In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval...In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval is singular or all four endpoints are regulars are the special cases).And these extensions yield"new"self-adjoint operators,which involve interactions between the two intervals.展开更多
In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequen...In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequence of L^(1)functions converging to the given function and verifying their representation in the form of Fourier transform to establish the desired result of the given function.Applying this main result,we further generalize the Paley-Wiener theorem for band-limited functions to the analytic function spaces L^(p)(0<p<∞)with general weights.展开更多
The mitogen-activated protein kinase kinase kinase kinases(MAP4Ks)signaling pathway plays a pivotal role in axonal regrowth and neuronal degeneration following insults.Whether targeting this pathway is beneficial to b...The mitogen-activated protein kinase kinase kinase kinases(MAP4Ks)signaling pathway plays a pivotal role in axonal regrowth and neuronal degeneration following insults.Whether targeting this pathway is beneficial to brain injury remains unclear.In this study,we showed that adeno-associated virus-delivery of the Citron homology domain of MAP4Ks effectively reduces traumatic brain injury-induced reactive gliosis,tauopathy,lesion size,and behavioral deficits.Pharmacological inhibition of MAP4Ks replicated the ameliorative effects observed with expression of the Citron homology domain.Mechanistically,the Citron homology domain acted as a dominant-negative mutant,impeding MAP4K-mediated phosphorylation of the dishevelled proteins and thereby controlling the Wnt/β-catenin pathway.These findings implicate a therapeutic potential of targeting MAP4Ks to alleviate the detrimental effects of traumatic brain injury.展开更多
Pb(Zr,Ti)O_(3)-Pb(Zn_(1/3)Nb_(2/3))O_(3) (PZT-PZN) based ceramics, as important piezoelectric materials, have a wide range of applications in fields such as sensors and actuators, thus the optimization of their piezoe...Pb(Zr,Ti)O_(3)-Pb(Zn_(1/3)Nb_(2/3))O_(3) (PZT-PZN) based ceramics, as important piezoelectric materials, have a wide range of applications in fields such as sensors and actuators, thus the optimization of their piezoelectric properties has been a hot research topic. This study investigated the effects of phase boundary engineering and domain engineering on (1-x)[0.8Pb(Zr_(0.5)Ti_(0.5))O_(3)-0.2Pb(Zn_(1/3)Nb_(2/3))O_(3)]-xBi(Zn_(0.5)Ti_(0.5))O_(3) ((1-x)(0.8PZT-0.2PZN)- xBZT) ceramic to obtain excellent piezoelectric properties. The crystal phase structure and microstructure of ceramic samples were characterized. The results showed that all samples had a pure perovskite structure, and the addition of BZT gradually increased the grain size. The addition of BZT caused a phase transition in ceramic samples from the morphotropic phase boundary (MPB) towards the tetragonal phase region, which is crucial for optimizing piezoelectric properties. By adjusting content of BZT and precisely controlling position of the phase boundary, the piezoelectric performance can be optimized. Domain structure is one of the key factors affecting piezoelectric performance. By using domain engineering techniques to optimize grain size and domain size, piezoelectric properties of ceramic samples have been significantly improved. Specifically, excellent piezoelectric properties (piezoelectric constant d_(33)=320 pC/N, electromechanical coupling factor kp=0.44) were obtained simultaneously for x=0.08. Based on experimental results and theoretical analysis, influence mechanisms of phase boundary engineering and domain engineering on piezoelectric properties were explored. The study shows that addition of BZT not only promotes grain growth, but also optimizes the domain structure, enabling the polarization reversal process easier, thereby improving piezoelectric properties. These research results not only provide new ideas for the design of high-performance piezoelectric ceramics, but also lay a theoretical foundation for development of related electronic devices.展开更多
To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions...To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions in the form of noise and artifacts,are kept to a bare minimum.The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality,while indirectly attacking the effectiveness of the diagnostic process.It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise.The solution to these challenges presents a complex dilemma at the acquisition stage,where image processing techniques must be adopted.The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices.This article hereby provides an extensive systematic review of the above techniques,with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise,i.e.,Gaussian,Poisson,and Rician.A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method.The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis,such as tumor detection in MRI and vascular imaging in CT.展开更多
