Distribution Of Students Research Articles

INVESTIGATION OF THE CORRELATION BETWEEN EMPLOYEE PRODUCTIVITY AND MOTIVATION IN THE COMPANY "INNOVATOR"

Nino Lazviashvili E-mail: lazviashvilinino11@gtu.ge Professor, Georgian Technical University Tbilisi, Georgia https://orcid.org/0009-0009-1251-4475 Zurab Gegenava E-mail: gege@innovator.ge PhD student, Georgian Technical University Tbilisi, Georgia https://orcid.org/0000-0001-7855-5987 Abstract: In the article - "Investigation of the correlation between employee productivity and motivation in the company "Innovator"" - in the period of 2018-2022 (number of observations n=5), the density of the correlation between productivity (y) and economic motivation (x) in the company "Innovator" is calculated and evaluated. The calculation is made both in terms of the entire number of employees and on average per employee. In any organization there is a resultative event and its cause (or causes). In this particular case, in the company "Innovator" the effective indicator is productivity, i.e. turnover (y), and the causal indicator is labor remuneration (x). The correlation is one-factor, i.e. pairwise, and also linear. In the paper, the density of the correlation connection is calculated by a special formula (Ч_yx ), which is perceived as positively functional if its value is placed in the norm of the numerical connection between the x and y values [-1; 1]. The research established that the correlation coefficient between productivity and motivation in the company "Innovator" is: for the total number of employees - 0.8075 (ie 80.7%) per employee on average - 0.7959 (ie 79.5%) Both these coefficients are placed in the interval between -1 and +1, which indicates the essentiality of this connection. The essentiality (significance) of these coefficients in the paper is substantiated using the student's distribution table. The paper concludes that more than 80% of overall productivity in the company "Innovator" is influenced by external motivation, i.e. the amount of remuneration, and the rest by the internal human factor, i.e. "motivation 3.0". Keywords: correlation; correlational connection; connection density; pairwise correlation; successful event; causal event; correlation coefficient; Student criteria. JEL classification: A2; M12;

Predicting Students' Performance by Using Data Mining Methods

Abstract
 The corona pandemic disrupted the educational process, especially in universities that use traditional education. Universities were therefore obliged to move from traditional education to e-education without adequate preparations. The aim of this research is to analyze the students' performance in the two educational environments and predict the result of any of them in the future. The k-means algorithm, an important data mining method, was used to analyze the results of the fourth-stage classes of five consecutive years of students from one Iraqi university's scientific departments. Four of these years were traditional education, while the last was E-education to see whether the student's performance distribution is normal or abnormal. The results indicate a 100 percent of students’ success rate in e-education, while the upper limit is 70 percent for the previous years.
 Moreover, the average class rate increased to 75 percent compared to 62 in previous years. The decision tree has been built based on a dataset created from the collected data to predict the distribution of both traditional and e-education with a 2% error tolerance. The study shows that using the exact mechanism in e-education will give abnormal results. Therefore, the study recommends the need for good infrastructure, the preparation of efficient staff, increasing students’ skills, and appropriate software platforms for an accurate assessment of students’ performance.

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

SummaryIn this paper, the parameter identification of bilinear state‐space model (SSM) in the presence of random outliers and time‐varying delays is investigated. Under the basis of the observable canonical form of the bilinear model, the system output can be written as a regressive form, and a bilinear state observer is applied to estimate the unknown states. To eliminate the influence of outliers and time‐varying delays on parameter estimation, we employ the Student's distribution to deal with the measurement noise and use a first‐order Markov chain to model the delays. In the framework of expectation‐maximization (EM) algorithm, the unknown parameters, delays, noise variance, states and transition probability matrix can be estimated iteratively. A numerical simulation and a continuous stirred tank reactor (CSTR) process demonstrate that the proposed algorithm has good immunity against outliers and time‐varying delays and offers good estimation accuracy for the bilinear SSM.

Thermal inertias and confidence intervals in the determination of activation energy by isoconversional methods applied for accelerating rate calorimetry

Isoconversional method has been preliminarily applied in accelerating rate calorimetry (ARC) and achieved good results. However, it is a new subject and needs to be explored in depth. In this study, the Friedman and the Vyazovkin methods were applied to investigate the influences of thermal inertia on the activation energy estimation of the single-step reaction and autocatalytic reaction. The accuracy and adaptability of the two kinetic methods are compared, and the confidence intervals for activation energy estimated by the Friedman and the Vyazovkin methods are evaluated based on the corrected Student's distribution and F distribution. Finally, the validity of the simulation result is verified by the experiments. The results show that the sets of thermal inertia have different effects on the estimation of activation energy in single-step reaction and multi-step reaction, and the activation energies in multi-step reaction change obviously under different sets of thermal inertia. The thermal inertia increment of 0.03-0.5 and the Vyazovkin method are recommended. This study is helpful to promote the development of isoconversional methods applied for ARC data.

