Chinese Journal of Notural Mdicines(CINM,ISSN 2095-6975,original ISSN 1672-3651)was founded in May 2003 and is sponsored by China Pharmaceutical University and Chinese Phar-maccutical Association.The printed version o...Chinese Journal of Notural Mdicines(CINM,ISSN 2095-6975,original ISSN 1672-3651)was founded in May 2003 and is sponsored by China Pharmaceutical University and Chinese Phar-maccutical Association.The printed version of CINM is published monthly by Science Press and is web edition is published by Elsevier.CJNM is devoted to communications among pharmaccutical and medicinal plant scientists who are interested in the advance-ment of the botanical,chemical,and biological sciences related to natural medicines,ineluding traditional Chinese medicines(TCM).展开更多
In this paper,we investigate the uniqueness of meromorphic functions and their derivatives in the unit disc and consider the relations between the Borel points and the shared-values of meromorphic functions in an angu...In this paper,we investigate the uniqueness of meromorphic functions and their derivatives in the unit disc and consider the relations between the Borel points and the shared-values of meromorphic functions in an angular domain by Nevanlinna value distribution theory.An admissible meromorphic function with orde or precise order has Borel point and shares IM common values with its derivative in an angular domain of the unit disc,then the meromorphic function and its derivative are unique.The obtained results improve and generalize some existing results and enrich the uniqueness theory of meromorphic functions.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
The development of β-titanium alloys with bone-mimicking elastic moduli remains a significant challenge.Although machine learning has the potential to accelerate alloy discovery,traditional methods often face data li...The development of β-titanium alloys with bone-mimicking elastic moduli remains a significant challenge.Although machine learning has the potential to accelerate alloy discovery,traditional methods often face data limitations such as sparsity,compositional discontinuity,and feature heterogeneity,leading to overfitting and restricting the exploration of novel compositional spaces.In this study,we introduce a domain-adversarial neural network framework that balances predictive accuracy with the generalization ability of unexplored composition space through integrated feature alignment and adversarial training.Using this approach,we successfully developed a non-intuitiveβ-Ti alloy with an ultra-low elastic modulus of 28±3 GPa,providing new insights beyond conventionally designed biomedical titanium alloys.This work establishes a screening framework for materials discovery in small-sample data spaces,with broad implications for the design of biomedical and other alloy systems.展开更多
Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater imag...Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.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
This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems unde...This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability.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
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
In sodium-ion hybrid capacitors(SIHCs),the high-capacity metal selenide anodes are severely limited by structural instability and polyselenide dissolution/shuttle during cycling.This study proposes an innovative strat...In sodium-ion hybrid capacitors(SIHCs),the high-capacity metal selenide anodes are severely limited by structural instability and polyselenide dissolution/shuttle during cycling.This study proposes an innovative strategy utilizing high-electronegativity N(χ=3.04)to modulate local electronic domains and stabilize amorphous Mo–Se coordination(N/Mo-Se).Through self-polymerization and tunable selenization,N-doped carbon(NC)nanospheres encapsulating N-stabilized amorphous Mo-Se clusters(N/Mo-Se@NC)are successfully constructed.Theoretical and experimental analyses reveal that N-optimization effectively reconstructs the electronic distribution of Mo–Se coordination via strong covalent Mo–N bonds.This significantly enhances the covalency of Mo-Se clusters and induces localized electronic domains,thereby substantially suppressing polyselenide dissolution/shuttle during cycling.Concurrently,the amorphous N/Mo-Se clusters provide isotropic ion diffusion pathways,and together with the threedimensional(3D)conductive networks of the NC,they jointly optimize charge transfer kinetics.The N/Mo-Se@NC anode exhibits a high reversible capacity of 328.7 mAh g^(-1)after 5000 cycles,even at 10.0 A g^(-1),with a remarkable capacity retention of 110%.The assembled N/Mo-Se@NC//AC SIHCs achieve high energy/power densities(236.1 Wh kg^(-1)/9990 W kg^(-1)),demonstrating superior comprehensive performance compared to most previously reported anodes.This study,through high-electronegativity atom modulation and amorphization engineering,opens new avenues for designing highly stable and high-rate Na^(+) storage 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
文摘Chinese Journal of Notural Mdicines(CINM,ISSN 2095-6975,original ISSN 1672-3651)was founded in May 2003 and is sponsored by China Pharmaceutical University and Chinese Phar-maccutical Association.The printed version of CINM is published monthly by Science Press and is web edition is published by Elsevier.CJNM is devoted to communications among pharmaccutical and medicinal plant scientists who are interested in the advance-ment of the botanical,chemical,and biological sciences related to natural medicines,ineluding traditional Chinese medicines(TCM).
