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The spin-orbit torque switching of ferrimagnetic Gdx(Fe90Co10)100-x films was studied for both transition metal (TM)-rich and rare earth (RE)-rich configurations. The spin-orbit torque driven magnetization switching follows the same sense in TM-rich and RE-rich samples with respect to the total magnetization, but the sense of the switching is reversed with respect to the TM magnetization. This indicates that the sign of the spin-orbit-torque-driven magnetic switching follows the total magnetization, although transport based techniques such as anomalous Hall effect are only sensitive to the transition metal magnetization. These results provide important insight into the physics of spin angular momentum transfer in materials with antiferromagnetically coupled sublattices.

This article presents a non-linear mathematical model that captures the dynamics of drinking prevalence within a population. The model is analyzed under an optimal control framework, dividing the total population into four compartments: susceptible, heavy drinker, drinker in treatment, and recovered classes. The model’s validity is affirmed through considerations of positivity, boundedness, reproduction number, stability, and sensitivity analysis. Stability theory is employed to explore both local and global stabilities. Sensitivity analysis identifies parameters with a significant impact on the reproduction number (R0{R}_{0}), with maximum sensitivity observed in parameters related to drinking transmission and transitions from heavy drinking to treatment stages. These parameters exhibit sensitivity indices of (0.538,1)\left(0.538,1), indicating that a 10% increase in these parameters would result in a (5.38,1)\left(5.38,1) increase in the threshold quantity. The study introduces an optimal control strategy that involves awareness campaigns and treatment as control variables. These controls aim to minimize the number of heavy drinkers while maximizing the number of recovered individuals. Pontryagin’s maximum principle is used to solve optimal control problems. Additionally, the research explores various parametric settings for each compartment, enriching the study environment. The effectiveness of the proposed control scheme is evaluated through rigorous numerical simulations, highlighting its competitive edge. The results, validated using MATLAB simulations, are detailed throughout the article.

This paper presents a new development of an Adaptive Network-based Fuzzy Inference System (ANFIS) for a Hybrid Ground Source Heat Pump (HGSHP). The HGSHP is equipped with a supplementary heat sink composter to process organic solid waste (OSW), utilizing excess hot air from the condensing unit to aerate the compost pile. The Fuzzy Logic Controller (FLC) was developed using data collected by effective sensors installed in the HGSHP system. The main objective is to control the water flow rate with a Variable Speed Drive (VSD) to improve overall system performance. The dataset for ANFIS has been created and trained using MATLAB® software, then implemented on a Raspberry Pi nano-computer with Python coding. This paper compares the performance of ANFIS with two different cases: ANFIS with Triangular Membership Function (TriMF) and ANFIS with Gaussian Membership Function (GaussMF). After implementing ANFIS with TriMF and GaussMF, the average COP during composter operation and system cooling significantly increased. In contrast, the HGSHP system power consumption is sufficiently reduced in both case studies. Moreover, ANFIS also benefits the composting process, as evidenced by the increase in composter operation time, and vice versa for system cooling time. Ultimately, the implementation of ANFIS can improve the HGSHP system performance in both the TriMF and GaussMF cases, with the TriMF case showing a significant improvement in the HGSHP system performance compared to the GaussMF case.

The influence of substrate temperature on the growth rate, crystal grain size and SiO2 mask stability in the selective epitaxial growth of silicon carbide deposited from SiH4, C2H4 and HCl on silicon dioxide masked silicon (100) was investigated. Results showed that deposition at atmospheric pressure and a Cl/Si ratio of 50 provided good selectivity. Raising the substrate temperature from 950 to 1000 deg C increased the growth rate and the crystal size and enhanced the film's surface morphology.

Since skewness plays a vital role in different engineering phenomena, its accurate measurement gains significance. Several measures have been taken to quantify the extent of skewness in distributions over the years, but each measure is subject to some serious limitations. In this regard, the present study aims to propose a new skewness measuring functional based on distribution function evaluated at mean with minimal assumptions and limitations. Four well-recognized properties for an appropriate measure of skewness were verified and demonstrated for the new measure. A comparison was made between the new measure and the conventional moment-based measure using both functionals over the range of distributions available in the literature. Furthermore, the robustness of the proposed measure against unusual data points was explored using influence function. The mathematical findings were verified through meticulous simulation studies; further, they were verified by real data sets derived from diverse fields of inquiries. As observed, compared to the classical moment-based measure, the proposed one passed all the checks with distinction. Given the computational simplicity, applicability in a more general environment, and preservation of c-ordering of distribution, the proposed measure may be regarded as an attractive addition to the family of skewness measures. [ABSTRACT FROM AUTHOR]