The functional and structural integrity of the blood-brain barrier is crucial in maintaining homeostasis in the brain microenvironment;however,the molecular mechanisms underlying the formation and function of the bloo...The functional and structural integrity of the blood-brain barrier is crucial in maintaining homeostasis in the brain microenvironment;however,the molecular mechanisms underlying the formation and function of the blood-brain barrier remain poorly understood.The major facilitator superfamily domain containing 2A has been identified as a key regulator of blood-brain barrier function.It plays a critical role in promoting and maintaining the formation and functional stability of the blood-brain barrier,in addition to the transport of lipids,such as docosahexaenoic acid,across the blood-brain barrier.Furthermore,an increasing number of studies have suggested that major facilitator superfamily domain containing 2A is involved in the molecular mechanisms of blood-brain barrier dysfunction in a variety of neurological diseases;however,little is known regarding the mechanisms by which major facilitator superfamily domain containing 2A affects the blood-brain barrier.This paper provides a comprehensive and systematic review of the close relationship between major facilitator superfamily domain containing 2A proteins and the blood-brain barrier,including their basic structures and functions,cross-linking between major facilitator superfamily domain containing 2A and the blood-brain barrier,and the in-depth studies on lipid transport and the regulation of blood-brain barrier permeability.This comprehensive systematic review contributes to an in-depth understanding of the important role of major facilitator superfamily domain containing 2A proteins in maintaining the structure and function of the blood-brain barrier and the research progress to date.This will not only help to elucidate the pathogenesis of neurological diseases,improve the accuracy of laboratory diagnosis,and optimize clinical treatment strategies,but it may also play an important role in prognostic monitoring.In addition,the effects of major facilitator superfamily domain containing 2A on blood-brain barrier leakage in various diseases and the research progress on cross-blood-brain barrier drug delivery are summarized.This review may contribute to the development of new approaches for the treatment of neurological diseases.展开更多
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me...Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.展开更多
Lithium niobate(LN)has remained at the forefront of academic research and industrial applications due to its rich material properties,which include second-order nonlinear optic,electro-optic,and piezoelectric properti...Lithium niobate(LN)has remained at the forefront of academic research and industrial applications due to its rich material properties,which include second-order nonlinear optic,electro-optic,and piezoelectric properties.A further aspect of LN’s versatility stems from the ability to engineer ferroelectric domains with micro and even nano-scale precision in LN,which provides an additional degree of freedom to design acoustic and optical devices with improved performance and is only possible in a handful of other materials.In this review paper,we provide an overview of the domain engineering techniques developed for LN,their principles,and the typical domain size and pattern uniformity they provide,which is important for devices that require high-resolution domain patterns with good reproducibility.It also highlights each technique's benefits,limitations,and adaptability for an application,along with possible improvements and future advancement prospects.Further,the review provides a brief overview of domain visualization methods,which is crucial to gain insights into domain quality/shape and explores the adaptability of the proposed domain engineering methodologies for the emerging thin-film lithium niobate on an insulator platform,which creates opportunities for developing the next generation of compact and scalable photonic integrated circuits and high frequency acoustic devices.展开更多
The partitioning of membrane proteins into lipid domains in cellular membranes is closely associated with the realization of the protein functions and it is influenced by various factors such as the post-translational...The partitioning of membrane proteins into lipid domains in cellular membranes is closely associated with the realization of the protein functions and it is influenced by various factors such as the post-translational modification of palmitoylation.However,the molecular mechanism of the effect of palmitoylation on membrane protein partitioning into the lipid domains remains elusive.In this work,taking human peripheral myelin protein 22(PMP22)as an example,we employ coarse-grained molecular dynamics simulations to investigate the partitioning of both the natural PMP22 and the palmitoylated PMP22(pal-PMP22)into the lipid domains of model myelin membranes.The results indicate that palmitoylation drives PMP22 to localize at the boundary of the liquid-ordered(Lo)and liquid-disordered(Ld)domains and increases the possibility of PMP22 partitioning into the Lo domains by changing the hydrophobic length of the proteins and perturbing the ordered packing of tails of the saturated lipids in the Lo domains.This work offers some novel insights into the role of palmitoylation in modulating the function of membrane proteins in cellular membranes.展开更多
