SCOPUS

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.

The caporalato system, a widespread form of labor exploitation in Italy and Europe, relies on caporali – intermediaries who manage and control migrant workers. Despite their central role, caporali remain understudied due to the difficulty of accessing and observing them directly. This study addresses this gap by analyzing narratives from three distinct datasets of recorded interactions with agricultural workers, using a dual framework of emerging social representations and ideologies. The analysis reveals that caporali are represented through overlapping and dynamic identities, including businessmen, criminal bosses, paternal figures, and religious leaders. These representations often merge, creating hybrid constructs that allow caporali to legitimize their power and maintain control. Similarly, the ideological values tied to productivity, loyalty, fear, and control interact, reinforcing the caporale’s dominance and making resistance difficult. This study highlights how the fluidity of representations and ideologies sustains the resilience of the caporalato system, offering new insights into its persistence and adaptability.

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.