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This study aims to investigate the coupling reaction of epoxide and CO₂ catalysed by alkali metal salts in the presence of ß-cyclodextrin (ß-CD) derivatives to generate cyclic carbonates at various conditions.

The coupling reaction was catalysed by alkali metal salts. The effects of the co-catalysts were investigated by using the conversion rate of raw materials. The affecting factors, such as reaction temperature, amount of the co-catalyst and reaction time, were explored. The possible mechanism of the coupling reaction was discussed.

Results showed that the structure of ß-CD is an important factor influencing the catalytic activity for the coupling reaction of epoxide with CO₂. The catalytic system of 2,3,6-trimethyl-ß-CD with potassium iodide (KI) showed a high catalytic activity. The protocol was expanded to various epoxides, which provided the corresponding cyclic carbonates in excellent yields. The apparent decrease in the yields was not detected after four recycling times. Moreover, the mechanism for the synergetic effect of the catalyst was proposed.

The coupling reactions were achieved in the presence of different structure of ß-CD as co-catalysts. The affecting of substituent of ß-CD were investigated.

Five halogen-free amphiphilic Brønsted acidic ionic liquids (ILs) based on 3-(N, N-dimethylalkylammonium)-propanesulphonate quaternary ammonium zwitterions and p-toluene sulphonic acid monohydrate were synthesized. The IL structures were characterized using ¹H NMR, ¹³C NMR and (ESI)-MS. The IL thermal instability was characterized using TG-DSC. The ILs were used as dual-solvent catalysts for esterification of several carboxylic acids, with different alcohols under mild conditions to study relevant structure-activity relationships. Results showed that the miscibility of the acidic IL, which is affected by the length of the carbon chain in the cation, is an important factor influencing the activity of the catalyst for the esterification reaction. Post-processing steps were simple, and the catalyst could be reused easily.

In order to make sure of the safety of a long-span suspension bridge under earthquake action, this paper aims to study the traveling wave effect of the bridge under multi-support excitation and optimize the semi-active control schemes based on magneto-rheological (MR) dampers considering reference index as well as economical efficiency.

The finite element model of the long-span suspension bridge is established in MATLAB and ANSYS software, which includes different input currents and semi-active control conditions. Six apparent wave velocities are used to conduct non-linear time history analysis in order to consider the seismic response influence in primary members under traveling wave effect. The parameters α and β, which are key parameters of classical linear optimal control algorithm, are optimized and analyzed taking into account five different combinations to obtain the optimal control scheme.

When the apparent wave velocity is relatively small, the influence on the structural response is oscillatory. Along with the increase of the apparent wave velocity, the structural response is gradually approaching the response under uniform excitation. Semi-active control strategy based on MR dampers not only restrains the top displacement of main towers and relative displacement between towers and girders, but also affects the control effect of internal forces. For classical linear optimal control algorithm, the values of two parameters (α and β) are 100 and 8 × 10–6 considering the optimal control effect and economical efficiency.

The emphasis of this study is the traveling wave effect of the triple-tower suspension bridge under multi-support excitation. Meanwhile, the optimized parameters of semi-active control schemes using MR dampers have been obtained, providing relevant references in improving the seismic performance of three-tower suspension bridge.

This paper aims to collect and compile the historical data of Guanfu Salt Farm, officially built by the Song Dynasty (960–1279) within modern Hong Kong territories, to reconstruct its history for the reflection of Hong Kong society of the time.

This paper is largely based on identification and analysis of historical documents, including keyword search on electronic databases and verification with the original sources, with reference to archaeological findings when necessary.

This paper reconstructs the history of Guanfu Salt Farm based on documentary sources with reference to archaeological findings. English translation of Chinese sources is also provided when necessary.

There has been an absence of systematic compilation of historical data of Hong Kong during the Song Dynasty, which are limited in quantity and scattered across different sources. This paper seeks to fill the vacuum of knowledge about pre-colonial Hong Kong, with a more comprehensive reconstruction of the history of Guanfu Salt Farm.

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.