Communities in PRCR
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- Colecciones de la Albizu University
- Colecciones digitales de Atenas College
- Colecciones de los Centros Sor Isolina Ferré
- Materiales utilizados para la certificación.
Recent Submissions
Design and development of an intelligent emergency state reporting system for Puerto Rico: Secure mobile application development
(Polytechnic University of Puerto Rico, 2026-09-10) González Lafontaine, Sebastian; Brennes Catinchi, Joanne; Pacheco Crosetti, Gustavo E.; Brennes Catinchi, Joanne; Pacheco Crosetti, Gustavo E.
Puerto Rico currently faces a critical shortfall in its emergency response framework—the absence of a comprehensive Emergency Management Application managed by government agencies. The government could leverage this widespread connectivity to crowdsource real-time emergency reporting but currently does not. This research project is predicated on the conviction that citizens, armed only with a smartphone, constitute the most immediate — and often underutilized — layer of emergency response capability. The core of creating an emergency reporting system is civilians. There are no reports if civilians don’t have a practical way to send them. Therefore, it is necessary to provide a simple way for emergency reports to be made via a mobile application. This mobile application will serve as a centralized service for Puerto Rican civilians to report various types of emergencies. The app strengthens both community preparedness and institutional responsiveness. Moreover, it empowers residents to become active participants in safety networks. This approach not only accelerates communication but also improves transparency, situational awareness, and trust between communities and response agencies.
Design and development of an intelligent emergency state reporting system for Puerto Rico: Geographic Information System: Vulnerability and Risk Mapping
(Polytechnic University of Puerto Rico, 2026-09-10) Tron Esqueda, Santiago; Brennes Catinchi, Joanne; Pacheco Crosetti, Gustavo E.; Brennes Catinchi, Joanne; Pacheco Crosetti, Gustavo E.
In emergency situations like natural disasters it's important to know what event happened and where, but an aspect of the response that could be overlooked is the very people who could need saving. A visual format of handling this information allows for faster parsing and decision making. A web map, to be integrated into the admin portal is an ideal way to do this. Highlighting the expected losses, or percentage of vulnerable groups in an area is useful to gauge how dire a situation is and what places might require more attention.
Chitosan and Alginate Based Conductive Gels for Electroencephalography: Comparative In Vitro Characterization of Fifteen Formulations
(Polytechnic University of Puerto Rico, 2026-09-10) Colón Rodríguez, Ruth M.; Ferrer Del Valle, Victor J.; Rondón Contreras, Jairo J.; Rondón Contreras, Jairo J.
Electroencephalography (EEG) relies on conductive gels for effective electrode–skin contact, but conventional gels dehydrate, leave residue, and are not biodegradable. This study developed marine-derived gels from chitosan, sodium alginate, and chitosan–alginate hybrids, with selected formulations reinforced with montmorillonite. Of 35 formulations, 15 homogeneous gels were characterized for physicochemical and electrical properties. Gel-only impedance decreased from 85–230 kΩ at ~20 Hz to 21–80 kΩ at 1 kHz, with phase angles shifting toward increasingly resistive behavior. ALG-6*, a montmorillonite-containing alginate gel, combined the highest viscosity, lowest contact angle, and favorable interfacial impedance, and was provisionally prioritized pending biocompatibility and EEG signal-quality testing.
Entropía del cambio climático: Análisis dinámico en los Andes de Cusco y el trópico de Puerto Rico
(Universidad Politécnica de Puerto Rico, 2026-09-10) Montilla Polanco, Roseliz Marie; Quispitupa Yupa, David; Quispitupa Yupa, David
El cambio climático puede manifestarse de manera diferente según las características de cada sistema climático, haciendo necesario contar con métricas que permitan comparar su variabilidad. Esta investigación utiliza la entropía de Shannon para estudiar los patrones de temperatura y precipitación en dos contextos contrastantes: San Juan, Puerto Rico, y Cusco, Perú, durante 2020–2025. Se analizaron registros meteorológicos de las estaciones del Aeropuerto Internacional Luis Muñoz Marín (TJSJ) y el Aeropuerto Internacional Alejandro Velasco Astete (SPZO), clasificando las observaciones en categorías fijas y calculando la entropía mensual en bits. El estudio busca identificar diferencias, patrones estacionales y tendencias descriptivas entre ambas localidades. Los resultados muestran comportamientos contrastantes y tendencias lineales débiles, destacando el potencial de la entropía de Shannon como métrica complementaria para caracterizar la variabilidad meteorológica y apoyar futuros análisis climáticos.
Deep learning framework for colon and lung cancer detection using CNNs
(Polytechnic University of Puerto Rico, 2026-09-10) Torres Díaz, Paola A.; Valera Márquez, Juan B.; Valera Márquez, Juan B.
Cancer remains one of the leading causes of mortality worldwide. The World Health Organization (2025) reports approximately 10 million cancer deaths in 2020, with lung and colorectal cancers among the largest contributors, accounting for an estimated 20% and 9% of cancer deaths, respectively. This study presents a convolutional neural network (CNN)-based framework for classifying histopathological images into five classes: lung adenocarcinoma (lungaca), lung squamous cell carcinoma (lungscc), benign lung tissue (lungn), colon adenocarcinoma (colonaca), and benign colon tissue (colonn), with the goal of supporting diagnostic workflows across clinical contexts. The model was trained on the publicly available LC25000 dataset and evaluated using accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and area under the curve (AUC). The architecture consists of four convolutional blocks, global average pooling, dropout, and a 512-unit dense layer, achieving 98.53% accuracy, 98.57% precision, and 98.53% F1-score. These results suggest that the proposed framework could serve as a decision-support tool to assist pathologists in the earlier and more consistent detection of lung and colon cancer from histopathological images.