It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly eval...It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.展开更多
In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded ...In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].展开更多
Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received in...Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.展开更多
For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing m...For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.展开更多
BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidat...BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).展开更多
Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small...Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.展开更多
ABSTRACT Satellite encounters during close operations,such as rendezvous,formation,and cluster flights,are typical long-term encounters.The collision probability in such an encounter is a primary safety concern.In thi...ABSTRACT Satellite encounters during close operations,such as rendezvous,formation,and cluster flights,are typical long-term encounters.The collision probability in such an encounter is a primary safety concern.In this study,a parametric method is proposed to compute the long-term collision probability for close satellite operations with initial state uncertainty.Random relative state errors resulting from system uncertainty lead to possible deviated trajectories with respect to the nominal one.To describe such a random event meaningfully,each deviated trajectory sample should be mapped to a unique and time-independent element in a random variable(RV)space.In this study,the RV space was identified as the transformed state space at a fixed initial time.The physical dimensions of both satellites were characterized by a combined hard-body sphere.Transforming the combined hard-body sphere into the RV space yielded a derived ellipsoid,which evolved over time and swept out a derived collision volume.The derived collision volume was solved using the reachable domain method.Finally,the collision probability was computed by integrating a probability density function over the derived collision volume.The results of the proposed method were compared with those of a nonparametric computation-intensive Monte Carlo method.The relative difference between the two results was found to be<0.6%,verifying the accuracy of the proposed method.展开更多
Conventional reachable domain(RD)problem with an admissible velocity increment,Δv,in an isotropic distribution,was extended to the general case withΔv in an anisotropic ellipsoidal distribution.Such an extension ena...Conventional reachable domain(RD)problem with an admissible velocity increment,Δv,in an isotropic distribution,was extended to the general case withΔv in an anisotropic ellipsoidal distribution.Such an extension enables RD to describe the effect of initial velocity uncertainty because a Gaussian form of velocity uncertainty can be regarded as possible velocity deviations that are confined within an error ellipsoid.To specify RD in space,the boundary surface of RD,also known as the envelope,should be determined.In this study,the envelope is divided into two parts:inner and outer envelopes.Thus,the problem of solving the RD envelope is formulated into an optimization problem.The inner and outer reachable boundaries that are closest to and farthest away from the center of the Earth,respectively,were found in each direction.An optimal control policy is then formulated by using the necessary condition for an optimum;that is,the first-order derivative of the performance function with respect to the control variable becomes zero.Mathematical properties regarding the optimal control policy is discussed.Finally,an algorithm to solve the RD envelope is proposed.In general,the proposed algorithm does not require any iteration,and therefore benefits from quick computation.Numerical examples,including two coplanar cases and two 3D cases,are provided,which demonstrate that the proposed algorithm works efficiently.展开更多
The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching proper...The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching property of antiferroelectric domains.Our results indicate that the ferroelectric domains nucleate preferably at the boundaries of the antiferroelectric domains,and antiferroelectrics with larger initial domain sizes possess a higher coercive electric field as demonstrated by hysteresis loops.Moreover,we introduce charge defects into the sample and numerically investigate their influence.It is also shown that charge defects can induce local ferroelectric domains,which could suppress the saturation polarization and narrow the enclosed area of the hysteresis loop.Our results give insights into understanding the antiferroelectric phase transformation and optimizing the energy storage property in experiments.展开更多
The cell membrane structure is closely related to the occurrence and progression of many metabolic bone diseases observed in the clinic and is an important target to the development of therapeutic strategies for these...The cell membrane structure is closely related to the occurrence and progression of many metabolic bone diseases observed in the clinic and is an important target to the development of therapeutic strategies for these diseases.Strong experimental evidence supports the existence of membrane microdomains in osteoclasts(OCs).However,the potential membrane microdomains and the crucial mechanisms underlying their roles in OCs have not been fully characterized.Membrane microdomain components,such as scaffolding proteins and the actin cytoskeleton,as well as the roles of individual membrane proteins,need to be elucidated.In this review,we discuss the compositions and critical functions of membrane microdomains that determine the biological behavior of OCs through the three main stages of the OC life cycle.展开更多
