Variance-based Method Research Articles

Estimating global identifiability using conditional mutual information in a Bayesian framework

A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No assumptions are made about the structure of the statistical model or the prior distribution while constructing the estimator. The estimator has the following notable advantages: first, no controlled experiment or data is required to conduct the practical identifiability analysis; second, unlike popular variance-based global sensitivity analysis methods, different forms of uncertainties, such as model-form, parameter, or measurement can be taken into account; third, the identifiability analysis is global, and therefore independent of a realization of the parameters. If an individual parameter has low identifiability, it can belong to an identifiable subset such that parameters within the subset have a functional relationship and thus have a combined effect on the statistical model. The practical identifiability framework is extended to highlight the dependencies between parameter pairs that emerge a posteriori to find identifiable parameter subsets. The applicability of the proposed approach is demonstrated using a linear Gaussian model and a non-linear methane-air reduced kinetics model. It is shown that by examining the information gained for each model parameter along with its dependencies with other parameters, a subset of parameters that can be estimated with high posterior certainty can be found.

Green perceived value and green product purchase intention of Gen Z consumers: Moderating role of environmental concern

The primary objective of this study is to examine the connection between Generation Z Indonesian consumers’ perceptions of value for green products and their purchase intentions, with a supplementary investigation into how environmental concerns moderate the influence of perceived green value on their intention to purchase green products. Between June and December 2021, a probability sampling technique, specifically stratified random sampling, was used to select a sample of 543 Indonesian Generation Z consumers. The data were analyzed using a variance-based method, namely partial least squares, within the context of structural equation modeling. The analysis reveals that functional value, conditional value, and environmental concern significantly and positively affect the intention to purchase green products. This indicates that Generation Z consumers are more inclined to choose green products when they perceive them to have good functional value, when specific conditions or requirements support the purchase, and when they have a high environmental concern. Moreover, environmental concern moderates the effect of social value on the intention to purchase green products. This suggests that more environmentally conscious consumers are more likely to be influenced by social value benefits when deciding to buy green products, highlighting the complex interplay between environmental and social motivations in shaping consumer behavior towards sustainability.

Mediating effect of e-learning quality on learning outcomes through student satisfaction in nursing education

Abstract Objective This study aimed to determine the relationships between e-learning quality, student satisfaction, and learning outcomes and the mediating effect of student satisfaction. Methods A cross-sectional quantitative correlational study using a predictive design and multivariate analysis method was employed in this study. A sample of 241 nursing students were recruited through an online survey based on a stratified random sampling technique. The variance-based Partial Least Squares Structural Modeling analysis method was used to test the possible relationship and mediating effect among the variables. Results The findings revealed statistically significant relationships between e-learning quality, student satisfaction, and learning outcomes. A mediating effect of 37.2% is predicted for e-learning quality on learning outcomes through student satisfaction. Conclusions This study emphasizes the learning needs of working nurses and the impact on their learning outcomes in e-learning nursing undergraduate programs. Advances in e-learning education have assisted nurses to be more self-sufficient in their pursuit of lifelong learning.

Measuring polycentric urban development with multi-temporal Sentinel-1 SAR imagery: A case study in Shanghai, China

Studies on polycentric urban development (PUD) have been done by using LandScan and point of interest data. So far, the quality of PUD products has been limited by the spatio-temporal resolution of these datasets. Using Sentinel-1 SAR missions with a 10 m pixel spacing and a weekly repeat cycle, this study, for the first time, explores the potential of using a time series of Sentinel-1 SAR images for measuring urban polycentricity. In particular, we develop a variance-based filtering method to mitigate speckle noise. We propose the Kittler and Illingworth with a Modified Model (KI-MM) method to accurately identify PUD-related changes. We focus on the mean distance of new-born patches and the mean patch area for a PUD-related change analysis. These allow us to associate SAR tailor-made output with PUDs. The proposed methods were implemented on Google Earth Engine platform using 304 Sentinel-1 SAR images on the city of Shanghai, China, acquired between 2015 and 2018. We have tested 80 distribution models and found that the Laplace distribution is the best model for the KI-MM method. Our results show 2526, 2409, 4232 new-born patches for 2015–2016, 2016–2017, 2017-2018, with areas equal to 18, 20, 36 km2, respectively. Using cross-validation with Sentinel-2 (optical) reference, we found that the matching rates and F1-score between detected changes and reference for the years 2015–2016, 2016–2017, 2017-2018 were equal to 89.79% and 91.67%, 100% and 100%, 88.64% and 94.06%, respectively. We conclude that Sentinel-1 SAR images are suited to PUD applications at an intra-city scale with a high spatio-temporal resolution.

POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes.

There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits. Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation. The code for this method is available at https://github.com/staylorhead/POIROT-POE.

Customer values and patronage intention in social media networks: mediating role of perceived usefulness

Purpose This paper aims to validates a framework using the Uses and Gratifications Theory (UGT) to study the effect of values, i.e. Functional Value (FV), Social Value (SV), Emotional Value (EV) and Monetary Value (MV), on the Patronage Intention (PI) of Social Network Users (SNU’s) with mediating role of Perceived Usefulness (PU). Design/methodology/approach A survey method was used to collect responses from 302 SNUs, and the variance-based structural equation method was used to understand the relationships among the constructs. Findings The results found a significant positive effect of FV and EV on Perceived Usefulness (PU) and MV and PU on Patronage intention (PI) of SNUs. Further, PU partially mediated the relationship of EV with PI. Originality/value This study used the UGT to understand the effect of values on the PI of SNUs. This research study contributes to the existing social networks/social media literature.

Comparison of local and global sensitivity analysis methods and application to thermal hydraulic phenomena

The best estimate plus uncertainty analysis have been widely applied for the safety assessment of nuclear systems, and sensitivity analysis is one of the essential steps to identify and rank the important phenomena and parameters. The conventionally used local sensitivity methods are based on linear hypothesis and have limitations in the application process of nonlinear systems, while the more complicated global sensitivity methods may have problem in time costs. To compare and propose the proper method for evaluation of thermal-hydraulic calculation in a reactor system, local methods such as Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient, global methods such as the variance-based method, moment-independent method, and elementary effects method are reviewed and applied for numerical examples and specific reactor thermal-hydraulic problem in this paper. Results show that the rank of parameter importance measured by various local analysis methods is the same, but away from the global methods. Different types of global sensitivity analysis methods have similar results for parameters with higher importance, and among these methods, the elementary effects method has the lowest computational cost with the same number of samples.

Coopetition configuration and performance of international joint ventures for high-speed rail projects

PurposeMembers in the international joint ventures (IJVs) for high-speed rail (HSR) projects usually engage in coopetition interactions to create common benefits (CB) and simultaneously safeguard private benefits (PB). Previous studies of coopetition and performance using variance-based methods usually ignore the combinational influence of diverse coopetition constructs on performance, which can be effectively compensated by adopting a configuration perspective. Therefore, this research aims to ascertain various combinations of three coopetition constructs (coopetition relationship, coopetition capability and coopetition strategy) that lead to high IJVs’ performance through a configuration approach.Design/methodology/approachFirst, the research framework of coopetition configuration was established, and the key constructs were operationalized, which were validated by expert interviews. Then the information on 12 HSR IJVs was collected and quantified through nine rounds of interviews and a questionnaire survey. Later, the fuzzy-set qualitative comparative analysis (fsQCA) was applied to explore what coopetition configurations benefit the CB or PB achievement.FindingsConfiguration results indicate that six coopetition configurations lead to CB outcome and seven configurations lead to PB outcome. Based on the results, coopetition contexts are divided into four categories: firm-based coopetition, project-based coopetition, firm-project-based coopetition and none-based coopetition. Then, a selection scheme for coopetition strategies in various contexts has been developed. The results also show that the core conditions mostly appear in the coopetition relationships and coopetition strategies dimensions, and the optimal coopetition strategies vary in different contexts.Originality/valueThis study enhances the theoretical understanding of coopetition in HSR IJVs and assists relative HSR industrialists, as well as the mega infrastructure project managers, in IJVs’ implementation. The configuration perspective of this paper also contributes to a systemic and holistic view of coopetition in HSR IJVs.

A mechanism motion error sensitivity analysis method based on principal component analysis and artificial neural network

Present sensitivity analysis of motion error usually focuses on the trajectory deviation of the mechanism, which inevitably introduces an intractable time dependent problem. For efficiently and accurately measuring the motion error of the planar mechanism with dimension and clearance uncertainties by global sensitivity analysis (GSA), a novel method is proposed in this work. By applying the principal component analysis (PCA), the motion error is transformed into new vector output and cleverly avoids the time dependent problem. To ensure the accuracy of PCA in the case of small samples, the Bootstrap method is introduced. Based on the PCA results, the artificial neural network (ANN) surrogate model is established between the input variables and the vector output. Then the classical variance-based GSA method is applied to obtain the variable importance ranking for different PCs, and the synthesized GSA indices are introduced. Four representative examples are studied to demonstrate the versatility and effectiveness of the proposed method.

