Association of American railroads (AAR) standard automatic couplers are designed for much higher capacity than the normal operating loads. However, failure of knuckles and coupler bodies is still a common occurrence. Recent studies have shown that fatigue is the main reason behind such failures below the expected load. Moreover, knuckle failures occur more frequently than coupler body failures, which cause operational disruptions and also influence overall coupler life because of nonconforming contact between a new knuckle and an old coupler. In addition to new and old counterparts, undesired contact conditions are often the case with the new assembly due to casting-based manufacturing inaccuracies.

A study is thus carried out in this paper to understand the variation of load transfer paths and its consequences caused by dimensional variability. A finite element model of an E-type coupler’s knuckle is developed and different possible contact conditions of the knuckle with the coupler head are simulated. Knuckles generally fail in pulling mode, during which the possible contacting elements of knuckle are pulling lugs, pin protector regions and pinholes. Due to dimensional variability, contact conditions may exist where an individual or a combination of these elements are in contact.

Simulation results indicate that under regular operational conditions, having only the pulling lugs in contact reduces the risk of knuckle failure and maintains assembly integrity even if the knuckle fails. However, under extreme loading conditions, the safest scenario is when both pulling lugs and pin protector regions are in contact.

These findings are believed to assist in defining the dimensional variability limits to ensure the desired contacts between the mating surfaces of the knuckle and coupler body of railway couplers of AAR type. This work contributes to understanding implications of dimensional variability in the railway couplers. The insight presented are useful in design, manufacturing and maintenance of railway coupler’s knuckle.

Amidst an increasingly severe cybersecurity landscape, the widespread adoption of Xinchuang endpoints has become a strategic imperative. Governments and enterprises have established terminal localization as a critical objective, aiming for comprehensive indigenous replacement through rapid technological iteration. Consequently, Xinchuang systems and Windows platforms are expected to coexist over an extended period. This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.

Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security, this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems. The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments. It automates the inspection of core baselines, including account password complexity, default shared service status and patch installation status. Furthermore, a comprehensive scoring model is established by incorporating differentiated weights for account security, patch management and log auditing, ultimately generating visualized risk reports to facilitate remediation prioritization.

This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices. Through a scalable detection engine and quantitative scoring model, the system can promptly identify and remediate potential risks, thereby reducing the attack surface and mitigating intrusion risks. However, on certain domestic chip architectures, compatibility issues persist in detecting specific configuration items. Further improvement in hardware–software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.

Through in-depth research on security baseline configurations across multiple operating systems, this study implements an automated and visualized baseline verification methodology. This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust, national-level cybersecurity defense framework.

This study investigates the impact of flagship trains on high-speed railway capacity utilization and develops a brand value-oriented optimization framework that balances service quality enhancement with operational efficiency.

A mathematical optimization model based on integer programming is developed, incorporating flagship train constraints into capacity optimization. Case studies compare scenarios with and without flagship train considerations using the Beijing–Shanghai High-Speed Railway data across 20 experimental groups.

Operating flagship trains with hourly departure constraints results in an average decrease of 0.9 trains and an 8.4% reduction in capacity utilization rate. When scheduling 2 flagship trains within a 2-h timeframe, capacity utilization decreases from 86.43% to 83.73%, quantifying the trade-off between brand positioning and operational capacity.

This research provides the first quantitative framework for brand value-oriented railway capacity optimization, establishing clear definitions for flagship trains and mathematical foundations for evaluating service quality versus efficiency trade-offs. The findings offer practical decision support for railway operators balancing competitive positioning with capacity maximization.

This study explores how managerial leadership and organizational innovation interact to enhance resilience and risk management in railway supply chains and how these capabilities contribute to sustained competitive advantage. It emphasizes the strategic importance of resilience in railway systems that face operational complexity, regulatory pressures and increasing exposure to systemic risks.

