Maria Cosio Leon

El papel del litio en la transición energética exige mediciones de campo más rápidas y confiables en las cadenas de suministro basadas en salmueras. Presentamos una estrategia de LIBS orientada a proceso para la cuantificación en tiempo real de litio en salmueras en flujo. Un láser de 1064 nm, espectrómetros UV–Vis–NIR y una celda diseñada para minimizar la cavitación permiten generar plasma estable. Un flujo de datos guiado por la física (sustracción de línea base, extracción de rasgos, normalización interna y validación por nivel retenido) ofrece una calibración robusta en ~30–1300 ppm en matrices representativas. Las rutas de despliegue abarcan salares continentales, fluidos geotérmicos, aguas producidas en yacimientos de hidrocarburos y charcas hipersalinas marinas, lo que permite un mapeo denso, cribado ágil y el control de DLE en un lazo cerrado. La metodología acorta los ciclos de exploración, reduce la incertidumbre en la optimización de procesos y mejora la resolución espaciotemporal en la gestión del yacimiento. También se discuten las implicaciones de la gobernanza para un desarrollo responsable mediante el monitoreo químico in situ.

This study proposes a conceptual framework to operationalize the transition from conventional, technical, and material-centric circularity to an inclusive socio-technical paradigm through the deployment of a Co-Creative Digital Process Twin (DPT) framework in Progreso, Hidalgo, Mexico. The primary objective is to bridge the gap between technocentric industrial applications and the socio-technical needs of developing regions, specifically by integrating informal waste pickers (pepenadores) into a formal circular economy ecosystem. The methodology is structured into three distinct stages: a systematic scoping review following PRISMA-ScR protocols to identify architectural gaps in the current literature; the generation of a conceptual framework and Business Model Canvas grounded in participatory design; and the formulation of Key Performance Indicators for future longitudinal testing. The results indicate a profound bias in existing digital twin research toward industrial automation, with a near-total absence of social inclusion mechanisms. In response, this study presents a multidimensional business model featuring service-oriented cooperative architecture. This system utilizes open standards (FIWARE, BPMN 2.0) and crowdsourcing platforms (Ushahidi) to maximize material recovery while ensuring socio-economic equity. This will need to be verified in future studies. A limitation of this study is its specificity to a single municipality with certain characteristics, which could hinder its immediate generalization to metropolitan areas. Furthermore, the success of the model could depend on basic digital literacy and a stable telecommunications infrastructure.

Digital twins offer significant potential for operationalizing a circular economy at the municipal level. This study aims to propose a business model framework that optimizes the circular management of municipal solid waste (MSW) by leveraging the concept of "Co-creative Digital Process Twins." The methodology was structured around two primary axes: first, a critical literature review conducted via the PRISMA-ScR protocol to identify process architectures and existing research gaps in digital process twin development; and second, a theoretical-practical integration using the Business Model Canvas tool, grounded in the paradigms of the circular economy and participatory design, applied to a case study in the municipality of Progreso, Hidalgo, Mexico. Our findings reveal a significant bias in the current state of the art: existing digital twin applications are predominantly industrial and notably lack social inclusion mechanisms. In response, this paper presents a multidimensional business model that integrates key social actors into the digital ecosystem, establishing an architecture explicitly designed to maximize material recovery rates. Conclusively, the adoption of co-creative digital process twins provides a robust socio-technical infrastructure that not only simulates and optimizes the waste value chain but also fosters social inclusion, thereby catalyzing the transition toward a genuinely circular municipal economy.

The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium and the cornea and modeling the eyeball as a dipole with a positive and negative hemisphere. Supervised learning algorithms were implemented to classify five eye movements; left, right, down, up and blink. Wavelet Transform was used to obtain information in the frequency domain characterizing the EOG signal with a bandwidth of 0.5 to 50 Hz; training results were obtained with the implementation of K-Nearest Neighbor (KNN) 69.4%, a Support Vector Machine (SVM) of 76.9% and Decision Tree (DT) 60.5%, checking the accuracy through the Jaccard index and other metrics such as the confusion matrix and ROC (Receiver Operating Characteristic) curve. As a result, the best classifier for this application was the SVM with Jaccard Index.