Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.

In medical validation experiments, such as drug testing and clinical trials, 3D bioprinted biomimetic tissues, especially those containing blood vessels, can be used to replace animal models. The difficulty in the viability of printed biomimetic tissues, in general, lies in the provision of adequate oxygen and nutrients to the internal regions. This is to ensure normal cellular metabolic activity. The construction of a flow channel network in the tissue is an effective way to address this challenge by both allowing nutrients to diffuse and providing sufficient nutrients for internal cell growth and by removing metabolic waste in a timely manner. In this paper, a three-dimensional TPMS vascular flow channel network model was developed and simulated to analyse the effect of perfusion pressure on blood flow rate and vascular-like flow channel wall pressure when the perfusion pressure varies. Based on the simulation results, the in vitro perfusion culture parameters were optimised to improve the structure of the porous structure model of the vascular-like flow channel, avoiding perfusion failure due to unreasonable perfusion pressure settings or necrosis of cells without sufficient nutrients due to the lack of fluid passing through some of the channels, and the research work promotes the development of tissue engineering in vitro culture.

Basic analysis of the flow field and aerosol deposition under different conditions when a spreader contains an upper airway tract is important to accurately predict the transmission of virus-laden aerosols. An upper airway was included to simulate aerosol transport and deposition. A flow field was simulated by the Transition SST model for validation. The simulation results show that, in the absence of the upper airway structure, an over-predicted aerosol deposition rate will occur. Higher upper-stream air velocity enhanced the intensity but added complexity to the recirculating flow between two manikins and increased the deposition rate of aerosol in the disseminator. A low-temperature environment can reduce the deposition rate of aerosol particles on the body of the disseminator due to a strong thermal plume. Therefore, the structure of the upper airway should be considered when predicting respiratory aerosol in order to increase the accuracy of aerosol propagation prediction.

The operation strategies of a distributed energy system (DES) are usually proposed according to the electrical load (FEL) and the thermal load (FTL), which take the cooling/heating load or electric load as unique constraint conditions that result in a too high or too low equipment load rate. This paper proposes a new hybrid operation strategy (HOS) that takes the full utilization of natural gas and the minimization of power consumption from the power grid as constraints and coordinates the cooling/electricity ratio and heating/electricity ratio of buildings and equipment. In the optimization phase of a DES, an optimization method based on the discretization of the load is proposed to investigate the influence of the uncertainty of the load on the DES, which helps to avoid repeated load simulations and provides stronger adjustability by quoting the normal distribution function to obtain multiple sets of load data with different fluctuations. Further, a multi-objective optimization model combining the genetic algorithm (GA) and mixed integer linear programming algorithm (MILP) was established to find the optimal configuration of equipment capacities by comprehensively considering the annual total cost, carbon emissions, and energy efficiency of the DES. Finally, an office building example was used to validate the feasibility of the above theories and methods. Compared with the FEL and FTL, the HOS reduced the energy waste of the DES by 19.7% and 15.5%, respectively. Compared with using a typical daily load, using an annual hourly load to optimize the DES-HOS produced a better comprehensive performance and lower adverse impacts derived from load fluctuations.