Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Dynamic Coupling Analysis of Semisubmersible Platform Float-over Method for Docking Case

In this paper,the multi-body coupled dynamic characteristics of a semisubmersible platform and an HYSY 229 barge were investigated.First,coupled hydrodynamic analysis of the HYSY 229 barge and the semisubmersible platform was performed.Relevant hydrodynamic parameters were obtained using the retardation function method of three-dimensional frequency-domain potential flow theory.The results of the hydrodynamic analysis were highly consistent with the test findings,verifying the accuracy of the multifloating hydrodynamic coupling analysis,and key hydrodynamic parameters were solved for different water depths and the coupling effect.According to the obtained results,the hydrodynamic influence was the largest in shallow waters when the coupling effect was considered.Furthermore,the coupled motion equation combined with viscous damping,fender system,and mooring system was established,and the hydrodynamics,floating body motion,and dynamic response of the fender system were analyzed.Motion analysis revealed good agreement among the surge,sway,and yaw motions of the two floating bodies.However,when the wave period reached 10 s,the motion of the two floating bodies showed severe shock,and a relative motion was also observed.Therefore,excessive constraints should be added between the two floating bodies during construction to ensure construction safety.The numerical analysis and model test results of the semisubmersible platform and HYSY 229 barge at a water depth of 42 m and sea conditions of 0°,45°,and 90° were in good agreement,and the error was less than 5%.The maximum movement of the HYSY 229 barge reached 2.61 m in the sway direction,whereas that of the semisubmersible platform was 2.11 m.During construction,excessive constraints should be added between the two floating bodies to limit their relative movement and ensure construction safety.

Population genomic analysis reveals key genetic variations and the driving force for embryonic callus induction capability in maize

Genetic transformation has been an effective technology for improving the agronomic traits of maize.However,it is highly reliant on the use of embryonic callus(EC)and shows a serious genotype dependence.In this study,we performed genomic sequencing for 80 core maize germplasms and constructed a high-density genomic variation map using our newly developed pipeline(MQ2Gpipe).Based on the induction rate of EC(REC),these inbred lines were categorized into three subpopulations.The low-REC germplasms displayed more abundant genetic diversity than the high-REC germplasms.By integrating a genome-wide selective signature screen and region-based association analysis,we revealed 95.23 Mb of selective regions and 43 REC-associated variants.These variants had phenotypic variance explained values ranging between 21.46 and 49.46%.In total,103 candidate genes were identified within the linkage disequilibrium regions of these REC-associated loci.These genes mainly participate in regulation of the cell cycle,regulation of cytokinesis,and other functions,among which MYB15 and EMB2745 were located within the previously reported QTL for EC induction.Numerous leaf area-associated variants with large effects were closely linked to several REC-related loci,implying a potential synergistic selection of REC and leaf size during modern maize breeding.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

Bioinformatics Analysis of the Association between Ewing’s Sarcoma and Tuberculosis Comorbidity

Objective To screen and analyze the differentially expressed genes of Ewing’s sarcoma (ES) and Tuberculosis (TB) by bioinformatics. Methods GEO gene chip public database in NCBI was used for data retrieval, and chip data GSE17674 and GSE57736 were selected as analysis objects. The R language limma toolkit was used to screen DEmRNAs, and the data were standardized, and the common differentially expressed genes were screened by Venn diagram. The GO function and KEGG pathway enrichment of common differentially expressed genes were analyzed by using the R cluster Profiler package. String database was selected for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Import Hub gene into BioGPS database. Results: A total of 3 Hub genes were screened, namely CD3D, LCK, KLRB1;The genes were imported into BioGPS database to obtain the specific genes. Conclusion The selected differential genes and related signaling pathways are helpful to understand the molecular mechanism of ES and TB, and can provide the basis for early diagnosis of ES complicated with TB. It also provides new ideas for clinical treatment and diagnosis.

Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

The prognostic and immunological impacts of DCX expression:a pan-cancer analysis

Background:Doublecortin(DCX),a microtubule-associated protein,is best known for its critical role in neuronal migration during neural development,where it stabilizes microtubules and guides neurons to their proper positions.Recently,DCX has been implicated in various cancer processes,suggesting it may influence tumor progression and the tumor microenvironment.Emerging evidence indicates that DCX can modulate cell migration,invasion,and interaction with immune cells,making it a potential player in oncogenesis.However,the role of DCX across different cancer types and its potential as a prognostic biomarker remain underexplored,necessitating a comprehensive analysis.Methods:We utilized The Cancer Genome Atlas to extract data on DCX expression in tumor and adjacent normal tissues across diverse cancer types.Differential expression analysis was conducted using differential expression sequencing 2.Survival analysis was performed with Kaplan-Meier estimates and Cox proportional hazards models.Correlations between DCX expression and tumor mutational burden,microsatellite instability,and immune infiltration were examined using Spearman’s correlation.Results:DCX showed variable expression across cancer types,with significant overexpression in certain tumors such as liver and lung cancer and downexpression in others like breast cancer.High DCX expression was correlated with poor prognosis in adrenocortical carcinoma but with better outcomes in low-grade glioma.Additionally,DCX expression was significantly associated with various immune markers and chemokines,suggesting a role in modulating the immune microenvironment.Conclusion:Our findings highlight the complex role of DCX in cancer,underlining its potential as a prognostic marker and its involvement in immune-related pathways.Targeting DCX could represent a novel approach to modulating tumor behavior and enhancing immune response in cancer therapy.

Analysis of Additional Damping Control Strategy and Parameter Optimization for Improving Small Signal Stability of VSC-HVDC System

The voltage source converter based high voltage direct current(VSC-HVDC)system is based on voltage source converter,and its control system is more complex.Also affected by the fast control of power electronics,oscillation phenomenon in wide frequency domain may occur.To address the problem of small signal stability of the VSCHVDC system,a converter control strategy is designed to improve its small signal stability,and the risk of system oscillation is reduced by attaching a damping controller and optimizing the control parameters.Based on the modeling of the VSC-HVDC system,the general architecture of the inner and outer loop control of the VSCHVDC converter is established;and the damping controllers for DC control and AC control are designed in the phase-locked loop and the inner and outer loop control parts respectively;the state-space statemodel of the control system is established to analyze its performance.And the electromagnetic transient simulation model is built on the PSCAD/EMTDC simulation platform to verify the accuracy of the small signal model.The influence of the parameters of each control part on the stability of the system is summarized.The main control parts affecting stability are optimized for the phenomenon of oscillation due to changes in operation mode occurring on the AC side due to faults and other reasons,which effectively eliminates system oscillation and improves system small signal stability,providing a certain reference for engineering design.

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.

SA SIGNAL TIME SERIES ANALYSIS

分析了进行GPS SA研究的必要性,选择Trimble公司的Model 4000RL GPS接收机于定点采集数据,通过定位计算和时钟偏移滤波相结合的方法分离SA信号,经检验指出SA信号基本是零均值的平稳随机过程,并具有ARMA(3,2)的结构,对200个模型参数的频域和时域的分析,认为所有模型都是稳定的,进行预测和滤波将是收敛的。

Wavelet analysis and its application to signal processing

The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.

RESEARCH OF WAVELET TRANSFORM INSTRUMENT SYSTEM FOR SIGNAL ANALYSIS

After brief describing the Principle of wavelet transform (WT) of signals, a new signals analysis system based on wavelet transform is introduced. The design and development of the instryment of wavelet transform are described. A number of practical uses of this system demonstrate that wavelet transform system is specially functional in identifying and processing impulse, singular and non-smooth signals, so that it should be evaluated the most advanced signal analyzing system.

A quantitative analysis method for GPR signals based on optimal biorthogonal wavelet

Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

SSTA SIGNAL CHARACTERISTIC ANALYSIS OVER THE INDIAN OCEAN DURING RAINY SEASON IN CHINA

The teleconnection distribution characteristics of sea surface temperature (SST) over the India Ocean and the precipitation during rainy season in China were studied by using the methods of EOF and CCA. The results indicate that the change of SST field will affect the change of rain belt during rainy seasons in China, and greatly affect the precipitation in northwest and southwest China, the Yangzi and Yellow River downstream basins. Strong signal phenomena of SSTA over India Ocean were revealed that showed the anoma-lous distribution of drought and flood in China. It shows that the precipitation during rainy seasons in China may be forecast by analyzing SST distribution characteristics over the India Ocean.

