level.Comment: 11 pages, 5 figures accepted for publication in Journal of Physics G: Nuclear and Particle Physic

Observations of TeV gamma-rays from Mrk 421 during Dec. 2005 to Apr. 2006 with the TACTIC telescope

The TACTIC $\gamma$-ray telescope has observed Mrk 421 on 66 clear nights from Dec. 07, 2005 to Apr. 30, 2006, totalling $\sim$ 202 hours of on-source observations. Here, we report the detection of flaring activity from the source at $\geq$ 1 TeV energy and the time-averaged differential $\gamma$-ray spectrum in the energy range 1-11 TeV for the data taken between Dec. 27, 2005 to Feb. 07, 2006 when the source was in a relatively higher state as compared to the rest of the observation period. Analysis of this data spell, comprising about $\sim$97h reveals the presence of a $\sim 12.0 \sigma$ $\gamma$-ray signal with daily flux of $>$ 1 Crab unit on several days. A pure power law spectrum with exponent $-3.11\pm0.11$ as well as a power law spectrum with an exponential cutoff $(\Gamma = -2.51\pm0.26$ and $E_0=(4.7\pm2.1) TeV)$ are found to provide reasonable fits to the inferred differential spectrum within statistical uncertainties. We believe that the TeV light curve presented here, for nearly 5 months of extensive coverage, as well as the spectral information at $\gamma$-ray energies of $>$ 5 TeV provide a useful input for other groups working in the field of $\gamma$-ray astronomy.Comment: 13pages,4figures; Accepted for publication in Astroparticle Physic

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