freederia blog

# AI-Driven Anomaly Detection and Defect Characterization in Advanced Borosilicate Glass Fiber Production **Abstract:** This paper introduces a novel AI-driven framework, the Multi-modal Evaluation Pipeline (MEP), for real-time anomaly detection and fine-grained defect characterization in the continuous production of advanced borosilicate glass fibers. Leveraging fusion of high-resolution optica..

# Adaptive Affective HRI through Multi-Modal Bayesian Network Fusion for Elderly Assistance Robots **Abstract:** This paper proposes a novel Adaptive Affective Human-Robot Interaction (AA-HRI) framework for elderly assistance robots, leveraging Multi-Modal Bayesian Network Fusion (MMBNF). Existing HRI systems often struggle with accurately interpreting nuanced human affective states and adaptin..

# **** Kinetic Modulation of Thermostable Cellulase Variants via Dynamic Enzyme-Polymer Hydrogels for Enhanced Biofuel Production **Abstract:** This research details a novel approach to enhancing the activity and stability of cellulases involved in biofuel production from cellulose. We introduce a dynamic enzyme-polymer hydrogel system which modulates enzyme kinetics through controlled polymer c..

# Real-Time Vapor Phase Ammonia Synthesis Optimization Through Quantum-Enhanced Dual-Catalyst Reactor Modeling **Abstract:** This work proposes a novel approach to optimizing ammonia synthesis in vapor phase reactors by leveraging real-time data analysis and quantum-enhanced computational modeling. Combining advanced machine learning techniques with a custom-built dual-catalyst reactor design an..

# Deep Residual Convolutional Autoencoder for Early Pathogen Detection via Stomatal Efflux Profiling - A Commercializable Framework **Abstract:** This paper introduces a novel, fully commercializable framework for detecting plant pathogen invasion at the stomatal level based on real-time profiling of volatile organic compound (VOC) efflux. Leveraging established deep residual convolutional autoe..

# Automated Semantic Scene Understanding via High-Dimensional Temporal Graph Convolutional Networks (HST-GCN) **Abstract:** This paper proposes a novel automated semantic scene understanding framework, HST-GCN, leveraging high-dimensional temporal graph convolutional networks for robust and efficient interpretation of complex visual environments. Unlike traditional approaches relying on feature ..

# Automated Phenotype-Driven Drug Repurposing via Multi-modal Knowledge Graph Fusion and Bayesian Optimization **Abstract:** Personalized medicine-as-a-service increasingly necessitates rapid and cost-effective identification of repurposed therapeutic compounds. This paper introduces a novel framework, **Phenotype-Guided Drug Repurposing Engine (PDRE)**, which integrates multi-modal biomedical ..

# Automated Pharmacogenomic Variant Prioritization via Graph Neural Networks and Bayesian Inference for Personalized Drug Selection **Abstract:** This paper introduces an automated framework for prioritizing pharmacogenomic variants impacting drug response, leveraging Graph Neural Networks (GNNs) and Bayesian Inference to enhance personalized drug selection. Traditional approaches struggle with ..