To estimate the rail axial force of high‐speed railway ballastless track, the reasonable index without complex measuring or error correction process is proposed. Taking the ballastless track structure in high‐speed railway as the research object, the wave motion of periodic ballastless track is studied using the wave finite element method. It is found that some standing wave modes are linearly correlated with the rail axial force and thus can be considered as the basic indices for rail axial force estimation. A further in situ experiment according to the modal test method is performed and the feasibility of different wave modes for estimating rail axial force is discussed. Experiment results show that the lateral wave mode coincides well with the theoretical result while there is a large difference for the vertical wave mode. To explicate the difference, the temperature‐dependent properties of the fastening are tested additionally. Parametric analysis shows that the frequency shift of vertical wave mode is greatly affected by the fastening temperature‐dependent characteristics including the rail pad, elastic pad, and fastener clamping force, while the frequency shift of lateral wave mode is mainly determined by the rail axial force.

Interior noise reduction has become an important concern in railway operating environments due to the influence of increased speeds and reduced vehicle weights on energy efficiency. Two types of measures, rail grinding and rail dampers, were proposed to reduce the internal noise within railway metro vehicles on a particular curve section of a Chinese metro line where the internal noise within metro vehicles is so excessive when the train passes that drivers and passengers complain. The dynamic property and track decay rate of the track system were carried out through mechanical shock method first. Then the measurement of rail vibration performance, external noise and internal noise were conducted when the metro train passed through this track section at the required speed. The rail corrugation with a wavelength of 0.06 m is a major cause of the excessive internal noise. After the application of rail grinding, the overall A-weighted sound pressure levels reduced from 85 to 82 dB due to the removal of the rail corrugation and other short-wave irregularities. Moreover, after the further use of rail dampers, the overall A-weighted sound pressure levels within the carriage further decreased from 82 to 74 dB, resulting from the reduction of the wheel/rail external noise transmitted into the vehicle body through leaks in the vehicle body. Therefore, the noise reduction effect of the two presented measures is significant.

Rail transit has now been widely popularized, with the demand for the electrical energy required for railway operations showing a significant upward trend. Traditional power supply systems face challenges meeting railway operations’ continuously expanding energy consumption and the global vision for green, low-carbon transportation. Therefore, green microgrids are proposed to be integrated with them, constructing a green microgrid structure for the rail transit system to enhance its safety and stability. This paper discusses the structure of green microgrids and conducts a comprehensive review and comparative analysis of various green energy sources, energy storage systems, and advanced control strategies in green microgrids. The application of microgrids in rail systems is forward-looking, and future rail transit is expected to achieve a deep integration of self-sufficiency in energy, intelligent scheduling, and sustainable development.

To overcome the ill-conditioned matrix problem of the traditional transfer matrix method, the Floquet transform method and supercell technology are used to study the defect states of the periodic track structure. By solving the equations of the supercell directly, the propagation characteristics of elastic waves in the track structure with defects are analyzed. The existence of defects destroys the periodicity of track structure, thus resulting in the formation of defect states within the band gaps. Moreover, the elastic wave is localized near the defect position at the frequency of the defect state. The formation mechanism of the defect state in track structure can be explained by the local resonance at the defect. With the expansion of the defect range, the number of local resonance modes that can be formed near the defect increases, thus generating multiple defect states. Furthermore, the defect state enhances the vibration of the structure adjacent to the defect. Therefore, the vibration transmission coefficient in a finite-length range can be used to detect the defect characteristics in the track structure, and the defect degree can be evaluated by the peak frequency of the vibration transmission coefficient within the band gap.

Large Language Models (LLMs) can generate creative and engaging narratives from user-specified input, but maintaining coherence and emotional depth throughout these AI-generated stories remains a challenge. In this work, we propose SCORE, a framework for Story Coherence and Retrieval Enhancement, designed to detect and resolve narrative inconsistencies. By tracking key item statuses and generating episode summaries, SCORE uses a Retrieval-Augmented Generation (RAG) approach to identify related episodes and enhance the overall story structure. Experimental results from testing multiple LLM-generated stories demonstrate that SCORE significantly improves the consistency and stability of narrative coherence compared to baseline GPT models, providing a more robust method for evaluating and refining AI-generated narratives.

As railway transportation rapidly expands, operational safety, including regular inspection of wheel polygonal wear, rail fastener clip failure, and rail corrugation, becomes increasingly crucial to ensure passenger well-being. Rail corrugation, a prevalent mode of railway track wear, is particularly challenging to manage due to its impact on operational safety. Current inspection methodologies are labor-intensive, dependent on external power supplies, and conduct damage identification offline. Addressing these challenges, this paper introduces an energy self-sufficient approach utilizing a nonlinear piezo-electro-magnet (PEM) energy transducer. This device generates multi-stable phase trajectories under the dynamic loads of vehicles operating on railways with corrugation defects. The piezoelectric (PE) energy transducer captures signals that identify sensitive mutation points in wavelet spectrograms. Concurrently, the electromagnetic (EM) energy harvester supplies power for the wireless transmission module to operate, generating 18.67 mW under normal rail excitation and 82.18 mW under corrugated rail excitation. The wireless transmission module then transmits rail corrugation status to the railway administration information center. This energy-self-sufficient approach enables online rail corrugation detection by leveraging multi-stable phase trajectories and wavelet analysis. Hence, our proposed solution, powered by piezoelectric-electromagnetic energy flow, offers an efficient, less labor-intensive means to enhance railway operational safety by promptly identifying rail corrugation defects.