Global sensitivity analysis based on Gaussian-process metamodelling for complex biomechanical problems.

Biomechanical models often need to describe very complex systems, organs or diseases, and hence also include a large number of parameters. One of the attractive features of physics-based models is that in those models (most) parameters have a clear physical meaning. Nevertheless, the determination of these parameters is often very elaborate and costly and shows a large scatter within the population. Hence, it is essential to identify the most important parameters (worth the effort) for a particular problem at hand. In order to distinguish parameters which have a significant influence on a specific model output from non-influential parameters, we use sensitivity analysis, in particular the Sobol method as a global variance-based method. However, the Sobol method requires a large number of model evaluations, which is prohibitive for computationally expensive models. We therefore employ Gaussian processes as a metamodel for the underlying full model. Metamodelling introduces further uncertainty, which we also quantify. We demonstrate the approach by applying it to two different problems: nanoparticle-mediated drug delivery in a complex, multiphase tumour-growth model, and arterial growth and remodelling. Even relatively small numbers of evaluations of the full model suffice to identify the influential parameters in both cases and to separate them from non-influential parameters. The approach also allows the quantification of higher-order interaction effects. We thus show that a variance-based global sensitivity analysis is feasible for complex, computationally expensive biomechanical models. Different aspects of sensitivity analysis are covered including a transparent declaration of the uncertainties involved in the estimation process. Such a global sensitivity analysis not only helps to massively reduce costs for experimental determination of parameters but is also highly beneficial for inverse analysis of such complex models.

A topological classifier to characterize brain states: When shape matters more than variance.

Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. By contrast, topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors and provides a quantitative characterization of specific topological features of the dataset under scrutiny. Here we introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data. We used this classifier with a high-dimensional electro-encephalographic (EEG) dataset recorded from eleven participants during a previous decision-making experiment in which three motivational states were induced through a manipulation of social pressure. We calculated silhouettes from persistence diagrams associated with each motivated state with a ready-made band-pass filtered version of these signals, and classified unlabeled signals according to their impact on each reference silhouette. Our results show that in addition to providing accuracies within the range of those of a nearest neighbour classifier, the TDA classifier provides formal intuition of the structure of the dataset as well as an estimate of its intrinsic dimension. Towards this end, we incorporated variance-based dimensionality reduction methods to our dataset and found that in most cases the accuracy of our TDA classifier remains essentially invariant beyond a certain dimension.

The effect of strategic management accounting on strategic supply chain through internal and external orientation

This paper aims to empirically examine how public sector auditing agencies have strategically positioned themselves in a changing external operating environment and policy context. Specifically, this study aims to examine the effect of strategic management accounting on strategic supply chain positioning. The mediating roles of external and internal orientations were employed as mediating variables in this study. The research was conducted in the Audit Board of the Republic of Indonesia (BPK) in December 2022 to January 2023 by involving a total sample of 325 respondents. The data obtained in this study were analyzed using a variance-based structural method using the Partial Least Squares path modeling method through SmartPLS software. The findings showed that there is a significant influence of strategic management accounting on strategic supply chain positioning. The findings also emphasized the mediating roles of internal orientation and external orientation in strengthening the effects of strategic management accounting on strategic supply chain positioning. The results highlighted that strategic management accounting plays an important role in helping public sector auditing agencies to strategically position themselves in auditing practices. In addition, internal orientation can also help organizations to optimize the use and the allocation of internal resources. The findings presented in this study theoretically contribute to explaining empirical evidence regarding the effect of strategic supply chain positioning in strategic management accounting activities in public sector accounting and auditing practices.

A new approach to preprocessing of EMG signal to assess the correctness of muscle condition

Purpose: Electromyography (EMG) is a technique dealing with the recording and analysis of myoelectric signals formed by physiological variations in the muscle fiber membrane. The voltage potential of surface myoelectrical signals (sEMG) varies over time and depends on the characteristics of the individual subject. One of the main drawbacks of sEMG analysis is that the given acquisition conditions strongly determine the amplitude of the signal. The analysis of sEMG requires appropriate preprocessing, including proper filtering and artifact removal. Moreover, the sEMG data must be converted to a scale standardized for all measurements. This research aimed both to propose a method of sEMG processing to eliminate the occurring disturbances, in particular impulsive artifacts, and to determine the level of muscle excitation based on normalized sEMG. The analysis of muscle excitation level can be applied to assess muscle activity during physical activity. Design/methodology/approach: The proposed algorithm uses set of digital filters, probabilistic distribution and the decomposition of the sEMG signal to attenuate artifacts. Variance analysis of the sEMG derivative is used to determine muscle excitation. The sEMG signals were acquired with the VICON system with the sampling frequency set at 1000 Hz, and processed in MATLAB. During sEMG recordings, standard silver/silver chloride (Ag/AgCl) surface electrodes were used. Findings: The suggested technique was validated using sEMG recorded for eight persons during deep squat. Normalized excitation was determined for the left and right muscles, the rectus femoris, the vastus medialis, and the biceps femoris. Obtained outcomes indicate a possibility to assess the correctness of muscles condition. Originality/value: The combination of the proposed filter and the analysis variance-based thresholding method can effectively eliminate impulse artifacts within the surface myoelectrical signals. Keywords: Spikes disturbance, EMG, Muscle excitation, Filtering, Kurtosis. Category of the paper: Research paper.

