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Silt has poor engineering properties, and lime and/or fly ash are widely used to treat silt. However, very few studies could be found to investigate the water resistance and compressibility of lime and fly-ash mixtures (lime–FA) solidified silt. This paper investigated the unconfined compressive strength (UCS), water resistance and consolidation properties, and the microscopic characteristics of lime–FA solidified silt. In addition, the paper also compared the UCS, water resistance of lime–FA and lime solidified silt in our previous studies. Results reveal that the UCS increased slowly with increasing lime–FA dosage at 7 and 28 days of curing, but increased rapidly at 90 days of curing. However, at 7 days of curing, the soaked UCS was 0, and the water resistance was not improved by increasing lime–FA dosage. As the curing time increased, the UCS gain rate of different dosages of lime–FA solidified silt was largely consistent during 7–28 days of curing, but UCS gain rate became bigger when lime–FA dosage increased during 28–90 days of curing. Meanwhile, the water resistance coefficient increased first and then stabilized with curing time. Compared with lime solidified silt, the lime–FA stabilized silt had superior water resistance and a higher UCS gain rate, and the change trend of water resistance coefficient with curing time was basically the same as that of alone lime. In addition, the compressive modulus increased with curing time increasing, and microscopic analysis shows that the silt had a denser structure and more hydrates were formed with lime–FA dosage and curing time increasing.

In recent years, deep learning methods have been used with increasing frequency to solve architectural design problems. This paper aims to study the spatial functional layout of deep learning-assisted generation subway stations. Using the PointNet++ model, the subway station point cloud data are trained and then collected and processed by the author. After training and verification, the following conclusions are obtained: (1) the feasibility of spatial deep learning for construction based on PointNet++ in the form of point cloud data is verified; (2) the effectiveness of PointNet++ for the semantic segmentation and prediction of metro station point cloud information is verified; and (3) the results show that the overall 9:1 training prediction data have 60% + MIOU and 75% + accuracy for 9:1 training prediction data in the space of 20 × 20 × 20 and a block_size of 10.0. This paper combines the deep learning of 3D point cloud data with architectural design, breaking through the original status quo of two-dimensional images as research objects. From the dataset level, the limitation that research objects such as 2D images cannot accurately describe 3D space is avoided, and more intuitive and diverse design aids are provided for architects.

In recent years, there has been increasing concern about the effects of indoor thermal environments on human physical and mental health. This paper aimed to study the current status of the thermal environment and thermal comfort in the classrooms of Northeastern University during the heating season. The indoor thermal environment was analyzed with the use of field measurements, a subjective questionnaire, regression statistics, and the entropy weight method. The results show that personnel population density is an important factor affecting the temperature and relative humidity variations in classrooms. The results also show that the temperature and relative humidity in a lecture state are respectively 4.2 °C and 11.4% higher than those in an idle state. In addition, in university classrooms in Shenyang, the actual thermal neutral temperature is 2.5 °C lower than the predicted value of the Predicted Mean Vote. It was found that increasing indoor relative humidity can effectively improve the overall thermal comfort of subjects. Furthermore, the temperature preference of women was higher than that of men. Therefore, when setting the initial heating temperature, the personnel population density and sufficient indoor relative humidity have been identified as the key factors for improving the thermal environment of the classroom.

By integrating rough set theory and neural network theory, this study combined their advantages. Drawing on the existing theoretical results for bridge influencing factors, a method for numerical simulation and data fusion was used in the application of multifactor data fusion for cable-stayed bridge safety evaluation. Based on studying existing bridge safety evaluation methods, a neural network and rough set theory were combined to perform a safety evaluation of PC cable-stayed bridge cables, which provided a new means for bridge safety evaluation. First, a cable-stayed bridge in Shenyang was used as the engineering background, the safety level of its cables was divided into five levels, and a safety evaluation database was established, clustered by a Kohonen neural network. This provided specific evaluation indicators corresponding to the five safety levels. A rough neural network algorithm integrating the rough set and neural network was applied to data fusion of the database, with the attribute-reduction function of the rough set used to reduce the input dimension of the neural network. Conclusions. The neural network was then trained and the resulting trained network was applied to the safety evaluation of the cables of the cable-stayed bridge. Four specific attribute index values, corresponding to the bridge cables, were directly input to obtain the safety status of the bridge and provide corresponding management suggestions.

Rapid changes in vertical illuminance trigger visual fatigue. Therefore, controlling the illuminance ratio of adjacent spaces can ensure the satisfaction and comfort of users. This study takes reaction time as the measure of adaptation and explores the correlation between visual adaptation and comfort in different light environments. The Landolt C ring was selected as the visual standard for the experimental test, the degree of visual comfort was assessed using a Likert scale, and experimental parameters were formulated according to relevant criteria. By analyzing the subjective visual comfort, visual task performance and physiological evaluations of the participants under different changing illuminance levels, we have concluded that there is a significant correlation between reaction time and visual comfort, and no significant effect of gender on visual comfort. Therefore, under the condition of meeting the required value of illumination standard, the smaller the illuminance ratio of adjacent rooms, the more the comfort and visual acuity of users can be guaranteed, and visual fatigue can also be avoided. The study is a useful resource for improving comfort and pleasure in a light environment as well as for lighting design.

