Y₃Al₅O₁₂:Ce³⁺ (YAG:Ce³⁺) single crystal phosphor (SCP) exhibits high internal quantum efficiency (IQE) and excellent thermal conductivity and stability. Such properties are promising in high-power laser-excited white lighting applications. However, YAG:Ce³⁺ SCP usually shows low luminous flux and color rendering index (CRI, Ra) when excited by a transmissive laser. In this study, a self-designed three-integrating sphere system was established to characerize the optical performances of YAG:Ce³⁺ SCP palates under both the reflective and transmissive 455 nm blue laser excitations. Next, its luminous and color rendering properties under transmissive laser excitation were improved by surface roughening treatment and sintering of a CaAlSiN₃:Eu²⁺ (CASN:Eu²⁺) phosphor-in-borate glass (PiBG) layer. The results revealed that: (1) When the YAG:Ce³⁺ SCP was excited by blue laser, its forward luminous flux was considerably lower than the backward one; (2) Under transmissive excitation, its luminous flux rolled up with the maximum increase rate of 109.19% when the averaged roughness (R_a) increased from 0.35 to 5.40 μm; (3) After sintering the CASN:Eu²⁺ PiBG layer on the roughening YAG:Ce³⁺ SCP, its luminous flux, CRI increased by 31.48% and 117.14%, and its color fidelity index (CFI, R_f) reached up to 82 under the transmissive 3.03 W blue laser excitation.

Some atrial contractile assist devices applied on the heart surface can be regarded as a laminated Liquid crystal elastomer (LCE) plate under steady temperature loads and a contact mechanical force. An exact solution for the deformation of the laminated LCE plate under combined thermal and mechanical loads is derived by solving the three-dimensional (3D) equilibrium equations including heat conduction and thermoelastic theory. The validity of mathematical formula and computer programming is proved by convergence and comparison examples with finite element method (FEM). In order to simplify the complex calculation of exact solution, a back propagation neural network (BPNN) is further trained with a database containing 9504 sets of thermo-mechanical load conditions and their corresponding deformation which is solved by the exact solutions. Then the deformations of LCE plate subject to combined thermo-mechanical load can be predicted by this BP neural network instead of complex numerical calculation. Moreover, it is also applied to inverse the contact mechanical force at the bottom surface of LCE plate with a given deformation and temperature conditions. The results show that: (1) The results from the exact theoretical solution are in consistence with that from FEM but have a higher computational efficiency and stability; (2) The deformation of the laminated plate is more sensitive to the layered thickness of LCE than the variation of the temperature; (3) 3-D elasticity solutions of a laminated LCE plate under the combined thermos-mechanical load can be effectively predicted by a trained BP neural network.