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This paper deals with the power supply queuing model M/M/∞. In this paper, we analyze total cost of power supply queuing model with infinite servers. We develop a total cost function and suject it to optimization, which consequently gives us non-linear equations. These non-linear equations are further solved by using Newton-Raphson method with the help of R-software. We also present analysis of sensitivity, tables and graphs of the model to exhibit the comprehensive interpretation of the same. [ABSTRACT FROM AUTHOR]

Natural catastrophes have a tremendous influence on the environment and our economy, which has raised significant concerns and spurred scientific research. Several studies have been done to model the economic losses brought on by natural disasters. In this article, we primarily concentrate on examining the distributions of economic losses resulting from big catastrophes including wildfires, earthquakes, droughts, volcanic eruptions, and harsh weather. We recommend utilizing five well-known statistical distributions, including the Weibull, Log-logistics, Gamma, Generalized Pareto, and Lognormal distributions since we observe the skewed forms of the empirical distributions. We employ the maximum likelihood technique for each distribution for the available data sets in order to estimate the distributions. The parameter estimations are numerically computed using the PSO method. We select the distribution that best fits the economic losses using the Akaike Information Criterion and Kolmogorov-Smirnov statistics. We discovered that the Log-logistic distribution is the distribution that fits the total economic losses caused by all-natural disasters the best. [ABSTRACT FROM AUTHOR]

Histological aberration is a common phenomenon in aquatic animals exposed to pollution. Pollution also influences the hormonal condition of an organism, including the green mussel Perna viridis. In the present study, degeneration and lyses were studied in the females' oocytes of P. viridis and the 17-β-Estradiol was measured in the gonads of female and male individuals. The damage analysis was carried out and the aberration level in the tissue was measured by image processing analysis using R-Software. The concentration of 17-β-Estradiol in the gonads was measured by the ELISA method. Histopathological aberration and 17-β-Estradiol concentration in the female gonad tissue of P. viridis were detected in the samples with variable quantities. The high value of 17-β-Estradiol in males cultured in the industrial city of Indonesia indicated that this mussel species experienced hormonal imbalances and represent a threat with regard to food safety.

In this academic work a comparison between a Bayesian-Structural Equation Modelling (B-SEM) and a Partial Least Squares-Structural Equation Modelling (PLS-SEM) on a relationship amongst self-directed learning readiness (SDLR), E-learning readiness, and learning motivation of undergraduate students throughout the outbreak of Covid-19 is studied. The B-SEM is built using prior distribution i.e., inverse-Gamma, inverse-Wishart, and normal distribution on specific parameters of the model with 19000 iterations on Markov Chain Monte Carlo (MCMC) algorithm. Whereas the PLS-SEM is established using Ordinary Least Squares (OLS) method, PLS algorithm with 300 iterations, and 5000 subsamples on bootstrapping. The objective of this study is to get the most compatible model which representsthe relationship between three latent variables in this study. Schwarz’s Bayesian Information Criteria (BIC) is used on model selection between these two models. Data were obtained from 214 undergraduate students with three majors of study at the Faculty of Information Technology, Sebelas April University in Indonesia. Both models produce the same output which depict that self-directed learning readiness significantly affects the learning motivation of the students, while there is not a significant effect of e-learning readiness on learning motivation. With the lower BIC value, which is a negative value, PLS-SEM is more fitted for portraying the influence of self-directed learning readiness, and e-learning readiness to learning motivation of students than B-SEM model. [ABSTRACT FROM AUTHOR]

Histological aberration is a common phenomenon in aquatic animals exposed to pollution. Pollution also influences the hormonal condition of an organism, including the green mussel Perna viridi s. In the present study, degeneration and lyses were studied in the females' oocytes of P. viridis and the 17-β-Estradiol was measured in the gonads of female and male individuals. The damage analysis was carried out and the aberration level in the tissue was measured by image processing analysis using R-Software. The concentration of 17-β-Estradiol in the gonads was measured by the ELISA method. Histopathological aberration and 17-β-Estradiol concentration in the female gonad tissue of P. viridis were detected in the samples with variable quantities. The high value of 17-β-Estradiol in males cultured in the industrial city of Indonesia indicated that this mussel species experienced hormonal imbalances and represent a threat with regard to food safety. [ABSTRACT FROM AUTHOR]

