We present the Quantum Informational Origin (QIO) model, a novel, minimal viable framework for cosmogenesis that avoids the infinite regress, bounce models, and pre-existing spacetime conditions that characterize many contemporary cosmological theories. The model posits that spacetime, matter, and physical laws emerge from a pre-geometric quantum network of entangled qubits—a holographic quantum error-correcting code. The universe's origin is characterized as a symmetry-breaking phase transition from a complete graph of N qubits to a three-dimensional lattice, with cosmic inflation naturally emerging as entanglement growth within this quantum network. Dark energy is identified as residual network entanglement, dark matter as topological defects within the network structure, and baryon asymmetry as a consequence of CP violation in the quantum circuit dynamics. The model demonstrates remarkable explanatory coherence while requiring only two fundamental parameters (N and the code distance d), and makes several testable predictions including specific relations between cosmological parameters and early-universe entanglement entropy. This framework offers a unified, information-theoretic approach to resolving long-standing cosmological puzzles while remaining consistent with established principles of quantum mechanics and general relativity.

A govhr package toy example quality control report generated from the standard harmonized contract, personnel and establishment modules of government payroll data.

his project explores the hidden structures within global health data by focusing on 46 Asian countries. Using the WHO Life Expectancy dataset, I applied various unsupervised learning techniques to group countries based on metrics like life expectancy, schooling, and adult mortality. Key features of this report include: Data Selection: Analysis focused on the year 2014 to ensure data completeness for critical variables like Alcohol consumption and Schooling. Methodology: A comparison of partitioning methods (K-means and PAM) and Hierarchical Clustering (Ward's method) to identify stable country groupings. Advanced Visualization: Use of dimensionality reduction techniques—MDS, t-SNE, and UMAP—to project complex, multi-dimensional data into intuitive 2D maps. Findings: The results reveal three distinct clusters: a "High-Performing" group (e.g., Japan, Singapore), an "Emerging" middle-income group (e.g., China, Thailand), and a "Challenged" group (e.g., Afghanistan, Yemen).