The rapid development of the industrial internet of things(IIoT)has brought huge benefits to factories equipped with IIoT technology,each of which represents an IIoT domain.More and more domains are choosing to cooper...The rapid development of the industrial internet of things(IIoT)has brought huge benefits to factories equipped with IIoT technology,each of which represents an IIoT domain.More and more domains are choosing to cooperate with each other to produce better products for greater profits.Therefore,in order to protect the security and privacy of IIoT devices in cross-domain communication,lots of cross-domain authentication schemes have been proposed.However,most schemes expose the domain to which the IIoT device belongs,or introduce a single point of failure in multi-domain cooperation,thus introducing unpredictable risks to each domain.We propose a more secure and efficient domain-level anonymous cross-domain authentication(DLCA)scheme based on alliance blockchain.The proposed scheme uses group signatures with decentralized tracing technology to provide domain-level anonymity to each IIoT device and allow the public to trace the real identity of the malicious pseudonym.In addition,DLCA takes into account the limited resource characteristics of IIoT devices to design an efficient cross-domain authentication protocol.Security analysis and performance evaluation show that the proposed scheme can be effectively used in the cross-domain authentication scenario of industrial internet of things.展开更多
The enhancement of coercivity in Nd-Fe-B sintered magnets modified by Pr_(58)Dy_(10)Cu_(32)alloy was investigated through scanning electron microscope(SEM)and in-situ magneto-optic Kerr effect(MOKE)microscopy.The modi...The enhancement of coercivity in Nd-Fe-B sintered magnets modified by Pr_(58)Dy_(10)Cu_(32)alloy was investigated through scanning electron microscope(SEM)and in-situ magneto-optic Kerr effect(MOKE)microscopy.The modification treatment resulted in the formation of a smooth and continuous weakly magnetic grain boundary layer and the(Nd,Pr,Dy)_(2)Fe_(14)B main phase with a high magnetocrystalline anisotropy field,leading to an increased coercivity of 23 kOe.MOKE observations revealed that the dynamic evolution of the maze domain area under an external magnetic field varied significantly between the original and modified magnets.Compared with the original magnets,the modified magnets exhibited a slower decrease in maze domain area during magnetization and a slower increase during reverse magnetization,contributing to the observed coercivity enhancement.展开更多
Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LST...Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LSTM)network(SAMFF-Conv-LSTM),a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples.The model incorporates the Fourier transform to extract time-and frequency-domain features from wave and wind variables.For the 12 h prediction,the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error(RMSE)of 1.350 m/s,implying a high prediction accuracy.For the 24 h prediction,the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%,14.26%,and 13.36%compared with those of the convolutional neural network,gated recurrent units,and convolutional LSTM(ConvLSTM),respectively.These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.展开更多
In this study,the wave motion in elastodynamics for unbounded media is modeled using an unsplit-field perfectly matched layer(PML)formulation that is solved by employing an isogeometric analysis(IGA).In the adopted co...In this study,the wave motion in elastodynamics for unbounded media is modeled using an unsplit-field perfectly matched layer(PML)formulation that is solved by employing an isogeometric analysis(IGA).In the adopted combination,the non-uniform rational B-spline(NURBS)functions are employed as basis functions.Moreover,the unbounded and artificial domains,defined in the PML method,are contained in a single patch domain.Based on the proposed scheme,the approximation of the geometry problem is set in a new scheme in which the PML’s absorbing and attenuation properties and the description of traveling waves can be represented.This includes a higher continuity and smoother approximation of the computed domain.As high-order NURBS basis functions are non-interpolatory,a penalty method is present to apply a time-dependent displacement load.The performance of the NURBS-based PML is analyzed through numerical examples for 1D and 2D domains,considering homogeneous and heterogeneous media.Further,we verify the long-time numerical stability of the present method.The developed method can be used to simulate hypothetical stratified domains commonly encountered in soil-structure interaction analyses.展开更多
Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target reg...Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.展开更多
