Evolution of Compact Stars and Dark Dynamical Variables

This work is aimed to explore the dark dynamical effects of $f(R,T)$ modified gravity theory on the dynamics of compact celestial star. We have taken the interior geometry as spherical star which is filled with imperfect fluid distribution. The modified field equations are explored by taking a particular form of $f(R,T)$ model, i.e., $f(R,T)=f_1(R)+f_2(R)f_3(T)$. These equations are then utilized to formulate the well-known structure scalars under the dark dynamical effects of this higher order gravity theory. Also, the evolution equations for expansion and shear are formulated with the help of these scalar variables. Further, all this analysis have been made under the condition of constant $R$ and $T$. We found a crucial significance of dark source terms and dynamical variables on the evolution and density inhomogeneity of compact objects.Comment: 18 pages, 4 figures, version accepted for publication in European Physical Journal

Influence of f(R)f(R) Models on the Existence of Anisotropic Self-Gravitating Systems

This paper aims to explore some realistic configurations of anisotropic spherical structures in the background of metric $f(R)$ gravity, where $R$ is the Ricci scalar. The solutions obtained by Krori and Barua are used to examine the nature of particular compact stars with three different modified gravity models. The behavior of material variables is analyzed through plots and the physical viability of compact stars is investigated through energy conditions. We also discuss the behavior of different forces, equation of state parameter, measure of anisotropy and Tolman-Oppenheimer-Volkoff equation in the modeling of stellar structures. The comparison from our graphical representations may provide evidences for the realistic and viable $f(R)$ gravity models at both theoretical and astrophysical scale.Comment: 23 pages, 13 figures, version accepted for publication in European Physical Journal

Hybrid iterative learning control of a flexible manipulator

This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied

Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN approach. By including a distribution for the noise error, an appropriate value can be found automatically in the sampling process, which optimizes the nuclear mass predictions. Furthermore, two quantities related to nuclear pairing and shell effects are added to the input layer in addition to the proton and mass numbers. As a result, the theoretical accuracies are significantly improved not only for nuclear masses but also for single-nucleon separation energies. Due to the inclusion of the shell effect, in the unknown region, the BNN approach predicts a similar shell-correction structure to that in the known region, e.g., the predictions of underestimation of nuclear mass around the magic numbers in the relativistic mean-field model. This manifests that better predictive performance can be achieved if more physical features are included in the BNN approach.Comment: 15 pages, 4 figures, and 3 table