Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.

The Impact of Transformational Leadership Style and Employee Creativity on Organizational Innovation in Universities During the Covid-19 Pandemic

Organizational innovation is one of the important issues for organizations in every country to adapt to changing operating environments, scientific and technological progress, and crisis issues. This study aims to evaluate the impact of transformational leadership style and employee creativity on organizational innovation in universities in Vietnam. We employed Bayesian exploratory factor analysis and Bayesian regression analysis with primary data of leader–employee pairs to explore the abovementioned effects. The findings show that the components of transformational leadership style, including idealized influence (II), inspirational motivation (IM), intellectual stimulation (IS), and individual consideration (IC), have positive impacts on organizational innovation (OI) and employee creativity (EC). The findings also imply that employee creativity (EC) is a mediating factor in the impact of transformational leadership style (TLS) on organizational innovation (OI). Finally, increasing intrinsic motivation (INM) can increase the positive impact of transformational leadership style (TLS) on employee creativity (EC). Based on the results, we propose policy implications to promote organizational innovation in Vietnamese universities in the context of the COVID-19 pandemic.

Toward ‘seeing’ critically: a Bayesian analysis of the impacts of a critical pedagogy

Critical reflection supports enactment of the social roles of care, like collaboration and advocacy. We require evidence that links critical teaching approaches to future critically reflective practice. We thus asked: does a theory-informed approach to teaching critical reflection influence what learners talk about (i.e. topics of discussion) and how they talk (i.e. whether they talk in critically reflective ways) during subsequent learning experiences? Pre-clinical students (n = 75) were randomized into control and intervention conditions (8 groups each, of up to 5 interprofessional students). Participants completed an online Social Determinants of Health (SDoH) module, followed by either: a SDoH discussion (control) or critically reflective dialogue (intervention). Participants then experienced a common learning session (homecare curriculum and debrief) as outcome assessment, and another similar session one-week later. Blinded coders coded transcripts for what (topics) was said and how (critically reflective or not). We constructed Bayesian regression models for the probability of meaning units (unique utterances) being coded as particular what codes and as critically reflective or not (how). Groups exposed to the intervention were more likely, in a subsequent learning experience, to talk in a critically reflective manner (how) (0.096 [0.04, 0.15]) about similar content (no meaningful differences in what was said). This difference waned at one-week follow up. We showed experimentally that a particular critical pedagogical approach can make learners’ subsequent talk, ways of seeing, more critically reflective even when talking about similar topics. This study offers the field important new options for studying historically challenging-to-evaluate impacts and supports theoretical assertions about the potential of critical pedagogies.

Soft Sensor Development Based on Quality-Relevant Slow Feature Analysis and Bayesian Regression with Application to Propylene Polymerization

Data-driven soft sensors are widely used to predict quality indices in propylene polymerization processes to improve the availability of measurements and efficiency. To deal with the nonlinearity and dynamics in propylene polymerization processes, a novel soft sensor based on quality-relevant slow feature analysis and Bayesian regression is proposed in this paper. The proposed method can handle the dynamics of the process better by extracting quality-relevant slow features, which present both the slowly varying characteristic and the correlations with quality indices. Meanwhile, a Bayesian inference model is developed to predict the quality indices, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. Finally, a case study is conducted with data sampled from a practical industrial propylene polymerization process to demonstrate the effectiveness and superiority of the proposed method.

Discrete Dynamic Modeling and Change Trend Analysis of Regional Economy Based on Big Data

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing.

Using a climate attribution statistic to inform judgments about changing fisheries sustainability

Sustainability—maintaining catches within the historical range of socially and ecologically acceptable values—is key to fisheries success. Climate change may rapidly threaten sustainability, and recognizing these instances is important for effective climate adaptation. Here, we present one approach for evaluating changing sustainability under a changing climate. We use Bayesian regression models to compare fish population processes under historical climate norms and emerging anthropogenic extremes. To define anthropogenic extremes we use the Fraction of Attributable Risk (FAR), which estimates the proportion of risk for extreme ocean temperatures that can be attributed to human influence. We illustrate our approach with estimates of recruitment (production of young fish, a key determinant of sustainability) for two exploited fishes (Pacific cod Gadus macrocephalus and walleye pollock G. chalcogrammus) in a rapidly warming ecosystem, the Gulf of Alaska. We show that recruitment distributions for both species have shifted towards zero during anthropogenic climate extremes. Predictions based on the projected incidence of anthropogenic temperature extremes indicate that expected recruitment, and therefore fisheries sustainability, is markedly lower in the current climate than during recent decades. Using FAR to analyze changing population processes may help fisheries managers and stakeholders to recognize situations when historical sustainability expectations should be reevaluated.

Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models

We consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. Crucial factors influencing accuracy of source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed estimation of noise covariance matrices with full structure. To address this shortcoming, we develop hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, which are estimated jointly from data. As an extension to classical sparse Bayesian learning (SBL), where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using the majorization-maximization framework and Riemannian geometry, we derive an efficient algorithm for updating the noise covariance along the manifold of positive definite matrices. We demonstrate that our algorithm has guaranteed and fast convergence and validate it in simulations and with real MEG data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and fully-structured. Our method has applications in many domains beyond biomagnetic inverse problems.