Linear Moment Research Articles

Comprehensive evaluation of rockburst risk by multiparameter characteristics based on microseismic signals: A case study

AbstractNowadays, the seismological monitoring system in China is a valuable tool in the rockburst risk evaluation for deep coal mines. In the past, only parameters, like source location and energy, are widely used to estimate the risk level of rockburst. Sometimes, it is effective; however, some other important physical parameters, such as apparent stress drop, static stress drop, P‐wave velocity, and moment tensor, should also be included in order to improve the accuracy of risk assessment. In this study, these parameters are calculated using mine tremor signals recorded in the LW35001 workface of Liangbaosi Coal Mine. These calculations provide an overall identification of periodical stress distribution and rock mass energy‐releasing type under high concentrated stress. Via linear moment tensor inversion procedure, the source mechanism of mine tremors and stress state of the rock mass is determined whether it is risk or not to underground roadway. This comprehensive analysis provides a specific guidance for rockburst prevention for coal mine management, that is, knowing when and where measures must be taken to decrease the risk level or induce the occurrence of rockburst under control.

The Effect of Students’ Interest in English Song on the Value of Learning Outcomes in English Subject for Class VIII of SMP Negeri 1 Haurgeulis

This research aims to (1) help increase students' learning motivation in English subjects by growing students' interest in English songs (2) obtain evidence that the singing method can improve student learning outcomes in English subjects. To find out whether this method is effective or not, the author used pre-experimental research with a quantitative approach. The research was carried out using stages: Pre-test, Treatment and Post-test. Because the method used is a quantitative approach, to analyze the data the author used Linear Product Moment Regression Analysis, while to test the effect of treatment on the sample the authors used the paired sample t test. The results of data analysis show that the correlation coefficient value rxy is 0.659. This means that the regression correlation coefficient value of students' interest in English songs is 0.659, which has a positive sign, meaning that the higher the students' interest in English songs, the higher the students' interest in English songs student learning outcomes will be. The validity test results of all questionnaire items have Pearson Correlation ® values > 0.3494 then it can be concluded that all items are valid. Meanwhile, the results of the reliability test used Cronbach Alpha with a value of 0.839 > 0.6, which means that the questionnaire items in the instrument were reliable for use. From the results above, the hypothesis states Student’s interest in English Songs can improve the value of learning outcomes in English Subjects for class VIII of SMP Negeri 1 Haurgeulis can be accepted

Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel

The assembled corrugated steel initial support structure is a new prefabricated structure in highway tunnel engineering, achieving a balance between economy and safety. This study proposes a simplified calculation method and elucidates the mechanical mechanisms of assembled corrugated steel initial support structures. Firstly, the stiffness characteristics of corrugated steel plates were studied based on full-scale tests. A general equivalent stiffness coefficient table was established. Numerical simulations of corrugated steel flange joints were conducted to explore their bending mechanical properties. A two-stage rotational stiffness model for corrugated steel flange joints was proposed. Finally, a plane strain-spring simplified calculation method for the assembled corrugated steel initial support structure was developed, and the monitoring data from the Qipanshan Tunnel validated the correctness and reliability of the proposed method. The results demonstrate that (1) the plane strain-spring simplified model consists of the planar strain equivalent calculation method for corrugated steel plates and the two-line stiffness equivalent spring of the corrugated steel flange joint. The simplified model was validated as effective by monitoring data. (2) Corrugated steel plates exhibit two stages under loading, namely gap elimination and elastic stages. The elastic stage stiffness of corrugated steel plates decreases with increasing ratio of depth to pitch (RDP), positively correlating with plate thickness when the RDP exceeds 0.333 and otherwise negatively correlated. (3) Corrugated steel lining flange joints exhibit distinct elastic and plastic stages in their linear moment–rotation curves under loading.

