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This study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization purposes.

Using adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. Genetic iterative methods were used to calculate the optimal texture structure. Genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex bodies.

After about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component surfaces.

Surface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often requires multiple experimental comparisons to determine the optimal texture structure, resulting in high trial-and-error costs. By using an adaptive genetic algorithm as an optimization tool, the optimal surface groove structure can be obtained through simulation and modeling, effectively saving costs in the process.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

The purpose of this paper is to study the aerodynamic characteristics and uplift force tendencies of pantographs within the operational height span of 1,600–2,980 mm, aiming to offer valuable insights for research concerning the adaptability of pantograph-catenary systems on double-stack high container transportation lines.

Eight pantograph models were formulated based on lines with the contact wire of 6,680 mm in height. The aerodynamic calculations were carried out using the SST k-ω separated vortex model. A more improved aerodynamic uplift force method was also presented. The change rule of the aerodynamic uplift force under different working heights of the pantograph was analyzed according to the transfer coefficients of the aerodynamic forces and moments.

The results show that the absolute values of the aerodynamic forces and moments of the upper and lower frame increase with the working height, whereas those of the collector head do not change. The absolute values of the transfer coefficients of the lower frame and link arm were significantly larger than those of the upper frame. Therefore, the absolute value of the aerodynamic uplift force increased and then decreased with the working height. The maximum value occurred at a working height of 2,400 mm.

A new method for calculating the aerodynamic uplift force of pantographs is proposed. The specifical change rule of the aerodynamic uplift force of the pantograph on double-stack high container transportation lines was determined from the perspective of the transfer coefficients of the aerodynamic forces and moments.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

While the adverse health consequences of obesity are well-documented, the causal effect between obesity and economic outcomes, particularly individual income in the labor market, has yielded inconsistent findings. At the same time, China has the highest number of obese or overweight people around the world in recent years. However, limited research has examined the causal effect of obesity on Chinese rural residents’ income. To answer this question, our study aims to identify the causal impact of obesity on Chinese rural residents.

First, we conduct ordinary least squares, instrumental variable and Mendelian randomization methods to evaluate the impact of obesity on Chinese rural residents’ income. Then, we employ quantile regression and instrumental variable quantile regression to investigate the potential distributional impacts of obesity across various income segments. For the generality of our results, we also use 485,849 samples from the UK Biobank and the two-sample Mendelian randomization method to analyze.

Our one-sample Mendelian randomization analysis, based on a sample of 441 people collected from rural China in 2019 and 2021, revealed that obesity reduced residents’ annual income by 647.87 China Yuan. Results from the UK further support our findings. We also find that the adverse causal impact of obesity on income is significant in individuals within the above 50th percentile of the income distribution. Additionally, childhood obesity has a long-term adverse effect on income in adulthood.

These results contribute to a more nuanced understanding of the economic costs of obesity. Weight penalties may lead to a deviation from the efficiency that should be pursued in the labor market and widen income disparity.

We provide novel and robust instrumental variables and utilize the Mendelian randomization method to analyze the effect of obesity on Chinese rural residents.

Given the growing external value chain disruptions, there have been many studies seeking to propose methods for improving the resilience of global value chains (GVCs). This study complements previous studies by proposing the perspective of firm strategy in switching governance modes to improve the resilience of GVCs. Specifically, this study explores under what conditions MNCs are more likely to switch governance toward non-equity mode (NEM) from the alternative ones. This study introduces three industry-specific factors that affect MNCs’ decision for NEM. It then applies this framework to explain how Korean MNCs’ strategic governance change for the co-productions with Chinese firms when entering their film market which is highly restricted by the Chinese government. This study enriches the research on GVC resilience by arguing that MNCs can avoid unfavorable environmental impacts by flexibly changing their GVC governance modes under certain conditions. This study also contributes to the understanding on why some countries maintain their high attractiveness for foreign MNCs, whereas other countries do not, given the similar level of restrictive government regulations. The protectionist policies of the host government are valid only in an industry where the three conditions are met, as they increase the possibility of domestic firms’ participation by encouraging foreign MNCs to shift their entry mode from sole venture toward alliances with domestic firms.

Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.

An integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.

The results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.

This study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.

Identified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.

This study aims to examine how different modes of knowledge flows affect the changes of asset specificity and how ownership control moderates the relationship between knowledge flows and asset specificity in the open innovation paradigm.

This paper selects information technology outsourcing as the research base. It uses the feasible weighted least squares modeling method for its analysis and has collected the data from 2,369 research and development contracts of multinational vendor firms in China.

The coupled and outbound knowledge flows have a direct and positive effect on asset specificity. Moreover, the results show that weak corporate control has significant moderating effects on the relationship between both coupled and outbound knowledge flows and asset specificity; the strong control positively moderates the relationship between outbound knowledge flows and asset specificity.

In open innovation, firms build a higher degree of asset specificity to maximize the efficiency of knowledge flows, which then helps them to enhance innovation capacity and market performance.

Preceding studies have tended to examine the influences of asset specificity as an independent variable in a closed innovation paradigm. Asset specificity is hence often left as the antecedent “black box.” This paper, however, opens the “black box” of asset specificity, which is set as a dependent variable, by investigating the influences of knowledge flows on the asset specificity in the context of open innovation. It also reinterprets the role of asset specificity by adopting the lens of open innovation theory.

This conceptual paper aims to explore under what conditions multinational companies (MNCs) are more likely to internationalize through externalization modes.

This paper complements previous studies by proposing three industry-specific factors that affect MNCs’ decision for externalization. It then applies this framework to the case of Korean MNCs’ strategic choice when entering the Chinese film market which is highly regulated by the government, to illustrate how such a framework works in practice.

This paper suggests that MNCs are more likely to choose externalization entry modes under the three industry conditions: when the business grows fast, when there are best practices of industry standard and when the business requires multi-competence. It also shows that the three conditions explain well the growing Korea–China co-productions in the Chinese film market.

This paper provides useful implications for the government’s regulatory effectiveness. The protectionist policies of the host government are valid only in an industry where the three conditions are met, as they increase the possibility of domestic firms’ participation by encouraging foreign MNCs to shift their entry mode from sole venture toward alliances with domestic firms.

This paper enriches the entry-mode research by indicating that MNCs’ externalization decisions need to consider these industry-specific factors. In addition, it also contributes to the understanding on why some countries maintain their high attractiveness for foreign MNCs, whereas other countries do not, given the similar level of restrictive government regulations.

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and thematic (topics, theories, key constructs and their relationships) perspectives.

Qualitative thematic review and quantitative systematic review were performed on 70 papers obtained from hospitality and tourism categories of two databases, namely, Web of Science and Scopus.

A total of 5 topics and 27 sub-topics were identified and the major theme is market intelligence. Sentiment variables were investigated not only as independent but also as dependent variables. The customer rating is the most investigated dependent variable, whereas moderators and mediators were rarely tested. Most reviewed studies did not use theory. The findings from the methodological review show that analysis of big data was rare. Moreover, testing the performance of sentiment analyzes was uncommon, and only one paper tested the performance of aspect/feature extraction.

This study extends prior review studies by providing a comprehensive view of how knowledge and methodologies of sentiment analysis have developed. The identified themes and key constructs serve as a solid base for future knowledge advancement. Future research directions on sentiment analysis are also provided.

To the best of the authors’ knowledge, this study is the first comprehensive methodological and thematic review of sentiment analysis in hospitality and tourism. Based on the identified findings, the authors propose several directions for future research.