A large range of applications have been identified based upon the communication of underground sensors deeply buried in the soil. The classical electromagnetic wave (EM) approach, which works well for terrestrial communication in air medium, when applied for this underground communication, suffers from significant challenges attributing to signal absorption by rocks, soil, or water contents, highly varying channel condition caused by soil characteristics, and requirement of big antennas. As a strong alternative of EM, various magnetic induction (MI) techniques have been introduced. These techniques basically depend upon the magnetic induction between two coupled coils associated with transceiver sensor nodes. This paper elaborates on three basic MI communication mechanisms i.e. direct MI transmission, MI waveguide transmission, and 3D coil MI communication with detailed discussion of their working mechanism, advantages and limitations. The comparative analysis of these MI techniques with each other as well as with EM wave method will facilitate the users in choosing the best method to offer enhanced transmission range (upto 250 m), reduced path loss (<100 dB), channel reliability, working bandwidth (1–2 kHz), & omni-directional coverage to realize the promising MI-based wireless underground sensor network (WUSN) applications.

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos.

Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm.

The flow of a two-dimensional incompressible hybrid nanofluid over a stretching cylinder containing microorganisms with parallel effect of inclined magnetohydrodynamic was examined in the current study in relation to chemical reactions, heat source effect, nonlinear heat radiation, and multiple convective boundaries. The main objective of this research is the optimization of heat transfer with inclined MHD and variation in different physical parameters. The governing partial differential equations are transformed into a set of ordinary differential equations by applying the appropriate similarity transformations. The Runge–Kutta method is recognized for using shooting as a technique. Surface plots, graphs, and tables have been used to illustrate how various parameters affect the local Nusselt number, mass transfer, and heat transmission. It is demonstrated that when the chemical reaction parameter rises, the concentration and motile concentration profiles drop. The least responsive is the rate of heat transfer to changes in the inclined magnetic field and most associated with changes in the Biot number and radiation parameter shown in contour plot. The streamline graph illustrates the way fluid flow is affected simultaneously by the magnetic parameter M and an angled magnetic field. Local Nusselt number and local skin friction are improved by the curvature parameter and mixed convection parameter. The contours highlight the intricate interactions between restricted magnetic field, significant radiation, and substantial convective condition factors by displaying the best heat transfer. The three-dimensional surface, scattered graph, pie chart, and residual plotting demonstrate the statistical analysis of the heat transfer. The results support their use in sophisticated energy, healthcare, and industrial systems and enhance our theoretical knowledge of hybrid nanofluid dynamics.

This study focuses on the analysis of the simultaneous impact of inclined magnetohydrodynamic Carreau hybrid nanofluid flow over a stretching sheet, including microorganisms with the effects of chemical reactions in the presence and absence of slip conditions for dilatant $(n>1.0)$ and quasi-elastic hybrid nanofluid $(n<1.0)$ limitations. Meanwhile, the transfer of energy is strengthened through the employment of heat sources and bioconvection. The analysis incorporates nonlinear thermal radiation, chemical reactions, and Arrhenius activation energy effects on different profiles. Numerical simulations are conducted using the efficient Bvp5c solver. Motile concentration profiles decrease as the density slip parameter of the motile microbe and Lb increase. The Weissenberg number exhibits a distinct nature depending on the hybrid nanofluid; the velocity profile, skin friction, and Nusselt number fall when $(n>1.0)$ and increase when $(n<1.0)$. For small values of inclination, the 3D surface plot is far the surface, while it is close to the surface for higher values of inclination but has the opposite behavior for the 3D plot of the Nusselt number. A detailed numerical investigation on the effects of important parameters on the thermal, concentration, and motile profiles and the Nusselt number reveals a symmetric pattern of boundary layers at various angles $\left(\alpha \right)$. Results are presented through tables, graphs, contour plots, and streamline and surface plots, covering both shear-thinning cases $(n<1.0)$ and shear-thickening cases $(n>1.0)$.

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging networks that offer a wide range of applications, including mine prevention, power grid maintenance, underground pipeline monitoring, and upstream oil monitoring. MI-based wireless underground sensor networks (WUSNs), utilizing small antenna coils, offer a viable solution by providing consistent channel conditions. The cross-layer protocols address the specific challenges of WUSNs, leading to improved network performance and enhanced operational capabilities in real-world applications. This work proposes a distributed cross-layer solution, leveraging the hybrid marine predator naked mole rat algorithm (MPNMRA) for MI-operated WUSNs. The solution, called DECMN (distributed energy-throughput efficient cross-layer network using MPNMRA), is designed to optimize the MI communication channels, MI relay coils (MI waveguide), and MI waveguide with 3D coils to fulfill quality of service (QoS) parameters, while achieving energy savings and throughput gains. DECMN utilizes the interactions between various layers to develop cross-layer protocols based on MPNMRA. Simulation results demonstrate the effectiveness of DECMN, offering energy savings, increased throughput, and reliable transmissions within the performance limits.