Ferroelectric domain walls appear as sub-nanometer-thick topological interfaces separating two adjacent domains in different orientations,and can be repetitively created,erased,and moved during programming into differ...Ferroelectric domain walls appear as sub-nanometer-thick topological interfaces separating two adjacent domains in different orientations,and can be repetitively created,erased,and moved during programming into different logic states for the nonvolatile memory under an applied electric field,providing a new paradigm for highly miniaturized low-energy electronic devices.Under some specific conditions,the charged domain walls are conducting,differing from their insulating bulk domains.In the past decade,the emergence of atomic-layer scaling solid-state electronic devices is such demonstration,resulting in the rapid rise of domain wall nano-electronics.This review aims to the latest development of ferroelectric domain-wall memories with the presence of the challenges and opportunities and the roadmap to their future commercialization.展开更多
Objective: To study the mechanism of Sijunzi decoction treating limb weakness in spleen Qi deficiency (SQD) based on the myonuclear domain (MND) theory. Methods: 40 male Sprague-Dawley rats were randomly divided into ...Objective: To study the mechanism of Sijunzi decoction treating limb weakness in spleen Qi deficiency (SQD) based on the myonuclear domain (MND) theory. Methods: 40 male Sprague-Dawley rats were randomly divided into the normal group, SQD model group (model group), SQD+ still water group (SW group) and SQD+ Sijunzi decoction group (CM group), 10 rats each group;Grip-Strength Meter was used to measure limb grip strength;transmission electron microscope was employed to observe the ultrastructural changes of the myofibers, Image Pro 6.0 was used to measure the myonuclear numbers, cross-section area (CSA) and then their ratios (the MND sizes) were calculated, immunofluorescence assay was chosen to test the expressions of paired box gene 7 (Pax7) and myogenic differentiation antigen (MyoD). Results: Compared with those in the normal group, limb grip strength was decreased, sarcomeres were abnormal, and all the myonuclear numbers, CSA and MND sizes were reduced, but the Pax7+ cell numbers were increased, significantly, in the model and SW groups;Compared with those in the model and SW groups, limb grip strength was increased, sarcomeres were basically normal, the myonuclear number and CSA were both greater, and the Pax7+ and MyoD+ cell numbers were both increased, significantly, in the CM group. Conclusion: Sijunzi decoction might increase the myonuclear number by activating the MSCs to treat limb weakness in SQD.展开更多
From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect o...From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect of the status quo maintenance and the moderating effect of the prevention focus orientation.The study re‐sults show that PEBs in the private domain significantly promote individuals’PEBs in the public domain.The status quo maintenance partially mediates the relationship between PEBs in the private and public domains.Specifically,individuals with a high-level prevention focus orientation strengthen the relationship between the PEBs in the private domain and the status quo maintenance,and that of the PEBs in the public domain.There‐fore,individuals with a high-level prevention focus will more likely engage in subsequent PEBs in the public domain after their initial PEBs in the private domain due to their increased status quo maintenance degree.Policymakers and practitioners should pay attention to the prevention-repetition effect and use the PEBs in the private domain to promote those in the public domain.展开更多
Let X be a Jordan domain satisfying certain hyperbolic growth conditions.Assume that φ is a homeomorphism from the boundary ?X of X onto the unit circle.Denote by h the harmonic diffeomorphic extension of φ from X o...Let X be a Jordan domain satisfying certain hyperbolic growth conditions.Assume that φ is a homeomorphism from the boundary ?X of X onto the unit circle.Denote by h the harmonic diffeomorphic extension of φ from X onto the unit disk.We establish the optimal Orlicz-Sobolev regularity and weighted Sobolev estimate of h.These generalize the Sobolev regularity of h in [A.Koski,J.Onninen,Sobolev homeomorphic extensions,J.Eur.Math.Soc.23(2021) 4065-4089,Theorem 3.1].展开更多
Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In ...Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of challenges.In this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each output.Such models increase confidence if the results appear plausible and match the clinicians expectations.However,the absence of a plausible explanation does not imply an inaccurate model.Especially in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.For example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health information.Unlike centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health data.This narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.展开更多
