DOAJ

Municipalities are increasingly recognized as key actors in addressing climate change adaptation. However, a significant challenge remains in translating legislative authority into effective, action-oriented implementation. This study evaluates municipal climate adaptation communication and its public perception in Lithuania. A mixed-methods approach was employed, combining data from a representative survey of Lithuanian residents (n=1013) and a content analysis of climate-related communications from all 60 municipalities in Lithuania, gathered from their official websites and Facebook accounts throughout 2024. The survey results revealed significant differences in how citizens perceive and access municipal climate information across key demographic groups. Notably, lower education levels are associated with higher municipal information scores (ANOVA: F = 3.92, p < 0.001), potentially indicating more effective outreach to this group but also raising questions about information interpretation. Significant variations were also found in municipal information scores across occupational categories (ANOVA: F = 3.63, p < 0.001), highlighting the need for tailored communication strategies that consider the diverse needs and constraints of different professions. The content analysis of municipal communications revealed that adaptation-specific messaging is often limited, comprising only 24.7% of climate-related messages. Interestingly, rural municipalities demonstrated higher climate communication intensity (1.83 messages per 10,000 inhabitants) compared to major urban centres (0.58 messages per 10,000 inhabitants), suggesting a greater emphasis on public outreach in less densely populated areas, though urban centres focused more on adaptation messaging. These findings emphasize that simply raising awareness is insufficient. To truly close the gap between authority and capacity, municipalities need to adopt nuanced, targeted communication strategies that not only inform but also empower citizens with actionable adaptation guidance. Enhanced citizen engagement through strategic communication is, therefore, essential for transforming legislative intentions into meaningful and effective climate adaptation actions.

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.

This study explores the role of social media algorithms in social media activism actions for the representation of LGBTQ+ civil partnerships. Social media platforms, particularly Facebook, categorize users in ways that expose them primarily to targeted content, what represents social media bubbles and echo chambers, which can distort public perception and amplify extreme viewpoints. Methodology: By utilizing a combination of qualitative and quantitative content analysis, alongside a review of relevant academic literature, this research identifies the predominant themes within these digital environments. Results: The findings reveal the existence of distinct filter bubbles surrounding the topic of same-sex civil partnerships, characterized by a lack of neutrality and a polarization of opinions. Six key themes emerged from the content analysis: a populist framing of legislation, Lithuania's position within a global context, perceptions of family, legal and social implications, the discourse on equal rights and protections, and external views of the LGBTQ+ community. Discussion: The analysis demonstrates that while clear divisions exist between supporters and opponents of civil partnership legislation, neutral perspectives are largely absent, with media sources remaining passive and ineffective in facilitating a balanced dialogue. This study highlights the crucial role of algorithms in shaping social discourse and the implications for LGBTQ+ activism in Lithuania.

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 work supports the circular economy and sustainable material by facilitating the creation of low-carbon materials with enhanced elimination of nutrients from wastewater, thereby assisting in preventing eutrophication. Porous geopolymers, owing to their distinctive pore structure and numerous superior properties, including noise reduction and thermal insulation, have a wide range of potential applications in the building sector, chemical industry, and water treatment. Developing low-carbon-footprint porous geopolymer materials is an important step toward creating multipurpose lightweight materials that can serve as structural materials and, at the same time, as adsorbents. In this study, it was revealed that the porous material created during the hydrothermal synthesis of (lime–Portland cement-based aerated composition), by replacement of sand with wood biomass bottom ash (WBA), can be used as porous aggregates (PA) for adsorbent development. PA was produced with an apparent porosity of 65%, a density of 610 kg/m3, and a compressive strength of 2.0 MPa. The effectiveness of employing an air-entraining additive (AEA) and creating PA in geopolymers was tested. A different-molarity activator was used, and wood biomass fly ash (WFA) and metakaolin (MK) waste were used as precursors for the synthesis of porous geopolymers. Using an air-entraining admixture in geopolymers allows for the production of lightweight geopolymers with densities up to 1400 kg/m3, compressive strengths up to 8.0 Mpa, and apparent porosities up to 38.4%. Such properties, together with their low cost, offer good prospects for geopolymers in the construction industry. By utilizing PA in the geopolymer composition, a lightweight geopolymer (GPA) with a density of 985 kg/m3 and a compressive strength of 3.9 Mpa, with 42.0% apparent porosity, was obtained. The materials effectively removed phosphorus from biologically treated wastewater: PA had an efficiency of up to 82.5%, the geopolymer with AEA had an efficiency of up to 88.4%, and GPA had an efficiency of up to 97%. The created GPA enhances the adsorbent’s sorption capacity, resulting in extremely high phosphorus uptake efficiency.

