United States Department of Transportation

Purpose Offline commercial complexes feature diverse services, sparse behavioural data and rapidly changing user interests, which limit conventional recommendation methods. This study develops a personalised recommendation framework designed for high-uncertainty offline environments and evaluates its performance across static and dynamic systems and across cold-start and stable user groups. Design/methodology/approach A lightweight system is built by encoding questionnaire data into a preference knowledge graph and applying a hybrid user similarity model for preference inference. A dynamic module updates user–category relations through explicit feedback and controlled semantic diffusion, enabling continuous refinement. Static and dynamic configurations are tested separately on cold-start and stable users and ablation experiments quantify the contribution of relation updating and diffusion. Findings The static system provides a strong baseline under sparse data. The dynamic system yields further improvements: for cold-start users, P@1 and HIT@3 rise by 5.6 and 6.6 percentage points; for stable users, P@1 increases by 10.0 percent with clear gains in mean reciprocal rank and discounted cumulative gain. Ablation results show that feedback-driven edge updating generates most improvements, while intra-cluster diffusion supplies additional benefits, particularly for cold-start users. Originality/value The study offers a structurally adaptable and computationally efficient framework for offline commercial environments without behavioural logs. Through a combined static-to-dynamic and cold-start-to-stable evaluation, it demonstrates how lightweight graph modelling and local feedback-based updates can deliver accurate, interpretable and deployable personalised recommendations for real facility and commercial management settings.

With the rapid urbanization, transportation demands are rising steadily, and urban rail transit has become one of the most effective means to meet this demand. The operational availability and cost-effectiveness of urban rail systems largely depend on the efficiency of their maintenance strategies. This paper presents a comprehensive case study focused on enhancing the 3- monthly (3M) periodic preventive maintenance process of a light rail vehicle fleet. The methodology begins with an initial work study to analyze current maintenance procedures and establish a performance baseline, which revealed critical inefficiencies and time losses. These inefficiencies were then systematically addressed through targeted engineering modifications combined with lean manufacturing principles. A follow-up work study was conducted to quantitatively assess the impact of the applied improvements. Results indicate that the Mean Time To Repair (MTTR) was reduced by 30.6% (from 14.30 h to 9.92 h), and the non-value-added ‘Waiting Time’ was decreased by 43%. Furthermore, the economic analysis projects an annual labor cost saving of approx. $20,000, with a return on investment (ROI) period of just 3 months. The novelty of this study lies in its integrated, data-driven framework that connects detailed process analysis with real engineering interventions, offering a replicable model for optimizing preventive maintenance in urban rail systems.

The selection of representative ground motions (GMs) for dynamic analysis of soil and structural systems considering soil–structure interaction (SSI) is critical for accurately predicting their performance under seismic loads. Site-specific GMs are typically generated first by selecting a set of seed time histories and then modifying them by matching to the target spectrum using either Time-Domain (TD) or Frequency-Domain (FD) techniques. While previous studies have recommended TD over FD techniques based on 1D site response analysis and engineering judgment, their effects on predicting soil and structural performance, considering SSI, are poorly understood in a 3D nonlinear domain. In this study, a set of seed ground motions corresponding to 3% and 50% in 75 years hazard conditions specific to Charleston, SC, was selected and modified using TD and FD techniques. Then, sixteen dynamic nonlinear 3D finite element simulations of a bridge bent supported by a 2 × 2 pile group were performed using the general-purpose finite element software ABAQUS by applying the motions generated using the FD and TD techniques. The geotechnical properties specific to Charleston were obtained from published documents, and the stress–strain behavior of the soil was represented by the Drucker-Prager model. The soil domain was spatially discretized with 3D brick elements. The structural components were represented by linear elastic beam elements. Results indicate that FD matching tends to distort nonstationary waveform components, particularly in displacement time histories. In contrast, TD matching preserves waveform features, including phase and frequency content, more effectively. Despite this, these distortions in FD-matched motions did not lead to substantial differences in structural response in most cases. However, noticeable response variations were observed in the 1 to 2.5-s period range. Overall, TD matching exhibited greater consistency and reliability in preserving input motion characteristics and is recommended for use in nonlinear SSI analyses.

Vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) technologies enable connected and automated vehicles (CAVs) to drive in platoons and realize cooperative driving. To investigate the impact of CAVs and platoons on multi-lane mixed traffic flow, this paper proposes a multi-lane cellular automata model considering the self-organized strategy of CAV platoons, which can describe the LC behaviors of CAV platoons. First, different car-following (CF) modes are introduced, and the CF characteristics and the lane-changing (LC) motivations of vehicles in different CF modes are analyzed. Then, based on CF characteristics and LC motivations, CF rules and LC rules for vehicles in different CF modes are designed, and the multi-lane cellular automata model considering the self-organized strategy of CAV platoons is developed. Finally, the impact of the LC behaviors of CAV platoons on the mixed traffic flow is investigated based on the numerical simulation. Numerical simulation results showed that: (1) benefiting from the LC behaviors of CAVs, CAVs can significantly improve traffic capacity and average velocity in the two-lane scenario compared to the single-lane scenario. When the penetration rate (PR) is 80%, the capacity of one lane and average velocity in the twolane scenario increases by 14.3% and 41.4%, respectively, compared to the single-lane scenario. (2) under the same PR of CAVs, the LC frequency showed a tendency of increasing and then decreasing with the density increase. Besides, as the PR of CAVs increases, the LC frequency decreases. (3) CAVs are significantly more effective in alleviating traffic congestion at a low density than at a high density. (4) when the PR of CAVs is not higher than 40%, the maximum platoon size has almost no effect on traffic capacity and velocity. As the PR of CAVs increases, the effect of maximum platoon size gradually becomes significant.

Neste artigo temos por objetivo analisar como um Corredor Ecológico em específico (o Corredor Ecológico da Região do Chapecó, no interior do estado de Santa Catarina), se situa diante da proposta global de reversão da crise ecológica global, que se apresenta cada vez mais como realidade irreversível. Na introdução buscamos identificar a relação entre o Corredor Ecológico do Chapecó (CE Chapecó) com o discurso da sustentabilidade, pensando a noção como justificativa para a promoção de mecanismos de direcionamento global econômico-ecológico. Depois disso, apresentamos um esforço em dimensionar o papel das ciências nos governos contemporâneos, entendendo-as como uma das principais sustentações estratégicas das relações de poder e saber interestatal. Neste sentido, trazemos Sequências Discursivas específicas de documentos que embasam a implementação do CE Chapecó, identificando como se estabelece a relação entre proteção ecológica, agricultura sustentável e desenvolvimento econômico. E, por fim, sinalizamos para algumas congruências e divergências entre sustentabilidade, produções do “agro” e os modos de governo das populações em perspectiva local e global.

A mysterious phenomenon can be observed under atomic force microscopy (AFM) in SBS-modified asphalt (SBSMA), where the bee-structure size significantly increases after aging. Numerous studies have focused on the factors that induce changes in bee structures but have overlooked the relationship between macromolecular accumulation and bee-structural evolution during aging. The work here investigated the essential reason for the increase in the bee structure of SBSMA during aging. The geometric and topological parameters of bee structures are used to quantify microstructural characteristics, including axial length, area, interquartile range (IQR), and standard deviation (S.D.) from the Delaunay triangulation method. Nanoindentation tests quantified nanomechanical property evolution, while molecular dynamics simulation was used to obtain the contribution of macromolecular accumulation for the bee structure of SBSMA during aging. Results revealed that aging induces significant geometric and topological restructuring of bee structures, marked by a pronounced increase in large-sized features (length >3 μm, area >500 μm2) and reduced distribution uniformity. Short-term aging enhanced interfacial adhesion, whereas long-term aging slightly diminished this property. Furthermore, aging promoted the self-aggregation of SBS-polymers and asphaltene molecules, forming clusters or entangled micelles. These interactions disrupted colloidal stability and degraded the 3D network homogeneity. Critical analysis identified π–π interactions between aromatic benzene rings as the primary driver of molecular aggregation, directly correlating with increased height and length of bee- structures. This finding provides mechanistic insights into aging-induced microstructural evolution in SBS-modified asphalt, offering implications for material durability enhancement.