High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-f...Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-forms,which leverage sparse coding(SC),have gained significant attention in the geosciences due to their ability to achieve high TF resolution.However,the iterative approaches typically employed in sparse TF transforms are computationally intensive,making them impractical for real seismic data analysis.To address this issue,we propose an interpretable convolutional sparse coding(CSC)network to achieve high TF resolution.The proposed model is generated based on the traditional short-time Fourier transform(STFT)transform and a modified UNet,named ULISTANet.In this design,we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm(LISTA)blocks,a specialized form of CSC.The LISTA block,which evolves from the traditional iterative shrinkage thresholding algorithm(ISTA),is optimized for extracting sparse features more effectively.Furthermore,we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet.Finally,the proposed method’s performance is subsequently validated using both synthetic and field data,demonstrating its potential for enhanced seismic data analysis.展开更多
The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improv...The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.展开更多
We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to ...We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to examine the connections between various data during the COVID-19 pandemic.We consider two characteristics:time and frequency.Based on Diebold and Yilmaz’s(Int J Forecast 28:57-66,2012)technique,our findings indicate that comparable data have a substantially stronger correlation(regarding return)than volatility.Per Baruník and Křehlík’(J Financ Econ 16:271-296,2018)approach,interconnectedness among returns(volatilities)reduces(increases)as one moves from the short to the long term.A moving window analysis reveals a sudden increase in correlation,both in volatility and return,during the COVID-19 pandemic.In the context of wavelet coherence analysis,we observe a strong interconnection between data corresponding to the COVID-19 outbreak.The only exceptions are the behavior of Bitcoin and Ethereum.Specifically,Bitcoin combinations with other data exhibit a distinct behavior.The period precisely coincides with the COVID-19 pandemic.Evidently,volatility spillover has a long-lasting impact;policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks(e.g.,the COVID-19 pandemic)and simultaneously reduce its side effects.展开更多
Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydo...Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.展开更多
BACKGROUND One of the main characteristics of oral squamous cell carcinoma(OSCC)is that it metastasizes to cervical lymph nodes frequently with a high degree of local invasiveness.A primary feature of malignant tumors...BACKGROUND One of the main characteristics of oral squamous cell carcinoma(OSCC)is that it metastasizes to cervical lymph nodes frequently with a high degree of local invasiveness.A primary feature of malignant tumors is their penetration of neighboring tissues,such as lymphatic and blood arteries,due to the tumor cells'capacity to break down the extracellular matrix(ECM).Matrix metalloproteinases(MMPs)constitute a family of proteolytic enzymes that facilitate tissue remodeling and the degradation of the ECM.MMP-9 and MMP-13 belong to the group of extracellular matrix degrading enzymes and their expression has been studied in OSCC because of their specific functions.MMP-13,a collagenase family member,is thought to play an essential role in the MMP activation cascade by breaking down the fibrillar collagens,whereas MMP-9 is thought to accelerate the growth of tumors.Elevated MMP-13 expression has been associated with tumor behavior and patient prognosis in a number of malignant cases.AIM To assess the immunohistochemical expression of MMP-9 and MMP-13 in OSCC.METHODS A total of 40 cases with histologically confirmed OSCC by incisional biopsy were included in this cross-sectional retrospective study.The protocols for both MMP-9 and MMP-13 immunohistochemical staining were performed according to the manufacturer’s recommendations along with the normal gingival epithelium as a positive control.All the observations were recorded and Pearson’sχ²test with Fisher exact test was used for statistical analysis.RESULTS Our study showed no significant correlation between MMP-9 and MMP-13 staining intensity and tumor size.The majority of the patients were in advanced TNM stages(III and IV),and showed intense expression of MMP-9 and MMP-13.CONCLUSION The present study suggests that both MMP-9 and MMP-13 play an important and independent role in OSCC progression and invasiveness.Intense expression of MMP-9 and MMP-13,irrespective of histological grade of OSCC,correlates well with TNM stage.Consequently,it is evident that MMP-9 and MMP-13 are important for the invasiveness and progression of tumors.The findings may facilitate the development of new approaches for evaluating lymph node metastases and interventional therapy techniques,hence enhancing the prognosis of patients diagnosed with OSCC.展开更多
