Cross-correlation Research Articles

A novel fault identification method for rolling bearing

Abstract One-dimensional local binary pattern (1D-LBP) identifies the running state of bearings by describing local textural features of vibration signals. However, 1D-LBP algorithm is rather sensitive to noise and intrinsic periodic features of rotating machinery which is not included in consideration. This makes it difficult for 1D-LBP algorithm to comprehensively dig fault information of bearings. To solve this problem, a new method has been brought forward by combining the intrinsic periodicity of rotating machinery and extraction of textural features. Before extraction of local features of signals, vibration signals are subjected to first-order difference operation to highlight local impact features of bearing failure. Meanwhile, to reduce the noise and further strengthen periodic features, differential signals are segmented by a complete period, and the signals after segmentation are subjected to cross-correlation successively to acquire the corresponding cross-correlation functions. Secondly, 1D-LBP is employed on cross-correlation functions (not raw signals) to extract local features and local textural signals (LTS) are obtained. Thirdly, feature parameters and vectors have been built according to LTS and the running state of bearings has been described from the perspective of textural feature. At last, according to established feature vector and classification algorithm, running state of bearing was determined. A comparative analysis is given to proposed method and other methods through public data sets and data from an experimental equipment of bearing. The results of comparative analysis indicate that with the 2 kinds of data sets, the identification rate of proposed method for unknown samples is over 97% and it can judge the running state and the type of faults more precisely.

Continuous overnight monitoring of body temperature during embryo transfer cycles as a proxy for establishing progesterone fluctuations by comparison with P4 blood progesterone results: a prospective, observational study

BackgroundA prospective, observational study to identify relationships between body temperature and levels of peripheral P4 blood progesterone, and examine if these differ according to body temperature cycle pattern.Methods62 data points from 18 patients undergoing hormone assisted embryo transfer cycles at IVIRMA IVF clinics in Madrid, Mallorca and Malaga, Spain volunteered to use OvuSense, an intra-vaginal body temperature monitor. Primary outcome measures were OvuSense Raw and Smooth Temperature (ST) (°C), P4 (ng/ml). Secondary outcome measures: Ongoing Pregnancy, Miscarriage or biochemical pregnancy. Graphical time based comparison analyses and multilevel regression analyses using MLwiN 3.10 [Charlton C, Rasbash J, Browne WJ, Healy M, Cameron B. MlwiN Version 3.10. Centre for Multilevel Modelling. University of Bristol; 2024.] software were conducted.ResultsA graphical analysis showed an apparent relationship between P4 levels and Temperature taken on P4 blood draw day. A multilevel regression analysis using MLwiN 3.10 Centre for Multilevel Modelling. University of Bristol software investigated this relationship, allowing between-patient variation to be accounted for and estimated. This established a strong linear relationship between LnP4 and ST, and cross correlation was carried out which identified the optimum predictor of levels of LnP4 was ST measured on the day prior to blood sampling. Further graphical analyses showed an apparent lower luteal level of P4 for cycles flagged as atypical by OvuSense, and for negative outcomes, except on embryo transfer day.ConclusionsThe results provide extremely strong evidence of a linear relationship between LnP4 and Smooth Temperature (ST) measured the day before blood sampling (Z = 15.6, p < 0.0001, 2 sided). This suggests that ST could provide a less invasive, continuous, and more practical method of assessing P4 response. Secondary outcomes may be related to ST pattern established during an embryo transfer cycle. Further investigation is required to establish the value of the ST pattern for improving outcomes.

Correlation Properties of Color Histograms in the Case of Image Quality Decreasing

