Statistical Probability Research Articles

Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy

The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particle swarm optimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algorithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water-accumulated goaf.

And Finally: Matters of Life and Death

And Finally: Matters of Life and Death

The Inertial properties of EWMA control charts

Since exponentially weighted moving average (EWMA) control charts combine information from the current and past samples, they are relatively more effective in detecting small process shifts than Shewhart control charts. A prevailing problem in control charts is the property of not responding quickly to process shifts, that is, showing statistical control of the process while a shift has occurred in the process parameter(s). This property of control charts, which is referred to as “inertia”, is an important issue in control charting and has been a major concern to engineers and statisticians. This study surveys the inertial properties of EWMA control charts in detail. First, the distribution of EWMA chart statistic and signal resistance probability (SRP) are studied for many different cases when there are undetected transient shifts. All measures are calculated and evaluated via a Markov chain approach. Then, an optimal scheme is proposed to minimize the out-of-control average run length (ARL) for a specified shift, subject to SRP constraints. The results reveal that the inertia can be sometimes a serious problem in EWMA control schemes that should not be overlooked. It is shown that an appropriate scheme based on both ARL and SRP measures could be useful for processes that may have sustained and/or transient out-of-control states.

The effect of employee engagement and training on employee performance through organizational commitment

The existence of organizational change results in a decrease in the quality of employee performance which must be supported by training so that it can respond to market changes and competition. The purpose of this study was to analyze the effect of employee engagement and training on employee performance through organizational commitment. The sample in this study amounted to 89 respondents. The statistical method used is inferential statistics (inductive statistics or probability statistics) with PLS (Partial Least Square) to complete the hypothesis. The results of this study are that there is a positive and significant influence of employee engagement, training, and organizational commitment on employee performance through organizational commitment. This research has implications for the managerial part of the company where training is needed, especially those related to benefits in completing work.

Probabilistic approach to Gramian inversion of multiphysics data

We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various components of the geological system. By joint inversion of the multiphysics data, one can produce enhanced subsurface images of the physical properties distribution, which improves our ability to explore natural resources. One powerful method of joint inversion is based on Gramian constraints. This technique enforces the relationships between different model parameters during the inversion process. We demonstrate that the Gramian can be interpreted as a determinant of the covariance matrix between different physical models representing the subsurface geology in the framework of the probabilistic approach to inversion theory. This interpretation opens the way to use all the power of the modern probability theory and statistics in developing novel methods for joint inversion of the multiphysics data. We apply the developed joint inversion methodology to inversion of gravity gradiometry and magnetic data in the Nordkapp Basin, Barents Sea to image salt diapirs.

A Survey of Herbig Ae/Be Multiplicity

Although the majority of Herbig Ae/Be (HAEBE) stars are likely multiple systems, relatively few multiples have been identified. We aim to create a statistically significant survey of HAEBE multiplicity as a function of stellar mass, to shed light on mixed-mass star formation. Two independent adaptive optics IR imaging surveys were conducted, between 1993 and 2007. A total of 143 stars were observed, including 126 HAEBE stars. These surveys are significantly more sensitive than previous imaging work and, combined with accurate distance and proper-motion data from GAIA Data Release 3, provide a broad survey of intermediate-mass multiplicity. We perform a statistical probability analysis to identify the likely physical companions, and analyze multiepoch observations for common proper motions. We find 250 stellar detections around 88 sample stars. Of these, 70 systems are likely multiple systems, and 42 companions around 29 systems are reported here for the first time. Correcting for completeness, we find a multiplicity fraction of 54%. Multiples are found randomly across all spectral types, but high-order multiples are more common among the hottest stars. Statistically likely companions are found at distances of up to several thousand astronomical units and ΔK magnitudes ranging from <0.1 to ∼9, although companions with ΔK < 1.5 are rare. Companions around Herbig Ae stars tend to be closer than those around Herbig Be stars. We confirm 24 proper-motion companions in 21 systems, including roughly half of the statistically very likely companions; however, many proper-motion results are inconclusive.

