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
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
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
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
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
Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global...Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.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
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
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
BACKGROUND The overexpression of the MYC gene plays an important role in the occurrence,development and evolution of colorectal cancer(CRC).Bromodomain and extraterminal domain(BET)inhibitors can decrease the function...BACKGROUND The overexpression of the MYC gene plays an important role in the occurrence,development and evolution of colorectal cancer(CRC).Bromodomain and extraterminal domain(BET)inhibitors can decrease the function BET by recognizing acetylated lysine residues,thereby downregulating the expression of MYC.AIM To investigate the inhibitory effect and mechanism of a BET inhibitor on CRC cells.METHODS The effect of the BET inhibitor JAB-8263 on the proliferation of various CRC cell lines was studied by CellTiter-Glo method and colony formation assay.The effect of JAB-8263 on the cell cycle and apoptosis of CRC cells was studied by propidium iodide staining and Annexin V/propidium iodide flow assay,respectively.The effect of JAB-8263 on the expression of c-MYC,p21 and p16 in CRC cells was detected by western blotting assay.The anti-tumor effect of JAB-8263 on CRC cells in vivo and evaluation of the safety of the compound was predicted by constructing a CRC cell animal tumor model.RESULTS JAB-8263 dose-dependently suppressed CRC cell proliferation and colony formation in vitro.The MYC signaling pathway was dose-dependently inhibited by JAB-8263 in human CRC cell lines.JAB-8263 dose-dependently induced cell cycle arrest and apoptosis in the MC38 cell line.SW837 xenograft model was treated with JAB-8263(0.3 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P<0.001).The MC38 syngeneic murine model was treated with JAB-8263(0.2 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P=0.003).CONCLUSION BET could be a potential effective drug target for suppressing CRC growth,and the BET inhibitor JAB-8263 can effectively suppress c-MYC expression and exert anti-tumor activity in CRC models.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
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
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
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
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
Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)...Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)3are fabricated by magnetron sputtering technology. We study the domain structure and switching field distribution in p-SAF by changing the thickness of the infrared space layer. The strongest exchange coupling field(Hex) is observed when the thickness of Ir layer(tIr) is 0.7 nm and becoming weak according to the Ruderman–Kittel–Kasuya–Yosida-type coupling at 1.05 nm,2.1 nm, 4.55 nm, and 4.9 nm in sequence. Furthermore, the domain switching process between the upper Co/Ni stack and the bottom Co/Ni stack is different because of the antiferromagnet coupling. Compared with ferromagnet coupling films, the antiferromagnet samples possess three irreversible reversal regions in the first-order reversal curve distribution.With tIrincreasing, these irreversible reversal regions become denser and smaller. The results from this study will help us understand the details of the magnetization reversal process in the p-SAF.展开更多
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 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.
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supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+1 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。
文摘Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
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 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Supported by the National Natural Science Foundation of China,No.81871317.
文摘BACKGROUND The overexpression of the MYC gene plays an important role in the occurrence,development and evolution of colorectal cancer(CRC).Bromodomain and extraterminal domain(BET)inhibitors can decrease the function BET by recognizing acetylated lysine residues,thereby downregulating the expression of MYC.AIM To investigate the inhibitory effect and mechanism of a BET inhibitor on CRC cells.METHODS The effect of the BET inhibitor JAB-8263 on the proliferation of various CRC cell lines was studied by CellTiter-Glo method and colony formation assay.The effect of JAB-8263 on the cell cycle and apoptosis of CRC cells was studied by propidium iodide staining and Annexin V/propidium iodide flow assay,respectively.The effect of JAB-8263 on the expression of c-MYC,p21 and p16 in CRC cells was detected by western blotting assay.The anti-tumor effect of JAB-8263 on CRC cells in vivo and evaluation of the safety of the compound was predicted by constructing a CRC cell animal tumor model.RESULTS JAB-8263 dose-dependently suppressed CRC cell proliferation and colony formation in vitro.The MYC signaling pathway was dose-dependently inhibited by JAB-8263 in human CRC cell lines.JAB-8263 dose-dependently induced cell cycle arrest and apoptosis in the MC38 cell line.SW837 xenograft model was treated with JAB-8263(0.3 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P<0.001).The MC38 syngeneic murine model was treated with JAB-8263(0.2 mg/kg for 29 d),and the average tumor volume was significantly decreased compared to the vehicle control group(P=0.003).CONCLUSION BET could be a potential effective drug target for suppressing CRC growth,and the BET inhibitor JAB-8263 can effectively suppress c-MYC expression and exert anti-tumor activity in CRC models.
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.
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.
基金Project supported by the Natural Science Foundation of Gansu Province, China (Grant No. 22JR5RA775)the Science and Technology Program of Lanzhou, China (Grant No. 2021-1-157)+2 种基金the Guangdong Basic and Applied Basic Research Foundation, China (Grant Nos. 2020A1515110998 and 2022A1515012123)the Outstanding Youth Foundation of Gansu Academy of Science, China (Grant No. 2021YQ01)the Innovative Team Construction Project of Gansu Academy of Sciences, China (Grant No. 2020CX005-01)。
文摘Perpendicular synthetic-antiferromagnet(p-SAF) has broad applications in spin-transfer-torque magnetic random access memory and magnetic sensors. In this study, the p-SAF films consisting of (Co/Ni)3]/Ir(tIr)/[(Ni/Co)3are fabricated by magnetron sputtering technology. We study the domain structure and switching field distribution in p-SAF by changing the thickness of the infrared space layer. The strongest exchange coupling field(Hex) is observed when the thickness of Ir layer(tIr) is 0.7 nm and becoming weak according to the Ruderman–Kittel–Kasuya–Yosida-type coupling at 1.05 nm,2.1 nm, 4.55 nm, and 4.9 nm in sequence. Furthermore, the domain switching process between the upper Co/Ni stack and the bottom Co/Ni stack is different because of the antiferromagnet coupling. Compared with ferromagnet coupling films, the antiferromagnet samples possess three irreversible reversal regions in the first-order reversal curve distribution.With tIrincreasing, these irreversible reversal regions become denser and smaller. The results from this study will help us understand the details of the magnetization reversal process in the p-SAF.
