INSPEC

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 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.

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.

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.

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.

The rapid evolution of smart building technologies has transformed the hotel industry, necessitating structured methodologies for evaluating building intelligence. This research, dedicated to engineering problems, proposes an integrated decision-making model that combines fuzzy Shannon entropy and fuzzy multi-objective optimization on the basis of ratio analysis (MOORA) to assess the intelligence level of buildings within the hospitality sector. The model systematically determines the relative importance of intelligence criteria, including engineering, environmental, economic, social and cultural, technological, and energy conservation criteria. By leveraging fuzzy Shannon entropy, the framework objectively assigns weights to criteria based on information distribution, minimising subjective biases in evaluation. Fuzzy MOORA is then applied to rank alternative intelligent buildings in hotels, ensuring an accurate comparative assessment. The proposed model is tested on real-world hotel data, demonstrating its effectiveness in identifying optimal intelligent building configurations. The results of applying fuzzy Shannon entropy reveal that human comfort, the emission of greenhouse gases (pollution), and system integration are the most important sub-criteria. Finally, by applying the importance of the criteria in the fuzzy MOORA model, the intelligence levels of hotels are evaluated. The results show that the Parsian Kowsar, Piroozy and Sepahan Hotels are the best hotels based on the intelligent building criteria.

To ensure the proper delivery of goods to the end user, it is necessary to overcome a number of challenges and obstacles. To optimize transport routes, strengthen customer relationships, and enhance customer satisfaction, timely last-mile delivery plays a crucial role. To achieve various benefits in urban areas, the trend of installing parcel lockers at optimal locations has emerged, which is the focus of this paper. The location problem of the Zvornik distribution center for the X Express company was considered based on seven locations and seven criteria. The applied methodology involves the integration of Fuzzy PIvot Pairwise Rela-tive Criteria Importance Assessment (PIPRECIA) and Alternative Ranking Order Method Accounting for two-step Normalization Modified (AROMAN-M) for assessing the values of the criteria and ranking the locations. The results obtained indicate three locations as the most suitable for parcel locker installation, taking into account both the preferences of decision-makers and the territorial structure of the distribution center.

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM10 (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM10 surface estimation. This study is an assessment of a national-scale, daily PM10 estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM10 observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R2) of 0.739 in validation and R2 = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m3, and root mean square error (RMSE) was 3.98 µg/m3. The results indicate a slight tendency to overestimate PM10 concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM10 prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM10 concentrations across monitoring stations, with R2 ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection.