Science Citation Index Expanded (Web of Science)

Data-driven decision-making has become a pivotal approach to enhancing processes in supply chain management (SCM), particularly in complex supplier selection and procurement strategy (SSPS). This study develops a novel data-driven SSPS model integrating multi-objective optimisation and real-world supplier performance data to improve supply chain efficiency, sustainability, and resilience against disruptions. Unlike traditional models focusing on limited or static scenarios, this framework simultaneously considers procurement costs, delivery delays, defect rates, sustainability performance, and disruption risks in multi-product procurement planning. The model employs augmented max–min fuzzy multi-objective linear programming, leveraging historical data to balance conflicting objectives and achieve a globally optimised solution. Furthermore, an innovative supplier negotiation mechanism supported by simulation-based analyses explores capacity adjustments, enabling win-win procurement outcomes. Experimental results show that, through capacity negotiation, procurement orders could be reallocated to only three suppliers instead of four, thereby reducing supplier management complexity. Notably, when the top-performing suppliers agreed to increase capacity by 45%, the average overall procurement performance improved from 81.77% to 88.28%, enhancing all objectives without additional costs. These results also demonstrate meaningful improvements in five objectives without incurring additional procurement costs. The findings underscore the practical potential of data-driven modelling in developing intelligent, sustainable, and resilient procurement decision-making frameworks.

Railway systems rely heavily on sensor networks to ensure the safety, reliability, and efficiency of operations. These networks monitor parameters such as track vibrations, structural integrity, and environmental conditions. However, sensor nodes deployed along tracks or on train components often operate in remote environments with limited power supplies. This study adapts the Threshold-sensitive Energy Efficient Sensor Network (TEEN) protocol to railway applications to optimize energy consumption and enhance data accuracy. Two methods for defining the Soft Threshold (ST) value are proposed: (1) using the difference between consecutive measured values and (2) using the rate of change over time. Experiments simulate railway scenarios, analyzing energy savings and data accuracy under various ST values. Results show that the second method significantly improves energy efficiency and data integrity, ensuring reliable monitoring with minimal energy expenditure. The proposed method achieved energy savings of up to 95% while maintaining data accuracy with an RMSE < 1 and Pearson’s correlation coefficient > 0.95. These findings have implications for extending sensor lifetimes and enhancing the robustness of railway monitoring systems. Future research will explore scalability in large railway networks and integration with IoT platforms.

To date, many exoplanets have been discovered which exhibit distinct characteristics not observed within our own Solar System, raising numerous unresolved questions regarding their compositions, atmospheres, formation processes, and evolutionary pathways. Several missions have been dedicated to enhance the understanding of the exoplanets like James Webb and Hubble Space Telescopes. However, they have a limited spectral range and resolution to allow for a complete characterisation of atmospheric dynamics. The Aetheras mission proposal was developed at the Summer School Alpbach 2023 and presents a satellite mission to overcome these limitations to better understand the formation, evolution and characteristics of exoplanets. This mission aims to unravel key enigmas in contemporary exoplanetary research by investigating atmospheric escape mechanisms and measuring proxies of magnetic fields’ influence on atmospheric loss. Focusing on objects in the Radius Valley and the Hot Neptune desert, the mission seeks to discover their origins. By defining mission needs and designing a potential instrument based on derived requirements, a space mission architecture is envisioned to fulfil the proposed mission objectives. A spacecraft design has been made with top down systems engineering approach. Employing transit spectroscopy in the near-infrared range (1070nm to 1090nm) and ultraviolet range (115nm to 285nm) outside the geocoronal influence, the mission gains valuable insights to planetary formation and evolution. The mission architecture comprises a 1302kg spacecraft equipped with a 1.5m main mirror to observe the sky over a mission lifetime of three years.

Rubber-metal springs are widely utilized in industrial applications, particularly as vibration absorbers, due to their ability to mitigate dynamic loads. The dynamic stiffness of rubber-metal springs plays a crucial role in determining the natural frequency of a system, as natural frequency is directly linked to dynamic stiffness. Therefore, the accurate determination of dynamic stiffness is essential when selecting an appropriate rubber-metal spring for a given application. However, the assessment of dynamic stiffness presents a significant challenge due to the complex interaction between rubber and metal components, particularly when considering the viscoelastic properties of rubber and the geometric properties of the spring. Rubber’s viscoelastic response and how it changes under different strain rates is fundamentally rooted in the micro- and meso-scale configuration of polymer chains, filler particles, and their bonding to metal components. Consequently, dynamic stiffness is often approximated using static stiffness measurements, which simplifies the problem but may lead to inaccuracies in predicting the true dynamic behaviour of the spring. In this paper, we present an experimental method for dynamic stiffness assessment using an electrodynamic shaker, which allows for a more accurate characterization of the spring’s response to dynamic loading. This method is compared to an analytical approach based on static stiffness, highlighting the limitations of the latter approach. Furthermore, we propose an improved range for calculating dynamic stiffness from static stiffness, enhancing the predictive accuracy for dynamic behaviour.

