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Slide 1 of 5 Publicación Acceso Abierto
Computational Thinking in Rural Education: Evidence of Gains in Problem-Solving Abilities
(IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 2026-02-27) Garcés Prettel, Miguel Efrén; Martínez-Caro, Elkin; Grupo de Investigación en Estudios Sociales y Humanísticos- GESH; Semillero de Investigación en Comunicación y Educación
Most research on computational thinking has been conducted in urban or technologically advantaged settings, leaving its applicability in rural environments largely unexplored. This study addresses that gap by evaluating the effectiveness of a computational thinking-based pedagogical strategy on problem-solving skills among tenth-grade students in a rural Colombian school. Using a quasi-experimental design with control group and pre/post-test measurements, three dimensions were assessed: computational concepts, required tasks, and evaluative capacities. The intervention, consisting of six constructivist-oriented sessions mediated through the MakeCode environment, led to significant improvements in the experimental group. A repeated measures analysis of variance confirmed substantial effects across all dimensions, with no significant influence from sociodemographic or academic variables. These findings indicate that complex problem-solving skills can be developed through accessible pedagogical strategies within rural school contexts characterized by limited technological infrastructure. The study provides empirical evidence supporting the integration of computational thinking in rural education through replicable and context-sensitive approaches.
Slide 2 of 5 Publicación Acceso Abierto
Detection of diseases in cucumber using deep neural networks
(Neural Computing and Applications, 2026-01-12) Menco Tovar, Andrea Carolina; Puertas Del Castillo, Edwin Alexander; Martínez Santos, Juan Carlos; Grupo de Investigación Tecnologías Aplicadas y Sistemas de Información (GRITAS); Semillero de Investigación en Inteligencia Artificial
The cucumber (Cucumis sativus L.), a globally essential crop, faces severe threats from various foliar diseases. This work explores deep neural networks (AlexNet, Vision Transformer, MobileNet, and U-Net) for the early and accurate detection of these pathologies based on leaf images. We analyzed 4,353 images classified as healthy or diseased through advanced preprocessing and data augmentation techniques. The results highlight Vision Transformer as the most effective architecture, achieving 99% accuracy, surpassing MobileNet with similar performance. Meanwhile, AlexNet and U-Net demonstrated more limited performance. The research underscores the practical applicability of these technologies in intelligent agriculture systems, promoting informed decision-making to reduce economic losses and environmental impact. Furthermore, it emphasizes the importance of integrating these tools into low-cost devices for implementation in rural areas. This approach contributes to the sustainability of cucumber cultivation. It sets a precedent for the efficient management of diseases in modern agriculture.
Slide 3 of 5
Slide 4 of 5 Publicación Acceso Abierto
Wind-turbine waste heat for desalination: a scoping review and research agenda
(International Journal of Sustainable Energy, 2026-02-02) Mejia Pinedo, Javier; Fajardo Cuadro, Juan Gabriel; Serrano-Florez, Dario; Buelvas Hernandez, Ana; Grupo de Investigación Energías Alternativas y Fluidos (EOLITO); Semillero de Investigación de la Economía Energética y el Desarrollo Sostenible (SEEDS)
Using wind-turbine waste heat for seawater desalination could boost efficiency and water security, but evidence is scarce. A scoping review (2018–2024) found 28 studies; only four quantified integration with thermal desalination. Available heat is low–mid grade (≈100–150 °C) with recoverable power of hundreds of kilowatts. Modeling a 7.58 MW turbine (~231 kW at 140 °C) driving multi-effect distillation yields ≈45 m³/day (~0.52 L/s), serving ~900 people at 50 L·cap−1·day−1. Simulated nanofluid enhancements show up to 30% gains, without experimental validation. No levelized cost of water estimates or field demonstrations were found. We conclude the concept is technically plausible but remains at the simulation stage. Key barriers include temperature mismatch, moving heat from nacelles, thermal storage, corrosion, and economics. Priorities include robust heat-recovery hardware, TES/heat-pump integration, corrosion control, techno-economic modeling, and pilot trials in wind-rich coasts such as La Guajira.
Slide 5 of 5 Publicación Acceso Abierto
Assessing Banana-Based Activated Carbon as a Biomaterial for the Adsorption of Drug Metabolites in Wastewater: Simulation of an Industrial-Scale Packed Column
(Processes, 2025-12-30) Tejada - Tovar, C; Villabona Ortiz, Angel; Coronado Hernández, Óscar Enrique; Haeckermann-Ruiz, Esmeralda; Méndez-Anillo, Rafael; Grupo de Investigación Sistemas Ambientales e Hidráulicos (GISAH)
The presence of paracetamol and ciprofloxacin in aquatic ecosystems is a cause for great concern due to their harmful effects on human health. The objectives of this investigation are to simulate an industrial-scale adsorption bed for the competitive removal of these pharmaceutical metabolites from effluents using banana-based activated carbon as the adsorbent. Aspen Adsorption simulation software (v.1) was used to model an industrial-scale packed-bed column under different conditions. Freundlich and Langmuir isothermal models were used in combination with the linear driving force (LDF) kinetic formulation. Adsorption efficiencies of 89.57% for paracetamol and 89.57% for ciprofloxacin were achieved using the Freundlich-LDF model, while the Langmuir-LDF model presented efficiencies of 89.60% for paracetamol and 89.59% for ciprofloxacin. This study used machine learning algorithms, combined with analyses of multiple statistical indicators (R2, RMSE, and MAE), to evaluate model performance. Coefficient of determination (R2) values of up to 0.99 were observed in validation and testing. The application of these mathematical models yielded high removal efficiencies, demonstrating the potential of this approach for drug-contaminated effluent remediation and for forecasting the performance of packed columns at scaled-up levels.
