Search

The turbine blades of aero-engines are subjected to cyclic high-low cycle loads in actual service environments，which is a typical high-low cycle micro motion fatigue condition. The frequent occurrence of fatigue fracture in turbine blades due to fretting fatigue seriously affects the safe operation of aero-engines. In order to study the high-low cycle fretting fatigue problems of hot end component materials，this paper designs an Inconel718 tenon connection simulation component and conducts high-low cycle composite fretting fatigue tests on it. Three different load/frequency experiment conditions were designed to compare the life distribution of the simulated parts under different conditions. The results show that high/low cycle load has significant influence on fretting fatigue life. With the increase of high/low cycle load or frequency，the fretting fatigue life of joints decreases. The test results have proven that the high-low cycle fretting fatigue test of the tenon joint structure effectively reproduces the actual service condition of turbine blades. By displaying the explicit finite element analysis，the predicted crack initiation location shows less dispersion compared to the experimental results.

Objective To evaluate the analgesic and sedative effect of single epidural application of esketamine in cesarean section during perioperative period, and analyze the clinical application value of esketamine in cesarean section epidural analgesia and sedation. Methods From October 2021 to March 2022, 100 patients undergoing lower segment cesarean section under combined spinal epidural anesthesia in Rugao Hospital Affiliated to Nantong University were selected and randomly divided into morphine group (n=50) and esketamine group (n=50). After delivery of newborns (Apgar score ≥ 8) and removal of the umbilical placenta for 5 min, individuals in esketamine group were administered 20 mg ketamine, 5 mg dexamethasone plus 9 mg ropivacaine via an epidural catheter diluted with 0.9% sodium chloride to 6 mL; while individuals in morphine group were administered 2 mg morphine, 5 mg dexamethasone plus 9 mg ropivacaine via an epidural catheter, diluted with same volume (6 mL) of sodium chloride. The VAS score and Ramsay score at different time points after epidural administration, postoperative supplemental analgesic use and adverse reactions of the two groups were recorded. Results There was no significant difference between the two groups in VAS scores at 5 min after epidural administration, at the end of operation, 24 h after operation, and 48 h after operation, as well as the time of first use of supplementary analgesic drugs after operation and the proportion of the number of people (P＞0.05). At 5 min after epidural administration, Ramsay scores in esketamine group were significantly higher than those in morphine group (P＜0.05), and that there was no statistically difference in Ramsay scores between the two groups after surgery (P＞0.05). The incidence of dysuria (22.00% vs 4.00%, χ2=7.162, P＜0.05) and pruritus (18.00% vs 0, χ2=7.814, P＜0.05) in morphine group were higher than those in esketamine group. Conclusion Esketamine and morphine both exert effective analgesic effects during the perioperative period of cesarean section, and more importantly, ketamine is superior to morphine in sedative effect during cesarean section and reducing postoperative adverse reactions.

The proliferation of artificial intelligence models has rendered them vulnerable to a myriad of security threats. The extensive integration of deep learning models into edge devices has introduced novel security challenges. Given the analogous structural characteristics of deep neural networks, adversaries can employ decompilation tactics to extract model structural details and parameters, facilitating the reconstruction of these models. Such actions can compromise the intellectual property rights of the model and increase the risk of white-box attacks. To mitigate the capability of model decompilers to locate and identify model operators, acquire parameters, and parse network topologies, an obfuscation framework was proposed. This framework was embedded within the model compilation process to safeguard against model extraction attacks. During the frontend optimization phase of deep learning compilers, three obfuscation techniques were developed and integrated: operator obfuscation, parameter obfuscation, and network topology obfuscation. The framework introduced opaque predicates, incorporated fake control flows, and embedded redundant memory access to thwart the reverse engineering efforts of model decompilers. The experimental findings demonstrate that the obfuscation framework, named DNNobfus, significantly diminishes the accuracy of state-of-the-art model decompilation tools in identifying model operator types and network connections to 21.63% and 48.24%, respectively. Additionally, DNNobfus achieves an average time efficiency of 67.93% and an average space efficiency of 88.37%, surpassing the performance of the obfuscation tool Obfuscator-LLVM in both respects.