ABSTRACT Under strong wind conditions, floatable plastics (i.e., plastic mulching films, plastic greenhouses, plastic dust-proof nets, etc.) are prone to be blown up to hang on the power transmission lines of high-speed railways, leading to the misfunction or even train service suspension. Therefore, timely and accurate mapping of these floatable plastics is of great significance to both railway administrations and the safety of passengers onboard. However, the potential of remote sensing has not been well justified in this study field. To tackle this issue, we take Beijing-Shanghai high-speed railway as an example, which is the busiest railway in China, and propose a novel deep learning based semantic segmentation model to map floatable plastics from very high-resolution optical satellite imagery. Specifically, a well-annotated sample dataset of floatable plastics is prepared, consisting of three typical categories of plastic mulching films, plastic greenhouses and plastic dust-proof nets. Afterwards, a hybrid Convolutional Neural Network-Mamba (CNN-Mamba) network is proposed, which integrates multi-scale convolutions with various local receptive fields and Mamba with global receptive fields into an end-to-end model. Specifically, the Multi-Perspective Fusion Block leverages multi-kernel convolutions to capture multi-scale local features, while the Feature Refinement Module integrates encoder-decoder multi-level features, thereby improving semantic consistency and boundary precision. Experimental results showed that the proposed model has achieved a high performance in floatable plastics mapping with an mIoU of 0.8641 and an average F1-score of 0.9261. Ablation studies have been done to justify the rationality of each module in the proposed hybrid model. Besides, the proposed model also outperformed several CNN-based and Mamba-based networks, not only in floatable plastics mapping but also in two other popular land use land cover datasets. Overall, this study provides an effective pipeline for monitoring the floatable plastics along high-speed railways.

Abstract A novel gas solid counter flow contact cyclone reactor (GS‐CFCCR), aimed at enhancing reactant mixing, has been proposed for the methanol‐to‐propylene process in this paper. The gas–solid mixing reaction is accelerated by the impinging flow generated through the collision of mixtures within the inlet tubes on both sides, and the separation of products is enhanced by the strong swirling flows induced by the guide vanes. An investigation was conducted on the residence time distribution (RTD), mean residence time (MRT), and flow field characteristics in the mixing chamber under three distinct inlet structural parameters, namely the particle incidence angle (α = 30°–150°), the effective length of accelerating tube (L = 120–160 mm), and the insertion depth of feed tube (l = 0–20 mm). The results indicate that the axial RTD curve follows a normal unimodal distribution. The interfacial contact area between the fluid phases reaches its maximum at the cross section of z = 30 mm. At α = 90°, homogeneous mixing is achieved with maximal mixing intensity observed across axial cross‐sections. Shallower l prevents flow separation induced by feed tube intrusion, thereby mitigating localized turbulence. L = 160 mm represents an equilibrium between stability and efficiency. Backmixing predominantly occurs along sidewalls proximal to the feed ports. It has been demonstrated through research that the GS‐CFCCR exhibits a commendable mixing efficiency with utilizing inlet parameters of α = 90°, l = 0 mm, and L = 160 mm.

The calculation of energy consumption in building plans is usually carried out after design completion, resulting in high time costs and hindering their application in the early design stage. This study focused on the heating and cooling demands of nearly zero energy residential buildings in Jinan and developed an envelope optimization model for the design stage. Firstly, field research on residential buildings in Jinan was conducted, and the shape coefficient based on research data was determined. Subsequently, ten design parameters were selected, and a prediction function was established through multiple linear regression. Finally, the mechanisms between the parameters and energy consumption were revealed, and the reliability of the model was verified. Results showed that the most energy-efficient shape coefficient is an 18-story rectangular building with a length of 52.6 m, a width of 15.1 m, and a floor-to-floor height of 3 m. The goodness of fit of the prediction function is 0.994. The adjusted R2 and RMSE of the neural network model in interpretable analysis are 0.933 and 0.089, respectively. The window-to-wall ratio significantly impacts energy consumption. This study addresses the lack of energy optimization by establishing a process that first determines energy-efficient parameter combinations and then refines the architectural scheme, and provides software to assist architects in design during schematic phases.

University campus teaching areas are essential spaces for students’ daily learning and recovery, in which soundscapes play a crucial role in shaping restorative experiences. This study aimed to explore the restorative effects of soundscapes in campus teaching areas and the factors influencing these effects. Field surveys, psychological assessments, and physiological experiments were conducted to evaluate restorative perceptions, which were characterized by three dimensions: Attractiveness, Coherence, and Being Away. The findings indicate that both the visual environment and acoustic characteristics significantly shaped restorative outcomes. Natural landscapes, particularly green areas and waterscapes, enhanced the restorative potential of soundscapes, while natural sounds, such as birdsong, fountain sound, and rustling leaves, were perceived as more restorative. In contrast, traffic noise, crowd noise, and class bell sound reduced restorative perceptions. Furthermore, the signal-to-noise ratio (SNR) played a critical role, with the higher SNR values of birdsong relative to traffic noise being associated with stronger restorative effects. These results suggest that campus soundscape design should prioritize green landscapes, introduce or amplify natural sounds, and optimize the SNR of restorative sounds. Overall, this research provides both theoretical support and practical guidance for designing healthier campus environments that foster students’ recovery and well-being.

Melting leakage and low thermal conductivity of stearic acid (SA) restrict its application in thermal storage. In this work, a shape-stabilized phase change material (ECNX/SA) with enhanced thermal storage performance and photothermal conversion is designed based on expanded graphite/chitin-derived carbon (ECNX). Thermal storage performance, including phase change temperature, enthalpy, thermal conductivity and shape stability, of ECNX/SA is investigated. With this, the influence mechanism of ECNX on the thermal storage performance is characterized via N2 isothermal adsorption–desorption, FTIR, XRD and SEM. Results show that the prepared ECN15/SA has ideal thermal storage performance, where its phase change enthalpy and thermal conductivity are 121.59 J/g and 1.573 W/(m·K), respectively, and possesses superior shape stability. Moreover, the thermal storage performance of ECN15/SA keeps stable even undergoing several thermal cycles, and its photothermal conversion is as high as 89.2%. Characterizations suggest that ECN15 with a hierarchical pore structure and a high graphitization degree to enhance the shape stability and thermal conductivity of SA. Therefore, the prepared ECN15/SA is potential using in thermal storage.