It is now crucial to accurately monitor the state of health (SoH) of batteries in a setting where the use of electric vehicles (EVs) and renewable energy technologies is still growing. To solve this issue and evaluate the SoH, this paper makes use of deep learning technology. The suggested method incorporates voltage, current, and temperature data, which are important indications of the SoH and can potentially be obtained directly from the battery management system (BMS). Although deep neural networks (DNNs) have previously been employed for SoH estimation, our study distinguishes itself by implementing a robust, completely configurable DNN application in MATLAB/Simulink R2019a. This design enables the adjustment of activation functions, layer depth, and neuron count to adapt to different battery aging conditions. To achieve optimal performance, numerous configurations were examined, highlighting the relevance of hyperparameter setting. Our technique avoids traditional feature engineering while providing a practical, adaptive, and accurate SoH estimate framework appropriate for real-world integration. The precision of the improved model was then verified against a Li-ion battery dataset with various discharge profiles given by the national aeronautics and space administration (NASA). The collected findings revealed that the proposed method is more accurate and robust than other regularly used models. The DNN model achieved a Mean absolute error (MAE) of 1.433% and a Coefficient of determination of 0.99998, outperforming previous methods such as CNN-BiGRU, which reported an MAE of 2.448% in a recent publication. This study demonstrates the reliable performance of the DNN in predicting the SoH of Li-ion cells.

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.

Organic lake sapropel was used as a binder to create a composite for thermal insulation and construction. To obtain good composite properties, the sapropel itself and the contact zones between the sapropel and the filler were modified. To improve the adhesion of wood chips to sapropel, the chips were treated mechanically, thermally, and chemically to roughen and uneven the surface and impart hydrophobic properties. To improve the adhesion properties of sapropel, it was mechanically cleaned and activated to increase its surface area, reduce impurities that destroy the contact zones, and increase the number of active bonds. To optimise the properties of the composite, the wood chip-to-sapropel ratio, the pressure applied to the mixture, and the use of a hydrophobizer and a flame retardant were varied. Binder amounts ranging from 8 to 72% by mass of sapropel relative to wood chips were used to bind the composite. It was found that the amount of sapropel determines the composite's density, thermal conductivity, and compressive strength. The optimal ratio among material density, thermal conductivity, and compressive strength was determined. It was found that using 32% sapropel by mass of wood chips yields a composite density of 251 kg/m3, a compressive stress at 10% deformation of 141 kPa, and a thermal conductivity coefficient of 0.0586 W/(m·K). Using 5% of expanded graphite, a non-combustible composite was obtained, and the use of natural linseed oil and calcite lime reduced the composite's water absorption from 12% to 1.7%.

Visual saliency modeling has achieved high predictive performance in natural image domains, yet its generalization to abstract art remains limited by the lack of explicit semantic structure and the scarcity of eye-tracking data. In such semantically ambiguous contexts, understanding the underlying drivers of attention is as critical as predictive accuracy. This paper presents an interpretable, ’white-box’ saliency framework tailored to abstract art, which constructs predictions through a weighted combination of 35 modular heuristics grounded in perceptual psychology and art theory, including contrast, grouping, isolation and symmetry. Heuristic weights are optimized via a genetic algorithm and refined by a context-aware modulation mechanism that adapts to image-level visual features. Evaluation against eye-tracking data from 40 abstract paintings demonstrates that the model with the expanded activation variant produces stable, meaningful predictions while achieving a competitive KL-divergence score (1.11 ± 0.55), which is comparable to the SalGAN baseline (1.11 ± 0.53). Analysis of the optimized weights reveals strong contributions from contrast, texture, and grouping mechanisms, while nearly half of the heuristics, including most horizontal symmetry heuristics are systematically pruned by the model. Moreover, context-aware modulation reveals that these weights are not static but shift dynamically based on image-level features such as edge density and intensity variation. By prioritizing transparency over raw predictive performance, this study demonstrates that explainable saliency models can function as robust investigative tools for decoding the principles of human visual perception in data-scarce domains.

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.