arXiv:2511.16923v1 Announce Type: cross
Abstract: Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated datasets, SCR-MF achieves robust and interpretable performance comparable to or exceeding existing imputation methods in most cases, while preserving biological fidelity and transparency. Runtime analysis demonstrates that SCR-MF provides a competitive balance between accuracy and computational efficiency, making it suitable for mid-scale single-cell datasets.
Development of a high-performance in-memory database architecture for intelligent video surveillance in critical patient care
ObjectivesThis research aims to engineer a specialized, high-speed database architecture tailored for intelligent video surveillance in critical healthcare environments. The primary objective is to overcome