Location Parameters Research Articles

Mine seismic source location via forward modeling of spherical waves in a multilayered horizontal or inclined medium

Seismic source location is a classic inverse problem in seismology. In mathematical physics, inverse problems have multiple natural solutions. The objective of this study was to develop a generic theory and method of seeking the true solution from multiple solutions for the location of a coal mine seismic source in an idealized velocity structure model of a coal mine with a small scale and complex geological environment. Starting from the simplest velocity structure model, the complexity of the model gradually increased, until it approached the real velocity structure model, i.e., the multilayered horizontal and inclined velocity structure model, in order to find a generic method for solving the multi-solution inverse problem of coal mine seismic source location. Specifically, the wavefront distribution equation in a two-layer horizontal medium was derived and then expanded to any multi-layer horizontal medium. Based on this equation, a positive definite nonlinear equation system was established from the perspective of any observation system. The equation system contained four unknown variables of the spatiotemporal position of the seismic source. To determine the spatiotemporal parameters of the seismic source, nonlinear equations for four stations were required. To solve the nonlinear equation system, an initial iteration value was determined. In order to reduce the difficulty of determining the initial iteration value, the variable substitution method was used to reduce the number of location parameters. By rotating the original geodetic coordinate system of the station to be parallel and orthogonal to the medium interface, the wavefront method was extended to inclined medium. In conclusion, in this study, the problem of coal mine seismic source location in a multi-layer horizontal or inclined medium was effectively solved. The method proposed in this study provides a reference for solving the true solution from multiple solutions for the location of a coal mine seismic source in small-scale coal mines with complex geological environments.

The Flexible Gumbel Distribution: A New Model for Inference about the Mode

A new unimodal distribution family indexed via the mode and three other parameters is derived from a mixture of a Gumbel distribution for the maximum and a Gumbel distribution for the minimum. Properties of the proposed distribution are explored, including model identifiability and flexibility in capturing heavy-tailed data that exhibit different directions of skewness over a wide range. Both frequentist and Bayesian methods are developed to infer parameters in the new distribution. Simulation studies are conducted to demonstrate satisfactory performance of both methods. By fitting the proposed model to simulated data and data from an application in hydrology, it is shown that the proposed flexible distribution is especially suitable for data containing extreme values in either direction, with the mode being a location parameter of interest. Using the proposed unimodal distribution, one can easily formulate a regression model concerning the mode of a response given covariates. We apply this model to data from an application in criminology to reveal interesting data features that are obscured by outliers.

Integrating the circular economy model into the management and treatment of Fischer–Tropsch effluents—a conversion of waste to energy (biogas) opportunity

AbstractThe Fischer–Tropsch (FT) process converts coal, biomass, or natural gas into liquid hydrocarbons via syngas generation and catalytic conversion. However, FT produces byproducts and effluents with substantial environmental consequences. This review explores the circular economy model's potential as a sustainable wastewater management strategy for FT effluent management. Furthermore, the paper investigates ameliorative measures to overcome the limitations of one biological treatment method, anaerobic digestion of FT effluents by examining the combination of nutrient augmentation, microbial augmentation, and enrichment from the integration of wastes derived from other industrial sectors as pivotal to the implementation of circular economics modelling in FT effluents valuation. This approach to the circular economy model can help overcome the environmental concerns posed by FT effluents in its advocacy of resource sharing, reuse, and recycling. This review also promotes circular economy principles to improve resource recovery, recycling, and collaboration with other industrial sectors, such as agriculture and mining, with FT application industries to create a more sustainable economy and reduce their environmental footprints. However, since the chemical compositions of wastes vary with location and other parameters, future case studies should optimize the waste mixtures to determine the optimal balance before valorizing them.

