Statistical Criteria Research Articles

Evaluation of the expression of LINCOO475 and FOXO1 genes as tumor suppressor genes in breast cancer: Correlation of bioinformatics tools and experimental approaches

Breast cancer is one of the most prevalent forms of malignancy in humans and is considered the most important life-threatening factor among women worldwide. Long non-coding RNAs (LncRNAs) are thought to be potential transcripts that regulate the expression of important genes in vital processes such as cell proliferation, apoptosis, survival, and oxidative stress. LINC00475 is an important lncRNA and is located on 9q22.31 and its expression is regulated by p53. FOXO1 gene is an important regulator in physiological processes such as cell maintenance and differentiation, stimulation of cell cycle arrest, and apoptosis. In this study, we focused on identifying a possible association between FOXO1 and LINC00475 expression levels in breast cancer. Bioinformatics expression and Microarray data analysis were utilized to find their potential role as novel biomarkers in breast cancer. The correlation of gene expression levels with the survival rate of patients was determined using GEPIA. LINC00475 and FOXO1 expression levels were validated using quantitative reverse transcription PCR (qRT-PCR) in 55 tumors and 40 normal tissues. Our experiments indicated a significant decrease in the expression level of LINC00475 and FOXO1 genes in tumor tissues compared to normal samples. Moreover, the specificity and sensitivity of all statistical criteria were significant between the two groups. Also, we revealed a positive relationship between LINC00475 and FOXO1 expression in tumor samples. Based on gene expression analysis and lncRNA data, it was shown that LINC00475 and FOXO1 could represent as potential diagnostic biomarkers for early diagnosis of breast cancer.

A new sine-inspired probability model: Theoretical features with statistical modeling of the music engineering and reliability scenarios

It is a proven authenticity that probability-schooled methodologies have a cardinal role in making decisions under the vagueness circumstances. Thus, to reach meaningful and fruitful decisions under vagueness circumstances, numerous probability-focused methodologies have been introduced, promoted, and implemented. This paper also contributes to adding another useful probability model to the existing literature for data modeling in engineering-oriented areas. The new model is called the weighted sine exponentiated exponential (WSEE) distribution, which is formed by implementing the exponentiated exponential model together with a sine-adapted method. Certain properties of the WSEE distribution are given. The parameter estimation of the WSEE distribution along with the simulation study is also provided. Last, in order, we apply the WSEE distribution for analyzing three data sets taken from the musical engineering and reliability settings. The optimality of the WSEE distribution is proved by comparing its results with other prominent modifications of the exponentiated exponential distribution. Using 4 statistical criteria as judgment tools, it was shown numerically and pictorially that the WSEE distribution is an optimal and suitable model for the musical engineering reliability data sets.

Statistical Methods to Examine Racial and Ethnic Disparities in the Surgical Literature: A Review and Recommendations for Improvement.

To characterize the quality of statistical methods for studies of racial and ethnic disparities in the surgical-relevant literature during 2021-2022. Hundreds of scientific papers are published each year describing racial and ethnic disparities in surgical access, quality, and outcomes. The content and design quality of this literature have never been systematically reviewed. We searched for 2021 to 2022 studies focused on describing racial and/or ethnic disparities in surgical or perioperative access, process quality, or outcomes. Identified studies were characterized in terms of 3 methodological criteria: (1) adjustment for variables related to both race/ethnicity and outcomes, including social determinants of health (SDOH), (2) accounting for clustering of patients within hospitals or other subunits ("providers"), and (3) distinguishing within-provider and between-provider effects. We identified 224 papers describing racial and/or ethnic differences. Of the 38 single-institution studies, 24 (63.2%) adjusted for at least one SDOH variable. Of the 186 multisite studies, 113 (60.8%) adjusted for at least one SDOH variable, and 43 (23.1%) accounted for the clustering of patients within providers using appropriate statistical methods. Only 10 (5.4%) of multi-institution studies made efforts to examine how much of the overall disparities were driven by within versus between-provider effects. Most recently published papers on racial and ethnic disparities in the surgical literature do not meet these important statistical design criteria and, therefore, may risk inaccuracy in the estimation of group differences in surgical access, quality, and outcomes. The most potent leverage points for these improvements are changes to journal publication guidelines and policies.

ScBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.

Comprehensive analysis of distinct circadian clock subtypes of adult diffuse glioma and their associations with clinicopathological, genetic, and epigenetic profiles.

The circadian clock (CC) has biological and clinical implications in gliomas. Most studies focused on CC effects on the tumor microenvironment and the application of chronotherapy. The present study focused on CC gene expression patterns and intracellular oncogenic activities. Glioma gene expression data were collected from The Human Cancer Genome Atlas (TCGA) project. After applying inclusion and exclusion criteria, we selected 666 patients from TCGA-GBM and TCGA-LGG projects and included important clinicopathological variables. The entire cohort was subjected to clustering analysis and divided into CC1 and CC2 subtypes based on statistical, biological, and clinical criteria. CC2 gliomas showed higher expression of BMAL1 and CRY1 and lower expression of CRY2 and PER2 (adjusted P < .001). CC2 gliomas had q higher activity of cell proliferation, metabolic reprogramming, angiogenesis, hypoxia, and many oncogenic signals (P < .001). The CC2 subtype contained a higher proportion of glioblastomas (P < .001) and had a worse prognosis (P < .001). Stratified Kaplan-Meier and multivariable Cox analyses illustrated that the CC subtype is an independent prognostic factor to clinicopathological characteristics (P < .001), genetic aberrations (P = .006), and biological processes (P < .001). Thus, this study shows statistical evidence of CC subtypes and their biological, and clinicopathological significance in adult gliomas.

Forecasting the Unemployment Rate in the Philippines using Box-Jenkins Methodology: Advancing Economic Strategies for Sustainable Development Goals

Unemployment remains a critical macroeconomic issue in Southeast Asia, significantly impacting the Philippines. This study aims to forecast the unemployment rate in the Philippines using ARIMA models to inform policy decisions and economic planning. The study utilized ARMA model for time series forecasting, leveraging past values and error terms to predict future data points. The methodology involved identifying suitable models through stationarity tests and autocorrelation analyses, estimating model parameters, and conducting diagnostic checks to ensure accuracy. Finally, the models were used to forecast future values and compute confidence intervals, providing insights into potential future trends. Monthly unemployment data from 2019 to 2023 was analyzed using the ARIMA (1,1,0) model, selected for its optimal fit based on statistical criteria. Initial findings indicated non-stationary unemployment data, necessitating first differencing to achieve stationarity. The ARIMA (1,1,0) model showed robustness, evidenced by diagnostic checks and Portmanteau test results confirming white noise residuals. Forecasts from 2024 to 2050 suggest fluctuating unemployment rates until 2034, followed by a gradual decline, indicating potential economic growth and improved labor market conditions. These forecasts align with the broader goal of sustainable economic development and highlight the importance of strategic policy interventions to foster job creation and economic stability in the Philippines. The study highlights the efficacy of ARIMA models in capturing unemployment trends and aiding policymakers in mitigating the socio-economic impacts of unemployment.

Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation

AbstractDrought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. Meteorological data, such as monthly precipitation, that could represent the region were obtained between 1955 and 2020 from the general directorate of state water works and the general directorate of meteorology. According to the findings, the M04 model, whose input structure was developed using SPI, various time steps, data delayed up to 5 months, and monthly precipitation data from the preceding month (time t − 1), produced the best results out of all the models examined using machine learning algorithms. Among machine learning algorithms, SVM has achieved the most successful results not only before applying WT but also after WT. The best results were obtained from M04, in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

Automated modal parameter tracking with neural network based normalization of environmental perturbations

Automated modal parameter tracking is an essential part of vibration-based structural health monitoring of engineering structures, e.g., bridges, high-rise building, wind turbines, amongst others. In this work, we conceive a robust algorithm for accomplishing this task. First, the modal parameters are extracted with by greedy clustering the stabilization diagram, which is based on covariance-driven subspace identification and the related uncertainty quantification. After the selection of a set of relevant modal parameters to be monitored, the tracking is performed by an active search of stable modes close to the reference ones, again using statistical criteria and automated clustering. The applied strategies allow for a robust method, allowing also for relatively strong variations in the natural frequencies or the MAC without losing track of the monitored modes. Finally, when EOVs (Environmental and Operational Variables) are measured and training and testing periods are defined, a neural network is trained based on the tracked frequencies and the EOVs, with the goal of correcting the environmental influence on the estimated frequencies based on the model predictions. In doing so, a distance metric is defined for damage detection, which is robust to environmental perturbations under the measured EOVs. The whole framework is showcased on monitoring data of a full-scale bridge.

