Coefficient Of Skewness Research Articles

Role of Lake Morphometric and Environmental Drivers of Ice Cover Formation and Occurrence on Temperate Lakes: A Case Study from the Eastern Baltic Lakeland, Poland

The presence of ice cover on temperate lakes is a crucial factor in determining the functioning of these ecosystems. The isolation of water from atmospheric influences significantly alters physical, chemical, and biological processes, and the intensity of this impact depends on the duration of the ice cover. This study analyzed the basic parameters of ice cover on several dozen lakes in Northeastern Poland. The aim of this study is to investigate the influence of morphometric parameters, alongside environmental factors, on the variation of ice cover characteristics in lakes located within the Eastern Baltic Lakeland. Characterization of ice conditions in the analyzed lakes was based on basic statistics such as minimum and maximum values, mean, standard deviation, coefficients of variation, skewness, and kurtosis. Given that the dataset contains variables describing ice phenomena in the studied lakes and data describing location, morphometric parameters, and land cover directly adjacent to the lake (treated as independent variables), a method of Spearman’s rank correlations and constrained ordination method were decided upon. Despite the relatively small study area, significant variability was observed, with average differences as follows: 26 days for the onset of ice cover, 17 days for the end date, 15 cm for ice thickness, and a 30-day difference in the average duration of ice cover. Key factors included parameters such as lake volume, average depth, and land use (urbanized and agricultural areas). Understanding parameters such as the onset and end of ice cover is essential for lake ecosystems, both from an ecological and economic perspective. This knowledge is crucial for interpreting the behavior of living organisms, water quality, and economic considerations.

Parallel high-speed random bit generation based on wideband chaotic microcomb and wavelet high-pass filtering.

We propose and experimentally demonstrate a parallel ultra-fast random bit generation (RBG) scheme based on wideband chaotic microcomb, which utilizes a phase modulation and dispersive component broadening spectrum. The effective bandwidth of each comb tooth is increased by over 10-fold. Wavelet high-pass filtering (WHPF) is employed to make the probability density functions (PDFs) of the chaotic signal's amplitude unbiased, achieving high symmetry with a skewness coefficient |S| of 0.0026, and the RBG rate of a single channel reaches 200 Gbps. Furthermore, the autocorrelation properties of the random sequences from each comb tooth and the cross-correlation properties between different comb teeth are analyzed, confirming both true randomness and orthogonality. This scheme can simultaneously generate dozens of wideband chaotic combs in the wavelength range of 1500-1600 nm.

A-164 Establishment and Verification of Reference Intervals for Red Blood Cell Series and Total Leukocytes in Whole Blood Counts of Chilean Adults Using refineR and Multi Criteria Expectation Maximization Methods

Abstract Background This study aimed to define RIs for red blood cell series and total leukocytes in the complete blood count, in adult patients. Methods We used a sample of 822 patients aged 18 to 60 years, both female and male. The study focused on the red blood cell series (RBC Count, hemoglobin, Hematocrit, MCV, MCH, MCHC and RDW) and total leukocytes. RIs and respective 90% confidence intervals were estimated using the refineR method and a multi-criteria method combining the Expectation Maximization algorithm, mode, antimode analysis, an adapted Horn algorithm (5 cycles of Tukey's test with intermediate trimming and 2 cycles of Tukey's test with Box-Cox transformed data if applicable after analyzing Lambda, kurtosis, and skewness coefficients) and distribution truncation based on clusters and antimode. The RIs estimates using refineR and multi-criteria methods were compared to the manufacturer's specifications (Mindray). Flagging Rates were used to validate the method that estimates the most representative RI for the population served by the laboratory. Equivalence Interval and Bias Ratio were utilized to assess the practical relevance of sex-based partitioning. Results The method based on the multicriteria approach demonstrated the best performance, with flagging rates showing results lower than 4.7% and higher than 0.9% after the truncation of the results distribution. In general, the refineR method generated very narrow RIs, while the manufacturer's specifications resulted in very wide or shifted RIs, which do not adequately represent the data distribution profile. It was identified the need to partition by sex for the parameters Red Blood Cell Count, Hemoglobin, and Hematocrit, which is in agreement with the manufacturer's specifications. Conclusions The estimated RIs better represent the laboratory-served population, highlighting the importance of adhering to ISO 15189 normative requirements. This involves verifying and/or determining RIs to provide representative information of the served population, thereby aiding in medical decision-making.

