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To test a social-psychological model of public acceptance of highway infrastructure projects in the Chinese architecture/engineering/construction industry.

Through a comprehensive literature review, we established a social-psychological model of public acceptance related to benefit perception, risk perception and public trust. We empirically validated our model by using structural equation model analysis based on a questionnaire survey in the S35 Yongjin Highway Infrastructure Project in Yunnan Province, China.

Benefit, trust and risk perception had a significant influence on local residents' public acceptance of highway infrastructure projects; benefit perception and trust perception had a greater influence than risk perception. Public acceptance among local male residents over the age of 35 or those with higher education levels was more likely to be determined by the relative dominance of risk and benefit perceived.

This study contributes empirical evidence to the theoretical literature related to locally unwanted land use (LULU) siting and stakeholders in the field of project management from the public perspective. This study also suggests valuable practical implications to authorities, project managers and the public in decision-making and risk communication.

Although previous studies addressed factors affecting public acceptance towards potentially hazardous facilities, understanding of the implications of these social-psychological factors and their effects are still far from sufficient. This study bridges this gap by exploring the determinants of public acceptance towards highway infrastructure projects based on a selected case in China.

The study adopts congruence theory to explore the structure of inter-organizational compatibility and its structural effects on knowledge transfer in cross-border merger and acquisitions (M&As).

This paper built a moderated-mediation model that presented the relationship between inter-organizational compatibility and knowledge transfer. Regression analysis was conducted with 182 samples from China to examine the model and hypotheses.

The results indicate that inter-organizational compatibility is a four-dimensional construct comprising culture, strategy, routine and knowledge. Additionally, inter-organizational compatibility has structural effects on knowledge transfer. Specifically, routine compatibility mediates the relationships between cultural compatibility and knowledge transfer and between strategic compatibility and knowledge transfer. Moreover, the mediating roles are moderated by knowledge compatibility.

This study updates the construct and provides a comprehensive and fresh understanding of inter-organizational compatibility. Additionally, it presents the structural effects of inter-organizational compatibility on knowledge transfer.

Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths, has recently been imposing stricter controls over its production and export. The purpose of this paper is to examine the domestic roots of the changes in China's rare earth industry production and exports in its three-decade rise to the current global monopoly.

This paper adopts the historical institutionalism approach to analyze the trajectory of industry and trade development. The author analyzes data collected from government whitepapers and reputed scholarly and news sources.

This paper argues that the Chinese rare earth industry has gone through three periods of development, in which the state attempted to control the market and industry through reformulating rules and institutions to achieve state goals. Domestic state institutions, combined with macroeconomic environment and state governance strategy shaped the three-decade experience of rare earth industry and trade development in China.

This paper builds on existing findings about Chinese state regulations to provide a novel analytical framework to analyze the role of the state in industry and trade development in the rare earth industry. The focus on a single strategic industry seldom studied in the current literature also provides ample empirical value to further scholarly understanding about this industry.

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R² of 0.887 and a slight overestimation bias of +0.01.

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.