Background Virtual-reality(VR)fusion techniques have become increasingly popular in recent years,and several previous studies have applied them to laboratory education.However,without a basis for evaluating the effects of virtual-real fusion on VR in education,many developers have chosen to abandon this expensive and complex set of techniques.Methods In this study,we experimentally investigate the effects of virtual-real fusion on immersion,presence,and learning performance.Each participant was randomly assigned to one of three conditions:a PC environment(PCE)operated by mouse;a VR environment(VRE)operated by controllers;or a VR environment running virtual-real fusion(VR VRFE),operated by real hands.Results The analysis of variance(ANOVA)and t-test results for presence and self-efficacy show significant differences between the PCE*VR-VRFE condition pair.Furthermore,the results show significant differences in the intrinsic value of learning performance for pairs PCE*VR VRFE and VRE*VR-VRFE,and a marginally significant difference was found for the immersion group.Conclusions The results suggest that virtual-real fusion can offer improved immersion,presence,and self efficacy compared to traditional PC environments,as well as a better intrinsic value of learning performance compared to both PC and VR environments.The results also suggest that virtual-real fusion offers a lower sense of presence compared to traditional VR environments.
The field of fluid simulation is developing rapidly,and data-driven methods provide many frameworks and techniques for fluid simulation.This paper presents a survey of data-driven methods used in fluid simulation in computer graphics in recent years.First,we provide a brief introduction of physical based fluid simulation methods based on their spatial discretization,including Lagrangian,Eulerian,and hybrid methods.The characteristics of these underlying structures and their inherent connection with data driven methodologies are then analyzed.Subsequently,we review studies pertaining to a wide range of applications,including data-driven solvers,detail enhancement,animation synthesis,fluid control,and differentiable simulation.Finally,we discuss some related issues and potential directions in data-driven fluid simulation.We conclude that the fluid simulation combined with data-driven methods has some advantages,such as higher simulation efficiency,rich details and different pattern styles,compared with traditional methods under the same parameters.It can be seen that the data-driven fluid simulation is feasible and has broad prospects.
