Variance-based Method Research Articles

Reference frequency variance-based rapid identification method for vortex flow rate under mixed vibration interference

Reference frequency variance-based rapid identification method for vortex flow rate under mixed vibration interference

Global Sensitivity Study of a Duffing-Type Nonlinear Vibration System

Abstract An interesting field of studying nonlinear systems is their sensitivity study. With sensitivity study the most influential parameters on a system can be obtained and then the simplification and improvement of the model will be possible. In this paper the global sensitivity study of a Duffing-type vibration system is carried out with Sobol’s variance-based method taking the root mean square of acceleration and the maximum acceleration as output variables. With the sensitivity study it was observed that the parameters of the excitation signal like the amplitude and the angular velocity are the most influential. It was also found that a single parameter has less influence on the system than the parameter combinations. The aim of the research is to carry out the global sensitivity study of a relatively simple nonlinear system. The study is the basis for further research tasks in order to perform the sensitivity study of more complex systems.

Uncertainty sensitivity analysis for vibration properties of composite doubly-curved shallow shells using Kriging method

This paper presents a Kriging based global sensitivity analysis (GSA) method for the frequency response of displacements of composite doubly-curved shallow shells. A unified solution is utilized to develop the dynamic vibration formulation using the First-order Shear Deformation Theory (FSDT) and the Rayleigh-Ritz method. Kriging surrogate model is employed to substitute the frequency response function (FRF) of displacements. Ten parameters including materials and geometrical dimension are considered as input uncertain variables. A variance-based GSA method for dynamic model is employed to quantify the influence of each uncertain parameter. In addition, to avoid the computational burden of Monte Carlo simulation method (MCS), the presented sensitivity indices are computed analytically based on the Kriging mode, which further improves computational efficiency. Based on the convergence studies and comparison with traditional methods, the accuracy and efficiency of the present method are validated. The results shows that the frequency response of displacements exhibits greater sensitivity to changes in width, and thickness is more influential than others in the example from this article. Finally, the presented numerical results demonstrate vibration characteristics of different types of shells and observation points, which can also serve as a reference for further study on uncertainty-propagation analysis.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

SCAMPI: A scalable statistical framework for genome-wide interaction testing harnessing cross-trait correlations.

Family-based heritability estimates of complex traits are often considerably larger than their single-nucleotide polymorphism (SNP) heritability estimates. This discrepancy may be due to non-additive effects of genetic variation, including variation that interacts with other genes or environmental factors to influence the trait. Variance-based procedures provide a computationally efficient strategy to screen for SNPs with potential interaction effects without requiring the specification of the interacting variable. While valuable, such variance-based tests consider only a single trait and ignore likely pleiotropy among related traits that, if present, could improve power to detect such interaction effects. To fill this gap, we propose SCAMPI (Scalable Cauchy Aggregate test using Multiple Phenotypes to test Interactions), which screens for variants with interaction effects across multiple traits. SCAMPI is motivated by the observation that SNPs with pleiotropic interaction effects induce genotypic differences in the patterns of correlation among traits. By studying such patterns across genotype categories among multiple traits, we show that SCAMPI has improved performance over traditional univariate variance-based methods. Like those traditional variance-based tests, SCAMPI permits the screening of interaction effects without requiring the specification of the interaction variable and is further computationally scalable to biobank data. We employed SCAMPI to screen for interacting SNPs associated with four lipid-related traits in the UK Biobank and identified multiple gene regions missed by existing univariate variance-based tests. SCAMPI is implemented in software for public use.