文摘In this paper,we investigate the uniqueness of meromorphic functions and their derivatives in the unit disc and consider the relations between the Borel points and the shared-values of meromorphic functions in an angular domain by Nevanlinna value distribution theory.An admissible meromorphic function with orde or precise order has Borel point and shares IM common values with its derivative in an angular domain of the unit disc,then the meromorphic function and its derivative are unique.The obtained results improve and generalize some existing results and enrich the uniqueness theory of meromorphic functions.
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 Guangdong S&T Program(Grant No.2025B1111130003).
文摘The development of β-titanium alloys with bone-mimicking elastic moduli remains a significant challenge.Although machine learning has the potential to accelerate alloy discovery,traditional methods often face data limitations such as sparsity,compositional discontinuity,and feature heterogeneity,leading to overfitting and restricting the exploration of novel compositional spaces.In this study,we introduce a domain-adversarial neural network framework that balances predictive accuracy with the generalization ability of unexplored composition space through integrated feature alignment and adversarial training.Using this approach,we successfully developed a non-intuitiveβ-Ti alloy with an ultra-low elastic modulus of 28±3 GPa,providing new insights beyond conventionally designed biomedical titanium alloys.This work establishes a screening framework for materials discovery in small-sample data spaces,with broad implications for the design of biomedical and other alloy systems.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.
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 the National Key Research and Development Program of China(No.2022YFC2204400)。
文摘This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability.
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 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.52573299)the Natural Science Foundation of Jiangxi province(Grant Nos.20242BAB25223,20232BCJ23025,20232BCJ25040,and 20232BAB214024)the Special Funding Program for Graduate Student Innovation of Jiangxi Province(No.YC2024-S594)。
文摘In sodium-ion hybrid capacitors(SIHCs),the high-capacity metal selenide anodes are severely limited by structural instability and polyselenide dissolution/shuttle during cycling.This study proposes an innovative strategy utilizing high-electronegativity N(χ=3.04)to modulate local electronic domains and stabilize amorphous Mo–Se coordination(N/Mo-Se).Through self-polymerization and tunable selenization,N-doped carbon(NC)nanospheres encapsulating N-stabilized amorphous Mo-Se clusters(N/Mo-Se@NC)are successfully constructed.Theoretical and experimental analyses reveal that N-optimization effectively reconstructs the electronic distribution of Mo–Se coordination via strong covalent Mo–N bonds.This significantly enhances the covalency of Mo-Se clusters and induces localized electronic domains,thereby substantially suppressing polyselenide dissolution/shuttle during cycling.Concurrently,the amorphous N/Mo-Se clusters provide isotropic ion diffusion pathways,and together with the threedimensional(3D)conductive networks of the NC,they jointly optimize charge transfer kinetics.The N/Mo-Se@NC anode exhibits a high reversible capacity of 328.7 mAh g^(-1)after 5000 cycles,even at 10.0 A g^(-1),with a remarkable capacity retention of 110%.The assembled N/Mo-Se@NC//AC SIHCs achieve high energy/power densities(236.1 Wh kg^(-1)/9990 W kg^(-1)),demonstrating superior comprehensive performance compared to most previously reported anodes.This study,through high-electronegativity atom modulation and amorphization engineering,opens new avenues for designing highly stable and high-rate Na^(+) storage 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.
