Journal of Multiscale Modelling

A novel mathematical model in hygrothermoelastic theory has developed to describe heat and moisture distribution in a nonsimple medium. The model incorporates non-Fourier and non-Fick laws using a single phase-lag model to establish a relation between hygrothermoelasticity and unstable conduction processes. The model is applied to an ellipse-shaped composite material subjected to humid-heat loading. The Laplace and Mathieu integral transform was employed to obtain an exact solution of linearly coupled partial differential equations in an elliptical coordinate system. The Gaver–Stehfest algorithm is utilized for the inversion of Laplace functions. Berger’s approximation equation was employed to simplify the calculation of significant deformations for elliptic plates. The model also provides quantitative results for the composition of T300/5208, which consists of epoxy resin reinforced with graphite fibers. The hygrothermal behaviors due to the single-phase-lag time, geometry dimensions, material type, and the two temperatures are explored. The results reveal that nonclassical theories with single-phase-lag time and two-temperature have significant influences on the hygrothermoelastic behaviors in elliptic-shaped objects. The investigation can be effectually led using micro- and nano-elliptic shape resonators in future research.

The IoT’s remarkable growth rate has attracted cybercriminals’ attention more than ever. The rising number of cyberattacks on IoT devices and intermediary communication mediums support the assertion. There are various ways for attackers to tamper with the IoT data distribution service. Since it affects data secrecy, the Blackhole attack was the most serious. For intrusion detection, deep learning algorithms are essential to IoT cyber security. Systems for detecting intrusions have been created to identify various attacks outside the firewall’s capability. The IDS categorizes the normal and abnormal aspects of the system based on its properties. In an integrated wired and wireless context, we developed a deep learning model to identify approved and unauthorized APs and detect Blackhole attacks in networks where raw traffic data has been gathered. To select the best feature sets exactly, we employ Sand Cat Swarm Optimization (SCSO), which tries to detect malicious attempts with the maximum detection rate possible. The Multiscale Deep Bi-directional GRU (MDBGRU) network also functions as a potent classifier, handling deep feature classification and threat detection in the complex IoT network environment. The outcomes of the suggested method’s detection accuracy are 99.27%, precision 89.34%, recall 84.32%, F1-score 77.31%, and Intrusion Detection Rate (IDR) 80.33%.

This study focuses on the photo-thermoelastic interaction for a functionally graded semiconductor in the context of nonlocal heat conduction law. The medium is assumed to be functionally graded in which, the graded changes have been justified by the exponential variation of the material gradients. The bounding plane of the medium is influenced by a periodically varying heat source. The heat transport equation is governed by the nonlocal Moore–Gibson–Thompson (MGT) theory, which assimilates the memory-dependent derivative within a slipping interval. Laplace and the Fourier transform techniques have been utilized to arrive at the solutions of the governing equations. Moreover, the solutions in real space-time domain can be determined upon successively applying the inversion of the Fourier transform with the help of residual calculus, in which, the poles of the integrand have calculated numerically in complex domain by accomplishing Laguerre’s method. Also, the inversion of the Laplace transform has been performed numerically using a method based on Fourier series expansion technique. From the numerical estimates, the profound effect of various parameters such as the nonlocal parameter, correlating length parameter and the time-delay parameter on the thermophysical quantities has been discussed. From the numerical study, the superiority of a nonlinear kernel function compared to a linear kernel is analyzed. A comparative study of the existing literature with the present results have been depicted. A validation example is also considered to show the accuracy and efficiency of the current demonstration.