Background: Scientific collaboration has become a cornerstone of sustainable development, particularly in regions where research capacity and funding face significant challenges. The Andean region, Panama, and Spain offer a unique perspective due to their cultural and linguistic ties, alongside varying levels of scientific production and innovation. These disparities present opportunities for collaboration and targeted interventions to foster regional growth and contribute to global priorities. According to UNESCO, Latin America invests merely 0.56% of its GDP in research and development, underscoring the pressing need for innovative strategies to enhance scientific capacity and align efforts with the United Nations Sustainable Development Goals (SDGs). Methods: This study employed HJ-Biplot and MANOVA-Biplot methodologies to analyze bibliometric data across various thematic areas. These multivariate techniques offer a comprehensive exploration of the interrelationships between scientific production, research talent, and international collaboration, revealing significant patterns and associations. The data were sourced from the Scimago Iberoamerican platform, which aggregates information from Elsevier’s Scopus database on scientific journals and countries. The platform provides data in five-year increments, capturing trends in scientific output, international collaboration, and thematic focus across the Andean region, Panama, and Spain, spanning the period from 2012 to 2022. Results: The analysis identified significant correlations between scientific productivity, research talent, and international partnerships. Clustering disciplines such as engineering, computer science, and energy highlights the strong intersections between technology and economic development. The proximity of psychology and environmental sciences emphasizes the importance of social and environmental factors in scientific research. Conclusion: This study provides a comprehensive bibliometric analysis of the Andean region, Panama, and Spain, identifying critical drivers of scientific productivity and collaboration. The integration of advanced statistical methodologies reveals key associations between research talent, international partnerships, and thematic focus areas. While areas such as environmental sciences and biochemistry demonstrate alignment with innovation and sustainability goals, disciplines like engineering and mathematics require targeted investment to enhance their contributions. These findings underscore the importance of a balanced approach to research funding and policymaking to ensure equitable and impactful scientific development across regions. The results serve as a roadmap for fostering collaboration, strengthening leadership, and aligning research efforts with sustainable development objectives globally.

This study analyzes the impact of research and development (R&D) investment on economic growth in Panama, an emerging economy with structural challenges in its innovation system. Using a multivariate econometric approach that included elastic net regularization and fixed-effect panel data estimation, the analysis incorporated key explanatory variables such as public education expenditure, inflation, infrastructure investment, population growth, and exports. The results indicated that both R&D and education spending have a positive and statistically significant effect on GDP growth, while inflation has a negative impact and exports show no significant effect. To ensure robustness, the study applied the augmented Dickey–Fuller test for stationarity, nonparametric bootstrapping (1000 replications), and multiple diagnostic tests, including RMSE, adjusted R², Durbin–Watson statistic, and White’s test. Scenario-based projections suggest that gradual and sustained increases in R&D investment, supported by stronger institutional coordination and absorptive capacity, could enhance Panama’s long-term productivity and innovation outcomes. The findings underscore that improving R&D funding alone is not sufficient; effective governance and coherent science, technology, and innovation (STI) policies are essential. This research contributes empirical evidence to a relatively underexplored area in the development literature and offers strategic insights for policymakers seeking to build more integrated and sustainable STI ecosystems in emerging economies.

Estimating sparse solutions in penalized tensor models poses a significant meth- odological challenge in high-dimensional settings. Although methods such as the Cenet- Tucker model incorporate Elastic Net penalties to achieve sparse and orthogonal repre- sentations, their stability under minimal perturbations remains a critical issue. This article proposes a bootstrap-based extension to stabilize the selected support, formulating a com- prehensive protocol for resampling, model fitting, and evaluation, applicable to penalized tensor models. Stability metrics are introduced, a rigorous simulation study is conducted, and the GSparseBoot package is being actively developed in R as a reproducible and mod- ular computational tool. Although still under development, GSparseBoot already shows promising performance and reflects a strong commitment to accessible, open-source im- plementation of advanced statistical methods. In simulations, the use of calibrated boot- strap increased the Stable Selection Index (SSI) by over 25%, reduced the standard devia- tion of the support by 40%, and improved the average Jaccard Index across replicates— demonstrating a substantial enhancement in the structural consistency of the model. This article presents a well-founded methodological contribution, empirically validated and aligned with modern standards of reproducibility in data science.

This paper introduces BCenetTucker, a novel bootstrap-enhanced extension of the CenetTucker model designed to address the instability of sparse support recovery in high-dimensional tensor settings. By integrating mode-specific resampling directly into the penalized tensor decomposition process, BCenetTucker improves the reliability and reproducibility of latent structure estimation without compromising the model′s interpretability. The proposed method is systematically benchmarked against classical CenetTucker, Stability Selection, and Bolasso, using real-world gene expression data from the GSE13159 leukemia dataset. Across multiple stability metrics—including support-size deviation, average Jaccard index, inclusion frequency, proportion of stable support, and Stable Selection Index (SSI)—BCenetTucker consistently demonstrates superior robustness and structural coherence relative to competing approaches. In the real data application, BCenetTucker preserved all essential signals originally identified by CenetTucker while uncovering additional marginal yet reproducible features. The method achieved high reproducibility (Jaccard index = 0.975; support-size deviation = 1.7 genes), confirming its sensitivity to weak but stable signals. The protocol was implemented in the GSparseBoot R library, enabling reproducibility, transparency, and applicability to diverse domains involving structured high-dimensional data. Altogether, these results establish BCenetTucker as a powerful and extensible framework for achieving stable sparse decompositions in modern tensor analytics.

Los tumores de las células de la granulosa (TCG) son poco frecuentes y representan sólo alrededor del 5 % de todos los cánceres de ovario. Se ha publicado acerca de los tumores epiteliales y los germinales recientemente en Panamá, pero falta información acerca de los TGC lo que motiva esta investigación. Se realizó un estudio retrospectivo descriptivo de los casos evaluados en el Instituto Oncológico Nacional (ION) entre los años 2012 y 2022. Hubo 90 casos y se incluyeron todos. La mediana de edad fue 47,5 años. La etapa FIGO IA fue la más frecuente con 43 casos. La presentación clínica inicial más común fue el dolor abdominal. La mayoría de las pacientes recibieron tratamiento quirúrgico y no recibieron quimioterapia. 10 de las pacientes tuvieron recurrencia, y de ellas, 9 murieron al momento del cierre del estudio. Los resultados obtenidos son similares a los registrados en la literatura médica, pero con una frecuencia un poco más alta en relación a los tumores de origen epitelial.