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.

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.

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].

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%.

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.

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.

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.

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.

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).

HF-VHF dual-channel multifunctional radio astronomy terminal system

The high frequency-very high frequency(HF-VHF)frequency band is of significant importance in astronomical observations,with applications studying various phenomena such as space weather,solar radio emissions,planetary eruptions in the solar system,pulsars,transient sources,and reionization of the early universe.This article introduces the HF-VHF frequency band multifunctional radio astronomical terminal system based on a dual-channel high-speed acquisition board with a frequency observation range of 1-250 MHz and a sampling rate of 500 Msps(Mega samples per second).The maximum quantization bit of the system is 14 bits,with a maximum time resolution of 0.1 s and a maximum spectral resolution of 16 kHz.The system combines spectral analysis of solar radio signals and recording of time-domain data of signals interfering with long baselines,and adopts a server-client separation mode to allow remote operation with separate permissions.It is used in the China-Malaysia joint astronomy project,which can carry out single-site observation of solar radio signals as well as interferometric observation of signals from multiple sites.

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.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): I. Mathematical Framework

This work presents the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (abbreviated as “nth-FASAM-N”), which will be shown to be the most efficient methodology for computing exact expressions of sensitivities, of any order, of model responses with respect to features of model parameters and, subsequently, with respect to the model’s uncertain parameters, boundaries, and internal interfaces. The unparalleled efficiency and accuracy of the nth-FASAM-N methodology stems from the maximal reduction of the number of adjoint computations (which are considered to be “large-scale” computations) for computing high-order sensitivities. When applying the nth-FASAM-N methodology to compute the second- and higher-order sensitivities, the number of large-scale computations is proportional to the number of “model features” as opposed to being proportional to the number of model parameters (which are considerably more than the number of features).When a model has no “feature” functions of parameters, but only comprises primary parameters, the nth-FASAM-N methodology becomes identical to the extant nth CASAM-N (“nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems”) methodology. Both the nth-FASAM-N and the nth-CASAM-N methodologies are formulated in linearly increasing higher-dimensional Hilbert spaces as opposed to exponentially increasing parameter-dimensional spaces thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems. Both the nth-FASAM-N and the nth-CASAM-N are incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities of any order with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces.

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.

Solution Domain Boundary Analysis Method and Its Application in Parameter Spaces of Nonlinear Gear System

Mastering the influence laws of parameters on the solution structure of nonlinear systems is the basis of carrying out vibration isolation and control.Many researches on solution structure and bifurcation phenomenon in parameter spaces are carried out broadly in many fields,and the research on nonlinear gear systems has attracted the attention of many scholars.But there is little study on the solution domain boundary of nonlinear gear systems.For a periodic non-autonomous nonlinear dynamic system with several control parameters,a solution domain boundary analysis method of nonlinear systems in parameter spaces is proposed,which combines the cell mapping method based on Poincaré point mapping in phase spaces with the domain decomposition technique of parameter spaces.The cell mapping is known as a global analysis method to analyze the global behavior of a nonlinear dynamic system with finite dimensions,and the basic idea of domain decomposition techniques is to divide and rule.The method is applied to analyze the solution domain boundaries in parameter spaces of a nonlinear gear system.The distribution of different period domains,chaos domain and the domain boundaries between different period domains and chaotic domain are obtained in control parameter spaces constituted by meshing damping ratio with excitation frequency,fluctuation coefficient of meshing stiffness and average exciting force respectively by calculation.The calculation results show that as the meshing damping increases,the responses of the system change towards a single motion,while the variations of the excitation frequency,meshing stiffness and exciting force make the solution domain presenting diversity.The proposed research contribution provides evidence for vibration control and parameter design of the gear system,and confirms the validity of the solution domain boundary analysis method.

Harmonic balance method with alternating frequency/time domain technique for nonlinear dynamical system with fractional exponential

Comparisons of the common methods for obtaining the periodic responses show that the harmonic balance method with alternating frequency/time （HB-AFT） do- main technique has some advantages in dealing with nonlinear problems of fractional exponential models. By the HB-AFT method, a rigid rotor supported by ball bearings with nonlinearity of Hertz contact and ball passage vibrations is considered. With the aid of the Floquet theory, the movement characteristics of interval stability are deeply studied. Besides, a simple strategy to determine the monodromy matrix is proposed for the stability analysis.

Time-Domain Analysis for 3-D Moored Systems

In the paper, a comprehensive numerical study on the moored system is performed in time domain. The moored system, which is composed of the floating body sub system and the mooring line sub system, is calculated as a whole system by coupling. A time domain method is applied to the analysis of the mooring line sub system, and at the same time, an indirect time domain method translated from frequency domain to time domain is developed to calculate the floating body sub system. In the end, an FPSO vessel is calculated as a numerical example by the present method. A comparison of the result of the model test and that of the numerical method indicates that the present method is exact and effective.

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

Sampled-Data Asynchronous Fuzzy Output Feedback Control for Active Suspension Systems in Restricted Frequency Domain

This paper proposes a novel sampled-data asynchronous fuzzy output feedback control approach for active suspension systems in restricted frequency domain.In order to better investigate uncertain suspension dynamics,the sampleddata Takagi-Sugeno(T-S)fuzzy half-car active suspension(HCAS)system is considered,which is further modelled as a continuous system with an input delay.Firstly,considering that the fuzzy system and the fuzzy controller cannot share the identical premises due to the existence of input delay,a reconstructed method is employed to synchronize the time scales of membership functions between the fuzzy controller and the fuzzy system.Secondly,since external disturbances often belong to a restricted frequency range,a finite frequency control criterion is presented for control synthesis to reduce conservatism.Thirdly,given a full information of state variables is hardly available in practical suspension systems,a two-stage method is proposed to calculate the static output feedback control gains.Moreover,an iterative algorithm is proposed to compute the optimum solution.Finally,numerical simulations verify the effectiveness of the proposed controllers.