The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functio...The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.展开更多
Increased matrix stiffness of nucleus pulposus(NP)tissue is a main feature of intervertebral disc degeneration(IVDD)and affects various functions of nucleus pulposus cells(NPCs).Glycolysis is the main energy source fo...Increased matrix stiffness of nucleus pulposus(NP)tissue is a main feature of intervertebral disc degeneration(IVDD)and affects various functions of nucleus pulposus cells(NPCs).Glycolysis is the main energy source for NPC survival,but the effects and underlying mechanisms of increased extracellular matrix(ECM)stiffness on NPC glycolysis remain unknown.In this study,hydrogels with different stiffness were established to mimic the mechanical environment of NPCs.Notably,increased matrix stiffness in degenerated NP tissues from IVDD patients was accompanied with impaired glycolysis,and NPCs cultured on rigid substrates exhibited a reduction in glycolysis.展开更多
Quantum dot(QD)-based infrared photodetector is a promising technology that can implement current monitoring,imaging and optical communication in the infrared region. However, the photodetection performance of self-po...Quantum dot(QD)-based infrared photodetector is a promising technology that can implement current monitoring,imaging and optical communication in the infrared region. However, the photodetection performance of self-powered QD devices is still limited by their unfavorable charge carrier dynamics due to their intrinsically discrete charge carrier transport process. Herein, we strategically constructed semiconducting matrix in QD film to achieve efficient charge transfer and extraction.The p-type semiconducting CuSCN was selected as energy-aligned matrix to match the n-type colloidal PbS QDs that was used as proof-of-concept. Note that the PbS QD/CuSCN matrix not only enables efficient charge carrier separation and transfer at nano-interfaces but also provides continuous charge carrier transport pathways that are different from the hoping process in neat QD film, resulting in improved charge mobility and derived collection efficiency. As a result, the target structure delivers high specific detectivity of 4.38 × 10^(12)Jones and responsivity of 782 mA/W at 808 nm, which is superior than that of the PbS QD-only photodetector(4.66 × 10^(11)Jones and 338 mA/W). This work provides a new structure candidate for efficient colloidal QD based optoelectronic devices.展开更多
Neuronal growth, extension, branching, and formation of neural networks are markedly influenced by the extracellular matrix—a complex network composed of proteins and carbohydrates secreted by cells. In addition to p...Neuronal growth, extension, branching, and formation of neural networks are markedly influenced by the extracellular matrix—a complex network composed of proteins and carbohydrates secreted by cells. In addition to providing physical support for cells, the extracellular matrix also conveys critical mechanical stiffness cues. During the development of the nervous system, extracellular matrix stiffness plays a central role in guiding neuronal growth, particularly in the context of axonal extension, which is crucial for the formation of neural networks. In neural tissue engineering, manipulation of biomaterial stiffness is a promising strategy to provide a permissive environment for the repair and regeneration of injured nervous tissue. Recent research has fine-tuned synthetic biomaterials to fabricate scaffolds that closely replicate the stiffness profiles observed in the nervous system. In this review, we highlight the molecular mechanisms by which extracellular matrix stiffness regulates axonal growth and regeneration. We highlight the progress made in the development of stiffness-tunable biomaterials to emulate in vivo extracellular matrix environments, with an emphasis on their application in neural repair and regeneration, along with a discussion of the current limitations and future prospects. The exploration and optimization of the stiffness-tunable biomaterials has the potential to markedly advance the development of neural tissue engineering.展开更多
This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh ...This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh principle and the perturbation of the spectral radius under moving the edge operation,and the extremal hypergraphs are characterized for both supertree and unicyclic hypergraphs.The spectral radius of the graph is generalized.展开更多
