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An unsteady computational study was carried out on GE-E3 high pressure (HP) turbine at inflow turbulence intensities of 5%, 10%, and 20% accompanying with inlet hot streak (HS) at two circumferential positions (impinging and nonimpinging relative to vane leading edge) to analyze the interacted turbulence and HS influences. Turbulence decay mechanisms in turbine passage were presented, and the airfoil heat transfer behaviors were explored by means of adiabatic wall temperature, heat transfer coefficient (HTC), and wall heat flux. The results indicate that the elevated turbulence leads to favorable turbine airfoil temperature distributions, and turbulence induced HS attenuation mainly occurs in vane passage. In addition, the HS dispersion is related directly to the temperature gradients. Although the endwall temperature increases by more than 20 K (2.8% inlet mass-averaged temperature) and midregion temperature decreases by 16 K at blade leading edge, the hot region on blade pressure surface (PS) is only weakened by about 7 K, when turbulence intensity is increased from 5% to 20%. Higher turbulence significantly affects the airfoil HTC, excepting the regions secondary and leakage flow effects are dominating. Therefore, the tip and blade suction surface (SS) trailing edge heat flux is decreased for the temperature decline at higher turbulence, which is beneficial to tip cooling. HS position not only affects the airfoil surface temperature variations but also slightly affects the vane and blade midspan HTC for the variation of heat transfer driving temperature.

Objectives. Gallstones can cause malnutrition in patients and further lead to cognitive impairment. This study is aimed at constructing a validated clinical prediction model for evaluating the risk of developing cognitive impairment from gallstones. Methods. The study was a single-centre crosssectional study. Four models or methods (SVM-RFE, random forest model, Lasso model, and logistics analysis) were analyzed and compared regarding their predictive performance. The model with the best classification performance and predictive power was selected. The AUC index, C-index, and calibration curves were applied to the chosen model to further evaluate its classification and prediction performance. Finally, the nomogram was plotted, and the clinical usability, efficacy, and safety of the nomogram were assessed using decision curve analysis (DCA). Results. This study included a total of 294 patients with gallstones, of which 110 had cognitive impairment. Factors such as gender, age, education, place of birth, history of alcohol consumption, abdominal circumference, sarcopenia, diabetes, anaemia, depression, and Pittsburgh Sleep Quality Index (PSQI) were incorporated into the model for nomogram construction. The calibration curve showed that the nomogram had good classification performance. Furthermore, the C-index of the model was 0.778 (95% CI, 0.674-0.882) in the test group. The DCA curves indicated that the constructed model had strong clinical applicability, efficacy, and safety. Conclusions. This study constructed a cognitive impairment risk prediction model for patients with gallstones with good classification and predictive power. The constructed predictive model allows us to screen patients with gallstones and at high risk of cognitive impairment. These efforts might also help to further increase patient compliance, assist healthcare professionals to better manage patients with gallstones, and ultimately improve their overall health status and quality of life. Future clinical studies should further evaluate the accuracy and clinical usability of this model.