In this study, a mathematical model of a railroad excavator was developed, along with a program to determine the spectrum of allowable loads in the entire working space of the excavator, according to which determines the spectrum of possible track loads at the excavator's supports on the rails. The results show that the track loads vary and depend on the position of the excavator and the size of the load. The study's findings can inform the structural analysis of rail elements and the selection of manipulator tools to ensure excavator stability.

Recently, technological innovations in road pavement production have been focused on extending the service life of road pavements by enhancing the anti-ageing resistance of the road bitumen in the asphalt mixture. In this area, high-cost polymer nanofibres with difficult compatibility to road bitumen are also used. Therefore, this study examined the possibility of using a new, cost-effective and environmentally friendly polymer modifier in the form of polythiophene nanofibres (PT_NFs) in the design of road pavements with increased service life. The research included an analysis of both modification as well as short-(TFOT) and long-term(PAV) ageing processes in terms of changes in the physico-chemical, structural and surface properties of road bitumen. The research showed that, relative to unmodified road bitumen, 1% w/w addition of PT_NFs caused an increase in penetration of ∼ 13%, a decrease in softening point of 0.5°C and an increase in dynamic viscosity of ∼ 6%. However, a 5-fold increase in the amount of nanofibres to 5% by weight resulted in a decrease in penetration of ∼ 0.5%, an increase in softening point of 1.4°C and an increase in dynamic viscosity of 24%. The obtained results indicate that use of PT_NFs modified road bitumen in the production of asphalt mixtures can effectively extend the service life of road surfaces. Their use reduces the VAI and SPI values by ∼ 13% and ∼ 12%, respectively. Furthermore, the use of PT_NFs also inhibits the oxidation of C and S atoms during road surface use by ∼ 25% and ∼ 90%, respectively.

This study evaluates a compression-ignition engine operated on neat diesel (D100), neat hydrotreated vegetable oil (HVO100), and dual-fuel (D–F) modes in which HVO serves as the pilot while the gaseous fuel is natural gas (NG), simulated biogas (BG: 70% CH4/30% CO2), or hydrogen-enriched biogas (BG + H2, 10–30 vol% of the CH4 fraction). The gas energy share (GES) varies from 0% to 80%. Relative to diesel, HVO100 shortens ignition delay, lowering premixed heat release, ensures similar brake thermal efficiency, and lowers CO, HC, NOx, smoke, and CO2. In D–F operation, CO2 in BG dilutes the charge, narrowing flammability and slowing combustion at high λ (>1.9). NOx decreases by up to ~90%, but incomplete-combustion pollutants rise sharply (CO up to 14 time, HC up to 6 time), smoke increases by ~70%, CO2 by ~22%, and efficiency drops by as much as ~45% relative to HVO100. Hydrogen acts as a main factor by widening lean flammability limits, accelerating burning, and stabilizing D–F combustion. With 30vol% H2 (HVO_BG + H30), medium-load efficiency approaches that of neat HVO100; CO and HC drop by ~30% and ~45% versus HVO_BG (though they remain above HVO100), while smoke, NOx, and CO2 decrease concurrently—by up to ~80%, ~53%, and ~12%, respectively, relative to HVO100. Considering life-cycle effects – biomass CO2 uptake and oxidation of biogenic CH4 – hydrogen-assisted dual biofuels further reduce greenhouse-gas impacts compared with fossil diesel.

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 research examines the feasibility of using waste cooking oil (WCO) as a substitute for traditional diesel fuel in internal combustion engines, with a focus on biodiesel production. The aim of this research is to evaluate the effects of WCO–diesel blends on engine performance, with particular emphasis on critical metrics including brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The study utilizes artificial neural networks (ANNs) to model and forecast the performance and emission characteristics of engines operating with different fuel combinations. The study employs a methodology that involves conducting experiments to evaluate the mixtures of waste cooking oil (WCO) and diesel fuel in diesel engines. Furthermore, artificial neural networks (ANNs) are employed to develop models for predicting engine performance. The analysis focuses on critical metrics, including BSFC and BTE, under various operating conditions. This research aims to improve sustainable energy solutions by demonstrating the benefits of alternative fuels and advanced artificial intelligence (AI) prediction models in automotive applications.

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.