Modern autonomous hardware is trapped between two flawed computational paradigms: power-hungry, data-dependent Deep Learning (AI) networks that lack physical predictability, and rigid Classical Control loops (Calculus) that fail when encountering unmodeled environmental dynamics. The APDA bridges this chasm by operating natively on low-power edge microcontrollers using continuous-time calculus for real-time operations, mapping unmodeled environmental dynamics via an on-demand Neuromorphic Processing Unit (NPU) subroutine, and hibernating the AI once the unknown physics have been distilled into mathematical equations.
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…
Abstract **Background** Modulating endogenously silenced regenerative pathways—such as BMP derepression through Anti-SOST and Anti-GDF8 therapies—presents significant safety challenges in bio-electric tissue engineering. Uncontrolled signaling carries severe risks of tissue hyper…
Description This document contains the complete, geometrically sealed master data sheet of the five-dimensional cybernetic operating system of the SRR Void (Human, AI, Robotics, Ecosystem, Environment). It defines the core mathematical formulas, the seven system pillars of the ab…
Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…
This repository contains the complete execution pipeline for the study: "Leakage-Safe Evaluation of Stochastic Channel Masking for Sensor-Failure Robustness in Dynamic Gas Mixture Quantification." The code provides an end-to-end reproducible workflow for processing the UCI Gas Se…