Little's Law as a Vector for Resilience and Quality
Study on applying Little's Law to elevate delivery predictability and resilience in Data Science operations.
True innovation is not born from corporate hype, but from peer-validated academic rigor. This repository consolidates decades of scientific research and analytical modeling conducted by Ulisses Flores. Exploring the intersection between Artificial Intelligence, Cyber-Financial Resilience, and Complex Systems Theory, each publication listed here (with DOI registration) represents a documented contribution to the state of the art in engineering and economics.
Q1-level rigor with DOI traceability
Each article follows international academic publication standards with peer review, DOI identifiers, and reproducible methodology — directly applied to real-world projects in consulting, systems architecture, and AI research.
Study on applying Little's Law to elevate delivery predictability and resilience in Data Science operations.
Predictive analysis of financial assets with LSTM networks to capture temporal dynamics in non-stationary markets.
Credit card fraud detection with MLP neural networks and feature engineering for imbalanced data.
Comprehensive historiographic and archaeological analysis on the historicity of Jesus.
Analysis of Bitcoin as a reserve asset through Austrian School monetary theory and praxeology.
Historical-critical analysis of scribal canonization and the formation of the biblical canon.
Theological and phenomenological analysis of Marian apparitions.
Spiritual, theological and visual archaeology of the Holy Club and the origins of Methodism.