BIG HANKEL OPERATORS ON HARDY SPACES OF STRONGLY PSEUDOCONVEX DOMAINS

In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f )is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H~2(Ω) of other pseudoconvex domains.In these arguments,Amar's L~p-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H~p(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.

MINIMIZERS OF L^(2)-SUBCRITICAL VARIATIONAL PROBLEMS WITH SPATIALLY DECAYING NONLINEARITIES IN BOUNDED DOMAINS

This paper is concerned with the minimizers of L^(2)-subcritical constraint variar tional problems with spatially decaying nonlinearities in a bounded domain Ω of R~N(N≥1).We prove that the problem admits minimizers for any M> 0.Moreover,the limiting behavior of minimizers as M→∞ is also analyzed rigorously.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Domain size and charge defects affecting the polarization switching of antiferroelectric domains

The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood.In this work,by using the phase field simulation,we have studied the polarization switching property of antiferroelectric domains.Our results indicate that the ferroelectric domains nucleate preferably at the boundaries of the antiferroelectric domains,and antiferroelectrics with larger initial domain sizes possess a higher coercive electric field as demonstrated by hysteresis loops.Moreover,we introduce charge defects into the sample and numerically investigate their influence.It is also shown that charge defects can induce local ferroelectric domains,which could suppress the saturation polarization and narrow the enclosed area of the hysteresis loop.Our results give insights into understanding the antiferroelectric phase transformation and optimizing the energy storage property in experiments.

Structure and function of the membrane microdomains in osteoclasts

The cell membrane structure is closely related to the occurrence and progression of many metabolic bone diseases observed in the clinic and is an important target to the development of therapeutic strategies for these diseases.Strong experimental evidence supports the existence of membrane microdomains in osteoclasts(OCs).However,the potential membrane microdomains and the crucial mechanisms underlying their roles in OCs have not been fully characterized.Membrane microdomain components,such as scaffolding proteins and the actin cytoskeleton,as well as the roles of individual membrane proteins,need to be elucidated.In this review,we discuss the compositions and critical functions of membrane microdomains that determine the biological behavior of OCs through the three main stages of the OC life cycle.

IMPROVED REGULARITY OF HARMONIC DIFFEOMORPHIC EXTENSIONS ON QUASIHYPERBOLIC DOMAINS

Let X be a Jordan domain satisfying certain hyperbolic growth conditions.Assume that φ is a homeomorphism from the boundary ?X of X onto the unit circle.Denote by h the harmonic diffeomorphic extension of φ from X onto the unit disk.We establish the optimal Orlicz-Sobolev regularity and weighted Sobolev estimate of h.These generalize the Sobolev regularity of h in [A.Koski,J.Onninen,Sobolev homeomorphic extensions,J.Eur.Math.Soc.23(2021) 4065-4089,Theorem 3.1].

Consistency between domain wall oscillation modes and spin wave modes in nanostrips

Investigations on domain wall(DW) and spin wave(SW) modes in a series of nanostrips with different widths and thicknesses have been carried out using micromagnetic simulation. The simulation results show that the frequencies of SW modes and the corresponding DW modes are consistent with each other if they have the same node number along the width direction. This consistency is more pronounced in wide and thin nanostrips, favoring the DW motion driven by SWs.Further analysis of the moving behavior of a DW driven by SWs is also carried out. The average DW speed can reach a larger value of ~ 140 m/s under two different SW sources. We argue that this study is very meaningful for the potential application of DW motion driven by SWs.

ON THE SOBOLEV DOLBEAULT COHOMOLOGY OF A DOMAIN WITH PSEUDOCONCAVE BOUNDARIES

In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

High patatin like phospholipase domain containing 8 expression as a biomarker for poor prognosis of colorectal cancer

BACKGROUND Patatin like phospholipase domain containing 8(PNPLA8)has been shown to play a significant role in various cancer entities.Previous studies have focused on its roles as an antioxidant and in lipid peroxidation.However,the role of PNPLA8 in colorectal cancer(CRC)progression is unclear.AIM To explore the prognostic effects of PNPLA8 expression in CRC.METHODS A retrospective cohort containing 751 consecutive CRC patients was enrolled.PNPLA8 expression in tumor samples was evaluated by immunohistochemistry staining and semi-quantitated with immunoreactive scores.CRC patients were divided into high and low PNPLA8 expression groups based on the cut-off va-lues,which were calculated by X-tile software.The prognostic value of PNPLA8 was identified using univariate and multivariate Cox regression analysis.The over-all survival(OS)rates of CRC patients in the study cohort were compared with Kaplan-Meier analysis and Log-rank test.RESULTS PNPLA8 expression was significantly associated with distant metastases in our cohort(P=0.048).CRC patients with high PNPLA8 expression indicated poor OS(median OS=35.3,P=0.005).CRC patients with a higher PNPLA8 expression at either stage I and II or stage III and IV had statistically significant shorter OS.For patients with left-sided colon and rectal cancer,the survival curves of two PN-PLA8-expression groups showed statistically significant differences.Multivariate analysis also confirmed that high PNPLA8 expression was an independent prog-nostic factor for overall survival(hazard ratio HR=1.328,95%CI:1.016-1.734,P=0.038).

Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

GATA binding protein 2 mediated ankyrin repeat domain containing 26 high expression in myeloid-derived cell lines

BACKGROUND Thrombocytopenia 2,an autosomal dominant inherited disease characterized by moderate thrombocytopenia,predisposition to myeloid malignancies and normal platelet size and function,can be caused by 5’-untranslated region(UTR)point mutations in ankyrin repeat domain containing 26(ANKRD26).Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1)have been identified as negative regulators of ANKRD26.However,the positive regulators of ANKRD26 are still unknown.AIM To prove the positive regulatory effect of GATA binding protein 2(GATA2)on ANKRD26 transcription.METHODS Human induced pluripotent stem cells derived from bone marrow(hiPSC-BM)INTRODUCTION Ankyrin repeat domain containing protein 26(ANKRD26)acts as a regulator of adipogenesis and is involved in the regulation of feeding behavior[1-3].The ANKRD26 gene is located on chromosome 10 and shares regions of homology with the primate-specific gene family POTE.According to the Human Protein Atlas database,the ANKRD26 protein is localized to the Golgi apparatus and vesicles,and its expression can be detected in nearly all human tissues[4].Moreover,UniProt annotation revealed that ANKRD26 is localized in the centrosome and contains coiled-coil domains formed by spectrin helices and ankyrin repeats[5,6].The most common disease related to ANKRD26 is thrombocytopenia 2(THC2),which is a rare autosomal dominant inherited disease characterized by lifelong mild-to-moderate thrombocytopenia and mild bleeding[7-9].Caused by the variants in the 5’-untranslated region(UTR)of ANKRD26,THC2 is defined by a decrease in the number of platelets in circulating blood and results in increased bleeding and decreased clotting ability[8,10].Due to the point mutations that occur in the 5’-UTR of ANKRD26,its negative transcription factors(TFs),Runt related transcription factor 1(RUNX1)and friend leukemia integration 1(FLI1),lose their repression effect[11].The persistent expression of ANKRD26 increases the activity of the mitogen activated protein kinase and extracellular signal regulated kinase 1/2 signaling pathways,which are potentially involved in the regulation of thrombopoietin-dependent signaling and further impair proplatelet formation by megakaryocytes(MKs)[11].However,the positive regulators of ANKRD26,which might be associated with THC2 pathology,are still unknown.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

拟连续Domain的SM^(*)性质

研究了拟连续Domain中比M^(*)性质更强的SM^(*)性质,并得到了一些有意义的结论.主要结果有:给出了拟连续Domain具有M^(*)性质的等价刻画;给出了拟连续Domain的SM^(*)性质的定义,并说明了M^(*)性质与SM^(*)性质的关系;给出了QL-Domain具有SM^(*)性质的等价条件;给出了两类具有性质SM^(*)的特殊的拟连续Domain.

Multilinear Calder′on Zygmund operators on variableexponent Morrey spaces over domains

The boundedness of multilinear singular integrals of Calder′on-Zygmund type onproduct of variable exponent Lebesgue spaces over both bounded and unbounded domains areobtained. Further more, the boundedness for this type multilinear operators on product ofvariable exponent Morrey spaces over domains is shown in the paper.

Protein-protein Interaction Between Domains of PDZ and BAR from PICK1

Two DNA fragments encoding PDZ domain （21-110 residues） and BAR domain （ 150-360 residues） from PICK1 （1-416 residues） were amplified by PCR and then introduced into vectors, pET-32M and pMAL-e2X respectively to generate recombinant plasmids, pE-pdz and pM-bar. Having been separately transferred into the hosts E. coli BL21 and E. coli JM109, these two strains can express fusion proteins： His-tagged PDZ（PDZ domain） and maltose binding protein-BAR（ MBP-BAR domain） respectively, as confirmed by both SDS-PAGE and Wostem blotting. The interaction between these two domains is dose-dependence, as identified by a pull-down test. Moreover, it has been shown from the ELISA analysis that the actual amount of PDZ bound to MBP-BAR-amylose beads reaches （ 16 ± 0. 5）%, as calculated by the molar ratio of PDZ to MBP-BAR. In addition, the interaction between BAR（bait） and PDZ（prey） in vivo was also examined with a yeast two-hybrid system.