Подавление шума и выделение когерентных структур сигнала на основе алгоритма нелинейной адаптации

An algorithm for noise suppression and extraction of coherent structures of a complex signal is proposed. The algorithm is based on the joint application of packet wavelet decompositions and adaptive stochastic thresholds. Thresholds are estimated with a given confidence level based on the α-quantiles of Student's distribution. The operations of the algorithm are described and a scheme for its implementation is proposed. On the example of data from neutron monitors (recording the intensity of secondary cosmic rays, www.nmdb.eu), the efficiency of the algorithm is shown. The selected coherent structures characterize the occurrence of anomalies in variations in the intensity of cosmic rays. Anomalies in cosmic ray data create a radiation hazard, disrupt radio communications, and cause satellites to malfunction, leading to disorientation and destruction. Therefore, the task of timely diagnosis of such events is relevant. The application of the algorithm made it possible to clearly detect and evaluate the moments of occurrence of anomalies in cosmic rays, which were observed during periods of magnetospheric disturbances.

Superintegrability of matrix Student's distribution

For ordinary matrix models, the eigenvalue probability density decays rapidly as one goes to infinity, in other words, has “short tails”. This ensures that all the multiple trace correlators (multipoint moments) are convergent and well-defined. Still, many critical phenomena are associated with an enhanced probability of seemingly rare effects, and one expects that they are better described by the “long tail” models. In absence of the exponential fall-off, the integrals for high moments diverge, and this could imply a loss of (super)integrability properties pertinent to matrix and eigenvalue models and, presumably, to non-perturbative (exact) treatment of more general quantum systems. In this paper, we explain that this danger to modern understanding could be exaggerated. We consider a simple family of long-tail matrix models, which preserve the crucial feature of superintegrability: exact factorized expressions for a full set of basic averages. It turns out that superintegrability can survive after an appropriate (natural and obvious) analytical continuation even in the presence of divergencies, which opens new perspectives for the study of the long-tail matrix models.

GARCH Models under Power Transformed Returns: Empirical Evidence from International Stock Indices

This study evaluates the empirical performance of four power transformation families: extended Tukey, Modulus, Exponential, and Yeo--Johnson, in modeling the return in the context of GARCH(1,1) models with two error distributions: Gaussian (normal) and Student-t. We employ an Adaptive Random Walk Metropolis method in Markov Chain Monte Carlo scheme to draw parameters. Using 19 international stock indices from the Oxford-Man Institute and basing on the log likelihood, Akaike Information Criterion, Bayesian Information Criterion, and Deviance Information Criterion, the use of power transformation families to the return series clearly improves the fit of the normal GARCH(1,1) model. In particular, the Modulus transformation family provides the best fit. Under Student's t-error distribution assumption, the GARCH(1,1) models under power transformed returns perform better in few cases.

Experimental study of the bearing capacity of lyophilized allogeneic bone material

Was to conduct an experimental study of the bearing capacity and determination of the Young's modulus of lyophilized allogeneic bone material. The study used 5 prototypes of lyophilized allogeneic bone blocks, which were alternately subjected to static loading by compressive force. The experimental values of Young's modulus of all bone samples were determined. When applying the Student's probability distribution in statistical processing of experimental data, the average indicators of the forces of destruction of the samples were obtained and Young's modulus was determined. As a result of the study, the indices of the maximum loading (6012H) and Young's modulus (147.2±0.228 MPa) of lyophilized allogeneic bone material were obtained. The obtained Young's modulus of lyophilized allogeneic bone material is of great importance in scientific activity, providing the possibility of further investigation of the properties of lyophilized allogeneic bone tissue to form a preoperative forecast of the behavior of an osteoplastic material and a dental implant installed in it.

