Purpose: This study aims to analyze the most frequently discussed topics in the scientific discourse on artificial intelligence (AI) in higher education using Natural Language Processing (NLP) techniques. Design/methodology/approach: This paper analyzes 52 peer-reviewed articles published between 2017 and 2024, utilizing NLP techniques to identify prevalent unigrams, bigrams and trigrams related to AI in higher education. Findings: The analysis identifies an emerging concern with utilizing AI tools to enhance educational processes, with “Higher education,” “artificial intelligence” and “generative AI” becoming ubiquitous terms in use. LLM and ChatGPT represent types of technology that evoke potential for personalized learning and enhanced practice in instruction. Research limitations/implications: In review studies, samples with a post-secondary educational background usually restrict generalizability to school environments. Future studies can examine the long-term consequences of AI technology in extended academic environments, longitudinal studies and educational environments. Practical implications: The frequency patterns from our analysis offer essential insights for educators and administrators regarding curriculum development and teaching practices. The high occurrence of terms like “artificial intelligence” (1,193 times) and “higher education” (824 times) highlights the need for incorporating AI literacy into curricula. This integration should include guidelines for responsible AI use and training programs for faculty. The frequent mentions of “teaching learning” (226 times) and “AI education” (319 times) highlight important implications for teaching practices. Educational institutions must establish frameworks that blend traditional methods with AI-enhanced strategies, including assessment plans that consider AI tools while upholding academic integrity. Additionally, institutions should prioritize investment in AI infrastructure and support systems. Social implications: Our findings highlight important societal implications beyond education. The frequency analysis reveals concerns about educational equity, including disparities in access to AI-enhanced education, digital literacy gaps and economic barriers to adopting AI tools. Addressing these issues is vital to prevent the worsening of social inequalities. Additionally, our results emphasize the need for workforce development. Educational institutions should focus on equipping students with the AI competencies that employers demand and bridging the gap between academic training and industry needs. The policy implications of our findings are equally significant. Our analysis suggests the need for educational policies that address AI integration while establishing clear guidelines for ethical AI use in academic settings. These policies should include standards for AI tool evaluation and implementation to guide institutions' adoption decisions. The economic impact of these developments is also noteworthy, as our results indicate the potential for enhanced workforce preparedness through AI-integrated education, improved educational efficiency through automation and new opportunities for educational technology development. Originality/value: This study contributes to the field by providing an overview of prominent trends in AI within higher education, discussing the practical application, future research opportunities, and challenges associated with the responsible and effective use of AI in education.

This paper identifies patterns in total and per capita CO₂ emissions among 208 countries considering different emission sources, such as cement, flaring, gas, oil, and coal. This research uses linear and non-linear dimensional reduction techniques, combining K-means clustering with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), which allows the identification of distinct emission profiles among nations. This approach allows effective clustering of heterogeneous countries despite the highly dimensional nature of emissions data. The optimal number of clusters is determined using Calinski–Harabasz and Davies–Bouldin scores, of five and six clusters for total and per capita CO₂ emissions, respectively. The findings reveal that for total emissions, t-SNE brings together the world’s largest economies and emitters, i.e., China, USA, India, and Russia, into a single cluster, while PCA provides clusters with a single country for China, USA, and Russia. Regarding per capita emissions, PCA generates a cluster with only one country, Qatar, due to its significant flaring emissions, as byproduct of the oil industry, and its low population. This study concludes that international collaboration and coherent global policies are crucial for effectively addressing CO₂ emissions and developing targeted climate change mitigation strategies.