On the Accuracy of Transport Spatial Discretization Methods for Diffusive Regimes

An infinite-medium analysis is performed for neutron transport spatial discretization methods in planar geometry. Angular flux solutions of the spatially continuous transport equation, which are driven by a linear (or quadratic) source, are shown to vary linearly (or quadratically) in space and angle; these are used to assess whether the discretized transport equations preserve certain cell-averaged and edge quantities. Each of the continuous angular flux solutions has a scalar flux that satisfies the standard diffusion equation; our analysis predicts whether the transport discretizations yield an accurate diffusion coefficient and (diffusion) spatial differencing scheme. The linear moment–based discretization methods under consideration, which are found to preserve certain features of the linear (or quadratic) infinite-medium angular flux solutions, are the familiar linear discontinuous (LD), lumped linear discontinuous (LLD), and linear characteristic (LC) schemes. The step characteristic scheme, which yields an unphysically large diffusion coefficient, is revisited and shown to possess, for diffusive problems, a solution error that would occur if an unphysical anisotropic scattering term had been included in the starting discretized S N transport equations. The numerical results verify the theoretical predictions and demonstrate the accuracy of the LD, LLD, and LC schemes in highly scattering problems that are optically thick. Our numerical results also illustrate the impact of inaccuracies in the diffusion coefficient on the numerical solutions of eigenvalue problems. The analysis in this paper has practical implications in the choice of spatial schemes used to solve realistic eigenvalue problems.

Study of Spatial Autoregressive Regression With Heteroskedasticity Using the Generalized Method of Moments and Bayesian Approach

Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containing heteroskedasticity using the maximum likelihood estimation (MLE) method provides biased and inconsistent estimators. The alternative method that can be used are generalized method of moments (GMM) and Bayesian method. GMM uses a combination of linear and quadratic moment functions simultaneously so that the computation is easier than MLE. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. The bias are used to evaluate the GMM and Bayes in estimating parameters of SAR model with heteroskedasticity disturbances in simulation data. The results show that GMM and Bayes provides the bias of parameter estimates relatively consistent and smaller with larger number of observations. GMM and Bayes methods are applied to district/city GRDP data in Indonesia. The result show GMM method with Eksponential Distance Weights (EDW) matrix produces the minimum variance and the largest pseudo-R2

On duration effects in non-life insurance pricing

The paper discusses duration effects on the consistency of mean parameter and dispersion parameter estimators in exponential dispersion families (EDFs) that are the standard models used for non-life insurance pricing. Focus is on the standard generalised linear model assumptions where both the mean and variance, conditional on duration, are linear functions in terms of duration. We derive simple convergence results that highlight consequences when the linear conditional moment assumptions are not satisfied. These results illustrate that: (i) the resulting mean estimators always have a relevant asymptotic interpretation in terms of the duration adjusted actuarially fair premium—a premium that only agrees with the standard actuarial premium using a duration equal to one, given that the expected value is linear in the duration; (ii) deviance based estimators of the dispersion parameter in an EDF should be avoided in favour of Pearson estimators; (iii) unless the linear moment assumptions are satisfied, consistency of dispersion and plug-in variance estimators can not be guaranteed and may result in spurious over-dispersion. The results provide explicit conditions on the underlying data generating process that will lead to spurious over-dispersion that can be used for model checking. This is illustrated based on real insurance data, where it is concluded that the linear moment assumptions are violated, which results in non-negligible spurious over-dispersion.

Trending Time-Varying Coefficient Spatial Panel Data Models

This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is satisfactory. As an empirical illustration, we investigate the trending pattern of the spillover effect of air pollution among Chinese cities from 2015 to 2021.

Frequency Analysis of Rainfall with Different Methods based on the Missing Data Processed by Random Forest Algorithm

Global warming is causing dramatic climate change, leading to rainfall that triggers a more severe risk of flooding. The conventional moment method and linear moment method’s basic theories for the design of flood frequency analyses were introduced and compared for frequency analysis of rainfall. The rainfall data from 149 long-series representative rainfall stations in Jiangsu Province were processed using the random forest (RF) algorithm to address the missing data encountered during the actual rainfall monitoring process. The frequency was calculated using conventional and linear moment methods, and the differences and advantages of the two methods were analyzed by comparing the calculation results of different sites under each method. The results show that under low design frequencies, both the conventional moment and linear moment methods exhibit minimal errors, rendering them suitable for calculating design rainfall. The linear moment method outperforms the conventional moment method in terms of the unbiasedness of the estimation process and for very large values, and that the parameters estimated by the linear moment method are more accurate. In practical hydrological frequency calculations, different computation methods can be chosen according to specific needs to enhance calculation accuracy.

