Traditional Structural Models Research Articles

Matrix Decomposition Approach for Structural Equation Modeling as an Alternative to Covariance Structure Analysis and Its Theoretical Properties

Matrix decomposition structural equation modeling (MDSEM) is introduced as a novel approach in structural equation modeling, contrasting with traditional structural equation modeling (SEM). MDSEM approximates the data matrix using a model generated by the hypothetical model and addresses limitations faced by conventional SEM procedures by emphasizing factor analysis with L 2 penalization. Key advantages of MDSEM include preventing improper solutions, the ability to compute observation-wise residuals without post-hoc factor score estimation and ease in identifying equivalent models. These benefits are attributed to its matrix decomposition techniques, allowing for direct model fitting to the data matrix, unlike the covariance structure fitting in CS-SEM. An iterative algorithm for parameter estimation is proposed, guaranteeing a monotonically decreasing function value. Theoretical properties of MDSEM are examined, revealing its shared characteristics with existing factor analysis and SEM. Numerical simulations and real data examples validate that MDSEM produces results comparable to existing methods when adequately calibrated.

Equipment Fault Propagation Path Identification Based on Unstable Points Detection

To prevent industrial fault propagation, it is important to clarify the relationship between industrial system components and identify the fault propagation path efficiently and timely, aiming at the problems in fault propagation path identification, this paper presents an equipment fault propagation analysis approach based on unstable points identification to solve such issues. First, the fault propagation diagram is created by analyzing the industrial complex system components. Second, to address the issues of traditional Interpretative Structural Modeling (ISM), the Bilateral Rotation Interpretative Structural Modeling (BRISM) method is proposed to stratify the components and detect the unstable points. Finally, the fault propagation graph is analyzed using the PageRank algorithm to update the unstable points and edge weights between different nodes to identify the fault propagation paths. The results indicate that the proposed method can effectively identify fault propagation paths between components and can be applied to various industrial systems.

A Novel Public Sentiment Analysis Method Based on an Isomerism Learning Model via Multiphase Processing.

The dissemination of public opinion in the social media network is driven by public sentiment, which can be used to promote the effective resolution of social incidents. However, public sentiments for incidents are often affected by environmental factors such as geography, politics, and ideology, which increases the complexity of the sentiment acquisition task. Therefore, a hierarchical mechanism is designed to reduce complexity and utilize processing at multiple phases to improve practicality. Through serial processing between different phases, the task of public sentiment acquisition can be decomposed into two subtasks, which are the classification of report text to locate incidents and sentiment analysis of individuals' reviews. Performance has been improved through improvements to the model structure, such as embedding tables and gating mechanisms. That being said, the traditional centralized structure model is not only easy to form model silos in the process of performing tasks but also faces security risks. In this article, a novel distributed deep learning model called isomerism learning based on blockchain is proposed to address these challenges, the trusted collaboration between models can be realized through parallel training. In addition, for the problem of text heterogeneity, we also designed a method to measure the objectivity of events to dynamically assign the weights of models to improve aggregation efficiency. Extensive experiments demonstrate that the proposed method can effectively improve performance and outperform the state-of-the-art methods significantly.

Bayesian inference for long memory term structure models

In this study, we propose a novel adaptation of the Dynamic Nelson–Siegel term structure model, incorporating long memory properties to enhance its forecasting accuracy. Our approach involves modelling the evolution of latent factors using fractional Gaussian noise processes, approximated by a weighted sum of independent first-order autoregressive components. The resulting formulation allows for a Gaussian Markov Random Field representation, facilitating the application of computationally efficient Bayesian techniques through Integrated Nested Laplace Approximations. Extensive simulation and empirical analysis demonstrate that integrating long memory significantly improves the model's forecasting performance, particularly for longer time horizons. By shedding light on the potential benefits of incorporating long memory concepts into traditional term structure models, our research highlights its utility in capturing intricate temporal dependencies and enhancing prediction precision.

Analysis of barriers affecting Industry 4.0 implementation: An interpretive analysis using total interpretive structural modeling (TISM) and Fuzzy MICMAC

The purpose of this study is to build a structural relationship model based on total interpretive structural modeling (TISM) and fuzzy input-based cross-impact matrix multiplication applied to classification (MICMAC) for analysis and prioritization of the barriers influencing the implementation of Industry 4.0 technologies. 10 crucial barriers that affect the deployment of Industry 4.0 techniques are identified in the literature. Also, the Fuzzy MICMAC approach is applied to classify the barriers. The importance of TISM over traditional interpretive structural modeling (ISM) is shown in this work. Results proved that the barriers, namely IT infrastructure, lack of cyber physical systems, and improper communication models, are identified as the most dependent barriers, and the barriers of lack of top management commitment and inadequate training are identified as the most driving barriers. This study makes it easier for decision-makers to take the necessary steps to mitigate the barriers. The bottom level of the TISM hierarchy is occupied by barriers that need more attention from top management in order to be effectively monitored and managed. This study explains the steps to execute TISM in detail, making it easy for researchers and practitioners to comprehend its principles.

