Large-eddy simulations (LES) of a planar turbulent lean hydrogen-air jet flame at Re = 11000 are performed using a tabulated flamelet model based on mixture-averaged diffusion that incorporates detailed transport, including differential and preferential diffusion, wall heat loss, and thermodiffusion. The approach is extended to turbulent combustion in LES using a presumed-shape probability density function formulation that accounts for sub-filter effects. The flame exhibits a highly corrugated front, driven by local variations of mixture fraction induced by strong thermodiffusive transport. These effects significantly alter both the flame structure and morphology. The LES results are systematically compared to a reference direct numerical simulation across varying LES filters through different mesh resolutions to evaluate the predictive capability of the model. The LES accurately reproduces instantaneous flow structures and thermodiffusive effects. Global flame characteristics including flame length, surface area, and consumption speed, are well captured and show limited sensitivity to mesh resolution. The role of thermodiffusion is also examined, showing that its incorporation leads to a more reactive flame and should not be neglected in the formulation. Heat losses are incorporated into the tabulated chemistry framework for completeness but are found to have a negligible impact, consistent with the walls weak influence in the present configuration. Overall, the results demonstrate that the proposed approach provides reliable predictions of the main flame characteristics, with remaining discrepancies primarily associated with unresolved sub-filter effects that deserve further investigation.
Roaming dynamics can provide an alternative to conventional transition state pathways and have been observed in acetaldehyde using XUV time-resolved photoelectron spectroscopy (XUV-TRPES) at 262–308 nm. Alongside CH3 roaming, acetyl radical formation was tentatively assigned at 308 nm, although its mechanism remained unclear. Here, we revisit acetyl formation following 308 nm excitation using multireference and coupled cluster calculations and reanalyse previously reported XUV-TRPES measurements. Although a shallow H-atom roaming region is identified on S1, its predicted spectral signatures are absent experimentally. Instead, the results support a mechanism of CH3 roaming followed by recombination and ground state H-atom loss.
With recent advancements in state-of-the-art sensing technologies, dense hybrid sensor networks enable integration of numerous multi-type sensing tools, empowering real-time monitoring of assets. Such sensor networks have significantly increased damage sensing capability and reliability at global and local levels, empowering real-time monitoring for intelligent asset maintenance. Such real-time monitoring, however, produces large data that exceeds the present knowledge and capacity to rapidly analyse and accurately discover their feature for structural damage detection, a challenge often referred to as “data deluge’’. To meet this challenge, this study establishes a new precise, generalised, automatic mathematical framework that intelligently tracks and analyses variations in large data produced from dense sensor networks in real-time. Such framework systematically integrates modern data techniques for anomaly or damage detection. This study proposes a novel mathematical framework for unsupervised machine learning, real-time structural health monitoring that enables adaptive damage detection directly from streaming vibration data without requiring labelled damage data or physics-based numerical models. By integrating symbolic data analysis, unsupervised learning, and adaptive thresholding into a unified framework, the proposed approach provides a general solution for automated anomaly detection and warning generation. In the proposed framework, exceeding the adaptively learned threshold prompts an instantaneous damage alarm. The proposed framework is validated using both numerical beam models and experimental tests on corroded and uncorroded columns, demonstrating its capability for accurate and fully automated real-time damage detection under both simulated and experimental conditions.
In 1999, Korea introduced the Preliminary Feasibility Study (PFS), a mandatory appraisal framework for large-scale public investment projects that designates balanced regional development as a core policy objective. As the primary gateway for major public construction projects, the PFS plays a pivotal role in shaping the scale and spatial allocation of transport investment, yet empirical evidence on whether it is associated with reduced regional economic disparities remains limited. Using panel data for 15 Korean regions from 2000 to 2023, this study estimates output elasticities of transport capital with a multilevel Cobb-Douglas framework that allows road and railway elasticities to vary by region and accounts for temporal dependence in the residuals. The results reveal substantial regional and modal heterogeneity: road elasticities average about 0.12 and are highest along the Seoul-Busan corridor, while railway elasticities average about 0.15 and are highest in the Seoul metropolitan area and the adjacent Chungcheong corridor. By contrast, several less developed regions, including areas prioritised by the PFS balanced development component, exhibit generally positive but low elasticities for both modes. These uneven productivity returns suggest that transport investment alone, as shaped by the PFS, is unlikely to resolve Korea’s interregional economic disparities, underscoring the need for complementary, place-based policies tailored to regional industrial structure and absorptive capacity.
Seaborne trade underpins global logistics chains and regional economic resilience, yet maritime piracy continues to impose significant safety, security, and operational risks on maritime transport systems. This study advances piracy risk analysis by developing a cost-sensitive multi-graphs attention network (CSmGAT) that explicitly accounts for the spatio-temporal heterogeneity of piracy behaviour and the pronounced class imbalance across attack types. Multiple complementary graph structures are formulated to represent geopolitical conditions, regional economic activity, and spatial proximity, allowing the underlying risk environment to be modelled as a multi-layered interaction network. A multi-graph attention mechanism is employed to adaptively refine these structures by down-weighting weak or spurious dependencies, thereby improving the fidelity of the learned risk representations. To address the unequal distribution of piracy categories, a cost-sensitive learning formulation is integrated to enhance minority-class recognition without compromising overall system performance. Using a global maritime piracy dataset containing 7391 records covering 1994-2020, the CSmGAT model achieves superior performance relative to nineteen state-of-the-art statistical and machine-learning baselines in terms of evaluation metrics such as overall summary score (SS), Macro-F1 (mF1), and Macro-Gmean (mGmean). In addition, the framework yields interpretable risk insights by quantifying the relative influence of key explanatory factors through pseudo-elasticity analysis. The resulting approach provides a robust, interpretable, and operationally relevant tool for analysing piracy risks, supporting maritime security planning, and strengthening the reliability and resilience of international shipping systems.