Intelligent automatic operational modal analysis

This work presents a novel methodology for the automatic output-only modal identification of linear structures under ambient vibrations named Intelligent Automatic Operational Modal Analysis (i-AOMA). The proposed methodology leverages the Covariance-based Stochastic Subspace (SSI-cov) algorithm for output-only modal parameter identification and encompasses two key phases. In the first phase, the SSI-cov algorithm is executed using quasi-randomly samples of the control parameters. The corresponding stabilization diagrams are next processed via Kernel Density Estimation in order to prepare a training database for the i-AOMA's intelligent core. This is a machine learning technique (namely a Random Forest algorithm) that predicts the optimal combinations of the control parameters involved in the SSI-cov algorithm. The second phase of i-AOMA rests on the iterative generation of quasi-random control parameter samples. If a sample is classified as feasible by the intelligent core of i-AOMA, then the SSI-cov algorithm is applied, while a new sample is considered otherwise. Such iterative procedure stops once a statistical convergence criterion is fulfilled. Hence, ultimate modal estimates are carried out from the stabilization diagrams, and relevant statistics are computed to quantify the effects of the uncertainties attributable to the variability of the control parameters. The entire Python source code for i-AOMA is made freely available through public web repositories. Some applications of the proposed methodology are finally illustrated.

Model adaptive phase space reconstruction.

Phase space reconstruction (PSR) methods allow for the analysis of low-dimensional data with methods from dynamical systems theory, but their application to prediction models, such as those from machine learning (ML), is limited. Therefore, we here present a model adaptive phase space reconstruction (MAPSR) method that unifies the process of PSR with the modeling of the dynamical system. MAPSR is a differentiable PSR based on time-delay embedding and enables ML methods for modeling. The quality of the reconstruction is evaluated by the prediction loss. The discrete-time signal is converted into a continuous-time signal to achieve a loss function, which is differentiable with respect to the embedding delays. The delay vector, which stores all potential embedding delays, is updated along with the trainable parameters of the model to minimize prediction loss. Thus, MAPSR does not rely on any threshold or statistical criterion for determining the dimension and the set of delay values for the embedding process. We apply the MAPSR method to uni- and multivariate time series stemming from chaotic dynamical systems and a turbulent combustor. We find that for the Lorenz system, the model trained with the MAPSR method is able to predict chaotic time series for nearly seven to eight Lyapunov time scales, which is found to be much better compared to other PSR methods [AMI-FNN (average mutual information-false nearest neighbor) and PECUZAL (Pecora-Uzal) methods]. For the univariate time series from the turbulent combustor, the long-term cumulative prediction error of the MAPSR method for the regime of chaos stays between other methods, and for the regime of intermittency, MAPSR outperforms other PSR methods.

Logistic and CSG Growth Models for Predicting Life Expectancy

The tables that allow calculating the probability of death at a certain age by recording the number of births/deaths in a population are called life tables. The concept of life expectancy, which is a measure that determines how long a creature will live, is also determined by mortality rates obtained from life tables. It is also possible to model the expected lifetime with some nonlinear mathematical functions. One of the functions that is often used in modeling mortality rates is the logistic growth function. The aim of this study is to propose a model that can be used as an alternative to the logistic growth model and to interpret the mortality rates of countries. In this study, the life expectancy of males and females in Turkey, Singapore, Norway, and China was modeled using the logistic and the CSG growth model, which was newly introduced to the literature. When modeling the life expectancy of countries, adjusted graph was drawn in accordance with the data of each growth model. Then, the performances of the logistic growth model and the CSG growth model were compared with R^2, RMSE, and MAPE statistical criteria. As a result of the comparison, it was revealed that the CSG growth model is more suitable than the logistic model for estimating life expectancy for overall data and for each gender. The originality of this study is the CSG model which is very new nonlinear model that predicts life expectancy effectively for related datasets.