In image recognition tasks, objects are identified by examining various features such as texture, color, contour detection, and statistical or semantic descriptions. One widely used approach for extracting image attributes is the analysis of intensity histograms. While the traditional RGB color model is commonly used in digital image processing, it is often more effective to analyze color properties in HS* systems (such as HSL, HSV, and HSI) since these systems more closely resemble the spectral representation of color. A key characteristic shared by these three systems is the use of the H (Hue) coordinate, which is represented as an angular value within a cylindrical coordinate system. The paper investigates the possibility of using color histograms generated in HS* spaces for identifying images that have undergone various types of distortions. The CQ100: A High-Quality Image Dataset for Color Quantiza-tion Research was chosen for the research. The non-quantized section of the CQ100 dataset consists of 100 RGB images in PNG format, each with a resolution of 768×512 pixels and a color depth of 24 bits. The study examines how different distortions, which can occur during real-time photo and video capture, affect the color properties of images. Specifically, the re-search focuses on distortions caused by rotation, noise, blurring, and optical aberration. His-tograms were compared using the Pearson cross-correlation coefficient, and the findings re-veal that the correlation remains high for the same image despite the applied distortions. Conversely, the correlation coefficient between different images is low for most of the studied objects. These results suggest that color histograms could be effectively used for image identi-fication tasks, even when images are significantly distorted, as is common in image registra-tion processes. The applicability of correlation detection as a method for histograms com-parison is considered regardless of the relative simplicity of its calculation. This approach could contribute to the development of faster image recognition systems.

Comparing three different platforms for the testing of intrinsic factor antibody and cross correlation with gastric parietal cell antibody status, in the diagnosis of pernicious anaemia

Comparing three different platforms for the testing of intrinsic factor antibody and cross correlation with gastric parietal cell antibody status, in the diagnosis of pernicious anaemia

Spatiotemporal Variability of Groundwater Quality for Irrigation: A Case Study in Mimoso Alluvial Valley, Semiarid Region of Brazil

Alluvial aquifers are vital for agricultural communities in semiarid regions, where groundwater quality is often constrained by seasonal and spatial salinity variations. This study employed geostatistical methods to analyze the spatial and temporal variability of electrical conductivity (EC) and the sodium adsorption ratio (SAR) and elaborate an indicative quality map in the Mimoso Alluvial Aquifer, Pernambuco, Brazil. Groundwater samples were collected and analyzed for cations, total hardness (TH), and the percentage of sodium (PS). Moreover, the relation between EC and the SAR was used to determine the groundwater quality for irrigation. Cation concentrations followed the order Ca2+ &gt; Mg2+ &gt; Na+ &gt; K+. EC and the SAR exhibited medium to high variability, with spatial dependence ranging from moderate to strong, and presented a strong cross-spatial dependence. Results showed that sequential Gaussian simulation (SGS) provided a more reliable groundwater classification for agricultural purposes compared to kriging methods, enabling a more rigorous evaluation. Based on the strong geostatistical cross correlation between EC and RAS, a novel water quality index was proposed, properly identifying regions with lower groundwater quality. The resulting spatial indicator maps classified groundwater as suitable (64.7%), restricted use (2.08%) and unsuitable (2.38%) for irrigation. The groundwater quality maps indicated that groundwater was mostly suitable for agriculture, except in silty areas, also corresponding to regions with low hydraulic conductivity at the saturated zone. Soil texture, rainfall, and water extraction significantly influenced spatial and temporal patterns of groundwater quality. Such correlations allow a better understanding of the groundwater quality in alluvial valleys, being highly relevant for water resources management in semiarid areas.

The effects of rain and drought on incidence of enteric disease in Pennsylvania (2010-2019).

Campylobacter, nontyphoidal Salmonella, Cryptosporidium, and Giardia cause an estimated 1 million cases of domestically acquired waterborne diseases annually in the United States. Acute symptoms can include diarrhea and vomiting; however, these illnesses can result in longer term complications such as reactive arthritis, Guillan Barré syndrome and death, particularly in immunocompromised individuals. Precipitation and drought can plausibly increase the risk of enteric infections, but consensus in the literature is lacking. To determine the effects of rain and drought on weekly counts of reportable enteric illness (due to Salmonella, Campylobacter, Giardia, or Cryptosporidium) in Pennsylvania, US between 2010 and 2019. We obtained 10-years of data on confirmed illness from 66 Pennsylvania counties due to: Salmonella (9,924), Campylobacter (15,854), Giardia (4,537), and Cryptosporidium (4,017). A zero-inflated negative binomial model with random-intercept for county was used to assess the relationship between illnesses caused by these pathogens between 2010 and 2019, and weekly rain (cm) and Palmer Drought Severity Index (PDSI) values from the National Weather Service. The lag times that were tested, between illness and weather event, were chosen by calculating the cross correlation between the statewide average weekly rain and the statewide number of weekly cases. A positive association was found between rain and counts of campylobacteriosis, salmonellosis, and giardiasis. An increase in prior wetness (PDSI value) was associated with increased incidence rates of cryptosporidiosis and campylobacteriosis. The relationship between rain and PDSI and illness varied by organism type. Complex relationships exist between enteric disease and precipitation and prior environmental wetness. Our findings suggest that rainfall may be contributing to increased waterborne exposure. Further investigation is needed to explore these relationships with factors such as drinking water source, local geological conditions, presence of combined sewer overflows and agricultural activities, recreational water use and irrigation water sources to better elucidate important waterborne transmission pathways.