Relocation of 3 April 2017 Moiyabana Earthquake in Botswana Using Seismological Data from Local Network of Autonomously Recording Seismographs and International Monitoring System Stations

AbstractOn the 3rd April 2017 a widely felt Moiyabana earthquake shook Botswana and the rest of southern Africa. Previous Moiyabana earthquake locations used mainly teleseismic or regional seismograms; and/or non-seismic methods which include Synthetic Aperture Radar (InSAR), and magnetotelluric (MT). These results did not agree, as evidenced by the depth of the earthquake that ranged from zero to 30 km (i.e. indicating either a man-made event or a natural event); thus motivating us to re-assess the location parameters. Unfiltered seismic waveform data from the recent project of the Network of Autonomously Recording Seismographs (NARS) in Botswana was complimented with stations from the International Monitoring System (IMS) to relocate the event. Relocated parameters are origin time, epicentre, focal depth, and magnitude. Geotool software from the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO), and the Regional Seismic Travel Time model (RSTT) were used to process vertical components waveforms from 9 NARS and 32 IMS stations. Geotool results are: earthquake epicentre (22.645 °S: 25.220 °E); origin time of 17:40:16.9 (UTC); hypocentral depth range of 22 to 24 km; body magnitude (mb) and local magnitude (ml) of 6.3 ± 0.6 and 6.0 ± 0.8, respectively. RSTT results are: earthquake epicentre (22.667 °S: 25.257 °E); origin time of 17:40:16.95 (UTC); hypocentral depth of 25 km; and mb of 6.65 ± 0.03. The seismological location parameters from Geotool and RSTT, agree very well within experimental uncertainties with the non-seismic geophysical methods.

Estimating Chilling and Growing Degree Hours Related to Deciduous Fruit Production in Kurdistan Region-Iraq

This study was carried out to estimate the annual chilling and heat accumulation in Kurdistan region-Iraq using three different models; chilling hours (CH), chilling units (CU) and chilling portions (CP), as well as estimating the accumulated growing degree hours (GDH) using the ChillR Package, Version 0.72.8 based on daily maxima and minima temperatures of 170 locations for 40 years (1983-2022) downloaded from NASA. The results showed that in Kurdistan region, the chilling hours with 7 classes ranged from 335 CH in Shahreban (Diyala) to 1083 CH in Amedi, Deraluk, Dinarte, Kani Masi, and Shiladize (Duhok), and Barzan (Erbil), with an average of 886 CH. The chilling units (CU, Utah Model) with 6 classes ranged from 507 CU (Shahreban, Diyala) to 1135 CU (Hizawa, Duhok), with an average of 927 CU. However, the chilling portions with 7 classes ranged from 30.0 CP in Shahreban to 58.3 CP in Ahmad Awa, Khurmal, Penjwin, Sirwan, and Zalm (Salaymaniyah), with an average of 49.5 CP. Growing degree hours with 7 classes ranged from 1136 GDH Choman, Galala, Haji Omaran and Samilan (Erbil) to 8564 GDH in Shahreban, with an average of 3256 GDH. All models were strongly correlated with location parameters, the highest strong correlations were found between each CP and GDH with AAT. The observations emphasized that both CU and CP models are more accurate than CH model for estimation of chilling and growing degree hours (units) in Kurdistan region-Iraq.

On a novel skewed generalized t distribution: Properties, estimations, and its applications

With the progress of information technology, large amounts of asymmetric, leptokurtic, and heavy-tailed data are arising in various fields, such as finance, engineering, genetics, and medicine. It is very challenging to model those kinds of data, especially for extremely skewed data, accompanied by very high kurtosis or heavy tails. In this article, we propose a class of novel skewed generalized t distribution (SkeGTD) as a scale mixture of skewed generalized normal. The proposed SkeGTD has excellent adaptiveness to various data, because of its capability of allowing for a large range of skewness and kurtosis and its compatibility of the separated location, scale, skewness, and shape parameters. We investigate some important properties of this family of distributions. The maximum likelihood estimation, L-moments estimation, and two-step estimation for the SkeGTD are explored. To illustrate the usefulness of the proposed methodology, we present simulation studies and analyze two real datasets.

