Hot tearing behaviors and in-situ thermal analysis of Mg-7Zn-xCu-0.6Zr alloys

Thermal analysis was used to investigate the microstructural evolution of Mg-7 Zn-x Cu-0.6 Zr alloys during solidification. The effect of Cu content（0, 1, 2 and 3, mass fraction, %） on the hot tearing behavior of the Mg-7 Zn-x Cu-0.6 Zr alloys was investigated with a constrained rod casting（CRC） apparatus, equipped with a load sensor and a data acquisition system. The thermal analysis results of Mg-7 Zn-x Cu-0.6 Zr alloy revealed that the alloy consisted of two distinct phases： α-Mg and Mg Zn2. Three distinct peaks were observed in the alloys with Cu addition, which were identified as α-Mg, Mg Zn Cu and Mg Zn2. In addition, the reaction temperature of α-Mg decreased and the reaction temperatures of Mg Zn2 and Mg Zn Cu increased as the Cu content increased. The experimental results of hot tearing demonstrated that the addition of Cu significantly reduced the hot tearing susceptibility（HTS） of Mg-7 Zn-x Cu-0.6 Zr alloys due to the higher eutectic temperature and the shorter solidification temperature region.

Hydraulic turbine system identification and predictive control based on GASA-BPNN

Based on the characteristics of nonlinearity,multi-case,and multi-disturbance,it is difficult to establish an accurate parameter mod-el on the hydraulic turbine system which is limited by the degree of fitting between parametric model and actual model,and the design of con-trol algorithm has a certain degree of limitation.Aiming at the modeling and control problems of hydraulic turbine system,this paper proposes hydraulic turbine system identification and predictive control based on genetic algorithm-simulate anneal and back propagation neural network(GASA-BPNN),and the output value predicted by GASA-BPNN model is fed back to the nonlinear optimizer to output the control quantity.The results show that the output speed of the traditional control system increases greatly and the speed of regulation is slow,while the speed of GASA-BPNN predictive control system increases little and the regulation speed is obviously faster than that of the traditional control system.Compared with the output response of the traditional control of the hydraulic turbine governing system,the neural network predictive control-ler used in this paper has better effect and stronger robustness,solves the problem of poor generalization ability and identification accuracy of the turbine system under variable conditions,and achieves better control effect.

Development and evaluation of a new curriculum based on the Delphi method for master of nursing progranls in China

Objective： To develop a new curriculum for a master＇s degree in nursing and provide a reference for nursing education in China. Methods： After a comprehensive literature review and item consolidation, an expert panel developed the initial version of the master＇s degree program objective and curriculum. A three-round Delphi study was subsequently conducted to revise and validate the curriculum. Results： Twenty-six experts participated in the evaluation and validation process. The authority coef- ficient was 0.88, and the Kendall coordination coefficient was statistically significant （X2 test, P 〈 0.01）. The final training objective contained 9 items, covering knowledge, skills and humanistic quality. The final established curriculum contained 22 courses, including 5 public compulsory courses, 5 specialized basic compulsory courses and 12 elective courses for professional development. Conclusions： The present study provided an operational curriculum for a master＇s degree in nursing in China.

Stark splitting of intense red emission for Er^(3+)in O_(h) symmetry sites realizing optical temperature sensing in biological applications

Optical temperature sensing based on the fluorescence intensity ratio(FIR)of red emission for lanthanide ions holds significant relevance in non-contact temperature measurement for biological application.In this study,the perovskite-structured KZnF_(3)is utilized as a host material for Er^(3+)to achieve a high-purity upconversion(UC)red emission.The observed Stark splitting of the red emission peak provides evidence of the energy level splitting of Er^(3+).Group theory is employed to decompose the spectral branching of Er^(3+)under the point group symmetry of KZnF_(3),allowing for the derivation of Stark splitting energy levels induced by the crystal field effect.The optical temperature-sensing behavior of the red UC luminescence was investigated,specifically examining the FIR of the splitting sub-peaks,which exhibited an exponential relationship with temperature.The KZnF_(3):Yb^(3+),Er^(3+)demonstrated a relative sensitivity(S_(r))of 0.00182%·K^(-1)at 298 K,highlighting its excellent response to temperature.Ex vivo bio-thermometry experiments conducted on chicken breast validated the material's ability to penetrate biological tissues and showed its significant sensitivity of the FIR to temperature.These results establish KZnF_(3):Yb^(3+),Er^(3+)as a promising material for optical thermometry in various biological applications.