Authors - Israel Herrera-Miranda, Miguel Apolonio Herrera-Miranda, Juan Villagomez-Mendez, Silvia Lizbeth Herrera-Lopez Abstract - This paper analyzes the characterization and adoption of Information and Communication Technologies (ICT) and GenAI (GAI) within the framework of digital governance and higher education policies in México. Framed around the early years of President Claudia Sheinbaum Pardo's administration (2024–2030), the study evaluates how emerging regulatory instruments and administrative agencies—specifically the newly enacted Telecommunications and Broadcasting Law (2025) and the Agency for Digital Transformation and Telecommunications (ADTT)—act as institutional backbones to reduce technological gaps and preserve digital sovereignty. Methodologically, this study relies on a documentary and reflective analysis, utilizing empirical indicators from the OECD Digital Government Index (DGI) and the results of México's 2025 National Survey on GAI in Higher Education. The findings indicate that while México exhibits a strong performance in digital-by-design policy frameworks, structural challenges persist regarding proactive public sector AI integration. Concurrently, higher education data reveals an accelerated, mass adoption of GAI by over 60% of students and faculty, transforming traditional pedagogical paradigms. In response, the Ministry of Public Education (SEP) has advanced a ten-point strategy targeting digital literacy, curricular overhauls, and ethical boundaries. Ultimately, this paper underscores that bridging the digital divide requires co-aligning public innovation frameworks with human-centric, ethically-governed higher education policies to foster national technological autonomy and sustainable socioeconomic development.
Authors - Humberto Merritt Abstract - In recent years, the use of digital applications for data management, information access, communication and resource exchange has grown worldwide. Advances in telecommunications have been particularly pervasive in Mexico, where the widespread use of smartphones has encouraged the adoption of digital technology. In this study, we examine the implementation of the APP CDMX electronic platform, which was designed to enhance Mexico City's functionality. The research aims to determine how this tool has helped citizens learn about available public services and how they evaluate it. In particular, the research describes how smartphone users are leveraging the application to access real-time data on local procedures and their fees, the traffic conditions, specific transit routes and stations, and other touristic venues, thereby achieving not only more efficient mobility but also encouraging a sustainable lifestyle. Using a qualitative methodology based on the lexical analysis of user opinions and adopting a multidisciplinary approach, the study concludes that the APP CDMX has positively transformed citizen interaction, accelerating administrative processes and democratizing access to key information.
Authors - Luis Romel Assis Oliveira Junior, Olivan da Silva Rabelo, Paulo Henrique da Silva Santos Abstract - The article proposes the Dodecahedral Framework as a conceptual model to analyze Metaverse Experience Networks, differentiating them from Internet Social Networks and characterizing them as a new object of academic study. The approach explores four dimensions – Immersive Realism, Spatiotemporal Disruption, Meta Culture and Tokenized Economy. The methodology consists of a bibliographic review and the theoretical formulation of the model. The results indicate that Metaverse Experience Networks offer new forms of socialization, interactivity, and engagement, profoundly impacting marketing, consumption, work, and digital culture. It is concluded that the Dodecahedral Framework can serve as a tool for researchers and professionals, assisting in the understanding, planning, and implementation of strategies within the metaverse, in addition to opening paths for new investigations on its technological and social implications.
Authors - Meshak Ratshikombo, Mehrdad Ghaziasgar Abstract - Deep Reinforcement Learning has emerged as a promising approach for algorithmic trading, but trading performance remains highly sensitive to reward design. This study investigates how reward engineering shapes learned trading behavior by training agents exclusively on FTSE market data under identical conditions while varying only the reward formulation. Evaluation across multiple unseen equity indices demonstrates that reward functions induce distinct behavioral biases governing exposure timing, volatility sensitivity, and downside risk. Profit-oriented rewards encourage aggressive trading and higher variability, whereas risk-aware and sparse rewards produce more stable policies with improved drawdown control. The findings show that reward engineering acts as a strong inductive bias influencing both trading behavior and cross-market generalization in reinforcement-learning-based trading systems.
Authors - Lydie Simone Tapsoba, Salifou Ouoba, Athanase Sawadogo Abstract - Malaria is the leading cause of child mortality in Burkina Faso and accounts for over 40% of public health expenditure. This study develops and validates a hybrid machine learning model combining Random Forest (40%) and XGBoost (60%) to predict the spatio-temporal dynamics of malaria across the country’s 13 health regions over the period 2010–2024. The model incorporates satellite-derived climatic variables (CHIRPS precipitation, MODIS NDVI, temperature) and demographic variables, enhanced by advanced feature engineering: 3- and 6-month moving averages of precipitation, temporal lags and circular encoding of seasonality. Validation combines a prospective temporal split for 2024 and spatial GroupKFold cross-validation. On the independent 2024 test set, the hybrid model achieves exceptional performance, substantially outperforming the best previously published approaches for this context. Interpretability analysis reveals surprising determinants of transmission, highlighting the equal role of environmental and anthropogenic factors in Burkina Faso. The SHAP analysis identifies the 3-month moving average of rainfall as the dominant variable, ahead of population density, highlighting the equal role of environmental and anthropogenic factors. The priority areas are Hauts-Bassins, the East and the South-West. The risk maps and 6-month predictions serve as operational tools for the National Malaria Control Programme.
Authors - Yasmany Prieto, Christopher Moyano Abstract - Communication systems are shifting toward more software-based development, driven by technologies such as Software-defined Networking and Network Function Virtualization. This new paradigm improves flexibility, scalability, and resource efficiency, shortens time-to-market, but opens a new dimension of system failure through bugs, backdoors, and software vulnerabilities. In this work, we build a Decision Tree (DT) classifier to predict vulnerability exploitability in networking systems. A list of vulnerabilities affecting those systems is obtained from the National Vulnerability Database. To measure exploitability, we employ the Cybersecurity and Infrastructure Security Agency Known Exploited Vulnerabilities Catalog, which lists vulnerabilities that have been exploited in the wild. The DT classifier achieved a recall of 0.878 in detecting exploitable vulnerabilities.
