CORTEXA
← Browse
openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24Cited by 0

Disaster e-Health Digital Twins: A Serverless IoT Architecture for Real-Time Healthcare Logistics in Crisis Environments Under Publication date, leave as the current date.

YINKA ADERIBIGBE

The deployment of Healthcare Digital Twins presents a transformative approach to hospital operations and medical logistics. However, in the context of Disaster e-Health, the cyber-physical infrastructure linking the physical healthcare environment to its digital replica is highly vulnerable to network fragmentation and catastrophic latency. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a highly resilient, serverless pipeline for Healthcare Digital Twins in disaster environments. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms, the proposed system ingests high-frequency Internet of Things telemetry from distributed medical supply nodes and acute care facilities. During simulated disaster shocks, the architecture dynamically reroutes medical assets and predicts system-wide supply chain bottlenecks. Preliminary architectural evaluations demonstrate that utilizing event-driven cloud computing significantly reduces the computational overhead of maintaining a live digital twin, providing health informatics researchers with a deterministic, highly scalable tool for ensuring continuous emergency healthcare operation.

View free PDFSource page

Related papers

openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Cloud-Native Accounting Measurement: A Serverless Machine Learning Architecture for Integrating Climate Risk into Real-Time Equity Valuation

YINKA ADERIBIGBE

The measurement of climate risk and its influence on accounting-based equity valuation has become a critical mandate in empirical financial research. Traditional methodologies utilize log-linear valuation models and historical panel data to observe how investors adjust their rela…

View free PDFSource page
openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

A Theoretically Grounded Spiking Neural Network Architecture for Real-Time Intracortical Signal Processing: Epistemological Foundations, Mathematical Guarantees, Computationally Verified Results, and Falsifiable Predictions for Closed-Loop Brain–Computer Interfaces

Sami Shibah

Epistemological position. This work adopts a critical-rationalist stance (Popper, 1959): every theoretical claim is stated as a conjecture subject to empirical falsification, with quantitative rejection thresholds fixed a priori. Every mathematical guarantee is derived from expli…

View free PDFSource page
openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Immersive Digital Twins of Viable Systems

Serhii Hostiunin

This paper introduces the concept of Immersive Digital Twins of Viable Systems as a new stage in the development of intelligent scientific infrastructures within the framework of Vitology. The proposed approach integrates digital twins, immersive technologies, artificial intellig…

View free PDFSource page
openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Architectural Benchmarking of Asynchronous State Synchronization in WebGL Spatial Graphs Driven by Real-Time Generative AI Pipelines

Abdullah

This preprint presents an empirical software engineering study on resolving main-thread performance bottlenecks in browser-based spatial computing. Abstract Real-time Retrieval-Augmented Generation (RAG) pipelines increasingly stream high-dimensional vector embeddings into browse…

View free PDFSource page
openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

VisionGuard: Explainable Deep Learning Framework for Real-Time Anomaly Detection in Surveillance Video

Jahnavi Somaraju, L. Mounika, M. Mounika, K. Mounika, BS. Karishma

Surveillance anomaly detection systems built around a single monolithic deep network are difficult to interpret, brittle to distribution shift, and offer operators no rationale on which to act. This paper presents VisionGuard, an explainable deep learning framework that reorganiz…

View free PDFSource page
openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Algorithmic Catastrophe Pricing: A Serverless Spatiotemporal Machine Learning Architecture for Evaluating Climate Risk and Disaster Insurance Retreat

YINKA ADERIBIGBE

The increasing frequency of severe climate anomalies and natural disasters has destabilized global insurance markets, precipitating a widespread retreat of private disaster insurance. Financial economists modeling the economics of natural hazard risks are frequently constrained b…

View free PDFSource page