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Objective: This study aimed to enhance the understanding of the role of estrogen in lymphangioleiomyomatosis(LAM) and to conclude the impact of estrogen-altering events on the condition and recent advances in estrogen-based treatments for LAM. Results: LAM development is strongly linked to mutations in the tuberous sclerosis gene (TSC1/2) and the presence of estrogen. Estrogen plays a significant role in the spread of TSC2-deficient uterine leiomyoma cells to the lungs and the production of pulmonary LAM. Menstruation, pregnancy, estrogen medication, and other events that cause an increase in estrogen levels can trigger the disorder, leading to a sudden worsening of symptoms. Current findings do not support using estrogen-blocking therapy regimens. However, Faslodex, which is an estrogen receptor antagonist, presents new possibilities for future therapeutic approaches in LAM. Conclusion: Estrogen is crucial in the development and spread of LAM. The use of estrogen inhibitors or estrogen receptor antagonists alone does not provide good control of the disease or even poses a greater risk, and the use of a combination of mTOR receptor inhibitors, complete estrogen receptor antagonists, estrogen inhibitors, and autophagy inhibitors targeting important signaling pathways in LAM pathogenesis may be of greater benefit to the patient. [ABSTRACT FROM AUTHOR]

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy. [ABSTRACT FROM AUTHOR]

Analysis of thermodynamic parameters' effects on the S-CO2 recompression Brayton cycle for recovering the main engine exhaust gas waste heat of an ocean-going 9000 TEU container ship are carried out first in this paper. NSGA-II algorithm-based multi-objective optimizations are conducted to maximise net power output and exergetic efficiency as well as to minimise the levelized cost of energy (LCOE). The final optimal result from Pareto front solutions is decided by decision-making processes. The results show that the LCOE increases along with the enhancement of net power output and exergetic efficiency, and the maximal objectives always appear near the upper boundary of exhaust gas temperature. The final optimal result decided by TOPSIS decision-making has higher rationality and accuracy than that obtained by the LINMAP method. Regarding the final optimal results of triple-objective and dual-objective optimizations obtained by the TOPSIS method, the former achieved more reasonable results, since its optimal net power output and exergetic efficiency are 1.29% and 5.11% higher than the latter, while its LCOE increased by 2. 28% as a result of the increase of the net power output. The recompression Brayton cycle is better for recovering the exhaust gas waste heat in ships compared with the simple recuperative cycle without recompression. [ABSTRACT FROM AUTHOR]