In recent years, there has been a notable increase in the development of social media and humanitarian applications based on bots, designed to provide assistance during large-scale natural disasters. These applications play a crucial role in managing the chaos and useful to satisfy the urgent needs for rescue and relief that arise when catastrophes disrupt daily routines. However, they often encounter challenges during emergencies, such as dynamic and unpredictable variations in user workload, which can affect service quality and application stability. To tackle these challenges, we propose a capacity planning methodology to determine the optimal number of replicas and partitions for each component of an application and distributes them across virtual machines in server clusters. By bridging the gap between the algorithms executed in the applications and the performance characteristics of their implementations, this methodology enables applications to scale efficiently. It helps maintain response times and average utilization within user-defined ranges while providing fault tolerance to prevent component saturation. We validate the proposed methodology with tree bot-based applications devised to be use after a natural disaster strikes. Our experimental results show the effectiveness of the methodology, with estimation errors ranging from 1% to 15% for utilization and average response times. Furthermore, the methodology serves as an effective elasticity tool, allowing for the adjustment of component replicas based on user’s requests.

Large-scale urban tree donation campaigns are widely implemented worldwide as nature-based solutions for climate adaptation and mitigation; however, most programs lack individual-level traceability and post-donation monitoring, limiting accountability and evidence-based management. A fundamental prerequisite for longitudinal survival assessment is the existence of a reliable traceability infrastructure capable of linking individual trees to verified planting records over time. This study proposes and empirically evaluates a participatory digital traceability system that establishes this foundational infrastructure, conceptualized as a distributed data validation architecture for donation-based urban afforestation programs. The framework integrates (i) persistent digital identifiers, (ii) geospatial registration, (iii) distributed multi-stage validation, and (iv) structured citizen reporting, and is operationalized through an installation-free progressive web application (ArborizaCL). The approach was deployed in five real-world campaigns conducted in Valdivia, Chile (May–September 2025), registering 642 trees distributed to 240 participants. A total of 190 georeferenced planting reports were submitted, corresponding to an overall reporting rate of 29.6%. Reporting behavior varied substantially by institutional follow-up strategy: campaigns with active follow-up achieved a mean reporting rate of 54.0%, compared with 13.0% under passive strategies, yielding a 41.0 percentage point difference (315.8% relative increase). Spatial analysis of reported plantings showed a predominance of urban (51.1%) and peri-urban (42.1%) locations, enabling differentiated territorial assessment. These results indicate that while digital infrastructure enables traceability and transparent monitoring, sustained citizen engagement is strongly associated with institutional coordination mechanisms. Beyond environmental monitoring, the proposed framework contributes to information governance by demonstrating how participatory digital traceability systems can support distributed public-sector oversight and outcome-oriented evaluation. The framework provides a transferable methodological basis for strengthening monitoring capacity, transparency, and governance design in publicly funded afforestation initiatives and other distributed civic programs.

Introducción: El uso de psicofármacos en la Atención Primaria de Salud (APS) plantea desafíos en la seguridad y calidad del tratamiento, especialmente en poblaciones vulnerables. Este estudio analiza los patrones de prescripción y características sociodemográficas y clínicas de usuarios de psicofármacos en la red de salud municipal de Valdivia durante 2022, con énfasis en polifarmacia y uso off-label. Métodos: Se realizó un estudio observacional retrospectivo basado en datos de mundo real (RWD) obtenidos del sistema de registro clínico electrónico Rayen en centros de APS municipal de Valdivia. Se analizaron los registros de 5.813 usuarios con diagnósticos CIE-10 F y 210.554 prescripciones asociadas, evaluando prevalencias, dosis diarias definidas (DDD) y patrones de polifarmacia. Resultados: El 76,88% de los usuarios fueron mujeres, con mayor prevalencia en mayores de 45 años. Las principales patologías diagnosticadas fueron ansiedad e hipertensión. Se observó mayor tendencia en las prescripciones de sertralina, zopiclona y fluoxetina en menores de 60 años. Mientras que, en mayores de 60 años, predominan quetiapina, risperidona y citalopram. El 41% presentó polifarmacia (≥5 fármacos). El uso prolongado de clonazepam y zopiclona superó las recomendaciones internacionales (> 4 semanas de tratamiento). La quetiapina se utilizó off-label para insomnio en el 77% de los casos. Conclusiones: Los resultados evidencian la necesidad de optimizar la prescripción de psicofármacos en APS, priorizando la seguridad y adherencia a las recomendaciones clínicas vigentes. La integración de ciencia de datos podría contribuir a mejorar las prescripciones generando alertas automatizadas para reducir la sobremedicación y mejorar la calidad del tratamiento.