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Primary hepatoid adenocarcinoma of the lung in Yungui Plateau,China:A case report 被引量:3
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作者 Yun-Fei Shi Jia-Gui Lu +4 位作者 Qing-Mei Yang Jin Duan you-ming lei Wei Zhao Yin-Qiang Liu 《World Journal of Clinical Cases》 SCIE 2019年第13期1711-1716,共6页
BACKGROUND Hepatoid adenocarcinoma(HAC)occurs in extrahepatic organs such as the gastrointestinal tract,testes,ovaries,lungs,mediastinum and pancreas,and frequently produces a-fetoprotein(AFP).HAC of the lung(HAL)is r... BACKGROUND Hepatoid adenocarcinoma(HAC)occurs in extrahepatic organs such as the gastrointestinal tract,testes,ovaries,lungs,mediastinum and pancreas,and frequently produces a-fetoprotein(AFP).HAC of the lung(HAL)is rare,characterized by difficult treatment and poor prognosis.There are no reports of HAL in Yunnan-Guizhou Plateau,China.CASE S UMMARY A 60-year-old male patient was clinically diagnosed with HAL pT3 NOM0,stageⅡB.Chest computed tomography revealed a 7.5 cm x 7.2 cm soft tissue mass located in the right lung upper lobe and the adjacent superior mediastinum.Right upper lobectomy was performed.The diagnosis of HAL was confirmed by pathological examination,and the patient received paclitaxel and carboplatin as adjuvant chemotherapy after surgery.CONCL USION Clinical manifestations,pathological features,imaging findings,auxiliary examination,and treatment planning of HAL are presented to help clinicians improve their diagnosis and treatment. 展开更多
关键词 HEPATIC ADENOCARCINOMA LUNG cancer IMMUNOHISTOCHEMISTRY α-Fetoprotein Case report
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Control of chaos in Frenkel-Kontorova model using reinforcement learning
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作者 you-ming lei Yan-Yan Han 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第5期247-254,共8页
It is shown that we can control spatiotemporal chaos in the Frenkel-Kontorova(FK)model by a model-free control method based on reinforcement learning.The method uses Q-learning to find optimal control strategies based... It is shown that we can control spatiotemporal chaos in the Frenkel-Kontorova(FK)model by a model-free control method based on reinforcement learning.The method uses Q-learning to find optimal control strategies based on the reward feedback from the environment that maximizes its performance.The optimal control strategies are recorded in a Q-table and then employed to implement controllers.The advantage of the method is that it does not require an explicit knowledge of the system,target states,and unstable periodic orbits.All that we need is the parameters that we are trying to control and an unknown simulation model that represents the interactive environment.To control the FK model,we employ the perturbation policy on two different kinds of parameters,i.e.,the pendulum lengths and the phase angles.We show that both of the two perturbation techniques,i.e.,changing the lengths and changing their phase angles,can suppress chaos in the system and make it create the periodic patterns.The form of patterns depends on the initial values of the angular displacements and velocities.In particular,we show that the pinning control strategy,which only changes a small number of lengths or phase angles,can be put into effect. 展开更多
关键词 chaos control Frenkel-Kontorova model reinforcement learning
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