Mixed matrix membranes(MMMs)have demonstrated significant promise in energy-intensive gas separations by amalgamating the unique properties of fillers with the facile processability of polymers.However,achieving a sim...Mixed matrix membranes(MMMs)have demonstrated significant promise in energy-intensive gas separations by amalgamating the unique properties of fillers with the facile processability of polymers.However,achieving a simultaneous enhancement of permeability and selectivity remains a formidable challenge,due to the difficulty of achieving an optimal match between polymers and fillers.In this study,we incorporate a porous carbon-based zinc oxide composite(C@ZnO)into high-permeability polymers of intrinsic microporosity(PIMs)to fabricate MMMs.The dipole–dipole interaction between C@ZnO and PIMs ensures their exceptional compatibility,mitigating the formation of non-selective voids in the resulting MMMs.Concurrently,C@ZnO with abundant interconnected pores can provide additional low-resistance pathways for gas transport in MMMs.As a result,the CO_(2) permeability of the optimized C@ZnO/PIM-1 MMMs is elevated to 13,215 barrer,while the CO_(2)/N_(2) and CO_(2)/CH_(4) selectivity reached 21.5 and 14.4,respectively,substantially surpassing the 2008 Robeson upper bound.Additionally,molecular simulation results further corroborate that the augmented membrane gas selectivity is attributed to the superior CO_(2) affinity of C@ZnO.In summary,we believe that this work not only expands the application of MMMs for gas separation but also heralds a paradigm shift in the application of porous carbon materials.展开更多
Analysis and design of linear periodic control systems are closely related to the periodic matrix equations.The biconjugate residual method(BCR for short)have been introduced by Vespucci and Broyden for efficiently so...Analysis and design of linear periodic control systems are closely related to the periodic matrix equations.The biconjugate residual method(BCR for short)have been introduced by Vespucci and Broyden for efficiently solving linear systems Aα=b.The objective of this paper is to provide one new iterative algorithm based on BCR method to find the symmetric periodic solutions of linear periodic matrix equations.This kind of periodic matrix equations has not been dealt with yet.This iterative method is guaranteed to converge in a finite number of steps in the absence of round-off errors.Some numerical results are performed to illustrate the efficiency and feasibility of new method.展开更多
Capacitive voltage transformers (CVTs) are essential in high-voltage systems. An accurate error assessment is crucial for precise energy metering. However, tracking real-time quantitative changes in capacitive voltage...Capacitive voltage transformers (CVTs) are essential in high-voltage systems. An accurate error assessment is crucial for precise energy metering. However, tracking real-time quantitative changes in capacitive voltage transformer errors, particularly minor variations in multi-channel setups, remains challenging. This paper proposes a method for online error tracking of multi-channel capacitive voltage transformers using a Co-Prediction Matrix. The approach leverages the strong correlation between in-phase channels, particularly the invariance of the signal proportions among them. By establishing a co-prediction matrix based on these proportional relationships, The influence of voltage changes on the primary measurements is mitigated. Analyzing the relationships between the co-prediction matrices over time allows for inferring true measurement errors. Experimental validation with real-world data confirms the effectiveness of the method, demonstrating its capability to continuously track capacitive voltage transformer measurement errors online with precision over extended durations.展开更多
Micro-and nano-to millimeter-scale magnetic matrix materials have gained widespread application due to their exceptional magnetic properties and favorable cost-effectiveness.With the rapid progress in condensed matter...Micro-and nano-to millimeter-scale magnetic matrix materials have gained widespread application due to their exceptional magnetic properties and favorable cost-effectiveness.With the rapid progress in condensed matter physics,materials science,and mineral separation technologies,these materials are now poised for new opportunities in theoretical research and development.This review provides a comprehensive analysis of these matrices,encompassing their structure,size,shape,composition,properties,and multifaceted applications.These materials,primarily composed of alloys of transition state metasl such as iron(Fe),cobalt(Co),titanium(Ti),and nickel(Ni),exhibit unique attributes like high magnetization rates,low eleastic modulus,and high saturation magnetic field strengths.Furthermore,the studies also delve into the complex mechanical interactions involved in the separation of magnetic particles using magnetic separator matrices,including magnetic,gravitational,centrifugal,and van der Waals forces.The review outlines how size and shape effects influence the magnetic behavior of matrices,offering new perspectives for innovative applications of magnetic matrices in various domains of materials science and magnetic separation.展开更多
