Spin-orbit torques from interfacial spin-orbit coupling for various interfaces

We use a perturbative approach to study the effects of interfacial spin-orbit coupling in magnetic multilayers by treating the two-dimensional Rashba model in a fully three-dimensional description of electron transport near an interface. This formalism provides a compact analytic expression for current-induced spin-orbit torques in terms of unperturbed scattering coefficients, allowing computation of spin-orbit torques for various contexts, by simply substituting scattering coefficients into the formulas. It applies to calculations of spin-orbit torques for magnetic bilayers with bulk magnetism, those with interface magnetism, a normal metal/ferromagnetic insulator junction, and a topological insulator/ferromagnet junction. It predicts a dampinglike component of spin-orbit torque that is distinct from any intrinsic contribution or those that arise from particular spin relaxation mechanisms. We discuss the effects of proximity-induced magnetism and insertion of an additional layer and provide formulas for in-plane current, which is induced by a perpendicular bias, anisotropic magnetoresistance, and spin memory loss in the same formalism.Comment: 24 pages, 9 figure

Prediction of Giant Spin Motive Force due to Rashba Spin-Orbit Coupling

Magnetization dynamics in a ferromagnet can induce a spin-dependent electric field through spin motive force. Spin current generated by the spin-dependent electric field can in turn modify the magnetization dynamics through spin-transfer torque. While this feedback effect is usually weak and thus ignored, we predict that in Rashba spin-orbit coupling systems with large Rashba parameter $\alpha_{\rm R}$, the coupling generates the spin-dependent electric field [\pm(\alpha_{\rm R}m_e/e\hbar) (\vhat{z}\times \partial \vec{m}/\partial t)], which can be large enough to modify the magnetization dynamics significantly. This effect should be relevant for device applications based on ultrathin magnetic layers with strong Rashba spin-orbit coupling.Comment: 4+ pages, 2 figure

A study on the effect of the physical environment in an airplane on customer loyalty

Purpose: The purpose of this study is to identify the effect of the in-flight physical environment on perceived quality and customer loyalty. Design/methodology: For this analysis, a survey was conducted with airline passengers at GimpoInternational Airport and Incheon International Airport. A total of 264 questionnaires were analyzed using structural equation modeling. Findings: This study found that physical environment factors such as spatiality, amenity, aesthetics and entertainingness would have a positive impact on perceived quality. In addition, this study found that perceived quality would have a positive impact on satisfaction, whereas satisfaction would have a positive impact on image and behavioral intention. Originality/value: This paper is the first research that examines the effect of in-flight physical environment on satisfaction, image, and customer loyalty simultaneously in Korea. Results of this study could be used as basic data for an enhancement strategy of the in-flight physical environment.Peer Reviewe

Attitudes toward Using and Teaching Confidence Intervals: A Latent Profile Analysis on Elementary Statistics Instructors

The use of confidence intervals (CIs) for making a statistical inference is gaining popularity in research communities. To evaluate college statistics instructors’ readiness to teach CIs, this study explores their attitudes toward teaching CIs in elementary statistics courses, and toward using CIs in inferential statistics. Data were collected with a survey that classifies instructors’ attitudes on the basis of three previously established pedagogical components: affective, cognitive, and behavioral. Based on the survey responses from 270 participants, we created three profiles (subgroups) via latent profile analysis, and identified each profile’s pattern of attitudes toward CIs and common characteristics of the instructors that fit each profile. In addition, we compared the profiles across groupings created by six variables: gender, academic background, statistics teaching experience, subject preference, degree level, and desire to improve teaching. The results of the latent profile analysis support three profiles within the population of statistics instructors, and the results of the comparative analysis of teacher characteristics indicate that the six variables are moderate to strong predictors of the grouping of the sample into three profiles

Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model

Use of work-related communication technology outside regular working hours and work-family conflict (work interference with family and family interference with work): results from the 6th Korean working conditions survey

Background: Recently, use of work-related communication technology - smartphones, tablets, and laptops - is increasing rapidly by development of technology with the coronavirus disease 2019 pandemic. Some studies have suggested that work-related communication technology has a significant link with work-family conflict (WFC) but these studies included only limited number of participants and lacked essential covariates. Therefore, this study analyzes this association using large representative data sample and selected waged workers who were married-couples with children. Methods: This study was conducted based on data from the 6th Korean Working Conditions Surveys (KWCS). A total of 17,426 waged workers having a marriage partner and one or more children were selected. Logistic regression analysis was performed to determine whether WFC was associated with communication technology use. The odds ratios (ORs) for WFC were stratified by sex and working hours. Results: In fully adjusted model, WFC was higher those who used communication technology outside regular working hours compared with those who did not use it (OR: 1.66; 95% confidence interval [CI]: 1.39-1.97). When stratified by sex and working hours, the effect was greater in women than in men (OR: 1.79; 95% CI: 1.42-2.26 vs. OR: 1.52; 95% CI: 1.17-1.97) and women who worked over 52 hours per week had the highest OR (3.40; 95% CI: 1.25-9.26). Conclusions: This study revealed that the work-related communication technology use outside regular working hours was associated with WFC. The association were greater among those having longer working hours and female workers. These results suggest that appropriate policy should be implemented to reduce working hours and right to disconnect after work, particularly in female workers