A New Retest-Stable Tortuosity Metric for Retinal Vessel Analyses.

Existing retinal vessel tortuosity metrics lack standardization and retest reliability, hindering their clinical utility. Our study addresses this gap by introducing a novel metric, coined as the "vascular curvature index" (VCI), to enhance accuracy and consistency in biomarkers associated with medical conditions. We assess VCI's performance in terms of retest reliability in healthy subjects to transform early detection and monitoring approaches for various diseases. We recruited 44 patients for a single-session study, capturing fundus images before and after a five-minute break. Using AutoMorph, we generated vessel segmentation maps and evaluated retest reliability using multiple tortuosity metrics. The VCI was introduced and statistically compared to existing metrics. We performed a paired one-sided t-test to test for significantly improved retest reliability of our newly proposed metric. We analyzed distribution histograms, Fisher-Pearson coefficient of skewness, and correlation matrices for further insights. VCI is the most retest-reliable metric, statistically surpassing other curvature metrics, except inverse spherical radius tortuosity. With a somewhat negatively distributed pattern (coefficient of skewness of -0.52), VCI exhibits the strongest correlation with the second most retest-reliable metric; inverse-radius-tortuosity (Pearson and Spearman correlation of 0.7 and 0.72, respectively). Its correlation with angle-tortuosity is lower (Pearson and Spearman correlation of 0.05 and 0.07, respectively). The VCI emerges as a highly retest-reliable metric with a relatively normal distribution in healthy patients. Further investigation is warranted to evaluate its clinical performance in real-world applications, potentially influencing proactive healthcare interventions and personalized treatment decision-making.

Unraveling the complex dynamics of temporal moments for multispecies contaminant transport in saturated porous media: A global sensitivity analysis approach

In this study, we focus on the application of global sensitivity analysis (GSA) to gain insight into the influence of model parameters on the outputs of multispecies contaminant transport model. In this context, employing GSA methods (Morris and FAST) allows us to comprehend the relative significance of each input parameter in the model. We specifically analyzed the effects of model parameters on the concentration breakthrough curves and associated temporal moments of concentration of contaminant species. The spatial variability of sensitivity indexes from Morris and FAST methods for parent and daughter contaminant species is also investigated. We represent uncertainties of longitudinal dispersivity, porosity, first-order degradation constants of parent and daughter contaminant species, and sorption distribution coefficient of parent species into the coupled multispecies contaminant transport model and GSA framework. Our results show a complex interplay between different model parameters and their varying influences on different output metrics. The ranking of parameters based on GSA shows that (i) the contaminant mass recovery of final daughter species of three species chain reaction is governed by the first-order degradation coefficient of parent and first daughter species; (ii) dispersivity and porosity are critical parameters that govern the mean residence time and variance of breakthrough curve of final daughter species; (iii) the coefficient of skewness is more sensitive to dispersivity, sorption distribution coefficient, and first-order degradation coefficient of parent species as compared to other model parameters. The variation in the sensitivity indices among contaminant species and with travel distance is observed. This study highlights the significance of global sensitivity analysis when simulating the multiple species contaminants in saturated porous media.

Geopolitical risk and energy price crash risk

This paper explores the link between geopolitical risks and energy prices crash risk. Studying energy price crashes is important given the sharp fall in oil prices in 2008 and 2014. The analysis focuses on three energy markets: natural gas, oil, and coal, while it employs two measures: the negative coefficient of skewness and the down-to-up volatility, to construct proxies for crash risks. The period of examination is January 2000 to December 2023, whereas that for coal is January 2010 to December 2023. The study employs a modified version of the smooth transition autoregressive model. The results show that, within the modelling framework, coal and oil crash risks are driven by the cyclical behavior of geopolitical acts, whereas natural gas crash risks by geopolitical threats. Causality tests confirm the prediction that geopolitical tensions cause crash risks in energy markets. The results also confirm that the “Economic Activity Channel” is only valid for energy markets driven by geopolitical threats. Energy market regulators should be concerned about crash risks, given that the energy supply shows cyclical boom and bust cycles in prices and production. Crash risks could also potentially cause a fall in investments required to enhance energy efficiency.

