35 research outputs found
Preparation and Characterization of Modified Bitumen using Waste Plastics, Recycled Eva and Nano Fly Ash
No full textCompetitive metastable behaviours of surface and bulk in Ising ferromagnet
No full textThe reversal of magnetisation has been studied in a three-dimensional Ising ferromagnet by Monte Carlo simulation with Metropolis single spin flip algorithm using random updating scheme. The outer layers are considered as surface. The surface interacts with core with a relative ferromagnetic interaction strength. Depending on the relative interaction strength, the time of reversal of the surface was found to be different from that of the bulk. For weaker relative strength, surface reversal was found to be faster than that of bulk and vice versa for stronger relative interaction strength. A critical value () of relative interaction strength provides same time of reversal of surface and bulk. This critical relative interaction strength was found to be a function of the temperature (T) and applied magnetic field (h). The scaling relation , where and , has been proposed, numerically by the method of data collapse. The metastable volume fractions, for both surface and bulk, were found to follow the Avrami’s law. The critical relative interaction strength () has been observed to decrease in an exponential ( fashion with the system size (L)
Intrinsically Heat Tolerant, UV Resistant EVA/ LDPE thermoplastic elastomeric encapsulant – An alternative for conventional crystalline silicon PV module encapsulant
No full textAnisotropy-driven reversal of magnetisation in Blume–Capel ferromagnet: a Monte Carlo study
No full textThe two-dimensional Spin-1 Blume–Capel ferromagnet is studied by Monte Carlo simulation with Metropolis algorithm. Starting from initial ordered spin configuration, the reversal of magnetisation is investigated in the presence of a magnetic field (h) applied in the opposite direction. The variations of the reversal time with the strength of single-site anisotropy are investigated in details. The exponential dependence was observed. The systematic variations of the mean reversal time with positive and negative anisotropy were found. The mean macroscopic reversal time was observed to be linearly dependent on a suitably defined microscopic reversal time. The saturated magnetisation after the reversal was noticed to be dependent of the strength of anisotropy D. An interesting scaling relation was obtained, with the scaling function of the form . The temporal evolution of density of (surrounded by all ) is observed to be exponentially decaying. The growth of mean density of has been fitted in a function . The characteristic time shows and a crossover in the rate of exponential falling is observed at . The metastable volume fraction has been found to obey the Avrami’s law
Theoretical studies on switching of magnetisation in thin film
No full textIn the present chapter, we focus on the switching of magnetisation, or the
metastable lifetime of a ferromagnetic system. In this regard, particularly the
Ising model and the Blume-Capel model, have been simulated in the presence of
an externally applied magnetic field by the Monte-Carlo simulation technique
based on the Metropolis algorithm. Magnetisation switching is found to be
faster in the presence of disorder, modelled here by a quenched random field.
The strength of the random field is observed to play a similar role to that
played by temperature. Becker-D\"oring theory of classical nucleation
(originally proposed for the spin-1/2 Ising system) has been verified in the
random field Ising model. However, a stronger random field affects the
nucleation regime. In a cubic Ising lattice, surface reversal time is found to
be different from the bulk reversal time. That distinct behaviour of the
surface in contrast to the bulk has been studied here by introducing a relative
interfacial interaction strength (). Depending on , temperature, and
applied field, a competitive switching of magnetisation of surface and bulk is
noticed. The effect of anisotropy () on the metastable lifetime has been
investigated. We report a linear dependency of the mean macroscopic reversal
time on a suitably defined microscopic reversal time. The saturated
magnetisation , after the reversal, is noticed to be strongly dependent on
. , , and (field) are found to follow a proposed scaling
relation. Finally, Becker-D\"oring theory as well as Avrami's law are verified
in spin- Ising and Blume-Capel models. The switching time depends on the
number of accessible spin states.Comment: Invited review article, to appear in Comprehensive Materials
Processing (2E), Elsevie
Investigation of the degradation of EVA encapsulation of photovoltaic module under different stress factors
No full text
Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach
No full text[EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481S2959296934
