The growing demand for lifelong learning and workforce adaptability has positioned micro-credentials as a strategic innovation in higher education systems worldwide. While individual micro-credentials initiatives have expanded rapidly, their effectiveness and sustainability increasingly depend on coherent national and system-level ecosystems that integrate governance, quality assurance, and technological infrastructure. This study conducts a scoping review of the international literature on national and institutional ecosystems of stackable micro-credentials in higher education, following the PRISMA-ScR guidelines. Searches were conducted primarily in Scopus, complemented by searches in Web of Science (Core Collection) and ERIC, as well as relevant grey literature from international organizations. A total of 18 studies were included after systematic screening and eligibility assessment. The results reveal converging conceptual frameworks that position micro-credentials within lifelong learning systems, alongside diverse governance models ranging from state-led to hybrid arrangements. Key enabling mechanisms include alignment with national qualifications frameworks, robust quality assurance processes, and interoperable digital credentialing systems. However, persistent challenges remain, particularly in developing regions, where fragmented governance and limited regulatory clarity constrain implementation. Drawing on these findings, the study highlights evidence-informed implications for the development of a national stackable micro-credentials ecosystem in Ecuador, contributing to policy-oriented debates on the future of higher education.
The optimal deployment of Low-Power Wide-Area Networks (LPWANs) such as LoRaWAN in complex urban environments remains an NP-Hard Set Covering Problem. Traditional network planning often relies on 2D mathematical grids that ignore physical RF barriers, leading to topographic shadowing and single points of failure. This research proposes the Native 3D Memetic Spatially Aware Genetic Algorithm (3D-M-SAGA), an optimization framework that operates over a Morphological Digital Twin. By fusing OpenStreetMap (OSM) vector topologies with NASA SRTM elevation data and autonomous urban clutter classification, the framework evaluates physical constraints—including ITU-R knife-edge diffraction and dielectric absorption—directly within the evolutionary loop. To counteract the epistatic variance inherent to standard genetic algorithms, the 3D-M-SAGA integrates a vectorized memetic “Smart Repair” operator driven by heuristic attraction and repulsion forces. Formulated as a multi-objective optimization problem balancing Capital Expenditure (CAPEX) and topological Quality of Service (QoS) through K-coverage, the framework is evaluated using a 36-scenario parametric grid search and a 50-iteration Monte Carlo benchmark. Results show that the 3D-M-SAGA tightly bounds stochastic CAPEX variance (σ=±0.51 gateways) while reducing single-point-of-failure network fragility (K=1) by up to 20%, guaranteeing fault tolerance (K≥2) without over-provisioning civic infrastructure.
In this work, we introduce a computational framework for marketing analytics. To demonstrate the advantages of our proposal, we conducted a statistical assessment in Milagro, Ecuador, with a case study specifically focused on brand experience, examining corporate communication and key factors that shape consumer behavior during the purchasing process. The data model presented in this manuscript primarily integrates the HJ-Biplot, hierarchical clustering, and the disjoint principal component analysis method, where the strength of our model lies in their integration, with the disjoint components providing a distinctive methodological advantage and enhanced interpretability. Our methodology is designed to support the marketing dimensions that companies wish to incorporate into their studies, providing insights into consumer behavior and helping firms optimize their strategies. A tailored survey was administered via a Google form to 712 participants, generating the real data matrix for the computational experiments. Moreover, we carried out two complementary types of simulations to illustrate the versatility of our proposal: (i) simulations based on latent dimensions, which evaluate the adaptability of the methodology across different structural components of marketing, and (ii) simulations based on empirical sampling, including row-wise and column-wise generation schemes that preserve or diversify observed variable combinations. Finally, we provide a Python software library that enables researchers to implement and extend our computational approach in diverse marketing contexts.
Pocas bebidas han acompañado a la humanidad con la fidelidad de la cerveza. Desde las tabletas sumerias que registraron el Himno a Ninkasi hace más de cuatro mil años hasta los micro lotes que hoy se elaboran en garajes de Quito, Guayaquil o Cuenca, la cerveza ha sido, en cada época, un espejo de la cultura que la produce: de su agricultura, de su ciencia, de su paladar y de su imaginación. Ecuador vive en los últimos años un renacimiento cervecero artesanal sin precedentes. Nuevas cervecerías, cerveceros caseros entusiastas, ferias especializadas y una demanda creciente de cervezas con identidad local han configurado un escenario vibrante. Sin embargo, este crecimiento ha estado, con frecuencia, desacoplado del conocimiento científico riguroso que sustenta al oficio. Existe abundante literatura técnica en inglés y en el contexto europeo o norteamericano, pero muy poca escrita desde, y para las condiciones reales del cervecero ecuatoriano: nuestra altitud, nuestros perfiles de agua, nuestras materias primas locales y los insumos importados a los que tenemos acceso.
The global increase in life expectancy has been accompanied by a parallel rise in the burden of chronic diseases, many of which are influenced by diet and lifestyle. Conventional dietary guidelines, though broadly beneficial, often fail to account for the wide heterogeneity in nutritional needs, metabolic responses, and health trajectories observed among older adults. This limitation has spurred interest in personalized nutrition—a precision approach that leverages individual genomic, metabolomic, and gut microbiome profiles to tailor dietary interventions for healthy aging. Recent advances in omics technologies have deepened our understanding of how genes, circulating metabolites, and microbial ecosystems interact with diet to influence disease risk, physical function, and longevity. This review explores the conceptual foundations, technological enablers, and clinical applications of personalized nutrition, highlighting key evidence from 2020 to 2025. It examines how precision models are being used to improve outcomes in older adults, discusses ethical and social barriers to implementation, and outlines future directions including artificial intelligence and real-time monitoring tools. Personalized nutrition represents a transformative opportunity to shift from population-based dietary advice to individualized strategies that can promote resilience, reduce frailty, and extend healthspan in the aging global population.
