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In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.

Based on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.

When the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.

Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

This paper examines the different features of China's economic development in different stages of economic globalization. The study finds that the investment- and export-based growth model drove China's high-speed economic growth between 2000 and 2007, which came into existence around 2000 when China plugged into the global production network.

This paper also finds that China slowed down to the New Normal because of the disruption to the socio-economic underpinnings of this growth model. As China adapts to and steers the New Normal, supply-side structural reforms can channel excess capacity to the construction of underground pipe networks in rural areas of central China and fix capital while advance rural revitalization.

At the same time, enterprises must strive to build a key component development platform for key component innovation and the standard-setting power in global manufacturing.

The establishment of a domestic production network integrating the integrated innovation-driven core enterprises and modular producers at different levels can satisfy the dynamic demand structure of China in which standardized demands and personalized demands coexist.

The purpose of this paper is to summarize the status and characteristics of rail technology of high-speed railway in China, and point out the development direction of rail technology of high-speed railway.

This study reviews the evolution of high-speed rail standards in China, comparing their chemical composition, mechanical attributes and geometric specifications with EN standards. It delves into the status of rail production technology, shifts in key performance indicators and the quality characteristics of rails. The analysis further examines the interplay between wheels and rails, the implementation of grinding technology and the techniques for inspecting rail service conditions. It encapsulates the salient features of rail operation and maintenance within the high-speed railway ecosystem. The paper concludes with an insightful prognosis of high-speed railway technology development in China.

The rail standards of high-speed railway in China are scientific and advanced, highly operational and in line with international standards. The quality and performance of rail in China have reached the world’s advanced level. The 60N profile guarantees the operation quality of wheel–rail interaction effectively. The rail grinding technology system scientifically guarantees the long-term good service performance of the rail. The rail service state detection technology is scientific and efficient. The rail technology will take “more intelligent” and “higher speed” as the development direction to meet the future needs of high-speed railway in China.

The development direction of rail technology for high-speed railway in China is defined, which will promote the continuous innovation and breakthrough of rail technology.

This paper evaluates the effects of tax incentives on business innovation in Brazil that were established by Law 11,196/05 (the “Fiscal Incentives Law”) to test whether they have had a positive impact on beneficiary firms' innovation input and output and on their performance.

The policy impacts are estimated using microdata on 13,706 firms available in the 2008 and 2011 editions of the Brazilian Innovation Survey (PINTEC) and by applying propensity score matching with difference-in-differences.

The results suggest a positive and statistically significant impact of the policy on research and development (R&D) expenditures (average of approximately US$ 264,000 in 2011), the number of research staff (average of five researchers) and total employment (approximately 5% of the beneficiary firms' mean size). However, no impact was found on the overall spending on innovative activities, the percentage of sales and exports from new products, net revenue or net revenue per employee.

The findings provide empirical support in favor of tax incentives as a policy tool to boost business innovation in the country. However, the absence of significant effects on innovative activities expenditures and on most indicators of innovation output and firms' performance reveals shortcomings of the policy that need to be addressed.

The study complements and advances the findings of previous studies by assessing policy impact on total innovative activities expenditures and on innovation output and firm performance.

This study examines the impact of firm-level climate risk exposure (FCRE) on firm stock liquidity by using a sample of Indian-listed firms from the financial years 2003–2004 to 2022–2023. Further, it endeavors to investigate the moderating role of environmental, social and governance (ESG) disclosure in this relationship.

A novel text-based FCRE metric is introduced using a sophisticated Word2Vec model through a Python-generated algorithm for each firm and year based on the management discussions and analysis (MD&A) reports. The panel fixed effect model is used to study how FCRE affects stock liquidity.

The result shows that FCRE negatively affects firms’ stock liquidity, and the effect remains robust after addressing endogeneity concerns. In addition, we find that a high ESG disclosure rating significantly moderated the adverse effect of FCRE. Furthermore, our analysis reveals that investor sentiment, information quality, corporate life cycle and institutional holdings moderate the impact of FCRE on liquidity.

The study offers valuable insights for investors, managers and policymakers on integrating climate risk into investment strategies, improving corporate climate governance and shaping policies that incentivize sustainable corporate behavior.