To avoid the laborious annotation process for dense prediction tasks like semantic segmentation,unsupervised domain adaptation(UDA)methods have been proposed to leverage the abundant annotations from a source domain,s...To avoid the laborious annotation process for dense prediction tasks like semantic segmentation,unsupervised domain adaptation(UDA)methods have been proposed to leverage the abundant annotations from a source domain,such as virtual world(e.g.,3D games),and adapt models to the target domain(the real world)by narrowing the domain discrepancies.However,because of the large domain gap,directly aligning two distinct domains without considering the intermediates leads to inefficient alignment and inferior adaptation.To address this issue,we propose a novel learnable evolutionary Category Intermediates(CIs)guided UDA model named Leci,which enables the information transfer between the two domains via two processes,i.e.,Distilling and Blending.Starting from a random initialization,the CIs learn shared category-wise semantics automatically from two domains in the Distilling process.Then,the learned semantics in the CIs are sent back to blend the domain features through a residual attentive fusion(RAF)module,such that the categorywise features of both domains shift towards each other.As the CIs progressively and consistently learn from the varying feature distributions during training,they are evolutionary to guide the model to achieve category-wise feature alignment.Experiments on both GTA5 and SYNTHIA datasets demonstrate Leci's superiority over prior representative methods.展开更多
Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
Cross-domain graph anomaly detection(CD-GAD)is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph.CD-GAD classifies anomalies as unique or c...Cross-domain graph anomaly detection(CD-GAD)is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph.CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs.However,existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies.Additionally,they tend to focus solely on node-level differences,overlooking structural-level differences that provide complementary information for common anomaly detection.To address these issues,we propose a novel method,Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple(GTGD),which effectively detects common and unique anomalies in the target graph.Specifically,our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features.Moreover,we simulta-neously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph,enabling comprehensive domain-common knowledge representation.Anomalies are detected using both common and unique features,with their synthetic score serving as the final result.Extensive experiments demonstrate the effectiveness of our approach,improving an average performance by 12.6%on the AUC-PR compared to state-of-the-art methods.展开更多
Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve pr...Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.展开更多
基金Supported by NSFC (No.12361027)NSF of Inner Mongolia (No.2018MS01021)+1 种基金NSF of Shandong Province (No.ZR2020QA009)Science and Technology Innovation Program for Higher Education Institutions of Shanxi Province (No.2024L533)。
文摘In this paper,we give a complete characterization of all self-adjoint domains of odd order differential operators on two intervals.These two intervals with all four endpoints are singular(one endpoint of each interval is singular or all four endpoints are regulars are the special cases).And these extensions yield"new"self-adjoint operators,which involve interactions between the two intervals.
基金Supported by the National Natural Science Foundation of China(12301101)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110019 and 2020A1515110585)。
文摘In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequence of L^(1)functions converging to the given function and verifying their representation in the form of Fourier transform to establish the desired result of the given function.Applying this main result,we further generalize the Paley-Wiener theorem for band-limited functions to the analytic function spaces L^(p)(0<p<∞)with general weights.
基金supported by the TARCC,Welch Foundation Award(I-1724)the Decherd Foundationthe Pape Adams Foundation,NIH grants NS092616,NS127375,NS117065,NS111776。
文摘The mitogen-activated protein kinase kinase kinase kinases(MAP4Ks)signaling pathway plays a pivotal role in axonal regrowth and neuronal degeneration following insults.Whether targeting this pathway is beneficial to brain injury remains unclear.In this study,we showed that adeno-associated virus-delivery of the Citron homology domain of MAP4Ks effectively reduces traumatic brain injury-induced reactive gliosis,tauopathy,lesion size,and behavioral deficits.Pharmacological inhibition of MAP4Ks replicated the ameliorative effects observed with expression of the Citron homology domain.Mechanistically,the Citron homology domain acted as a dominant-negative mutant,impeding MAP4K-mediated phosphorylation of the dishevelled proteins and thereby controlling the Wnt/β-catenin pathway.These findings implicate a therapeutic potential of targeting MAP4Ks to alleviate the detrimental effects of traumatic brain injury.