Trivariate Probabilistic Assessments of the Compound Flooding Events Using the 3-D Fully Nested Archimedean (FNA) Copula in the Semiparametric Distribution Setting

Severe flooding in coastal areas can result from the joint probability of oceanographic, hydrological, and meteorological factors, resulting in compound flooding (CF) events. Recently, copula functions have been used to enable a much more flexible environment in joint modelling. Incorporating a higher dimensional copula framework via symmetric 3-D Archimedean or elliptical copulas has statistical limits, and the preservation of all lower-level dependencies would be impossible. The heterogeneous dependency in CF events can be effectively modelled via a fully nested Archimedean (FNA) copula. Incorporating FNA under parametric settings is insufficiently flexible since it is restricted by the prior distributional assumption of the function type for both marginal density and copulas in parametric fittings. If the marginal density is of a specific parametric distribution, it could be problematic if underlying assumptions are violated. This study introduces a 3-D FNA copula simulation in a semiparametric setting by introducing nonparametric marginal distributions conjoined with parametric copula density. The derived semiparametric FNA copula is applied in the trivariate model in compounding the joint impact of rainfall, storm surge, and river discharge based on 46 years of observations on the west coast of Canada. The performance of the derived model has also been compared with the FNA copula constructed with parametric marginal density. It is concluded that the performance of FNA with nonparametric marginals outperforms the FNA copula built under parametric settings. The derived model is employed in multivariate analysis of flood risks in trivariate primary joint and conditional joint return periods. The trivariate hydrologic risk is analyzed using failure probability (FP) statistics. The investigation reveals that trivariate events produce a higher FP than bivariate (or univariate) events; thus, neglecting trivariate joint analysis results in FP being underestimated. Moreover, it indicates that trivariate hydrologic risk values increase with an increase in the service time of hydraulic facilities.

Study of the Deposition Characteristics of Particles on the Slag Wall of a Gasifier

In this work, the behavior of coal char particles adhering to the slag wall was studied. The effects of slag thickness and particle size on the restitution coefficient and probability of deposition were mainly discussed. In addition, the types of particle deposition were also classified. The results of exploring the effect of particle size on the coefficient of restitution show that the restitution coefficient of coal char particles hitting the slag wall surface has a complex dimensional effect, with a maximum and minimum coefficient of restitution occurring at the same particle size, a phenomenon referred to in this paper as the discrete effect. The minimum restitution coefficient greater than 0 is regarded as the critical restitution coefficient (εf), and it is considered that particles with theoretical restitution coefficient higher than εf will bounce off the slag wall. With the particle diameter of 600 μm as the boundary, εf first decreases and then increases. The maximum restitution coefficient increases with an increasing particle size of less than 600 μm and stays at a maximum value of 0.62 when the particle size is more than 600 μm. Moreover, the fragmentation of particles occurs at the particle size of 840–1600 μm. The discrete effect was also observed in the experimental data for the investigation of slag thickness, εf increases with the increase of the slag thickness, and the maximum possible restitution coefficient does not change with the thickness of the slag. In addition, according to the statistics of the deposition probability, it is found that the smaller the particle size, the greater the deposition probability, the thicker the slag thickness, and the easier it is to deposit.

Seismic performance and fragility of two-story and three-span underground structures using a random forest model and a new damage description method

This paper proposes a new method for evaluating the seismic performance and fragility of large underground structures. A typical underground subway station consisting of a two-story and three-span structure was used as the research subject. Considering the variation of sites and the uncertainty of input ground motions, we developed a finite element model of a nonlinear dynamic soil-underground structure interaction system. Based on the incremental dynamic analysis method, the nonlinear seismic responses of the subway station structure were calculated for 900 cases. Python (software) was used to accelerate the processing of the large number of numerical simulation data. Based on the seismic damage characteristics of an underground structure, this paper proposes a new concept of average seismic damage value to evaluate the damage condition of the underground structure to overcome the inadequacies induced by an expert’s intuitive judgment. Meanwhile, the random forest prediction model was adopted to predict the seismic performance levels (SPLs) of the underground structure, and a non-parametric test was performed to verify the correlation between the degree of seismic damage and the inter-story drift ratios (IDR) of the large underground structure. This proved that the IDR could describe the seismic damage of shallow-buried underground structures; we advanced a precise quantification system of the SPL for shallow-buried subway station structures with two-story and three-span using probability statistics. The peak ground velocity (PGV) is more suitable than the peak ground acceleration (PGA) for use as the seismic intensity measure (IM) for large underground structures when the stiffness of the soil foundation becomes soft. In addition, the seismic fragility curves of the shallow-buried subway station structure were constructed and analyzed using the developed quantification system for the SPL.

