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.

This paper proposes an innovative methodological framework that combines machine-learning and deep learning algorithms with established econometric methods for the critical problem of expenditure forecasting in the budget process. The paper aims to develop, test, and validate an artificial intelligence model capable of improving the accuracy of expenditure forecasting in the budget process and supporting financial accounting decisions in public institutions. Using historical and statistical data from a group of public institutions, the research applies both univariate and multivariate forecasting strategies, evaluated with performance metrics. The research focuses on the development of an innovative forecasting model based on AI, using historical and statistical data from public sources and case studies of local public institutions to transform them into smart cities. The selected AI algorithms include artificial neural networks, support vector machines, and deep learning models, implemented and evaluated using Python v3.14. The research results show that AI can significantly improve the accuracy of budget forecasts compared to traditional methods, such as linear regression and econometric models. The use of AI contributes to increasing transparency and accountability in the management of public funds, providing more detailed and well-founded forecasts.

This paper investigates the potential of leveraging artificial intelligence to automate and optimize the verification process, particularly in generating System Verilog assertions for an Advance Peripheral Bus verification environment using Universal Verification Methodology. Generative artificial intelligence, such as ChatGPT, demonstrated its ability to produce accurate and valuable assertions by employing text-based prompts and image-fed inputs, significantly reducing the required manual effort. This research presents a way of generating System Verilog assertions using the ChatGPT prompt, presenting an image to the Large Language Models, and requesting the assertions needed for the respective protocol. This approach shows the potential for artificial intelligence to revolutionize functional verification by automating complex tasks, ultimately ensuring faster and more reliable System-on-Chip development. The assertions generated by the Large Language Models are integrated into an existing Advance Peripheral Bus verification environment. This process involves running the assertions on a free EDA Playground platform with all three simulators (Cadence Incisive, Mentor Questa, and Synopsys Verilog Compiler Simulator). The main conclusions are that using ChatGPT-4.0 for generating System Verilog assertions significantly reduces the time and effort required for functional verification, demonstrating its potential to enhance efficiency and accuracy in verifying complex System-on-Chip designs.

This study aims to analyze the relationships between corporate social responsibility and corporate financial performance using only content analysis from 1971 to 2018 available in all databases of Web of Knowledge. Our findings attempts to fill this void that past researchers are deepening because the uncertainty can be find in lots of papers. If there is a link between CSP and CFP is no longer the question. Our question is what kind of link is it and what is its impact? Some authors are proposing some kind of theories linking this concepts but we must understand better the implications for the companies.