A class of infinite number of unbiased estimators using weighted squared distance for two-deck randomized response model

ABSTRACT We develop a collection of unbiased estimators for the proportion of a population bearing a sensitive characteristic using a randomized response technique with two decks of cards for any choice of weights. The efficiency of the estimator depends on the weights, and we demonstrate how to find an optimal choice. The coefficients of skewness and kurtosis are introduced. We support our findings with a simulation study that models a real survey dataset. We suggest that a careful choice of such weights can also lead to all estimates of proportion lying between [0, 1]. In addition, we illustrate the use of the estimators in a recent study that estimates the proportion of students, 18 years and over, who had returned to the campus and tested positive for COVID-19.

A New Multimodal Modification of the Skew Family of Distributions: Properties and Applications to Medical and Environmental Data

The skew distribution has the characteristic of appropriately modeling asymmetric unimodal data. However, in practice, there are several cases in which the data present more than one mode. In the literature, it is possible to find a large number of authors who have studied extensions based on the skew distribution to model this type of data. In this article, a new family is introduced, consisting of a multimodal modification to the family of skew distributions. Using the methodology of the weighted version of a function, we perform the product of the density function of a family of skew distributions with a polynomial of degree 4, thus obtaining a more flexible model that allows modeling data sets, whose distribution contains at most three modes. The density function, some properties, moments, skewness coefficients, and kurtosis of this new family are presented. This study focuses on the particular cases of skew-normal and Laplace distributions, although it can be applied to any other distribution. A simulation study was carried out, to study the behavior of the model parameter estimates. Illustrations with real data, referring to medicine and environmental data, show the practical performance of the proposed model in the two particular cases presented.

Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19.

Predicting the endemic/epidemic transition during the temporal evolution of a contagious disease. Indicators for detecting the transition endemic/epidemic, with four scalars to be compared, are calculated from the daily reported news cases: coefficient of variation, skewness, kurtosis, and entropy. The indicators selected are related to the shape of the empirical distribution of the new cases observed over 14 days. This duration has been chosen to smooth out the effect of weekends when fewer new cases are registered. For finding a forecasting variable, we have used the principal component analysis (PCA), whose first principal component (a linear combination of the selected indicators) explains a large part of the observed variance and can then be used as a predictor of the phenomenon studied (here the occurrence of an epidemic wave). A score has been built from the four proposed indicators using the PCA, which allows an acceptable level of forecasting performance by giving a realistic retro-predicted date for the rupture of the stationary endemic model corresponding to the entrance in the epidemic exponential growth phase. This score is applied to the retro-prediction of the limits of the different phases of the COVID-19 outbreak in successive endemic/epidemic transitions for three countries, France, India, and Japan. We provided a new forecasting method for predicting an epidemic wave occurring after an endemic phase for a contagious disease.

Models and methods for RZ-signals distinction in non-Gaussian noise for information-measurement systems

Abstract Polynomial Decision Rules (DR) for the problems of discrete RZ (Return-to-Zero) signal distinction in asymmetric-excess non-Gaussian noise are proposed. A new approach is proposed, which is based on the use of a modified moment quality criterion of statistical hypothesis testing and the application of higher-order statistics to describe the characteristics of non-Gaussian noise. Simulation of the DR with different parameters of the signal and noise was carried out. It is shown that taking into account the coefficients of skewness and kurtosis of the non-Gaussian noise the efficiency of signal distinction increases with non-linear processing DR compared to known results, which are optimal for the Gaussian noise model. The conducted studies demonstrate a reduction in false decisions in the processing of RZ signals when considering the coefficients of skewness and kurtosis of non-Gaussian noise. Such an increase in efficiency can exceed twofold, depending on the noise parameters. It is shown that the efficiency of the proposed approach is much higher for small SNR (Signal-to-Noise Ratio) values, for example, less than 1.