Unsupervised feature selection based on bipartite graph and low-redundant regularization

Unsupervised feature selection (UFS) has attracted increasing attention because of the difficulty and high cost of obtaining data labels. Since the ignorance of redundancy between features and the use of a fixed similarity matrix, current UFS algorithms do not perform well. Moreover, because of the high computational complexity, existing UFS algorithms usually cannot handle large-scale data. A novel UFS method is proposed to solve these problems through bipartite graph learning with low-redundant regularization (BGLR). Firstly, BGLR uses a variance-based de-correlation anchor selection method to ensure that selected anchors can be evenly distributed across classes. Secondly, BGLR builds an adaptive bipartite graph by using selected anchors and original data in the projection space. L2,0-norm constraint is applied to the projection matrix. Thus, the feature subset can be obtained directly. Finally, a regularization term is applied to control the redundancy of selected features. Moreover, BGLR is solved by an iterative method, which can converge quickly. Experiments are performed on public datasets in comparison with some relevant methods. The experimental results demonstrate the BGLR’s effectiveness.

Generalized Spin in the Variance-Based Wave Function Optimization Method within the Doubly Occupied Configuration Interaction Framework.

In this work, we implement a generalized spin formulation of the doubly occupied configuration interaction methodology using the energy variance of the N-electron Hamiltonian. We perform the optimization of the N-electron wave functions and calculate their corresponding energies, using a unified variational treatment for ground and excited states based on the energy variance, which allows us to describe the entire energy spectra on an equal footing. We analyze the effects produced by the breakdown of the Ŝ2 and Ŝz symmetries in the spectra of model hydrogenic clusters in terms of energies and spin-related quantities, arising from the restricted, unrestricted, and generalized spin methods. The results are compared with other related methods as well as full configuration interaction.

Computational modelling and prediction of nonlinear deflection responses of bonded joint component with multiple damage (crack and delamination) – An experimental validation

The present research evaluates the presence of damage, including delamination and cracks, affects the deflection response of an adhesively bonded single lap joint (SLJ). In this study, the numerical analysis is performed for intact and influence of different damages in the adherend of SLJ in the commercial simulation software (ABAQUS). The circular delamination of radius ‘R’ 10 mm and crack having length ‘l’ 20 mm, the radius of curvature ‘Rc’ 0.2 mm are imposed into the laminate adherend using node-to-surface interaction technique. The stability of the simulation model is verified by comparing the deflection values from published results and experimental data. The confirmed simulation model is broadened to examine the impact of design factors (fiber orientation, loading position, delamination shape, crack position and orientation, and delamination position with and without crack) on the deflection response for various overlapping lengths (25, 30, 35, and 40 mm) for final design recommendation. Additionally, the sensitivity analysis was performed using a surrogate model (i.e., variance-based method) by constructing the second-order polynomial function to quantify the influence of damages under the input parameters. The detailed features and multivariate model functions have been analysed for the various damage cases of SLJ.

THE INFLUENCE OF DESTINATION IMAGE AND PERCEIVED VALUE ON SATISFACTION OF LITERACY PARK

The public's low interest in reading has caused Indonesia to become one of the countries with the lowest literacy rate in the world. Literacy is an activity that includes skills in reading and writing, skills in interpreting information used for the development of education, development and quality human resources.This research has the purpose to determine the influence of destination image and perceived value on satisfaction. The research method used was a survey with data collection through a questionnaire distributed to 251 respondents by using purposive sampling. The measurement scale in this study used a 5-point Likert scale.The analysis of the variance-based SEM method, PLS-SEM, shows that destination image significantly affects satisfaction and perceived value significantly. This research aims to positively contribute to advancing the tourism industry, especially in a literacy park destination, bolster sustainable government policies, and shape visitors loyalty towards literacy park. The study results provide the implication that destination image and perceived value have an important role in shaping visitors satisfaction. Besides that, this research uses variables rarely used in the tourism industry to measure satisfaction. This research proves that destination image and perceived value have a positive and significant relationship to satisfaction. This research's empirical basis and findings can serve as a reference for future studies. This research was conducted to strengthen research regarding destination image variables, perceived value, satisfaction and destination loyalty in a tourist destination, especially the Martha Christina Tiahahu Literacy Park. It is also hoped that this research can contribute to increasing literacy activities in Indonesia. It is hoped that the results of this research can be used as material for innovation and evaluation by marketers in managing destinations. Apart from that, it is hoped that the results of this research can also provide input for similar tourist destinations in increasing visitor loyalty.

