Autocorrelation Research Articles

An improved wiener filter-based method for identifying stall inception of transonic compressor

The development of the modern aviation industry poses high demands on the design of aircraft engines recently. However, the stability of compressor flow is one of the key factors affecting further improvements in engine performance. The design of next-generation aircraft engines imposes higher requirements on compressor loading, which leads to the emergence of many new stall inceptions. As a result, the onset and evolution of flow instability become more complex. For accurately capturing stall inceptions of transonic compressors, the strong pressure disturbances caused by shock waves at the blade tip and the complex flow within blade passages result in significant challenges. To address this issue, this study draws inspiration from the design methods of Wiener filters in the field of speech recognition. Based on the characteristic signal mutations during rotating stall in compressors, a Wiener filter approach that uses time-delayed signals as noise estimates during filter training is developed. The method can be used for both offline and online analysis. It was applied to analyze the stall signals of a single rotor from a 1.5-stage transonic axial compressor under distorted inlet conditions at transonic rotational speeds and the entire stage under uniform inlet conditions at subsonic rotational speeds. The results indicate that, under inlet distortion, the compressor generates disturbance signals in the distorted sector before stall, and the earliest spike-inception disturbance occurs at the circumferential position of the rotor leaving the distorted sector. Under uniform inlet conditions, random disturbances could be detected throughout the circumference before stall onset, developing into spike waves at a circumferential location that subsequently triggered stall. Compared to conventional low-pass filters, discrete wavelet transforms, and empirical mode decomposition, the Wiener filter yielded more prominent spike wave structures in the filtered signals. Under distorted inlet conditions, the Wiener-filtered signals showed a 1 % decrease in autocorrelation coefficient and a 3.7 % increase in root mean square (RMS) upon the appearance of spike waves, more pronounced than the 0.5 % decrease in autocorrelation coefficient and 1.8 % increase in RMS achieved by conventional methods. Under uniform inlet conditions, the Wiener filter also detected a 9.7 % increase in RMS upon the appearance of spike waves, more pronounced than the 6.3 % increase observed with conventional methods.

Driving low-carbon mechanisms through smart investments in renewable resources and green finance initiatives among G20 nations

In light of the escalating concerns regarding climate change and environmental decline, major nations are actively exploring strategies to mitigate environmental harm and achieve future sustainability. The surge in economic expansion in developed economies is linked to an increase in CO2 emissions. Consequently, in their pursuit of carbon-neutral policies, these countries are increasingly turning towards renewable energy as a means to enhance resource conservation and efficiency. This research investigates the varied impacts of renewable energy investment, green finance, geopolitical risk, GDP growth, foreign direct investment, and gross fixed capital formation on the carbon emissions of G20 countries. The study uses the CUP-FM (Continuously Updated Fully Modified) and CUP-BC (Continuously Updated Bias-Corrected) estimators, which are sophisticated econometric approaches designed to handle non-stationary panel data and cross-sectional dependency, to produce robust long-term parameter estimates. The CUP-FM estimator adjusts for potential endogeneity and serial correlation, improving the accuracy of long-run relationships in panel data. The CUP-BC estimator provides bias-corrected estimates to further enhance the precision of these long-run connections.The long-term parameter estimates reveal a negative correlation between renewable energy investment, green finance, and carbon emissions. In contrast, foreign direct investment, gross fixed capital formation, GDP growth, and geopolitical risk are positively associated with CO2 emissions. This suggests that financial stability often leads to investments in carbon-heavy economic ventures, thereby implicating economic growth as a contributing factor to environmental degradation in G20 countries.

Structural Damage Detection under Ambient Excitation Using Symbolic Three-Order Square Matrix Formed by Specific-Interval-Sampled Time-Domain Signals.

In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam-a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient.

Investigating the Effect of Capital Expenditure and Age on Profitability: A Study of Listed Manufacturing Firms in Bangladesh

The study has been conducted to investigate whether firms’ capital expenditure and age have any impact on profitability. To serve this purpose, around 38 listed manufacturing companies of Dhaka Stock Exchange have been considered. Capital expenditure and age of the business since its commencement have been considered as independent variables where return of assets (ROA) as a proxy for profitability has been considered as dependent variable. To normalize the variability, the study used the natural logarithm of the said variables. To consider the confounding effect, the study also considered five control variables: total assets as proxy for firm size, debt to total assets as proxy for financial leverage, GDP growth as proxy for economic growth, the presence of COVID and one period lag on dependent variable. Based on the results of Hausman test, the study used Random Effect Model. In addition, owing to presence of heteroskedasticity and serial correlation, the study further accommodated Panel Corrected Standard Errors (PCSE) to interpret its findings. The findings of the study suggest that both capital expenditure and age of the firm have positive and significant effect on the profitability of the firms.