A mixed-methods design was employed, integrating survey data from 186 railway organizations with six case studies involving railway operators, rolling stock manufacturers and supply chain partners across multiple regions. Constructs were measured using validated scales and hypothesized relationships were tested using Structural Equation Modeling (SEM). Case study interviews were analyzed thematically to provide contextual understanding of leadership practices and innovation strategies.

The results confirm that transformational managerial leadership significantly predicts innovation adoption, which in turn strengthens resilience and risk management capabilities. Resilience emerged as a powerful driver of competitive advantage, reinforcing its role as a strategic capability rather than a reactive response to disruptions. Furthermore, innovation was shown to partially mediate the relationship between leadership and resilience, highlighting its function as the operational channel through which vision translates into capability.

This study contributes to the literature by integrating the Resource-Based View (RBV) and Dynamic Capabilities (DC) framework into the context of railway supply chains. It is among the first to empirically validate the mediating role of innovation between leadership and resilience, offering both theoretical advancements and actionable strategies for building resilient and competitive railway systems.

This research aims to monitor seismic intensity along railway lines, study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes, thereby achieving graded post-earthquake response measures.

The seismic intensity monitoring system for railways adopts a two-level architecture, namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform. The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm. A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.

The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang. During the operational period, the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times. Among these, earthquakes triggering the Kriging interpolation algorithm occurred twice. The system operated stably throughout the application period and successfully visualized relevant seismic impact data, such as earthquake intensity distribution maps and affected railway mileage sections. These results validate the system's practicality and effectiveness.

The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network. It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope, thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality. The system can provide clear graded interval recommendations for post-earthquake disposal, effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.

This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure, with a focus on China's Shuohuang Railway and Daqin Railway. It summarizes the technological progression from traditional manual inspections to integrated and intelligent inspection systems, analyzes their practical application outcomes and outlines future research directions to support the safe, efficient and sustainable operation of heavy-haul railways.

The study employs a combination of historical and empirical analysis, primarily drawing on academic literature and operational data from Shuohuang Railway. The development of inspection technologies is categorized into two distinct phases: traditional inspection and integrated inspection. The comprehensive effectiveness of these technologies is evaluated based on actual inspection efficiency, defect detection capability, cost savings and other relevant data.

The adoption of integrated inspection vehicles has significantly improved inspection efficiency and accuracy. In 2014, the world's first heavy-haul integrated inspection vehicle enabled synchronous multidisciplinary inspections, greatly reducing reliance on manual labor. By 2024, the intelligent heavy-haul integrated inspection vehicle further enhanced detection precision by 30%. Practical applications demonstrate that the annual number of track defects decreased from 25,000 to 3,800, while the track quality index (TQI) remained stable below 6 mm. Additionally, annual maintenance costs were reduced by more than 40 m yuan.

This paper provides the first systematic review of the development of inspection technologies for heavy-haul railway infrastructure, highlighting China's leading achievements in integrated and intelligent inspection. It clarifies the practical value of these technologies in enhancing safety, reducing costs and optimizing maintenance operations. Furthermore, it proposes future directions for development, including system integration, onboard computing capabilities and unmanned operations, offering valuable insights for technological innovation and policymaking in the field.

Severe scarcity of natural river sand (RS), exacerbated by environmental protection policies and extraction constraints, has significantly impacted aggregate supply for railway concrete. While manufactured sand (MS) offers a substitute for RS in railway applications, its widespread adoption in high-strength railway prestressed structures is challenged by lack of drying shrinkage and creep research data on concrete.

High-strength manufactured sand concrete (MSC) was prepared using MS with varying lithologies and stone powder contents. Its drying shrinkage and creep behaviors were evaluated in accordance with the Chinese standard GB/T 50082. The deformation mechanism was analyzed by combining nano-scratch testing.

