Pair Of Latent Variables Research Articles

Analyzing Teaching Self-Efficacy Correlates in Virtual Education: A Gender-Driven Structural Equation Modeling Approach

The study focused on the Philippines' education system, which abruptly adopted to online education due to the COVID-19 pandemic. The study used different theories to explore the relationships between online teaching self-efficacy and other affective variables. The researchers used structural equation modeling (SEM) to develop a model that showed the correlations among the identified variables in general and when grouped according to gender. The study found that only three pairs of latent variables were positively correlated at an average extent, indicating a low correlation between the variables. The findings drawn out from the final model can certainly benefit teacher-educators and institutional leaders alike who would most probably explore the idea of introducing the online modality as a supplementary requirement for the preservice teachers’ internship in the near future. Thus, institutions that offer education programs must find ways and means to motivate preservice teachers to online teaching as there are revealed to be the rudiments, based on the final model to a positive attitude and a favorable satisfaction level toward online language teaching.

Forecasting of iron ore sintering quality index: A latent variable method with deep inner structure

Accurate and real-time estimation of iron ore sintering quality index is essential for the stability of the production process. However, the sintering process data is generally characterized by high dimensionality, collinearity, nonlinearity, and dynamic features, which seriously hinder the modeling performance. To cope with the complex properties mentioned above, an effective fusion of recurrent neural network with the gated recurrent unit and partial least squares (GRU-PLS) is introduced for ferrous oxide (FeO) prediction of the finished sinter. The proposed GRU-PLS model takes the advantage of conventional latent variable method but incorporates the deep inner structure between each pair of latent variables that captures the nonlinear and dynamic information simultaneously. The modeling performance of the proposed model is evaluated by the actual data collected from the iron ore sintering process in a large iron and steel group in South China. The results show the lowest prediction error of the GRU-PLS model in comparison with other counterparts. More specifically, the rootmean-square error of the GRU-PLS model is decreased by 35.29% in comparison with that for the recurrent neural network with the gated recurrent unit.

Adaptive dynamic prediction of effluent quality in wastewater treatment processes using partial least squares embedded with relevance vector machine

Recycling of water resources through wastewater treatment process (WWTP) is widely considered for a viable option for different water demands and sustainable production due to water shortage and pollution. Thus, accurate and real-time prediction of effluent quality is of vital importance to maintain the stable process control of WWTP system. Taking into account the complex biochemical characteristics of WWTP, an adaptive dynamic nonlinear partial least squares (PLS) model has been proposed to improve the prediction performance and stability of effluent quality indexes. First, an adaptive matrix expansion scheme is introduced to exploit the specific dynamic features of different data sets. Then the linear PLS model is conducted and the inner function between each pair of latent variables is rebuilt by relevance vector machine (RVM) with sparse and probabilistic structure, which is robust for the nonlinearity and random noises among process variables. The performance of the proposed adaptive dynamic RVM-based PLS (D-RVM-PLS) model is evaluated through two case studies based on benchmark simulation model No.1 and a real industrial WWTP, respectively. Experimental results show the superiority of the proposed model in prediction accuracy, stability, and execution efficiency. For the modeling accuracy of effluent chemical oxygen demand in real industrial case, adaptive D-RVM-PLS provides the lowest root-mean-square error, which is decreased by approximately 5.01%–49.19% in comparison with linear PLS model, Gaussian process regression-based PLS model, and their dynamic models without adaptive selection procedure.

Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression

A dynamic Gaussian process regression based partial least-squares (D-GPR-PLS) model is proposed to improve estimation ability and compared to the conventional nonlinear PLS. Considering the strong ability of GPR in nonlinear process modeling, this method is used to build a nonlinear regression between each pair of latent variables in the partial least-squares. In addition, augmented matrices are embedded into the D-GPR-PLS model to obtain better prediction accuracy in nonlinear dynamic processes. To evaluate the modeling performance of the proposed method, two simulated cases and a real industrial process based on wastewater treatment processes (WWTPs) are considered. The simulated cases use data from two high fidelity simulators: benchmark simulation model no. 1 and its long-term version. The second study uses data from a real biological wastewater treatment process. The results show the superiority of D-GPR-PLS in modeling performance for both data sets. More specifically, in terms of the prediction for effluent chemical oxygen demand of the real WWTP data, the value of the root-mean-square error is decreased by 31%, 16%, and 52%, respectively, in comparison with that for linear PLS, quadratic PLS, and least-squares support vector machine based PLS.

A Metabolic Obesity Profile Is Associated With Decreased Gray Matter Volume in Cognitively Healthy Older Adults.