Comparison of the Meta-Heuristic Algorithms for Maximum Likelihood Estimation of the Exponentially Modified Logistic Distribution

Generalized distributions have been studied a lot recently because of their flexibility and reliability in modeling lifetime data. The two-parameter Exponentially-Modified Logistic distribution is a flexible modified distribution that was introduced in 2018. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, log-logistic, and others. In this study, the unknown parameters of the Exponentially-Modified Logistic distribution are estimated using the maximum likelihood method. Five meta-heuristic algorithms, including the genetic algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and sine cosine algorithm, are applied in order to solve the nonlinear likelihood equations of the study model. The efficiencies of all maximum likelihood estimates for these algorithms are compared via an extensive Monte Carlo simulation study. The performance of the maximum likelihood estimates for the location and scale parameters of the Exponentially-Modified Logistic distribution developed with the genetic algorithm and grey wolf optimization algorithms is the most efficient among others, according to simulation findings. However, the genetic algorithm is two times faster than grey wolf optimization and can be considered better than grey wolf optimization considering the computation time criterion. Six real datasets are analyzed to show the flexibility of this distribution.

A note on the Hendrickson-Lattman phase probability distribution and its equivalence to the generalized von Mises distribution.

Hendrickson & Lattman [Acta Cryst. (1970), B26, 136-143] introduced a method for representing crystallographic phase probabilities defined on the unit circle. Their approach could model the bimodal phase probability distributions that can result from experimental phase determination procedures. It also provided simple and highly effective means to combine independent sources of phase information. The present work discusses the equivalence of the Hendrickson-Lattman distribution and the generalized von Mises distribution of order two, which has been studied in the statistical literature. Recognizing this connection allows the Hendrickson-Lattman distribution to be expressed in an alternative form which is easier to interpret, as it involves the location and concentration parameters of the component von Mises distributions. It also allows clarification of the conditions for bimodality and access to a simplified analytical method for evaluating the trigonometric moments of the distribution, the first of which is required for computing the best Fourier synthesis in the presence of phase, but not amplitude, uncertainty.

Risk adjustment-based statistical control charts with a functional covariate

ABSTRACT In real applications, there are situations in which quality characteristics are significantly affected by function-valued covariates such as electrocardiogram (ECG) signals; however, previous surveillance methods with covariate adjustment mainly use scalar-valued covariates to construct control charts to investigate the stability of nonindustrial processes, such as in the medical and public health fields. Few existing approaches address the infinite-dimensional regression encountered with function-valued covariates. Thus, an applicable scheme needs to be developed. Here, by relaxing the assumption of normality, a novel surveillance strategy for functional logistic regression (FLR) is proposed. In this approach, the response is a binary variable, and the covariate information is obtained from functional data. To address the above problems, functional principal component analysis (FPCA) is used to extend the score test to situations with functional covariates. Moreover, the score statistic is integrated into an exponentially weighted moving average (EWMA) charting scheme to detect abnormal changes in the location and scale parameters based on additional information. The simulation results show that the proposed approach is more efficient than previous approaches in detecting small to moderate shifts. Finally, the proposed charting scheme is applied to a real case study to demonstrate its utility and applicability.

Detecting outliers in the multivariate control charts for dispersion monitoring

AbstractOutlier detection is an important aspect of statistical process monitoring (SPM) because outliers affect the performance of control charts. SPM researchers study the negative impact of outliers on control charts for monitoring location parameters. However, there is little research on outlier detection in multivariate charts for monitoring process dispersion. This study aims to investigate the impact of outliers in multivariate control charts for monitoring covariance matrix of a process, and then to recommend techniques for detecting potential outliers present from Phase I samples. We propose a new multivariate dispersion chart that employs the determinant of logarithm of estimated covariance matrix as the monitoring statistic. Through Monte Carlo simulations, the results show how outliers from the first phase affect the overall performance of multivariate charts. The results also demonstrate that the minimum volume ellipsoid (MVE) estimator is effective in reducing the effect of outliers on the proposed control scheme than the other compared estimators.