Dispersion of a non-uniform solute slug in pulsatile viscoelastic fluid flow

Solute transport in pulsatile viscoelastic fluid flow is relevant in nutrient transport and drug delivery in blood flow. Previous studies have extensively analyzed the effect of the shear-thinning nature of the blood but neglected the elastic property. The present study aims to fill this lacuna by analyzing the role of blood viscoelasticity on solute transport. To accomplish this, we study solute transport for a non-uniformly distributed solute slug in the pulsatile flow of an Oldroyd-B fluid through a tube in the presence of wall absorption. We employ Gill's procedure and Aris' method of moments to compute the transport coefficients Km(t) (m≤4). We also numerically solve the species transport equation using a finite difference scheme to directly determine local solute concentration C(t,z,r). Consistent results for a non-viscoelastic fluid predict a negative convection coefficient K1 and a positive effective diffusivity K2 for realistic values of the parameters. However, the present analysis predicts positive K1 and negative K2 for small tubes due to flow reversal caused by the fluid elasticity. For high Λ1, the amplitude of oscillation for K1 and K2 exhibits scaling K1∼Λ11.5 and K2∼Λ12 indicating an enhancement in the dispersion due to fluid elasticity, where Λ1 is the dimensionless relaxation time. The analysis of the skewness and (excess) kurtosis coefficients reveals inconsistency in previous studies on Newtonian fluids. Thus, we present consistent results not only for a viscoelastic fluid but also for a Newtonian fluid subjected to a pulsatile pressure gradient. In addition, the solute dispersion is significantly influenced by the non-uniformity of a solute slug. As the radius of a slug increases, solute dispersion reduces in short and moderate times; however, at large times, it is independent of the radius of a slug.

Examination of Physical Activity Attitudes and Anti-Social Behaviors of Middle School Students

The aim of this study is to examine the physical activity attitudes and antisocial behaviors of middle school students. The research group of the study consisted of a total of 1046 students, 509 boys and 537 girls, aged between 11 and 13, studying in 5-6-7-8th grades in three secondary schools affiliated to the Ministry of National Education in Esenyurt district of Istanbul province in the 2022-2023 academic year. "Personal Information Form", "Physical Activity Attitude Scale for Secondary School Students" and "Antisocial Behavior Scale for Secondary School Students" were used as data collection tools. SPSS 25.0 statistical package program was used to evaluate the data and to find calculated values. Data were summarized by giving mean and standard deviations. Whether the data were normally distributed was checked with the range of Kurtosis and Skewness coefficients and it was determined that the data were normally distributed. Since the data were normally distributed, independent group t-test was used for pairwise cluster comparisons and One-Way Analysis of Variance (ANOVA) was used for more than two cluster comparisons. Tukey HSD multiple comparison test was used to determine the source of significant differences as a result of ANOVA. The Pearson Product Moment Correlation Coefficient was used to determine whether there was a significant relationship between the mean scores of the Physical Activity Attitude Scale and Antisocial Behavior Scale sub-dimensions of the participants who participated in the study. Within the scope of this study, the scale reliability coefficient was calculated as .77. The significance level was taken as 0.05. As a result of the analysis, no significant difference was found between the physical activity attitudes of secondary school students and family income level and mother's working status (p>0.05), while a significant difference was found between gender, grade level, mother's education level and father's education level variables (p<0.05). While no significant difference was observed between antisocial behaviors and family income level (p>0.05), a significant difference was found between other demographic variables (p<0.05). In addition, while no significant difference was found between the self-confidence sub-dimension of the Physical Activity Attitude Scale and the inappropriate behavior sub-dimension of the Antisocial Behavior Scale (p>0.01), significant differences were found between the other sub-dimensions (p<0.01) As a result, it can be said that students' physical activity attitudes affect antisocial behaviors.

