The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is develop...The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is developed.In this representation,an expansion center is adopted within the neighborhood of the sources.Then a high-frequency electromagnetic scattering evaluation algorithm is formulated,combining the refined physical optics(PO)and equivalent edge current(EEC)algorithm.The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field(NF)scattering estimation.Afterwards,two basic concepts of the angular glint are briefly introduced and formulated.The proposed procedure makes preparation for the computation of NF linear deviation.Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm.The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.展开更多
The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on e...The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric ana...Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.展开更多
Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an uns...Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
The precise tuning of magnetic nanoparticle size and magnetic domains,thereby shaping magnetic properties.However,the dynamic evolution mechanisms of magnetic domain configurations in relation to electromagnetic(EM)at...The precise tuning of magnetic nanoparticle size and magnetic domains,thereby shaping magnetic properties.However,the dynamic evolution mechanisms of magnetic domain configurations in relation to electromagnetic(EM)attenuation behavior remain poorly understood.To address this gap,a thermodynamically controlled periodic coordination strategy is proposed to achieve precise modulation of magnetic nanoparticle spacing.This approach unveils the evolution of magnetic domain configurations,progressing from individual to coupled and ultimately to crosslinked domain configurations.A unique magnetic coupling phenomenon surpasses the Snoek limit in low-frequency range,which is observed through micromagnetic simulation.The crosslinked magnetic configuration achieves effective low-frequency EM wave absorption at 3.68 GHz,encompassing nearly the entire C-band.This exceptional magnetic interaction significantly enhances radar camouflage and thermal insulation properties.Additionally,a robust gradient metamaterial design extends coverage across the full band(2–40 GHz),effectively mitigating the impact of EM pollution on human health and environment.This comprehensive study elucidates the evolution mechanisms of magnetic domain configurations,addresses gaps in dynamic magnetic modulation,and provides novel insights for the development of high-performance,low-frequency EM wave absorption materials.展开更多
Objective:Breast cancer is the most common malignancy in women and is characterized by a high recurrence rate that severely impacts patient survival.Regulatory T cells(Tregs)in the tumor microenvironment(TME)promote i...Objective:Breast cancer is the most common malignancy in women and is characterized by a high recurrence rate that severely impacts patient survival.Regulatory T cells(Tregs)in the tumor microenvironment(TME)promote immune evasion and metastasis,increasing recurrence risk.This study determined how the epigenetic regulators,DNMT3A and METTL7A,modulate Treg infiltration via the DDR1/STAT3/CXCL5 axis and influence breast cancer recurrence and prognosis.Methods:RNA sequencing(RNA-seq)was used to identify differentially expressed genes(DEGs),followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),supported vector machine-recursive feature elimination(SVM-RFE)and ElasticNet identified DDR1 as a key gene.Validation included RT-qPCR,western blot,MSP,MeRIP-qPCR,and Co-IP to assess epigenetic regulation.Functional assays(CCK-8,Transwell,and Treg differentiation/chemotaxis)and xenograft models evaluated the role of DDR1 in tumor progression and recurrence.Results:DNMT3A upregulated DDR1 via DNA methylation,while METTL7A enhanced DDR1 mRNA stability via m6A modification.Co-regulation activated the DDR1/STAT3/CXCL5 axis,which boosted cancer cell proliferation,migration,and invasion.CXCL5 secretion increased Treg infiltration and accelerated tumor growth in vivo.DDR1 silencing reversed these effects,confirming that DDR1 has a pivotal role in breast cancer recurrence.Conclusion:DNMT3A and METTL7A were shown to cooperatively regulate DDR1 via DNA/m6A methylation,which drives Tregmediated immune suppression and recurrence.This study provided novel insights and therapeutic targets for breast cancer prognosis and treatment.展开更多
In this article, we deal with the uniqueness problems on meromorphic functions sharing two finite sets in an angular domain instead of the whole plane C. In particular, we investigate the uniqueness for meromorphic fu...In this article, we deal with the uniqueness problems on meromorphic functions sharing two finite sets in an angular domain instead of the whole plane C. In particular, we investigate the uniqueness for meromorphic functions of infinite order in an angular domain and obtain some results. Moreover, examples show that the conditions in theorems are necessary.展开更多