Women and insurance pricing policies: a gender-based analysis with GAMLSS on two actuarial datasets

In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers’ decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions.

Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algorithm. The main reason for this is that differential evolution is highly sensitive to the scaling factor and crossover rate parameters. In this study, a novel adaptation technique is proposed which uses the success rate to replace the popular success history-based adaptation for scaling factor tuning. In particular, the scaling factor is sampled with a Cauchy distribution, whose location parameter is set as an nth order root of the current success rate, i.e., the ratio of improved solutions to the current population size. The proposed technique is universal and can be applied to any differential evolution variant. Here it is tested with several state-of-the-art variants of differential evolution, and on two benchmark sets, CEC 2017 and CEC 2022. The performed experiments, which include modifications of algorithms developed by other authors, show that in many cases using the success rate to determine the scaling factor can be beneficial, especially with relatively small computational resource.

Association of Lesion Location and Functional Parameters with Vision-Related Quality of Life in Geographic Atrophy Secondary to Age-related Macular Degeneration

The primary goal of this study was to determine how structural and functional parameters influence the vision-related quality of life (VRQoL) in patients suffering from geographic atrophy (GA) secondary to age-related macular degeneration. This study was designed as a prospective, noninterventional, natural-history study (Directional Spread in Geographic-Atrophy study, NCT02051998). The research involved 82 patients with bilateral GA. The study examined parameters including GA location as assessed by the ETDRS grid, best-corrected visual acuity, low-luminance visual acuity (LLVA), reading acuity, and speed. These parameters were then correlated with VRQoL, which was gauged using the National Eye Institute Visual Function Questionnaire 25. The analysis method employed was the least absolute shrinkage and selection operator with linear mixed-effects models. The central parameters measured in this study encompassed GA area, VRQoL scores associated with different GA subfields, and the significance of LLVA for foveal-sparing patients. On average, patients showed a total GA area of 2.9 ± 1.2 mm2 in the better eye (BE) and 3.1 ± 1.3 mm2 in the worse eye. The most significant associations with VRQoL scores for distance and near activities were observed in the inner lower and inner left subfields of the BE, respectively. For patients with foveal-sparing GA, the LLVA of the BE stood out as the most influential variable across all VRQoL scales. The study's findings point toward the pivotal role of GA location, especially the inner lower and inner left subfields of the BE, in relation to VRQoL in GA patients. The LLVA's importance becomes even more pronounced for foveal-sparing patients. These observations highlight the need for health care professionals to better understand the association between lesion location and patient-reported outcomes. This is critical for informing treatment decisions and refining the planning of interventional trials. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Change-point design-based charting schemes for monitoring process variability

ABSTRACT Most of the recommended nonparametric control charts apply to monitor location parameter. In this paper, we propose a nonparametric exponentially weighted moving average (EWMA) control chart for monitoring scale parameter, which combines the Ansari–Bradley test and the framework of change point detection. As with most nonparametric control charts, a large number of historical observations are used to motivate the control chart, and we propose a nonparametric EWMA control chart that achieves the same purpose with a very small number of historical observations. Simulation results demonstrate that our control chart is effective and robust for monitoring scale parameter shifts. For illustration of our main results, an applied example demonstrates the practicality of this control chart for monitoring scale parameter.

Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks

In this paper, we investigate mathematical models of meteorological forecasting based on the work of neural networks, which allow us to calculate presumptive meteorological parameters of the desired location on the basis of previous meteorological data. A new method of grouping neural networks to obtain a more accurate output result is proposed. An algorithm is presented, based on which the most accurate meteorological forecast was obtained based on the results of the study. This algorithm can be used in a wide range of situations, such as obtaining data for the operation of equipment in a given location and studying meteorological parameters of the location. To build this model, we used data obtained from personal weather stations of the Weather Underground company and the US National Digital Forecast Database (NDFD). Also, a Google remote learning machine was used to compare the results with existing products on the market. The algorithm for building the forecast model covered several locations across the US in order to compare its performance in different weather zones. Different methods of training the machine to produce the most effective weather forecast result were also considered.

Cognitive outcomes in patients with essential tremor treated with deep brain stimulation: a systematic review.

Essential tremor (ET) is a common neurological disease. Deep brain stimulation (DBS) to the thalamic ventral intermediate nucleus (VIM) or the adjacent structures, such as caudal zona incerta/ posterior subthalamic area (cZi/PSA), can be effective in treating medication refractory tremor. However, it is not clear whether DBS can cause cognitive changes, in which domain, and to what extent if so. We systematically searched PubMed and the Web of Science for available publications reporting on cognitive outcomes in patients with ET who underwent DBS following the PICO (population, intervention, comparators, and outcomes) concept. The PRISMA guideline for systematic reviews was applied. Twenty relevant articles were finally identified and included for review, thirteen of which were prospective (one also randomized) studies and seven were retrospective. Cognitive outcomes included attention, memory, executive function, language, visuospatial function, and mood-related variables. VIM and cZi/PSA DBS were generally well tolerated, although verbal fluency and language production were affected in some patients. Additionally, left-sided VIM DBS was associated with negative effects on verbal abstraction, word recall, and verbal memory performance in some patients. Significant cognitive decline after VIM or cZi/PSA DBS in ET patients appears to be rare. Future prospective randomized controlled trials are needed to meticulously study the effect of the location, laterality, and stimulation parameters of the active contacts on cognitive outcomes while considering possible medication change post-DBS, timing, standard neuropsychological battery, practice effects, the timing of assessment, and effect size as potential confounders.

An EM/MCMC Markov-Switching GARCH Behavioral Algorithm for Random-Length Lumber Futures Trading

This paper tests using two-regime Markov-switching models with asymmetric, time-varying exponential generalized autoregressive conditional heteroskedasticity (MS-EGARCH) variances in random-length lumber futures trading. By assuming a two-regime context (a low s=1 and high s=2 volatility), a trading algorithm was simulated with the following trading rule: invest in lumber futures if the probability of being in the high-volatility regime s=2 is lower or equal to 50%, or invest in the 3-month U.S. Treasury bills (TBills) otherwise. The rationale tested in this paper was that using a two-regime Markov-switching (MS) algorithm leads to an overperformance against a buy-and-hold strategy in lumber futures. To extend the current literature in MS trading algorithms, two location parameter scenarios were simulated. The first uses an unconditional mean or expected value (no factors), and the second incorporates market and behavioral factors. With weekly simulations form 2 January 1994 to 28 July 2023, the results suggest that using MS-EGARCH models in a no-factors scenario is appropriate for active lumber futures trading with an accumulated return of 158.33%. Also, the results suggest that it is not useful to add market and behavioral factors in the MS-GARCH estimation because it leads to a lower performance.

Estimation of non-stationary return levels of extreme temperature by CMIP6 models

Abstract This study aims to investigate the effects of climate change on return level of extreme maximum temperature (EMT) events in Iran. To this end, the CRU gridded dataset was used to collect EMT for the 1901–2014 period and future data were projected from four available CMIP6 models, where the BCC-CSM2-MR performed best under the latest Shared Socioeconomic Pathways-Representative concentration pathways (SSPs-RCPs) emission scenarios for the 2015–2100 period. The non-stationary state of the distribution was considered under three models GEV0 (location and scale parameters are constant), GEV1 (nonstationary of location), and GEV2 (nonstationary of scale) based on the evaluation criteria . The findings indicate that, when using a non-stationary approach and considering the SSP5-8.5 scenario for a 2-year return period, the return level of extreme temperature increased by up to +4°C compared with the stationary approach, while considering a non-stationary approach without climate change, the increase in the return level of extreme temperature was much smaller(up to +0.7°C). MCMC and DE-MC showed no significant differences and demonstrated that all stations are non-stationary in terms of the location parameter (GEV1).

