Biopolymeric films based on chitosan and starch offer an ecological alternative for food protection. Nevertheless, their practical application is often limited by their low mechanical properties and high solubility in aqueous solutions, due to weak interactions between the chains of the biopolymers. One approach to resolve this problem is to obtain biopolymeric films based on (bio)polyelectrolyte complex ((bio)PEC). These films exhibit stronger electrostatic interactions and homogeneous biopolymeric structure. In this study, films based on (bio)PEC were obtained by the casting method, using chitosan and carboxymethyl cassava starch with different degrees of substitution with a biopolymer concentration of 2.5 wt.% at pH = 6. The obtained films were analyzed using the optical and scanning microscopy, color method, ATR-FTIR spectroscopy, thermogravimetry, mechanical analysis under tension, solubility in water, simulated gastric fluid (SGF), and phosphate-buffered saline (PBS) solutions, and contact angle of water. The results demonstrated that the tensile strength and Young’s modulus of films based on (bio)PEC increased by 2–4 times, and the elongation at break by 20% compared to films based on a mixture chitosan and starch. This is due to the increase in the attraction between oppositely charged polyelectrolytes in (bio)PEC films. Additionally, the solubility of (bio)PEC films was reduced by ~40%, 35% and 70% in water, SGF and PBS solutions, respectively, when the carboxymethyl starch with highest degree of substitution was used, and z was near to 1.

La investigación buscó determinar la eficacia de la aplicación del ABP para mejorar la comprensión de los grandes conceptos del cálculo diferencial en estudiantes de educación superior. La investigación partió de la hipótesis de que, al poner al estudiante como protagonista de su aprendizaje y trabajar en la resolución colaborativa de problemas contextualizados, el ABP puede convertirse en una metodología capaz de superar las dificultades persistentes en la apropiación de conceptos matemáticos abstractos. Se optó por un enfoque mixto secuencial articulado en metodologías cuantitativas y cualitativas. En una fase inicial, se administró una prueba pre-test y una prueba post-test a una muestra de estudiantes de primer año de ingeniería, y se llevó a cabo un análisis estadístico de la calificación total. En una fase subsecuente, se llevaron a cabo entrevistas semiestructuradas y observaciones en el aula con el objetivo de indagar en las perspectivas y vivencias de los estudiantes. Además, se integraron tecnologías emergentes —como las plataformas de aprendizaje basado en plataformas (LMS) y las simulaciones interactivas en GeoGebra— que respaldan la representación visual de conceptos diferenciales y fomentan la autonomía en la búsqueda activa del conocimiento. Los hallazgos señalan un avance notable en el desempeño académico, particularmente en las cuestiones relativas a límites, derivaciones y aplicaciones. Adicionalmente, se registró un incremento en la participación en las actividades académicas, un incremento en la cantidad de interacciones colaborativas y una mejora en las prácticas metacognitivas. Se postula que la implementación del Aprendizaje Basado en Problemas (ABP, por sus siglas en inglés) constituye una estrategia pedagógica sólida y apropiada para el cálculo diferencial de nivel superior, un modelo replicable que respalda la instrucción matemática y el fomento de competencias transversales reconocidas como esenciales en el ámbito universitario.

Reliable discrimination of Volcano-Tectonic (VT) and Long-Period (LP) seismic signals is central to volcanic surveillance, yet operational deployment is hindered by pronounced inter-volcano domain shift, scarce annotated catalogues, and heterogeneous acquisition instrumentation. We present a self-supervised trimodal deep learning framework that jointly encodes spectrograms, raw one-dimensional waveforms, and physical tabular features, and we evaluate its capacity for cross-domain generaliza-tion across three leave-one-volcano-out transfer settings among Galeras, Cumbal, and Chiles-Cerro Negro (Colombia). Beyond classification accuracy, we systematically characterise the geometry of the learned latent space using class- and domain-separability landscapes projected into two dimensions, quantifying silhouette coefficients, Davies–Bouldin indices, separation indices, and decision-boundary alignment for every ablation variant. The trimodal framework (SSL Trimodal) attains the highest bootstrap balanced accuracy on the Galeras+Cumbal→Chiles-Cerro Negro transfer (0.9080±0.0072) and the most favourable class-separation index (Sep-Idx_2D = 2.06) among all eight evaluated variants in that scenario. It further achieves the best class-separation index (1.72) even on the most divergent transfer setting, Galeras+Chiles-Cerro Negro→Cumbal, despite not attaining the highest accuracy there. This dissociation between point accuracy and embedding quality is, we argue, the more in-formative signal for assessing cross-domain robustness than accuracy alone. We report this result alongside its limitations transparently: the CORAL covariance-alignment baseline outperforms the trimodal framework on two transfer settings with comparable mean accuracies ( 0.81), underscoring that discriminative contrastive pretraining and second-order domain alignment address complemen-tary, rather than redundant, failure modes. To our knowledge, this is the first trimodal self-supervised framework reported for cross-domain generalization in seismo-volcanic signal classification.