Statistical characteristics and correlations of morphometric traits of Asparagus officinalis L. fruits in the M.M. Gryshko National Botanical Garden of the NAS of Ukraine

The current research was conducted in 2020–2023 on the botanical-geographical plot “Steppes of Ukraine” at the M.M. Gryshko National Botanical Garden of the NAS of Ukraine and was focused on ripe fruits of Asparagus officinalis. This study aimed to find out the main statistical characteristics of morphometric traits of A. officinalis fruits: fruit diameter, number of locules per fruit, number of seeds per fruit, number of seeds per locule, and to confirm or deny the hypothesis of positive pairwise correlations of the first three traits. Eighteen combinations of four morphometric traits were found in the A. officinalis fruits. For each morphometric feature of A. officinalis fruits, ten statistical characteristics were determined: arithmetic mean, median, mode, range of variation, oscillation coefficient, standard deviation, coefficient of variation, standard error of the mean, coefficient of skewness, and coefficient of kurtosis. The coefficient of variation showed that the number of seeds per fruit is the most variable of the studied morphometric traits of A. officinalis fruits, and the fruit diameter is the least variable. According to the comprehensive assessment of the coefficients of skewness and kurtosis, the distributions of the number of seeds per fruit and the diameter of the fruits are closest to the normal distribution. It was established that the correlations between pairs of such traits as fruit diameter and number of locules per fruit, fruit diameter and number of seeds per fruit, number of locules per fruit and number of seeds per fruit are positive, direct, and strong. Thus, considering the economic feasibility, it is more practical to collect A. officinalis fruits of a larger size to obtain a larger number of seeds. The results of this work are of interest for the morphology and biometrics of A. officinalis and its cultivation.

A methodology for ranking of critical links in transportation networks based on criticality score distributions

Criticality of transportation networks links is calculated through utilizing criticality metric(s) for various disruption scenarios that generally include one link removal/degradation at a time or simultaneous removal/degradation of multiple links. As also shown in this paper, single link removals do not reveal the actual criticality of link(s) due to network dependencies. Multiple simultaneous link removal scenarios can capture the network interactions; however, the results relate to the criticality ranking of scenarios than the links, e.g., the links in scenario-1 are more critical than the links in scenario-2. This paper addressed the need for a methodology that provides individual link criticality rankings based on failure scenarios. The proposed approach utilizes the distribution of the criticality scores for each link based on running traffic assignment for each failure scenario, e.g., the distribution for link #X is composed of the criticality scores of all link failure combinations that include link #X. The criticality ranking for each link is identified based on its criticality distribution's mean, coefficient of variation and skewness. The use of the proposed approach is illustrated on test networks to show that it provides robust link criticality rankings that accounts for network interactions across varying scales of link failure scenarios.

Evaluation of Fracture Strength of Roots to Determine the Duration Limit of Activated Ethylene Diamine Tetraacetic Acid Irrigation for Intracanal Calcium Hydroxide Dressing Removal In Vitro

This study evaluated the effect of the use of EDTA solution at various intervals to remove intracanal calcium hydroxide medication on fracture resistance. One hundred and one extracted lower premolar teeth were randomly allocated into one control group (n = 5), two main experimental groups (CH(+), with calcium hydroxide dressing, and CH(−), without calcium hydroxide dressing), with four subgroups (EDTA intervals: 1 min, 3 min, 5 min, 10 min, n = 12). Ready-to-use injectable calcium hydroxide was used for the samples in the four CH(+) subgroups, and the samples were kept in an incubator at 37 °C and 100% humidity for seven days. All samples (CH(+), CH(−)) were irrigated with 17% EDTA, which was accompanied by activation with EndoActivator for 1, 3, 5, and 10 min, and then rinsed with 2.5% NaOCl in a standardized manner. The obturation was conducted using the warm obturation technique; experiments were conducted with a universal testing device, and their fracture strength was recorded. Compliance with the normal distribution was examined with ±2 skewness coefficients. Two-way ANOVA, Tukey’s, one-way ANOVA, and Dunnett’s tests were used for statistics (p < 0.050). A statistically significant difference was found between the mean values of the force between the groups (p = 0.009). The mean strength of the tooth at fracture that CH(−) was 401.7, CH(+) was 335.35. There was no statistically significant difference between the mean values of the forces according to time intervals (p = 0.387). While there was no significant difference among the experimental groups (p = 0.229), the mean fracture strength of the negative control group was found to be significantly higher (p < 0.001). The highest fracture resistance was observed in the nonprepared group. Irrigation with EDTA for up to 5 min showed more acceptable fracture resistance results than the other groups. Using calcium hydroxide significantly and negatively affected the root strength.

