Railway tracks at bridge approaches experience significant vertical stiffness transitions, leading to adverse effects such as settlement and increased dynamic loads, accelerating track degradation. This study explores various structural solutions, including geosynthetics, reinforced ballast, transition slabs, under sleeper pads (USPs), under ballast mats (UBMs), jet grouting, and special rail fasteners. Despite their application, these solutions often fail due to their static nature. This paper introduces an adaptive approach using special rail fastenings with real-time adjustable stiffness. This system dynamically modifies rail support characteristics based on train speed and track conditions, improving track durability, ride quality, and maintenance strategies. The findings demonstrate the potential of adaptive systems to enhance railway infrastructure performance.

Unmanned aerial vehicles have become essential tools in a wide range of applications. As drone operations grow more complex, the accurate prediction of battery runtime and aerodynamic flight safety risks, particularly those caused by wind, becomes increasingly important. This study employs the Wind Resistance Risk Index (WRRI), to quantify the impact of wind conditions on UAV performance. While several predictive models have been introduced to address these issues, many have not been thoroughly validated under real operational conditions. This study focuses on the post-validation of a previously developed fuzzy-based predictive model, using telemetry data collected from four UAV missions. Key flights and battery parameters were analyzed. The results demonstrate that real-world flight data provide valuable insight into model reliability and highlight discrepancies that can guide future model refinement. This work contributes to enhancing UAV safety by bridging the gap between theoretical predictions and empirical evaluations, specifically under varying wind conditions.

Optimizing the operating temperature of lithium-ion batteries is critical for safe, reliable, and efficient cell operation. Manufacturers’ recommendations vary in this area, which is primarily determined by the cells’ chemical composition and internal structural characteristics. Most manufacturers define the maximum charging voltage level as the same or close to the same value, but there are significant differences in the lower threshold voltage. Lithium-ion cells exhibit increased internal resistance at lower state-of-charge levels, resulting in elevated heat generation during operation, with intensity proportional to the depth of discharge. However, using a too low voltage threshold causes a significant loss of usable capacity, which reduces the cell’s energy utilization. The present research aims to define and analyze the optimal value of the lower voltage threshold more precisely, considering both thermal development and usable capacity aspects. A further objective is to determine an optimal energy safety margin level that provides a suitable compromise for longer-term storage. Different 18650 and 21700 standard lithium-ion cell types were tested using various load profiles. The results show that the two cell formats have different electro-thermal behaviors. The 21700 cells show a clear increase in thermal efficiency at around 3.1 V. In contrast, the 18650 cells have a heating pattern that depends heavily on the load. This requires selecting a cutoff that adapts to the discharge rate to prevent excessive thermal stress. These findings indicate that a fixed lower threshold voltage for all cells is not ideal. Instead, we need cutoff strategies that are specific to each cell and can change dynamically. The TER-based evaluation introduced in this work provides a practical framework for defining these adaptive limits. It may improve control in battery management systems in real-world applications.