{
  "api": "christianecg.com",
  "version": 1,
  "endpoint": "/api/papers.json",
  "docs": "https://christianecg.com/es/api",
  "generated_at": "2026-07-10T02:20:19.727Z",
  "count": 2,
  "data": [
    {
      "id": "blockchain-messaging-security",
      "title": "Security Issues of a Decentralized Blockchain-Based Messaging System",
      "title_es": null,
      "source": "IEEE Xplore",
      "issn": null,
      "year": 2021,
      "url": "https://ieeexplore.ieee.org/document/9619732",
      "pdf": "https://christianecg.com/papers/blockchain-messaging-security.pdf",
      "description": {
        "es": "Sistema de mensajería descentralizado basado en blockchain con cifrado de clave pública (aes128-ctr). Analiza vulnerabilidades clave de la arquitectura — ataque del 51%, problema del doble gasto y limitaciones de velocidad de verificación — y propone soluciones aplicables a cualquier red blockchain. Presentado en congreso internacional.",
        "en": "Decentralized blockchain-based messaging system using public-key cryptography (aes128-ctr). Analyzes key architectural vulnerabilities — 51% attacks, double-spending, and verification speed — and proposes solutions applicable to any blockchain network. Presented at an international conference.",
        "lat": "Systema nuntiorum in catena solutorum (blockchain) cum cryptographia clavium publicarum (aes128-ctr). Vulnerabilitates architecturae examinat — impetum LI%, dispendium duplex et velocitatem verificationis — et solutiones proponit cuilibet retis catenae applicabiles."
      }
    },
    {
      "id": "covid19-ml-mexico",
      "title": "Statistical Analysis of the Spread of the COVID-19 Pandemic in Mexico Applying Machine Learning Models",
      "title_es": "Análisis Estadístico de la Propagación de la Pandemia COVID-19 en México Aplicando Modelos de Machine Learning",
      "source": "REINGTEC",
      "issn": "2448-7198",
      "year": 2020,
      "url": "https://reingtec.itsoeh.edu.mx/reingtec/docs/vol8_2020reingtec/51-53%20ITICS%20Cruz%20Gonza%CC%81lez%202020.pdf",
      "pdf": "https://christianecg.com/papers/covid19-ml-mexico.pdf",
      "description": {
        "es": "Modelo de Densidad de Kernel (Kernel Ridge) para predecir la tendencia de contagios de COVID-19 en México. Entrenado con datos del CSSE de Johns Hopkins durante los primeros 50 días del brote para estimar los siguientes 150 días. Implementado en Python con sklearn.",
        "en": "Kernel Ridge model to predict COVID-19 contagion trends in Mexico. Trained on Johns Hopkins CSSE data from the first 50 days of the outbreak to estimate the following 150 days. Implemented in Python with sklearn.",
        "lat": "Exemplar Kernel Ridge ad tendentias contagionis COVID-19 in Mexico praedicendas. Eruditum ex datis CSSE Universitatis Hopkins ex primis L diebus ad sequentes CL dies aestimandos. In Python cum sklearn effectum."
      }
    }
  ]
}