The study was designed to know the year-wise research growth rate, author collaboration pattern and productivity, sub-domain research in library science, research keyword network, thematic and cluster analysis of journal Annals of Library and Information Science. The data search and download has been done under (Scopus database) sources by selecting the subject area Social Science/Library and Information Science and the journal Annals of Library and Information Science. A total of 388 articles from the study period 2011 to 2022 have been downloaded with all bibliographic information from the Scopus database. VOSviewer (version, 1.6.9) and R (Biblioshiny) software have been used for data visualization and keyword analysis. Total 388 articles were published in which 2014 noted as the most productive year (11.60%) and 2019 as least productive (4.64%) year. Citation analysis indicates that highest 315 citation (18.39%) for year 2011 and lowest 32 (1.87%) in 2022, noted so for averaging 4.41 per article annually. multi-authored articles were found prominent (48.20%), followed by single authors (33.76%). The Collaborative Index ranged from 1.81 to 2.03 (average 1.88), and the Degree of Collaboration found between 0.88 to 0.73 (average 0.82). The Collaboration Coefficient, reflecting averaged collaboration 0.365, with values from 0.29 to 0.41. B K Sen, B Dutta, and K C Garg, noted as highest contributor for this journal where with article "Internet of Things and Libraries" published by Pujar S M & Satyanarayana K V in year 2015 has highest number of citations for any article. Keyword analysis indicate that term "Scientometrics," "Bibliometrics," and "India," occurred most time where Library Services, H-index, and Covid-19 noted as latest occurred term for year 2021. The thematic analysis of subject shows that koha, vufind, ethics, open source, ontology, academic integrity, citation impact, altmetrics, lexicon etc. found as emerging subject areas for research. As expected, India emerged as the primary contributor in publications (76.80%) and citations (75.55%), followed by Nigeria (6.44% publications, 5.78% citations) which shows the journal must make its presence on international level. The institute analysis indicates that research institutes are sharing more publication comparing to the central university where the library science department exist. [ABSTRACT FROM AUTHOR]

Aegilops L. is a large genus of the tribe Triticeae, allied to the family Poaceae. It spreads in almost all temperate regions, mainly in the Mediterranean basin, Southwest and Central Asia. The goal of this study was to use morphometric and anatomical methodologies to reappraise the taxonomy of the genus Aegilops in Egypt. Clustering was applied to determine the relationship between seven studied species based on sectional delimitation. Results generated from principal component analysis (PCA) and correlation revealed the significance of morphometric and anatomical descriptors traits in the characterization and identification of Aegilops species. Heatmap analyses of combining morphological and anatomical data confirmed the sectional classification of the seven species. [ABSTRACT FROM AUTHOR]

Background: Statistical reasoning is an important clinical skill that informs evidence-based medicine and is desirable for 21 st century practice. Yet many physicians struggle to apply biostatistical techniques correctly, potentially undermining patient care decision-making and adversely affecting outcomes. The overarching concepts of foundational biostatistics and probabilistic reasoning can be challenging to teach and hard for students to internalize given time constraints, lack of intrinsic motivation, and varied pre-existing knowledge or cognitive schema. We investigated how supplemental, interactive online webtools affected first-year medical students’ learning of sensitivity, specificity, negative/positive predictive values, and prevalence. Methods: Online webtool design was informed by constructivism and sought to have learners 1) build conceptual representations by connecting new information to existing knowledge, 2) interact with primary sources of biostatistical data, and 3) begin with whole concepts then explore component parts. A total of 59 students were assigned randomly to two cohorts that had access to the webtools before or after in-class teaching; an additional 35 students had no access and served as the control cohort. Access metrics, summative exam scores, prior knowledge measurements, and qualitative data on learners’ experiences from three focus groups (n = 6/group) were collected. Results: Mean exam scores were 87.5 ± 10.8%, 90.7 ± 11.2%, and 88.9 7 ±12.9% for the before, after, and control cohorts (p = 0.49). Students repeatedly accessed the online webtools: mean access was 4.3 (± 2.6) (before) and 2.6 (± 1.5) (after). Qualitative data showed the webtools facilitated learning in a time-efficient manner. Conclusions: Online interactive webtools facilitated a constructivism-informed learning model and were found by first-year medical student to supplement and enhance their learning experience without detracting from established biostatistical teaching or negatively affecting summative assessment outcomes.

Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by GS to perform predictions of candidate genotypes that were not phenotyped. Because there is no universally better model for prediction and models for each type of response variable are needed (continuous, binary, ordinal, count, etc.), an active area of research aims to develop statistical models for the prediction of univariate and multivariate traits in GS. However, most of the models developed so far are for univariate and continuous (Gaussian) traits. Therefore, to overcome the lack of multivariate statistical models for genome-based prediction by improving the original version of the BMTME, we propose an improved Bayesian multi-trait and multi-environment (BMTME) R package for analyzing breeding data with multiple traits and multiple environments. We also introduce Bayesian multi-output regressor stacking (BMORS) functions that are considerably efficient in terms of computational resources. The package allows parameter estimation and evaluates the prediction performance of multi-trait and multi-environment data in a reliable, efficient and user-friendly way. We illustrate the use of the BMTME with real toy datasets to show all the facilities that the software offers the user. However, for large datasets, the BME() and BMTME() functions of the BMTME R package are very intense in terms of computing time; on the other hand, less intensive computing is required with BMORS functions BMORS() and BMORS_Env() that are also included in the BMTME package.

In this paper an attempt has been made to develop the pre-harvest forecast model for rice yield based on biometrical character by using the concept of stepwise principal component regression technique and stepwise multiple regression technique. The data for two years was utilized from the experiment conducted by the rice research station, Kaul for the final yield trial for medium and early duration genotypes during 2015-16 and 2016-17. On applying the stepwise multiple regression technique and principal component regression technique thorough R-software, it was found that principal component regression technique provides the better forecast yield with less mean square error and less percentage standard deviation as compared to the stepwise multiple regression technique. It was found that coefficient of determination for PCR (Principal component regression) model are 83 percent and 92 percent for the final yield trial during medium and early duration whereas for the stepwise multiple regression models the coefficient of determination was found to be 61 percent and 85 percent. For the final yield trial during medium and early duration the overall mean square error of the stepwise multiple regression was found to be 125028 and 178578 respectively whereas the overall mean square error for the same trial during medium and early duration was found to be 52432 and 90904 for principal component regression. [ABSTRACT FROM AUTHOR]

Activities in the construction sector are assumed to be influenced by inflow of mortgage funding in the private housing sector and public finances targeted at large infrastructure projects, apart from climate variables. In this study, we modeled seasonal time series representing monthly output in the Czech construction sector in CZK mil. during 2000:1 through 2016 : 12 (T = 204) adjusted for calendar variations and seasonal movements via TRAMO‑SEATS and then transformed to natural logarithms of gross returns. A Markov‑Switching model with two states, no intercept, average monthly temperature, average monthly precipitation and parameters of first‑order autoregression process was specified and estimated by the Expectation‑Maximization. In State 1 of regular performance, the log‑differenced returns were significantly and positively influenced by precipitation levels, but not by ambient outdoor temperature. In State 2 of non‑standard operation of the construction sector, the transformed series was unaffected by precipitation levels, but instead by ambient outdoor temperatures. First‑order autocorrelation dependency in both regimes was established. Changes in legal and macroeconomic environment pertinent to tax law amendments affecting VAT or corporate tax, country’s accession to EU or large construction project deadlines were shown to induce nonstandard regime in the construction sector (State 2). The model classified 91 % observations in the first state, while only 9 % data belonged to the State 2. Transition probability matrix indicates that change from model State 1 to State 2 is difficult to attain. At the same time, once State 2 was established, it tends to persist or change to State 1 with near equal probability. Ability of the Markov‑Switching model to identify both states is reasonably good. [ABSTRACT FROM AUTHOR]

Most countries of the Visegrad group presently undertake meticulous preparation for entry to the Eurozone, which makes the issue of business cycle synchronization contemporary. In this study, we identified business cycle in V4 member states, Germany and the Eurozone and investigated the degree of business cycle synchronization among V4 countries, with Germany and the Eurozone. Quarterly GDP data, season and calendar adjusted from 1995 : 1 through 2016 : 4 (T = 88) were broken down to trend and cyclical elements by the Hodrick-Prescott filter and used to obtain the relative output gap. Phases of the business cycle were identified through modified BBQ algorithm. Business cycle synchronization was quantified with Pearson correlation coefficient, concordance index and Mink similarity index calculated separately for time segments before EU accession in 2004 and after. Tests of statistical significance were carried out, where relevant. In the period before EU enlargement, few economic cycles were correlated weakly, but many were not synchronized at all or were even negatively correlated. After EU accession, the cycles became closely synchronized among V4 countries or between V4 state and Germany or Eurozone. Although the degree of correlation between V4 countries and Eurozone is not as strong, as for Germany and Eurozone, it is obvious that V4 countries are becoming increasingly prepared for adopting Euro, as their currency, with respect to the requisite of economic cycle synchronization. [ABSTRACT FROM AUTHOR]