Simulation‐data‐driven load disaggregation based on multi‐channel neural network for industrial and commercial users

AbstractThe immeasurable energy‐saving potential of industrial parks and commercial buildings with unprecedented growth in scale and quantity can be well exploited by load disaggregation, a promising technique for demand‐side refinement management. However, traditional load disaggregation studies mainly focus on residential users and neglect the collection and analysis of industrial and commercial loads. This study employs a simulated response model of versatile load categories with voltage‐dependent load functions to overcome the unavailability of industrial and commercial datasets. Specially, a weighting factors allocation method considering probability statistics of power consumption distribution characteristics is proposed to construct a typical daily multi‐type load aggregation model. Furthermore, an intelligent load disaggregation strategy is developed based on a multi‐channel neural network to constructively improve multi‐feature tracking and convergence efficiency. Finally, the simulation results of the detailed study case confirm the tractability and effectiveness of the proposed approach to carry out load identification among multi‐type users, with an accuracy of 94.9% and errors of individual categories controlled between 3.32% and 10.686%. Moreover, additional interference tests demonstrate that the developed model has superiority over common load disaggregation structures, with better tolerance to exceptional voltage fluctuation and noise.

Research on the integrated and innovative teaching mode of probability and statistics courses under the background of big data

Big data has become a buzzword in many industries, forums and economic exchanges, and its development is interrelated and permeated with mathematics, computers and mathematical statistics courses. Probability theory and statistics, as tools for big data analysis, often belong to different teaching departments, and teaching and research are separated, this paper is based on the Department of Economics and Statistics doing innovative reforms in the discipline foundation, course class connotation and talent training direction to achieve two This paper is based on an innovative reform of the Department of Economics and Statistics in terms of the foundations of the subject, the content of the course and the direction of personnel training. Vertically, realize the theoretical content integration of "high school probability statistics university probability theory and mathematical statistics university statistics big data and statistics"; In the horizontal expansion of the teaching process, grasp the context of "theoretical system interesting cases mathematical modeling ideas project-based embedded teaching competition participation guidance theory and practice review and strengthening"; Form a student-centered and output-oriented integrated and innovative teaching model.

Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications

This paper presents the design of dominance relations to reduce the space traversed in machine learning for solving two applications in financial trading and real-time multimedia. A machine-learning algorithm designed for an application with a huge search space will need to perform an efficient traversal of the space during learning. It will be more effective if it employs a powerful pruning mechanism to eliminate suboptimal candidates before using them in the learning algorithm. In our approach, we present dominance relations for pruning subspaces with suboptimal kernels that are otherwise evaluated in learning, where kernels represent the statistical quality, average density, or probability of solutions in a subspace. Specifically, when one subspace dominates another by a dominance relation, we can prune the latter and guarantee without searching both that the kernel of the latter cannot be better than that of the first. As a result, a significant portion of the search space will be pruned by those non- dominated subspaces during learning. In the financial trading application studied, we use mean reversion as our strategy for learning the set of promising stocks and Pareto-optimality as our dominance relation to reduce the space evaluated in learning. In the multimedia application, we propose a dominance relation using an axiom from our past work to approximate the subspace of perceptual qualities within an error threshold. The pruning mechanism allows the learning of the mapping from controls to perceptual qualities while eliminating the evaluation of all those mappings that are within the error thresholds. In both cases, we can harness the complexity of machine learning by reducing the candidate space evaluated.

Residential Harmonic Injection Models Based on Field Measurements

In recent years, harmonic current levels have increased in residential distribution networks due to the growing penetration of nonlinear loads at customer premises. To properly model the harmonic current injection of residences, probabilistic models that account for the stochastic behavior of domestic loads are needed. As a part of the study reported in this paper, a field measurement campaign was performed in 24 dwellings during a one-week period with a one-minute resolution. Harmonic current magnitudes and phase angles of odd harmonic orders up to the 25th as well as the active and reactive power demanded were recorded. By applying two different unsupervised learning techniques (i.e., nonparametric density estimation and Gaussian mixture models) to these measurements, two probabilistic models of harmonic injection of individual residential sites have been derived. Statistical probability distributions have been determined for the magnitude and phase of each harmonic order segmented in different power demand intervals. The developed models are validated on a test feeder by comparing harmonic voltages caused by the injection of the synthetically generated currents with those obtained from measurements. Both field measurement results and the detailed data defining the probabilistic harmonic current models are made public in an Open Science database repository.

