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| 1 | A novel modified binary differential evolution algorithm and its applications显示文摘 | Ling Wang Xiping Fu Yunfei Mao Muhammad Ilyas Menhas Minrui Fei | 2012 | Neurocomputing2012，，： | 1 |
| 2 | The microRNA-218 and ROBO-1 signaling axis correlates with the lymphaticmetastasis of pancreatic cancer显示文摘 | Hang He Yang Di Minrui Liang Feng Yang Lie Yao Sijie Hao Ji Li Yongjian Jiang Chen Jin Deliang Fu | 2013 | Oncology Reports2013，，2： | 1 |
| 3 | The Hemodynamic Study on the Effects of Entry Tear and Coverage in Aortic Dissection显示文摘In this work,the hemodynamic effects of the type-A aortic dissection in different entry and covering entry tear positions were mainly studied.It provides a new method or idea in the field of the aortic dissection hemodynamics,and it is of profound significance to provide basic theoretical research on the development of aortic dissection in the aspect of clinical judgment.Two type-A aortic dissection models with different entry tear positions(Model 1:The entry tear was located at the entrance of the ascending aorta,Model 2:The entry tear was located at the starting position of the descending aorta)were reconstructed according to the computed tomography(CT)images of the patients.In our study,the thoracic aortic endovascular repair was simulated by covering the entry tear(Model 3).To clarify the hemodynamic effects of entry tear and coverage,the comparative study on the true lumen(TL)and false lumen(FL)blood flow patterns of three models were carried out numerically.The velocity vector,flow ratio,pressure,time-averaged wall shear stress(TAWSS)and relative residence time(RRT)were calculated to evaluate the hemodynamic changes.The results of this work indicated that(I)the velocity of entry tear at the aorta entrance was higher;(II)The helical development of the TL and FL might be related to the helical nature of aortic arch;(III)The blood flow which passing the FL of Model 1,Model 2 and Model 3 in one cardiac cycle were approximately 26.63%,13.39%and 1%,respectively;(IV)The difference in intima wall pressure of the TL and FL were showed a strong pulsation;(V)The TAWSS distribution in TL and FL were completely different(the TAWSS in TL intima>8 Pa,the TAWSS in FL intima<4 Pa).In brief,the aortic morphology and location of the entry tear were found to have a significant effect on the hemodynamics of the aortic dissection.In addition,the CFD method is used to obtain multi-dimensional hemodynamic information such as velocity field,pressure,TAWSS and RRT,which can help clinicians better understand the development of type-A aortic dissection and provide a theoretical basis for clinical treatment. | Zhenxia Mu Xiaofei Xue Minrui Fu Dawei Zhao Bin Gao Yu Chang | 2019 | Computer Modeling in Engineering & Sciences2019，，12： | 0 |
| 4 | An Intelligent Algorithm for Solving Weapon-Target Assignment Problem:DDPG-DNPE Algorithm显示文摘Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions. | Tengda Li Gang Wang Qiang Fu Xiangke Guo Minrui Zhao Xiangyu Liu | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 5 | The Hemodynamic Study on the Effects of Entry Tear and Coverage in Aortic Dissection显示文摘Nowadays,cardiovascular disease has gradually become the number one killer of human health.The Stanford type-A aortic dissection is a relatively common cardiovascular disease with potential hazards.The disease is caused by a partialtearing of the aortic intima.Thoracic endovascular aortic repair(TEVAR)is one of the most effective treatment for type A aortic dissection and has been widely used clinically to achieve thrombosis and reduce pressure by covering the incision tear in the FL.However,the effect of entry tear location on type-A aortic dissection and the prognosis of TEVAR intervention are unclear.In this work,the hemodynamic effects of the type-A aortic dissection in different entry and covering entry tear position were mainly studied.It can provide a new method or idea in the field of the aortic dissection hemodynamics,which is of great significance to provide a basic theoretical research on the development of aortic dissection in the aspect of clinical judgment.Two type-A aortic dissection models with different entry tear positions(Model 1:the entry tear was located at the entrance of the ascending aorta,Model 2:the entry tear was located at the starting position of the descending aorta)were reconstructed according to the computed tomography(CT)images of the patients.In our study,the thoracic aortic endovascular repair was simulated by covering the entry tear(Model 3).Then,the semi-automatic adaptive technology HyperMeshl0.0(Altair HyperWorks,Troy,Ml,USA)mesh generator was used to generate the high-quality tetrahedral 3D mesh.To clarify the hemodynamic effects of entry tear and coverage in aortic blood flow pattern,the comparative study on the true lumen(TL)and false lumen(FL)blood flow patterns of three models were carried out numerically(the time-dependent pulsatile waveform of pressure boundary conditions used at the aortic inlet were consistent with Rapezzis’s work,time-dependent pulsatile waveform of velocity at the descending aorta outlet and the time-dependent pulsatile waveform of pressure at the brachiocephalic artery,left common carotid artery,and left subclavian artery were obtained from the work of Olufsenet et al).The velocity vector,flow ratio,pressure,time-average wall shear stress(TAWSS)and relative residence time(RRT)were calculated to evaluate the hemodynamic changes.The results of this work indicated that(Ⅰ)the velocity was higher at the entry tear in aorta entrance;(Ⅱ)the helical development of the TL and FL might be related to the helical nature of aortic arch;(Ⅲ)the blood flow which passing the FL of Model 1,Model 2 and Model 3 in one cardiac cycle were approximately 26.63%,13.39%and 1%,respectively;(Ⅳ)the difference in intima wall pressure among the TL and FL was found varied a lot and shown a strong pulsation;(Ⅴ)the TAWSS distribution in TL and FL were quite different(the TAWSS in TL intima>8 Pa,the TAWSS in FL intima<4 Pa).In brief,the aortic morphology and location of the entry tear were found to have a significant effect on the hemodynamic of the aortic dissection.What’s more,we found that the risk of the aortic rupture could be higher if the position of entry tear is closer to the ascending aorta.By covering the entry tear to simulate TEVAR,the pressure of FL was reduced,which could provide some help for the treatment of early retrograde type-A dissection. | Zhenxia Mu Xiaofei Xue Minrui Fu Dawei Zhao Bin Gao Yu Chang | 2019 | 医用生物力学2019，34，A01： | 0 |
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