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This study aims to analyze the relevance of the city spatial structure for smart city innovation from the perspective of agglomeration externalities, and discusses whether there is heterogeneity in innovation across different geographical areas and population scales of cities.

The authors construct the centralization and concentration indexes to conceptualize the city spatial structure of 286 cities (prefecture-level) in China based on the LandScan Global Population Dataset from 2001 to 2016. A fixed-effects panel data model is employed to analyze the relationship between the spatial structure and the innovation ability of smart cities; the results were validated through robustness tests and heterogeneity analyses.

The study found that the more concentrated and more evenly the distribution of urban population, namely the more city spatial structure tends to be weak-monocentricity, the higher the level of innovation in smart cities. The relevance of the weak-monocentricity structure and smart city innovation varies significantly depending on their geographical location and the size of the city. This result is more applicable to cities in the eastern and central regions, as well as to cities with smaller populations.

The adjustment and optimization of the city spatial structure is important for enhancing smart city construction. Unlike previous studies, which mostly use a single dimension of “the proportion of population in sub-centres to the population of all central areas” to measure city spatial structure, the authors employed the spatial centralization and spatial concentration. It is hoped that this study can guide smart city construction from the perspective of the development model of city spatial structure.

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Corporate digital transformation (DT) and top management team (TMT) internationalization seem to be concomitant phenomena in recent years, the former is a major change and a thorough transformation requiring continuously new technologies and ideas. Excitingly, the latter shows a relatively broad vision, a large risk appetite and interest in new things. Does TMT internationalization promote DT? This question is essential for DT. Given that, this article will aim to discuss and examine whether and how TMT internationalization affects corporate DT.

This article takes China's A-share listed manufacturing companies from 2011 to 2019 as a sample. The quantitative text analysis method is used to measure attention related to digitalization. This paper discusses: (1) The decision-making logic and cognitive process mechanism of “TMT internationalization–attention related to digitalization–corporate DT”. (2) The moderating effect of shared corporate mission of the TMT on the cognitive process of decision-making, that is, the social process of transforming individual cognition into team cognition, and the social process of transforming team cognition into corporate decision-making.

TMT internationalization promotes DT. As an external manifestation of team cognition, attention plays a positive role as an intermediary mechanism. Specifically, executives with overseas experience have higher urgency assessment and manageable assessment, thus affecting their attention to digitalization positively, thereby promoting DT. This article does not demonstrate the moderating effect of shared corporate mission on the cognitive process, but it promotes DT directly, and only plays a role in the precognitive stage.

This article is the first one to study the relationship between TMT internationalization and corporate DT, which has practical guiding significance for DT and the “going out” strategies of the TMT. Also, the combination of upper echelons theory and cognitive theory opens up the black box of the strategic process. Lastly, this research explores the formation process of team cognition, which is always neglected by previous studies of the TMT demographic characteristics.

Given the complexity of building information modeling (BIM) adoption decisions in small- and medium-sized enterprises (SMEs) in the Architecture, Engineering and Construction (AEC) industry, understanding BIM adoption decision-making through the net effect of a single factor on BIM adoption decisions alone is limited. Therefore, this paper analyzed the co-movement effect of managers' psychological factors on the BIM adoption decisions from the perspective of managers' perceptions. The purpose is to let managers have a deep understanding of their BIM adoption decisions, and put forward targeted suggestions for the AEC industry to promote the adoption of BIM by SMEs.

Data from 192 managers in SMEs collected by the questionnaire were used in a fuzzy set qualitative comparative analysis (fsQCA). Due to the limitations of fsQCA in making the best use of the data used, as a complement to fsQCA, necessary conditions analysis (NCA) was used to analyze the extent to which necessary conditions influenced the outcome.

(1) NCA analysis shows that high perceived resource availability (PRA) and high performance expectancy (PE) are necessary conditions for high BIM adoption intention (AI). (2) fsQCA analysis shows that high PE is the single core condition for high AI. fsQCA analysis identifies three configurations of managers' psychological factors, reflecting three types of managers' decision preferences, namely benefit preference, loss aversion and risk avoidance, respectively. Different decision preferences may lead to different BIM adoption strategies, such as full in-house use, partial in-house/outsourcing and full outsourcing of BIM processes. (3) High perceived risk (PR) and low perceived business value of BIM (PBV) are the core conditions for low AI.

