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Cutting force prediction is pretty important for manufacture management. Thus, the purpose of this paper is to obtain the cutting force of the machining process with high efficiency and low cost. A method based on the improved auto regressive moving average (ARMA) model is proposed for cutting force predictions in milling process.

First, classification and normalization are made for initial cutting force. Second, the cutting force sequences are compressed followed singular and valid value removed. At last, the improved ARMA model is used for cutting force fit and extrapolation considered the time domain characteristics.

A series of cutting force with the spindle speed 595r/min is carried out in the research. It is showed that the mean absolute percentage error value of cutting force extrapolation results which is based on the improved model is smaller. The percentage value is approximately 5.80%. Then the root mean square error test value is only 72.49, which is smaller than that with other traditional method, such as hidden Markov model. The extrapolation results with the proposed model performed good consistency and accuracy in terms of peaks, valleys and volatility compared with the experiment results.

The proposed method that is based on the improved ARMA model can be used for cutting force predictions conveniently. And the predictions can be used for improving the qualities in milling process.

This study examines how project uncertainties (environmental uncertainty and participant uncertainty) affect guanxi and contractual governance and assesses the mediating role of guanxi governance between project uncertainty and contractual governance.

Data were collected in two stages from Chinese contractors. First, in-depth interviews were conducted with nine construction engineering project practitioners in different contracts as a pilot for questionnaire designing. Second, a cross-sectional questionnaire survey was conducted with professionals and practitioners of construction enterprises to collect primary data. Partial least squares structural equation modeling (PLS-SEM) was used to test seven hypotheses based on data collected from 198 respondents.

Project environmental uncertainty promotes the use of guanxi governance, while project participant uncertainty hinders it; the relationship between both types of uncertainty and contractual governance is the same as with guanxi governance. Furthermore, guanxi governance promotes contractual governance and partially mediates project environmental uncertainty and contractual governance and a complete mediating role between project participant uncertainty and contractual governance.

As the interviewed samples are mainly from China, the study should be replicated using large representative samples from East Asian countries, such as Japan and South Korea, to gain a more comprehensive understanding of the influence of guanxi governance. Further, while the internal consistency reliability and convergent validity of the questionnaire data in this study align with the standards, a larger sample size would improve the reliability and validity of the research results and better represent the overall work situation of contractors, owners and public policymakers.

The results provide insights into project governance research and have implications for construction practitioners in deploying governance-related resources.

This study aims to argue that responses in economic growth (EG) resulting from positive and negative shocks in energy consumption could be a non-linear phenomenon. Thus, the study aims to investigate the existence of non-linear long-run effects of positive and negative shocks in green and conventional energy consumption on EG for China and India. By decomposing energy consumption in positive and negative shocks, the study seeks to determine the distinct impact of positive and negative shocks in energy (conventional and green) consumption on EG of China and India.

A non-linear autoregressive distributed lag (NARDL) model based on energy-augmented environment Kuznets curve (EKC) framework is used on annual time series covering the period 1965–2021. The study uses a precise econometric methodology, starting with unit root tests to assess stationarity, moving to the estimation of the NARDL model, which resulted in the calculation of long-run coefficients and error correction terms to analyse the rate of adjustment towards equilibrium.

The empirical findings demonstrate that there exists a non-linear cointegrating relationship among EG, carbon emissions and green and conventional energy consumption for both economies. In the long run, a non-linear impact of green energy consumption (GEC) on EG is evident for China only, whereas non-linear impact of conventional energy consumption (CEC) on EG is visible for both countries.

While China and India prioritise energy diversification by embracing green energy to promote energy security and limit rising carbon emissions, it is interesting to investigate how positive and negative shocks in GEC and CEC have affected their EG. Second, this paper examines the trade-offs between EG and GEC/CEC in China and India, two high-carbon emitters. The disparities in trade-offs may indicate how well each country’s energy policies address increased EG with fewer energy-induced carbon emissions.

This study examines non-linear cointegration among the variables of interest, whereas most prior studies have focused on linear cointegration. The existence of non-linear cointegration may suggest that positive and negative shocks in GEC and CEC can result in non-linear reactions in EG. Thus, it establishes a basis for examining the non-linear long-term effects of GEC and CEC on EG. The research findings indicate significant consequences and necessitate prompt intervention to alleviate the detrimental impacts of shocks in GEC and CEC on EG in China and India and provide several important inputs to address the inherent challenges of energy transition goals.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.