The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
BACKGROUND As cardiovascular mortality continues to increase globally,percutaneous coronary intervention(PCI)with stent placement stands out as a cutting-edge and highly effective treatment for severe cardiovascular d...BACKGROUND As cardiovascular mortality continues to increase globally,percutaneous coronary intervention(PCI)with stent placement stands out as a cutting-edge and highly effective treatment for severe cardiovascular diseases.However,the inherent invasiveness of any endovascular procedure introduces the risk of coronary vessel and myocardial damage.AIM To evaluate the utility of a novel electrocardiographic metric in detecting subtle myocardial injuries after coronary stenting.METHODS This investigation was conducted in 2021 at the Kyiv Heart Institute of the Ministry of Healthcare of Ukraine.The study involved 23 patients who underwent PCI,each subjected to a meticulous preoperative examination.A paired measurement approach was employed,encompassing 3-minutes electrocardiogram(ECG)recordings both before and several hours following the operation,using a compact ECG device.Each pair of ECG underwent a thorough analysis,scrutinizing 240 primary and computed ECG parameters.RESULTS The analysis delineated a distinct subgroup exhibiting significant myocardial damage post-stenting.This subgroup was characterized by an older average age and more stents than their counterparts.Notably,a concurrent reduction in the psychoemotional state index was observed alongside the ECG alterations in these patients,suggesting a correlation between myocardial damage and psychoemotional distress.Introducing a new electrocardiographic index has illuminated the often-subtle myocardial damage incurred during PCI.CONCLUSION The newly devised electrocardiographic metric is a significant advancement in the early detection of myocardial damage following PCI,able to capture not only physiological but also psychoemotional changes.展开更多
In observational science,data is the foundation of a scientific model;satellite-derived parameters serve as data for earth sciences models.The building of science is imprecise if data is ambiguous.Remote sensing‘big ...In observational science,data is the foundation of a scientific model;satellite-derived parameters serve as data for earth sciences models.The building of science is imprecise if data is ambiguous.Remote sensing‘big data’provides a wealth of information for unlocking the mysteries of earth sciences.The parameter estimation from remote sensing measurements is extremely ill-posed and the inverse method plays a significant role in extracting parameter information.In this paper,predominant stochastic inverse methods in satellite retrieval applications are critically investigated from different schools of thought and several basic flaws are revealed,e.g.error being treated as definite information.The major drawbacks of these methods include a high reliance on a priori information and binding the satellite retrievals to in situ measurements.A fundamentally different and transformative approach is explored as an alternative.A rational,reliable,and repeatable determination of geophysical parameter values from remote sensing measurements is possible using the total least squares based deterministic inverse method.It is a physical model-based data-driven optimization,where the error quantity is extracted from the problem itself for regularization on a case-by-case basis using singular vector decomposition of the augmented function of the Jacobian and the residual.By moving from the prevalent to the proposed inverse method,a paradigm shift in results from“information loss”to‘information gain’is achieved.展开更多
We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of pa...We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved,it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives:(1) maximizing the Fisher information, improving the parameter estimation precision, and(2)minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ε-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.展开更多
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金Supported by The National Research Foundation of Ukraine,No.2023.04/0094。
文摘BACKGROUND As cardiovascular mortality continues to increase globally,percutaneous coronary intervention(PCI)with stent placement stands out as a cutting-edge and highly effective treatment for severe cardiovascular diseases.However,the inherent invasiveness of any endovascular procedure introduces the risk of coronary vessel and myocardial damage.AIM To evaluate the utility of a novel electrocardiographic metric in detecting subtle myocardial injuries after coronary stenting.METHODS This investigation was conducted in 2021 at the Kyiv Heart Institute of the Ministry of Healthcare of Ukraine.The study involved 23 patients who underwent PCI,each subjected to a meticulous preoperative examination.A paired measurement approach was employed,encompassing 3-minutes electrocardiogram(ECG)recordings both before and several hours following the operation,using a compact ECG device.Each pair of ECG underwent a thorough analysis,scrutinizing 240 primary and computed ECG parameters.RESULTS The analysis delineated a distinct subgroup exhibiting significant myocardial damage post-stenting.This subgroup was characterized by an older average age and more stents than their counterparts.Notably,a concurrent reduction in the psychoemotional state index was observed alongside the ECG alterations in these patients,suggesting a correlation between myocardial damage and psychoemotional distress.Introducing a new electrocardiographic index has illuminated the often-subtle myocardial damage incurred during PCI.CONCLUSION The newly devised electrocardiographic metric is a significant advancement in the early detection of myocardial damage following PCI,able to capture not only physiological but also psychoemotional changes.
基金This work was supported by the NASA ROSES[80NSSC18K0705].
文摘In observational science,data is the foundation of a scientific model;satellite-derived parameters serve as data for earth sciences models.The building of science is imprecise if data is ambiguous.Remote sensing‘big data’provides a wealth of information for unlocking the mysteries of earth sciences.The parameter estimation from remote sensing measurements is extremely ill-posed and the inverse method plays a significant role in extracting parameter information.In this paper,predominant stochastic inverse methods in satellite retrieval applications are critically investigated from different schools of thought and several basic flaws are revealed,e.g.error being treated as definite information.The major drawbacks of these methods include a high reliance on a priori information and binding the satellite retrievals to in situ measurements.A fundamentally different and transformative approach is explored as an alternative.A rational,reliable,and repeatable determination of geophysical parameter values from remote sensing measurements is possible using the total least squares based deterministic inverse method.It is a physical model-based data-driven optimization,where the error quantity is extracted from the problem itself for regularization on a case-by-case basis using singular vector decomposition of the augmented function of the Jacobian and the residual.By moving from the prevalent to the proposed inverse method,a paradigm shift in results from“information loss”to‘information gain’is achieved.
基金supported by the National Natural Science Foundation of China(Grant No.11404113)the Guangzhou Key Laboratory of Brain Computer Interaction and Applications(Grant No.201509010006)
文摘We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved,it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives:(1) maximizing the Fisher information, improving the parameter estimation precision, and(2)minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ε-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.