Probabilistic stability assessment of slope considering soft soil and silty clay foundations

Although deterministic analysis can provide initial insights into slope stability, there is no way to reflect the true distribution of soil properties within a slope. To further investigate the effects of the spatial variability of soil properties on the stability and failure mechanism of slope under different foundation types, this study develops a framework combining simple limit equilibrium method (LEM), Monte Carlo Simulation (MCS), and random field to incorporate these factors into slope probabilistic stability analysis. The slope models of two typical foundations (e.g., soft soil and silty clay foundations) are used to illustrate the proposed methodology. The results show that the shape of critical failure slip surface in slope with a soft foundation is deep and passes through the soft soil layer, indicating that the slope with a soft foundation has a variable failure mechanism. Due to the presence of soft soil foundation, the correlation between different critical failure slip surfaces is low and deterministic analysis may significantly overestimate slope stability. When performing a probabilistic stability analysis of the slope under the silty clay foundation, it is noted that the shape of critical failure slip surface is relatively shallow and the correlation between different critical failure slip surfaces is high. The degree of slope stability overestimation caused by deterministic analysis is significantly reduced. Note that when the factor of safety is used to evaluate slope stability, deterministic analysis can only evaluate slope stability more reasonably when the correlation of failure slip surface is high, while probabilistic method can evaluate slope stability more comprehensively and reasonably. The sensitivity analysis results show that the sensitivity of internal friction angle to slope stability is much higher than that of cohesive under different scales of fluctuation, coefficients of variation, and cross correlation coefficients.

Improvement of Seismic Noise Cross-correlation Function by Phase-velocity Probability Density Selection

TThe noise cross-correlation function (NCF) derived from continuous seismic records approximates the Green's Function (GF) of the far-field surface wave, which is crucial for S-wave velocity tomography. A critical step is stacking short-term NCFs over extended periods to improve the signal-to-noise ratio (S/N) and enhance the imaging quality of seismic data. However, incoherent noise significantly affects the accuracy of NCF calculations. Moreover, strong directional sources in the wavefield pose a challenge to conventional linear or phase-weighted stacking (PWS), often leading to overestimates of surface-wave phase velocity due to unresolved azimuthal effects. Selecting stationary phase zone (SPZ) data and correcting surface wave propagation paths based on source orientation requires a 2D array setup, which imposes stringent layout constraints. To address these challenges, we propose a novel data selection workflow for a 1D linear array based on the probability density distribution of surface-wave phase velocity. Firstly, we divide the noise data into different frequency bands and calculate the probability density curve of the phase velocity distribution for each frequency band. Then, based on the differences in phase velocities between SPZ data, directional source data, and incoherent noise, we select the probability density segments of SPZ data in each frequency band for stacking, effectively reducing the impact of incoherent noise and strong directional sources. The typical synthetic data and distributed acoustic sensing (DAS) field data examples demonstrate that the proposed workflow significantly improves the S/N in NCF and increases the useful bandwidth of the surface- waves dispersion curve, which furnishes reliable dispersion curve and waveform data, thereby facilitating subsequent S-wave velocity tomography.