In this paper, the main stress is on the evaluation of reliability measures of a series-parallel system by considering Weibull failure laws. For non-identical components, the expressions for mean time to system failure (MTSF) and reliability of the system are derived. Using R-software, the results for these reliability measures are obtained for arbitrary values of the parameters such as number of components, failure rates and operating time of the components. A particular case of Weibull failure laws i.e. Rayleigh laws is considered to examine the variation of MTSF and Reliability with respect to the parameters taken in the study. The behaviour of reliability and MTSF of the system comprising five subsystems each having five components have also been observed through graphs by considering different operating time and failure rates. [ABSTRACT FROM AUTHOR]

Chemometric calibrations namely, Partial least square (PLS), principal component regression (PCR) and classical least square (CLS), were developed for the simultaneous determination of domeperidone and lansoprazole from combined pharmaceutical dosage forms. In these methods with chemometric techniques, the calibrations were constructed by using the absorption data matrix, with measurements in the range of 260-310 nm (ДХ = 1 nm) in their zero order spectra. The chemometric calibrations were realized by using R - software (version 2.1.1). The linearity range was found to be 4-36 pg ml-1 and 2-18 pgml-1 for DOMP and LANS respectively. The lower correlation coefficient values 0.9998, 0.9999 and 0.9992 for DOMP and 0.996, 0.998 and 0.999 for LANS respectively estimated for CLS, PCR and PLS respectively. The validity of the proposed methods was successfully assessed for analysis of both drugs in laboratory prepared mixtures and in commercial formulations. [ABSTRACT FROM AUTHOR]

Modern speech synthesis systems implement prosody features to achieve more naturally sounding voices, trying to produce sounds similar to the human speech. This article deals with obtaining one of prosody characteristics (sentence melody contour) from human speech recordings. Similarities of melody contours are studied using cluster analysis. Several clustering methods are evaluated for this purpose, estimation of proper number of clusters is described and typical melodies for different types of Slovak language sentences (declarative, interrogative, exclamatory, clauses with final "," etc.) are found and recommended for implementation in our text-to-speech system.

In this study, we analyzed a system of five monthly time series integrated I(1 ): average price of crude oil imported to the U.S. from OPEC countries (Opec), imported oil price from other than OPEC countries (NonOpec) in USD per barrel, average price of regular gasoline in the U.S. (Regular), premium quality gasoline price (Premium) and kerosene price (Kerosene) in U.S. cents per gallon. Cointegration was established by EG test and the series were analyzed by VECM model with lag selected via BIC criterion. Cointegration rank was determined by the Johansen procedure. According to VECM coefficients, prices of oil from OPEC countries and beyond OPEC exert influence upon all commodity prices in the system, but in a contradictory manner. Responses to innovation shocks in Opec and NonOpec stabilized within 8 to 10 months upon a nonzero shift and further became permanent. Innovation shock in both types of gasoline and Kerosene had only short-term significant impact upon the system. Forecast error variance in all variables is explained mainly by variation in oil prices, especially Opec, which persists with increased horizon. For a short horizon h = 1, FEVDs in gasoline and kerosene prices are primarily made of variation in the respective fuel prices. [ABSTRACT FROM AUTHOR]

We present a (selective) review of recent frequentist highdimensional inference methods for constructing p-values and confidence intervals in linear and generalized linear models. We include a broad, comparative empirical study which complements the viewpoint from statistical methodology and theory. Furthermore, we introduce and illustrate the Rpackage h d i which easily allows the use of different methods and supports reproducibility. [ABSTRACT FROM AUTHOR]

Background: Soybean is an important oilseed crop that its development and production are affected by environmental stresses (such as saline-alkaline and water deficit)., Objectives: This experiment was performed with the aim of identifying candidate genes in saline-alkaline stress and water-deficit stress conditions using transcriptome analysis and to investigate the expression of these genes under water deficit stress conditions using RTqPCR., Materials and Methods: In this experiment, soybean transcriptome data under saline-alkaline and water-deficit stress were downloaded from the NCBI website, and then the co-expression modules were determined for them and the gene network was plotted for each module, and finally, the hub genes were identified. To compare the expression of genes in saline-alkaline and water deficit conditions, soybean plants were subjected to water deficit stress and their gene expression was determined using RTqPCR., Results: The filtered (Log FC above +2 and below -2) genes of soybean were grouped under saline-alkaline stress in 15 modules and under water-deficit stress in 2 different modules. Within each module, the interaction of genes was identified using the gene network, then three genes of ann11 , cyp450 and zfp selected as hub genes. These hub genes are highly co-expression with other network genes, which not only display differential expression but also differential co-expression. The results of RT-PCR indicated that cyp450 gene expression was not significantly different from the control, while ann11 gene expression significantly increased under water deficit stress, but zfp gene expression decreased significantly under water deficit stress., Conclusions: We identified three genes, ann11 , cyp450 and zfp , as hub genes. According to our results, ann11 gene had a significant increase in expression under water deficit stress, which can indicate the importance of this gene under drought conditions. Therefore, according to the results of this experiment as well as other researchers, we introduce this gene as a key gene in water deficit tolerance and recommend its use in genetic engineering to increase the tolerance of other plants., (Copyright: © 2021 The Author(s); Published by Iranian Journal of Biotechnology.)