Obesity is a risk factor for cognitive decline and gray matter volume loss in aging. Studies have shown that different metabolic factors, e.g., dysregulated glucose metabolism and systemic inflammation, might mediate this association. Yet, even though these risk factors tend to co-occur, they have mostly been investigated separately, making it difficult to establish their joint contribution to gray matter volume structure in aging. Here, we therefore aimed to determine a metabolic profile of obesity that takes into account different anthropometric and metabolic measures to explain differences in gray matter volume in aging. We included 748 elderly, cognitively healthy participants (age range: 60 – 79 years, BMI range: 17 – 42 kg/m2) of the LIFE-Adult Study. All participants had complete information on body mass index, waist-to-hip ratio, glycated hemoglobin, total blood cholesterol, high-density lipoprotein, interleukin-6, C-reactive protein, adiponectin and leptin. Voxelwise gray matter volume was extracted from T1-weighted images acquired on a 3T Siemens MRI scanner. We used partial least squares correlation to extract latent variables with maximal covariance between anthropometric, metabolic and gray matter volume and applied permutation/bootstrapping and cross-validation to test significance and reliability of the result. We further explored the association of the latent variables with cognitive performance. Permutation tests and cross-validation indicated that the first pair of latent variables was significant and reliable. The metabolic profile was driven by negative contributions from body mass index, waist-to-hip ratio, glycated hemoglobin, C-reactive protein and leptin and a positive contribution from adiponectin. It positively covaried with gray matter volume in temporal, frontal and occipital lobe as well as subcortical regions and cerebellum. This result shows that a metabolic profile characterized by high body fat, visceral adiposity and systemic inflammation is associated with reduced gray matter volume and potentially reduced executive function in older adults. We observed the highest contributions for body weight and fat mass, which indicates that factors underlying sustained energy imbalance, like sedentary lifestyle or intake of energy-dense food, might be important determinants of gray matter structure in aging.

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

This paper proposes a novel nonlinear partial least square (PLS) approach for dealing with the modeling problem of industrial processes with input variables in collinearity. The new method combines the external linear PLS framework with the internal extreme learning machine (ELM) function. First, PLS is used as the outer framework to extract the input and output latent variables as well as eliminating the collinearity of the original variables, and then ELM is employed to describe the nonlinear relation between pairs of latent variables. Besides, the weight updating strategy based on errors minimization is also involved to improve the prediction accuracy. Then, the pH-neutralization process is taken as a benchmark to verify the validity of the new model. Finally, this method is applied to model the NOx emission of a 1000-MW coal-fired boiler, and root-mean-square error (RMSE) values are 5.9541 for the training dataset and 6.8323 for the testing dataset. Compared with linear PLS and another two nonlinear PLS methods, smaller prediction errors is obtained. The results indicate new nonlinear PLS model can be a better choice for establishing the model of NOx emission for coal-fired boilers.

Nonlinear PLS Integrated with Error-Based LSSVM and Its Application to NOx Modeling

This article presents a novel nonlinear partial least-squares (PLS) method to address a nonlinear problem with input collinearity in an industrial process. The proposed method integrates an inner least-squares support vector machine (LSSVM) function with an outer linear PLS framework. First, the input and output latent variables are extracted to eliminate the collinearity through PLS projection, and then LSSVM is used to construct nonlinear relation between each pair of latent variables. Moreover, a weight-updating procedure is incorporated to enhance the accuracy of prediction. Then, training and predicting algorithms based on modified nonlinear iterative partial least-squares (NIPALS) steps are also described in detail. The performance of the new method is also investigated with a benchmark data set. Finally, this approach is applied to a real industrial process to predict the NO x emissions of a coal-fired boiler. The root-mean-square errors (RMSEs) on the training and testing data decreased to only 12.6632 and 37.6609, respectively. Compared with the original linear PLS and other kinds of nonlinear PLS methods, a reduction of approximately 40.8―47.4% in the prediction errors is attained. The results reveal that the new approach is capable of modeling the nonlinear relation of NO x emissions with other process parameters and improving the prediction performance.

Modeling Aspects of Theory of Mind with Markov Random Fields

We propose Markov random fields (MRFs) as a probabilistic mathematical model for incorporating the internal states of other agents, both human and robotic, into robot decision making. By using estimates of Theory of Mind (ToM), the mental states of other agents can be incorporated into decision making through statistical inference, allowing robots to balance their own goals and internal objectives with those of other collaborating agents. The MRF model is well-suited to domains in which the joint probability over latent (action) and observed (perceived) variables can be factored into pairwise interactions between these variables. Specifically, these interactions occur through functions that evaluate “local evidence” between an observed and latent variable and “compatibility” between a pair of latent variables. We will describe how experimental findings from the ToM literature can be explained using MRF models, and then show how this framework can be applied to a social robotics task. We will also describe how to use belief propagation on a multi-robot MRF as a novel approach to multi-robot coordination, with parallels to human collaboration strategies.