Isotopic analysis based on terahertz spectrum

As a new promising detection technology in the terahertz research field,the terahertz time-domain spec-troscopy(THz-TDS)has very broad application potential in many fields because its advantage on the characteristic spectrum,wide spectrum and non-destructive analysis of interested substances.In this paper,the terahertz absorption spectra of gases mixed with 12 CO and 13 CO in the spec-trum range of 0.5–2.5 THz are measured by terahertz time-domain spectroscopy for the first time.Several isotopo-logues can be clearly distinguished based on the difference in their rotational energies and the consequent terahertz spectrum.The experimental results show that 12 CO and 13 CO have obvious characteristic absorption peaks in the spectrum range of 0.5–2.5 THz due to the difference in rotational energy,and the rotational constant B can be calculated according to the experimental values to distin-guish the two gaseous isotopologues.The frequency posi-tions of the characteristic absorption peak measured by this experiment and the rotation constant B calculated accord-ing to the experimental values are compared with those previous theoretical calculations and experimental results,and they are in good agreement.This result lays a foun-dation for developing more sophisticated terahertz instru-ments to the detection of different isotopologues.

Analysis of Significant Genes and Pathways in Esophageal Cancer Based on Gene Expression Omnibus Database

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes,and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis,protein-protein interaction(PPI)network,and survival analysis based on the Gene Expression Omnibus(GEO)database.Methods By screening with highly expressed genes,we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites.Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis,PPI network,and survival analysis.Several software and platforms including Prism 8,R language,Cytoscape,DAVID,STRING,and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma(ESCC)tissue.Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer.Four genes including ALDH3A1,C2,SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer.Keratinization may greatly impact the pathogenesis of esophageal cancer.Genes ALDH3A1,C2,SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.

Time-Domain Analysis of Underground Station-Layered Soil Interaction Based on High-Order Doubly Asymptotic Transmitting Boundary

Based on the modified scale boundary finite element method and continued fraction solution,a high-order doubly asymptotic transmitting boundary(DATB)is derived and extended to the simulation of vector wave propagation in complex layered soils.The high-order DATB converges rapidly to the exact solution throughout the entire frequency range and its formulation is local in the time domain,possessing high accuracy and good efficiency.Combining with finite element method,a coupled model is constructed for time-domain analysis of underground station-layered soil interaction.The coupled model is divided into the near and far field by the truncated boundary,of which the near field is modelled by FEM while the far field is modelled by the high-order DATB.The coupled model is implemented in an open source finite element software,OpenSees,in which the DATB is employed as a super element.Numerical examples demonstrate that results of the coupled model are stable,accurate and efficient compared with those of the extended mesh model and the viscous-spring boundary model.Besides,it has also shown the fitness for long-time seismic response analysis of underground station-layered soil interaction.Therefore,it is believed that the coupled model could provide a new approach for seismic analysis of underground station-layered soil interaction and could be further developed for engineering.

Exploring the Application of Vibration Analysis Technology in the Medical Field

In the field of engine maintenance and assurance, the technology of unit condition detection through vibration analysis is relatively mature. More and more patents and technical products have been released, proving the practical value of the technology in mechanical vibration from the application level. In medical science, signals such as heart sounds and pulses are also vibration signals in nature, in order to expand the application of the technology and explore the value of the technology in medical applications. In order to extend the application of the technology and to explore the value of the technology in medical applications, the wavelet analysis technology was used to program the Labview2022 software to implement the corresponding analysis program for the analysis of the collected physiological signals. Finally, the wavelet transform-based analysis of the physiological signals was successfully implemented. It is demonstrated that the design concept can be achieved by applying this technique, which makes it valuable in the field of physiological signal detection and analysis.

Transcriptome Analysis of Molecular Mechanisms Underlying Phenotypic Variation in Phaseolus vulgaris Mutant‘nts’

The phenotype of a common bean plant is often closely related to its yield,and the yield of plants with reduced height or poor stem development during growth is low.Mutants serve as an essential gene resource for common bean breeding genetic research.Although model plants and crops are studied to comprehend the molecular mechanisms and genetic basis of plant phenotypes,the molecular mechanism of phenotypic variation in common beans remains underexplored.We here used the mutant‘nts’as material for transcriptome sequencing analysis.This mutant was obtained through 60Co-γirradiation from the common bean variety‘A18’.Differentially expressed genes were mainly enriched in GO functional entries such as cell wall organization,auxin response and transcription factor activity.Metabolic pathways significantly enriched in KEGG analysis included plant hormone signal transduction pathways,phenylpropanoid biosynthesis pathways,and fructose and mannose metabolic pathways.AUX1(Phvul.001G241500),the gene responsible for auxin transport,may be the key gene for auxin content inhibition.In the plant hormone signal transduction pathway,AUX1 expression was downregulated and auxin transport across the membrane was blocked,resulting in stunted growth of the mutant‘nts’.The results provide important clues for revealing the molecular mechanism of‘nts’phenotype regulation in bean mutants and offer basic materials for breeding beneficial phenotypes of bean varieties.