Measurement and Calculation of Sediment Transport on an Ephemeral Stream

Sediment transport remains a significant challenge for researchers due to the intricate nature of the physical processes involved and the diverse characteristics of watercourses worldwide. A type of watercourse that is of particular interest for study is the ephemeral streams, found primarily in semiarid and arid regions. Due to their unique nature, a new measurement algorithm was created and a modified bed load sampler was built. Measurement of the bed load transport rate and calculation of the water discharge were conducted in an ephemeral stream in Northeastern Greece, where the mean calculated streamflow rate ranged from 0.019 to 0.314 m3/s, and the measured sediment load transport rates per unit width varied from 0.00001 to 0.00213 kg/m/s. The sediment concentration was determined through various methods, including nonlinear regression equations and formulas developed by Yang, with the coefficients of these formulas calibrated accordingly. The results demonstrated that the equations derived from Yang’s multiple regression analysis offered a superior fit compared to the original equations. As a result, two modified versions of Yang’s stream sediment transport formulas were developed and are presented to the readership. To assess the accuracy of the modified formulas, a comparison was conducted between the calculated total sediment concentrations and the measured total sediment concentrations based on various statistical criteria. The analysis shows that none of Yang’s original formulas fit the available data well, but after optimization, both modified formulas can be applied to the specific ephemeral stream. The results indicate also that the formulas derived from the nonlinear regression can be successfully used for the determination of the total sediment concentration in the ephemeral stream and have a better fit compared to Yang’s formulas. The correlation from the nonlinear regression equations suggests that total sediment transport is primarily influenced by water discharge and rainfall intensity, with the latter showing a high correlation coefficient of 0.998.

Locus of control as a predictor of procrastination in employed and non-working education undergraduates

Introduction. The article examines how employment influences various aspects of prospective students’ personal, professional and academic development. The purpose of the study is to identify the relationship between the locus of control and the degree of procrastination in working and non–working students of a pedagogical university. Materials and Methods. The authors used the following research methods: an analytical literature review, a comparative analytical literature review, terminological analysis, as well as quantitative, qualitative and interpretative methods. Quantitative and qualitative methods included testing with subsequent interpretation of the results based on Spearman and Pearson statistical criteria. Using the generally accepted methodology for studying the locus of control, the study used J. Rotter’s method of studying the locus of control and a questionnaire on procrastination. The sample included 111 students of the pedagogical university. Results. The article presents a theoretical review and the results of the study on procrastination and the locus of control in education students working and not working within the educational system. Based on the data obtained, the relationships between a high level of procrastination and various parameters of the locus of control were revealed. Conclusions. Based on the testing, statistically significant differences in the level of procrastination were revealed among students who combine university studies and employment in the education system.

Categorical data analysis using discretization of continuous variables to investigate associations in marine ecosystems

AbstractUnderstanding and predicting interactions between predators and prey and their environment are fundamental for understanding food web structure, dynamics, and ecosystem function in both terrestrial and marine ecosystems. Thus, estimating the conditional associations between species and their environments is important for exploring connections or cooperative links in the ecosystem, which in turn can help to clarify such directional relationships. For this purpose, a relevant and practical statistical method is required to link presence/absence observations with biomass, abundance, and physical quantities obtained as continuous real values. These data are sometimes sparse in oceanic space and too short as time series data. To meet this challenge, we provide an approach based on applying categorical data analysis to present/absent observations and real‐number data. The real‐number data used as explanatory variables for the present/absent response variable are discretized based on the optimal detection of thresholds without any prior biological/ecological information. These discretized data express two different levels, such as large/small or high/low, which give experts a simple interpretation for investigating complicated associations in marine ecosystems. This approach is implemented in the previous statistical method called CATDAP developed by Sakamoto and Akaike in 1979. Our proposed approach consists of a two‐step procedure for categorical data analysis: (1) finding the appropriate threshold to discretize the real‐number data for applying an independent test; and (2) identifying the best conditional probability model to investigate the possible associations among the data based on a statistical information criterion. We perform a simulation study to validate our proposed approach and investigate whether the method's observation includes many zeros (zero‐inflated data), which can often occur in practical situations. Furthermore, the approach is applied to two datasets: (1) one collected during an international synoptic krill survey in the Scotia Sea west of the Antarctic Peninsula to investigate associations among krill, fin whale (Balaenoptera physalus), surface temperature, depth, slope in depth (flatter or steeper terrain), and temperature gradient (slope in temperature); (2) the other collected by ecosystem surveys conducted during August–September in 2014–2017 to investigate associations among common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0‐group fish) in Arctic Ocean waters to the west and north of Svalbard, Norway. The R code summarizing our proposed numerical procedure is presented in S4S1.

