Superconductivity and Magnetism at Nuclear-matter Densities: An Astronomical Challenge

We report on a study of the evolution of magnetic fields of neutron stars, driven by the expulsion of magnetic flux out of the proton superconducting core of the star. The rate of expulsion, or equivalently the velocity of outward motion of flux-carrying proton-vortices is determined from a solution of their equation of motion. A determination of the effective forces on the fluxoids moving through the quantum liquid interior of neutron stars is however confronted with many ambiguities about the properties of this special case of superconductivity in the nature. Also, the behaviour of the fluxoids at the core boundary, and the subsequent evolution of the expelled flux within the highly conductive surrounding crust, are other related issues that have not been so far explored in any great details.Comment: 8 papegs, 2 figures, accepted at the 1st Regional Conference on Magnetic and Superconducting Materials (MSM-99), Tehran, Sept. 199

The possible role of r-modes in post-glitch relaxation of Crab

The loss of angular momentum through gravitational radiation, driven by the excitation of r-modes, is considered in neutron stars having rotation frequencies smaller than the associated critical frequency. We find that for reasonable values of the initial amplitudes of such pulsation modes of the star, being excited at the event of a glitch in a pulsar, the total post-glitch losses correspond to a negligible fraction of the initial rise of the spin frequency in the case of Vela and the older pulsars. However, for the Crab pulsar the same effect would result, within a few months, in a decrease in its spin frequency by an amount larger than its glitch-induced frequency increase. This could provide an explanation for the peculiar behavior observed in the post-glitch relaxations of the Crab.Comment: 9 pages, 4 figures, RevTe

Flux Expulsion - Field Evolution in Neutron Stars

Models for the evolution of magnetic fields of neutron stars are constructed, assuming the field is embedded in the proton superconducting core of the star. The rate of expulsion of the magnetic flux out of the core, or equivalently the velocity of outward motion of flux-carrying proton-vortices is determined from a solution of the Magnus equation of motion for these vortices. A force due to the pinning interaction between the proton-vortices and the neutron-superfluid vortices is also taken into account in addition to the other more conventional forces acting on the proton-vortices. Alternative models for the field evolution are considered based on the different possibilities discussed for the effective values of the various forces. The coupled spin and magnetic evolution of single pulsars as well as those processed in low-mass binary systems are computed, for each of the models. The predicted lifetimes of active pulsars, field strengths of the very old neutron stars, and distribution of the magnetic fields versus orbital periods in low-mass binary pulsars are used to test the adopted field decay models. Contrary to the earlier claims, the buoyancy is argued to be the dominant driving cause of the flux expulsion, for the single as well as the binary neutron stars. However, the pinning is also found to play a crucial role which is necessary to account for the observed low field binary and millisecond pulsars.Comment: 23 pages, + 7 figures, accepted for publication in Ap

Effects of organic fertilisers and compost extracts on organic tomato production

The effects of various fertilizers and different compost extracts on crop health and tomato yield were studied in the field in 2004–2005 in two locations in Iran. Treatments included different fertilizers (cattle, sheep and chicken manures, green waste and household composts and chemical fertilizers) and five aqueous extracts (from cattle manure, chicken manure, green-waste and house-hold composts and water as control). The effect of fertilizer type on tomato yield was significant in both locations (P < 0.05). Organic fertilizer use did not obtain higher yields compared to using chemical fertiliser. Generally, chicken manure and green-waste compost led to the highest and lowest tomato yield among different organic fertilizers, respectively. The effect of aqueous extracts was not significant on either crop health or tomato yield with these results were being very limited and inconsistent. Improved efficacy of acceptable alternatives to agrochemicals, especially in organic farming, is required

Cyber bullying identification and tackling using natural language processing techniques

Abstract. As offensive content has a detrimental influence on the internet and especially in social media, there has been much research identifying cyberbullying posts from social media datasets. Previous works on this topic have overlooked the problems for cyberbullying categories detection, impact of feature choice, negation handling, and dataset construction. Indeed, many natural language processing (NLP) tasks, including cyberbullying detection in texts, lack comprehensive manually labeled datasets limiting the application of powerful supervised machine learning algorithms, including neural networks. Equally, it is challenging to collect large scale data for a particular NLP project due to the inherent subjectivity of labeling task and man-made effort. For this purpose, this thesis attempts to contribute to these challenges by the following. We first collected and annotated a multi-category cyberbullying (10K) dataset from the social network platform (ask.fm). Besides, we have used another publicly available cyberbullying labeled dataset, ’Formspring’, for comparison purpose and ground truth establishment. We have devised a machine learning-based methodology that uses five distinct feature engineering and six different classifiers. The results showed that CNN classifier with Word-embedding features yielded a maximum performance amidst all state-of-art classifiers, with a detection accuracy of 93\% for AskFm and 92\% for FormSpring dataset. We have performed cyberbullying category detection, and CNN architecture still provide the best performance with 81\% accuracy and 78\% F1-score on average. Our second purpose was to handle the problem of lack of relevant cyberbullying instances in the training dataset through data augmentation. For this end, we developed an approach that makes use of wordsense disambiguation with WordNet-aided semantic expansion. The disambiguation and semantic expansion were intended to overcome several limitations of the social media (SM) posts/comments, such as unstructured content, limited semantic content, among others, while capturing equivalent instances induced by the wordsense disambiguation-based approach. We run several experiments and disambiguation/semantic expansion to estimate the impact of the classification performance using both original and the augmented datasets. Finally, we have compared the accuracy score for cyberbullying detection with some widely used classifiers before and after the development of datasets. The outcome supports the advantage of the data-augmentation strategy, which yielded 99\% of classifier accuracy, a 5\% improvement from the base score of 93\%. Our third goal related to negation handling was motivated by the intuitive impact of negation on cyberbullying statements and detection. Our proposed approach advocates a classification like technique by using NegEx and POS tagging that makes the use of a particular data design procedure for negation detection. Performances using the negation-handling approach and without negation handling are compared and discussed. The result showed a 95\% of accuracy for the negated handed dataset, which corresponds to an overall accuracy improvement of 2\% from the base score of 93\%. Our final goal was to develop a software tool using our machine learning models that will help to test our experiments and provide a real-life example of use case for both end-users and research communities. To achieve this objective, a python based web-application was developed and successfully tested