Effectiveness of public health spending in Sub-Saharan Africa: The moderating role of health system efficiency

ABSTRACT Public health spending (PHS) has been extensively investigated concerning population health outcomes (PHO), but its effectiveness has been less analysed. To this end, this study examines the association between domestic general government health expenditure (DGGHE) and PHO in terms of disability-adjusted life year (DALY) while testing the role of health system efficiency (HSE) in its effects. The under-five mortality(U5MR) is also used in the analysis to assess the effectiveness of services. The study uses publicly available data from 2008 to 2018 across forty-three countries in SSA and applies the GMM estimator based on linear and nonlinear moment conditions. The findings indicate that an increase in DGGHE was significantly associated with a decrease in DALY and U5MR, with the lowest decrease in countries with the lowest HSE scores. Therefore, the study urges governments in SSA countries to consider mechanisms that enhance health system efficiency while planning interventions based on increased spending on health.

Best linear and quadratic moments for spatial econometric models with an application to spatial interdependence patterns of employment growth in US counties

SummaryWe provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross‐sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi‐maximum likelihood estimator when the disturbances follow a non‐normal and unknown distribution. We apply this procedure to a high‐order spatial autoregressive model with spatial errors, where the disturbances are heteroskedastic. Two normality tests of disturbances are developed. We apply the model to employment data in US counties, which demonstrates spatial interdependence patterns of regional employment growth.

Linear Moments-Based Monte Carlo Simulation for Reliability Analysis With Unknown Probability Distributions

Abstract Within the realm of structural reliability analysis, the uncertainties tied to resistance and loads are conventionally embodied as random variables possessing established cumulative distribution functions (CDFs). Nevertheless, real-world scenarios often involve cases where the CDFs of random variables are unknown, necessitating the probabilistic traits of these variables solely through statistical moments. In this study, for the purpose of integrating random variables characterized by an unknown CDF into the framework of Monte Carlo simulation (MCS), a linear moments (L-moments)-based method is proposed. The random variables marked by an unknown CDF are rendered as a straightforward function of a standard normal random variable, and the formulation of this function is determined by utilizing the L-moments, which are typically attainable from the statistical data of the random variables. By employing the proposed approach, the generation of random numbers associated with variables with unknown CDFs becomes a straightforward process, utilizing those derived from a standard normal random variable constructed by using Box-Muller transform. A selection of illustrative examples is presented, in which the efficacy of the technique is scrutinized. This examination reveals that despite its simplicity, the method demonstrates a level of precision that qualifies it for incorporating random variables characterized by unspecified CDFs within the framework of MCS for purposes of structural reliability analysis.

Multidisciplinary Robust Design Optimization Incorporating Extreme Scenario in Sparse Samples

Abstract Robust design optimization (RDO) is a potent methodology that ensures stable performance in designed products during their operational phase. However, there remains a scarcity of robust design optimization methods that account for the intricacies of multidisciplinary coupling. In this article, we propose a multidisciplinary robust design optimization (MRDO) framework for physical systems under sparse samples containing the extreme scenario. The collaboration model is used to select samples that comply with multidisciplinary feasibility, avoiding time-consuming multidisciplinary decoupling analyses. To assess the robustness of sparse samples containing the extreme scenario, linear moment estimation is employed as the evaluation metric. The comparative analysis of MRDO results is conducted across various sample sizes, with and without the presence of the extreme scenario. The effectiveness and reliability of the proposed method are demonstrated through a mathematical case, a conceptual aircraft sizing design, and an energy efficiency optimization of a hobbing machine tool.

Employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed: implications for flood management

Abstract. Extreme rainfall events in the Chi watershed of northeastern Thailand have significant implications for the safe and economic design of engineered structures and effective reservoir management. This study investigates the characteristics of extreme rainfall events in the watershed and their implications for flood risk management. We apply extreme value theory to historical maximum cumulative rainfall data for consecutive rainy days from 1984 to 2022. The generalized Pareto distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using maximum likelihood estimation (MLE) and linear moment estimation (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with p values exceeding 0.05. Our findings reveal that certain regions, notably Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet., Roi Et, and Sisaket provinces, show the highest return levels for consecutive 2 d (CONS-2) and 3 d (CONS-3) rainfall. These results underscore the heightened risk of flash flooding in these regions, even with short periods of continuous rainfall. Based on our findings, we developed 2D return level maps using the Q-geographic information system (Q-GIS) program, providing a visual tool to assist with flood risk management. The study offers valuable insights for designing effective flood management strategies and highlights the need for considering extreme rainfall events in water management and planning. Future research could extend our findings through spatial correlation analysis and the use of copula functions. Overall, this study emphasizes the importance of preparing for extreme rainfall events, particularly in the era of climate change, to mitigate potential flood-related damage.

Data-Driven Model Order Reduction Simultaneously Matching Linear and Nonlinear Moments

Data-Driven Model Order Reduction Simultaneously Matching Linear and Nonlinear Moments

Homogeneous Dephasing in Photosynthetic Bacterial Reaction Centers: Time Correlation Function Approach.

A new tractable linear electronic transition dipole moment time correlation function (ETDMTCF) that accurately accounts for electronic dephasing, asymmetry, and width of 1-phonon profile, which the zero-phonon line (ZPL) contributes to it, in Rhodopseudomonas viridis bacterial reaction center is derived. This time correlation function proves to be superior to other frequency-domain expressions in case of strong electron-phonon coupling (which is often the case in bacterial RCs and pigment-protein complexes), many vibrational modes involved, and high temperature, whereby more vibronic and electronic (sequence) transitions would arise. The Fourier transform of this ETDMTCF leads to asymmetric multiphonon profiles composed of Lorentzian distribution and Gaussian distribution on the high- and low-energy sides, respectively, whereby the overtone widths fold themselves with that of the one-phonon profile. This ETDMTCF also features expedient computation in large systems using asymmetric phonon profiles to account correctly for dephasing and pigment-protein interaction (electron-phonon coupling). The derived ETDMTCF allows computing all nonlinear optical signals in both time and frequency domains, through the nonlinear dipole moment time correlation functions (as guided by nonlinear optical response theory) in line with the eight Liouville space pathways. The linear transition dipole moment time correlation function is of a central value as the nonlinear transition dipole moment time correlation function is expressed in terms of the linear transition dipole moment time correlation function, derived herein. One of the great advantages of presenting this ETDMTCF is its applicability to nonlinear transition dipole moment time correlation functions in line with the eight Liouville space pathways needed in computing nonlinear signals. As such, there is more to the utility and applicability of the presented ETDMTCF besides computational expediency and efficiency. Results show good agreement with the reported literature. The intimate connection between a one-phonon profile and the corresponding bath spectral density in photosynthetic complexes is discussed.

Promising impact of push–pull configuration into designed octacyclic naphthalene-based organic scaffolds for nonlinear optical amplitudes: a quantum chemical approach

