Search

In the past three decades, fire engineering has experienced fast development worldwide. Fire regulations for building industry were moved from prescriptive description to performance-based specification. This trend started in the UK in the 1980s and spread to other western countries. Hong Kong closely followed the UK development and adopted the approach of performance-based design (PBD) for the Hong Kong International Airport and MTR stations in the early 1990s. The Government of Hong Kong SAR formally recognised the PBD approach in the prescriptive codes of practice in the mid-1990s and developed the performance-based code (PBC) in the 2000s. This PBC was promulgated in 2011. The evolution in fire engineering impacted the fire community in Mainland China. The State Key Laboratory of Fire Science (SKLFS) was established in University of Science and Technology of China (USTC) in 1991. Then fire science and fire engineering stepped into the fast development track. The building industry in Mainland China started to adopt the PBD approach in the early 2000s for the significant developments including large airport terminals, railway and metro stations, super high-rise buildings, and mega commercial centres. However, a structured PBC system is not available within the regulatory framework to date. Adoption of PBD approach is slowing down in recent years. This paper presents a review of fire engineering development in Hong Kong and Mainland China. The review focuses on the development of the PBC in Hong Kong, and the PBD approach and fire research in Mainland China in the past 30 years. [ABSTRACT FROM AUTHOR]

With the opening of the Stock Connect programs, the mainland China and Hong Kong stock markets are becoming more closely linked. In this paper, we develop a China's stock market risk early warning system. The proposed early warning system consists of three components. First, we use value at risk (VaR) to identify the stock market risk in which stock market risk is divided into multiple categories instead of two categories. Second, we construct a comprehensive indicator system in which basic indicators, technical indicators, overseas return rate indicators, and macroeconomic indicators are considered simultaneously. Third, we use four machine learning models, namely long short-term memory (LSTM), gate recurrent unit (GRU), multilayer perceptron (MLP), and EXtreme Gradient Boosting algorithm (XGBoost), to predict China's stock market risk. Experimental results show that: (1) Considering the macroeconomic indicators and basic indicators of Shanghai Composite Index (SSEC), ShenZhen Component Index (SZCZ) and Hang Seng Index (HSI) can significantly improve the performance of predicting China's stock market risk. (2) The opening of SH-HK Stock Connect program improves the predictive performance, but the opening of SZ-HK Stock Connect program decreases the predictive performance. (3) The indicators related to Hong Kong become more important after the SZ-HK Stock Connect program. [ABSTRACT FROM AUTHOR]