Axis Mũndũ × UMTL: A Hybrid Temporal Computing Framework for Phase-Aware Anomaly Detection and Real-Time Kinematics
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This paper introduces the Axis Mũndũ × UMTL framework, a hybrid temporal computing model integrating indigenous (Gĩkũyũ/Kikuyu) African knowledge systems with modern signal processing and kinematic analysis. The framework reconceptualizes time as a continuous-discrete structure, combining digital root-based cyclic coherence states (ℤ₉) with real-time system dynamics.
A total of 1,024 daily temporal coherence points are identified, enabling phase-aware anomaly detection that interprets system deviations relative to temporal context rather than static statistical thresholds. The system models key variables including system energy, kinematic momentum, and phase alignment (kinship), producing context-sensitive intelligence.
Results demonstrate that phase-aligned systems outperform rigid control models in efficiency and resilience. Applications include artificial intelligence optimization, cyber-security anomaly detection, and adaptive resource monitoring systems.
This work establishes a novel paradigm in temporal computing and contributes to the development of indigenous-informed, context-aware intelligent systems.
This work is licensed under CC BY 4.0 and is intended for open academic and applied research use.