Segregation flow behavior of polydisperse particle mixture with skewed distribution in a rotating drum

Industrial particle mixtures are naturally polydisperse due to the variations in size distribution during mineral processing, which makes it necessary to investigate segregation behaviors of polydisperse particle mixtures. In this work, five groups of polydisperse particle mixtures were defined separately by different skewed distributions. Based on discrete element method, the polydisperse particle segregation behaviors of in rotary drums were simulated. The results indicate that in the polydisperse particle system with normal distribution, larger particles tend to accumulate towards the both sides, while smaller particles tend to be in the middle of the drum. In this case, the medium-sized particles are uniformly distributed throughout the system. However, once the polydisperse particle mixture transitions from the coarse particle to the fine particle system, the size of medium particles exhibiting uniform distribution characteristics decreases with an increase in the skewness coefficient. Moreover, the smaller the skewness coefficient of the polydisperse particles, the slower the initial particle segregation rate, and the lower the steady particle segregation degree. Furthermore, whether the drum length to diameter (L / D) ratio or drum filling rate increases, the particle segregation rate in initial stage and the particle segregation degree in steady stage are subsequently significantly reduced, while the size of medium particles with uniform distribution characteristics remains essentially constant.

Nomogram of magnetic resonance imaging (MRI) histogram analysis to predict telomerase reverse transcriptase promoter mutation status in glioblastoma.

Telomerase reverse transcriptase promoter (pTERT) status is a strong biomarker to diagnose and predict the prognosis of glioblastoma (GBM). In this study, we explored the predictive value of preoperative magnetic resonance imaging (MRI) histogram analysis in the form of nomogram for evaluating pTERT mutation status in GBM. The clinical and imaging data of 181 patients with GBM at our hospital between November 2018 and April 2023 were retrospectively assessed. We used the molecular sequencing results to classify the datasets into pTERT mutations (C228T and C250T) and pTERT-wildtype groups. FireVoxel software was used to extract preoperative T1-weighted contrast-enhanced (T1C) histogram parameters of GBM patients. The T1C histogram parameters were compared between groups. Univariate and multivariate logistic regression analyses were used to construct the nomogram, and the predictive efficacy of model was evaluated using calibration and decision curves. Receiver operating characteristic curve was used to assess model performance. Patient age and percentage of unenhanced tumor area showed statistically significant differences between the pTERT mutation and pTERT-wildtype groups (P<0.001). Among the T1C histogram features, the maximum, standard deviation (SD), variance, coefficient of variation (CV), skewness, 5th, 10th, 25th, 95th and 99th percentiles were statistically significantly different between groups (P=0.000-0.040). Multivariate logistic regression analysis showed that age, percentage of unenhanced tumor area, SD and CV were independent risk factors for predicting pTERT mutation status in GBM patients. The logistic regression model based on these four features showed a better sample predictive performance, and the area under the curve (AUC) [95% confidence interval (CI)], accuracy, sensitivity, specificity were 0.842 (0.767-0.917), 0.796, 0.820, and 0.729, respectively. There were no significant differences in the T1C histogram parameters between the C228T and C250T groups (P=0.055-0.854). T1C histogram parameters can be used to evaluate pTERT mutations status in GBM. A nomogram based on conventional MRI features and T1C histogram parameters is a reliable tool for the pTERT mutation status, allowing for non-invasive radiological prediction before surgery.

