Analysis of Non-stationary Signals Based on Nonlinear Chaotic Theories

In the paper, two nonlinear parameter estimation methods based on chaotic theory, surrogate data method and Lyapunov exponents, are used to distinguish the difference of non-stationary signals. After brief introducting of the corresponding algorithms, two typical different non-stationary signals with different nonlinear constraining boundaries are taken to be compared by using the above two methods respectively. The obtained results demonstrate that the apparently similar signals are distinguished effectively in a quantitative way by applying above nonlinear chaotic analyses.

Feature Extraction Techniques of Non-Stationary Signals for Fault Diagnosis in Machinery Systems

Previously, fault diagnosis of fixed or steady state mechanical failures (e.g., pumps in nuclear power plant turbines, engines or other key equipment) applied spectrum analysis (e.g., fast Fourier transform, FFT) to extract the frequency features as the basis for identifying the causes of failure types. However, mechanical equipment for increasingly instant speed variations (e.g., wind turbine transmissions or the mechanical arms used in 3C assemblies, etc.) mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems.

Discrete Time-Frequency Signal Analysis and Processing Techniques for Non-Stationary Signals

This paper presents the methodology, properties and processing of the time-frequency techniques for non-stationary signals, which are frequently used in biomedical, communication and image processing fields. Two classes of time-frequency analysis techniques are chosen for this study. One is short-time Fourier Transform (STFT) technique from linear time-frequency analysis and the other is the Wigner-Ville Distribution (WVD) from Quadratic time-frequency analysis technique. Algorithms for both these techniques are developed and implemented on non-stationary signals for spectrum analysis. The results of this study revealed that the WVD and its classes are most suitable for time-frequency analysis.

Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Multi-components sinusoidal engineering signals who are non-stationary signals were considered in this study since their separation and segmentations are of great interests in many engineering fields. In most cases, the segmentation of non-stationary or multi-component signals is conducted in time domain. In this paper, we explore the advantages of applying joint time-frequency (TF) distribution of the multi-component signals to identify their segments. The Spectrogram that is known as Short-Time Fourier Transform (STFT) will be used for obtaining the time-frequency kernel. Time marginal of the computed kernel is optimally used for the signal segmentation. In order to obtain the desirable segmentation, it requires first to improve time marginal of the kernel by using two-dimensional Wiener mask filter applied to the TF kernel to mitigate and suppress non-stationary noise or interference. Additionally, a proper choice of the sliding window and its overlaying has enhanced our scheme to capture the discontinuities corresponding to the boundaries of the candidate segments.

Hypoglycemic mechanism of Tegillarca granosa polysaccharides on type 2 diabetic mice by altering gut microbiota and regulating the PI3K-akt signaling pathwaye

Type 2 diabetes mellitus(T2DM)is a complex metabolic disease threatening human health.We investigated the effects of Tegillarca granosa polysaccharide(TGP)and determined its potential mechanisms in a mouse model of T2DM established through a high-fat diet and streptozotocin.TGP(5.1×10^(3) Da)was composed of mannose,glucosamine,rhamnose,glucuronic acid,galactosamine,glucose,galactose,xylose,and fucose.It could significantly alleviate weight loss,reduce fasting blood glucose levels,reverse dyslipidemia,reduce liver damage from oxidative stress,and improve insulin sensitivity.RT-PCR and Western blotting indicated that TGP could activate the phosphatidylinositol-3-kinase/protein kinase B signaling pathway to regulate disorders in glucolipid metabolism and improve insulin resistance.TGP increased the abundance of Allobaculum,Akkermansia,and Bifidobacterium,restored the microbiota abundance in the intestinal tracts of mice with T2DM,and promoted short-chain fatty acid production.This study provides new insights into the antidiabetic effects of TGP and highlights its potential as a natural hypoglycemic nutraceutical.

2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Emotion Measurement Using Biometric Signal

In recent years, research on the estimation of human emotions has been active, and its application is expected in various fields. Biological reactions, such as electroencephalography (EEG) and root mean square successive difference (RMSSD), are indicators that are less influenced by individual arbitrariness. The present study used EEG and RMSSD signals to assess the emotions aroused by emotion-stimulating images in order to investigate whether various emotions are associated with characteristic biometric signal fluctuations. The participants underwent EEG and RMSSD while viewing emotionally stimulating images and answering the questionnaires. The emotions aroused by emotionally stimulating images were assessed by measuring the EEG signals and RMSSD values to determine whether different emotions are associated with characteristic biometric signal variations. Real-time emotion analysis software was used to identify the evoked emotions by describing them in the Circumplex Model of Affect based on the EEG signals and RMSSD values. Emotions other than happiness did not follow the Circumplex Model of Affect in this study. However, ventral attentional activity may have increased the RMSSD value for disgust as the β/θ value increased in right-sided brain waves. Therefore, the right-sided brain wave results are necessary when measuring disgust. Happiness can be assessed easily using the Circumplex Model of Affect for positive scene analysis. Improving the current analysis methods may facilitate the investigation of face-to-face communication in the future using biometric signals.