基金National Natural Science Foundation of China (52202139, 52072178)。
文摘Pb(Zr,Ti)O_(3)-Pb(Zn_(1/3)Nb_(2/3))O_(3) (PZT-PZN) based ceramics, as important piezoelectric materials, have a wide range of applications in fields such as sensors and actuators, thus the optimization of their piezoelectric properties has been a hot research topic. This study investigated the effects of phase boundary engineering and domain engineering on (1-x)[0.8Pb(Zr_(0.5)Ti_(0.5))O_(3)-0.2Pb(Zn_(1/3)Nb_(2/3))O_(3)]-xBi(Zn_(0.5)Ti_(0.5))O_(3) ((1-x)(0.8PZT-0.2PZN)- xBZT) ceramic to obtain excellent piezoelectric properties. The crystal phase structure and microstructure of ceramic samples were characterized. The results showed that all samples had a pure perovskite structure, and the addition of BZT gradually increased the grain size. The addition of BZT caused a phase transition in ceramic samples from the morphotropic phase boundary (MPB) towards the tetragonal phase region, which is crucial for optimizing piezoelectric properties. By adjusting content of BZT and precisely controlling position of the phase boundary, the piezoelectric performance can be optimized. Domain structure is one of the key factors affecting piezoelectric performance. By using domain engineering techniques to optimize grain size and domain size, piezoelectric properties of ceramic samples have been significantly improved. Specifically, excellent piezoelectric properties (piezoelectric constant d_(33)=320 pC/N, electromechanical coupling factor kp=0.44) were obtained simultaneously for x=0.08. Based on experimental results and theoretical analysis, influence mechanisms of phase boundary engineering and domain engineering on piezoelectric properties were explored. The study shows that addition of BZT not only promotes grain growth, but also optimizes the domain structure, enabling the polarization reversal process easier, thereby improving piezoelectric properties. These research results not only provide new ideas for the design of high-performance piezoelectric ceramics, but also lay a theoretical foundation for development of related electronic devices.
文摘To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions in the form of noise and artifacts,are kept to a bare minimum.The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality,while indirectly attacking the effectiveness of the diagnostic process.It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise.The solution to these challenges presents a complex dilemma at the acquisition stage,where image processing techniques must be adopted.The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices.This article hereby provides an extensive systematic review of the above techniques,with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise,i.e.,Gaussian,Poisson,and Rician.A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method.The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis,such as tumor detection in MRI and vascular imaging in CT.
基金supported by the National Natural Science Foundation of China,No.82104412(to TD)Shaanxi Provincial Key R&D Program,No.2023-YBSF-165(to TD)+1 种基金the Natural Science Foundation of Shaanxi Department of Science and Technology,No.2018JM7022(to FM)Shaanxi Provincial Key Industry Chain Project,No.2021ZDLSF04-11(to PW)。
文摘The functional and structural integrity of the blood-brain barrier is crucial in maintaining homeostasis in the brain microenvironment;however,the molecular mechanisms underlying the formation and function of the blood-brain barrier remain poorly understood.The major facilitator superfamily domain containing 2A has been identified as a key regulator of blood-brain barrier function.It plays a critical role in promoting and maintaining the formation and functional stability of the blood-brain barrier,in addition to the transport of lipids,such as docosahexaenoic acid,across the blood-brain barrier.Furthermore,an increasing number of studies have suggested that major facilitator superfamily domain containing 2A is involved in the molecular mechanisms of blood-brain barrier dysfunction in a variety of neurological diseases;however,little is known regarding the mechanisms by which major facilitator superfamily domain containing 2A affects the blood-brain barrier.This paper provides a comprehensive and systematic review of the close relationship between major facilitator superfamily domain containing 2A proteins and the blood-brain barrier,including their basic structures and functions,cross-linking between major facilitator superfamily domain containing 2A and the blood-brain barrier,and the in-depth studies on lipid transport and the regulation of blood-brain barrier permeability.This comprehensive systematic review contributes to an in-depth understanding of the important role of major facilitator superfamily domain containing 2A proteins in maintaining the structure and function of the blood-brain barrier and the research progress to date.This will not only help to elucidate the pathogenesis of neurological diseases,improve the accuracy of laboratory diagnosis,and optimize clinical treatment strategies,but it may also play an important role in prognostic monitoring.In addition,the effects of major facilitator superfamily domain containing 2A on blood-brain barrier leakage in various diseases and the research progress on cross-blood-brain barrier drug delivery are summarized.This review may contribute to the development of new approaches for the treatment of neurological diseases.
基金the Deanship of Scientific Research at King Khalid University for funding this work through large group under grant number(GRP.2/663/46).
文摘Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.
基金supported by the Australian Research Council Centre of Excellence in Optical Microcombs for Breakthrough Science COMBS(CE230100006)the Australian Research Council grants DP220100488 and DE230100964funded by the Australian Government.