Analysis of the Relationship Between Climate Change Impacts and Terrorism in ASEAN (1990-2018)

In August 1967, realizing that peace and stability is the catalyst to economic growth, 5 nations (Indonesia, Malaysia, Singapore, Thailand, and The Philippines) formed the Association of Southeast Asian Nations (ASEAN). The group eventually evolved and adding more members resulting with a grouping of 10 states with the addition of Brunei Darussalam, Cambodia, Lao’s PDR, Myanmar, and Vietnam. This paper intends to review the link between climate change and terrorism in member countries of the Association of Southeast Asian Nations (Association of Southeast Asian Nations/ASEAN). The datasets used to form the empirical model are the number of terrorist attacks from 1990-2018 recorded in the Global Terrorism Database (National Consortium for the Study of Terrorism and Responses to Terrorism (START) and Climate Change Indicators 1990-2018 recorded in The World Bank. Using multiple regression analysis, the study answered the research question as ‘An increase in access to electricity, rainfall, and farmland size has a statistical probability of increasing terrorism attacks in ASEAN, while an increase in greenhouse gas emissions, statistically has a probability of reducing terrorism attacks in ASEAN member states.

Some Refinements of Hermite–Hadamard Type Integral Inequalities Involving Refined Convex Function of the Raina Type

The aim of this work is to elaborate and define the idea of refined convex function of the Raina type. In addition, we have attained some associated properties in the manner of the newly introduced idea. To add some more comprehension into the newly investigated definition, we obtain the estimations of the Hermite-Hadamard inequality. For the reader’s interest, we add some remarks regarding the Mittag-Leffer function. During the last four decades, the term Mitag-Leffler function has acquired popularity on account of its many importance in the fields of engineering and science, i.e statistical distribution theory, rheology, electric networks, fluid flow, and probability. The amazing perception regarding this function provides the solution of certain boundary value problems. The asymptotic status of this function plays a very vital performance in various problems of physics associated with fractional calculus. The methodology and amazing tools of this work may serve as an impetus for further research activities in this direction as well.

Machine Learning-Assisted Rapid Screening of Four Types of New Psychoactive Substances in Drug Seizures.

Over the past few years, new psychoactive substances (NPS) have become a global health and social problem because of their wide variety, constant structural renewal, vague legal definitions, and rapid adaptation to legal restrictions. The rapid structural modifications of NPS have posed significant challenges for the screening and identification of these new substances using traditional mass spectrometric techniques based on reference substances or a mass spectral database. Here, we propose supervised machine learning (ML) classification models such as k-nearest neighbors, support vector machine, random forest, and multigrained cascade forest for the rapid screening of NPS using mass spectrometric data. This approach utilizes ML methods to learn the statistical probability distributions of mass spectral data for NPS and non-NPS. Four classification ML models were generated and evaluated using a data set comprising 567 LC-MS and 732 GC-MS spectra. Through cross validation, we achieved an F1 score of 0.35-0.97. These algorithms were applied in conjunction with mass spectrometry techniques for the detection of six seizures including electronic cigarette oil and suspected powdered substances netted in drug trafficking cases. The models provided warning signals for synthetic cannabinoids, synthetic cathinones, and fentanyl. Thus, an early warning system was successfully established, which provided a useful method for reliable and effective identifications of unknown NPS.

Analisis Keakuratan Kualitas dan Kuantitas Sustainability Report Disclosure; Strategi Menarik Minat Investasi di Bank Syariah Indonesia

Advances in digital technology that penetrase so quickly create serious changes in various fields including the economy. Sustainability report disclosure is one of the crucial issues that is interesting and always hotly discussed by economic actors in the 5.0 era, including sharia economic actors. This research is important because the accuracy of the sustainability report disclosure is not only a form of compliance with sharia values but also as a trend of needs for stakeholders, and becomes an added value for companies including Bank Syariah Indonesia (BSI) in order to reach wider stakeholders and investors. Increasing the accuracy of the quality and quantity of the sustainability report disclosure can be a solution for BSI in developing its Islamic finance industry. As for the research objectives; First, knowing the effect of the accuracy of the quality and quantity of the sustainability report disclosure on financial performance; Second, knowing the impact of the quality and quantity of the sustainability report disclosure on investor interest in investing. This research is a type of field research with a mix method. The sample was determined through purposive sampling and obtained as many as 34 respondents. The research data used are primary data and secondary data, while the data collection method is done through questionnaires, interviews and documentation. The analysis technique was carried out by using multiple linear regression, t test, F test, and R-Square test. The results of the study show that the accuracy of the quality and quantity of the sustainability report’s disclosure, either partially or simultaneously, has a positive and significant effect on investment interest, as evidenced by the probability statistics obtained, namely 0.000 &lt; 0.05. Both variables have an effect of 94,3% on interest in investing. From the results of the study, it is also known that the variable quality of sustainability report disclosure is the dominant variable that influences the level of investor interest in investing with the value obtained reaching 1.254085 on investors in the Valu Capital stock community in Banda Aceh.

