The University of Santiago, Chile (Usach) (Spanish: Universidad de Santiago de Chile) is one of the oldest public universities in Chile. The institution was born as Escuela de Artes y Oficios (Spanish: School of Arts and Crafts) in 1849 by Ignacy Domeyko, under the government of Manuel Bulnes. It became Universidad Técnica del Estado (Spanish: Technical University of the State) in 1947, with various campuses throughout the country. In 1981, as a consequence of a reform on higher education under the dictatorship of Augusto Pinochet, it became what is now known as Universidad de Santiago de Chile, with all activities centered in a single 340,000 m2 campus in the capital Santiago.
Purpose This study aims to evaluate the Anderson acceleration (AA) method as a strategy to improve convergence efficiency in thermally coupled, convection-dominated non-Newtonian flows with temperature-dependent properties. Although AA has proven effective in accelerating iterative methods in other computational contexts, its application to fluid flow and heat transfer problems with variable thermophysical properties has not yet been systematically assessed. These systems are particularly challenging, as the interaction between non-Newtonian rheology and thermal feedback introduces strong nonlinearities that make them difficult to solve using traditional iterative methods. Design/methodology/approach To assess the benefits of AA, the method was implemented by an in-house finite volume code and tested on benchmark natural convection problems. Both Newtonian and pseudoplastic fluids with temperature-dependent viscosity were analyzed to capture the influence of rheology on thermally coupled flow behavior. Convergence performance was evaluated in terms of the number of iterations required for both steady and transient regimes and compared directly with the standard Picard fixed-point approach. Findings The results show that AA preserves the accuracy of the reference solutions while substantially reducing the number of iterations required for convergence. For steady problems, the iteration count decreased by up to 11.3 times, while in transient cases the savings reached up to 2.2 times for Newtonian fluids and 2.4 times for pseudoplastic cases. These improvements result in a significant reduction in computational effort for thermally coupled non-Newtonian flows. Originality/value This work introduces AA as a practical, reliable and efficient technique for simulating thermally coupled non-Newtonian flows with temperature-dependent properties within a finite volume framework. Beyond demonstrating its effectiveness in this challenging class of problems, this study suggests that the present formulation could serve as a foundation for extending AA to more complex multiphysics scenarios, such as multiphase or solid–liquid phase-change systems.
Este estudio investiga las pr & aacute;cticas de facilitaci & oacute;n empleadas en un Programa de Desarrollo Profesional (PDP) online en una escuela vulnerable en Chile, enfatizando su impacto en la participaci & oacute;n docente y la pedagog & iacute;a durante una transici & oacute;n significativa debido a la pandemia de COVID-19. La literatura existente subraya el papel esencial de los PDP eficaces para mejorar las habilidades de los docentes y, posteriormente, mejorar los resultados de los estudiantes; sin embargo, muchos programas no logran los resultados deseados. A trav & eacute;s de un estudio de caso cualitativo que involucr & oacute; a 20 participantes, incluidos 13 docentes de aula y 7 facilitadores, la investigaci & oacute;n identifica tres pr & aacute;cticas de facilitaci & oacute;n clave: interacci & oacute;n, contenido y pedagog & iacute;a. Estas pr & aacute;cticas resaltan la articulaci & oacute;n din & aacute;mica entre facilitadores y docentes, enfatizando la construcci & oacute;n de relaciones, la relevancia del contenido y las estrategias pedag & oacute;gicas adaptativas para abordar los desaf & iacute;os contextuales. Los hallazgos revelan que la capacidad de los facilitadores para fomentar la confianza, brindar retroalimentaci & oacute;n espec & iacute;fica y adaptar el contenido para alinearlo con las prioridades del plan de estudios contribuy & oacute; significativamente al crecimiento profesional de los docentes y a la participaci & oacute;n de los estudiantes en actividades de resoluci & oacute;n de problemas. Adem & aacute;s, la transici & oacute;n a un formato online requiri & oacute; enfoques innovadores para mantener la colaboraci & oacute;n y entornos de aprendizaje efectivos. En & uacute;ltima instancia, esta investigaci & oacute;n subraya la necesidad cr & iacute;tica de una preparaci & oacute;n eficaz de los facilitadores y el desarrollo de estrategias flexibles en el dise & ntilde;o del PDP para garantizar una mejora sostenible en las pr & aacute;cticas educativas, llamando la atenci & oacute;n sobre la intrincada relaci & oacute;n entre los facilitadores, los docentes y los contextos en los que operan.
Demand forecasting in competitive and uncertain business environments requires models that can integrate multiple evaluation perspectives, rather than being restricted to hyperparameter optimization through a single metric. This traditional approach tends to prioritize one error indicator, which can bias results when metrics provide contradictory signals. In this context, the Hierarchical Evaluation Function (HEF) is proposed as a multi-metric framework for hyperparameter optimization that integrates explanatory power (R2), sensitivity to extreme errors (RMSE), and average accuracy (MAE). The performance of HEF was assessed using four widely recognized benchmark datasets in the forecasting domain: the Walmart, M3, M4, and M5 datasets. Prediction models were optimized through Grid Search, Particle Swarm Optimization (PSO), and Optuna, and statistical analyses based on difference-of-proportions tests confirmed that HEF delivers superior results compared to a unimetric reference function, regardless of the optimizer employed, with particular relevance for heterogeneous monthly time series (M3) and highly granular daily demand scenarios (M5). The findings demonstrate that HEF improves stability, generalization, and robustness at a low computational cost, consolidating its role as a reliable evaluation framework that enhances model selection, enables more accurate demand forecasts, and supports decision-making in dynamic and competitive business environments
This study introduces a cost-effective green hydrogen generation system based on the direct coupling of a discarded photovoltaic module with a proton exchange membrane electrolyzer for domestic natural gas blending (up to 20% v/v). Electrical parameters were identified using an A+A+A+ solar simulator for precise modeling. To mitigate identified impedance mismatches, a physical structural reconfiguration involving the parallel connection of internal substrings was proposed, facilitating stable operation without additional power electronics. This strategy achieved an annual energy extraction yield of 88% relative to an ideal maximum power point tracking-based system. A 30-year sensitivity analysis further demonstrated that this voltage-matching condition remains resilient to long-term parameter drift. Experimental validation under real outdoor conditions confirmed a daily production of 0.345m3, significantly surpassing the 0.12m3 domestic target. The system achieved a daily average Solar-to-Hydrogen efficiency of 7.0%, capturing over 70% of the theoretical maximum. Economic assessment indicated a Levelized Cost of Hydrogen of $5.79/kg, a 18% reduction compared to a benchmark system ($7.05/kg). The results demonstrate that the methodology is fundamentally generalizable to other standard architectures, such as 60 and 96-cell modules. These findings underscore the techno-economic potential of repurposing discarded photovoltaic modules for decentralized hydrogen production, offering a sustainable alternative aligned with circular economy principles.