In this article, we study the uniqueness question of nonconstant meromorphic functions whose nonlinear differential polynomials share 1 or have the same fixed points in an angular domain. The results in this article i...In this article, we study the uniqueness question of nonconstant meromorphic functions whose nonlinear differential polynomials share 1 or have the same fixed points in an angular domain. The results in this article improve Theorem 1 of Yang and Hua [26], and improve Theorem 1 of Fang and Qiu [6].展开更多
In computer aided geometric design （CAGD）, B′ezier-like bases receive more andmore considerations as new modeling tools in recent years. But those existing B′ezier-like basesare all defined over the rectangular do...In computer aided geometric design （CAGD）, B′ezier-like bases receive more andmore considerations as new modeling tools in recent years. But those existing B′ezier-like basesare all defined over the rectangular domain. In this paper, we extend the algebraic trigono-metric B′ezier-like basis of order 4 to the triangular domain. The new basis functions definedover the triangular domain are proved to fulfill non-negativity, partition of unity, symmetry,boundary representation, linear independence and so on. We also prove some properties of thecorresponding B′ezier-like surfaces. Finally, some applications of the proposed basis are shown.展开更多
We investigate the angular-dependent multi-mode resonance frequencies in CoZr magnetic thin films with a rotatable stripe domain structure.A variable range of multi-mode resonance frequencies from 1.86 GHz to 4.80 GHz...We investigate the angular-dependent multi-mode resonance frequencies in CoZr magnetic thin films with a rotatable stripe domain structure.A variable range of multi-mode resonance frequencies from 1.86 GHz to 4.80 GHz is achieved by pre-magnetizing the CoZr films along different azimuth directions,which can be ascribed to the competition between the uniaxial anisotropy caused by the oblique deposition and the rotatable anisotropy induced by the rotatable stripe domain.Furthermore,the regulating range of resonance frequency for the CoZr film can be adjusted by changing the oblique deposition angle.Our results might be beneficial for the applications of magnetic thin films in microwave devices.展开更多
In this paper, a new analytical method of symplectic system, Hamiltonian system, is introduced for solving the problem of the Stokes flow in a two-dimensional rectangular domain. In the system, the fundamental problem...In this paper, a new analytical method of symplectic system, Hamiltonian system, is introduced for solving the problem of the Stokes flow in a two-dimensional rectangular domain. In the system, the fundamental problem is reduced to an eigenvalue and eigensolution problem. The solution and boundary conditions can be expanded by eigensolutions using adjoint relationships of the symplectic ortho-normalization between the eigensolutions. A closed method of the symplectic eigensolution is presented based on completeness of the symplectic eigensolution space. The results show that fundamental flows can be described by zero eigenvalue eigensolutions, and local effects by nonzero eigenvalue eigensolutions. Numerical examples give various flows in a rectangular domain and show effectiveness of the method for solving a variety of problems. Meanwhile, the method can be used in solving other problems.展开更多
Rectangular reflector antennas have motivated the time-domain analysis of electromagnetic scattering problems. The asymptotic time domain physical-optics (TDPO) is applied to the analysis of a rectangular reflector il...Rectangular reflector antennas have motivated the time-domain analysis of electromagnetic scattering problems. The asymptotic time domain physical-optics (TDPO) is applied to the analysis of a rectangular reflector illuminated by a Gaussian-impulse. The effects of time-delayed mutual coupling between points on the surface will be ignored as a result of utilizing the TDPO method for determining the equivalent surface-current density on the reflector. Finally, in this work the scattered signals at the specular reflection point, at the edges, and at the corners can be clearly distinguished.展开更多
In order to increase the capacity of encrypted information and reduce the loss of information transmission, a three-dimensional(3 D) scene encryption algorithm based on the phase iteration of the angular spectrum doma...In order to increase the capacity of encrypted information and reduce the loss of information transmission, a three-dimensional(3 D) scene encryption algorithm based on the phase iteration of the angular spectrum domain is proposed in this paper. The algorithm, which adopts the layer-oriented method, generates the computer generated hologram by encoding the three-dimensional scene. Then the computer generated hologram is encoded into three pure phase functions by adopting the phase iterative algorithm based on angular spectrum domain,and the encryption process is completed. The three-dimensional scene encryption can improve the capacity of the information,and the three-phase iterative algorithm can guarantee the security of the encryption information. The numerical simulation results show that the algorithm proposed in this paper realized the encryption and decryption of three-dimensional scenes. At the same time, it can ensure the safety of the encrypted information and increase the capacity of the encrypted information.展开更多