Shopping malls are large buildings and thus have extremely high heating or cooling costs and energy requirements. This study explored the response patterns of human physiological parameters to sudden temperature changes (temperature difference >30 °C) at moderate activity levels in a Shenyang shopping mall. The temperature was set to −12 °C for cold conditions and indoor temperature conditions were set to 18 and 24 °C. Fifty participants underwent hot—cold—hot room temperature exposure. The following results were observed for short-duration stays in the shopping mall. (1) When the temperature difference between hot and cold environments was >30 °C and the indoor temperature did not exceed 24 °C, 12–18 min on average was required for the body to reach a new physiological equilibrium. Children required less time to return to a steady state than other age-groups. (2) Children, adolescents, and young adults preferred indoor temperature conditions of 18 °C, whereas middle-aged adults preferred a slightly warmer environment. Thus, in view of the excessively high indoor temperature of the mall, its temperature should be maintained within a range that not only conserves energy but also provides human comfort.

The polarization and diffusion effects of landscape patterns are important features of megalopolis development. Under the urbanized effects, green space is a key spatial unit in delivering vital ecosystem services for sustainable urban planning. However, currently, fast urban developing is swamping the green space. In this study, by tracing landscape pattern changes of a fast-developing megalopolis, the Chengdu-Chongqing Megalopolis in the southeast of China, and using land-use data from 1980 to 2020, we aimed to determine the polarization and diffusion effects of the megalopolis and their impacts on the green space within and between the cities. We found that: (1) during the past four decades, spatial expansion of the megalopolis mainly occupied grassland and farmland, triggering an increase in landscape fragmentation; (2) based on socio-economic indicators, the spatial-attraction network analysis showed a significant polarization effect; however, based on the natural landscape, this analysis demonstrated a more scattered pattern; (3) importantly, the megalopolis developed at quite a similar pace, which caused the green rural area between the central cities demonstrating an encroached trend by the urbanization. To promote sustainability of the fast-developing megalopolis, we suggest that the boundary of the green space should be broadened to form a green network in which natural green space and urban green space are interconnected, improving the connectivity of habitats within the megalopolis for urban biodiversity. Our study implied that maintaining the green buffer shall be considered in advance for sustainable megaregional planning and establishing resilience of the fast-developing megalopolis.

In recent decades, increasing urbanization has increased construction land shortages, which has made people pay more attention to the utilization of vertical space. The emergence of more and more high-rise buildings has affected the wind environment at the pedestrian level, especially in residential areas. In this research, the typical patterns of the layouts of residential buildings in Shenyang were investigated and summarized, and the wind environment of the residential areas of different architecture layouts was simulated according to the climatic conditions in Shenyang. After analyzing the simulation results, a typical layout mode for the residential areas in Shenyang was developed to facilitate the establishment of a favorable wind environment. In comparison with different building layouts, a staggered layout of slab buildings, half-enclosed layout of point buildings with openings on the south side, slab-point combined buildings with slab buildings on the north side, and point buildings on the south side were found to be the most suitable layouts for Shenyang’s climate. Thus, this study can provide guidance to designers and urban planners in addition to practical suggestions for residential planning.

This study investigated the possible involvement of microRNAs in the regulation of genes that participate in peripheral neural regeneration. A microRNA microarray analysis was conducted and 23 microRNAs were identiifed whose expression was signiifcantly changed in rat dorsal root ganglia after sciatic nerve transection. The expression of one of the downregulated microRNAs, microRNA-214, was validated using quantitative reverse transcriptase-PCR. MicroRNA-214 was predicted to target the 3′-untranslated region of Slit-Robo GTPase-activating protein 3. In situ hybridization veriifed that microRNA-214 was located in the cytoplasm of dorsal root ganglia primary neurons and was downregulated following sciatic nerve transection. Moreover, a com-bination of in situ hybridization and immunohistochemistry revealed that microRNA-214 and Slit-Robo GTPase-activating protein 3 were co-localized in dorsal root ganglion primary neu-rons. Western blot analysis suggested that Slit-Robo GTPase-activating protein 3 was upregulated in dorsal root ganglion neurons after sciatic nerve transection. These data demonstrate that mi-croRNA-214 is located and differentially expressed in dorsal root ganglion primary neurons and may participate in regulating the gene expression of Slit-Robo GTPase-activating protein 3 after sciatic nerve transection.

The concerns about the contradiction between agricultural production and Agricultural Non-Point (ANPS) pollution has become increased with economic development in China. Government interventions are key to controlling ANPS pollution through the establishment of laws and policies. This paper uses the entropy method to calculate the emissions amount of ANPS pollution and policy strength of 31 provinces from 2010 to 2019 in China. The dynamic panel data model with system generalized moment is used to estimate the impacts of policies with different measures on ANPS pollution emission. According to our findings, China's policies have been helpful in controlling ANPS pollution though there are important regional differences. Moreover, four types of policy measures all contribute to the reduction in ANPS pollution. These findings improve our understanding of the relationship between policies and ANPS pollution in the analyzed period, thus providing support for the formulation of pollution management strategies in the next stage.