Dimensions

The rapid development of electromobility increases the need for fast, accessible and robust charging stations devoted to EVs (electric vehicles). Planning a network of such stations poses new challenges—amongst others, a power supply that may power such chargers. One major concept is to utilise wind energy as a power source. The paper analyses meteorological data gathered since 2001 in several stations across Poland to achieve quantitative indexes, which summarise (a) wind power density (WPD) as a metric of energy amount, (b) long-term (multiannual) time trends of amount of energy, (c) short-term stability (and thus predictability) of the wind power. The indexes that cover the abovementioned factors allow the authors to answer the research questions, where the local wind conditions allow the authors to consider the integration of a wind powerplant and a network of EV chargers. Additionally, we investigated locations where the amount of available energy is sufficient, but the variability of wind power impedes its practical exploitation. In such cases, the power system may be extended by an energy storage system that acts as a buffer, smoothing power fluctuations and thereby improving the robustness and reliability of downstream charging systems.

Every year, road accidents cause significant human and social losses, posing one of the key challenges for public policy in Poland. The aim of this article is to quantitatively assess the relationship between selected infrastructural and economic conditions and the scale of road accidents in Poland in the period 2010–2024. The analysis was carried out using a log–linear regression model, which allows the results to be interpreted in terms of elasticity. The dependent variable was the total number of road accidents, while the set of explanatory variables included the density of paved roads, the length of expressways and motorways, urban population density, the level of private car ownership, and average gross wages. The results indicate that the development of road infrastructure and an increase in the population’s income contribute to reducing the number of accidents, while the growing number of passenger cars significantly increases the risk of accidents. The estimated model explains approximately 94% of the variation in accident counts (R2 = 0.94). The elasticity of passenger car ownership is positive (β = 1.39), indicating increased accident exposure with rising motorization, while paved road density (β = −46.56) and expressways (β = −2.03) show negative elasticities. Average wages are also negatively associated with accidents (β = −4.64). These results quantify the proportional structure of long-term accident dynamics rather than merely confirming directional relationships. The analysis also revealed a negative correlation between urban population density and the number of accidents, which may indicate greater effectiveness of traffic management and control systems in urban areas. The results of the study provide empirical evidence relevant for the development of investment and regulatory strategies in the area of transport infrastructure and road safety.

This study investigates the resistance of polymer modified bitumen (PMB) to ultraviolet (UV) radiation and oxidative aging, focusing on the role of styrene–butadiene–styrene (SBS) characteristics (structure, styrene/vinyl content, molecular weight) and the chemical composition of the base binder. Twelve PMBs were produced using three 70/100 base binders with different chemical composition and four SBS types (linear vs. radial, high vs. low vinyl). Samples were subjected to three laboratory aging protocols: short-term ( R ), short-term with UV exposure (R + UV), and extended long-term (R + P, pressure aging vessel (PAV) 40 h). Aging effects were evaluated by Fourier-transform infrared (FTIR) spectroscopy and rheological tests, including multiple stress creep and recovery (MSCR), stress relaxation, and frequency sweeps. Results showed that extended long-term aging caused the most severe changes, with FTIR indices rising to 1.53–1.87, recovery decreasing more than twofold, and stress relaxation modulus increasing by up to 2 times, accompanied by a 28–34% loss in relaxation capacity. Base binder B (low saturates, high aromatics) produced the weakest unaged performance but the largest aging-induced changes, while base binder C (balanced maltenes) provided the most stable results. Radial SBS (molecular weight 118–144 kDa) was more prone to oxidative transformations than linear SBS (molecular weight 77–79 kDa). Vinyl content had only a minor effect, although high-vinyl SBS (31.3%) showed slightly lower resistance to UV aging compared with low-vinyl (7.3%) SBS. Overall, PMB durability is governed by both polymer architecture and base binder chemistry. Linear SBS combined with balanced maltene base binders offers the best resistance to UV and oxidative aging.

In English for specific purposes, students need to manage dialogue as well as prepared delivery; yet, classroom speaking tasks often underemphasize how prepared language is extended into co-constructed interaction. This study examines how a panel discussion format, taught as an extension of preparation, fosters speaking, defined as the coordinated use of planned contributions, reasoning, audience alignment, and collaborative synthesis. The study was conducted at Vilnius Gediminas Technical University, Lithuania, with 68 undergraduates in creative programmes. The intervention consisted of preparation with explicit language aims, a peer-moderated panel with audience questions, and a structured debrief. Evidence was collected through a Mentimeter survey. Results indicate increased confidence in discussion, faster retrieval of discipline-specific vocabulary, turn-taking, and readiness to move beyond rehearsed material. The contribution lies in operationalizing multifaceted speaking as an interactional route to creative communication within English for specific purposes through a repeatable panel design with transfer to pitches, critiques, and short presentations.

The Indian Railway Catering and Tourism Corporation (IRCTC) operates one of the most heavily utilized railway reservation systems globally, reflecting the central role of Indian Railways (IR) as an affordable and essential mode of transportation across the country. However, selecting an appropriate train remains a complex decision-making task for passengers, primarily due to the uncertainty surrounding ticket availability on preferred travel dates. To address this challenge, the present study proposes a hybrid decision support system designed to aid passengers in selecting optimal train options, particularly for tourism-related travel. This research employs a Dominance-Based Rough Set Approach (DRSA) within a Multi-Criteria Decision-Making (MCDM) framework to analyze preference-based data and extract interpretable decision rules in the form of “if...then” statements. These rules assist decision makers in evaluating multiple train-related criteria simultaneously. For comparative purposes, the Classical Rough Set Approach (CRSA) is also implemented to identify the relative advantages and limitations of both rough set methodologies in addressing train selection complexity. In addition, the study integrates machine learning techniques by utilizing two predictive models – Extreme Gradient Boosting (XGBoost) and Support Vector Machine Classifier (SVMC) – to estimate overall train ratings based on user preferences and historical data. Model performance is evaluated using standard classification metrics, including accuracy and precision. By combining MCDM techniques with machine learning algorithms, the proposed hybrid framework enhances the train reservation experience, enabling passengers to make informed, preference-aligned travel decisions through the Indian Railways reservation system.