Impact of STAT-signaling pathway on cancer-associated fibroblasts in colorectal cancer and its role in immunosuppression

Colorectal cancer(CRC)remains one of the most commonly diagnosed and deadliest types of cancer worldwide.CRC displays a desmoplastic reaction(DR)that has been inversely associated with poor prognosis;less DR is associated with a better prognosis.This reaction generates excessive connective tissue,in which cancer-associated fibroblasts(CAFs)are critical cells that form a part of the tumor microenvironment.CAFs are directly involved in tumorigenesis through different mechanisms.However,their role in immunosuppression in CRC is not well understood,and the precise role of signal transducers and activators of transcription(STATs)in mediating CAF activity in CRC remains unclear.Among the myriad chemical and biological factors that affect CAFs,different cytokines mediate their function by activating STAT signaling pathways.Thus,the harmful effects of CAFs in favoring tumor growth and invasion may be modulated using STAT inhibitors.Here,we analyze the impact of different STATs on CAF activity and their immunoregulatory role.

Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication technology.Unlike other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental platform.Based on such a platform,we first construct a real signal dataset with ten modulation methods.Then,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature classification.We also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated signals.Finally,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other algorithms.It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

Adaptive Bistable Stochastic Resonance Based Weak Signal Reception in Additive Laplacian Noise

Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.

Digital signal acquisition system for complex nuclear reaction experiments

A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.

Sounding the alarm:Functionally referential signaling in Azure-winged Magpie

Functionally referential signals are a complex form of communication that conveys information about the external environment.Such signals have been found in a range of mammal and bird species and have helped us understand the complexities of animal communication.Corvids are well known for their extraordinary cognitive abilities,but relatively little attention has been paid to their vocal function.Here,we investigated the functionally referential signals of a cooperatively breeding corvid species,Azure-winged Magpie(Cyanopica cyanus).Through field observations,we suggest that Azure-winged Magpie uses referential alarm calls to distinguish two types of threats:’rasp’ calls for terrestrial threats and ’chatter’ calls for aerial threats.A playback experiment revealed that Azure-winged Magpies responded to the two call types with qualitatively different behaviors.They sought cover by flying into the bushes in response to the ’chatter’ calls,and flew to or stayed at higher positions in response to ’rasp’ calls,displaying a shorter response time to ’chatter’ calls.Significant differences in acoustic structure were found between the two types of calls.Given the extensive cognitive abilities of corvids and the fact that referential signals were once thought to be unique to primates,these findings are important for expanding our understanding of social communication and language evolution.

Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

Stirred reactors are key equipment in production,and unpredictable failures will result in significant economic losses and safety issues.Therefore,it is necessary to monitor its health state.To achieve this goal,in this study,five states of the stirred reactor were firstly preset:normal,shaft bending,blade eccentricity,bearing wear,and bolt looseness.Vibration signals along x,y and z axes were collected and analyzed in both the time domain and frequency domain.Secondly,93 statistical features were extracted and evaluated by ReliefF,Maximal Information Coefficient(MIC)and XGBoost.The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor.Finally,the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model.The validation results showed that the model achieves 100%accuracy in detecting the fault/normal state of the stirred reactor and 98%accuracy in diagnosing the type of fault.

Suppressing a mitochondrial calcium uniporter activates the calcium signaling pathway and promotes cell elongation in cotton

Mitochondrial calcium uniporter(MCU)is a conserved calcium ion(Ca^(2+))transporter in the mitochondrial inner membrane of eukaryotic cells.How MCU proteins regulate Ca^(2+)flow and modulate plant cell development remain largely unclear.Here,we identified the gene GhMCU4 encoding a MCU protein that negatively regulates plant development and fiber elongation in cotton(Gossypium hirsutum).GhMCU4expressed constitutively in various tissues with the higher transcripts in elongating fiber cells.Knockdown of GhMCU4 in cotton significantly elevated the plant height and root length.The calcium signaling pathway was significantly activated and calcium sensor genes,including Ca^(2+)dependent modulator of interactor of constitutively active ROP(GhCMI1),calmodulin like protein(GhCML46),calciumdependent protein kinases(GhCPKs),calcineurin B-like protein(GhCBLs),and CBL-interacting protein kinases(GhCIPKs),were dramatically upregulated in GhMCU4-silenced plants.Metabolic processes were preferentially enriched,and genes related to regulation of transcription were upregulated in GhMCU4-silenced plants.The contents of Ca^(2+)and H_(2)O_(2)were significantly increased in roots and leaves of GhMCU4-silenced plants.Fiber length and Ca^(2+)and H_(2)O_(2)contents in fibers were significantly increased in GhMCU4-silenced plants.This study indicated that GhMCU4 plays a negative role in regulating cell elongation in cotton,thus expanding understanding in the role of MCU proteins in plant growth and development.

