Characterization and Low Temperature Study of Iron Telluride Thin Films Upon Ageing and Oxygen Ion Irradiation

The occurrence of superconductivity in iron telluride thin films has been observed upon ageing. The superconducting transition is found to be very robust under an application of magnetic fields up to 10 T. Infrared and Raman spectroscopic characterization of the aged films reveal the formation of Fe_1+δTeO _x upon ageing. As an alternative method of introducing oxygen in FeTe thin film, oxygen ion irradiation has also been carried out It is found that the irradiated film becomes increasingly disordered and ultimately transforms to an amorphous phase upon increasing the irradiation dose. Investigation of electrical resistivity and optical reflectivity of the irradiated FeTe films indicates an interesting possibility of an ion irradiationinduced phase change memory material in analogy to the phase change characteristics of laserirradiated FeTe films.     

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

     

Low Temperature Studies of Granularity and Upper Critical Field of Superconducting Nd<Subscript>1.85</Subscript>Ce<Subscript>0.15</Subscript>CuO<Subscript>4</Subscript>+Ag Composite System

Electrical resistivity studies reveal large granular effects in the superconductivity behaviour of the electron-doped cuprate, Nd_1.85Ce_0.15CuO₄, in polycrystalline form, much larger than their hole-doped counterparts. With the aim of investigating inter-granular coupling, as well as its variation with magnetic field and addition of Ag, AC susceptibility and magneto-resistance (MR) studies have been carried out on Nd_1.85Ce_0.15CuO₄ + Ag composite system. A comparative study of the superconducting properties of the Nd_1.85Ce_0.15CuO₄ + Ag composite system for Ag = 0, 5 and 15 wt%, including upper critical field [ H _C2(0)], has been carried out and is presented.