A support is a machine element that transmits loads to the base or other structures. A simple support is designed to withstand forces acting in the longitudinal direction, and a flexible support is designed to withstand forces in both longitudinal and transverse directions. The possibilities for the use of flexible supports are very wide. In precision mechanics, flexible supports are used in positioning systems, micropositioning systems, vibration damping systems, as well as in fastening applications requiring adjustment and other structural configurations. The main problem of flexible supports is ensuring stability. This work examines the dependence of the stiffness of supports used in mechanical and mechatronic systems on the material and dimensions of the flexible element. A theoretical analysis of the stiffness of flexible supports, finite element method (FEM) modeling, and experimental stiffness research were performed. A special stand was manufactured for experimental research. A research methodology was developed, according to which experimental research was carried out. After theoretical, FEM and experimental research, the results obtained were compared and conclusions were formulated. The obtained data can be practically used in the research and design of new flexible supports that ensure desired stability, as well as in the improvement of existing support structures.

Science and technology parks (STP) play a special role in the knowledge-based economy. An STP is a place where scientific ideas and thoughts are transformed into products and services. As socio-economic systems, they create a basis for the development of start-ups, knowledge transfer, cooperation between enterprises and scientific institutions and, thus, the commercialization of innovations and other research results. For this reason, the focus of STP functioning is the object of scientific research. On the other hand, there are enough unresolved issues. In most cases, individual aspects of STP activities are upset. There is a lack of research related to the assessment of park activities in a comprehensive, systemic manner. Without a general indicator, it is impossible to compare the activities of individual STPs, it is impossible to study their impact on the performance of enterprises, etc. The aim of the article is to develop a methodology for a comprehensive quantitative assessment of the activities of scientific and technological progress and to test it using the example of parks in Poland.

This research examines the relationship between Self-Determination Theory (SDT) constructs and objective measures of worker performance in an international call center environment. It analyzes workplace learning as a continuous process where formal training intersects with on-the-job skill application, investigating why employees exposed to identical learning experiences demonstrate different levels of knowledge transfer in their daily work. It focuses on the relationship between satisfaction of psychological needs (autonomy, competence, and relatedness) and seven aspects of performance. With the high training intensity needed in call center operations, the research gains an insight into adult learning motivation and its linkage to post-training performance results in diverse competency areas. Using confirmatory factor analysis and structural equation modeling, this research tests the hypothesis that self-determination-oriented workers will exhibit superior performance results. Outcomes are useful in explaining quality of motivation in work environments and organizational performance implications.  .

This article presents a thorough methodology for the planning of new urban metro systems by incorporating multi-regional empirical data alongside city-specific correction factors. This approach facilitates a more precise estimation of the necessary line length and the number of stations required. This research presents a systematic algorithm that produces various design alternatives, taking into account factors such as population, geographical area, and transportation demand, thereby tackling the significant variability noted among current global metro systems. A notable aspect of this work is the incorporation of operational performance forecasting, which includes elements such as crime rates, emergency response times, passenger density, and renewable energy utilization. This illustrates that these indicators are influenced not solely by the physical parameters of the system but also by wider social and infrastructural contexts. The proposed methodology offers urban planners a versatile, data-informed instrument that can be utilized across various urban contexts, thereby improving initial decision-making processes that promote sustainable metro development.

Cryptocurrency markets are highly volatile, creating challenges for accurate risk management and forecasting. As digital assets become more integrated into financial systems, understanding their volatility dynamics is essential for investors and policymakers. Previous research has primarily applied standard GARCH models to cryptocurrencies, often neglecting advanced specifications that capture asymmetry, regime-switching, and long-memory effects. This limits the accuracy of volatility forecasts and fails to reflect the unique behaviour of digital assets. This study aims to identify the most effective GARCH-class models for forecasting volatility in Bitcoin, Ethereum, Binance Coin, and Ripple. We analyse daily returns from August 2017 to December 2024, applying eight advanced GARCH specifications: EGARCH, GJR-GARCH, FIGARCH, HYGARCH, MSGARCH, CS-GARCH, and Log-GARCH. Hyperparameter tuning is conducted via grid search across lag orders (p, q ∈ [1, 5]), mean equations, and error distributions. Model performance is evaluated using AIC, BIC, RMSE, and MAE. Results show that MSGARCH and EGARCH outperform symmetric and short-memory models, highlighting the importance of regime-switching and leverage effects. FIGARCH provides the best fit for Bitcoin and Ethereum, confirming long-memory persistence. Skewed Student’s t and GED distributions improve accuracy by capturing heavy tails and asymmetry. These findings demonstrate the limitations of standard GARCH models and underscore the value of advanced specifications in modelling cryptocurrency volatility. The study offers practical insights for traders and risk managers, contributing to more robust forecasting in non-stationary markets. Advanced GARCH models significantly enhance volatility prediction for digital assets. Future research could extend this framework to other speculative instruments or integrate machine learning techniques to further improve performance.