Birla Institute of Technology, Patna is an educational institute offering undergraduate and postgraduate courses located in Patna, Bihar, India. It is an off campus of Birla Institute of Technology, Mesra, Ranchi .
In recent years, enormous people in all age category were affected with epilepsy throughout the world. To detect and evaluate the epilepsy seizure, the measurable component Electroencephalography (EEG) plays a major role in it. In the olden days, manual detection by the following medical process is carried, the accuracy of the detection is good, but some human error may occur which leads to the critical situation. The EEG plays a major role in the medical field to analyze the instant health condition of the subject under monitoring. The manual detection of EEG consumes much time and have to face the critical consequence, to avoid this situation world need alternate detecting methods. For more than a decade, to help medical specialists, many techniques, methodologies have been followed to detect with technology advancement. The detection of EEG signal was automated with various methods which reduce the detection time and gives the earlier response which is useful for medical diagnosis. During automated signaling the device also generates a noisy signal that causes difficulty in detection and prediction. In this paper, a new technique is proposed with an adaptive artificial neural network (AANN) to detect a normal and epileptic signal. The optimized proposed oppositional crow search algorithm (OCSA) gives better performance in the detection of epileptic seizures with an accuracy of 96.45% over others and the same has been reported.
Bulk metallic glasses (BMGs) have been proved to be a new family of materials that possess unusual atomic arrangements and exceptional mechanical properties against traditional crystalline alloys. The composition of alloys and environmental conditions greatly influences their corrosion resistance. This review captures prevailing knowledge on the corrosion behavior of BMGs in aqueous and physiological solutions. Focus is given to zirconium-, titanium-, magnesium-, copper-, and calcium-based BMGs, which are of interest for biomedical and structural uses. The influence of alloying elements on determining the stability of the passive film, susceptibility to localized corrosion, and resistance to pitting is discussed. Emphasis is placed specifically on the corrosion response of BMGs in bio-applications, where parameters like biocompatibility, ion release, and localized corrosion are significant to enable their viability as implant materials. Results from electrochemical characterization and in vivo tests are integrated to offer mechanistic explanations of corrosion mechanisms. The knowledge of the corrosion behavior of these materials, thus such an understanding of the application of metallic glasses in various environments, is required for both designers and engineers.
Solids in their noncrystalline phases become soft and exhibit viscoelastic behavior as a result of the application of mechanical forces. To understand the viscoelastic behavior of materials in their noncrystalline solid phases, a constitutive model is not available in the literature to the best of our knowledge. In some extent, continuum hyperelastic models can predict the behavior in macroscopic length scales. However, for very thin micro/nano-noncrystalline structures, these models may not be appropriate because they are not developed explicitly by considering molecular interactions. The novelty of the current work is the development of a constitutive relation to capture the viscoelastic response of noncrystalline solid phases of materials using molecular interactions. The noncrystalline solid phase is modeled by a large number of particles interacting with a hard-sphere intermolecular potential, so it becomes consistent with a continuum body. Using the existing continuum framework of viscoelasticity and Helmholtz free energy potential of noncrystalline phase of solids, Cauchy stress and corresponding evolution equation for viscoelastic stretch are derived. The current framework of constitutive modeling is also correlated with Maxwell’s representation of viscoelasticity, so it has versatile applicability on engineering materials. Current theoretical model is used to capture the viscoelasticity for uniaxial and equibiaxial extension, as well as pure shear. The experimental data available in the literature for uniaxial extension of polymeric and hydrogel are compared with the developed model. We achieve a very close agreement with the experimental results. The range of values of the parameter used in the energy dissipation function is determined from the convergence solution of the evolution equation for the equibiaxial extension. Because the current model considers microscopic molecular interactions, it can be utilized to predict the viscoelasticity of very thin micro/nano-noncrystalline structures.
Timely intervention and behavioral correction are contingent upon the early detection of juvenile delinquency. With the use of several decision-based classifiers and the random subspace technique, this study suggests a strong ensemble learning framework for automated assessment of adolescent delinquency. Based on a delinquency dataset, we assess the ensemble framework’s predictive performance in four experimental setups: (i) various base learners in the random subspace ensemble; (ii) Logistic Model Tree (LMT) under various train-test splits; (iii) conventional single classifiers; and (iv) comparison with other ensemble methods such as AdaBoost, Bagging, and Logit Boost. The results demonstrate that the random subspace technique significantly enhances classification performance when paired with more potent base learners. LMT continuously outperformed other classifiers, with a peak accuracy of 96.77
Coal mining operations produce substantial soil and rock materials, often called Overburden (OB). These wastes are often dumped nearby, and this dumping results in the formation of large mounds of debris several meters in height, known as OB-dump slopes. The present study addresses the stability problem of variably saturated OB-dump slopes under a probabilistic framework by employing the kinematically admissible solution of the classical plasticity theorem. For the study, OB-dump slopes, located in several locations of the Dhanbad region in India, are selected. Varying magnitudes of steady-state surface flux boundary conditions have been simulated in the numerical analysis to understand the time-dependent behaviour of the OB-dump slope. Furthermore, for a comprehensive and detailed investigation, the influence of seismic stresses has also been accounted for by performing a series of rigorous pseudo-static analyses considering the strength non-linearity of the OB-dump materials. In addition, a probabilistic approach has been implemented to quantify the uncertainties associated with the critical random input parameters through the application of the design of experiments and factorial design methodology. The relative significance or sensitivity of each parameter was obtained using analysis of variance (ANOVA), and multiple regression analysis was employed to develop a predictive model for assessing the safety factor of the dump slope. Finally, the developed prediction model has been coupled with a rigorous Monte-Carlo simulation technique to obtain the probability of failure of the OB-dump slope. Based on the study, it was found that the saturation and its variation of OB-dump materials play a crucial role in its stability, with a percentage variation of up to 6.6% in the FoS. Additionally, seismic earthquake coefficients and slope angle are the two most critical parameters affecting the slope stability, followed by the infiltration rate ratio.