The effect of educational situations according to the dimension (sequential - total) in learning the net lift

The aim of the research is to identify the learners’ preferred educational style according to the dimension (sequential and total), by designing educational situations according to the dimension (sequential and total) in learning the deadlift. The researcher assumed that there were statistically significant differences between the results of the pre- and post-tests for the two experimental research groups in learning the deadlift. For the sake of post-testing for students, the researcher used the experimental method to suit the nature of the research. The research population was intentionally selected from first-level students in the College of Physical Education and Sports Sciences - Samarra University for the academic year (2023-2024), as it reached (97) male and female students distributed into three divisions. Study section: Section (A) numbered (33), Section (B) numbered (32), and Section (C) numbered (32). As for the research sample, it was identified and chosen randomly. The first experimental group was represented by Section (B), which adopted the dimension ( Sequential) and its number is (15) students who tend to the sequential style according to the Felder and Silverman scale. As for the second experimental group, it was represented by Section (C) and its number is (15) students, which adopted the (total) dimension of those who tend to the holistic style according to the Felder and Silverman scale. Thus, it reached The proportion of the research sample was (30.927%) from members of the overall population, and the researcher used the assessment of the apparent form of performance as a means of collecting data. The main research experiment lasted (5) weeks with two educational units during one week for each group, and the statistical means were (arithmetic mean, standard deviation, Percentage, mode, skewness coefficient, simple correlation coefficient, T-test for related samples - for independent samples. The results showed that the educational situations prepared according to the dimension (sequential - total) have a positive impact on learning the movement of the net for the two experimental research groups under study, and it was recommended The researcher uses educational situations that take into account the different learning styles of learners because this has a positive impact on learning outcomes.

Laboratory investigations of wave-induced transport of plastic debris over a rippled bottom

Laboratory experiments are conducted in a wave flume to investigate the effect of water waves on the transport of plastic pellets over a rippled bottom. The horizontal velocities of plastic debris are analyzed over the rippled bottom for different wave conditions and plastic elements with different properties. Laboratory investigations determined the characteristic transport patterns of wave-induced plastic debris with a density of ∼2.0g/cm3 moving along the rippled bottom. In the first, swing-type motion, the grains move only in the ripple trough with velocities lower than 0.10 m/s. For sliding-type movement, the grains move along the entire rippled surface with velocities in the range of 0.10–0.13 m/s. For higher velocities in the range of 0.15–0.20 m/s, a saltation-type motion becomes dominant. The results show that plastic grains may move up to 2–3 cm above the ripple crest depending on hydrodynamic conditions. The analysis shows that for velocity-skewed flows, sliding-type motion and onshore transport dominate. For acceleration-skewed flows, saltation-type motion and offshore transport dominate, which is attributed to higher boundary layer thickness and phase lag effects. The analysis of the relationship between the particle Reynolds number and the thickness of the turbulent boundary layer reveals that for values of Rep≥1000 and a boundary layer thicknessδ≥50 mm saltation-type motion becomes dominant. The direction of transport is affected not only by the density of the sediment and the wave skewness coefficients but also by the dimensions of the bottom ripples. The laboratory investigations also provide insight into the hydrodynamic conditions affecting the transport of plastic debris along the bottom covered with ripples in oscillating nonlinear water flows.

Study of Variations of Citation-based Parameters for Selected Indian Science Journals

The ratio of the total number of citations to the total number of cited papers was predicted as a constant by Garfield. But, later he observed the changing nature of this constant over time. Scientometricians called it the ‘Garfield Ratio’ rather than the ‘Garfield Constant’. ‘Garfield Ratio’ is a well-known citation-based parameter that indicates the average citation per cited article. However, Garfield still pointed out that behind this ratio some deeper regularity may be found. In this paper, an analysis of this indicator, the Garfield Ratio (GR)Y is attempted for twelve prominent Indian science journals from 2009 to 2020. Two other formulae for Garfield Ratio are introduced here, i.e. Modified Garfield Ratio for the year ‘Y’ (GRM)Y and Time-Normalised Garfield Ratio for the year ‘Y’ (GRN)Y. The year-wise analysis of these two Garfield Ratios, i.e., (GRM)Y and (GRN)Y for the twelve journals over twelve years is also carried out. The temporal variations of (GR)Y, (GRM)Y and (GRN)Y are interpreted by seven statistical parameters, i.e. mean, median, standard deviation, range, coefficient of variation, skewness and kurtosis. The temporal variations of (GR)Y for twelve journals are viewed by regression analysis to find out the best-fit function.