Pre-osteoblast MC3T3-E1 cells were cultured in hyaluronic acid-modified chitosan/collagen/nano-hydroxyapatite (HA-CS/Col/nHAP) composite scaffolds and treated with phytoestrogen α-zearalanol (α-ZAL) to improve bone tissue formation for bone tissue engineering. Perfusion and dynamic strain were applied to three-dimensional (3D) cultured cells, which simulates mechanical microenvironment in bone tissue and solves mass transfer issues. The morphology of cell-scaffold constructs in vitro was then examined and markers of osteogenesis were assessed by immunohistochemistry staining and western blotting. The results showed that cells expanded their pseudopodia in an irregular manner and dispersed along the walls in 3D-dynamic culture. Osteogenic phenotype was increased or maintained by enhanced collagen I (COLI) levels, decreased osteopontin expression and having little effect on osteocalcin expression during the 12 days of in vitro culture. In response to α-ZAL, the cell-scaffold constructs showed inhibited cellular proliferation, enhanced the alkaline phosphatase (ALP) activity and increased ratio of osteoprotegerin to receptor activator of nuclear factor kappa B (NF-κB) ligand (RANKL). Application of perfusion and dynamic strain to cells-scaffold constructs treated with α-ZAL represents a promising approach in the studies of osteogenesis stimulation of bone tissue engineering.

China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system.

Near-wall microenvironment of a building refers to parameters such as wind speed, temperature, relative humidity, solar radiation near the building’s façade, etc. The distribution of these parameters on the building façade shows a certain variation based on changes in height. As a technology of passive heating and ventilation, the effectiveness of this application on heat collection wall is significantly affected by the near-wall microclimate, which is manifested by the differences, and rules of the thermal process of the components present at different elevations. To explore the feasibility and specificity of this application of heat collection wall in high-rise buildings, this study uses three typical high-rise buildings from Zhengzhou, China, as research buildings. Periodic measurements of the near-wall microclimate during winter and summer were carried out, and the changing rules of vertical and horizontal microclimate were discussed in detail. Later, by combining these measured data with numerical method, thermal process and performance of heat collection wall based on increasing altitude were quantitatively analyzed through numerical calculations, and the optimum scheme for heat collection wall components was summarized to provide a theoretical basis for the structural design of heat-collecting wall in high-rise buildings.

Since the 21st century, in several public health emergencies that have occurred across the world, the humid enclosed environment of the toilet has become one of the places where bacteria, viruses, and microorganisms breed and spread. Mobile public toilets, as a supplement of urban fixed public toilets, are also widely used in densely populated areas. According to statistics, since the outbreak of COVID-19 in 2019, multiple incidents of people being infected by the COVID-19 virus due to aerosol proliferation in public toilets have been confirmed. It is an urgent issue to resolve the internal environmental pollution of mobile public health and reduce the risk of virus transmission in public spaces under the global epidemic prevention. This paper utilized a typical combined mobile public toilet as the research object and measured and evaluated the indoor thermal environment in real time over a short period of time. The diffusion mode and concentration change law of pollutants in mobile public toilets were predicted and analyzed based on CFD. Regression analysis was also used to clarify the relationship between indoor thermal environment variables and aerosol diffusion paths, and a ventilation optimization scheme was proposed to reduce the risk of virus transmission.