Compared to RS concrete, MSC from all tested lithologies showed higher drying shrinkage but lower creep deformation. The drying shrinkage rose steadily with increased stone powder content, while the creep strain displayed a distinct non-linear trend, decreasing first before rising. To prepare low-deformation MSC, select high-strength MS and limit stone powder content not greater 10%. Nano-scratch tests indicated that harder MS particles suppress microcracking at the interfacial transition zone (ITZ), improving the creep resistance. The predictive models for drying shrinkage and creep were also developed by incorporating coefficients for stone powder and lithology effects.

These findings serve as a foundation for the application of MSC in railway prestressed structures, offering both theoretical and practical guidance.

This paper aims to offer a novel viewpoint for improving performance and reliability by developing and optimizing suspension components in a Y25 bogie through material optimization based on wheel-rail interactions under variable load and track conditions.

The suspension system, a critical component ensuring adaptation to road and load conditions in all vehicle types, is especially vital in heavy freight and passenger trains. In this context, the suspension set of the Y25 bogie - commonly used in Türkiye and Europe - was modelled using CATIAV5, and stress analyses have been performed by way of ANSYS using the finite element analysis (FEA) method. E300-520-M cast steel was selected for the bogie frame, while two different spring steels, 61SiCr7 and 51CrV4, were considered for the suspension springs. The modeled system was subjected to numerical analysis under loading conditions. The resulting stresses and displacements were compared with the mechanical properties of the selected materials to validate the design.

The results demonstrate that the mechanical strength and deformation characteristics of the suspension components vary according to the applied external loads. The stress and displacement responses of the system were found to be within the allowable limits of the selected materials, confirming the structural integrity and reliability of the design. The suspension set is deemed suitable for the prescribed material and environmental conditions, suggesting potential for practical application in real-world rail systems.

This research contributes to the design and optimization of bogie suspension systems using advanced CAD/CAE tools. It thinks that the material selection and numerical validation approach presented here can guide future designs in heavy load rail applications and potentially improve both safety and performance.

This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method, thereby realizing the systematic risk identification of regional hidden dangers.

The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis (ICA) theories. Firstly, the Gray Wolf Optimization (GWO) algorithm is used to partition each group of monitoring data in the hidden danger area, so that the data distribution characteristics within each sub-block are similar. Then, a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions. For the online evaluation process, the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated. The Bayesian method is applied to fuse online parameters with offline parameters, yielding statistics under a specific confidence interval. These statistics are then compared with the control limits - specifically, checking whether they exceed the pre-set confidence parameters - thus realizing the systematic risk identification of the hidden danger area.

Through simulation experiments, the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area, thereby realizing the systematic risk identification of the hidden danger area.

The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters, thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.

In recent years, the rapid advancement of artificial intelligence (AI) has exerted profound impacts on and provided strong impetus to numerous fields in the industrial sector. Within the railway industry, AI has driven continuous upgrading and optimization of intelligent train control technology, thanks to its enhanced computational capabilities derived from advanced algorithms and models, as well as its role in improving safety performance. Integrating AI technology more extensively into train autonomous driving and control has thus become an inevitable trend in the global development of railways.

This paper, therefore, conducts a comprehensive analysis of the development progress and current status of AI technology applications in the field of train driving and control on a global scale. It systematically sorts out and analyzes the advantages of various AI technologies and the positive impacts they bring to the upgrading of train control technology, elucidates the feasibility and future prospects of applying a range of emerging AI technologies from the perspective of technical theory and provides guidance for the intelligent development of this field from a practical perspective.

The application of AI technology in the train driving and control field is still in its infancy. While a large number of AI technologies have been widely adopted, there remains significant room for further optimization and improvement of these technologies. Additionally, a variety of AI technologies that have been applied in other industrial sectors but not yet widely implemented in training autonomous driving and control have demonstrated tremendous development potential.

The research findings provide references and guidance for advancing train control technology, promoting the digital transformation of railways, accelerating the overall optimization and upgrading of railway industry technologies, and facilitating the accelerated development of global railways.