This paper expands on the application of fsQCA to context of BIM adoption decisions. Based on the results of fsQCA analysis, this paper also establishes the relationship between managers' decision-making psychology and BIM adoption strategy choice and analyzes the impact of different decision biases on BIM adoption strategy choice. It concludes with suggestions for encouraging managers to adopt BIM and for avoiding decision-making bias.

The purpose of this paper is to examine the relationship between financial literacy and entrepreneurial activities, and the moderating effects of industrial regulation in the relationship between financial literacy and entrepreneurship.

In this study, the role of financial literacy on entrepreneurial participation and performance is investigated through multi-sourced data from the Chinese Family Panel Studies with manually merged provincial and industrial data from 2014. Four hypotheses are tested based on Probit and Tobit models. Moreover, instrumental variable method and principal component analysis are applied to provide robustness checks.

The empirical results demonstrate that financial literacy has significantly positive effects on entrepreneurial participation, as well as on entrepreneurial performance. In addition, industrial regulation positively moderates the effects of financial literacy on entrepreneurial participation and performance, which indicates that financial literacy plays a more important role in promoting entrepreneurship in tightly regulated industries.

This study proposes and tests the effects of financial literacy on entrepreneurial activities, which fills an important gap in the literature. The results in this paper provide evidence that financial literacy has positive impacts in both the entry and operation stages of entrepreneurship. This evidence provides theoretical foundations for policy making in popularizing financial knowledge and supporting entrepreneurial activities. Moreover, this research further reveals the effects of industrial regulation in the context of China, suggesting that the government should be more effective in promoting administrative decentralization and reducing unnecessary interventions.

The purpose of this paper is to empirically analyze the impacts of short prohibitions on stock prices.

The authors adopt event study in this paper. First, the authors match each shortable stocks with one unshortable stocks by the propensity score matching method. Second, the authors check the performance difference between treatment group and control group after the event date. Third, the authors check the performance difference among sub-groups sorted by other factors associated with stock returns.

The authors find that stocks do not decline necessarily after removal of short prohibitions; only those heavily overpriced stocks, such as small stocks, lower B/M or P/E stocks and higher turnover stocks, decline significantly.

The media falsely stated that short selling lead to market crash; otherwise, short selling is beneficial for improving market efficiency as it is helpful for keeping overpriced stocks in line with the fundamental value.

This is the first paper showing that removal of short prohibitions only impacts heavily overpriced stocks significantly, which is valuable for policy making.

The purpose of this paper was to obtain the lubrication characteristics of heavy hydrostatic bearing in heavy equipment manufacturing industry through theoretical analysis and numerical simulation.

This paper discusses the influence of oil film thickness variation on velocity field, outlet-L and outlet-R flow velocity under the hydrostatic bearing running in no-load 0 N, load 400 KN, full load 1,500 KN and rotating speeds of 10 r/min, 20 r/min, 30 r/min, 40 r/min, 50 r/min and 60 r/min, by using dynamic mesh technology and FLUENT software.

When the working table rotates clockwise, in the change process of oil film thickness, the fluid flow pattern of the lubricating oil at the edge of the sealing oil is the rule of laminar flow, and the oil cavity has a vortex. The outlet-R flow velocity becomes higher and higher by increasing the bearing load and working table speed, and the flow velocity increases with the decrease in oil film thickness; the outlet-L flow velocity increases with the decrease in oil film thickness under low rotating speed (less than 10 r/min) condition and decreases with the decrease of oil film thickness under high rotating speed (more than 60 r/min) condition.

The influence of the oil film thickness on the flow state distribution of the oil film was analyzed under different working conditions, and the influence rules of oil film thickness on the flow velocity of hydrostatic bearing oil pad was obtained by using dynamic mesh technology.

The adverse effects of temperature on the lubricating properties of nano magnetorheological grease are reduced by applying of a magnetic field.

Nano magnetorheological grease was prepared via a thermal water bath with stirring. The lubricating properties of the grease were investigated at different temperatures. Then the lubricity of the prepared nano magnetorheological grease was investigated under the effect of thermomagnetic coupling.

As the temperature rises, the coefficient of friction of grease lubrication gradually increases, surface wear gradually increases and lubrication performance gradually decreases. Compared with grease, magnetorheological grease has a decreased coefficient of friction and enhanced lubrication effect under the action of a magnetic field at different temperatures.

A lubrication method using a magnetic field to reduce the effect of temperature is established, thereby providing new ideas for lubrication design under a wide range of temperature conditions.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.