Applicability of 3D-printed models in hepatobiliary surgey: results from “LIV3DPRINT” multicenter study

BackgroundHepatobiliary resections are challenging due to the complex liver anatomy. Three-dimensional printing (3DP) has gained popularity due to its ability to produce anatomical models based on the characteristics of each patient. MethodsA multicenter study was conducted on complex hepatobiliary tumours. The endpoint was to validate 3DP model accuracy from original image sources for application in the teaching, patient-communication, and planning of hepatobiliary surgery. ResultsThirty-five patients from eight centers were included. Process testing between 3DP and CT/MRI presented a considerable degree of similarity in vascular calibers (0.22 ± 1.8 mm), and distances between the tumour and vessel (0.31 ± 0.24 mm). The Dice Similarity Coefficient was 0.92, with a variation of 2%. Bland–Altman plots also demonstrated an agreement between 3DP and the surgical specimen with the distance of the resection margin (1.15 ± 1.52 mm). Professionals considered 3DP at a positive rate of 0.89 (95%CI; 0.73–0.95). According to student's distribution a higher success rate was reached with 3DP (median:0.9, IQR: 0.8–1) compared with CT/MRI or 3D digital imaging (P = 0.01). Conclusion3DP hepatic models present a good correlation compared with CT/MRI and surgical pathology and they are useful for education, understanding, and surgical planning, but does not necessarily affect the surgical outcome.

Spillover effects in the financial year cycle for Indian markets

PurposeThe stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.Design/methodology/approachThe oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.FindingsThe findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.Research limitations/implicationsThe study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.Originality/valueThe paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

Modeling cryptocurrencies volatility using GARCH models: a comparison based on Normal and Student's T-Error distribution

This study measures the volatility of cryptocurrency by utilizing the symmetric (GARCH 1,1) and asymmetric (EGARCH, TGARCH, PGARCH) model of GARCH family using a daily database designated in different digital monetary standards. The results for an explicit set of currencies for entire period provide evidence of volatile nature of cryptocurrency and in most of the cases, the PGARCH is a better-fitted model with student’s t distribution. The findings show positive shocks heavily affected conditional volatility as a contrast with negative stuns. Those additional analyses can be provided further support their findings and worthwhile information for economic thespians who are engrossed in adding cryptocurrency to their equity portfolios or are snooping about the capabilities of cryptocurrency as a financial asset.

Estimating the integral of the square of the probability density of a one-dimensional random variable

When choosing the optimal number of sampling intervals of the range of values of a one-dimensional random variable, it is established that the functional of the square of the probability density is a constant. The values of the constant are independent of the probability density parameters. The functional dependencies of the studied constant on the coeffi cient of antikurtosis of the distribution law of a random variable are determined. The analysis of the established dependences for families of lognormal probability densities, Student's distribution laws and families of probability densities with Gauss distribution is carried out. Based on the results obtained, a generalized model is formed between the studied constant and the antikurtosis coeffi cient. The generalized model does not depend on the type of probability density, but is determined by the estimation of the antikurtosis coeffi cient. On this basis, we develop a method for estimating the integral of the square of the probability density, which involves the following actions. The random variable interval and the antikurtosis coeffi cient are estimated from the initial sample. At known values of these estimates, the integral of the square of the probability density is calculated. The eff ectiveness of the proposed method is confi rmed by the results of computational experiments. The conditions of the computational experiment diff er signifi cantly from the information used in the synthesis of models of the dependence between the studied constant and the antikurtosis coeffi cient. The conditions of competence of the method of estimating the probability density square integral from the antikurtosis coeffi cient are established using the proposed models of the dependence of the studied constant on the conditions of the computational experiment.

Anthropometric Parameters in Kyrgyz Males of Various Somatotypes

The aimof research was to obtain data about “somatotypical profile” and some anthropometric parameters in young and adult males, the ethnic Kyrgyz.Material and methods.The study included 955 healthy young and adult males, ethnic Kyrgyzs, residents of Osh city and its suburbs. To assess their physical development the authors used a method of complex anthropometry and somatotyping. Morphometric processing of the obtained data was performed using statistical programs Microsoft Excel and package STATISTICA (v. 6.0). Differences between two compared values were estimated by Student's distribution at p<0.05.Results.The most common constitutional types in the Kyrgyz young and adult men appeared to be an abdominal (35–37%), muscular (25–27%) and indefinite (27–30%) constitutional types; thoracic somatotype (9–11%) being the rarest. The performed analysis demonstrated a tendency to increasing the diameter of the wrist and calf during the development from the youth into the 1st and then the 2nd period of adulthood; this was observed in the representatives of all somatotypes. The individual minimum and maximum values of the width of the shoulder girdle were higher in men of the muscular somatotype than in men of the thoracic somatotype regardless of their age; the same parameters had intermediate values in men of the abdominal and indefinite somatotypes.Conclusion.The obtained data on the distribution of different somatotypes in the Kyrgyz male population and certain anthropometric parameters in the representatives of different somatotypes will contribute to the design of the national standards of physical development; the fact being significant for practical health and theoretical medicine

