The incorporation of generative artificial intelligence tools in higher education is transforming traditional university learning dynamics. In this context, this study aims to analyze the association between ChatGPT use and the self-perceived development of critical thinking among law students in the city of Cochabamba, Bolivia. The research adopted a quantitative approach with a descriptive cross-sectional design. The population consisted of law students from four private universities in Cochabamba, from which a non-probabilistic convenience sample of 111 participants was selected. Data were collected through a structured questionnaire composed of six items using a five-point Likert scale, which showed high reliability (α = 0.954). The results indicate that 77% of students frequently use ChatGPT to complete academic tasks. Additionally, 76% consider that this tool facilitates rapid access to legal information but does not significantly contribute to the development of solid legal arguments. Furthermore, 68% acknowledge that frequent use of ChatGPT may generate academic dependency and reduce the intellectual effort required to analyze legal problems independently. Pearson chi-square tests confirmed statistically significant associations (p < .001) between frequency of use, academic dependency, and the perceived impairment of critical legal reasoning. The study concludes that, although ChatGPT is a useful tool for quickly obtaining information, its intensive use without adequate pedagogical guidance is associated with difficulties in the development of critical thinking in legal education.

The representation of the State, as a manifestation of its political power, rests on the consent of the true sovereign and their guidelines for its legitimate exercise. Exercising such power effectively entails a great personal and professional responsibility that also requires leadership reflected in actions. Current Equatorial Guinean society lacks indicators that reflect its capacity to discern between leadership and mere followers or disorganization, and one of the causes is the correlation between ethnic origin and the perceived objectivity of those best qualified to lead or perform representative functions. Although ethnic identity is personal and private, national identity, for its part, is the secondary origin that permanently connects us to a specific territory among the subjects that make up the international community. The representation of the State must consider both identities: on one hand, having the presence of each ethnic group established in the country in the powers and organs of the State; and, on the other hand, designing a system of representation that recognizes those best qualified above ethnic identity, that is, meritocracy and ability. Article 9 of the Fundamental Law is essential for this matter, as in a broad sense it is a cornerstone for the adoption of integrative formulas and social cohesion.

This article focuses on "Angola's role in conflict resolution in the Great Lakes Region: the case of the Democratic Republic of Congo 2014-2023". Based on this theme, the approach adopted is qualitative in nature, seeking to understand the political and diplomatic context through a detailed analysis of the meanings, motivations, and values ​​underlying the events studied. Documentary and historical methods were used, with emphasis on bibliographic and documentary data collection instruments. Based on this data, it was confirmed that most current conflicts are located on the African continent, with the Democratic Republic of Congo, Angola's neighbor, being one of the countries that causes great instability in the Great Lakes Region. During its two terms at the International Conference on the Great Lakes Region (ICGLR), Angola played an important role in African regional organizations, specifically in mediating conflicts in the Great Lakes Region, neutralizing the negative forces of the March 23 Movement (M23) in the DRC and the ceasefire in the Central African Republic (CAR), weakening the Allied Armed Forces (ADF), and ensuring that the Democratic Forces for the Liberation of Rwanda (FDLR) voluntarily surrendered their weapons.

Introduction. Corporate recreational event agencies often rely on manual routines that extend production cycles and increase rework. This study examines whether adopting generative AI is associated with reduced event production time. Methodology. A quasi-experimental before–after single-case design was conducted in a corporate recreational events company (Bogotá, Colombia) between June and September 2025. Three comparable events were produced under a traditional approach (no AI) and then with systematic ChatGPT support for creative and administrative tasks (concept development, proposal drafting, budgeting, and supplier selection). The dependent variable was total production time (hours) from brief reception to final approval. Given the small sample, the analysis prioritized paired differences, confidence intervals, and effect sizes. Results. A consistent reduction in total production time was observed across events (51 h, 60 h, and 69 h), yielding an average saving of 60 h (19.1%). The largest savings occurred in Concept Development (23.3 h) and Proposal Preparation (20.0 h), while Final Validation slightly increased (+1.3 h), reflecting additional quality checks. Conclusions. Findings suggest generative AI can be an efficiency lever for mid-sized agencies when combined with governance, training, and human verification. Results should be interpreted as applied and exploratory due to the small sample and single organizational setting. Future research should test replicability with more events and additional outcomes (cost, quality, and client satisfaction).

This research aimed at the hydraulic design of a large-scale looped water distribution network for the Bairro 11 de Novembro (Luanda), a critical locality facing severe deficiencies in the existing system. The methodology integrated the determination of water demand through demographic analysis, interviews, and technical consultations with hydraulic analysis using the Hardy-Cross method and the Hazen-Williams equation (C=140), applying iterative corrections (ΔQ) until loop equilibrium was achieved (ε ≤ 0.01 m). Sensitivity analysis identified the roughness coefficient (C) as the most critical parameter for head losses (S ≈ 2.6), while the demand coefficients (K₁, K₂) directly impact flow rates (S ≈ 1). The results demonstrated efficient convergence (3.7 iterations/loop on average) and hydraulic equilibrium (ΣΔH=0 m). The designed network features final flow rates ranging from 125.41 L/s (main pipes) to 5.90 L/s (secondary pipes), diameters from 350 mm to 80 mm, velocities between 0.6 and 2.9 m/s, and minimum pressures above 15 mH₂O at 92% of the nodes. The proposed solution addresses the identified technical problem and, once implemented, will transform water access. The methodology proves to be replicable for other localities with similar challenges, constituting a significant contribution to sanitation engineering in contexts of rapid urban growth.

The present research focuses on the development of a prediction system for the EUR/USD currency pair, recognized for its stability and attractive profitability in the global market, yet subject to episodes of volatility. The study seeks to mitigate the impact of traders’ emotional management, known as “psychotrading,” a variable that can compromise the effectiveness of technical strategies in the field of trading. With the purpose of reducing the influence of the human factor in decision-making, the methodology proposed by Tinoco-Figueroa S. (2024) was adopted, who applied algorithms such as Lorentz distance and approximate nearest neighbors in the analysis of the BTC/USD currency pair. This research incorporates the use of Recurrent Neural Networks, specifically the LSTM (Long Short-Term Memory) model, given its ability to model financial time series with high accuracy. The technical implementation was carried out in the Visual Studio Code – VSCODE development environment, using the Python programming language. Historical data were obtained from the MetaTrader5 platform, establishing a real-time connection to collect information from the last 60 days with 15-minute intervals. Initially, a diagnostic assessment was conducted, followed by the design of the strategy and evaluation of the system’s performance through specific metrics. The results were compared with those obtained by Tinoco-Figueroa S. (2024), and improvement actions were proposed along with the presentation of the model’s limitations.