Probabilistic deconvolution of PET images using informed priors.

We present a probabilistic approach to medical image analysis that requires, and makes use of, explicit prior information provided by a medical expert. Depending on the choice of prior model the method can be used for image enhancement, analysis, and segmentation. The methodology is based on a probabilistic approach to medical image analysis, that allows integration of 1) arbitrarily complex prior information (for which realizations can be generated), 2) information about a convolution operator of the imaging system, and 3) information about the noise in the reconstructed image into a posterior probability density. The method was demonstrated on positron emission tomography (PET) images obtained from a phantom and a patient with lung cancer. The likelihood model (multivariate log-normal) and the convolution operator were derived from phantom data. Two examples of prior information were used to show the potential of the method. The extended Metropolis-Hastings algorithm, a Markov chain Monte Carlo method, was used to generate realizations of the posterior distribution of the tracer activity concentration. A set of realizations from the posterior was used as the base of a quantitative PET image analysis. The mean and variance of activity concentrations were computed, as well as the probability of high tracer uptake and statistics on the size and activity concentration of high uptake regions. For both phantom and in vivo images, the estimated images of mean activity concentrations appeared to have reduced noise levels, and a sharper outline of high activity regions, as compared to the original PET. The estimated variance of activity concentrations was high at the edges of high activity regions. The methodology provides a probabilistic approach for medical image analysis that explicitly takes into account medical expert knowledge as prior information. The presented first results indicate the potential of the method to improve the detection of small lesions. The methodology allows for a probabilistic measure of the size and activity level of high uptake regions, with possible long-term perspectives for early detection of cancer, as well as treatment, planning, and follow-up.

Population Genomic Analyses Suggest a Hybrid Origin, Cryptic Sexuality, and Decay of Genes Regulating Seed Development for the Putatively Strictly Asexual Kingdonia uniflora (Circaeasteraceae, Ranunculales).

Asexual lineages are perceived to be short-lived on evolutionary timescales. Hence, reports for exceptional cases of putative 'ancient asexuals' usually raise questions about the persistence of such species. So far, there have been few studies to solve the mystery in plants. The monotypic Kingdonia dating to the early Eocene, contains only K. uniflora that has no known definitive evidence for sexual reproduction nor records for having congeneric sexual species, raising the possibility that the species has persisted under strict asexuality for a long period of time. Here, we analyze whole genome polymorphism and divergence in K. uniflora. Our results show that K. uniflora is characterized by high allelic heterozygosity and elevated πN/πS ratio, in line with theoretical expectations under asexual evolution. Allele frequency spectrum analysis reveals the origin of asexuality in K. uniflora occurred prior to lineage differentiation of the species. Although divergence within K. uniflora individuals exceeds that between populations, the topologies of the two haplotype trees, however, fail to match each other, indicating long-term asexuality is unlikely to account for the high allele divergence and K. uniflora may have a recent hybrid origin. Phi-test shows a statistical probability of recombination for the conflicting phylogenetic signals revealed by the split network, suggesting K. uniflora engages in undetected sexual reproduction. Detection of elevated genetic differentiation and premature stop codons (in some populations) in genes regulating seed development indicates mutational degradation of sexuality-specific genes in K. uniflora. This study unfolds the origin and persistence mechanism of a plant lineage that has been known to reproduce asexually and presents the genomic consequences of lack of sexuality.

Functionalized diketopyrrolopyrrole compounds for NIR-to-visible photon upconversion

Diketopyrrolopyrrole derivatives demonstrating moderate NIR-to-Vis TTA-mediated photon upconversion quantum yield (up to 3.8% out of 50%) and statistical probabilityf(up to 15.6%) are promoted as stable alternatives to a rubrene emitter.