ABSTRACT This paper presents a prototype XR system for disaster medical response, demonstrating the feasibility of real‐time interactive elastodynamics simulations in emergency scenarios. The system delivers an end‐to‐end workflow utilizing XR technology, from on‐site data acquisition to remote simulation. Specifically, we propose an image‐guided mesh‐processing pipeline that converts photographs of injured individuals into solver‐ready tetrahedral meshes. We also develop a constraint‐based elastodynamics solver capable of simulating deformable bodies and visualizing internal stresses. Additionally, the system integrates multiple advanced XR devices and addresses the coordinate‐alignment problem between these devices and the simulator. We validate the system's performance in both AR/VR modes, under textured and stress‐visualization configurations, and demonstrate its applicability for remote medical guidance. Beyond whole‐body elastic simulations, we conduct preliminary organ‐level experiments to inform future remote surgical applications. This prototype, validated using a two‐room setup, provides a feasible solution for remote emergency medical response.

Position Based Dynamics (PBD) has been widely adopted for interactive simulation, particularly in applications such as virtual surgery and elastodynamics. However, many existing frameworks focus exclusively on interactive deformation, often neglecting the comprehensive analysis of stress distribution, which is a critical factor in engineering assessments. In remote disaster rescue scenarios, real-time stress visualization can provide vital insights that enable rescue teams to make informed decisions when interacting with deformable objects. In this work, we introduce a fully GPU-parallel, stress-driven simulation framework for real-time deformable human body dynamics, specifically designed for disaster rescue applications. Our approach computes the von Mises stress for each tetrahedral element using a practical Neo-Hookean material model, projects the stress onto the corresponding mesh vertices, and maps these values onto the surface for intuitive rendering. In particular, we address the convergence limitations of general Neo-Hookean constraints under a Jacobi parallel scheme by developing a robust, improved approach. This improved approach avoids the typical volume loss observed in conventional methods and better replicates the qualitative behavior of the Gauss-Seidel scheme. Quantitative experiments using 100 human body models with diverse shapes, heights, and weights demonstrate that our framework effectively maintains the original pose while delivering enhanced physical realism and informative stress visualization. This capability provides disaster rescue teams with critical insights to optimize decision-making during emergencies.

As an efficient multiplication method for polynomial rings, Number Theoretic Transform (NTT) is a fundamental algorithm that is both practically useful and theoretically established. Chung et al. proposed a method to perform NTT-based polynomial multiplication for NTT-unfriendly rings that do not have suitable primitive roots. They applied their proposal to lattice-based cryptography using NTT-unfriendly rings and speeded up several schemes. At ARITH 2021, Plantard proposed a modular multiplication algorithm that improves the speed of NTT if moduli are not large (a few dozen of bits), which is the case for typical lattice-based cryptography. It is natural to expect that Plantard's method improves Chung et al.‘s NTT when applied to them, however, this is not possible as Chung et al. requires the use of signed integers while Plantard's method assumes unsigned integers. A simple fix would cause a slowdown and a non-constant-time operation. To overcome this problem, we propose an efficient method for calculating the modular multiplication for signed integers based on Plantard's method. Our proposal generally incurs no overhead from the original and works in a constant-time fashion. To show the effectiveness of our proposal, we provide experimental implementation results on a lattice-based cryptographic scheme Saber. Currently, NIST is selecting candidates for standardization of post-quantum cryp-tography in preparation for the compromise of current public key cryptography by quantum computers, and has completed the selection of the final candidates. Saber is one of the finalists for the NIST standardization project,