Correlation of instantaneous fading in bidirectional channels of atmospheric laser communication

The instantaneous fading correlation of the bidirectional channel is an important characteristic of the atmospheric random channel. The instantaneous channel state of the transmitting end can be directly obtained through the instantaneous channel state of the receiving end, reducing the overhead of the additional feedback branch. In this paper, an autoregressive (AR) model and an inverse transformation method are used to generate time-dependent optical signals obeying an APD exponential Weibull distribution. Based on the channel reciprocity function, a channel cross-correlation model on the laser communication transmission path is established, and the influence of factors such as transmitting and receiving apertures, zenith angle, transmission distance, altitude, wavelength, and detector responsivity on the cross-correlation coefficient is studied. Based on theoretical research, an experimental system was built to verify the heterogeneous reciprocity model, and the experimental results were basically consistent with the simulation results. A channel state information prediction and evaluation are achieved through a relevant reciprocity theory, providing a basis for the development of adaptive technology in atmospheric laser communications.

Computational spectral imaging reconstruction via a spatial–spectral cross-attention-driven network

Compared with traditional hyperspectral imaging, computational spectral imaging (CSI) has the advantage of snapshot imaging with high spatial and temporal resolution, which has attracted considerable attention. The core challenge of CSI is to achieve computational imaging reconstruction from a single 2D measurement image to the corresponding 3D spatial–hyperspectral image (HSI). Existing reconstruction methods still face problems in exploring spatial–spectral cross correlation, leading to significant spatial–spectral distortion. Furthermore, due to neglect of multi-scale feature reconstruction, their reconstruction quality still needs to be improved. In this paper, to solve the above problems, we propose a spatial–spectral cross-attention-driven network (SSCA-DN). In SSCA, a proposed multi-scale feature aggregation (MFA) module and a spectral-wise transformer (SpeT) are used for multi-scale spatial feature reconstruction and long-range spectral feature reconstruction, respectively. Using spatial attention and spectral attention to interactively guide the reconstruction of the target HSI in spectral and spatial dimensions, the proposed SSCA models spatial–spectral cross correlation with considering multi-scale features. Using the SSCA as a basic module, a novel SSCA-DN network is constructed, in which a proposed supervised preliminary reconstruction subnetwork (SPRNet) learns the generalized prior, and a proposed unsupervised multi-scale feature fusion and refinement subnetwork (UMFFRNet) learns the specific prior. The SSCA module ensures that the learned generalized and specific priors can capture the spatial–spectral cross correlation while considering multi-scale features. In addition, in UMFFRNet, driven by MFA and SSCA, a novel multi-scale fusion and refinement mechanism for multi-level adjacent features is proposed to effectively model the correlation between adjacent level features and the multi-scale spatial–spectral cross correlation, which further improves the reconstruction accuracy. Extensive experiments show that our method achieves state-of-the-art performance on both simulated and real datasets.

Near-infrared Integral-field Spectroscopy of the Wind Forming Region of CW Leo

Abstract The circumstellar envelope of the carbon star CW Leo exhibited various unexpected changes in recent optical imaging observations. We have performed a follow-up observation using the Near-infrared Integral-Field Spectrograph (NIFS) equipped on the Gemini-North telescope. We report the near-infrared counterparts of a local brightness peak in the optical at the stellar position of CW Leo. On the other hand, a second peak detected at short wavelengths in the J band coincides with the brightest, bluest position in the optical images. The absorption features in the K band are minimized at a radius of 0 . ′ ′ 2 from the predicted stellar position. The reduction of the absorption depths likely indicates dilution of the absorption features by thermal emission of dust grains newly formed at such a radius and heated by radiation from the central star. The broad absorption feature at 1.53 μm is significantly deeper than in template carbon stars, consistent with the presence of a substantial amount of circumstellar material around CW Leo. Its northeastern quadrant lacks circumstellar absorption features and scattered light in the near-infrared regime, which are possibly manifestations of its conical cavity in both gas and dust. In addition, a cross correlation of CO overtone bands indicates that the average expansion velocity of dust grains is smaller to the northern direction, likewise the velocity of transverse wind components derived using the differential proper motion of a circumstellar whirled pattern. The gradual brightening of CW Leo and the changes in its innermost circumstellar envelope need further continuous monitoring observations to properly understand its transitional phase toward the post-asymptotic-giant-branch stage.

