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From the atom economy and environmentally friendly point of views, the development of clean and green approaches using ionic liquids (ILs) as recyclable catalysts has attracted increasing attention. The purpose of this study is to investigate the effect of task-specific ILs content on the one-pot three-component Biginelli reaction.

A series of halogen-free quaternary ammonium ILs functionalized with –SO₃H group were prepared and characterized by ¹H nuclear magnetic resonance (NMR), ¹³C NMR and electrospray ionization mass spectrometry. The ILs were used as catalysts for Biginelli reaction among aromatic aldehydes, urea or thiourea and β-dicarbonyl compounds. Anions and cations of ILs were varied to observe their effects on and contributions to the catalysts. The influencing factors, such as the amount of catalyst, solvent, reaction time and reaction temperature, were investigated.

The effect and contribution of cations of ILs were observed. Results showed that 3-(N, N-dimethylhexadecylammonium) propanesulfonic acid toluene sulfate ([DHPA][Tos]) showed comparable catalytic activity. Good adaptability to the reaction substrate and maximum product yield was observed when [DHPA][Tos] was used as catalyst. It was found that Biginelli reaction catalyzed by 10 mol% [DHPA][Tos] for 3 h under solvent-free conditions at 80 °C gave the best yield of 94%. Post-processing steps were simple, and the catalyst could be reused easily.

This paper demonstrates that ILs containing a long carbon chain and a bulky Tos anion efficiently promoted the reaction, in which the long carbon chains facilitate mass transfer in the reaction system.

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.

For a thermal protection system (TPS) of long endurance hypersonic flight vehicle (HFV), its thermal insulation property not only determines by the manufactured morphology but also changes along time. A thermal conductivity prediction model for aerogel considering heat treatment effect is carried out and applied to solve the heat conduction problem of a TPS. The aim of this study is to provide theoretical and numerical references for further development of aerogels applying to TPSs.

A thermal conductivity prediction model for aerogel is established considering treatment effect. The heat conduction problem of a TPS is derived and solved by combining the differential quadrature method and the Runge–Kutta method. The prediction results of aerogel thermal conductivities are verified by comparing with those in literature, while the calculated temperature field of TPS is verified by comparing with that by ABAQUS.

Numerical results show that when applying the current prediction model, the calculated high temperature area in the aerogel layer is narrowed due to the decrease of the thermal conductivity during heat treatment process.

This study will be beneficial to carry out the precise design of TPS for long endurance HFVs.

This paper aims to report the conductivity measurement of ten different surfactant-free microemulsions (SFMEs)

The variations of electrical conductivity as a function of water volume fraction are examined at one constant alcohol (or DMF, ethyl lactate, γ-valerolactone)/water, alcohol (or DMF, ethyl lactate, γ-valerolactone)/oil volume ratios for each sample.

Most of the results are consistent with percolation character. The conductive mechanism of these SFMEs is discussed by the percolation model, and it is found that it might be described with the static percolation model below the percolation threshold.

Our report gives a systematic research on the percolation mechanism of as many species of SFMEs as possible by the theoretical models

This paper proposes a distributed multi‐channel MAC protocol which is an extension of our early work GRID [1]. GRID is characterized by the following features: (i) it integrates a location‐aware channel assignment, (ii) it follows an “on‐demand” style to access the medium, (iii) the number of channels required is independent of the network topology, and (iv) no form of clock synchronization is required. The proposed protocol wants to further improve the GRID in two parts. First, we propose a fully distributed medium access mechanism without using a single control channel such that all of the network traffic can be distributed evenly over all data channels. Therefore, the network throughput will be increased significantly. Second, we can set the more suitable transmission range and GRID size by considering the factors of host density and packet arrival rate of the network within an specified area. Thus, all of channels will be reused more efficiently than GRID. Simulation results show that the throughput of our protocol is superior to GRID and IEEE 802.11.

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.

The author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.

The results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.

The main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

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.

The purpose of this paper is to develop and test a model that identifies the impact of organizational justice on work performance. The model examined the mediating role played by organizational commitment and leader‐member exchange (LMX) in linking organizational justice and work performance.

The data were collected from 793 completed questionnaires sampling employees from industries across the People's Republic of China. The questionnaire included scales to measure organizational justice, organizational commitment, LMX, and work performance. The measurement of constructs and the hypothesized relationships among variables were assessed by the use of structural equation modeling. The Baron and Kenny approach was used to test the mediating effects.

First, the relationship of organizational justice to work performance was mostly indirect, mediated by organizational commitment and LMX. Second, among the three kinds of organizational justice, interactional justice was the best predictor of performance. Lastly, organizational commitment accounted for more of the variance than LMX did in the mediating mechanism.

The model developed in this article provides important insights in the study of the relationship between organizational justice and work performance. Future research needs to examine the model using a cross‐national sample.

The link between organizational justice and work performance was mostly mediated by organizational commitment and LMX.

New technology tends to invite speculation on the future of societies, inspiring visions of both hope and horror. This chapter continues that tradition, exploring the application of emerging technology, such as artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) to processes of governance and learning in education. Drawing on both utopian visions and twin nightmares of machinic-dystopias, the analysis reflects on the application of new technology designed to fulfill the UN’s post-2015 agenda in education. In highlighting divergent traditions, the analysis then shifts to the application of the same technology in China as part of the Chinese Dream, under which the Chinese government aims to become the world leader in AI while revitalizing the nation’s cultural traditions. These ambitions are explored through the introduction of Smart Cities, a system of Social Credit, and Smart Schools. Finally, the chapter reflects on these visions of twenty-first century pedagogy and possible resources for thinking about a future that cannot be fully apprehended.