The purpose of this study is to explore the Web of Science Database (WOS) and review the significant contributions to the research of Fuzzy Logic or Fuzzy Sets theory from the beginning to the present. This study analyzes the most eminent authors, institutions, countries, and journals in Fuzzy Logic research by applying science mapping methods and bibliometric measures. Also, we paid attention to link strength and h-index to represent the visibility, influence, and link between the representative authors. Moreover, we added descriptive statistics to highlight strong linearity and a connection between fuzzy publications and Fuzzy Logic research. Also, we applied regression analyses and prevision functions to predict the evolution of the Fuzzy Logic topic. The results showed a significant increase in the number of papers published annually in a portfolio of internationally representative journals. This leads us to the idea that Fuzzy Logic research is now a transdisciplinary topic that continually develops. Therefore, it can be found in more and more related areas such as artificial intelligence, IoT, medicine, economics, or the environment. Most of the results are consistent with other bibliometric studies. Still, some results are different, results related to the current cited works that show a polarization in the Asia area and the top journals that is continuously changing depending on the number of papers and the quotations of scientific personalities that publish. We used the VOS Viewer software to map the main trends in the field. The results indicate that the use of concepts has long exceeded traditional boundaries.

Our research investigates the connection between firm characteristics and leverage based on a sample of firms listed in the Chinese Stock Index 300. We aim to examine the sustainability of the financial structure of Chinese enterprises covering the period 2010–2019. We employ a conditional quantile regression that discloses the behavior of regressions across the leverage distribution and compares its results for different leverage levels with those achieved by the linear regression model. The results confirm the effects of the determinants of capital structure change since the quantile of leverage varies. We find that both the trade-off theory (TOT) and the pecking order theory (POT) confirm the validity of Chinese firms’ financing decisions at different quantiles of leverage. Specifically, the empirical results support the POT more over the TOT at higher levels of the quantile. Furthermore, the relationship between firm size and leverage strongly switches to support the POT at the highest quantile. All empirical results are obtained from quantile regression, consistent with the prediction for an increase in asymmetric information of the POT when Chinese firms employ more debt in their capital structure.

The current development of multimodal transport networks focuses on the realization of intelligent transport systems (ITS) to manage the prediction of traffic congestion and urban mobility of vehicles and passengers so that alternative routes can be recommended for transport, especially the use of public passenger transport, to achieve sustainable transport. In the article, we propose an algorithm and a methodology for solving multidimensional traffic congestion objectives, especially for intersections, based on combining machine learning with the templates method—the golden template algorithm with the multiagent game theory. Intersections are modeled as independent players who had to reach an agreement using Nash negotiation. The obtained results showed that the Nash negotiation with multiagents and the golden template modeling have superior results to the model predictive control (MPC) algorithm, improving travel time, the length of traffic queues, the efficiency of travel flows in an unknown and dynamic environment, and the coordination of the agents’ actions and decision making. The proposed algorithm can be used in planning public passenger transport on alternative routes and in ITS management decision making.

The emergence of advanced technologies brings various opportunities for all kinds of business organizations. This topic was selected for research and discussion to figure out the possible impacts of using the ever-increasing development of digitalization, big data analytic capabilities (BDACs) and innovation in the field of sport-based entrepreneurship, which is the main pillar for the economic wellbeing, development and growth of sport activities. This study highlights the function of the BDAC of entrepreneurs in the acceleration of their readiness and innovation activities. Beyond the direct association of BDAC with an entrepreneur’s readiness and innovation performance (IP), we also tested the mediation of entrepreneurial readiness between the BDAC and IP of sport-based entrepreneurs. Moreover, the moderation of entrepreneurial orientation (EO) was also considered for the readiness and IP link. In this study, data were collected from 562 sport-based entrepreneurs. Online questionnaires were used for data collection, and various statistical techniques, including correlation, regression and structural equation modeling (AMOS 7.0), were applied for the analyses of the collected data. The outcomes of this study disclosed that BDAC and entrepreneur readiness positively predicted the IP. The results revealed that entrepreneurial readiness mediated between BDAC and IP. The findings suggested that sport-based entrepreneurs should enhance their BDAC for the execution of sport-related innovative activities. In spite of its valuable findings and suggestions, the current study is subject to some limitations. Firstly, this study is limited to sport-based entrepreneurs only. Secondly, AIT theory was used here, so for future considerations, other pillars of the economy such as the manufacturing sector should also be considered and the many other theories available should be employed.

The surrounding reality can be analyzed due to the interaction of complex nonlinear dynamic systems. The article’s main objective is to develop and analyze the models that best describe the efficient behavior of the Romanian capital market that generated the analyzed time series. The empirical analysis carried out in this paper does not aim to classify the Romanian market capital as efficient or ineffective but rather to identify the degree of deviation from efficiency relative to other markets, namely, an analysis of the dynamics of the degree of deviation over time. To describe the distribution of returns, we focused on the family of generalized hyperbolic distributions, which have statistical properties similar to financial returns. The presence of wide tails in the distributions (of extreme values) suggests using statistical tests and measures to detect dependencies, which take this behavior into account. Statistical methods and efficiency indicators are used, such as the Hurst exponent, Taken’s theorem, and the fractal dimension, which facilitate the detection of the main types of dependencies that could be present in the return series; measures that are robust to the heteroscedastic behavior of the returns. These statistical measures are applied to the entire period and sliding windows.