Accelerating quantum imaginary-time evolution with random measurements

Quantum imaginary-time evolution (QITE) is a promising tool to prepare thermal or ground states of Hamiltonians, as convergence is guaranteed when the evolved state overlaps with the ground state. However, its implementation using a a hybrid quantum-classical approach, where the dynamics of the parameters of the quantum circuit are derived by McLachlan's variational principle, is impractical as the number of parameters m increases, since each step in the evolution takes Θ(m2) state preparations to calculate the quantum Fisher information matrix (QFIM). In this work, we accelerate QITE by rapid estimation of the QFIM, while conserving the convergence guarantees to the extent possible. To this end, we prove that if a parameterized state is rotated by a 2-design and measured in the computational basis, then the QFIM can be inferred from partial derivative cross correlations of the probability outcomes. One sample estimate costs only Θ(m) state preparations, leading to rapid QFIM estimation when a few samples suffice. The second family of estimators takes greater liberties and replace QFIMs with averaged classical Fisher information matrices (CFIMs). In an extreme special case optimized for rapid (over accurate) descent, just one CFIM sample is drawn. We justify the second estimator family by proving rapid descent. Guided by these results, we propose the algorithm, which we showcase and test in several molecular systems, with the goal of preparing ground states. Published by the American Physical Society 2025

Investigation of oscillations above the acoustic cut-off frequency for a sample of Kepler stars. II. Identifying temporal frequency shifts

Abstract Stellar oscillations with frequencies greater than the acoustic cut-off frequency are not trapped within the star’s interior. Geometric interference between these high-frequency waves produces a peak-like structure in the power spectrum of the star, known as the pseudo-modes. In this study, we aim to analyse 51 stars previously identified (from a sample of 91 Kepler stars) to contain statistically significant evidence for pseudo-modes, to determine if the pseudo-mode frequencies vary in time. We obtained these frequency variations and the uncertainty by the resampled periodogram approach, where periodogram realisations were created from successive, overlapping 90-day time segments, and a cross-correlation function was generated between them. The computed shifts were compared against temporal p-mode frequency shifts and a stellar magnetic activity proxy, Sph. We found that for 16 % of the stars pseudo-mode frequency shifts were significantly anti-correlated with p-mode shifts, as is the case for the Sun. However, we also found pseudo-mode and p-mode shifts to be significantly correlated in-phase in 8 % of our sample. The remainder showed no significant correlation. We also searched for trends between the maximum variation of pseudo-mode frequency shifts and stellar parameters. Whilst we found no correlation between the maximum variation and stellar photospheric and chromospheric proxies, we found frequency variations to have a greater amplitude for cooler and slower rotating stars, which is in opposition to p-mode frequency shift variation. Temporal changes to pseudo-mode frequencies may be attributable to variations in stellar magnetic activity, and therefore can contribute to our understanding of stellar activity cycles.

Multifractal detrended cross-correlation analysis of Islamic stock markets in the pacific Asia region

This study aims to uncover the multifractal characteristics and complex interactions among six Islamic stock markets in the pacific Asia region, enhancing understanding of their dynamics. Employing Multifractal Detrended Cross-Correlation Analysis (MF-DCCA), this research analyzes a dataset covering the period from January 1, 2011, to August 1, 2024, with approximately 3315 observations. In the preliminary analysis, the application of the DCCA Cross-Correlation Coefficient method revealed that the cross-correlations are persistent and exhibit long-term stability across most index pairs. Utilizing core components of the MF-DCCA method, such as Generalized Hurst Exponents, Rényi Exponents, and the Hölder Singularity Spectrum, further confirmed that the index pairs exhibit long-range persistent cross-correlations and multifractal behavior. Surrogate and shuffling transformations showed that the observed multifractality is influenced by both long-term cross-correlations and heavy-tailed distributions. The findings have important implications for investors, policymakers, and financial analysts concerned with portfolio diversification, risk management, and the efficiency of Islamic financial markets in this economically vital region. Understanding the interconnectedness of these markets can aid in developing more effective investment strategies and regulatory frameworks. This study contributes original insights into the dynamics of Islamic stock markets in the Pacific Asia region, offering a nuanced perspective on their complex behaviors and interactions through the lens of multifractality.