This paper explores the asymmetric effect of COVID-19 pandemic news,as measured by the coronavirus indices(Panic,Hype,Fake News,Sentiment,Infodemic,and Media Coverage),on the cryptocurrency market.Using daily data fro...This paper explores the asymmetric effect of COVID-19 pandemic news,as measured by the coronavirus indices(Panic,Hype,Fake News,Sentiment,Infodemic,and Media Coverage),on the cryptocurrency market.Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heter-oskedasticity model,the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns,indicating fear of missing out behavior does not prevail.Furthermore,when the nonlinear autoregressive distributed lag model is esti-mated,both positive and negative shocks in pandemic indices promote Bitcoin’s daily changes;thus,Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil.The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns,whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index.These findings may have significant policy implications for investors and governments because they highlight the impor-tance of news during turbulent times.The empirical results indicate that pandemic news could significantly influence Bitcoin’s price.展开更多
Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity ...Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.展开更多
Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-c...Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.展开更多
In this paper, we consider the second-grade fluid equations in a 2D exterior domain satisfying the non-slip boundary conditions. The second-grade fluid model is a wellknown non-Newtonian fluid model, with two paramete...In this paper, we consider the second-grade fluid equations in a 2D exterior domain satisfying the non-slip boundary conditions. The second-grade fluid model is a wellknown non-Newtonian fluid model, with two parameters: α, which represents the length-scale,while ν > 0 corresponds to the viscosity. We prove that, as ν, α tend to zero, the solution of the second-grade fluid equations with suitable initial data converges to the one of Euler equations, provided that ν = o(α^(4/3)). Moreover, the convergent rate is obtained.展开更多
基金supported by the National Natural Science Foundation of China (12072365)the Natural Science Foundation of Hunan Province of China (2020JJ4657)。
文摘It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
文摘In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].
基金the Natural Science Foundation of Henan Province(232300420094)the Science and TechnologyResearch Project of Henan Province(222102220092).
文摘Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.
基金supported in part by the National Natural Science Foundation of China,China(Grant No.52102420)the National Key Research and Development Program of China,China(Grant No.2022YFE0102700)the China Postdoctoral Science Foundation,China(Grant No.2023T160085)。
文摘For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.
基金This study was approved by the Clinical Research Ethics Committee of Zhongshan Hospital,Fudan University.
文摘BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).
文摘Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.
基金This work was supported by the National Natural Science Foundation of China(Grant No.11702293).
文摘ABSTRACT Satellite encounters during close operations,such as rendezvous,formation,and cluster flights,are typical long-term encounters.The collision probability in such an encounter is a primary safety concern.In this study,a parametric method is proposed to compute the long-term collision probability for close satellite operations with initial state uncertainty.Random relative state errors resulting from system uncertainty lead to possible deviated trajectories with respect to the nominal one.To describe such a random event meaningfully,each deviated trajectory sample should be mapped to a unique and time-independent element in a random variable(RV)space.In this study,the RV space was identified as the transformed state space at a fixed initial time.The physical dimensions of both satellites were characterized by a combined hard-body sphere.Transforming the combined hard-body sphere into the RV space yielded a derived ellipsoid,which evolved over time and swept out a derived collision volume.The derived collision volume was solved using the reachable domain method.Finally,the collision probability was computed by integrating a probability density function over the derived collision volume.The results of the proposed method were compared with those of a nonparametric computation-intensive Monte Carlo method.The relative difference between the two results was found to be<0.6%,verifying the accuracy of the proposed method.
基金This work was supported by the National Natural Science Foundation of China(Grant No.11702293).
文摘Conventional reachable domain(RD)problem with an admissible velocity increment,Δv,in an isotropic distribution,was extended to the general case withΔv in an anisotropic ellipsoidal distribution.Such an extension enables RD to describe the effect of initial velocity uncertainty because a Gaussian form of velocity uncertainty can be regarded as possible velocity deviations that are confined within an error ellipsoid.To specify RD in space,the boundary surface of RD,also known as the envelope,should be determined.In this study,the envelope is divided into two parts:inner and outer envelopes.Thus,the problem of solving the RD envelope is formulated into an optimization problem.The inner and outer reachable boundaries that are closest to and farthest away from the center of the Earth,respectively,were found in each direction.An optimal control policy is then formulated by using the necessary condition for an optimum;that is,the first-order derivative of the performance function with respect to the control variable becomes zero.Mathematical properties regarding the optimal control policy is discussed.Finally,an algorithm to solve the RD envelope is proposed.In general,the proposed algorithm does not require any iteration,and therefore benefits from quick computation.Numerical examples,including two coplanar cases and two 3D cases,are provided,which demonstrate that the proposed algorithm works efficiently.
