Recognizing the importance of competition analysis in telecommunications markets is essential to improve conditions for users and companies. Several indices in the literature assess competition in these markets, mainly through company concentration. Artificial Intelligence (AI) emerges as an effective solution to process large volumes of data and manually detect patterns that are difficult to identify. This article presents an AI model based on the LINDA indicator to predict whether oligopolies exist. The objective is to offer a valuable tool for analysts and professionals in the sector. The model uses the traffic produced, the reported revenues, and the number of users as input variables. As output parameters of the model, the LINDA index is obtained according to the information reported by the operators, the prediction using Long-Short Term Memory (LSTM) for the input variables, and finally, the prediction of the LINDA index according to the prediction obtained by the LSTM model. The obtained Mean Absolute Percentage Error (MAPE) levels indicate that the proposed strategy can be an effective tool for forecasting the dynamic fluctuations of the communications market.

Choosing a university is a crucial decision for each field of study, as it significantly influences the quality of graduates. An important factor in this decision is the university’s annual benchmark scores. The benchmark score represents the minimum score required for admission. This study evaluates the benchmark scores in the logistics sector for several prominent universities in Vietnam during the period 2021–2023. The research process utilized data on the benchmark scores for the years 2021, 2022, and 2023. The weights of these benchmark scores were calculated using the Rank Order Centroid (ROC) method, and the Probability method was employed to compare the benchmark scores of the universities. The analysis identified C3 as the criterion with the highest importance, while U3 emerged as the top-ranked alternative. The two-stage comprehensive sensitivity analysis revealed that universities consistently ranked high or low regardless of the method used to calculate benchmark score weights or the method employed for ranking. Additionally, the smallest weight change that affected the overall Probability ranking was 4.61%. This study provides significant guidance for students in selecting a university for logistics studies and serves as a foundational reference for universities to assess their capabilities in logistics education, thereby fostering healthy competition among institutions.

This study examined the role of cryptocurrencies in tourism and their acceptance across EU regions, with particular attention to the digital transformation precipitated by the COVID-19 pandemic. The analysis focuses on the relationship between cryptocurrency acceptance points and the intensity of tourism, highlighting that the acceptance of cryptocurrencies is significantly correlated with tourism services. The literature review highlighted that Web 3.0, especially blockchain technology and decentralized applications, opens new possibilities in tourism, including secure and transparent transactions, and more personalized travel experiences. The research investigated cryptocurrency acceptance points and the intensity of tourism within the EU. The study illuminates that the acceptance of cryptocurrencies significantly correlates with tourism services. The data and methodology demonstrated the analysis methods for examining the relationship between cryptocurrency acceptance points and tourism intensity, including the use of clustering neural networks and Eurostat data utilization. The results showed a positive correlation between the number of cryptocurrency acceptance points and tourism intensity in the EU, affirming the research hypothesis. According to the regression analysis results, each additional cryptocurrency acceptance point is associated with an increase in tourism intensity. The significance of the research lies in highlighting the growing role of digital payment solutions, especially cryptocurrencies, in tourism, and their potential impacts on the EU economy. The analysis supports that the intertwining of tourism and digital financial technologies opens new opportunities in the sector for both providers and tourists.

The Consumer Price Index (CPI) is a vital gauge of economic performance, reflecting fluctuations in the costs of goods, services, and other commodities essential to consumers. It is a cornerstone measure used to evaluate inflationary trends within an economy. In Saudi Arabia, forecasting the Consumer Price Index (CPI) relies on analyzing CPI data from 2013 to 2020, structured as an annual time series. Through rigorous analysis, the SARMA (0,1,0) (12,0,12) model emerges as the most suitable approach for estimating this dataset. Notably, this model stands out for its ability to accurately capture seasonal variations and autocorrelation patterns inherent in the CPI data. An advantageous feature of the chosen SARMA model is its self-sufficiency, eliminating the need for supplementary models to address outliers or disruptions in the data. Moreover, the residuals produced by the model adhere closely to the fundamental assumptions of least squares principles, underscoring the precision of the estimation process. The fitted SARMA model demonstrates stability, exhibiting minimal deviations from expected trends. This stability enhances its utility in estimating the average prices of goods and services, thus providing valuable insights for policymakers and stakeholders. Utilizing the SARMA (0,1,0) (12,0,12) model enables the projection of future values of the Consumer Price Index (CPI) in Saudi Arabia for the period from June 2020 to June 2021. The model forecasts a consistent upward trajectory in monthly CPI values, reflecting ongoing economic inflationary pressures. In summary, the findings underscore the efficacy of the SARMA model in predicting CPI trends in Saudi Arabia. This model is a valuable tool for policymakers, enabling informed decision-making in response to evolving economic dynamics and facilitating effective policies to address inflationary challenges.

This study aims to empirically analyze the impact of budget allocation by the Korea Institute of Science and Technology Information (KISTI) on national research competitiveness, thereby reassessing the value of investing in research infrastructure within a knowledge-based society. In the 21st century, research and development (R&D) have emerged as a pivotal element of national competitiveness, underlining the increasing importance of investments aimed at constructing and enhancing research infrastructure. However, empirical studies examining the causal relationship between research infrastructure investment and national research competitiveness are still notably scarce. Accordingly, this research endeavors to systematically delineate the effect of research infrastructure investment, with a focus on KISTI’s budget allocation, on enhancing national R&D outcomes. To achieve this, the structural relationship between KISTI’s budget, national R&D budget, and various academic and industrial performance indicators was analyzed using multiple regression and simple regression analysis. In particular, by demonstrating the mechanism through which the budget management of research support organizations like KISTI contributes to strengthening national research competitiveness, this study aims to shed new light on the strategic value of research infrastructure investment in a knowledge-based society. Furthermore, these findings are expected to provide valuable evidence for the formulation of national R&D policies in Korea and the strategic planning of budget operations for research support organizations. Through strategic investment of limited budgets, this could enhance the efficiency of national R&D investments and contribute to strengthening the capacity for scientific and technological innovation required in a knowledge-based society.

This article explores the properties of Fibonacci sequences and their widespread applications.