文摘Lithium niobate(LN)has remained at the forefront of academic research and industrial applications due to its rich material properties,which include second-order nonlinear optic,electro-optic,and piezoelectric properties.A further aspect of LN’s versatility stems from the ability to engineer ferroelectric domains with micro and even nano-scale precision in LN,which provides an additional degree of freedom to design acoustic and optical devices with improved performance and is only possible in a handful of other materials.In this review paper,we provide an overview of the domain engineering techniques developed for LN,their principles,and the typical domain size and pattern uniformity they provide,which is important for devices that require high-resolution domain patterns with good reproducibility.It also highlights each technique's benefits,limitations,and adaptability for an application,along with possible improvements and future advancement prospects.Further,the review provides a brief overview of domain visualization methods,which is crucial to gain insights into domain quality/shape and explores the adaptability of the proposed domain engineering methodologies for the emerging thin-film lithium niobate on an insulator platform,which creates opportunities for developing the next generation of compact and scalable photonic integrated circuits and high frequency acoustic devices.
基金supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ25A040005)the National Natural Science Foundation of China(Grant No.11674287).
文摘The partitioning of membrane proteins into lipid domains in cellular membranes is closely associated with the realization of the protein functions and it is influenced by various factors such as the post-translational modification of palmitoylation.However,the molecular mechanism of the effect of palmitoylation on membrane protein partitioning into the lipid domains remains elusive.In this work,taking human peripheral myelin protein 22(PMP22)as an example,we employ coarse-grained molecular dynamics simulations to investigate the partitioning of both the natural PMP22 and the palmitoylated PMP22(pal-PMP22)into the lipid domains of model myelin membranes.The results indicate that palmitoylation drives PMP22 to localize at the boundary of the liquid-ordered(Lo)and liquid-disordered(Ld)domains and increases the possibility of PMP22 partitioning into the Lo domains by changing the hydrophobic length of the proteins and perturbing the ordered packing of tails of the saturated lipids in the Lo domains.This work offers some novel insights into the role of palmitoylation in modulating the function of membrane proteins in cellular membranes.
文摘The rapid development of the industrial internet of things(IIoT)has brought huge benefits to factories equipped with IIoT technology,each of which represents an IIoT domain.More and more domains are choosing to cooperate with each other to produce better products for greater profits.Therefore,in order to protect the security and privacy of IIoT devices in cross-domain communication,lots of cross-domain authentication schemes have been proposed.However,most schemes expose the domain to which the IIoT device belongs,or introduce a single point of failure in multi-domain cooperation,thus introducing unpredictable risks to each domain.We propose a more secure and efficient domain-level anonymous cross-domain authentication(DLCA)scheme based on alliance blockchain.The proposed scheme uses group signatures with decentralized tracing technology to provide domain-level anonymity to each IIoT device and allow the public to trace the real identity of the malicious pseudonym.In addition,DLCA takes into account the limited resource characteristics of IIoT devices to design an efficient cross-domain authentication protocol.Security analysis and performance evaluation show that the proposed scheme can be effectively used in the cross-domain authentication scenario of industrial internet of things.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFB3500300,2023YFB3507000,and 2023XYJG0001-01-03)the National Natural Science Foundation of China(Grant No.52171167)Inner Mongolia Northern Rare Earth Advanced Materials Technology Innovation Co.,Ltd.Project(Grant No.CXZX-B-202304-0004).
文摘The enhancement of coercivity in Nd-Fe-B sintered magnets modified by Pr_(58)Dy_(10)Cu_(32)alloy was investigated through scanning electron microscope(SEM)and in-situ magneto-optic Kerr effect(MOKE)microscopy.The modification treatment resulted in the formation of a smooth and continuous weakly magnetic grain boundary layer and the(Nd,Pr,Dy)_(2)Fe_(14)B main phase with a high magnetocrystalline anisotropy field,leading to an increased coercivity of 23 kOe.MOKE observations revealed that the dynamic evolution of the maze domain area under an external magnetic field varied significantly between the original and modified magnets.Compared with the original magnets,the modified magnets exhibited a slower decrease in maze domain area during magnetization and a slower increase during reverse magnetization,contributing to the observed coercivity enhancement.
基金supported by the National Natural Science Foundation(No.42176020)the Open Research Fund of State Key Laboratory of Target Vulnerability Assessment(No.YSX2024KFYS001)+1 种基金the National Key Research and Development Program(No.2022YFC3105002)the Project from Key Laboratory of Marine Environmental Information Technology(No.2023GFW-1047).