In this paper, some size-biased probability distributions and their generalizations have been introduced. These distributions provide a unifying approach for the problems where the observations fall in the non-experimental, non- replicated, and nonrandom categories. These distributions take into account the method of ascertainment, by adjusting the probabilities of actual occurrence of events to arrive at a specification of the probabilities of those events as observed and recorded. Failure to make such adjustments can lead to incorrect conclusions. This paper surveys some of the possible uses of size- biased distribution theory to some real life data. [ABSTRACT FROM AUTHOR]

Thin film of CdS was prepared by vacuum evoparation method. The variation in Hall coefficient RH of prepared thin film was recorded with electromagnetic field of different frequencies (5-15 MHz at 1MHz steps) and of 10mV, and 15mV amplitudes. The statistical analysis of obtained data was done by R-software which showed that the value of Hall coefficient decreases significantly with increment in frequency of E-M field. [ABSTRACT FROM AUTHOR]

Investigators use Bland-Altman plot (Limits of Agreement plot) to compare two methods measuring the same continuous variable to determine interchangeability or agreement of the methods. The method has evolved to deal with heteroscedastic data and fixed or proportional biases (or both). Although an ordinary Bland-Altman plot can be readily made with various software applications, there is no free, open-source application that is dedicated to producing Bland-Altman plots and constructing limits of agreement for data that do not meet the assumptions of a simple comparison. To fill this gap, we created BA-plotteR, a web-based, open-source, freeware tool created in Shiny/R that is dedicated to creating Bland-Altman plots. We validated the tool using 20 datasets with various data distributions by comparing the output from the tool against manually derived results. The webtool handles data that requires a more complex analysis than is commonly available through commercial statistical programs. Moreover, the automated analysis of the data distribution will guide users and help them to correctly plot and analyse their data. The tool agreed perfectly with manually constructed plots. The Bland-Altman graphing tool provides clinical researchers with a tool that correctly analyzes and graphs studies involved in method comparisons. The tool can be accessed here: https://huygens.science.uva.nl/BA-plotteR., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Forecasting is a focal subject in the area of finance and economics which has urged the interest of researchers and financial analysts to develop better predictive models. The Autoregressive Integrated Moving Average (ARIMA) models have been explored in the literature and are extensively used in prediction of variables with temporal force. This paper has endeavored equity price prediction using ARIMA procedure expending 246 daily closing prices. To this end, daily equity prices for Shirpur Gold Refinery Ltd., India from April 2017 to March 2018 has been considered to build an appropriate ARIMA model employing R software. The best obtained model, ARIMA (0, 1, 1) of its several variants has been used for securing equity price prediction intervals for the next few days. Additional effort was made to judge predictive performance of the fitted model taking out-of-sample closing equity prices. The results divulged that ARIMA model has a strong potential for short-term prediction and can contest favorably with other forecasting techniques used in stock price prediction. [ABSTRACT FROM AUTHOR]

Emerging and re-emerging infectious diseases are a significant public and animal health threat. In some zoonosis, the early detection of virus spread in animals is a crucial early warning for humans. The analyses of animal surveillance data are therefore of paramount importance for public health authorities to identify the appropriate control measure and intervention strategies in case of epidemics. The interaction among host, vectors, pathogen and environment require the analysis of more complex and diverse data coming from different sources. There is a wide range of spatiotemporal methods that can be applied as a surveillance tool for cluster detection, identification of risk areas and risk factors and disease transmission pattern evaluation. However, despite the growing effort, most of the recent integrated applications still lack of managing simultaneously different datasets and at the same time making available an analytical tool for a complete epidemiological assessment. In this paper, we present EpiExploreR, a user-friendly, flexible, R-Shiny web application. EpiExploreR provides tools integrating common approaches to analyze spatiotemporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements data and remotely sensed data. Data exploration and analysis results are displayed through an interactive map, tables and graphs. EpiExploreR is addressed to scientists and researchers, including public and animal health professionals wishing to test hypotheses and explore data on surveillance activities. [ABSTRACT FROM AUTHOR]