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Purpose: It is an indispensable part of airworthiness certification to evaluate the fuel tank flammability exposure time for transport aircraft. There are many factors and complex coupling relationships affecting the fuel tank flammability exposure time. The current work not only lacks a comprehensive analysis of these factors but also lacks the significance of each factor, the interaction relationship and the prediction method of flammability exposure time. The lack of research in these aspects seriously restricts the smooth development of the airworthiness forensics work of domestic large aircraft. This paper aims to clarify the internal relationship between user input parameters and predict the flammability exposure time of fuel tanks for transport aircraft. Design/methodology/approach: Based on the requirements of airworthiness certification for large aircraft, an in-depth analysis of the Monte Carlo flammability evaluation source procedures specified in China Civil Aviation Regulation/FAR25 airworthiness regulations was made, the internal relationship between factors affecting the fuel tank flammability exposure time was clarified and the significant effects and interactions of input parameters in the Monte Carlo evaluation model were studied using the response surface method. And the BP artificial neural network training samples with high significance factors were used to establish the prediction model of flammability exposure time. Findings: The input parameters in the Monte Carlo program directly or indirectly affect the fuel tank flammability exposure time by means of the influence on the flammability limit or fuel temperature. Among the factors affecting flammability exposure time, the cruising Mach number, balance temperature difference and maximum range are the most significant, and they are all positively correlated with flammability exposure time. Although there are interactions among all factors, the degree of influence on flammability exposure time is not the same. The interaction between maximum range and equilibrium temperature difference is more significant than other factors. The prediction model of flammability exposure time based on multifactor interaction and BP neural network has good accuracy and can be applied to the prediction of fuel tank flammability exposure time. Originality/value: The flammability exposure time prediction model was established based on multifactor interaction and BP neural network. The limited test results were combined with intelligent algorithm to achieve rapid prediction, which saved the test cost and time. [ABSTRACT FROM AUTHOR]

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm. [ABSTRACT FROM AUTHOR]

Coal combustion is the primary source of air pollution in China, especially the heavy metals (HMs) concentrated in the submicron particles. This paper discusses the enhancement of HMs absorption efficiencies during coal combustion experiments. By adding the modified Ca-based sorbents, the orthogonal design was used in this experiment. Combining with the analysis of variance and capturing rate of HMs, the optimal combination was arrived at. BP neural network model was established and trained using orthogonal capturing tests data to reach the best compositions. Confirmatory trial was conducted, thus the BP neural network model can be used to solve complex nonlinear optimization problems. [ABSTRACT FROM AUTHOR]

Typhoon is one of the most important natural disasters in the coastal areas of China and caused severe economic losses every year. A typhoon disaster early warning evaluation system for historical buildings is established, and the influence of risk factors is analyzed using the analytic hierarchy process. The corresponding prevention strategies are proposed to reference the disaster prevention and protection for historical buildings under typhoon. Theoretically, this paper uses the algorithm advantages of the BP neural network algorithm to achieve the purpose of early warning of typhoon disaster risk. It constructs a historical building typhoon warning model based on BP neural network to evaluate the risk level of historical buildings. The risk early warning model is found to have some validity and reliability by training the neural network with sample data and comparing the performance data with the predicted data. [ABSTRACT FROM AUTHOR]

Li, D. and Zhang, Y., 2019. Maritime communication regulation: A model for evaluating the performance. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 997–1001. Coconut Creek (Florida), ISSN 0749-0208. China's maritime communication regulatory departments have issued some corresponding regulatory policies to ensure the sound development of China's maritime communication, but the evaluation of the regulatory performance of these policies has not been taken seriously, and some existing studies still lack a set of performance evaluation models verified by empirical studies. Based on this, this paper first uses principal component analysis method to establish an exploratory model of China's maritime communication regulation performance evaluation based on the maritime communication data of 31 provinces, municipalities and autonomous regions in China. On this basis, it uses BP neural network algorithm to verify the model and finally establishes a model of China's maritime communication regulation performance evaluation, thus providing a useful reference for China's maritime communication regulation. [ABSTRACT FROM AUTHOR]