Global sensitivity analysis of bioretention cell design for stormwater system: A comparison of VARS framework and Sobol method

Bioretention cell (BC) design variation significantly changes its hydrological dynamics at unit-scale leading to major changes in its target design goals when designed unit replicated at the catchment-scale. Improved understanding of the behaviors of BC design parameters under different rainfall conditions is critical for effective implementation of BCs to achieve design goals. This paper illustrates and compares two global sensitivity analysis methods (i.e., VARS and Sobol) for the identification of influential parameters and their variations in hydrological dynamics (model responses) under different rainfall conditions and perturbation scales, so as to quantify their uncertainty and reliability for influential factors ranking. From the application results of both sensitivity analysis methods, six parameters out of the total seventeen including conductivity, berm height, vegetation volume, suction head, porosity and wilting point were indicated for their significant sensitivities to surface infiltration, surface outflow and peak flow. In addition, soil thickness, conductivity slope and field capacity (in a total of nine) were categorized as influential to storage outputs. Conductivity and vegetation volume were ranked as the most influential parameters, followed by berm height and suction head, porosity and wilting point. The results analysis also demonstrated that the behavior of design parameters towards model response significantly changes with different rainfall conditions and perturbation scales, as well as the used sensitivity analysis methods. In particular, it is indicated from this study that the VARS is preferred over other sensitivity analysis approaches including the Sobol method (i.e., variance-based method) because of its higher accuracy, reliability, and computational efficiency.

Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data.

Cell-fate transitions are fundamental to development and differentiation. Studying them with single-cell omic data is important to advance our understanding of the cell-fate commitment process, yet this remains challenging. Here we present a computational method called DICE, which analyzes the entropy of expression covariation patterns and which is applicable to static and dynamically changing cell populations. Using only single-cell RNA-Seq data, DICE is able to predict multipotent primed states and their regulatory factors, which we subsequently validate with single-cell epigenomic data. DICE reveals that primed states are often defined by epigenetic regulators or pioneer factors alongside lineage-specific transcription factors. In developmental time course single-cell RNA-Seq datasets, DICE can pinpoint the timing of bifurcations more precisely than lineage-trajectory inference algorithms or competing variance-based methods. In summary, by studying the dynamic changes of expression covariation entropy, DICE can help elucidate primed states and bifurcation dynamics without the need for single-cell epigenomic data.

Modeling the implications of sustainable supply chain practices on sustainable performance in Ghana’s petroleum industry: the role of stakeholders’ pressure

ABSTRACT The influx of Agenda 2030 for Sustainable Development and the COP 21 has necessitated the need for a paradigm shift in traditional consumption and production to reflect a balance between environmental safety, social justice and profitability. The Ghanaian Petroleum Industry is predominately fossil fuel–based with higher level of carbon and methane emissions. Ensuing from the Stakeholders’ and the Resource-Based View theories, this paper aims to develop a model to explain the relationship between sustainable supply chain management (SSCM) practices and sustainable performance (SP) by examining the extent to which pressure from stakeholders could strengthen the proposed model in the context of downstream petroleum supply chain. Variance-Based Partial Least Square method has been used to analyse a cross-sectional data from Oil Marketing companies in Ghana. Our results have revealed that SSCM practices such as environmental, economic, and social are positively related to SP. Moreover, stakeholder’s pressure positively and significantly relates to SP. Again, stakeholders’ pressure significantly moderate the relationships between environmental and social dimensions of SSCM practices and SP. By implications, there is an emergency of an SP model with enhanced predictability. Again, policymakers and advocates can achieve greater sustainability by maximising stakeholders’ pressure.

Bullying in Adolescents Practising Sport: A Structural Model Approach.