基金Project supported by the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20190405)the LOEWE program of the State of Hesse,Germany,within the project FLAME(Fermi Level Engineering of Antiferroelectric Materials for Energy Storage and Insulation Systems)。
文摘The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching property of antiferroelectric domains.Our results indicate that the ferroelectric domains nucleate preferably at the boundaries of the antiferroelectric domains,and antiferroelectrics with larger initial domain sizes possess a higher coercive electric field as demonstrated by hysteresis loops.Moreover,we introduce charge defects into the sample and numerically investigate their influence.It is also shown that charge defects can induce local ferroelectric domains,which could suppress the saturation polarization and narrow the enclosed area of the hysteresis loop.Our results give insights into understanding the antiferroelectric phase transformation and optimizing the energy storage property in experiments.
基金supported by the National Nature Science Fund of China(Grant No.82102313)Zhejiang Province Traditional Chinese Medicine Science and Technology Plan Project(Grant No.2023ZL497)+1 种基金Zhejiang Province Medical and Health Science and Technology Project(Grant No.2022519563)National Health and Medical Research Council of Australia(Grant No.app1107828,app1163933)。
文摘The cell membrane structure is closely related to the occurrence and progression of many metabolic bone diseases observed in the clinic and is an important target to the development of therapeutic strategies for these diseases.Strong experimental evidence supports the existence of membrane microdomains in osteoclasts(OCs).However,the potential membrane microdomains and the crucial mechanisms underlying their roles in OCs have not been fully characterized.Membrane microdomain components,such as scaffolding proteins and the actin cytoskeleton,as well as the roles of individual membrane proteins,need to be elucidated.In this review,we discuss the compositions and critical functions of membrane microdomains that determine the biological behavior of OCs through the three main stages of the OC life cycle.
基金Project supported by the National Key Basic Research Program of China (Grant Nos.2019YFA0308500 and 2022YFA1402900)the National Natural Science Foundation of China (Grant No.61904034)。
文摘Ferroelectric domain walls appear as sub-nanometer-thick topological interfaces separating two adjacent domains in different orientations,and can be repetitively created,erased,and moved during programming into different logic states for the nonvolatile memory under an applied electric field,providing a new paradigm for highly miniaturized low-energy electronic devices.Under some specific conditions,the charged domain walls are conducting,differing from their insulating bulk domains.In the past decade,the emergence of atomic-layer scaling solid-state electronic devices is such demonstration,resulting in the rapid rise of domain wall nano-electronics.This review aims to the latest development of ferroelectric domain-wall memories with the presence of the challenges and opportunities and the roadmap to their future commercialization.
文摘Objective: To study the mechanism of Sijunzi decoction treating limb weakness in spleen Qi deficiency (SQD) based on the myonuclear domain (MND) theory. Methods: 40 male Sprague-Dawley rats were randomly divided into the normal group, SQD model group (model group), SQD+ still water group (SW group) and SQD+ Sijunzi decoction group (CM group), 10 rats each group;Grip-Strength Meter was used to measure limb grip strength;transmission electron microscope was employed to observe the ultrastructural changes of the myofibers, Image Pro 6.0 was used to measure the myonuclear numbers, cross-section area (CSA) and then their ratios (the MND sizes) were calculated, immunofluorescence assay was chosen to test the expressions of paired box gene 7 (Pax7) and myogenic differentiation antigen (MyoD). Results: Compared with those in the normal group, limb grip strength was decreased, sarcomeres were abnormal, and all the myonuclear numbers, CSA and MND sizes were reduced, but the Pax7+ cell numbers were increased, significantly, in the model and SW groups;Compared with those in the model and SW groups, limb grip strength was increased, sarcomeres were basically normal, the myonuclear number and CSA were both greater, and the Pax7+ and MyoD+ cell numbers were both increased, significantly, in the CM group. Conclusion: Sijunzi decoction might increase the myonuclear number by activating the MSCs to treat limb weakness in SQD.
