Conference Agenda

This study addresses the optimization of humanitarian
logistics during natural disasters by implementing the Artificial
Bee Colony (ABC) algorithm. Natural disasters frequently
disrupt infrastructure and supply chains, complicating aid distribution.
The paper reviews bio-inspired and metaheuristic
algorithms, with a focus on ABC, which mimics the foraging behavior
of bees to explore and exploit optimal solutions efficiently.
The ABC algorithm is compared against a genetic algorithm
using statistical tests, including Shapiro-Wilk, F-test, and Z-test,
confirming ABC’s superior performance in resource distribution.
The research identifies key challenges such as uncertain data,
limited transportation capacity, and the need for equitable aid
allocation. Numerical experimentation demonstrates that the
ABC algorithm delivers higher efficiency and effectiveness in
planning relief operations. Additionally, the study presents a
software interface to facilitate parameter configuration and result
visualization. This work contributes to the field by introducing
the ABC algorithm in a new context, offering a reliable tool for
disaster response planning.

This systematic review brings together evidence from seventeen peer‐reviewed studies published between 2020 and 2025 to offer a comprehensive portrait of how administrative processes in public management are evolving under the pressure of digital transformation. Following the PRISMA 2020 guidelines, we conducted an exhaustive search across Scopus, SciELO and Dialnet, narrowing an initial pool of forty‐seven records to seventeen articles that met our methodological and temporal criteria. Through these works, we explored four interrelated dimensions: the enabling factors and critical barriers that shape digital adoption in public institutions; the ways in which citizens experience and evaluate online services; the strengths and shortcomings of existing measurement frameworks; and the patterns of international collaboration that drive knowledge exchange and innovation. Our findings reveal that successful digital initiatives hinge on clear regulatory frameworks, strong leadership, technical interoperability and expanding mobile connectivity, even as they struggle with infrastructure gaps, limited budgets and cultural resistance. Citizens appreciate the speed and reach of digital platforms but lament usability flaws and opaque information flows that undermine trust. We also show that current indices—such as DESI, e‐Government rankings and recovery‐plan metrics—capture only slices of the transformation, lacking sectoral granularity and user‐centric outcomes. Finally, transnational networks and funding programs (PNRR, H2020, CEPAL/OAS forums) emerge as critical engines for shared standards and capacity building. Building on these insights, we propose an integrated evaluation framework that couples infrastructure and competency indicators with measures of user satisfaction, transparency and operational efficiency, all governed by unified methodological standards and disaggregated by sector.

The main objective of the research was to determine the relationship between digital tools and the professional performance of architects in an architectural firm in La Libertad, Peru. The research was basic; no variables were manipulated, and therefore non-experimental. It was also cross-sectional and quantitative. It was simple correlational. Two questionnaires were administered to the participants. The population and sample consisted of 13 workers. The result was a correlation between both variables (0.000<0.050), and this correlation was highly positive (0.834). Therefore, it was concluded that the greater the use of digital tools, the higher the professional performance of architects in an architectural firm in La Libertad, Peru.

This research analyzed the incidence of unrecognized operations in the financial system of northern Peru between 2018 and 2021, with an emphasis on credit cards (54.8%) and savings accounts (29.8%). Through a quantitative-descriptive approach, more than 2800 complaints reported to Indecopi were processed. The results show that 90% of the sanctions were directed at the financial system, with BCP, Interbank and Banco Azteca being the most denounced entities. The measures adopted were mostly reactive and uneven, without a unified technological strategy. Improvement strategies inspired by international experiences were proposed, including biometric authentication, artificial intelligence for transaction monitoring and an interbank anti-fraud network. It is also suggested to integrate digital security content into the school curriculum as a preventive measure. The study shows the need to articulate technology, regulation and education to strengthen financial customer protection and reduce operational risk.

Phishing remains one of the most persistent threats in the field of cybersecurity, driving the development of Deep Learning (DL) models to enhance their detection. This Systematic Literature Review aims to identify, categorize, and analyze DL models applied to phishing attack detection in controlled environments. Based on the analysis of recent studies, these models have demonstrated strong performance, particularly in metrics such as Accuracy and F1- score. The review also examines commonly used architectures such as RNNs, CNNs, and hybrid models, the types of attack vectors addressed, and the experimental conditions under which the models were evaluated. However, recurring limitations were identified, including the use of non-representative datasets, a lack of standardization in evaluation metrics and attack vectors, and limited validation in real-world scenarios. This review offers a structured synthesis of the current state of DL-based phishing detection and serves as a reference point for future research aimed at improving model performance and practical applicability.

The increasing number of mental disorders among university students, particularly depression, highlights the urgency of early and effective interventions to prevent serious consequences on their academic and social performance. This study proposes an Ensemble Stacking-based model that combines Logistic Regression, Random Forest, and Decision Tree to predict depression, using variables such as gender, age, academic performance, and lifestyle factors. The featured model (Case 3) achieved 97.67% accuracy and 97.00% cross-validation, optimized by specific hyperparameter settings. Advanced preprocessing techniques, such as SMOTE for class balancing and PCA for dimensionality reduction, ensured the robustness and generalizability of the model. The methodology included an XGBoost metamodel, leveraging the diversity of the base models to improve accuracy and mitigate overfitting on complex data. These results demonstrate the effectiveness of the proposed approach for early detection of depression in educational contexts, providing practical tools that can facilitate timely and personalized interventions.

The rapid digitalization of education has created an urgent need for digitally literate educators capable of integrating big data and emerging technologies into pedagogical practices. This quasi-experimental study investigated digital literacy levels among 50 pre-service teachers from Universidad Peruana del Norte in Trujillo, Peru, and evaluated the effectiveness of a targeted digital literacy intervention. Using R Studio for statistical analysis and visualization, we assessed participants through a 32-item digital knowledge test (Cronbach's α = 0.82) and implemented a pre-test/post-test design. Results revealed moderate baseline digital literacy (M = 24.97/50, SD = 14.84) with significant variability among participants. The intervention proved highly effective, showing substantial improvement in digitalization scores (mean difference = -35.17, p < 2.2e-16). However, commitment levels remained low (19.9% vs. expected 40%, p = 1), and training effectiveness varied significantly across educational levels (ANOVA, p = 0.0019). Despite high academic performance in traditional assessments (M = 80.32/100), the study reveals a critical gap between academic achievement and digital competencies. These findings highlight the need for differentiated digital literacy training and institutional support to prepare educators for data-driven educational environments in Latin America.