Abstract This paper presents a novel and comprehensive approach to minimizing track deformation by assembling recycled truck tire cells infilled with granular material in a square pattern as an alternative to a traditional capping layer (i.e., uppermost layer of subballast) constructed with compacted sandy gravel. A full-scale test track was constructed at the National Facility for Heavy-Haul Rail Testing, supported by numerical modeling using the finite element method, to evaluate the effectiveness of a tire cell assembly used as the capping layer beneath the ballast. This system builds on prior research demonstrating that a tire cell assembly, when infilled and compacted with granular material, forms a more confined and resilient substructure layer with enhanced energy dissipation capacity (i.e., higher damping ratio) that effectively limits lateral movement and reduces ballast breakage. In this study, multistage cyclic loading is applied to simulate heavy-haul freight trains with typical axle loads ranging from 20 to 35 t, subjected to equivalent loading–unloading frequencies of 10 and 15 Hz, corresponding to typical train speeds of 50 – 80 km / h . Test results on track settlements, lateral displacements, resilient modulus, ballast breakage are reported, with the infilled tire cells contributing to enhanced substructure stability. The presence of a semirigid capping layer beneath the ballast layer contributes to reduced lateral displacement of the track, while providing a robust and stiff foundation for the primary load-bearing ballast-sleeper system. The analysis of stress distribution with depth confirms that the tire cell–reinforced layer has contributed to an increase in the stiffness of the load-bearing stratum as well as an accompanying reduction of the stress transferred to the underlying softer subgrade. Even in the final loading stage under an increased simulated load of 35 t at 15 Hz, the track did not encounter any significant settlement. A finite element model was also developed and validated by full-scale experimental results, where the emphasis was placed more on the understanding of how tire cells would integrate within the track substructure to control stress distribution and deformation (mobilized strain).
Abstract The thermal conductivity of unsaturated soils is critical for thermal management in shallow energy infrastructures. Despite growing recognition that soil fabric and drying shrinkage can substantially affect thermal behavior, the underlying physical mechanisms remain poorly understood. This study combines laboratory experiments and numerical simulations to investigate these effects. Experiments were conducted on completely decomposed granite (CDG) and Nanyang clay (NYC), using reconstituted specimens (RS) and compacted specimens (CS) to represent different initial fabric conditions. Results show that RS consistently exhibits higher thermal conductivity than CS at the same saturation and void ratio. Along the drying path, CDG displays a monotonic decrease in thermal conductivity with decreasing saturation, whereas NYC exhibits a nonmonotonic trend, with conductivity increasing at intermediate saturation and peaking at low levels. To interpret these trends, a physics-constrained quartet structure generation set method was developed for pore-scale simulations using experimentally constrained structures and a simplified geometric representation of drying shrinkage. The method captures both the particle-aggregated, dual-porosity structure of CS and the uniform particle arrangement with unimodal pore size distribution found in RS, as well as their distinct fabric evolution induced by drying shrinkage. Simulation results show that RS achieves higher thermal conductivity through well-connected “solid particle chain” pathways, whereas CS depends on more tortuous “solid–liquid bridge–solid” paths caused by aggregates and larger interaggregate pores. The differing thermal conductivity trends in CDG and NYC are further attributed to their distinct shrinkage behaviors, which drive different fabric evolutions and thermal path reconstructions during drying. Furthermore, the anisotropy of shrinkage leads to distinct directional variations in conductivity, though its effect on the average remains limited.
