Supplementary Data for the Development and Evaluation of the Robotic Concept-Based Framework (RCF): PRISMA Materials and Learner Knowledge Representations

Deciphering the Host-Range Grammar of Orthoflaviviruses Using Foundation Model Embeddings: A Leakage-Aware Evaluation Framework — Data and Code

Brhanu F. Znabu, Qiuming Yao, Nicole R. Sexton

Data and code for a leakage-aware evaluation of machine-learning predictors of orthoflavivirus host range. Contains the full analysis pipeline, DNABERT-2 embeddings, window-level sequence data, results, and figure-generation scripts to reproduce every figure and result. verify_re…Read more

Code and data for "Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction"

Wenbin Wan, Kexin Liu, Shanlin Tong, Wu Lu, Liu Y, Xingwen Jiang, et al.

This record contains the supplementary code and data supporting the manuscript “Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction.” The archive contains raw and processed data tables, crystallographic structures, Materials…Read more

Narsi Regression and Narsi Intelligence: A Unified Theoretical Framework for Dynamic Representation Evolution, Recursive Cognitive Adaptation, and Self-Evolving Artificial Intelligence

A Chaudhary

Narsi Regression is a theoretical framework that extends conventional machine learning by treating representation evolution as an explicit optimisation problem rather than an implicit consequence of parameter optimisation. The framework models a learner as a dynamic state consist…Read more

# Artificial Intelligence in Metallurgical Engineering: A Comprehensive Review of Applications, Challenges, and Future Direction

Sudhakar Geruganti

## 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…Read more

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more

Nonlinear Industrial Robot Stabilization Based on Differential Topology

ÖZTÜRK SEDAT

This study presents a nonlinear stabilization framework for industrial robotic systems based on differential topology and geometric control theory. System dynamics are modeled on smooth manifolds, where stabilization is achieved through topological invariants and non-smooth feedb…Read more

Also available via: European Organization for Nuclear Research