The Gaia-ESO survey: New spectroscopic binaries in the Milky Way

Context. The Gaia-ESO survey (GES) is a large public spectroscopic survey that acquired spectra for more than 100 000 stars across all major components of the Milky Way. In addition to atmospheric parameters and stellar abundances that have been derived in previous papers of this series, the GES spectra allow us to detect spectroscopic binaries with one (SB1), two (SB2), or more (SBn ≥ 3) components. Aims. The present paper discusses the statistics of GES SBn ≥ 2 after analysing 160 727 GIRAFFE HR10 and HR21 spectra, amounting to 37 565 unique Milky Way field targets. Methods. Cross-correlation functions (CCFs) have been re-computed thanks to a dozen spectral masks probing a range of effective temperatures (3900 K &lt; Teff &lt; 8000 K), surface gravities (1.0 &lt; log g &lt; 4.7), and metallicities (−2.6 &lt; [Fe/H] &lt; 0.3). By optimising the mask choice for a given spectrum, the newly computed, so-called NACRE (NArrow CRoss-correlation Experiment) CCFs are narrower and allow more stellar components to be unblended than standard masks. The DOE (Detection Of Extrema) extremum-finding code then selects the individual components and provides their radial velocities. Results. From the sample of HR10 and HR21 spectra corresponding to 37 565 objects, the present study leads to the detection of 322 SB2, ten SB3 (three of them being tentative), and two tentative SB4. In particular, compared to our previous study, the NACRE CCFs allowed us to multiply the number of SB2 candidates by ≈1.5. The colour-magnitude diagram reveals, as expected, the shifted location of the SB2 main sequence. A comparison between the SB identified in Gaia DR3 and the ones detected in the present work was performed and the complementarity of the two censuses is discussed. An application to the mass-ratio determination is presented, and the mass-ratio distribution of the GES SB2 is discussed. When accounting for the SB2 detection rate, an SB2 frequency of ≈1.4 % is derived within the present stellar sample of mainly FGK-type stars. Conclusions. As primary outliers identified within the GES data, SBn spectra produce a wealth of information and useful constraints for the binary population synthesis studies.

Rethinking asymmetric image deformation with post-correction for particle image velocimetry

Symmetric image deformation has been considered as the only method for achieving second-order accuracy in particle image velocimetry (PIV). However, two deformed images with interpolation errors might lead to a doubling of the measurement uncertainty. Alternatively, this work proposed a post-correction method (FDI2CDI) to correct the velocity results of asymmetric image deformation to second-order accuracy, aiming at reducing the random interpolation error because only one deformed particle image is required. Specifically, the implicit geometric relationship between asymmetric forward difference interrogation (FDI) and symmetric central difference interrogation (CDI) is derived. And the correction problem is thus modeled as a fixed-point problem, which is solved using iterative updates. Tested on several synthetic velocity fields, massive synthetic particle image pairs, and two captured recordings, our FDI2CDI method demonstrates fast convergence, noise robustness, and significant improvement in accuracy. Besides, our FDI2CDI method also exhibits strong generalizability across different one-pass displacement estimators, as shown through experiments with optical flow and cross correlation. In addition, we provide a publicly available repository of FDI2CDI, including all reported results for the interested practitioners. In summary, our FDI2CDI post-correction method revitalizes the asymmetric image deformation for more accurate PIV measurement.

Novel design of fractional cholesterol dynamics and drug concentrations model with analysis on machine predictive networks.

Within the intricate fabric of human physiology, cholesterol, a lipid present in cell membranes exerts a discernible effect on the concentration of the drug in human body that influence the aspects of drug pharmacokinetics. The objective of this work is to design a case study based fractional order cholesterol drug interaction model that encapsulates the nuanced dynamics inherent in the multifaceted human physiology with identification of essential variables including drug concentration Ksb and cholesterol level γ. The strength of nonlinear autoregressive with exogenous inputs (NARX) neural networks are exploited to predict the temporal dynamics that reveal the hidden intricacies and subtle patterns within the fractional model. Grünwald-Letnikov (GL) based fractional solver is used to generate the synthetic data, serving as a robust foundation for training, testing and validation of the NARX neural networks for different use cases of cholesterol drug interaction control strategies. A thorough comparative analysis based on exhaustive simulation unveiled a marginal distinction between the results obtained from NARX and the outcomes of fractal technique showing remarkably low MSE in the range of 10-12. The strength of the designed methodology is further verified by using other performance metrics such as MSE, regression index, autocorrelation and cross correlation. The integration of genetic and genomic information tailor the model to address the unique characteristics of individual patient facilitating advancement in precision medicines.

