This review examines hydrogen-assisted fatigue crack growth rate (HA-FCGR) in pipeline steels with a focus on implications for integrity assessment of hydrogen transport systems. Existing natural gas pipelines offer a cost-effective pathway for hydrogen transmission; however, hydrogen embrittlement (HE) significantly alters fatigue behavior. This paper integrates scientometric analysis with a systematic review to evaluate the influence of material microstructure, welds, loading conditions, hydrogen pressure, and environmental variables on fatigue crack growth rates (FCGR). The synthesis confirms that HA-FCGR is most pronounced in the Paris region and is strongly governed by hydrogen pressure and loading frequency, while the role of material strength is less definitive than traditionally assumed. Recent advances in machine learning demonstrate strong predictive capability for FCGR; however, their integration into risk-based inspection and pipeline integrity frameworks remains limited. To bridge the gap between laboratory-scale understanding and field implementation, the concept of a near-real-world hydrogen pipeline testbed is introduced, enabling synchronized measurement of pressure cycling, material degradation, and system-level response. The review identifies critical research needs, including weld-focused fatigue datasets, realistic pressure-cycle validation, uncertainty-aware modeling, and integration of physics-based and data-driven approaches for decision-making. These findings provide a pathway toward reliable and scalable integrity assessment for hydrogen transport in existing pipeline infrastructure.

Reliable crack detection is essential for ensuring the safety, serviceability, and long-term performance of civil structures. Conventional manual inspections are labor-intensive and subjective, while existing computer vision models often exhibit reduced accuracy under variable field conditions. This study develops a computer vision-based automated crack detection framework utilizing a hybrid Transformer–CNN architecture to support infrastructure inspection and condition assessment. The proposed model leverages the global context modeling capability of Transformers and the local feature sensitivity of convolutional neural networks (CNNs) to enhance detection robustness. The optimized hybrid model achieved an Intersection over Union (IoU) of 91.8% and an accuracy of 98.7%, outperforming baseline CNN, Transformer-only, and LSTM architectures. Field validation on bridge inspection imagery demonstrated strong resilience to variations in illumination and texture. The developed approach contributes to digital inspection and intelligent lifecycle management of infrastructure assets by enabling reliable, automated, and non-intrusive structural condition evaluation under realistic field conditions.

Thermally sprayed metallic coatings on carbon fiber-reinforced polymer (CFRP) substrates often contain pores and microstructural defects that can reduce adhesion, durability, and protective performance. In this study, a hybrid graphene nanoplatelet (GNP)-fullerene C60 reinforced epoxy coating was applied to HVOF-metallized CFRP, while a carbon nanotube (CNT)-based surface treatment was used to modify surface wettability. Micro-CT and SEM analyses indicated morphological changes consistent with partial coverage of accessible surface-connected defects and modification of the metallized layer surface. Pull-off adhesion strength increased from 320 psi to 650 psi, accompanied by a shift from adhesive to cohesive failure. The optimal nanofiller formulation improved tensile strength from approximately 25 MPa to 56 MPa (124%) and Young’s modulus by approximately 47% compared with neat epoxy. Abrasion testing showed more than 50% reduction in mass loss, and electrochemical impedance spectroscopy indicated improved barrier performance after 200 h of salt spray exposure. CNT surface modification transformed the coating from hydrophilic to superhydrophobic behavior, achieving water contact angles above 155°, delaying ice formation, and reducing ice accumulation. These results indicate that combining hybrid nanocomposite coatings with CNT functionalization can improve mechanical, protective, and surface-functional performance of HVOF-metallized CFRP systems under the laboratory conditions investigated.

Background/Objectives: Guided by the Social Ecological Model, this study examined the relationships between ecological environmental factors, executive function, physical activity, and sedentary behavior among youth. Methods: A cross-sectional study was conducted among 283 youth (M_age = 15 years; Male = 33.6%) in the North Texas region. Parent-report surveys were used to assess ecological environmental factors, executive function, physical activity, and sedentary behavior. Sociodemographic characteristics, including household income, ethnicity, age, and sex were also collected. Results: Neighborhood safety/crime was significantly associated with all components of executive function (p < 0.01). Community walkability was significantly associated with overall executive function and planning (β = 0.16 and 0.18, p < 0.05) and with screen-based sedentary behavior (r = −0.23, p < 0.01). Neighborhood safety/crime and the home media environment significantly predicted physical activity (β = −0.34; β = −0.29, p < 0.01; R² = 20.4%), whereas the socioeconomic environment emerged as a significant predictor of daily sedentary behavior (β = 0.16; p < 0.05; R² = 18.1%). The mediation model showed acceptable fit, chi-square (df = 25) = 420.32, p = 0.017, CFI = 0.956, and RMSEA = 0.050, 90% CI [0.021, 0.075]. The direct path from environmental factors to executive function was statistically significant (p < 0.01); however, the indirect effect of environmental factors on executive function through physical activity was not supported. Conclusions: These findings suggest that neighborhood safety and walkability are associated with executive function in school-aged youth, independent of physical activity levels. Because the data are cross-sectional, these implications should be tested in longitudinal and intervention studies before informing clinical guidelines.

Phase change materials (PCMs) have been widely investigated for latent thermal energy storage in building envelopes; however, conventional single-melting-point PCMs often exhibit limited adaptability under dynamically varying thermal conditions. This study investigates the thermodynamic feasibility of hybrid-melting-point PCMs to improve transient thermal regulation in multilayer building wall systems. A transient numerical model was developed to evaluate wall assemblies incorporating single and hybrid PCM configurations under structured dynamic thermal loading conditions representing mild, hot, and cold regimes. To isolate the influence of melting-point distribution, hybrid systems containing multiple phase-transition temperatures were compared against conventional single-transition PCM systems with identical total latent heat capacities. The results demonstrate that distributing melting thresholds broadens the effective activation temperature range and enhances attenuation of indoor temperature fluctuations under varying thermal loads. Compared with the conventional single-melting-point system, the proposed hybrid configuration reduced peak indoor temperature by up to 18.5% and increased the minimum indoor temperature by up to 51.9%. Additional material-level simulations revealed that staged phase transitions promote sequential latent heat activation and prolong thermal buffering behavior. The findings suggest that hybrid-melting-point PCMs can improve the transient thermal adaptability of PCM-integrated building envelopes without increasing total latent heat storage capacity. The present study is intended as an exploratory thermodynamic feasibility assessment rather than a climate-specific annual building-energy prediction framework.

Solid particle erosion presents a significant operational challenge in gas and oil transport, leading to component failure, increased maintenance costs, and reduced pipeline lifespan. Pipeline elbows are particularly susceptible to severe erosion, owing to the change in flow direction. This study presents a laboratory-scale experimental investigation into the erosion characteristics of a 90° elbow subjected to particle-laden flow with high-inertia sand particles. An innovative erosion paint method is employed for accelerated visualization of surface wear patterns, while a Lagrangian particle tracking algorithm is used to quantify the particle dynamics and their link to erosion severity. The results reveal that erosion is highly localized, with particle inertia and secondary flows driving a congregation of particles and subsequent intense impacts on the outer wall of the elbow. A direct correlation between particle kinetic energy loss and the erosion severity is established. Furthermore, a novel passive mitigation strategy using an array of guide vanes installed upstream of the elbow is proposed and tested. The guide vane system is found to significantly alter particle trajectories, leading to a more uniform particle distribution and a substantial delay in the onset of erosion in the 90° elbow, demonstrating its potential as an effective method for extending component life.