Vascular endothelial growth factor and its mimic peptide KLTWQELYQLKYKGI(QK)are widely used as the most potent angiogenic factors for the treatment of multiple ischemic diseases.However,conventional topical drug deliv...Vascular endothelial growth factor and its mimic peptide KLTWQELYQLKYKGI(QK)are widely used as the most potent angiogenic factors for the treatment of multiple ischemic diseases.However,conventional topical drug delivery often results in a burst release of the drug,leading to transient retention(inefficacy)and undesirable diffusion(toxicity)in vivo.Therefore,a drug delivery system that responds to changes in the microenvironment of tissue regeneration and controls vascular endothelial growth factor release is crucial to improve the treatment of ischemic stroke.Matrix metalloproteinase-2(MMP-2)is gradually upregulated after cerebral ischemia.Herein,vascular endothelial growth factor mimic peptide QK was self-assembled with MMP-2-cleaved peptide PLGLAG(TIMP)and customizable peptide amphiphilic(PA)molecules to construct nanofiber hydrogel PA-TIMP-QK.PA-TIMP-QK was found to control the delivery of QK by MMP-2 upregulation after cerebral ischemia/reperfusion and had a similar biological activity with vascular endothelial growth factor in vitro.The results indicated that PA-TIMP-QK promoted neuronal survival,restored local blood circulation,reduced blood-brain barrier permeability,and restored motor function.These findings suggest that the self-assembling nanofiber hydrogel PA-TIMP-QK may provide an intelligent drug delivery system that responds to the microenvironment and promotes regeneration and repair after cerebral ischemia/reperfusion injury.展开更多
Intestinal ischemia-reperfusion injury(IIRI)is a complex and severe pathophysiological process characterized by oxidative stress,inflammation,and apoptosis.In recent years,the critical roles of extracellular matrix(EC...Intestinal ischemia-reperfusion injury(IIRI)is a complex and severe pathophysiological process characterized by oxidative stress,inflammation,and apoptosis.In recent years,the critical roles of extracellular matrix(ECM)genes and microRNAs(miRNAs)in IIRI have garnered widespread attention.This review aims to systematically summarize the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI.First,we review the molecular mechanisms of IIRI,focusing on the dual role of the ECM in tissue injury and repair processes.The expression changes and functions of ECM components such as collagen,elastin,and matrix metalloproteinases during IIRI progression are deeply analyzed.Second,we systematically summarize the regulatory roles of miRNAs in IIRI,particularly the mechanisms and functions of miRNAs such as miR-125b and miR-200a in regulating inflammation,apoptosis,and ECM remodeling.Additionally,this review discusses potential diagnostic biomarkers and treatment strategies based on ECM genes and miRNAs.We extensively evaluate the prospects of miRNA-targeted therapy and ECM component modulation in preventing and treating IIRI,emphasizing the clinical translational potential of these emerging therapies.In conclusion,the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI provides new directions for further research,necessitating additional clinical and basic studies to validate and expand these findings for improving clinical outcomes in IIRI patients.展开更多
Aneurysms and dissections represent some of the most serious cardiovascular diseases.The prevailing theory posits that mechanical overloading of the vessel wall is the underlying cause.Inspired by Barkhordarian et al,...Aneurysms and dissections represent some of the most serious cardiovascular diseases.The prevailing theory posits that mechanical overloading of the vessel wall is the underlying cause.Inspired by Barkhordarian et al,the authors present matrix metalloproteinases(MMPs)and their inhibitors in immunohistological analyses as contributing factors in the pathophysiology of aortic aneurysms(AA).Data analysis of MMP-1,MMP-9,tissue inhibitors of metalloproteinases(TIMPs),including TIMP-1 and TIMP-2 expression reveals a varied distribution between the adventitia and media and a non-uniform expression of the investigated markers.These elements,as key components of the extracellular matrix(ECM),indicate that the formation of AA is not solely driven by endoluminal pressure loading of the aortic wall.Instead,degenerative processes within ECM elements contribute significantly.Importantly,AA do not necessarily imply dissection.Tissue destruction,allowing blood flow entry,arises from reduced oxygen supply to the media,primarily due to incomplete capillarization or neocapillarization.展开更多
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
基金supported by the National Natural Science Foundation of China under Grant 42474139the Key Research and Development Program of Shaanxi under Grant 2024GX-YBXM-067.