A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data.

We didactically derive a correlated traits correlated (methods - 1) [CTC(M - 1)] multitrait-multimethod (MTMM) model for dyadic round-robin data augmented by self-reports. The model is an extension of the CTC(M - 1) model for cross-classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round-robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance-structure of the augmented round-robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed.

NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data.

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.

Predicting Academic Self-Efficacy Based on Self-Directed Learning and Future Time Perspective.

The purpose of this study was to investigate the relationship between teacher candidates' academic self-efficacy, self-directed learning, and future time perspective. A dual-stage analytical approach, utilizing both traditional structural equation modeling (SEM) and Machine Learning Classification Algorithms, was employed to test the proposed hypotheses. The study included a sample of 879 teacher candidates. The SEM analysis revealed that self-directed learning had a significant positive effect on academic self-efficacy. Furthermore, future time perspective was found to significantly predict academic self-efficacy. The combined endogenous constructs accounted for a substantial portion of the explained variance. Additionally, the study employed LMT and Multiclass classifiers from Machine Learning algorithms to predict academic self-efficacy. In summary, the findings of this study suggest that self-directed learning and future time perspective are significant factors in predicting teacher candidates' academic self-efficacy. The study utilized both traditional SEM and Machine Learning algorithms to provide a comprehensive analysis of the relationships between these variables.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.Graphical abstract

Revisit the causal inference between organizational commitment and job satisfaction: A meta-analysis disentangling its sources of inconsistencies.

Existing theories and studies have exclusively focused on the direct temporal ordering of organizational commitment (OC) and job satisfaction (JS). However, their ordering varies highly across empirical studies. It is unclear whether such high variation is caused by statistical artifacts (i.e., spurious variation) or substantive moderators (i.e., true variation). Therefore, to disentangle artificial and substantial sources of this variation, we adopted traditional and full information meta-analytic structural equation modeling based on 71 independent samples (N = 16,698) with panel designs. After correcting for statistical artifacts, there was moderate heterogeneity in the lagged effects of OC and JS, suggesting that their ordering was not direct or simple but moderated by other variables. Further, the conceptualizations and/or measurements of OC and JS, time lag, and the timing of the investigation (e.g., newcomers vs. nonnewcomers) moderated their ordering. Finally, different specifications of the ordering between OC and JS resulted in differential cross-lagged relationships among JS, OC, and turnover intentions (k = 31, N = 6,876). Our meta-analysis provides new insights into the conceptualizations and/or measurements of OC and JS, adds some theoretical clarification to the dissenting theories by incorporating the time element, nuanced differences in the OC conceptualizations and the timing of the investigation, and raises concern over theory and research informed by studies overlooking alternative orderings of JS and OC. Looking forward, we suggest several promising future directions for explaining the heterogeneous cross-lagged relationships between OC and JS. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Structural sources of oil market volatility and correlation dynamics

This paper explores the structural driving sources of oil market volatility and correlation dynamics using our derived volatility impulse response and correlation decomposition functions. A focal point of this paper is to conduct hypothesis tests for the observed diverging dynamics of oil market volatilities (a steady decline in oil production volatility and a concurrent increase in oil price volatility). In order to circumvent the restriction of uncorrelated disturbances in traditional structural models with stochastic volatility, we specify a time-varying level-volatility transmission matrix that allows for time-varying correlations between level and volatility innovations. Our model generalization hence captures transmissions of unexpected changes in oil levels to oil volatilities, and proves more adequate than traditional models for estimating oil market dynamics. Our results reveal several new useful insights for policy-makers, firms and investors. First, we find that a decrease in oil price persistence is at the origin of the diverging pattern of oil volatilities. Second, decreased variability of flow supply shocks is responsible for about 32% of the reduction in oil production volatility, while increased variability of demand-side shocks explains almost 60% of the increase in oil price volatility. Lastly, we find a near-zero correlation between oil production and oil price since the mid-1980s – a result that supports the theoretical prediction that oil production is inelastic to oil price changes.

Ensemble-function relationships to dissect mechanisms of enzyme catalysis.

