Urban pavements influence heat exchange between transport infrastructure and the surrounding atmosphere, yet how surface evolution alters their cooling performance remains unclear. This study investigates the reflective–evaporative cooling of water-retentive semi-flexible pavements with progressively exposed aggregates. Controlled polishing, optical–thermal characterization, outdoor monitoring, mechanical evaluation, and ENVI-met simulation were integrated to link surface evolution with pavement and street-canyon thermal response. Aggregate exposure increased albedo to 57.8% for limestone and 47.4% for granite, whereas diabase and basalt remained optically constrained. Relative to SMA, the proposed pavements reduced peak surface temperature by up to 11.01 °C; each 10 percentage-point albedo gain corresponded to a 0.67 °C reduction. Street-canyon simulation predicted a near-ground air temperature decrease of up to 1.07 °C. Granite provided a favorable thermal–mechanical balance, suggesting aggregate selection as a practical pathway for heat-mitigating urban pavement design.
Urban Rail Transit (URT) station areas are pivotal nodes for shaping urban structure and fostering Urban Vibrancy. However, the spatio-temporal heterogeneity of the built environment’s impact on urban vibrancy remains underexplored, as existing research predominantly relies on spatially invariant global models, which overlook how the effects of the built environment vary across geographical locations and temporal contexts. To address this gap, this study uses Xi’an Metro Line 2 in China as a case study. Employing multi-source big data, including Location-Based Service (LBS) population heatmaps and Points of Interest (POIs) at a fine-grained grid scale, we utilize a Geographically Weighted Regression (GWR) model to quantitatively deconstruct the spatially varying interactions between built environment’s variables and urban vibrancy across different time periods (morning, noon, and evening).The results first statistically confirm the significant spatial non-stationarity of the built environment’s impacts. Based on this, the study identifies three distinct vibrancy generation archetypes: (1) "endogenous service-oriented" vibrancy, driven by localized built capacity and public services; (2) "exogenous attraction-based" vibrancy, defined by city-level functional synergies and public space quality; and (3) "nodal absorption-oriented" vibrancy, characterized by the efficient absorption of commuter flows through high-density development and functional diversity.These findings empirically challenge the universal applicability of principles within Transit-Oriented Development (TOD) theory. The core contribution of this study, therefore, is its advocacy for a paradigm shift in planning-from a "prescriptive framework" that applies standardized principles to a "diagnostic framework" predicated on the precise identification of local, dominant driving mechanisms. This conclusion provides a solid scientific basis and theoretical guidance for formulating place-based and time-sensitive strategies to enhance station area vibrancy.
As outsourcing evolves from a temporary solution into an entrenched practice, efficiently scheduling production under critical operation constraints has become a pivotal challenge.Thus,this paper proposes a flexible job shop scheduling problem with critical operation driven outsourcing strategy and interval grey processing time (G-FJSPOC). The strategy is based on a pre-defined classification where critical operations are mandatory for in-house processing as a hard constraint, while non-critical operations can be outsourced based on real-time machine utilization. This approach ensures security and quality for critical operations while introducing flexibility for non-critical ones. To characterize temporal uncertainty in the manufacturing process, We employ interval grey numbers to describe processing times and refine their operational rules. A multi-objective model is established with makespan, total cost, and dynamic carbon emissions as optimization targets. For the proposed G-FJSPOC, an enhanced imperialist competitive algorithm with embedded simulated annealing (EICA-ESA) is developed. First, Logistic-Tent dual chaotic mapping generates the initial population to enhance solution diversity and quality. Subsequently, the simulated annealing algorithm was incorporated into the evolutionary phase, achieving a balance between local development and global exploration. Furthermore, a multi-strategy competition mechanism is applied to continuously optimize the population structure and enhance the algorithm’s exploration and convergence capabilities. The hybrid design enables effective exploration of the multi-modal solution space under uncertainty and operational constraints. Experimental analysis through 20 test cases demonstrates the effectiveness of the proposed EICA-ESA for solving G-FJSPOC.
Abstract To investigate the diffusion behavior of warm-mix rejuvenators in aged styrene-butadiene-styrene-modified asphalt (ASBSMA), the effects of rejuvenator type, diffusion time, and temperature on diffusion performance were studied using rotational viscosity tests, the Fickian diffusion model, infrared spectroscopy tracing techniques, and molecular dynamics simulations. Results indicate that the self-developed rejuvenator, self-developed warm mix rejuvenator (SWR), exhibits superior diffusion performance compared to commercial rejuvenators RA1 and RA2. Its diffusion coefficient increases with rising temperature but decreases with extended diffusion time. After 120 minutes of diffusion at 145°C, SWR’s diffusion coefficient reached 1.125 × 10 − 8 m 2 / s , approximately 1.45 times that of RA1. Infrared tracing revealed that the migration depth of the warm-mix rejuvenator within ASBSMA increased with both temperature and time. Compared to RA1 and RA2, SWR migrated to the innermost layer of the asphalt film within 90 minutes at 145°C. Molecular dynamics simulations revealed bidirectional diffusion behavior between SWR and ASBSMA, with SWR molecules exhibiting more vigorous motion. In summary, the suitable operating temperature for SWR is 140°C–150°C, with a diffusion time of no less than 90 min.
Graph Neural Networks (GNNs) often struggle with node classification under class imbalance because standard methods fail to adequately leverage graph topology. Specifically, majority nodes dominate message passing, causing the aggregated information from minority nodes to become statistically diluted, which masks relational signals associated with under-represented classes. Traditional techniques such as oversampling and cost-sensitive learning mitigate data skew but typically neglect structural dependencies, leading to under-representation of minority classes or overfitting to local graph substructures. We propose CL3E-GNN, a novel curriculum learning framework that addresses class imbalance through three synergistic phases: Engage, Enact, and Embed. The Engage phase employs multi-head attention to identify critical node and edge connections, generating enhanced feature representations via dual graph convolution network (GCN) and graph attention network (GAT) aggregation. The Enact phase refines minority class representations through three complementary mechanisms, each targeting a distinct source of imbalance: importance sampling for active feature learning, metapath propagation for knowledge transfer across heterogeneous relations, and anomaly detection for identifying structural irregularities. The Embed phase introduces a memory-augmented reinforcement learning mechanism that progressively refines node embeddings via reward-modulated updates. A dual-loss optimisation strategy combining cross-entropy with adaptive curriculum loss ensures gradual difficulty progression, offering a principled alternative to simple loss functions or data augmentation alone. Extensive evaluations on eight benchmark datasets, with results averaged over five independent runs, demonstrate consistent improvements over state-of-the-art methods. These results establish CL3E-GNN as a new benchmark for imbalanced node classification, effectively preserving topological integrity while mitigating class distribution skew. The implementation code is available at https://github.com/afofanah/CL3E-GNN.
