This article focuses on agar biopolymer films that offer promise for developing biodegradable packaging, an important solution for reducing plastics pollution. At present there is a lack of data on the mechanical performance of agar biopolymer films using a simple plasticizer. This study takes a Design of Experiments approach to analyze how agar-glycerin biopolymer films perform across a range of ingredients concentrations in terms of their strength, elasticity, and ductility. Our results demonstrate that by systematically varying the quantity of agar and glycerin, tensile properties can be achieved that are comparable to agar-based materials with more complex formulations. Not only does our study significantly broaden the amount of data available on the range of mechanical performance that can be achieved with simple agar biopolymer films, but the data can also be used to guide further optimization efforts that start with a basic formulation that performs well on certain property dimensions. We also find that select formulations have similar tensile properties to thermoplastic starch (TPS), acrylonitrile butadiene styrene (ABS), and polypropylene (PP), indicating potential suitability for select packaging applications. We use our experimental dataset to train a neural network regression model that predicts the Young’s modulus, ultimate tensile strength, and elongation at break of agar biopolymer films given their composition. Our findings support the development of further data-driven design and fabrication workflows.

The design of biodegradable polymeric materials is of increasing scientific interest due to accelerating levels of plastics pollution. One area of increasing interest is the design of biodegradable polymer films based on seaweed as a raw material. The goal of the study is to explore whether machine learning techniques can be used to predict the properties of unknown compositions based on existing data from the literature. Clustering algorithms are used, which show how some ingredients components at certain concentration levels alter the mechanical properties of the films. Robust regression algorithms with three popular models, namely decision tree, random forest, and gradient boosting. Their predictive capabilities are compared, resulting in the random forest algorithm being the most stable with the greatest predictive capacity. These analyses offer a decision support system for biomaterials manufacturing and experimentation. The results and conclusions of the study indicate that bioplastics made from seaweed have promising potential as a sustainable alternative to traditional plastics, discovering interesting additives to improve the performance of biopolymers. In addition, the machine learning approaches used provide effective tools for analyzing and predicting the properties of these materials in structured but highly sparse data.

Automated analysis of the scientific literature using natural language processing (NLP) can accelerate the identification of potentially unexplored formulations that enable innovations in materials engineering with fewer experimentation and testing cycles. This strategy has been successful for specific classes of inorganic materials, but their general application in broader material domains such as bioplastics remains challenging. To begin addressing this gap, we explore correlations between the ingredients and physicochemical properties of seaweed-based biofilms from a corpus of 2000 article abstracts from the scientific literature since 1958, using a supervised word co-occurrence analysis and an unsupervised approach based on the language model MatBERT without fine-tuning. Using known relations between ingredients and properties for test scenarios, we discuss the potential and limitations of these NLP approaches for identifying novel combinations of polysaccharides, plasticizers, and additives that are related to the functionality of seaweed biofilms. The model demonstrates a valuable predictive ability to identify ingredients associated with increased water vapor permeability, suggesting its potential utility in optimizing formulations for future research. Using the model further revealed alternative combinations that are underrepresented in the literature. This automated method facilitates the mapping of relationships between ingredients and properties, guiding the development of seaweed bioplastic formulations. The unstructured and heterogeneous nature of the literature on bioplastics represents a particular challenge that demands ad hoc fine-tuning strategies for state-of-the-art language models for advancing the field of seaweed bioplastics.

Purpose: This paper aims to determine the effect of a set of factors (attitude toward entrepreneurship, perceived behavioral control or entrepreneurial self-efficacy, previous experience, internal locus of control and entrepreneurial alertness) on the entrepreneurial intentions of Chilean secondary vocational school students.Design/methodology/approach: An online survey was sent to all secondary-level students from four Chilean vocational schools (3,052 students). A total of 1,644 valid questionnaires were received. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).Findings: The results show that attitude toward entrepreneurship and perceived behavioral control positively and significantly impact the students’ entrepreneurial intentions, but not the previous experience.Research limitations/implications: Since this research was carried out in a specific reality, the results may lack generalizability. Also, the proposed model requires further development through the incorporation of other factors. It is then suggested to replicate this study in other countries as well as to incorporate new variables and test new relationships.Practical implications: Entrepreneurship education programs at the secondary level should emphasize changing/stimulating adolescents’ attitudes toward entrepreneurship as well as improving their confidence in their entrepreneurial abilities.Originality/value: This is one of the few studies that provide empirical evidence about the determinants of entrepreneurial intentions in adolescents, particularly in Latin America, and with a large sample.

El artículo presenta una revisión sistemática de la literatura sobre el emprendimiento tradicional, social y sostenible en contextos universitarios, aplicando el protocolo PRISMA para analizar un total de 7.184 artículos publicados hasta el año 2024. El objetivo central es mapear el estado actual del conocimiento, identificar las estructuras temáticas predominantes, analizar las tendencias evolutivas y detectar vacíos conceptuales en este campo de estudio. Los hallazgos revelan un crecimiento sostenido de la producción científica desde 2010, con un auge significativo entre 2020 y 2024, periodo que concentra el 49 % del total de publicaciones. Se consolidan tres grandes clústeres temáticos: la “intención emprendedora”, como eje articulador del campo, estrechamente vinculado a la Teoría del Comportamiento Planificado (TPB) y validado mediante técnicas estadísticas como SEM/PLS-SEM; la “educación emprendedora”, como tema básico que conecta teoría y práctica educativa, incluyendo currículos, metodologías innovadoras y competencias emprendedoras; y las “actividades emprendedoras”, como tema de nicho orientado a la transferencia tecnológica, desarrollo económico y formación de ecosistemas emprendedores. Además, emergen nuevas líneas centradas en el rol activo de las universidades en la promoción de emprendimientos sociales y sostenibles, especialmente en escenarios postpandemia. La evolución temática muestra un desplazamiento desde perspectivas macroeconómicas hacia enfoques centrados en el individuo, con una transición conceptual de capital humano a capital social. Como conclusiones, el estudio constata una madurez estructural del campo, donde la intención emprendedora se posiciona como constructo dominante, mientras que la componente social y de sostenibilidad comienza a integrarse progresivamente, aunque aún con menor centralidad. Las universidades destacan como agentes estratégicos para incubar emprendimientos con valor económico, social y ambiental. Este trabajo contribuye a ofrecer una visión panorámica del campo, e identifica oportunidades futuras para explorar enfoques pedagógicos emergentes, sinergias con políticas públicas y métricas de impacto institucional en el fomento del emprendimiento social y sostenible.

Este artículo proporciona una revisión sistemática realizada con la metodología PRISMA y un estudio bibliométrico sobre el estado del arte de las cooperativas en el contexto de la sostenibilidad, explorando cómo se ha desarrollado el campo a través del tiempo. Esta técnica de investigación es relevante para la identificación de conceptos claves y emergentes, permitiendo delimitar las brechas y desafíos para el campo de estudio. Se logra observar distintas aristas del cooperativismo y como se ha ido vinculando con las diferentes ramas enlazadas a la sostenibilidad, en los primeros periodos más enfocado en un aspecto medioambiental, pero luego transformándose a un concepto que integra dimensiones sociales y organizacionales. Mediante el análisis de literatura y ejemplos específicos, el estudio mapea el panorama actual, los estudios más relevantes y sintetiza la principal contribución. Finalmente, se sugieren direcciones futuras de investigación para promover un entendimiento más profundo de la relación entre cooperativas y sostenibilidad. Este estudio busca contribuir al desarrollo sostenible, identificando como la Economía Social a través del sector cooperativo ha implementado prácticas y acciones que han permitido enfrentar los desafíos globales actuales.