Radial velocity variability fractions of different types of hot subdwarf stars

Context. It is generally thought that hot subdwarfs are helium-core- or helium-shell-burning objects with extremely thin hydrogen envelopes and that binary interactions are always needed in their formation. Different types of hot subdwarfs may have different origins, which will cause them to present different radial velocity (RV) variability properties. Aims. We plan to study the RV-variability fractions of different types of hot subdwarfs, as well as their distributions in the Teff − log g diagram. This provides insights into the formation of hot subdwarfs. Methods. The cross-correlation function method was adopted to measure RV variations in 434 hot subdwarfs based on spectra obtained with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Light curves from the Transiting Exoplanet Survey were also used to search for short-period binary hot subdwarfs. Results. Only 6 ± 4% of our single-lined He-rich hot subdwarfs that only show spectroscopic features of hot subdwarfs are found to be RV variable, which is lower than the fraction of single-lined He-poor sdB stars (31 ± 3%). Single-lined sdB stars with effective temperatures (Teff) ∼ 25 000–33 000 K show an RV-variability fraction of 34 ± 5%, while lower RV-variability fractions are observed for single-lined sdB stars cooler than about 25 000 K (11 ± 4%), single-lined sdB/OB stars with Teff ∼ 33 000–40 000 K and surface gravities ∼ 5.7–6.0 (13 ± 3%), as well as single-lined sdO/B stars with Teff ∼ 45 000–70 000 K (10 ± 7%). Single-lined hot subdwarfs with Teff ∼ 35 000–45 000 K located above the extreme horizontal branch (EHB) show a similar RV-variability fraction of 34 ± 9% as single-lined sdB stars at about 25 000–33 000 K. The largest RV-variability fraction of 51 ± 8% is found in single-lined hot subdwarfs below the canonical EHB. The detected RV-variability fraction of our composite hot subdwarfs with an infrared excess in their spectral energy distributions is 9 ± 3%, which is lower than that fraction of single-lined hot subdwarfs. Since the average RV uncertainty we measured in the LAMOST spectra is about 7.0 km/s, the lower detected RV-variability fraction for composite hot subdwarfs is expected because the RV amplitudes associated with long-period systems are lower. Conclusions. The results here are generally consistent with the canonical binary evolution channels for forming hot subdwarfs. Most single-lined He-rich hot subdwarfs may form through merger channels, while the stable Roche-lobe overflow channel could play an important role in the formation of composite hot subdwarfs. Single-lined hot subdwarfs with Teff ∼ 35 000–45 000 K located above the EHB may have an evolutionary connection to the sdB stars at about 25 000–33 000 K. The different detected RV-variability fractions for the different subclasses of single-lined hot subdwarfs indicate that their formation channels may differ.

Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means.

This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features. We introduce a fuzzy C-means clustering method enhanced by quantum particle swarm optimization (QPSO) to manage data uncertainties, improving classification accuracy and convergence speed. Additionally, we employ a cross-correlation function to eliminate uncertainties from tonal transition redundancies. We designed a detection algorithm using trend data to validate our clustering method, thereby enhancing the accuracy of the analysis of tonal ranges and potential models. This method detects whether Yue Opera adheres to traditional rhythmic norms and models the regularity of musical tones and vocal patterns. Simulation results reveal that our approach achieves a 91.4% accuracy in classifying vocal styles, surpassing traditional methods and demonstrating its potential for identifying various styles. This research offers technical support for Yue Opera music education and interdisciplinary research. The findings enhance the quality of artistic creation and performance in Yue Opera, ensuring its preservation and development.

High Accuracy Source Localization Based on Parallel Factor Analysis of TDOA in the Cross Correlation Domain

High Accuracy Source Localization Based on Parallel Factor Analysis of TDOA in the Cross Correlation Domain