In opto-electronics, non-fullerene (NF) derivatives are regarded as efficient non-linear optical (NLO) materials. The present investigation was based on designing NF naphthalene-based derivatives (PCMD1–D9) with D-π-A configuration from PCMR. DFT analysis at M06/6-311G (d,p) level was accomplished to explore the photonic behavior of PCMD1–D9 compounds. Various kind of analysis like; UV–Vis, density of state (DOS), natural bond orbitals (NBOs), transition density matrix (TDM) and frontier molecular orbitals (FMOs) analyses were accomplished to understand the NLO properties of said chromophores. The configuration change led to considerable charge distribution over highest occupied and lowest unoccupied molecular orbitals with minimum band difference. The energy gap trend for all the entitled compounds was observed as; PCMD8 < PCMD5 = PCMD9 < PCMD6 < PCMD7 < PCMD4 < PCMD3 < PCMD2 < PCMD1 with the least band gap of 2.048 eV in PCMD8 among all the compounds. The UV–Visible spectrum of the entitled chromophores manifested high values of λmax in derivatives contrary to PCMR. Additionally, NBO findings explored effective intramolecular charge transfer and maximum energy of stabilization (34.31 kcal/mol) for PCMD8 chromophore. The highest linear polarizability (<α>) and dipole moment (µtot) values were exhibited by PCMD5 at 2.712 × 10–22. and 1.995 × 10–17 esu, respectively. PCMD8 push–pull configured molecular entity exhibited highest first hyper-polarizability (βtot) at 4.747 × 10–27 esu and second hyper-polarizability at 6.867 × 10–32 esu. Overall, all the formulated chromophores exhibited significant NLO results contrary to PCMR. Hence, through this structural tailoring via various acceptors, effective NLO materials were obtained for optoelectronic applications.

Nonlinear finite element analysis of partially encased composite columns under non-uniform moments

This paper presents a numerical investigation to evaluate the behavior of partially encased composite (PEC) columns with compact sections of three different heights submitted to non-uniform moment distribution. A 3D finite element model developed using the software ABAQUS was calibrated based on results from previous experimental research. The influence of concrete constitutive model parameters, column slenderness, load eccentricities, and moment distribution along height was investigated. The parametric study showed that constant moment distribution (case 1) is the most critical compared to linear moment variation (cases 2 and 3). This pattern is a direct consequence of the larger displacements at the mid-height section that causes higher second-order moments in the case 1 column. Long columns activated more confinement when a single curvature bending moment was applied at a minor axis. An increase in slenderness decreased the load capacity for non-uniform bending moments through the major axis.

On Well-Posed Boundary Conditions for the Linear Non-Homogeneous Moment Equations in Half-Space

On Well-Posed Boundary Conditions for the Linear Non-Homogeneous Moment Equations in Half-Space

Dynamic spatiotemporal ARCH models

ABSTRACT Geo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squared outcome variable, the spatial effect, and (iii) the spatiotemporal effect on the volatility of an outcome variable. We derive a generalised method of moments (GMM) estimator based on the linear and quadratic moment conditions. We show the consistency and asymptotic normality of the GMM estimator. After studying the finite-sample performance in simulations, the model is demonstrated by analysing monthly log-returns of condominium prices in Berlin from 1995 to 2015, for which we found significant volatility spillovers.

Assessing Flood Risk: LH-Moments Method and Univariate Probability Distributions in Flood Frequency Analysis

This study examines all of the equations necessary to derive the parameters for seven probability distributions of three parameters typically used in flood frequency research, namely the Pearson III (PE3), the generalized extreme value (GEV), the Weibull (W3), the log-normal (LN3), the generalized Pareto Type II (PG), the Rayleigh (RY) and the log-logistic (LL3) distributions, using the higher-order linear moments method (LH-moments). The analysis represents the expansion of previous research whose results were presented in previous materials, and is part of hydrological research aimed at developing a standard for calculating maximum flows based on L-moments and LH-moments. The given methods for calculating the parameters of the examined distributions are used to calculate the maximum flows on Romania’s Prigor River. For both methods, the criterion for selecting the most suitable distribution is represented by the diagram of the L-skewness–L-kurtosis and LH-skewness–LH-kurtosis. The results for Prigor River show that the PG distribution is the best model for the L-moments method, the theoretical values of the statistical indicators being 0.399 and 0.221. The RY distribution is the best model for the LH-moments technique, with values of 0.398 and 0.192 for the two statistical indicators.