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The twisted wind flow (TWF), referring to the phenomenon of wind direction varying with height, is a common feature of atmospheric boundary layer (ABL) winds, noticeably affecting the wind-resistant structural design and the wind environment assessment. The TWF can be effectively simulated by a guide vane system in wind tunnel tests, but the proper design and configuration of the guide vanes pose a major challenge as practical experience in using such devices is still limited in the literature. To address this issue, this study aims to propose an approach to determining the optimal wind tunnel setup for TWF simulations using a numerical wind tunnel, which is a replica of its physical counterpart, using computational fluid dynamics (CFD) techniques. By analyzing the mechanisms behind guide vanes for generating TWF based on CFD results, it was found that the design must take into account three key parameters, namely, (1) the distance from the vane system to the side wall, (2) the distance from the vane system to the model test region, and (3) the separation between the vanes. Following the optimal setup obtained from the numerical wind tunnel, TWF profiles matching both the power-law and Ekman spiral models, which, respectively, reflect the ABL and wind twist characteristics, were successfully generated in the actual wind tunnel. The findings of this study provide useful information for wind tunnel tests as well as for wind-resistant structural designs and wind environment assessment. [ABSTRACT FROM AUTHOR]

Freeze–thaw (F-T) is one of the principal perils afflicting concrete pavements. A remedial strategy used during construction encompasses the integration of hybrid fibers into the concrete matrix. An extant research gap persists in elucidating the damage mechanism inherent in hybrid steel fiber (SF)- and basalt fiber (BF)-reinforced concrete subjected to F-T conditions. This paper empirically investigated the durability performance of hybrid fiber-reinforced concrete (HFRC) subjected to F-T cycles. The impact of SF/BF hybridization on mass loss, abrasion resistance, compressive strength, flexural strength, damaged layer thickness, and the relative dynamic modulus of elasticity (RDME) was examined. The damage mechanism was explored using micro-hardness and SEM analysis. The results indicate that incorporating hybrid SF/BF effectively enhances the F-T resistance of concrete and prolongs the service life of concrete pavement. The mechanisms underlying these trends can be traced back to robust bonding at the fiber/matrix interface. Randomly dispersed SFs and BFs contribute to forming a three-dimensional spatial structure within the concrete matrix, suppressing the expansion of internal cracks caused by accumulated hydrostatic pressure during the F-T cycle. This research outcome establishes a theoretical foundation for the application of HFRC to concrete pavements in cold regions. [ABSTRACT FROM AUTHOR]

The session-based recommendation predicts the next user's interest item based on an anonymous user–item interaction sequence. However, most existing methods focus on capturing sequential signals or item-transition patterns within the current session while ignoring potential collaborative behaviors among different users from other sessions that could positively affect the recommended performance of the current session. To address these issues, we propose a Neighbor-Enhanced Graph Transition Network , which uses a diverse graph neural network to model complex interactions at the item level between the current session and its neighboring sessions. We create a Current Feature Encoder to investigate the user's current preference and a Neighbor Feature Encoder to generate useful collaborative information by considering the popularity of item-transition pairs from neighbor sessions. Then, we propose a fusion function that combines the two types of features mentioned above. We use a positional attention mechanism to investigate the impact of items in different positions on the user's true intention. The experimental results over three real-world datasets demonstrate that our proposed model generally outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]