While extensive research has examined the contribution of urban parks to well-being, empirical evidence on the role of cultural attributes in historical urban parks and their impact on visitors’ well-being remains limited. This study explores the impact of physical characteristics of historical urban parks on well-being from the perspective of human settlement environment. Quantitative data were collected from 11 urban parks in Wuhan, China, combining online crowdsourcing for physical characteristic assessments and questionnaire surveys for psychological evaluations. Machine learning techniques, spatial analysis, and statistical methods including multistep regression and Bootstrap sampling were employed to test our hypotheses. Our results demonstrate that objective physical features—including park area, green coverage rate, green space shape index, and the proportion of heritage landmarks—positively influence well-being, whereas road density exhibits a negative association. Cultural perception and place attachment serve as significant mediators between physical characteristics and well-being outcomes, with the proportion of heritage landmarks influencing well-being through a dual mediation path. Additionally, we found interaction effects between physical and psychological factors, with education level moderating the relationship between cultural perception and well-being. These findings advance environmental psychology theory by elucidating how historical elements foster unique pathways to well-being, distinct from those offered by conventional green spaces. Our research provides evidence-based guidance for historical urban park design and renovation in the context of urban renewal, where balancing preservation and modernization presents significant challenges.

Hydraulic fracturing is an effective method for stimulating reservoirs, making the economic development of ultra-tight shale gas and coalbed methane reservoirs possible. These formations are rich in nanopores, in which the fracturing fluid, such as fresh water, the flow, and the behavior of this flow differ significantly from those described in the classic Navier-Stokes formula. In bulk space, the interaction force exerted by the solid phase can be ignored, but the solid–fluid interaction plays a dominant role in nanoconfinement spaces in which the pore size is comparable to the molecular diameter. Nanoconfined water molecules tend to approach the water-wet pore surface, enhancing the water viscosity, which is a key parameter affecting the water flow capacity. Conversely, water molecules tend to stay in the middle of nanopores when subjected to a hydrophobic surface, leading to a decrease in viscosity. Thus, nanoconfined water viscosity is a function of the strength of the surface–fluid interaction, rather than a constant parameter, in classic theory. However, the influence of varying the viscosity on the nanoscale water flow behavior is still not fully understood. In this research, we incorporate wettability-dependent viscosity into a pore network modeling framework for stable flow for the first time. Our results show that: (a) the increase in viscosity under hydrophilic nanoconfinement could reduce the water flow capacity by as much as 11.3%; (b) the boundary slip is the primary mechanism for boosting the water flow in hydrophobic nanopores, as opposed to the slight enhancement contributed by a viscosity decline; and (c) water flow characterization in nanoscale porous media must consider both the pore size and surface wettability. Revealing the varying viscosity of water flow confined in nanopores can advance our microscopic understanding of water behavior and lay a solid theoretical foundation for fracturing-water invasion or flowback simulation.

Due to its unique chemical structure, hydrogen energy inherently has a high calorific value without reinforcing global warming, so it is expected to be a promising alternative energy source in the future. In this work, we focus on nanoconfined hydrogen flow performance, a critical issue in terms of geological hydrogen storage. For nanopores where the pore scale is comparable to hydrogen’s molecular size, the impact on hydrogen molecules exerted by the pore surface cannot be neglected, leading to the molecules near the surface gaining mobility and slipping on the surface. Furthermore, hydrogen adsorption takes place in the nanopores, and the way the adsorption molecules move is completely different from the bulk molecules. Hence, the frequently applied Navier–Stokes equation, based on the no-slip boundary condition and overlooking the contribution of the adsorption molecules, fails to precisely predict the hydrogen flow capacity in nanopores. In this paper, hydrogen molecules are classified as bulk molecules and adsorption molecules, and then models for the bulk hydrogen and the adsorption hydrogen are developed separately. In detail, the bulk hydrogen model considers the slip boundary and rarefaction effect characterized by the Knudsen number, while the flow of the adsorption hydrogen is driven by a chemical potential gradient, which is a function of pressure and the essential adsorption capacity. Subsequently, a general model for the hydrogen flow in nanopores is established through weight superposition of the bulk hydrogen flow as well as the adsorption hydrogen, and the key weight coefficients are determined according to the volume proportion of the identified area. The results indicate that (a) the surface diffusion of the adsorption molecules dominates the hydrogen flow capacity inside nanopores with a pore size of less than 5 nm; (b) improving the pressure benefits the bulk hydrogen flow and plays a detrimental role in reducing surface diffusion at a relatively large pressure range; (c) the nanoconfined hydrogen flow conductance with a strong adsorption capacity (P_L = 2 MPa) could reach a value ten times greater than that with a weak adsorption capacity (P_L = 10 MPa). This research provides a profound framework for exploring hydrogen flow behavior in ultra-tight strata related to adsorption phenomena.

