VINITI

This article presents a study aimed at integrating two road safety approaches to effectively prioritise road sections requiring safety improvements. The study examines the role of injury severity assessment in evaluating the safety of road networks. It outlines the fundamental principles of the MAIS3+ methodology, discusses its relevance to road safety analysis, and highlights its potential applications for assessing road infrastructure risk. A pilot safety assessment of the Lithuanian main road network is presented, based on the severity of traffic accident outcomes classified according to the MAIS3+ criterion. Furthermore, the study addresses the challenges of linking hospital and police data and provides recommendations to improve data integration.

Personality traits are indicative of consistent patterns in thoughts, emotions, and behaviours, and they consequently impact students' academic performance. Numerous scientific investigations have explored the determinants of student learning success, with some emphasizing the significance of considering both student personality traits and generational cohorts. This study seeks to examine the associations between the Big Five personality dimensions and the learning strategies employed in mathematics lectures. A random sample of first- and second-year informatics engineering science students from Vilnius Gediminas Technical University (VILNIUS TECH) was selected for this research. Data were gathered through an anonymous survey utilizing a questionnaire developed by the authors. Non-parametric statistical tests were employed to analyse the data. The findings revealed that the sample predominantly consisted of students exhibiting the personality dimensions of agreeableness and conscientiousness. Furthermore, students' selection of learning strategies was generally independent of their personality dimensions. Nonetheless, the study underscored students' preferences for diverse learning strategies, irrespective of their personality traits.

The analysed research question raises the problem of modernisation of technical specifications of the locomotive on electric traction. The authors of the scientific research consider the modernisation of the DS3 locomotive with the transition from a single-phase (25 kV 50 Hz AC) to a two-phase (25 kV 50 Hz AC/3 kV DC) power supply system, which aims to increase interoperability with neighbouring railway networks. A multi-level literature review has been carried out, transient and dynamic characteristics have been modelled in MATLAB, and the principles of control of traction converters and motors have been formulated. Ensuring stable operating modes when switching power systems is confirmed by optimal attenuation ζ ≈ 0.7, and the electromagnetic compatibility analysis has revealed characteristic interference of 500–3000 Hz, thus allowing us to propose filters in accordance with EN 50121. The radar graph of the comparative analysis provides an improvement in the main metrics (power, traction efforts, efficiency, and reaction time) in the context of an ideal 100% scale.The modernisation of DS3 is recognised as technically feasible, cost-effective and compliant with international technical aspects, which ensures prospects for joint operation in Ukraine and the EU.

This study investigates the seasonal structural behaviour of flexible pavement structures constructed on subgrades with varying types of treatment. Eight road sections in Lithuania, featuring natural subgrades or soils stabilised with lime, cement, or hydraulic road binder (HRB), were evaluated using Falling Weight Deflectometer (FWD) testing during thawed and recovered states. Structural condition was assessed using deflection-based indices: the Surface Curvature Index (SCI), Base Damage Index (BDI), and Base Curvature Index (BCI). Seasonal changes were quantified, and Wilcoxon signed-rank tests were applied to assess the statistical significance of deflection differences. The results revealed that the untreated subgrades experienced the largest seasonal softening, with BCI increases of up to 45%. Cement stabilization provided the most effective mitigation, limiting the BCI to 14% and preserving the stability of SCI. Lime-treated sections showed a dosage-dependent improvement, while HRB treatment yielded results comparable to high-percentage lime stabilisation. The study confirms that the type and dosage of subgrade treatment significantly influence pavement resistance under freeze-thaw conditions and highlights the importance of evaluating the geometry of the deflection bowl to correctly interpret structural indicators. These findings contribute to improved mechanistic understanding of seasonal load response in flexible pavements and inform best practices for subgrade stabilisation.

Pavement must have the characteristics that allow safe, fast, comfortable, economical and reliable vehicle traffic. The normative documents introduce limit values and permissible deviations of the controlled parameters of the road pavement, for which penalty deductions are levied if they are not satisfied or exceeded. The asphalt pavement installation rules for the ĮT ASFALTAS 08 contain 10 such indicators, whose relevance in respect of road quality is investigated in this paper. The relative weights of each indicator (defect) have been determined using assessments from 91 experts. The methods used in the study are rank correlation, ARTIW-L, ARTIW-N (Average Rank Transformation into Weight Linear and Non-linear), and DPW (Direct Percentage Weight). The expert team’s opinions are consistent because the empirical concordance coefficient value of 0.715 is 34.6 times higher than the minimum concordance coefficient value of 0.021. The most important indicators for the experts are the lower degree of compaction, the lower thickness of the layer, and the lower amount of binder. A pilot project dedicated to determining the required number of experts has shown that the number of experts in a team, exceeding 20, has almost no effect on the average of ranks and percentage weights of the criteria.

The paper investigates new estimation techniques for repeated surveys, focusing on improving the precision of finite population parameter estimates at the current time t by incorporating auxiliary time series and calibration methods. Repeated surveys generate temporally correlated estimates, which time series models capture effectively. Calibration further enhances estimation by adjusting estimators with auxiliary data, reducing variance, and improving precision. Several new estimators of a time-dependent finite population characteristic (usually the mean, which is used in various statistical analyses) at time t are developed and evaluated under diverse scenarios, considering factors such as the correlation between the errors of the target and auxiliary time series, sampling variance, number of surveys, and model complexity. Numerical results demonstrate that calibrated estimators, particularly those incorporating time series adjustments, achieve superior accuracy in high-correlation settings. Regression-based estimator also shows robust performance across varying conditions, while traditional estimators relying solely on survey data are less precise.