ПРОГНОЗИРОВАНИЕ ВЫРУЧКИ БЫСТРОРАСТУЩЕЙ КОМПАНИИ С ИСПОЛЬЗОВАНИЕМ ЛОГИСТИЧЕСКОЙ КРИВОЙ

The aim of the study is to assess the advancement potential of fast-growing companies, to create prerequisites for predicting the gain of such companies. The objective is to identify and forecast the growth points of the country’s economy, which is generally based on the advancement of individual economic entities, namely organizations. The research method for forecasting revenue as the main indicator of fast-growing companies (FGC) is considered. As an approximation, a logistic curve (the Ferhulst curve) is examined, the parameters of which are identified by the least squares method. Open data from financial statements of organizations are applied as a source. This work uses the criterion for distinguishing fast-growing companies; the average annual growth is of at least 50% at current prices. The novelty of the work lies in studying fast-growing organizations using a logistic curve (sigmoid). Approximation parameters are identified. A growth assessment (forecast) for organizations for 2040 is made. The statistical reliability of the selected approximation is established for 714 large and medium-sized fast-growing companies from the sample. The research results state, that a sample of about 900 fast-growing Russian companies is identified using the methods for processing large data sets from 2.5 million organizations. The illustrative calculations are based on actual sample data, highlighting the example of Wildberries in more detail. An assessment of the growth opportunities of the organizations under consideration is made. Studying the applicability of the proposed method using statistical criteria is carried out. The findings show that studying fast-growing organizations’ advancement for a medium or longer period using a logistic curve looks more preferable than applying an exponential one. For the vast majority of such organizations, the approximation of the logistic curve is statistically significant.

A Comparative Study of Data Mining Methods for Solar Radiation and Temperature Forecasting Models

Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter&amp;#39;s Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).&amp;nbsp;

Intelligent system for monitoring the operational properties of ship power equipment

The peculiarity of monitoring the technical condition of ship vehicles is that during the service life the main energetic installation is not changed, but its continuous maintenance and periodic repairs are carried out. In the organization of such activities, the leading role belongs to technical diagnostics, which allows you to determine the technical condition of the material, as well as predict possible changes for a certain period. Intelligent system of monitoring of operational properties of ship power equipment with Markov circuits, probabilistic dynamics elements, calculations on probabilistic models, multi-criteria optimization of diagnostic parameters, simulation and scenario generation is proposed. In detail, the content of the main structural units of the system for monitoring the operational properties of ship power equipment is considered. The input information was a generalization of experience in the operation of vehicles. The stages of construction of Markov circuits are described in relation to diagnostics of turbochargers, the feature of which is a replacement of discrete time by a continuous sequence of states. Considered by a separate unit of calculation on probabilistic models on the basis of spectra of vibration signals by combining their main discrete features and establishing new diagnostic parameters. The visualization of relationships consists in the construction of digraphs of interactions of the main structural elements of the SES taking into account probabilistic models. Multi-criteria optimization in the presented system is considered from the standpoint of statistical criteria and their convolution. Simulation and generation of basic scenarios is described, from the point of view, the conversion of analog data about workflows to digital form. Provided information support and tools of the system for monitoring the operational properties of ship power equipment