By introducing noncanonical vortex pairs to partially coherent beams, spatial correlation singularity (SCS) and orbital angular momenta (OAM) of the resulting beams are studied using the Fraunhofer diffraction integra...By introducing noncanonical vortex pairs to partially coherent beams, spatial correlation singularity (SCS) and orbital angular momenta (OAM) of the resulting beams are studied using the Fraunhofer diffraction integral. The effect of noncanonical strength, off-axis distance and vortex sign on spatial correlation singularities in far field is stressed. Furthermore, far-field OAM spectra and densities are also investigated, and the OAM detection and crosstalk probabilities are discussed. The results show that the number of dislocations of SCS always equals the sum of absolute values of topological charges for canonical or noncanonical vortex pairs. Although the sum of the product of each OAM mode and its power weight equals the algebraic sum of topological charges for canonical vortex pairs, the relationship no longer holds in the noncanonical case except for opposite-charge vortex pairs. The changes of off-axis distance, noncanonical strength or coherence length can lead to a more dominant power in adjacent mode than that in center detection mode, which also indicates that crosstalk probabilities of adjacent modes exceed the center detection probability. This work may provide potential applications in OAM-based optical communication, imaging, sensing and computing.展开更多
Eddy current (EC) distribution induced by EC sensors determines the interaction between the defectin the testing specimen and the EC, so quantitatively evaluating EC distribution is crucial to the design of ECsensors....Eddy current (EC) distribution induced by EC sensors determines the interaction between the defectin the testing specimen and the EC, so quantitatively evaluating EC distribution is crucial to the design of ECsensors. In this study, two indices based on the information entropy are proposed to evaluate the EC energyallocated in different directions. The EC vectors induced by a rotational field EC sensor varying in the timedomain are evaluated by the proposed methods. Then, the evaluating results are analyzed by the principle ofEC testing. It can be concluded that the two indices can effectively quantitatively evaluate the EC distributionsvarying in the time domain and are used to optimize the parameters of the rotational EC sensors.展开更多
文摘The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is developed.In this representation,an expansion center is adopted within the neighborhood of the sources.Then a high-frequency electromagnetic scattering evaluation algorithm is formulated,combining the refined physical optics(PO)and equivalent edge current(EEC)algorithm.The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field(NF)scattering estimation.Afterwards,two basic concepts of the angular glint are briefly introduced and formulated.The proposed procedure makes preparation for the computation of NF linear deviation.Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm.The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.
基金supported by the National Key Research and Development Program of China(No.2023YFB3712401),the National Natural Science Foundation of China(No.52274301)the Aeronautical Science Foundation of China(No.2023Z0530S6005)the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C).
文摘The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by grants for development of new faculty staff,Ratchadaphiseksomphot Fund,Chulalongkorn University,Thailand.
文摘Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375255,51935007)the Shanghai Rising-Star Program(Grant No.24QB2705000)。
文摘Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the National Natural Science Foundation of China(22265021,52231007,and 12327804)the Aeronautical Science Foundation of China(2020Z056056003)Jiangxi Provincial Natural Science Foundation(20232BAB212004).
文摘The precise tuning of magnetic nanoparticle size and magnetic domains,thereby shaping magnetic properties.However,the dynamic evolution mechanisms of magnetic domain configurations in relation to electromagnetic(EM)attenuation behavior remain poorly understood.To address this gap,a thermodynamically controlled periodic coordination strategy is proposed to achieve precise modulation of magnetic nanoparticle spacing.This approach unveils the evolution of magnetic domain configurations,progressing from individual to coupled and ultimately to crosslinked domain configurations.A unique magnetic coupling phenomenon surpasses the Snoek limit in low-frequency range,which is observed through micromagnetic simulation.The crosslinked magnetic configuration achieves effective low-frequency EM wave absorption at 3.68 GHz,encompassing nearly the entire C-band.This exceptional magnetic interaction significantly enhances radar camouflage and thermal insulation properties.Additionally,a robust gradient metamaterial design extends coverage across the full band(2–40 GHz),effectively mitigating the impact of EM pollution on human health and environment.This comprehensive study elucidates the evolution mechanisms of magnetic domain configurations,addresses gaps in dynamic magnetic modulation,and provides novel insights for the development of high-performance,low-frequency EM wave absorption materials.