文摘Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-forms,which leverage sparse coding(SC),have gained significant attention in the geosciences due to their ability to achieve high TF resolution.However,the iterative approaches typically employed in sparse TF transforms are computationally intensive,making them impractical for real seismic data analysis.To address this issue,we propose an interpretable convolutional sparse coding(CSC)network to achieve high TF resolution.The proposed model is generated based on the traditional short-time Fourier transform(STFT)transform and a modified UNet,named ULISTANet.In this design,we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm(LISTA)blocks,a specialized form of CSC.The LISTA block,which evolves from the traditional iterative shrinkage thresholding algorithm(ISTA),is optimized for extracting sparse features more effectively.Furthermore,we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet.Finally,the proposed method’s performance is subsequently validated using both synthetic and field data,demonstrating its potential for enhanced seismic data analysis.
基金Supported by the National Science Foundation of China(42055402)。
文摘The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.
文摘We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to examine the connections between various data during the COVID-19 pandemic.We consider two characteristics:time and frequency.Based on Diebold and Yilmaz’s(Int J Forecast 28:57-66,2012)technique,our findings indicate that comparable data have a substantially stronger correlation(regarding return)than volatility.Per Baruník and Křehlík’(J Financ Econ 16:271-296,2018)approach,interconnectedness among returns(volatilities)reduces(increases)as one moves from the short to the long term.A moving window analysis reveals a sudden increase in correlation,both in volatility and return,during the COVID-19 pandemic.In the context of wavelet coherence analysis,we observe a strong interconnection between data corresponding to the COVID-19 outbreak.The only exceptions are the behavior of Bitcoin and Ethereum.Specifically,Bitcoin combinations with other data exhibit a distinct behavior.The period precisely coincides with the COVID-19 pandemic.Evidently,volatility spillover has a long-lasting impact;policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks(e.g.,the COVID-19 pandemic)and simultaneously reduce its side effects.
文摘Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.
文摘BACKGROUND One of the main characteristics of oral squamous cell carcinoma(OSCC)is that it metastasizes to cervical lymph nodes frequently with a high degree of local invasiveness.A primary feature of malignant tumors is their penetration of neighboring tissues,such as lymphatic and blood arteries,due to the tumor cells'capacity to break down the extracellular matrix(ECM).Matrix metalloproteinases(MMPs)constitute a family of proteolytic enzymes that facilitate tissue remodeling and the degradation of the ECM.MMP-9 and MMP-13 belong to the group of extracellular matrix degrading enzymes and their expression has been studied in OSCC because of their specific functions.MMP-13,a collagenase family member,is thought to play an essential role in the MMP activation cascade by breaking down the fibrillar collagens,whereas MMP-9 is thought to accelerate the growth of tumors.Elevated MMP-13 expression has been associated with tumor behavior and patient prognosis in a number of malignant cases.AIM To assess the immunohistochemical expression of MMP-9 and MMP-13 in OSCC.METHODS A total of 40 cases with histologically confirmed OSCC by incisional biopsy were included in this cross-sectional retrospective study.The protocols for both MMP-9 and MMP-13 immunohistochemical staining were performed according to the manufacturer’s recommendations along with the normal gingival epithelium as a positive control.All the observations were recorded and Pearson’sχ²test with Fisher exact test was used for statistical analysis.RESULTS Our study showed no significant correlation between MMP-9 and MMP-13 staining intensity and tumor size.The majority of the patients were in advanced TNM stages(III and IV),and showed intense expression of MMP-9 and MMP-13.CONCLUSION The present study suggests that both MMP-9 and MMP-13 play an important and independent role in OSCC progression and invasiveness.Intense expression of MMP-9 and MMP-13,irrespective of histological grade of OSCC,correlates well with TNM stage.Consequently,it is evident that MMP-9 and MMP-13 are important for the invasiveness and progression of tumors.The findings may facilitate the development of new approaches for evaluating lymph node metastases and interventional therapy techniques,hence enhancing the prognosis of patients diagnosed with OSCC.
基金supported by National Institute on Aging(NIH-NIA)R21 AG074152(to KMA)National Institute of Allergy and Infectious Diseases(NIAID)grant DP2 AI171150(to KMA)Department of Defense(DoD)grant AZ210089(to KMA)。
文摘The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.
