Publication

The study focuses on the polarization dynamics of the ferroelectric phase under an external magnetic field in a trilayered magnetoelectric (ME) composite of 0.94(Na0.5Bi0.5TiO3)-0.06BaTiO3 (NBT-BT)/CoFe2O4(CFO)/NBT-BT. With the estimation of gradient size of the strain across the interface, the thin films with varying top layer (NBT-BT) thicknesses were fabricated. The piezoelectric displacement curves revealed the linear characteristics for the 30 nm NBT-BT ME composite due to the presence of dominant interfacial strain. Time-resolved polarization switching studies confirmed the role of interfacial strain on the time scale of polarization switching of the ferroelectric phase. Magnetic field-assisted piezoresponse force microscopy studies confirmed the presence of nonlinear contribution like polarization rotation in the 100 nm NBT-BT ME composite. The interfacial strain was found to operate in a way that imposes constraints on the polarization rotation in a spatial region of ∼20-30 nm away from the interface. However, at a spatial region >30 nm, the interfacial strain was found to supplement the field-induced strain and assisted the polarization rotation to happen. The spatial-dependent behavioral analysis of the interface strain on the polarization dynamics will help in using the ME composite for targeted device applications such as actuators or energy harvesters.

The east coast of India is highly prone to devastating winds, torrential rainfall, and storm surges caused by tropical cyclones. The storm surge is affected by ocean basin characteristics involving the width and slope of the continental shelf. The bed roughness plays a major role in surge formation. The east coast of India is characterized by a broader shelf in the north and a narrow shelf in the south. This paper uses a hybrid Finite Volume Method–Finite Element Method based Shallow water equation (SWE) solver to predict the storm surges during different cyclone events, and the roughness parameter Manning’s n is used in bed friction calculations. The bottom friction coefficient parameterization involving bed roughness is used to calibrate the resistance to flow in the numerical model. The calibration exercises are carried out with different values of n for each surge simulations for different cyclones to predict the surface elevation. Different statistical parameters against the measured values are used to analyze the impact of varying n values on predicted surge levels, and the most suitable n value is carefully chosen. The relationship between n and the bed slope is established as an expression, to replace the formulations involving Manning’s n, thereby minimizing the usual computational efforts. The performance of the novel bed friction formulation involving the physical parameter in bed slope is demonstrated through statistical evaluations.

This paper presents adaptive, graded and uniform mesh schemes to approximate the solution of a fractional order advection-diffusion model, which generally shows a weak singularity at the initial time level. The temporal fractional derivative in the underlying problem is described in a Caputo form and is discretized by means of L1 scheme on a nonuniform mesh. The space derivative is discretized on a uniform mesh employing a fourth-order compact finite difference scheme. The adaptive grid is generated via equidistribution of a positive monitor function. Stability and convergence results for the proposed method on graded mesh are established. Numerical examples are provided to study the accuracy and efficiency of the proposed techniques and to support the theoretical results. A discussion about the advantages of the graded and adaptive meshes over the uniform one is also presented. The CPU times for the proposed numerical schemes are provided.

Purpose: Surface electromyography (sEMG) is a non-invasive technique to characterize muscle electrical activity. The analysis of sEMG signals under muscle fatigue play a crucial part in the branch of neurorehabilitation, sports medicine, biomechanics, and monitoring neuromuscular pathologies. In this work, a method to transform sEMG signals to complex networks under muscle fatigue conditions using Markov transition field (MTF) is proposed. The importance of normalization to a constant Maximum voluntary contraction (MVC) is also considered. Methods: For this, dynamic signals are recorded using two different experimental protocols one under constant load and another referenced to 50% MVC from Biceps brachii of 50 and 45 healthy subjects respectively. MTF is generated and network graph is constructed from preprocesses signals. Features such as average self-transition probability, average clustering coefficient and modularity are extracted. Results: All the extracted features showed statistical significance for the recorded signals. It is found that during the transition from non-fatigue to fatigue, average clustering coefficient decreases while average self-transition probability and modularity increases. Conclusion: The results indicate higher degree of signal complexity during non-fatigue condition. Thus, the MTF approach may be used to indicate the complexity of sEMG signals. Although both datasets showed same trend in results, sEMG signals under 50% MVC exhibited higher separability for the features. The inter individual variations of the MTF features is found to be more for the signals recorded using constant load. The proposed study can be adopted to study the complex nature of muscles under various neuromuscular conditions.