Decades of structure-function studies have established our current extensive understanding of enzymes. However, traditional structural models are snapshots of broader conformational ensembles of interchanging states. We demonstrate the need for conformational ensembles to understand function, using the enzyme ketosteroid isomerase (KSI) as an example. Comparison of prior KSI cryogenic x-ray structures suggested deleterious mutational effects from a misaligned oxyanion hole catalytic residue. However, ensemble information from room-temperature x-ray crystallography, combined with functional studies, excluded this model. Ensemble-function analyses can deconvolute effects from altering the probability of occupying a state (P-effects) and changing the reactivity of each state (k-effects); our ensemble-function analyses revealed functional effects arising from weakened oxyanion hole hydrogen bonding and substrate repositioning within the active site. Ensemble-function studies will have an integral role in understanding enzymes and in meeting the future goals of a predictive understanding of enzyme catalysis and engineering new enzymes.

Estimating and investigating multiple constructs multiple indicators social relations models with and without roles within the traditional structural equation modeling framework: A tutorial.

The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials, we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison with alternative modeling options are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Supplemental Material for Estimating and Investigating Multiple Constructs Multiple Indicators Social Relations Models With and Without Roles Within the Traditional Structural Equation Modeling Framework: A Tutorial

Supplemental Material for Estimating and Investigating Multiple Constructs Multiple Indicators Social Relations Models With and Without Roles Within the Traditional Structural Equation Modeling Framework: A Tutorial

Loa-Geo1: A field regional transect to unravel the structure of the western Central Andes

The structure of the Central Andes forearc of northern Chile has been an enigma for many years. Along this region, the preservation of a long pediplain constituted by gravels and unconsolidated sediments has hidden the structure of the region, making it difficult to understand its tectonic evolution. The traditional and regional-scale structural models are mainly supported by outcrop-scale and local observations. To unravel the structure of this Andes region, we carried out a regional-scale study based on local and regional observations, geological mapping, and the elaboration of local and regional balanced cross-sections, thereby illustrating the geometry and distribution of the first-order structural styles. We examined four specific sectors along a regional transect: the Loa and San Salvador rivers (parallel to each other), Sierra de Limón Verde, and Cerros de Tuina. Three structural styles were identified in the hybrid thick- and thin-skinned and doubly verging fold-and-thrust belt. These styles comprise partially inverted Permian to Jurassic normal faults, thick-skinned reverse faults affecting Paleozoic crystalline basement blocks, and shallow and thin-skinned fault-related folds. The shallow and thin-skinned fault-related folds usually result from the propagation of large, thick-skinned thrust ramps into stratigraphic successions. The restoration of a regionally balanced cross section constrained by field data and the previously interpreted two-dimensional (2-D) seismic profiles suggest that much of the crustal shortenings are accommodated by the reverse reactivation of Triassic and Jurassic normal faults bounding half-graben structures. It is also accommodated by blind thrust faults, whose geometry, kinematic, and depth are debatable. However, we have proposed a large, thick-skinned thrust ramp as the structure responsible for the greater shortening in the region, along which a significant thick- to thin-skinned transition occurs. The age of the synorogenic deposits identified here is unknown, specially those exposed at the Loa and San Salvador rives, however, the basal successions exposed at the Tuina sector reported Upper Cretaceous ages, thus suggesting that the orogeny in the forearc could have started during this time spam. Regional correlations with similar synorogenic deposits recognized in neighboring regions (e.g., Salar de Atacama Basin and Coastal Cordillera) also affirm an Upper Cretaceous–Paleocene age for the initiation of the crustal contraction in the region.

Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer's disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging.

BackgroundTextural features of the hippocampus in structural magnetic resonance imaging (sMRI) images can serve as potential diagnostic biomarkers for Alzheimer’s disease (AD), while exhibiting a relatively poor discriminant performance in detecting early AD, such as amnestic mild cognitive impairment (aMCI). In contrast to sMRI, functional magnetic resonance imaging (fMRI) can identify brain functional abnormalities in the early stages of cerebral disorders. However, whether the textural features reflecting local functional activity in the hippocampus can improve the diagnostic performance for AD and aMCI remains unclear. In this study, we combined the textural features of the amplitude of low frequency fluctuation (ALFF) in the slow-5 frequency band and structural images in the hippocampus to investigate their diagnostic performance for AD and aMCI using multimodal radiomics technique.MethodsTotally, 84 AD, 50 aMCI, and 44 normal controls (NCs) were included in the current study. After feature extraction and feature selection, the radiomics models incorporating sMRI images, ALFF values and their combinations in the bilateral hippocampus were established for the diagnosis of AD and aMCI. The effectiveness of these models was evaluated by receiver operating characteristic (ROC) analysis. The radiomics models were further validated using the external data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.ResultsThe results of ROC analysis showed that the radiomics models based on structural images in the hippocampus had a better diagnostic performance for AD compared with the models using ALFF, while the ALFF-based model exhibited better discriminant performance for aMCI than the models with structural images. The radiomics models based on the combinations of structural images and ALFF were found to exhibit the highest accuracy for distinguishing AD from NCs and aMCI from NCs.ConclusionIn this study, we found that the textural features reflecting local functional activity could improve the diagnostic performance of traditional structural models for both AD and aMCI. These findings may deepen our understanding of the pathogenesis of AD, contributing to the early diagnosis of AD.