This article aims to analyse the relationship between the bullying aggressor and bullying victim profile related to practising or not practising sport in adolescents living in southern Spain. The research includes male and female participants aged between 12 and 16 years in different secondary schools in the provinces of Andalusia, Ceuta and Melilla in the period between February 2022 and June 2022. The study aims to extend the existing scientific, theoretical and empirical knowledge on the influence of playing sport or not on disruptive bullying attitudes in adolescents. To this end, two initial hypotheses were designed; the first hypothesises that bullying victim behaviours are associated with future bullying aggressor behaviours when practising sport; and the second states that victim behaviours are associated with future bullying aggressor behaviours when not practising sport. To verify them, SPSS software was used for the preliminary analysis of the scale and sociodemographic profile. Additionally, the study is based on structural equation modelling methodology and variance-based methods employing SmartPLS v3.3 software. The results show the importance of sport or physical activity to reduce the chances of carrying out bullying actions on other peers and/or classmates. Therefore, it is considered necessary to prevent bullying in the classroom by implementing sports intervention programmes in educational centres.

Measuring the β-decay Properties of Neutron-rich Exotic Pm, Sm, Eu, and Gd Isotopes to Constrain the Nucleosynthesis Yields in the Rare-earth Region

The β-delayed neutron-emission probabilities of 28 exotic neutron-rich isotopes of Pm, Sm, Eu, and Gd were measured for the first time at RIKEN Nishina Center using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The existing β-decay half-life (T 1/2) database was significantly increased toward more neutron-rich isotopes, and uncertainties for previously measured values were decreased. The new data not only constrain the theoretical predictions of half-lives and β-delayed neutron-emission probabilities, but also allow for probing the mechanisms of formation of the high-mass wing of the rare-earth peak located at A ≈ 160 in the r-process abundance distribution through astrophysical reaction network calculations. An uncertainty quantification of the calculated abundance patterns with the new data shows a reduction of the uncertainty in the rare-earth peak region. The newly introduced variance-based sensitivity analysis method offers valuable insight into the influence of important nuclear physics inputs on the calculated abundance patterns. The analysis has identified the half-lives of 168Sm and of several gadolinium isotopes as some of the key variables among the current experimental data to understand the remaining abundance uncertainty at A = 167–172.

A variance-based approach for the detection and localization of cracks in a beam

A variance-based method is proposed for the detection and localization of cracks in a beam. A crack in a beam introduces a slope discontinuity in the beam elastic line. The local slope discontinuity due to the crack results in a localized change in the beam deflection variance values. In the present work, the local variance is obtained using a rectangular moving window along the beam length. A significant change in the local variance values is observed at the crack location. A two-time application of moving window variance leads to a clear spike at the crack location. Localization of cracks in cantilever and simply supported beams is presented. The proposed method is compared with the widely used wavelet-based method for crack localization. The present method is more suitable for locating the crack in the high slope region of beam deflection. Finite element analysis is used to obtain the simulated cracked cantilever and simply supported beam responses. White Gaussian noise is added to the simulated beam deflection to test the noise-robustness of the algorithm. A key challenge in the detection of cracks in a beam is to obtain a high-resolution spatial beam deflection measurement. For experimental verification, the high-resolution spatial measurement of the vibrating beam is obtained by photographic measurement. The high spatial resolution gives localized spikes corresponding to the crack location. The proposed algorithm is capable of localizing multiple simultaneous cracks in a beam. Experimental localization of cracks in single and double cracked cantilever beams is presented.

Frequency Monitoring Analysis based Power Quality Operational Data Management System

In this work, a low-cost measurement solution (named PyFreqDAQ) has been developed to monitor the frequency of the electrical network, which is regarded a quality parameter of the electrical supply, and at the same time provide a more precise result thanks to the usage of the Allan variance. Software (Grafana) is available for free and can be used to track the frequency’s change over time in order to better understand how it is maintained within certain parameters. Two distinct techniques of calculating the frequency have been used to show the device’s power consumption. For starters, we’ll follow the guidelines established by IEC 61000-4-30 The Allan variance-based approach, on the other hand, uses an algorithm that offers a more precise frequency measurement and a GPS-traceable reference value. With the Allan variance-based method, one can expect improved precision and accuracy, as well as faster calculations and a wider range of observations. A significant advantage over other applications (e.g. labVIEWTM) is the flexibility demonstrated by Python while programming the data capture card and interacting with the database Because Python is a more powerful programming language, it allows for operations and calculations to be carried out using simple codes, while also allowing for minimal computational load and consuming considerably less working memory. To date, there hasn’t been any comparative research done between Python and LabVIEWTM.