Key parameters that influence surgical strategy in localised kidney cancer.

423 Background: The goal of this studywas to select the key factors affecting choice between radical nephrectomy (RN) and partial nephrectomy (PN) for patients with localized RCC based on clinical and nephrometry data. Methods: A special retrospective cohort study was conducted in National Cancer Institute of Ukraine, which results were further validated on patient dataset in urological department of University Clinic of Cologne. The Institutional Review Boards and the local ethics committees of both high-volume centres approved the study. The main nephrometry parameters of tumor location in the kidney were analysed according to the R.E.N.A.L nephrometry score. The remaining functional parenchymal volume (RFPV) was calculated using the special formula. To determine the relationship between the risk of RN or PN, the multivariate predictive modelling method containing 12 parameters was used (Artificial Neural Networks [ANN]). Data validation based on referential centre experience using ROC-curve analysis to detect clinical applicability of the null hypothesis was performed. Results: Based on the analysis, for polary and laterally located tumors, the risk of RN was conditioned only by RFPV. The average critical value of RFPV for polar lesions was X6crit = 58% (in X6 < X6crit, RN was predicted); for lateral tumors - X6crit = 67% (in X6 < X6crit, RN was predicted). For medial location, the risk of RN only depended on the tumor size. Average critical value of the tumor size in the medial location was X7crit = 38 mm (in X7 > X7crit, RN was predicted). Based on the ROC curve comparison, there were no statistically significant differences between the AUCLin_12 and AUCMLP_3 (p = 0.12); thus, the reduced amount of the factor indicators from 12 to 3 did not worsen the model predictive qualities. Designed during primary analysis hypothesis was successfully validated in a referent centre on the cohort of 300 patients. Out of the cohort - 14 (4.6%) patients experienced false positive/negative outcome, which resulted in a radical/partial nephrectomy out of the hypothesis margins. Predictive model is characterized by high sensitivity (95.2%) and specificity (95.4%) in selecting patients for partial nephrectomy. Conclusions: For the polar and lateral tumor locations, the functioning parenchymal volumes of over 58 and 67% respectively serve as PN indications. However, for the medial lesions, the primary PN indication is a tumor size less than 38 mm.

Absence of lunar phobia in European swarming vespertilionid bats

“Lunar phobia” in bats has been widely discussed since its description in tropical bats in 1978. The phenomenon has been frequently contested and supported and was first reported in European bats in 2020. Our study seeks to clarify the debate by describing the relationship between the activity of selected swarming vespertilionid bats (Family: Vespertilionidae) and moonlight levels. To verify a potential connection to the latter, a swarming dataset was analysed in respect of estimated moonlight illumination. Moonlight estimates were based on geographical location and several lunar parameters, to accurately characterise the non-linear relationship between moon phase and illumination (lux). The swarming data consisted of 32 netting and 14 echolocation recording sessions collected between August and October 2014 and 2015. Our data included 3,265 netted bats from 13 species and 15,919 bat calls from 10 confirmed species. Data was collected at the large Central European hibernation/swarming site – Natura 2000 PLH080003 “Nietoperek” in western Poland (N 52.394400, E 15.480600). Generalised linear mixed models (GLMMs) determined insignificant relationships between bats and moonlight illumination. Our analysis confirms an absence of impact of moonlight intensity on swarming bats and thereby rejects the lunar phobia phenomena in at least six insectivorous bat species (Myotis myotis, M. daubentonii, M. nattereri, M. bechsteinii, Barbastella barbastellus, Plecotus auritus) swarming in the autumn.