Abstract P4-10-05: High risk for cardiovascular disease in postmenopausal breast cancer survivors

Abstract Background: The majority of women diagnosed with breast cancer are considered to have a good prognosis and can expect to survive the disease. Cardiovascular risk in postmenopausal women treated for breast cancer is higher than in women without cancer. Objective: To evaluate cardiovascular risk factors in postmenopausal breast cancer survivors, in comparison to postmenopausal women without breast cancer. Methods: In this cross-sectional study, 96 postmenopausal breast cancer survivors were compared to 192 postmenopausal women (controls), aged 45 to 75 years. The main group included women with amenorrhea > 12 months, aged ≥45 years, with a histological diagnosis of breast cancer, without metastatic disease and without established cardiovascular disease (CVD). The control group consisted of women with amenorrhea > 12 months, aged ≥45 years, without breast cancer and CVD. Groups were matched by age, time since menopause, and body mass index (BMI) in a ration of 1 case to 2 controls, according to sample calculation, with a minimum of 92 breast cancer survivors. Clinical and anthropometric data (BMI and waist circumference) were collected by interview and physical examination. For biochemical analysis, total cholesterol, HLD, LDL, triglycerides, glucose and insulin levels were measured. Women presenting with three or more of the following criteria were diagnosed with metabolic syndrome (MetS): waist circumference (WC)> 88 cm; TG≥ 150 mg/dL; HDL cholesterol <50 mg/dL; blood pressure ≥ 130/85 mmHg; glucose ≥ 100 mg/dL. For measurement of plasma HSP 60 and 70 concentrations, immunoassays were used (ELISA test). Atherosclerotic disease was determined by intima-media thickness (IMT> 1 mm) of the carotid arteries and / or by the presence of atheromatous plaque, assessed by carotid artery ultrasound (scanner duplex). For statistical analysis, Student's t-test, Gamma Distribution (asymmetric variables), Chi-Square Test and Logistic Regression (odds ratio-OR) were used. Results: Breast cancer survivors had high mean systolic and diastolic blood pressure (p <0.001), and mean blood triglycerides and glucose, above desirable levels (p <0.05). Breast cancer patients had higher HSP60 levels and lower HSP70 levels than controls (p <0.05). Atheromatous plaque occurred more frequently in breast cancer survivors than in controls (19.8% vs. 9.4% respectively) (p <0.05). Analysis of risk adjusted for age, time since menopause and BMI, showed that women treated for breast cancer have a significantly higher risk of MetS (OR = 4.21, 95% CI 2.28-7.76), atheromatous plaque (OR = 2.61, 95% CI 1.19-5.72), diabetes (OR=4.42; 95%CI 1.86- 10.49), hypertriglyceridemia (OR = 2.32, 95% CI 1.33-4.0) and increased waist circumference (OR = 11.22, 95% CI 4.0 - 31.65) than women without breast cancer. Conclusion: Postmenopausal breast cancer survivors are at higher risk for metabolic syndrome, diabetes, atherosclerotic disease, hypertriglyceridemia and abdominal obesity (major risk factors for cardiovascular disease), in comparison to postmenopausal women without breast cancer. Citation Format: Buttros DdAB, Branco MT, Orsatti CL, Nahas-Neto J, Nahas EAP. High risk for cardiovascular disease in postmenopausal breast cancer survivors [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-10-05.

Probabilistic n/γ discrimination with robustness against outliers for use in neutron profile monitors.

A method to stochastically discriminate neutron and γ-ray signals measured with a stilbene organic scintillator is proposed. Each pulse signal was stochastically categorized into two groups: neutron and γ-ray. In previous work, the Expectation Maximization (EM) algorithm was used with the assumption that the measured data followed a Gaussian mixture distribution. It was shown that probabilistic discrimination between these groups is possible. Moreover, by setting the initial parameters for the Gaussian mixture distribution with a k-means algorithm, the possibility of automatic discrimination was demonstrated. In this study, the Student's t-mixture distribution was used as a probabilistic distribution with the EM algorithm to improve the robustness against the effect of outliers caused by pileup of the signals. To validate the proposed method, the figures of merit (FOMs) were compared for the EM algorithm assuming a t-mixture distribution and a Gaussian mixture distribution. The t-mixture distribution resulted in an improvement of the FOMs compared with the Gaussian mixture distribution. The proposed data processing technique is a promising tool not only for neutron and γ-ray discrimination in fusion experiments but also in other fields, for example, homeland security, cancer therapy with high energy particles, nuclear reactor decommissioning, pattern recognition, and so on.