Most existing Simultaneous Localization and Mapping (SLAM) systems rely on the assumption of static environments to achieve reliable and efficient mapping. However, such methods often suffer from degraded localization accuracy and mapping consistency in dynamic settings, as they lack explicit mechanisms to distinguish between static and dynamic elements. To overcome this limitation, we present BMP-SLAM, a vision-based SLAM approach that integrates semantic segmentation and Bayesian motion estimation to robustly handle dynamic indoor scenes. To enable real-time dynamic object detection, we integrate YOLOv5, a semantic segmentation network that identifies and localizes dynamic regions within the environment, into a dedicated dynamic target detection thread. Simultaneously, the data association Bayesian mobile probability proposed in this paper effectively eliminates dynamic feature points and successfully reduces the impact of dynamic targets in the environment on the SLAM system. To enhance complex indoor robotic navigation, the proposed system integrates semantic keyframe information with dynamic object detection outputs to reconstruct high-fidelity 3D point cloud maps of indoor environments. The evaluation conducted on the TUM RGB-D dataset indicates that the performance of BMP-SLAM is superior to that of ORB-SLAM3, with the trajectory tracking accuracy improved by 96.35%. Comparative evaluations demonstrate that the proposed system achieves superior performance in dynamic environments, exhibiting both lower trajectory drift and enhanced positioning precision relative to state-of-the-art dynamic SLAM methods.

The effect of molecule–surface interaction strength on water becomes pronounced when pore size shrinks to the nanoscale, leading to the spatially varying viscosity and water slip phenomena that break the theoretical basis of the classic Lucas–Washburn (L-W) equation for the spontaneous imbibition of water. With the purpose of fulfilling the knowledge gap, the viscosity of nanoconfined water is investigated in relation to surface contact angle, a critical parameter manifesting microscopic molecule–surface interaction strength. Then, the water slip length at the nanoscale is determined in accordance with the mechanical balance of the first-layer water molecules, which enlarges gradually with increasing contact angle, indicating a weaker surface–molecule interaction. After that, a novel model for the spontaneous imbibition of nanoconfined water incorporating spatially inhomogeneous water viscosity and water slip is developed for the first time, demonstrating that the conventional model yields overestimations of 16.7–103.2%. Hydrodynamics affected by pore geometry are considered as well. The results indicate the following: (a) Enhanced viscosity resulting from the nanopore surface action reduces the water imbibition distance, the absolute magnitude of which could be 3 times greater than the positive impact of water slip. (b) With increasing pore size, the impact of water slip declines much faster than the enhanced viscosity, leading to the ratio of the nanoconfined water imbibition distance to the result of the L-W equation dropping rapidly at first and then approaching unity. (c) Water imbibition performance in slit nanopores is superior to that in nanoscale capillaries, stemming from the fact that the effective water viscosity in nano-capillaries is greater than that in slit nanopores by 5.1–22.1%, suggesting stronger hydrodynamic resistance. This research is able to provide an accurate prediction of spontaneous imbibition of nanoconfined water with microscopic mechanisms well captured, sharing broad application potential in hydraulic fracturing water analysis and water-flooding-enhanced oil/gas recovery.

Fire detection primarily relies on sensors such as smoke detectors, heat detectors, and flame detectors. However, due to cost constraints, it is impractical to deploy such a large number of sensors for fire detection in outdoor gardens and landscapes. To address this challenge and aiming to enhance fire detection accuracy in gardens while achieving lightweight design, this paper proposes an improved symmetry SSS-YOLOv8 model for lightweight fire detection in garden video surveillance. Firstly, the SPDConv layer from ShuffleNetV2 is used to preserve flame or smoke information, combined with the Conv_Maxpool layer to reduce computational complexity. Subsequently, the SE module is introduced into the backbone feature extraction network to enhance features specific to fire and smoke. ShuffleNetV2 and the SE module are configured into a symmetric local network structure to enhance the extraction of flame or smoke features. Finally, WIoU is introduced as the bounding box regression loss function to further ensure the detection performance of the symmetry SSS-YOLOv8 model. Experimental results demonstrate that the improved symmetry SSS-YOLOv8 model achieves precision and recall rates for garden flame and smoke detection both exceeding 0.70. Compared to the YOLOv8n model, it exhibits a 2.1 percentage point increase in mAP, while its parameter is only 1.99 M, reduced to 65.7% of the original model. The proposed model demonstrates superior detection accuracy for garden fires compared to other YOLO series models of the same type, as well as different types of SSD and Faster R-CNN models.