ECO-FRIENDLY CARS’ PURCHASES: THE ROLE OF CONSUMER ENVIRONMENTAL CONCERN, MONETARY POLICY, AND VALUE PERCEPTIONS

In the automotive market environment of Indonesia, a more in-depth insight into the factors that influence the purchase intention of eco-friendly cars is essential. Environmentally friendly cars are increasingly gaining attention amidst global environmental and climate change concerns. The research method used was a survey with data collection through a questionnaire distributed to 300 respondents by using purposive sampling. The measurement scale in this study used a 7-point Likert scale. Most respondents are female, comprising 56 percent, compared to 44 percent of male respondents. Most respondents with entrepreneurial backgrounds and bachelor’s degrees fall within the productive age range of 35 to 45 years. Additionally, a significant portion of the respondents earns an annual income ranging from 5 to 10 million Indonesian Rupiah. The analysis of the variance-based SEM method, PLS-SEM, shows that consumer environmental concern, monetary incentive policy measures, and perceived value significantly contribute to consumer purchase intentions. This research aims to positively contribute to advancing the eco-friendly car industry, bolster sustainable government policies, and shape consumer perceptions favorably towards greener transportation solutions. The study contributes valuable insights to this field’s existing literature and research. The empirical basis and findings derived from this research can serve as a reference for future studies on eco-friendly cars, providing a foundation for further exploration and understanding sustainable mobility choices

Stability Evaluation of Slope Based on Global Sensitivity Analysis

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability (pf) are different. Therefore, the approaches based on GSA which can integrate the Fs with pf will be a promising approach for slope stability evaluation.

An Improved Sobol Sensitivity Analysis Method

The Sobol method is a variance-based global sensitivity analysis method that evaluates single-input and multiple-input interaction effects by calculating the contribution of a single input to the output variance and the contribution of multiple inputs to the output variance. The Sobol method requires each input to obey a uniform distribution of U [0,1], but it is difficult to meet the requirements in practice. Taking the sum function as an example, this paper analyzes the inapplicability of the existing Sobol method when the input does not obey the uniform distribution U [0,1]. To solve the inapplicability of the Sobol method and broaden the application scope, an improved Sobol sensitivity analysis method is proposed. First, the effect of the joint probability density function not 1 on sensitivity calculation is studied; second, the input parameters are changed to uniform distribution U [0,1] through variable substitution; finally, a complete algorithm model is presented and logical sensitivity analysis results are obtained. Application verification shows that the improved Sobol method is more scientific, applicable and practical.

The Influence of Building Quality, Strategic Location, and Price on the Decision to Purchase Subsidized Housing in the Cileungsi Area

This study aims to present a comprehensive depiction of the factors influencing the decision-making process regarding the purchase of subsidized housing, focusing specifically on variables such as building quality, strategic location, and price. The research objectives include identifying the key variables impacting the choice of acquiring subsidized housing in the Cileungsi region through a quantitative descriptive approach. Utilizing purposive sampling, the study surveyed one hundred local residents who recently acquired homes through government assistance programs. Structural Equation Modeling (SEM) employing variance-based methods or component-based structural equation modeling, specifically Partial Least Squares (PLS), was utilized for data analysis. The anticipated outcomes of this investigation are expected to contribute significantly to understanding consumer behavior in the context of subsidized housing purchases in Cileungsi. Moreover, the application of PLS analysis methods is poised to provide an in-depth comprehension of the interrelationships among variables within the tested model and furnish strategic recommendations for developers of subsidized housing and pertinent stakeholders to enhance product appeal.

Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States.

