RESUMEN
Aims: Short-wavelength blue light damaged retina by the oxidative stress in the retinal pigment epithelial (RPE) cells. Filtering blue light from screen could reduce blue hazard, whereas it inevitably altered color-gamut coverage and color-deviation level. Although abnormal fundus-vascular density (FVD) sometimes indicated fundus disease, few researchers noticed its responses to the variation of color-gamut coverage and color-deviation level. Methods: In this study, we performed cellular experiments and analyzed the RPE cell viabilities (CVs) in spectrums with different blue (455-475 nm) ratios to describe the corresponding oxidative-stress levels. Further, we investigated the effects of color-gamut and deviation on FVD variations during the screen-watching task using human factor experiments with 30 participants (university students, including 17 males and 13 females, 21 to 30 years old). Results: RPE CVs were similar in different spectrums, implying that non-oxidative blue filtering hardly contributed to CV improvement. Color-deviation level seems to induce more significant effects on the visual function compared to color-gamut coverage, and MTF and FVD presents similar variation trends during the visual task. Conclusion: Oxidative-free blue filtering contributed little to decrease retinal oxidative stress yet caused color-deviation increase, which caused significant FVD reduction.
Asunto(s)
Luz , Estrés Oxidativo , Masculino , Femenino , Humanos , Adulto Joven , Adulto , Oxidación-Reducción , Pigmentos RetinianosRESUMEN
This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that: (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.