A Small Sample Correction for Factor Score Regression.

Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores.

Terrestrial Laser Scanning to Assess Eucalyptus Aboveground Biomass: A Surface Reconstruction Approach

Terrestrial laser scanners (TLS) provide an alternative to traditional field sampling for gathering accurate volumetric data about vegetations without destroying the tree samples. However, popular volumetric modeling approaches (e.g., those cylindrical methods) oversimplify vegetation structure by underutilizing surface undulations provided by the point cloud. Thus, this study aimed to test the capability of two specialized surface reconstruction methods against a traditional cylindrical method to obtain the stem volumes and masses from 3D-cloud points generated by terrestrial laser scanners under relatively controlled conditions in a eucalyptus plantation in Muang, Nakornratchasima Province, Thailand. The TLS point data were collected from the test plots, and then three algorithms, the Poisson Surface Reconstruction (PSR), the Screen Poisson Surface Reconstruction (SPSR), and the traditional Quantitative Structure Model (QSM) were applied in order to build volumetric models of the sample eucalyptus trees. It is notable that this is the first study to test the SPSR method on real tree samples (N=40). The results were then compared with the reference values measured by a water replacement method. The root means square errors (RMSE) were estimated between the xylometric referenced aboveground biomass (AGB) and the TLS three methods. The SPSR approach yielded the most accurate results (RMSE of 0.49 kg or 6.91%), while the PSR method resulted in an RMSE of 0.60 kg (8.37%). The QSM method had the worst results, with an RMSE of 1.09 kg (15.31%). Despite the occlusion problem that caused 20% systematic error, this outcome provides evidence that the use of two Poisson reconstruction methods (e.g., PSR and SPSR) provides effective alternatives to accurately quantify aboveground biomass for eucalyptus trees.

Estimating Successful Internal Mobility: A Comparison Between Structural Equation Models and Machine Learning Algorithms

Internal mobility often depends on predicting future job satisfaction, for such employees subject to internal mobility programs. In this study, we compared the predictive power of different classes of models, i.e., (i) traditional Structural Equation Modeling (SEM), with two families of Machine Learning algorithms: (ii) regressors, specifically least absolute shrinkage and selection operator (Lasso) for feature selection and (iii) classifiers, specifically Bagging meta-model with the k-nearest neighbors algorithm (k-NN) as a base estimator. Our aim is to investigate which method better predicts job satisfaction for 348 employees (with operational duties) and 35 supervisors in the training set, and 79 employees in the test set, all subject to internal mobility programs in a large Italian banking group. Results showed average predictive power for SEM and Bagging k-NN (accuracy between 61 and 66%; F1 scores between 0.51 and 0.73). Both SEM and Lasso algorithms highlighted the predictive power of resistance to change and orientation to relation in all models, together with other personality and motivation variables in different models. Theoretical implications are discussed for using these variables in predicting successful job relocation in internal mobility programs. Moreover, these results showed how crucial it is to compare methods coming from different research traditions in predictive Human Resources analytics.

Do Executive Attentional Processes Uniquely or Commonly Explain Psychometric g and Correlations in the Positive Manifold? A Structural Equation Modeling and Network-Analysis Approach to Investigate the Process Overlap Theory.

One of the best-established findings in intelligence research is the pattern of positive correlations among various intelligence tests. Although this so-called positive manifold became the conceptual foundation of many theoretical accounts of intelligence, the very nature of it has remained unclear. Only recently, Process Overlap Theory (POT) proposed that the positive manifold originated from overlapping domain-general, executive processes. To test this assumption, the functional relationship between different aspects of executive attention and the positive manifold was investigated by re-analyzing an existing dataset (N = 228). Psychometric reasoning, speed, and memory performance were assessed by a short form of the Berlin Intelligence Structure test. Two aspects of executive attention (sustained and selective attention) and speed of decision making were measured by a continuous performance test, a flanker task, and a Hick task, respectively. Traditional structural equation modeling, representing the positive manifold by a g factor, as well as network analyses, investigating the differential effects of the two aspects of executive attention and speed of decision making on the specific correlations of the positive manifold, suggested that selective attention, sustained attention, and speed of decision making explained the common but not the unique portions of the positive manifold. Thus, we failed to provide evidence for POT’s assumption that the positive manifold is the result of overlapping domain-general processes. This does not mean that domain-general processes other than those investigated here will not be able to show the pattern of results predicted by POT.