Technology of regular urban passenger transportation operation improvement

Introduction. The degree of passenger vehicle capacity utilisation affects the economic component of regular urban passenger transport operation, as it depends on the number of passengers transported, which affects the revenue from transportation, as well as on the passenger vehicle capacity, which affects the cost of transportation. One of the tasks formulated in the programme-target documents of the Republic of Belarus is to achieve full selfsufficiency of urban regular passenger transport. One of the ways to ensure this can be maximising the degree of capacity utilisation set by the manufacturer of the passenger vehicle.The aim of the paper is to estimate the degree of passenger vehicle capacity utilisation and dynamics of its change, as well as to develop measures to improve the efficiency of urban regular passenger transport.Materials and methods. When writing the article, the data obtained by a full-scale survey of passenger flows in one of the cities of the Republic of Belarus, conducted in 2019 and in 2024, were used. When writing the article, statistical methods (descriptive statistics, fitting, visualisation, assessment of significance of differences using statistical criteria), analysis of scientific literature and work technology, synthesis of proposals were used.Conclusions. The low degree of utilisation of passenger vehicle capacity is shown. The new technology of the organisation of work of the city passenger transport of regular communication, allowing to increase use of capacity of passenger vehicles and, at the expense of this efficiency of such work is offered.The possibility of further use of the results of scientific work. The received results substantiate necessity of development of scientific toolkit for optimisation of assignment of passenger vehicles to flights taking into account passenger load on them and other restrictions.Practical significance. This article shows the need to develop measures to improve the efficiency of passenger vehicle capacity utilisation for the purposes of increasing the efficiency of urban regular passenger transport.Originality. This paper shows the change in the degree of use of passenger vehicle capacity, shows the factors affecting it, and proposes a new technology for organising the work of urban regular passenger transport. The article will be useful for officials of the management bodies responsible for the organisation of the work of urban passenger transport of regular communication, as well as employees of the enterprises of urban passenger transport, transport scientists.

Ecoregional height–diameter models for Scots pine in Turkiye

Ecoregion-based height-diameter models were developed in the present study for Scots pine (Pinus sylvestris L.) stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system. The data were obtained from 2831 sample trees in 292 sample plots. Ten generalized height–diameter models were developed, and the best model (HD10) was selected according to statistical criteria. Then, nonlinear mixed-effects modeling was applied to the best model. The R2 for the generalized height‒diameter model (Richards function) modified by Sharma and Parton is 0.951, and the final model included number of trees, dominant height, and diameter at breast height, with a random parameter associated with each ecoregion attached to the inverse of the mean basal area. The full model predictions using the nonlinear mixed-effects model and the reduced model (HD10) predictions were compared using the nonlinear sum of extra squares test, which revealed significant differences between ecoregions; ecoregion-based height–diameter models were thus found to be suitable to use. In addition, using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.

Metformin Use in the First Trimester of Pregnancy and Risk for Nonlive Birth and Congenital Malformations: Emulating a Target Trial Using Real-World Data.

Metformin is a first-line pharmacotherapy for type 2 diabetes, but there is limited evidence about its safety in early pregnancy. To evaluate the teratogenicity of metformin use in the first trimester of pregnancy. In an observational cohort of pregnant women with pregestational type 2 diabetes receiving metformin monotherapy before the last menstrual period (LMP), a target trial with 2 treatment strategies was emulated: insulin monotherapy (discontinue metformin treatment and initiate insulin within 90 days of LMP) or insulin plus metformin (continue metformin and initiate insulin within 90 days of LMP). U.S. Medicaid health care administration database (2000 to 2018). 12 489 pregnant women who met the eligibility criteria. The risk and risk ratio of nonlive births, live births with congenital malformations, and congenital malformations among live births were estimated using standardization to adjust for covariates. A total of 850 women were in the insulin monotherapy group and 1557 in the insulin plus metformin group. The estimated risk for nonlive birth was 32.7% under insulin monotherapy (reference) and 34.3% under insulin plus metformin (risk ratio, 1.02 [95% CI, 1.01 to 1.04]). The estimated risk for live birth with congenital malformations was 8.0% (CI, 5.7% to 10.2%) under insulin monotherapy and 5.7% (CI, 4.5% to 7.3%) under insulin plus metformin (risk ratio, 0.72 [CI, 0.51 to 1.09]). Possible residual confounding by glycemic control and body mass index. Compared with switching to insulin monotherapy, continuing metformin and adding insulin in early pregnancy resulted in little to no increased risk for nonlive birth among women receiving metformin before pregnancy. Under conventional statistical criteria, anything between a 49% decrease and a 9% increase in risk for congenital malformations was highly compatible with our data. National Institutes of Health.