This letter to the editor addresses the persistent innovation gap between Latin American universities and small and medium-sized enterprises (SMEs), exacerbated by academic incentive structures and financial constraints in the local productive sector. It argues that undergraduate engineering students, formally embedded in co-creation hubs, serve as an underutilized and short-term actionable mechanism to bridge this gap without requiring new funding instruments. Through curricular integration into internships and capstone projects, public policy support, and a two-way cultural transformation, students act as active bridges for applied knowledge transfer. This strategy enhances students' professional skills while fostering regional economic resilience and sustainable development.
Biodegradable food-service items are increasingly sought to reduce the environmental impact of single-use plastics. This study evaluated the biodegradation performance of plates made from sugarcane bagasse (SB) and rice husk (RH) under tropical field conditions and different soil types. Plates were produced via thermoforming using six starch and glycerol formulations and their physico-mechanical properties were characterized prior to selecting the best formulation. Biodegradation trials were conducted over 90 days in three soil environments: open soil (OS), eucalyptus-reforested soil (ER), and compost-amended soil (OC). Weight loss was measured periodically, alongside monitoring of environmental parameters and soil physicochemical and microbiological changes. Both SB and RH plates exceeded 96% biodegradation within 90 days, meeting ISO 14855-2:2018 and ASTM D5338-15 criteria. SB generally degraded faster than RH in OS and ER, while no significant differences were observed in OC. The highest degradation occurred within the first 15 days, followed by a slower phase. Biodegradation contributed to increased soil moisture, total nitrogen, and apparent density, and altered microbial populations. These findings demonstrate that agricultural-residue-based plates can serve as viable alternatives to plastics, effectively reintegrating organic matter into soils. However, limitations include low physico-mechanical performance, the restricted range of environmental conditions tested, and the absence of performance assessment under actual food-service use. Future work should address durability improvements and evaluate degradation in diverse real-world settings.
Saqta (Colignonia parviflora var. biumbellata) is an Andean perennial herb whose root is used by rural communities as a source of "natural detergent" for washing clothes and personal hygiene. Its use has been documented by ancient cultures; however, the chemical components of Saqta root have been largely unexplored despite their potential applications. This study aims to extract and characterize saponins from Saqta root and assess their suitability as surfactants. A saponin-rich extract was obtained using a 70% ethanol-water solution under reflux conditions. Characterization was performed using Fourier-transform infrared spectroscopy (FTIR), thin-layer chromatography (TLC), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Foam stability and surface tension were assessed through the afrosymetric method and capillary rise method, respectively. The extraction yield was approximately 6.28%, calculated based on the dry mass of the Saqta root material used in the extraction process. FTIR and TLC confirmed the presence of saponins, revealing structural similarities to commercial Quillaja saponins. The extracted saponins demonstrated high solubility in water and formed stable foam, with a critical micelle concentration (CMC) of 4.59 wt%. Surface tension measurements revealed a significant reduction in surface tension compared to that of pure water for solutions with saponin concentrations greater than 0.1%. In conclusion, saponins were successfully extracted from Saqta root, exhibiting promising surfactant properties. These findings suggest potential applications in alternative detergent formulations, cosmetics, and other soap-like products.
Reliable and up-to-date information on crop distribution is essential for ensuring food security in the face of rapid population growth and climate change, and with recent advancements in remote sensing and artificial intelligence, large-scale analyses of agricultural landscapes can now be performed more quickly and accurately. The main objective of this study is to improve the classification of strategic crops (wheat, barley, and corn) through an ensemble learning approach that integrates three complementary neural network architectures—TempCNN, BiLSTM, and Transformer-Encoder—using Sentinel-2 time series data. To achieve this objective, several vegetation indices were first computed from the spectral bands of Sentinel-2 time series data. A Random Forest (RF) model was then employed to identify and select the most relevant features. Finally, these features were used to train the individual models, and their prediction probabilities were combined using soft voting to produce the final ensemble predictions. Model performance was evaluated across four spatial cross-validation configurations, in which the data from each of the four departments of Brittany was held out in turn as an independent test region, ensuring a rigorous assessment of the model’s spatial generalization capability. The ensemble model achieves a mean Overall Accuracy (OA) of 98.14% across all configurations, with per-class F1-scores exceeding 0.93 for barley, 0.97 for wheat, and 0.99 for corn. Compared to TempCNN and LSTM baseline models, as well as Recurrent Convolutional Neural Network (R-CNN), RF, and Support Vector Machine (SVM), the proposed ensemble model consistently achieves higher OA and per-class metrics. It also outperforms the same ensemble trained without vegetation indices. These findings highlight the effectiveness of combining complementary deep learning architectures and demonstrate the added value of vegetation indices for crop type classification using satellite time series.
This study investigates the catalytic glycolysis of post-consumer poly (ethylene terephthalate) (PET) using Zn-Al and Zn-Ni-Al mixed oxides derived from hydrotalcite-like precursors. The catalysts were synthesized by constant-pH coprecipitation and calcined at 500 degrees C to generate mixed oxide phases. Structural characterization by XRD revealed the collapse of the layered structure and the formation of ZnO domains together with spinel-type MAl(2)O(4 )phases (M = Zn, Ni, or mixed Ni-Zn). Nitrogen physisorption showed a strong composition-dependent evolution of the mesostructure, with the Ni0.44Zn0.22Al0.33 catalyst exhibiting the highest surface area (177 m2 & centerdot;g-1). Control experiments demonstrated that PET depolymerization does not occur in the absence of catalyst, that ZnO promotes polymer degradation but with low selectivity toward BHET, and that bulk NiO is essentially inactive. The best catalytic performance was obtained for Ni0.44Zn0.22Al0.33, reaching 96.4% PET depolymerization and 82.0% selectivity toward bis (2-hydroxyethyl) terephthalate (BHET). The recovered monomer was identified by FTIR spectroscopy, differential scanning calorimetry (DSC), and morphological analysis confirming the formation of crystalline BHET as the main reaction product. The results indicate that catalytic performance is governed by the structural integration of Zn and Ni within the mixed oxide matrix rather than by the simple coexistence of individual oxides. A mechanistic interpretation suggests that Zn-related Lewis acid sites activate the ester carbonyl groups of PET, while the incorporation of Ni into aluminate structures modifies the surface environment and improves site accessibility within the mesoporous framework, leading to enhanced depolymerization and selectivity.