The recent developments of quantum computing present novel potential pathways for quantum chemistry as the scaling of the computational power of quantum computers could be harnessed to naturally encode and solve electronic structure problems. Theoretically exact quantum algorithms for chemistry have been proposed (e.g., quantum phase estimation), but the limited capabilities of current noisy intermediate-scale quantum devices motivated the development of less demanding hybrid algorithms. In this context, the variational quantum eigensolver (VQE) algorithm was successfully introduced as an effective method to compute the ground-state energies of small molecules. This study investigates the folded spectrum (FS) method as an extension of the VQE algorithm for the computation of molecular excited states. It provides the possibility of directly computing excited states around a selected target energy using the same quantum circuit as for the ground-state calculation. Inspired by the variance-based methods from the quantum Monte Carlo literature, the FS method minimizes the energy variance, thus, in principle, requiring a computationally expensive squared Hamiltonian to be applied. We alleviate this potentially poor scaling by employing a Pauli grouping procedure to identify sets of commuting Pauli strings that can be evaluated simultaneously. This allows for a significant reduction in the computational cost. We applied the FS-VQE method to small molecules (H2, LiH), obtaining all electronic excited states with chemical accuracy on ideal quantum simulators. Furthermore, we explore the application of quantum error mitigation techniques, demonstrating improved energy accuracy on noisy simulators compared with simulations without mitigation.

Prediction of the Waviness Error in Ultra-Precision Fly Cutting Using the Direct Integration Method

Abstract Fly cutting is widely used in manufacturing of large-scale, high-precision optical components. However, the discontinuity of fly cutting machining leads to significant relative vibrations between the tool and the workpiece. The cutting process generates periodic waves along the cutting direction, which will deteriorate the wavefront characteristics of optical components. Based on the machining dynamics, this paper proposes a direct integration method to predict the waviness error of the machined surface. The cutting force model of fly cutting is established. The multi-mode characteristics of the spindle-tool system are measured by the experimental method. Then, the influence of uncertainties on the calculation results is analyzed by the variance-based sensitivity analysis method. Finally, the plane cutting experiment verifies that the direct integration method effectively predicts the waviness error and its variation trend, and the waviness prediction research is important for optimization of the machining parameters.

Contact Force Surrogate Model and Its Application in Pantograph–Catenary Parameter Optimization

The significant increase in the speed of high-speed trains has made the optimization of pantograph–catenary parameters aimed at improving current collection quality become one of the key issues that urgently need to be addressed. In this paper, a method and solutions are proposed for optimizing multiple pantograph–catenary parameters, taking into account the speed levels and engineering feasibility, for pantograph–catenary systems that contain dozens of parameters and exhibit strong nonlinear coupling characteristics. Firstly, a surrogate model capable of accurately predicting the standard deviation of contact force based on speed and 14 pantograph–catenary parameters was constructed by using the pantograph–catenary finite element model and feedforward neural network. Secondly, sensitivity analysis and rating of the pantograph–catenary parameters under different speeds were conducted using the variance-based method and the surrogate model. Finally, by combining the sensitivity analysis results and the Selective Crow Search Algorithm, joint optimization of 10 combinations of the pantograph–catenary parameters across the entire speed range was performed, providing efficient pantograph–catenary parameter optimization solutions for various engineering conditions.

Torsional Vibration and Sensitivity Analysis of Microcantilever in Liquid

Development of microsystems and ever-increasing advances of nanotechnology have resulted in growing applications of microcantilevers (MCs), including Atomic Force Microscopy (AFM). In AFM, transverse vibration of microfiber from a liquid medium results in resonance of unwanted hydrodynamic and electrostatic forces, which can be minimized by torsional mode. In the present study, the differential equation of the vibrational motion is extracted by acquiring the hydrodynamic force applied to MCs aiming to investigate the torsional vibration behavior of MCs in a liquid medium; and the extracted equation is solved to study the behavior of MCs in a liquid medium. Considering the significant effect of MC geometry on its torsional vibrations in a liquid medium, Sobol's variance-based sensitivity analysis method was utilized to investigate the effectiveness of MC geometry on torsional vibration. The sensitivity analysis results show that length and width have the most significant effects on the quality factor and the resonance frequency of MCs.