基金supported by the National Nature Science Foundation of China(No.82002345 to J.D and 81902179 to L.S)the Gusu Talent Program(No.Qngg2022008 and GSWS2021027 to J.D)the Preliminary Research Project of the Second Affiliated Hospital of Soochow University(No.SDFEYBS1905 to J.D).
文摘Increased matrix stiffness of nucleus pulposus(NP)tissue is a main feature of intervertebral disc degeneration(IVDD)and affects various functions of nucleus pulposus cells(NPCs).Glycolysis is the main energy source for NPC survival,but the effects and underlying mechanisms of increased extracellular matrix(ECM)stiffness on NPC glycolysis remain unknown.In this study,hydrogels with different stiffness were established to mimic the mechanical environment of NPCs.Notably,increased matrix stiffness in degenerated NP tissues from IVDD patients was accompanied with impaired glycolysis,and NPCs cultured on rigid substrates exhibited a reduction in glycolysis.
基金supported by the National Natural Science Foundation of China (No. 62204079)the Science and Technology Development Project of Henan Province (Nos.202300410048, 202300410057)+2 种基金the China Postdoctoral Science Foundation (No. 2022M711037)the Intelligence Introduction Plan of Henan Province in 2021 (No. CXJD2021008)Henan University Fund。
文摘Quantum dot(QD)-based infrared photodetector is a promising technology that can implement current monitoring,imaging and optical communication in the infrared region. However, the photodetection performance of self-powered QD devices is still limited by their unfavorable charge carrier dynamics due to their intrinsically discrete charge carrier transport process. Herein, we strategically constructed semiconducting matrix in QD film to achieve efficient charge transfer and extraction.The p-type semiconducting CuSCN was selected as energy-aligned matrix to match the n-type colloidal PbS QDs that was used as proof-of-concept. Note that the PbS QD/CuSCN matrix not only enables efficient charge carrier separation and transfer at nano-interfaces but also provides continuous charge carrier transport pathways that are different from the hoping process in neat QD film, resulting in improved charge mobility and derived collection efficiency. As a result, the target structure delivers high specific detectivity of 4.38 × 10^(12)Jones and responsivity of 782 mA/W at 808 nm, which is superior than that of the PbS QD-only photodetector(4.66 × 10^(11)Jones and 338 mA/W). This work provides a new structure candidate for efficient colloidal QD based optoelectronic devices.
基金supported by the Natio`nal Natural Science Foundation of China,No. 81801241a grant from Sichuan Science and Technology Program,No. 2023NSFSC1578Scientific Research Projects of Southwest Medical University,No. 2022ZD002 (all to JX)。
文摘Neuronal growth, extension, branching, and formation of neural networks are markedly influenced by the extracellular matrix—a complex network composed of proteins and carbohydrates secreted by cells. In addition to providing physical support for cells, the extracellular matrix also conveys critical mechanical stiffness cues. During the development of the nervous system, extracellular matrix stiffness plays a central role in guiding neuronal growth, particularly in the context of axonal extension, which is crucial for the formation of neural networks. In neural tissue engineering, manipulation of biomaterial stiffness is a promising strategy to provide a permissive environment for the repair and regeneration of injured nervous tissue. Recent research has fine-tuned synthetic biomaterials to fabricate scaffolds that closely replicate the stiffness profiles observed in the nervous system. In this review, we highlight the molecular mechanisms by which extracellular matrix stiffness regulates axonal growth and regeneration. We highlight the progress made in the development of stiffness-tunable biomaterials to emulate in vivo extracellular matrix environments, with an emphasis on their application in neural repair and regeneration, along with a discussion of the current limitations and future prospects. The exploration and optimization of the stiffness-tunable biomaterials has the potential to markedly advance the development of neural tissue engineering.
基金Supported by Natural Science Foundation of HuBei Province(2022CFB299).
文摘This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh principle and the perturbation of the spectral radius under moving the edge operation,and the extremal hypergraphs are characterized for both supertree and unicyclic hypergraphs.The spectral radius of the graph is generalized.