Simultaneous Bayesian Clustering and Feature Selection Through Student's ${t}$ Mixtures Model.

In this paper, we proposed a generative model for feature selection under the unsupervised learning context. The model assumes that data are independently and identically sampled from a finite mixture of Student's distributions, which can reduce the sensitiveness to outliers. Latent random variables that represent the features' salience are included in the model for the indication of the relevance of features. As a result, the model is expected to simultaneously realize clustering, feature selection, and outlier detection. Inference is carried out by a tree-structured variational Bayes algorithm. Full Bayesian treatment is adopted in the model to realize automatic model selection. Controlled experimental studies showed that the developed model is capable of modeling the data set with outliers accurately. Furthermore, experiment results showed that the developed algorithm compares favorably against existing unsupervised probability model-based Bayesian feature selection algorithms on artificial and real data sets. Moreover, the application of the developed algorithm on real leukemia gene expression data indicated that it is able to identify the discriminating genes successfully.

A discussion on the innovation distribution of the Markov regime-switching GARCH model

The Markov Regime-Switching Generalized autoregressive conditional heteroskedastic (MRS-GARCH) model is a widely used approach to model the financial volatility with potential structural breaks. The original innovation of the MRS-GARCH model is assumed to follow the Normal distribution, which cannot accommodate fat-tailed properties commonly existing in financial time series. Many existing studies point out that this problem can lead to inconsistent estimates. To overcome it, the Student's t-distribution and General Error Distribution (GED) are the two most popular alternatives. However, a recent study points out that the Student's t-distribution lacks stability. Also, it incorporates the α-stable distribution in the GARCH-type model. The issue of the α-stable distribution is that its second moment does not exist. To solve this problem, the tempered stable distribution, which retains most characteristics of the α-stable distribution and has defined moments, is a natural candidate. In this paper, we conduct a series of simulation studies to demonstrate that MRS-GARCH model with tempered stable distribution consistently outperform that with Student's t-distribution and GED. Our empirical study on the S&P 500 daily return volatility also generates robust results. Therefore, we argue that the tempered stable distribution could be a widely useful tool for modeling the financial volatility in general contexts with a MRS-GARCH-type specification.

Box–Cox realized asymmetric stochastic volatility models with generalized Student's t-error distributions

ABSTRACTThis study proposes a class of non-linear realized stochastic volatility (SV) model by applying the Box–Cox (BC) transformation, instead of the logarithmic transformation, to the realized estimator. The non-Gaussian distributions such as Student's t, non-central Student's t, and generalized hyperbolic skew Student's t-distributions are applied to accommodate heavy-tailedness and skewness in returns. The proposed models are fitted to daily returns and realized kernel of six stocks: SP500, FTSE100, Nikkei225, Nasdaq100, DAX, and DJIA using an Markov chain Monte Carlo Bayesian method, in which the Hamiltonian Monte Carlo (HMC) algorithm updates BC parameter and the Riemann manifold HMC algorithm updates latent variables and other parameters that are unable to be sampled directly. Empirical studies provide evidence against both the logarithmic transformation and raw versions of realized SV model.

On double hysteretic heteroskedastic model

ABSTRACTThis paper proposes a hysteretic autoregressive model with GARCH specification and a skew Student's t-error distribution for financial time series. With an integrated hysteresis zone, this model allows both the conditional mean and conditional volatility switching in a regime to be delayed when the hysteresis variable lies in a hysteresis zone. We perform Bayesian estimation via an adaptive Markov Chain Monte Carlo sampling scheme. The proposed Bayesian method allows simultaneous inferences for all unknown parameters, including threshold values and a delay parameter. To implement model selection, we propose a numerical approximation of the marginal likelihoods to posterior odds. The proposed methodology is illustrated using simulation studies and two major Asia stock basis series. We conduct a model comparison for variant hysteresis and threshold GARCH models based on the posterior odds ratios, finding strong evidence of the hysteretic effect and some asymmetric heavy-tailness. Versus multi-regime threshold GARCH models, this new collection of models is more suitable to describe real data sets. Finally, we employ Bayesian forecasting methods in a Value-at-Risk study of the return series.

Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function.

Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT). Two biomedical images and a standard digital image were considered for experiments. The original images were corrupted with additive Gaussian noise with three different levels. Based on peak-signal-to-noise ratio, the experimental results show that PDE, LCDP, NLCDP, and DWT all perform better in the tBEMD than in the classical BEMD domain. It is also found that tBEMD is faster than classical BEMD when the noise level is low. When it is high, the computational cost in terms of processing time is similar. The effectiveness of the presented approach makes it promising for clinical applications.