Machine learning method for shale gas adsorption capacity prediction and key influencing factors evaluation

Shale gas plays a pivotal role in the global energy landscape, emphasizing the need for accurate shale gas-in-place (GIP) prediction to facilitate effective production planning. Adsorbed gas in shale, the primary form of gas storage under reservoir conditions, is a critical aspect of this prediction. In this study, a machine learning Gaussian process regression (GPR) model for methane adsorption prediction was established and validated using published experimental data. Five typical variables, i.e., total organic carbon (TOC), clay minerals, temperature, pressure, and moisture were considered, which were derived from the Marine shale of the Longmaxi formation in the Sichuan Basin through correlation analysis. The performance of the GPR model was compared with the widely used an extreme gradient boosting model. It turned out that our GPR model had better accuracy for predicting methane adsorption in shale with an average relative error of less than 3%. Furthermore, a variance-based sensitivity analysis method in conjunction with kernel density estimation theory was employed to conduct a global sensitivity analysis, quantifying the nonlinear influence of each variable methane adsorption. The findings indicate that TOC is the most significant factor affecting methane adsorption, while clay minerals have a limited direct impact but can enhance their influence through interactions with other influencing factors. Finally, based on the GPR model, a GIP prediction method was proposed that eliminates the need for calculating the density of the adsorbed phase. These findings are expected to extend the shale gas reserve assessment methodologies and offer valuable insight for further exploring the adsorption mechanisms of shale gas.

Comparing global sensitivity analysis methods for the joint thermal design parameters of a space manipulator based on the Sobol’ and PAWN

The Global Sensitivity Analysis method works well for screening and ranking impacting factors. It can help with thermal design optimization and thermal model calibration. This study compares the use of Sobol's variance-based method with PAWN's density-based method for 12 thermal design parameters (such as the properties of thermal control coating and the contact heat transfer coefficient between components) of a space manipulator joint thermal model. The two techniques have high consistent parameter sensitivity. The analysis's findings indicate that the junction electric box and shell's temperature variations are significantly influenced by the cooling surface's coating properties. The cooling surface's absorption rate has a bigger impact than emissivity does. Only the characteristics of the multilayer mask and the state of parcel coverage have a significant impact on the temperature of the joint shell. The outcomes offer technical backing for cooperative optimization in thermal design. The identical no-effect parameters are discovered to be recognized by Sobol' and PAWN at the same time, but they are ordered in different ways. The limitation of Sobol's technique, which uses variance as a sensitivity index, may be the cause of this outcome. Finally, the sensitivity response results are re-substituted into the finite element thermal model. The changing rule of temperature curve is used to further confirm the accuracy of the sensitivity results.

The effect of humble leadership on the success of international development projects: the mediating role of organizational learning

PurposeThe purpose of this paper is to examine the effect of humble leadership on project success. In addition, we examine the mediating effect of organisational learning on the relationship between humble leadership and the success of international development projects.Design/methodology/approachThis study adopted a quantitative research methodology based on questionnaire data collected from 80 international development project managers from different sectors in Senegal (West Africa). The variance-based structural equation method, following the partial least squares approach, was used to test the research hypotheses.FindingsThe results showed that humble leadership is positively related to project success. Furthermore, organizational learning mediates the relationship between humble leadership and project success.Research limitations/implicationsThis research has several limitations. The authors did not examine the role of organizational culture as a moderating variable. However, the authors believe that the cultural variable can have an impact on project success and team building, and future studies should consider this aspect as well. In the African context, each country has its own culture, which may affect the behaviour of the project manager. Also, the authors admit that the sample size is relatively small, which greatly reduces the generalizability of the results.Practical implicationsThese findings have important implications. First, because a project leader’s humility enhances project success, it is critical for development projects to select leaders who demonstrate modest conduct in the workplace. The perfect selection of a humble leader depends heavily on judgements about the characteristics of a humble leader from new project manager candidates.Originality/valueDrawing on conservation of resources theory, this study found that humble leadership is important for project success, thus extending the utility of the concept of humble leadership to the project literature.