基金financial support from the National Natural Science Foundation of China(Nos.22108258 and 52003251)Program for Science&Technology Innovation Talents in Universities of Henan Province(24HASTIT004)+1 种基金Outstanding Youth Fund of Henan Scientific Committee(222300420085)Science and Technology Joint Project of Henan Province(222301420041)。
文摘Mixed matrix membranes(MMMs)have demonstrated significant promise in energy-intensive gas separations by amalgamating the unique properties of fillers with the facile processability of polymers.However,achieving a simultaneous enhancement of permeability and selectivity remains a formidable challenge,due to the difficulty of achieving an optimal match between polymers and fillers.In this study,we incorporate a porous carbon-based zinc oxide composite(C@ZnO)into high-permeability polymers of intrinsic microporosity(PIMs)to fabricate MMMs.The dipole–dipole interaction between C@ZnO and PIMs ensures their exceptional compatibility,mitigating the formation of non-selective voids in the resulting MMMs.Concurrently,C@ZnO with abundant interconnected pores can provide additional low-resistance pathways for gas transport in MMMs.As a result,the CO_(2) permeability of the optimized C@ZnO/PIM-1 MMMs is elevated to 13,215 barrer,while the CO_(2)/N_(2) and CO_(2)/CH_(4) selectivity reached 21.5 and 14.4,respectively,substantially surpassing the 2008 Robeson upper bound.Additionally,molecular simulation results further corroborate that the augmented membrane gas selectivity is attributed to the superior CO_(2) affinity of C@ZnO.In summary,we believe that this work not only expands the application of MMMs for gas separation but also heralds a paradigm shift in the application of porous carbon materials.
基金Supported by NSFC (No.12371378)NSF of Fujian Province (Nos.2024J01980,2023J01955)。
文摘Analysis and design of linear periodic control systems are closely related to the periodic matrix equations.The biconjugate residual method(BCR for short)have been introduced by Vespucci and Broyden for efficiently solving linear systems Aα=b.The objective of this paper is to provide one new iterative algorithm based on BCR method to find the symmetric periodic solutions of linear periodic matrix equations.This kind of periodic matrix equations has not been dealt with yet.This iterative method is guaranteed to converge in a finite number of steps in the absence of round-off errors.Some numerical results are performed to illustrate the efficiency and feasibility of new method.
文摘Capacitive voltage transformers (CVTs) are essential in high-voltage systems. An accurate error assessment is crucial for precise energy metering. However, tracking real-time quantitative changes in capacitive voltage transformer errors, particularly minor variations in multi-channel setups, remains challenging. This paper proposes a method for online error tracking of multi-channel capacitive voltage transformers using a Co-Prediction Matrix. The approach leverages the strong correlation between in-phase channels, particularly the invariance of the signal proportions among them. By establishing a co-prediction matrix based on these proportional relationships, The influence of voltage changes on the primary measurements is mitigated. Analyzing the relationships between the co-prediction matrices over time allows for inferring true measurement errors. Experimental validation with real-world data confirms the effectiveness of the method, demonstrating its capability to continuously track capacitive voltage transformer measurement errors online with precision over extended durations.
基金Project(52174245)supported by the National Natural Science Foundation of ChinaProject(2021J01640)supported by the Natural Science Foundation of Fujian Province,ChinaProject(BGRIMM-KJSKL2022-03)supported by Open Foundation of the State Key Laboratory of Mineral Processing,China。
文摘Micro-and nano-to millimeter-scale magnetic matrix materials have gained widespread application due to their exceptional magnetic properties and favorable cost-effectiveness.With the rapid progress in condensed matter physics,materials science,and mineral separation technologies,these materials are now poised for new opportunities in theoretical research and development.This review provides a comprehensive analysis of these matrices,encompassing their structure,size,shape,composition,properties,and multifaceted applications.These materials,primarily composed of alloys of transition state metasl such as iron(Fe),cobalt(Co),titanium(Ti),and nickel(Ni),exhibit unique attributes like high magnetization rates,low eleastic modulus,and high saturation magnetic field strengths.Furthermore,the studies also delve into the complex mechanical interactions involved in the separation of magnetic particles using magnetic separator matrices,including magnetic,gravitational,centrifugal,and van der Waals forces.The review outlines how size and shape effects influence the magnetic behavior of matrices,offering new perspectives for innovative applications of magnetic matrices in various domains of materials science and magnetic separation.