Mechanism of Wendan decoction in preventing obesity by regulating multiple signal pathway networks based on gene promoter methylation

Objective:To investigate the potential mechanism of Wendan decoction in obesity by screening target genes with promoter region methylation changes and constructing a multiple signaling pathways network based on promoter methylation.Methods:The methylation degree of Itgad,Col8a1,Adra2b,Jund,Rab2a,Wnt8b,Fzd9,B4galt7,Pik3cd,Creb1,Stard8,and Mmp1 in the abdominal adipose tissue of obese rats was determined using the Agena MassARRAY system.Western blot was performed to assess protein expression levels.Target genes were identified based on the methylation degree in the promoter region and protein expression.Enrichment analysis of signaling pathways was conducted to identify relevant target genes and obtain a multiple signaling pathway network associated with obesity.Core and terminal effector molecules in the pathway networks were selected as research targets for reverse transcription-polymerase chain reaction(RT-PCR)analysis.Results:Four genes(Adra2b,Creb1,Itgad,and Pik3cd)showed a degree of promoter methylation consistent with their respective protein expression levels.Among them,Adra2b,Creb1,and Pik3cd expression increased,while that of Itgad decreased.Enrichment analysis revealed that Creb1 and Pik3cd were involved in 6 signaling pathways related to obesity:tumor necrosis factor(TNF)signaling pathway,growth hormone synthesis/secretion and action,adenosine 5'-monophosphate-activated protein kinase(AMPK)signaling pathway,relaxin signaling pathway,cyclic nucleotide(cAMP)signaling pathway,and phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt)signaling pathway.Subsequently,a multiple signaling pathways network was constructed based on promoter methylation.Key molecules including protein kinase B(AKT),mechanistic target of rapamycin complex 1(mTORC1),and unc-51 like autophagy activating kinase 1(ULK1),as well as terminal effector molecules interleukin-1β(IL-1β),interleukin-6(IL-6),and chemokine(C-X-C motif)ligand 2(CXCL2)were selected as research targets.Wendan decoction decreased the expressions of AKT,mTORC1,IL-1β,IL-6,and CXCL2 while up-regulating ULK1 expression.Conclusion:The mechanism of Wendan decoction in preventing obesity involves the regulation of multiple signaling pathways through the control of Creb1 and Pik3cd gene promoter methylation.However,the associated multi-path gene regulation mechanism in preventing obesity is complex.Thus,further exploration is needed to elucidate the role of methylation changes in this mechanism.

Enhanced extracellular production of alpha-lactalbumin from Bacillus subtilis through signal peptide and promoter screening

Alpha-lactalbumin(α-LA)is a major whey protein found in breast milk and plays a crucial role in the growth and development of infants.In this study,Bacillus subtilis RIK1285 harboring AprE signal peptide(SP)was selected as the original strain for the production ofα-LA.It was found thatα-LA was identified in the pellet after ultrasonic disruption and centrifugation instead of in the fermentation supernatant.The original strain most likely only producedα-LA intracellular,but not extracellular.To improve the expression and secretion ofα-LA in RIK1285,a library of 173 homologous SPs from the B.subtilis 168 genome was fused with target LALBA gene in the pBE-S vector and expressed extracellularly in RIK1285.SP YjcN was determined to be the best signal peptide.Bands in supernatant were observed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and purified by nickel column to calculate the highest yield signal peptide.In addition,different promoters(P_(aprE),P_(43),and P_(glv))were compared and applied.The results indicated that the strain RIK1285-pBE-P_(glv)-YjcN-LALBA had the highestα-LA yield,reaching 122.04μg/mL.This study demonstrates successful expression and secretion of humanα-LA in B.subtilis and establishes a foundation for simulating breast milk for infant formulas and developing bioengineered milk.

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.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge.During the malignant transformation of hepatocytes,a variety of oncogenic cellular signalling molecules,such as novel high mobility group-Box 3,angiopoietin-2,Golgi protein 73,glypican-3,Wnt3a(a signalling molecule in the Wnt/β-catenin pathway),and secretory clusterin,can be expressed and secreted into the blood.These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy.This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.