To the best of our knowledge, this study is an early study to explore the relationship between firm-specific climate risk exposure and stock liquidity using advanced machine learning techniques. It contributes to the existing literature by illustrating how climate risk can lead to adverse market reactions while highlighting the critical roles of corporate ESG practices, investor sentiment and disclosure quality in influencing this relationship.

The purpose of this paper is to extend the knowledge of social entrepreneurial ecosystems and test their effect on social entrepreneurial activity in a cross-border context.

The current research used the fuzzy set Qualitative Comparative Analysis method on a sample of 4,357 cross-border cooperation (CBC) projects implemented between 2014 and 2020, spread over 40 Euroregions.

Single ecosystem elements can be sufficient conditions but with a limited effect on cross-border social entrepreneurship. Configurations of ecosystem elements can be necessary conditions with synergetic effects. A geographical pattern was identified in the spread of configurations across Europe.

Geographical, quantitative and project data constraints exist. The authors call for research into synergies between ecosystem elements in cross-border contexts and ecosystem patterns across Europe.

Policymakers, their cross-border counterparts and Euroregions could coordinate their efforts to improve ecosystems’ impact and involve social entrepreneurs to scale impact in neighboring countries.

Involving social entrepreneurs in CBC projects will show how social impact in one country can be valuable for solving issues in the neighboring country. This will increase the valuation of innovative solutions, create opportunities for scaling social impact and contribute to the European (EU) Cohesion Policy.

The study uses a novel approach by investigating the effect of social entrepreneurial ecosystems in Euroregions on social entrepreneurial activity in a cross-border context. The study shows that the impact of social entrepreneurial ecosystems does not stop at the country’s borders.

The main objective of this article is to visualize the structure and trends of pro-environmental behavior (PEB) between 1999 and 2023 through mapping and in-depth analysis. The aim is to analyze PEB, which has received considerable academic attention in recent years due to its key role in the conservation of the environment and the protection of local communities in tourist destinations. This paper provides an important summary of the recent research that has explored the role that tourists have in protecting the environment through PEB.

This study presents a visual analysis of 2005 scholarly articles between the years 1999 and 2023 related to PEB. Using the knowledge mapping based on VOSviewer it presents the current status of research, which includes the analysis of citation analysis, co-citation analysis, co-citation network and longitudinal analysis.

PEB is an emerging topic due to its relevance to protecting the environment in the context of travel. The citation and co-citation analysis show the relevance of the behavior of tourists with regard to protecting the environment. The co-word analysis highlights the current significance of research concerning green hotels and the destination image of environmentally responsible destinations.

This study sheds light on the current research progress of PEB in the context of tourism through a comprehensive analysis (citation, co-citation and co-word). In addition, we provide theories and factors that have been previously used to study PEB in the context of tourism. The findings contribute to a broad and diverse understanding of the concept of PEB, which can provide important insights for policymakers in formulating management strategies and policies aimed at reducing environmental impacts in destinations.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This study embraces the call for exploring the determinants of continuous intention in TikTok. Taking the perspective of social influence, this study not only tries to explore the contextual sources of two types of social influence but also aims to unveil the influence mechanism of how social influence affects TikTok viewers’ continuous intention.

This study empirically analyzes how TikToker attractiveness, co-viewer participation, platform reputation and content appeal affect informative and normative social influence and then lead to the continuous intention of TikTok. Based on 547 valid survey data, this study adopts a mixed analytical approach for data analysis by integrating structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).

SEM results unveil that content appeal is the most critical antecedent of informational social influence, while the TikToker attractiveness and platform reputation have no effect on it. Differently, all four external sources positively lead to normative social influence. Among them, content appeal and co-viewer participation influence the most. The influences of both two types of social influence on continuous intention are demonstrated. FsQCA results reveal seven alternative configurations that are sufficient for influencing continuance intention and further complement and reinforce the SEM findings.

Addressing the critical contextual elements of TikTok, this study explores and confirms the sources which may engender social influence. The authors also demonstrate the critical role of social influence in affecting TikTok viewers’ continuous intentions by the hybrid analytical approach, which contributes to existing academic literature and practitioners.