基金support provided by the Zhejiang Province Planning Project of Philosophy and Social Science[Grant No.22NDJC107YB]Zhejiang Provincial Natural Science Foundation of China[Grant No.LY21G020009].
文摘From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect of the status quo maintenance and the moderating effect of the prevention focus orientation.The study re‐sults show that PEBs in the private domain significantly promote individuals’PEBs in the public domain.The status quo maintenance partially mediates the relationship between PEBs in the private and public domains.Specifically,individuals with a high-level prevention focus orientation strengthen the relationship between the PEBs in the private domain and the status quo maintenance,and that of the PEBs in the public domain.There‐fore,individuals with a high-level prevention focus will more likely engage in subsequent PEBs in the public domain after their initial PEBs in the private domain due to their increased status quo maintenance degree.Policymakers and practitioners should pay attention to the prevention-repetition effect and use the PEBs in the private domain to promote those in the public domain.
基金partially supported by the Young Scientist Program of the Ministry of Science and Technology of China(2021YFA1002200)supported by National Natural Science Foundation of China(12101226)+1 种基金partially supported by the National Natural Science Foundation of China(12101362)supported by Shandong Provincial Natural Science Foundation(ZR2021QA032)。
文摘Let X be a Jordan domain satisfying certain hyperbolic growth conditions.Assume that φ is a homeomorphism from the boundary ?X of X onto the unit circle.Denote by h the harmonic diffeomorphic extension of φ from X onto the unit disk.We establish the optimal Orlicz-Sobolev regularity and weighted Sobolev estimate of h.These generalize the Sobolev regularity of h in [A.Koski,J.Onninen,Sobolev homeomorphic extensions,J.Eur.Math.Soc.23(2021) 4065-4089,Theorem 3.1].
基金This work was supported in part by the National Natural Science Foundation of China(82260360)the Foreign Young Talent Program(QN2021033002L).
文摘Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of challenges.In this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each output.Such models increase confidence if the results appear plausible and match the clinicians expectations.However,the absence of a plausible explanation does not imply an inaccurate model.Especially in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.For example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health information.Unlike centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health data.This narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.
文摘This paper explores the asymmetric effect of COVID-19 pandemic news,as measured by the coronavirus indices(Panic,Hype,Fake News,Sentiment,Infodemic,and Media Coverage),on the cryptocurrency market.Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heter-oskedasticity model,the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns,indicating fear of missing out behavior does not prevail.Furthermore,when the nonlinear autoregressive distributed lag model is esti-mated,both positive and negative shocks in pandemic indices promote Bitcoin’s daily changes;thus,Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil.The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns,whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index.These findings may have significant policy implications for investors and governments because they highlight the impor-tance of news during turbulent times.The empirical results indicate that pandemic news could significantly influence Bitcoin’s price.
基金funded by the National Natural Science Foundation of China(Grant No.52274048)Beijing Natural Science Foundation(Grant No.3222037)+1 种基金the CNPC 14th Five-Year Perspective Fundamental Research Project(Grant No.2021DJ2104)the Science Foundation of China University of Petroleum-Beijing(No.2462021YXZZ010).
文摘Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.
基金supported by the National Natural Science Foundation of China(NSFC)(U1704158)Henan Province Technologies Research and Development Project of China(212102210103)+1 种基金the NSFC Development Funding of Henan Normal University(2020PL09)the University of Manitoba Research Grants Program(URGP)。
文摘Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.
基金Aibin Zang was supported partially by the National Natural Science Foundation of China (11771382, 12061080, 12261093)the Jiangxi Provincial Natural Science Foundation (20224ACB201004)。
文摘In this paper, we consider the second-grade fluid equations in a 2D exterior domain satisfying the non-slip boundary conditions. The second-grade fluid model is a wellknown non-Newtonian fluid model, with two parameters: α, which represents the length-scale,while ν > 0 corresponds to the viscosity. We prove that, as ν, α tend to zero, the solution of the second-grade fluid equations with suitable initial data converges to the one of Euler equations, provided that ν = o(α^(4/3)). Moreover, the convergent rate is obtained.