Autonomous ground vehicles (AGVs) operating in mountainous and off-road environments need to perform complex local navigation while simultaneously handling severe terrain undulation, intricate obstacle distributions, and task-driven observation requirements. Traditional navigation architectures often rely on decoupled planning and control modules, which makes it difficult to rigorously coordinate nominal guidance with terrain related safety and kinematic constraints. To address these challenges, this paper proposes a unified terrain-aware local navigation framework based on dynamical system modulation and control barrier functions (DSM-CBF), where traversal safety, operational agility, and observation quality are jointly considered. A DSM-based nominal guider is developed to generate continuous and smooth motion commands with natural obstacle avoidance behavior by reshaping the local vector field through anisotropic modulation. The resulting nominal control is then refined by a CBF-based safety filter formulated as a slack-variable-augmented hard quadratic program, so that forward invariance of the safety set can be guaranteed under obstacle clearance, terrain traversability, and speed constraints. Task-related observation metrics, including bistatic geometry as well as range and azimuth resolutions, are further incorporated into the optimization objective to preserve favorable sensing quality during bounded-speed terrain-following motion. Comprehensive simulations and real-vehicle experiments verify the effectiveness and practical executability of the proposed framework. The simulation results show that it satisfies the radial-resolution requirement with an achieved value of approximately 1.01 m relative to the 1.13 m design threshold and improves mission efficiency by about 27% under the observation-quality-prioritized setting; compared with the SOTA methods, the proposed method achieves zero terminal error, maintains an admissible obstacle-clearance margin with min Gamma obs = 1.0439, reduces the turn-RMS by 86.1% and 83.6% relative to A* and D* Lite, respectively, and reduces the computational runtime by 97.9% compared with MPC-CBF, while real-vehicle tests further demonstrate safe and smooth traversal on a physical rugged-terrain AGV platform.
Established data governance frameworks provide valuable guidance for large enterprises, yet their assumptions about stable funding, dedicated governance teams, and mature infrastructure often do not reflect the realities of small and medium-sized enterprises (SMEs). Recent research has explored the use of semantic technologies and knowledge graphs to automate governance tasks, improve traceability, and support policy enforcement. Yet, whether these approaches effectively address SME-specific needs remains unclear. This paper presents a systematic literature review of knowledge-graph-based data governance approaches, focusing on their relevance to SMEs. Following the PRISMA method, we analyzed evidence from 43 peer-reviewed studies published between 2020 and 2025 through multiple lenses, including core data governance activities (Define, Implement, Monitor), components of the Data Governance Institute framework, and stages of the data lifecycle. Using thematic network analysis we synthesize recurring limitations and structural patterns across the literature. Our findings show that while semantic technologies demonstrate strong potential for automating policy enforcement, metadata integration, and interoperability, most approaches are designed for mature enterprises. They commonly assume formal governance hierarchies, specialized expertise, and sustained operational capacity, conditions that are often absent in SMEs. As a result, governance designs are skewed toward technical controls and early lifecycle stages, with limited attention to monitoring, organizational roles, decision rights, and end-to-end governance. Building on these findings, this study advances a socio-technical perspective on data governance in SME contexts by reframing governance as a lifecycle-embedded and practice-oriented capability rather than a purely technical implementation. It further conceptualizes knowledge graphs as boundary objects that mediate between governance intent, technical enforcement, and organizational practice. Our key contributions include a structured synthesis of knowledge-graph-based data governance research, identifies eight recurring gaps that hinder SME applicability, and derives nine design principles alongside a conceptual research framework to guide future research and the development of SME-suitable governance solutions.
Aims To perform a scoping review to examine how nursing educators prepare nurses to engage with upstream social determinants of health (SDOH), and to explore educators' understandings of nurses' roles in addressing these determinants. Background International policy frameworks increasingly position nurses as key actors in addressing the upstream SDOH. Nursing educators play a pivotal role in developing nurses' capacity to engage with health inequities at this level; however, little is known about how educators prepare nurses for this work or how they conceptualize this role within contemporary nursing practice. Design Scoping review guided by PRISMA Extension for Scoping Reviews (PRISMA-ScR) reporting framework. Data sources CINAHL, MEDLINE, Embase, and Scopus were searched on 5 September 2026 for peer-reviewed primary studies published with no date restrictions. Review methods Following full-text screening of 125 papers, 27 studies met the inclusion criteria and were included in the final scoping review. Results Studies (2010–2025) were predominantly from the United States. Fifteen examined educational approaches to teaching SDOH at all levels, while 12 explored nursing educators' understandings of SDOH in practice. No studies were explicitly designed to prepare nurses for upstream engagement as a central aim (e.g. policy engagement or structural and systemic advocacy). Nursing educators themselves held varied, and at times conflicting, understandings of the role of upstream engagement within nursing practice. Conclusions This review identifies a critical gap in nursing education related to preparing nurses to address upstream SDOH. Existing educational approaches primarily focus on downstream and midstream determinants, with upstream factors largely absent from curricula. Findings also suggest inconsistent understandings among nursing educators regarding nurses' roles in addressing upstream determinants. Strengthening nurses' capacity to influence the structural and systemic drivers of health inequities will require targeted educational strategies supported by knowledgeable and engaged nursing educators.