基金supported by the National Natural Science Foundation of China(Grant No.82060479)Key Research and Development Program of Ningxia Hui Autonomous Region(Grant No.2021BEG03062)Ningxia Natural Science Fund Key Project(Grant No.2024AAC02080).
文摘Objective:Breast cancer is the most common malignancy in women and is characterized by a high recurrence rate that severely impacts patient survival.Regulatory T cells(Tregs)in the tumor microenvironment(TME)promote immune evasion and metastasis,increasing recurrence risk.This study determined how the epigenetic regulators,DNMT3A and METTL7A,modulate Treg infiltration via the DDR1/STAT3/CXCL5 axis and influence breast cancer recurrence and prognosis.Methods:RNA sequencing(RNA-seq)was used to identify differentially expressed genes(DEGs),followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),supported vector machine-recursive feature elimination(SVM-RFE)and ElasticNet identified DDR1 as a key gene.Validation included RT-qPCR,western blot,MSP,MeRIP-qPCR,and Co-IP to assess epigenetic regulation.Functional assays(CCK-8,Transwell,and Treg differentiation/chemotaxis)and xenograft models evaluated the role of DDR1 in tumor progression and recurrence.Results:DNMT3A upregulated DDR1 via DNA methylation,while METTL7A enhanced DDR1 mRNA stability via m6A modification.Co-regulation activated the DDR1/STAT3/CXCL5 axis,which boosted cancer cell proliferation,migration,and invasion.CXCL5 secretion increased Treg infiltration and accelerated tumor growth in vivo.DDR1 silencing reversed these effects,confirming that DDR1 has a pivotal role in breast cancer recurrence.Conclusion:DNMT3A and METTL7A were shown to cooperatively regulate DDR1 via DNA/m6A methylation,which drives Tregmediated immune suppression and recurrence.This study provided novel insights and therapeutic targets for breast cancer prognosis and treatment.
基金Supported by the NNSFC (10671109)the NSFFC(2008J0190)+1 种基金the Research Fund for Talent Introduction of Ningde Teachers College (2009Y019)the Scitific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
文摘In this article, we deal with the uniqueness problems on meromorphic functions sharing two finite sets in an angular domain instead of the whole plane C. In particular, we investigate the uniqueness for meromorphic functions of infinite order in an angular domain and obtain some results. Moreover, examples show that the conditions in theorems are necessary.
基金supported by the NSFC(11171184)the NSF of Shandong Province,China(Z2008A01)
文摘In this article, we study the uniqueness question of nonconstant meromorphic functions whose nonlinear differential polynomials share 1 or have the same fixed points in an angular domain. The results in this article improve Theorem 1 of Yang and Hua [26], and improve Theorem 1 of Fang and Qiu [6].
基金Supported by the National Natural Science Foundation of China( 60933008,60970079)
文摘In computer aided geometric design （CAGD）, B′ezier-like bases receive more andmore considerations as new modeling tools in recent years. But those existing B′ezier-like basesare all defined over the rectangular domain. In this paper, we extend the algebraic trigono-metric B′ezier-like basis of order 4 to the triangular domain. The new basis functions definedover the triangular domain are proved to fulfill non-negativity, partition of unity, symmetry,boundary representation, linear independence and so on. We also prove some properties of thecorresponding B′ezier-like surfaces. Finally, some applications of the proposed basis are shown.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51871117 and 51671099)the Program for Changjiang Scholars and Innovative Research Team in University,China(Grant No.IRT-16R35)the Gansu Provincial Science Foundation for Distinguished Young Scholars,China(Grant No.20JR10RA649).
文摘We investigate the angular-dependent multi-mode resonance frequencies in CoZr magnetic thin films with a rotatable stripe domain structure.A variable range of multi-mode resonance frequencies from 1.86 GHz to 4.80 GHz is achieved by pre-magnetizing the CoZr films along different azimuth directions,which can be ascribed to the competition between the uniaxial anisotropy caused by the oblique deposition and the rotatable anisotropy induced by the rotatable stripe domain.Furthermore,the regulating range of resonance frequency for the CoZr film can be adjusted by changing the oblique deposition angle.Our results might be beneficial for the applications of magnetic thin films in microwave devices.