This paper proposes a novel approach to improve the visualization capabilities of self-organizing maps and facilitate the identification of the resulting clusters. Unlike other clustering algorithms, self-organizing maps lack the feature to select a predefined number of clusters, and the boundaries of the clusters are not explicitly represented on the self-organizing maps. The main advantage of our proposed approach is that the option for selecting the desired number of clusters has been implemented. The experimental investigation was performed using four datasets with different characteristics. The improved visualization leverages various similarity distances to assess their impact on performance. The effectiveness of the novel approach to clustering results has been compared with those of the well-known k-means and hierarchical clustering methods, which allow for the selection of the desired number of clusters. Additionally, the visualization results, obtained by the proposed approach, were compared with those produced using the Orange Data Mining tool, where the u-matrix is applied to visualize a self-organizing map. The advantage of our approach compared to the u-matrix visualization has been highlighted in this paper. The performance of clustering algorithms has been measured by calculating the ratio of data items correctly assigned to clusters in the case when the clusters are predefined in the analyzed dataset. The results obtained showed that the most effective similarity distances are the cosine and correlation distances, which help to detect the correctly predefined clusters in the visualization of self-organizing maps.

The goal of the paper is to present the FSI (fluid-structure interaction) CFD (computational fluid dynamics) simulations of the blood flow in the LA (left atrium) for patient-specific geometry of the left atrium appendage (LAA). These simulations are important for the decision making in cardiology and cardiac surgery of the patients with atrial fibrillation. Nowadays according current atrial fibrillation treatment guidelines, initiation of oral anticoagulant therapy is recommended for patients with a CHA2DS2–VASc score greater or equal to 2 for males, and greater or equal to 3 for females, for lowering stroke risk. This therapy although has undesirable effects and provokes bleeding in a part of cases. That is why it is important to detect the stagnation zones of the blood flow in LAA. The presence of such zones justifies the necessity of the anticoagulant therapy. The FSI CFD simulations in the heart is a challenging problem: the existing softwares are not too robust for real life Reynolds numbers and often do not converge to the solution of the Navier–Stokes equations for the blood coupled with the elasticity equations of the wall. That is why we first provide the CFD computations with the rigid wall when the codes are more stable. Using this solution as a source of parameters for the implicit numerical scheme solver, we then provide the FSI computations, which become much more robust.

Micromobility is gaining momentum in many countries, helping reduce congestion and pollution in the streets of major cities. The use of e-scooters has increased rapidly over the past three years, posing additional risks to road safety. Therefore, it is necessary to identify the key factors influencing the number of accidents and their consequences. The current article assesses eight e-scooter safety importance criteria, applying various methods and consulting highly qualified experts. It is noteworthy that the maximum weights of criteria determined by the ARTIW-L, ARTIW-N (Average Rank Transformation into Weight Linear and Non-Linear), and AHP (Analytic Hierarchy Process) methods are consistent. Evaluating the opinions of 15 experts using the ARTIW-L, ARTIW-N, DPW (Direct Percentage Weight), and AHP methods allowed for the determination of average criterion weights and the ranking of their priorities. Averaging the criteria weights calculated by the four expert evaluation methods yields the following overall priority order: Type and quality of road surface≻Road or street element≻Maximum power≻Speed limits≻Mandatory helmet use≻Seasonality≻Age of the road user≻Educational activities. The findings highlight that the type and quality of the road surface, as well as road or street design, have the greatest impact on e-scooter safety. These insights can guide urban planners and policymakers in prioritizing infrastructure improvements and developing evidence-based safety regulations for micromobility users.

Nowadays, it is agreed that fuzzy sets are suitable for capturing and representing the concept of vagueness and uncertainty, and various fuzzy reasoning systems are being developed based on them. Researchers have proposed fuzzy set extensions to improve the performance and accuracy of these systems. The research questions arise regarding how fuzzy sets have evolved and what the main trends in their evolution are. To address these questions, our research presents a chronological and bibliometric analysis of fuzzy sets based on papers extracted from the Web of Science database. The main findings and contributions have been identified, systematized and visualized in a fuzzy set keyword map of 65 fuzzy set extensions. These extensions are primarily used for decision-making, reasoning, and prediction, particularly in the context of digital transformation, by integrating digital technologies into all areas of business, transforming operations and enhancing value delivery to customers. As organisations increasingly adopt digital technologies, the need for robust frameworks to manage uncertainty becomes critical. The main trends indicating the directions of fuzzy sets development, an overview of the variety and popularity of fuzzy sets over the years, and the impact of countries engaged in fuzzy set research are also identified and reported. The results support researchers and practitioners working on fuzzy sets and their applications by providing valuable insights into the fuzzy set topic, its existing extensions, and, more generally, to any field of investigation where fuzzy sets are relevant, particularly in the realm of digital transformation.