文摘Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LSTM)network(SAMFF-Conv-LSTM),a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples.The model incorporates the Fourier transform to extract time-and frequency-domain features from wave and wind variables.For the 12 h prediction,the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error(RMSE)of 1.350 m/s,implying a high prediction accuracy.For the 24 h prediction,the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%,14.26%,and 13.36%compared with those of the convolutional neural network,gated recurrent units,and convolutional LSTM(ConvLSTM),respectively.These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.
文摘In this study,the wave motion in elastodynamics for unbounded media is modeled using an unsplit-field perfectly matched layer(PML)formulation that is solved by employing an isogeometric analysis(IGA).In the adopted combination,the non-uniform rational B-spline(NURBS)functions are employed as basis functions.Moreover,the unbounded and artificial domains,defined in the PML method,are contained in a single patch domain.Based on the proposed scheme,the approximation of the geometry problem is set in a new scheme in which the PML’s absorbing and attenuation properties and the description of traveling waves can be represented.This includes a higher continuity and smoother approximation of the computed domain.As high-order NURBS basis functions are non-interpolatory,a penalty method is present to apply a time-dependent displacement load.The performance of the NURBS-based PML is analyzed through numerical examples for 1D and 2D domains,considering homogeneous and heterogeneous media.Further,we verify the long-time numerical stability of the present method.The developed method can be used to simulate hypothetical stratified domains commonly encountered in soil-structure interaction analyses.
基金the National Natural Science Foundation of China(Grant No.42301002,and 52109118)Fujian Provincial Water Resources Science and Technology Project(Grant No.MSK202524)Guidance fund for Science and Technology Program,Fujian province(Grant No.2024Y0002).
文摘Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.
基金Australian Research Council Project(FL-170100117).
文摘To avoid the laborious annotation process for dense prediction tasks like semantic segmentation,unsupervised domain adaptation(UDA)methods have been proposed to leverage the abundant annotations from a source domain,such as virtual world(e.g.,3D games),and adapt models to the target domain(the real world)by narrowing the domain discrepancies.However,because of the large domain gap,directly aligning two distinct domains without considering the intermediates leads to inefficient alignment and inferior adaptation.To address this issue,we propose a novel learnable evolutionary Category Intermediates(CIs)guided UDA model named Leci,which enables the information transfer between the two domains via two processes,i.e.,Distilling and Blending.Starting from a random initialization,the CIs learn shared category-wise semantics automatically from two domains in the Distilling process.Then,the learned semantics in the CIs are sent back to blend the domain features through a residual attentive fusion(RAF)module,such that the categorywise features of both domains shift towards each other.As the CIs progressively and consistently learn from the varying feature distributions during training,they are evolutionary to guide the model to achieve category-wise feature alignment.Experiments on both GTA5 and SYNTHIA datasets demonstrate Leci's superiority over prior representative methods.
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
基金supported by the National Nature Science Foundation of China,Grant/Award Numbers:62337001,62037001“Pioneer”and“Leading Goose”R&D Program of Zhejiang,Grant/Award Number:2022C03106.
文摘Cross-domain graph anomaly detection(CD-GAD)is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph.CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs.However,existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies.Additionally,they tend to focus solely on node-level differences,overlooking structural-level differences that provide complementary information for common anomaly detection.To address these issues,we propose a novel method,Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple(GTGD),which effectively detects common and unique anomalies in the target graph.Specifically,our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features.Moreover,we simulta-neously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph,enabling comprehensive domain-common knowledge representation.Anomalies are detected using both common and unique features,with their synthetic score serving as the final result.Extensive experiments demonstrate the effectiveness of our approach,improving an average performance by 12.6%on the AUC-PR compared to state-of-the-art methods.
基金supported by National Natural Science Foundation of China(Grant Nos.62473277,62473275,62133004,52105072,and 62073230)Jiangsu Provincial Outstanding Youth Program(Grant No.BK20230072)+5 种基金National Key R&D Program of China(Grant Nos.2022YFC3802302 and 2023YFB4705600)Suzhou Industrial Foresight and Key Core Technology Project(Grant No.SYC2022044)Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ24E050004)Shenzhen Polytechnic High-level Talent Start-up Project(Grant No.6023330006K)Shenzhen Science and Technology Program(Grant No.JCYJ20210324132810026)a Grant from Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grants from Jiangsu QingLan Project and Jiangsu 333 high-level talents.
文摘Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.