基金supported by the Natural Science Foundation of Shandong Province,No.ZR2023MC168the National Natural Science Foundation of China,No.31670989the Key R&D Program of Shandong Province,No.2019GSF107037(all to CS).
文摘Vascular endothelial growth factor and its mimic peptide KLTWQELYQLKYKGI(QK)are widely used as the most potent angiogenic factors for the treatment of multiple ischemic diseases.However,conventional topical drug delivery often results in a burst release of the drug,leading to transient retention(inefficacy)and undesirable diffusion(toxicity)in vivo.Therefore,a drug delivery system that responds to changes in the microenvironment of tissue regeneration and controls vascular endothelial growth factor release is crucial to improve the treatment of ischemic stroke.Matrix metalloproteinase-2(MMP-2)is gradually upregulated after cerebral ischemia.Herein,vascular endothelial growth factor mimic peptide QK was self-assembled with MMP-2-cleaved peptide PLGLAG(TIMP)and customizable peptide amphiphilic(PA)molecules to construct nanofiber hydrogel PA-TIMP-QK.PA-TIMP-QK was found to control the delivery of QK by MMP-2 upregulation after cerebral ischemia/reperfusion and had a similar biological activity with vascular endothelial growth factor in vitro.The results indicated that PA-TIMP-QK promoted neuronal survival,restored local blood circulation,reduced blood-brain barrier permeability,and restored motor function.These findings suggest that the self-assembling nanofiber hydrogel PA-TIMP-QK may provide an intelligent drug delivery system that responds to the microenvironment and promotes regeneration and repair after cerebral ischemia/reperfusion injury.
基金Supported by Health Science and Technology Programme of Zhejiang Province,No.2022KY1391.
文摘Intestinal ischemia-reperfusion injury(IIRI)is a complex and severe pathophysiological process characterized by oxidative stress,inflammation,and apoptosis.In recent years,the critical roles of extracellular matrix(ECM)genes and microRNAs(miRNAs)in IIRI have garnered widespread attention.This review aims to systematically summarize the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI.First,we review the molecular mechanisms of IIRI,focusing on the dual role of the ECM in tissue injury and repair processes.The expression changes and functions of ECM components such as collagen,elastin,and matrix metalloproteinases during IIRI progression are deeply analyzed.Second,we systematically summarize the regulatory roles of miRNAs in IIRI,particularly the mechanisms and functions of miRNAs such as miR-125b and miR-200a in regulating inflammation,apoptosis,and ECM remodeling.Additionally,this review discusses potential diagnostic biomarkers and treatment strategies based on ECM genes and miRNAs.We extensively evaluate the prospects of miRNA-targeted therapy and ECM component modulation in preventing and treating IIRI,emphasizing the clinical translational potential of these emerging therapies.In conclusion,the diagnostic and therapeutic potential of ECM gene sets and miRNA regulatory networks in IIRI provides new directions for further research,necessitating additional clinical and basic studies to validate and expand these findings for improving clinical outcomes in IIRI patients.
文摘Aneurysms and dissections represent some of the most serious cardiovascular diseases.The prevailing theory posits that mechanical overloading of the vessel wall is the underlying cause.Inspired by Barkhordarian et al,the authors present matrix metalloproteinases(MMPs)and their inhibitors in immunohistological analyses as contributing factors in the pathophysiology of aortic aneurysms(AA).Data analysis of MMP-1,MMP-9,tissue inhibitors of metalloproteinases(TIMPs),including TIMP-1 and TIMP-2 expression reveals a varied distribution between the adventitia and media and a non-uniform expression of the investigated markers.These elements,as key components of the extracellular matrix(ECM),indicate that the formation of AA is not solely driven by endoluminal pressure loading of the aortic wall.Instead,degenerative processes within ECM elements contribute significantly.Importantly,AA do not necessarily imply dissection.Tissue destruction,allowing blood flow entry,arises from reduced oxygen supply to the media,primarily due to incomplete capillarization or neocapillarization.