This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies’ moats) according to their financial ratios in order to improve the performance of investment portfolios. First, we computed the financial ratios of companies belonging to the S&P 500. Subsequently, we assessed the stocks’ moats according to an evaluation defined between 0 and 5 for each financial ratio. The sum of all the ratios provided a score between 0 and 100 to classify the companies as wide, narrow or null moats. Finally, several ML models were applied for classification to obtain an efficient, faster and less expensive method to select companies with lasting competitive advantages. The main findings are: (1) the model with the highest precision is the Random Forest; and (2) the most important financial ratios for detecting competitive advantages are a long-term debt-to-net income, Depreciation and Amortization (D&A)-to-gross profit, interest expense-to-Earnings Before Interest and Taxes (EBIT), and Earnings Per Share (EPS) trend. This research provides a new combination of ML tools and information that can improve the performance of investment portfolios; to the authors’ knowledge, this has not been done before. The algorithm developed in this paper has a limitation in the calculation of the stocks’ moats since it does not consider its cost, price-to-earnings ratio (PE), or valuation. Due to this limitation, this algorithm does not represent a strategy for short-term or intraday trading.

Objetivo: analizar los componentes cíclicos entre variables económicas cuyos acoplamientos instauraron los mecanismos de propagación y causalidad en la formación del ciclo económico de la economía estadounidense. Método: se utiliza la técnica de sincronización de fase en series de tiempo para los componentes cíclicos de las variables sumada a un análisis de causalidad con un modelo VAR (Vectores Autorregresivos). Resultados: se encuentran los mecanismos de propagación entre variables mediante la secuenciación de los ciclos, reflejando los impactos de las decisiones de política monetaria en la economía agregada, transmitiendo inconscientemente ineficiencias en el desempeño y comportamiento de variables financieras y macroeconómicas. Recomendaciones: implementar análisis de componentes principales para capturar mejor los mecanismos de transmisión de política monetaria. Limitaciones e implicaciones: la dinámica de ajuste cíclico no es del todo pertinente para ser modelada en sistemas lineales. Principal contribución: se expone evidencia de una fuerte relación en subsistemas cíclicos de variables y la dinámica de transmisión y causalidad cíclica, revelando la generación de periodos de crisis económicas y financieras debidos a las decisiones de política monetaria.

This study analyzes the linguistic patterns and rhetorical strategies employed in the 2024 U.S. presidential debates from the exchanges between Donald Trump, Joe Biden, and Kamala Harris. This paper examines debate transcripts to find underlying themes and communication styles using Natural Language Processing (NLP) advanced techniques, including an n-gram analysis, sentiment analysis, and lexical diversity measurements. The methodology combines a quantitative text analysis with qualitative interpretation through the Jaccard similarity coefficient, the Type–Token Ratio, and the Measure of Textual Lexical Diversity. The empirical results reveal distinct linguistic profiles for each candidate: Trump consistently employed emotionally charged language with high sentiment volatility, while Biden and Harris demonstrated more measured approaches with higher lexical diversity. Finally, this research contributes to the understanding of political discourse in high-stakes debates through NLP and can offer information on the evolution of the communication strategies of the presidential candidates of any country with this regime.

A partir de la implementación de Solvencia II en México, se generó un cambio significativo en la normatividad para aseguradoras y afianzadoras, lo que implicó incremento en sus costos y gastos, en particular para el sector afianzador, ya que les representa una mayor carga regulatoria con respecto a su Ley anterior; teniendo un impacto directo en el costo de las primas y la generación de utilidades. El objetivo del presente trabajo es analizar la dinámica entre el total de primas emitidas por el sector afianzador y la utilidad bruta reportada por las mismas; lo anterior permite determinar si la captación de primas es suficiente para incrementar las utilidades reportadas por el sector. Se utilizan diagramas de fase con la finalidad de comprender la dinámica temporal entre las variables y conocer sus trayectorias en relación al Estado Estacionario. A partir de la metodología propuesta se señala que las afianzadoras mexicanas, a pesar de sus utilidades, presentan comportamientos globalmente inestables, asimismo, se verifican los casos donde una mayor aversión al riesgo de las afianzadoras (representadas por las primas emitidas) no siempre reflejan mayores utilidades.