文摘In this paper, a new analytical method of symplectic system, Hamiltonian system, is introduced for solving the problem of the Stokes flow in a two-dimensional rectangular domain. In the system, the fundamental problem is reduced to an eigenvalue and eigensolution problem. The solution and boundary conditions can be expanded by eigensolutions using adjoint relationships of the symplectic ortho-normalization between the eigensolutions. A closed method of the symplectic eigensolution is presented based on completeness of the symplectic eigensolution space. The results show that fundamental flows can be described by zero eigenvalue eigensolutions, and local effects by nonzero eigenvalue eigensolutions. Numerical examples give various flows in a rectangular domain and show effectiveness of the method for solving a variety of problems. Meanwhile, the method can be used in solving other problems.
文摘Rectangular reflector antennas have motivated the time-domain analysis of electromagnetic scattering problems. The asymptotic time domain physical-optics (TDPO) is applied to the analysis of a rectangular reflector illuminated by a Gaussian-impulse. The effects of time-delayed mutual coupling between points on the surface will be ignored as a result of utilizing the TDPO method for determining the equivalent surface-current density on the reflector. Finally, in this work the scattered signals at the specular reflection point, at the edges, and at the corners can be clearly distinguished.
基金supported by the Natural Science ResearchProject of the Colleges and Universities of Anhui Province(KJ2016A056)Natural Science Foundation of Anhui Province of China(1508085MF121)National Natural Science Foundation of China(61572032)。
文摘In order to increase the capacity of encrypted information and reduce the loss of information transmission, a three-dimensional(3 D) scene encryption algorithm based on the phase iteration of the angular spectrum domain is proposed in this paper. The algorithm, which adopts the layer-oriented method, generates the computer generated hologram by encoding the three-dimensional scene. Then the computer generated hologram is encoded into three pure phase functions by adopting the phase iterative algorithm based on angular spectrum domain,and the encryption process is completed. The three-dimensional scene encryption can improve the capacity of the information,and the three-phase iterative algorithm can guarantee the security of the encryption information. The numerical simulation results show that the algorithm proposed in this paper realized the encryption and decryption of three-dimensional scenes. At the same time, it can ensure the safety of the encrypted information and increase the capacity of the encrypted information.
文摘By introducing noncanonical vortex pairs to partially coherent beams, spatial correlation singularity (SCS) and orbital angular momenta (OAM) of the resulting beams are studied using the Fraunhofer diffraction integral. The effect of noncanonical strength, off-axis distance and vortex sign on spatial correlation singularities in far field is stressed. Furthermore, far-field OAM spectra and densities are also investigated, and the OAM detection and crosstalk probabilities are discussed. The results show that the number of dislocations of SCS always equals the sum of absolute values of topological charges for canonical or noncanonical vortex pairs. Although the sum of the product of each OAM mode and its power weight equals the algebraic sum of topological charges for canonical vortex pairs, the relationship no longer holds in the noncanonical case except for opposite-charge vortex pairs. The changes of off-axis distance, noncanonical strength or coherence length can lead to a more dominant power in adjacent mode than that in center detection mode, which also indicates that crosstalk probabilities of adjacent modes exceed the center detection probability. This work may provide potential applications in OAM-based optical communication, imaging, sensing and computing.
基金Foundation item:the National Natural Science Foundation of China(No.51807086)the Young Doctoral Fund of Education Department of Gansu Province(No.2021QB-047)the Hongliu Youth Fund of Lanzhou University of Technology(No.07/062003)。
文摘Eddy current (EC) distribution induced by EC sensors determines the interaction between the defectin the testing specimen and the EC, so quantitatively evaluating EC distribution is crucial to the design of ECsensors. In this study, two indices based on the information entropy are proposed to evaluate the EC energyallocated in different directions. The EC vectors induced by a rotational field EC sensor varying in the timedomain are evaluated by the proposed methods. Then, the evaluating results are analyzed by the principle ofEC testing. It can be concluded that the two indices can effectively quantitatively evaluate the EC distributionsvarying in the time domain and are used to optimize the parameters of the rotational EC sensors.
