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This study aims to examine the effects of institutions on private investment (PI) using panel data analysis, where the sample countries consist of 100 countries around the world and the time period is covering from 2007 to 2016. The system generalized method of moments (GMM) estimator, introduced by Arellano and Bond (1991) and further developed by Blundell and Bond (1998) is used to analyze the data sets.

This study uses the panel data approach to estimate the empirical model due to the panel nature of the data. In particular, due to the presence of lagged dependent variables and the ability to capture individual country-specific effects, the system GMM estimator, introduced by Arellano and Bond (1991) and further developed by Blundell and Bond (1998), is adopted to analyze the roles of institutions in PI. The system GMM is developed specifically to solve the problems of weak instruments and persistency (Blundell and Bond, 1998). Jointly, they suggest to adopt additional moment conditions where lagged difference of the dependent variable is orthogonal to the level form of the disturbances. The system GMM estimator is able to combine the moment conditions for the different models, as well as the level model, thereby (is capable of) generate consistent and efficient parameters. Due to the dynamic nature of the data, this study uses one-step and two-step system GMM to investigate the roles of institutions in PI.

The empirical results based on the two-step system GMM demonstrate that the quality of institutions plays an important role in stimulating PI. The finding is reinforced by the analysis of the institutional sub-components’ effects on PI.

This study is unique as its measurement of institutions is multi-dimensional (including law and order, rules and regulation, government stability, bureaucratic quality, control of corruption, socio-economic condition, etc.), and hence are more comprehensive. Second, it is different than the previous studies as its sample of countries includes both democracies and non-democracies, as well as both developed and non-developed economies in which policy implications are widely acceptable. Third, this study contributes to the policymakers especially those in the debt-ridden economies where governments are budget-tightening (limited capacity for public investment), as to which practical direction should be focused on so as to attract PI and eventually sustainable growth can take place.

This paper aims to study the tribological characteristics of the electrical contact system under different displacement amplitudes.

First, the risk frequency of real nuclear safety distributed control system (DCS) equipment is evaluated. Subsequently, a reciprocating friction test device which is characterized by a ball-on-flat configuration is established, and a series of current-carrying tribological tests are carried out at this risk frequency.

At risk frequency and larger displacement amplitude, the friction coefficient visibly rises. The reliability of the electrical contact system declines as amplitude increases. The wear morphology analysis shows that the wear rate increases significantly and the degree of interface wear intensifies at a larger amplitude. The wear area occupied by the third body layer increases sharply, and the appearance of plateaus on the surface leads to the increase of friction coefficient and contact resistance. EDS analysis suggests that oxygen elements progressively arise in the third layer as a result of increased air exposure brought on by larger displacement amplitude.

Results are significant for recognizing the tribological properties of electrical connectors in nuclear power control systems.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0098/

Nowadays, more and more Chinese consumers purchase luxury goods on live streaming platforms. However, the existing literature rarely focuses on this emerging phenomenon. This article attempts to construct a theoretical model based on the perceived value theory to explain this phenomenon.

In total, 354 online questionnaires are collected, and the partial least square structural equation model is used to analyze the model empirically.

The results show that consumers' perceived luxury values (financial value, functional value, individual value and social value) have a significant and positive effect on customer engagement, which further leads to purchase intention.

In view of fact that there is a big difference between luxury goods and nonluxury goods, yet the existing literature rarely distinguishes between luxury goods and nonluxury goods in the context of live streaming shopping, this article attempts to use perceived value theory to examine consumers' luxury purchase intentions in live streaming shopping and explores whether customer engagement is a mediating mechanism of perceived luxury values that influences purchase intention in live streaming.

The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods.

The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed.

The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly.

A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.