Random Telegraphic Signals with Fractal-like Probability Transition Rates

Many physical processes feature random telegraph signals, e.g., a time signal c(t) that randomly switches between two values over time. The present study focuses on the class of telegraphic processes for which the transition rates are formulated by using fractal-like expressions. By considering various restrictive hypotheses regarding the statistics of the waiting times, the present analysis provides the corresponding expressions of the unconditional and conditional probabilities, the mean waiting times, the mean phase duration, the autocorrelation function and the associated integral time scale, the spectral density, and the mean switching frequency. To assess the relevance of the various hypotheses, synthetically generated signals were constructed and used as references to evaluate the predictive quality of the theoretically derived expressions. The best predictions were obtained by considering that the waiting times probability density functions were Dirac peaks centered on the corresponding mean values.

The Impact of Socio-cultural and Environmental Deterioration Factors on Economic Sustainability in G7 Countries

The paper provides the examination of economic sustainability based on the impact of socio-cultural factors and the environmental deterioration factors. This empirical analysis focuses particularly on G7 countries, and panel time series during the period 2000-2022 are used to test this relationship. It applies several variables to determine these impacts that are tested with a principal component analysis and generalized least square estimation. According to Hofstede (1980) which is explained the culture dimensions (masculinity, power distance, uncertainty avoidance, and indulgence); and U-shaped in Environmental Kuznets curve (EKC) which showed the relationship between environmental deterioration and income level. Socio-cultural variables are divided into three groups: “socio-development”, “Hofstede dimensions”, and “economic development”. Environmental deterioration variables are divided into two groups: “air pollution”, and “water pollution”. The paper indicates the hypothesis that socio-cultural variables significantly impact economic sustainability; and the significant impact of environmental deterioration variables on economic sustainability. The findings shows that socio-cultural negatively impacts economic sustainability, and environmental deterioration positively affects economic sustainability. Different tests apply to determine these effects, Hausman test to determine either Fixed effect or Random effect, Heteroscedasticity test (Durbin Watson test), Persan’s CD to test the serial correlation and Jarque-Berra test to confirm the normality distribution.

Linking network- and neuron-level correlations by renormalized field theory

It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations. Published by the American Physical Society 2024

TRNG Entropy Model in the Presence of Flicker FM Noise

Flicker Frequency Modulated (FM) noise, which influences free-running Ring Oscillators (ROs), can make a substantial contribution to the entropy generated by RO-based True Random Number Generators (TRNGs). While current TRNG stochastic models predominantly concentrate on white FM noise, the addition of flicker FM noise could remarkably enrich the analysis of the TRNG entropy production rate. This paper introduces an entropy model for TRNGs, employing Gaussian processes, to estimate entropy generation from both white FM and flicker FM noise. We analytically derive the flicker FM noise Auto-Correlation Function (ACF), enabling assessment of entropy contributions conditioned on partial knowledge of the TRNG’s internal state. Utilizing the developed model with commonly reported noise magnitudes found in literature, it is determined that flicker FM noise holds the potential to substantially enhance the TRNG’s entropy rate. However, due to considerable variation in reported magnitudes across limited available research on flicker FM noise, it cannot yet be universally accepted as a dependable source of TRNG entropy.

Chemical short-range order increases the phonon heat conductivity in a refractory high-entropy alloy

We study the effects of the chemical short-range order (SRO) on the thermal conductivity of the refractory high-entropy alloy HfNbTaTiZr using atomistic simulation. Samples with different degrees of chemical SRO are prepared by a Monte Carlo scheme. With increasing SRO, a tendency of forming HfTi and TiZr clusters is found. The phonon density of states is determined from the velocity auto-correlation function and chemical SRO modifies the high-frequency part of the phonon density of states. Lattice heat conductivity is calculated by non-equilibrium molecular dynamics simulations. The heat conductivity of the random alloy is lower than that of the segregated binary alloys. Phonon scattering by SRO precipitates might be expected to reduce scattering times and, therefore, decrease thermal conductivity. We find that, in contrast, due to the increase of the conductivity alongside SRO cluster percolation pathways, SRO increases the lattice heat conductivity by around 12 %. This is expected to be a general result, extending to other HEAs.

Nanobubbles in Ultrapure Water Can Self-Propel.

Nanobubbles are sub- micron-sized gas entities that find applications in a wide range of scientific fields. Typically, they are thought to diffuse according to Brownian motion. We report the existence of self-propelled motion of oxygen bulk nanobubbles in ultrapure water at body temperature. Their motion, to a large extent, is self-affine; there are different scaling exponents along the x- and y-axes as well as for the lateral displacement. We use fractal analysis, and we calculate the structure function, the normalised velocity autocorrelation function, the skewness, and the kurtosis. All descriptors attest the existence of a quasi-Gaussian stochastic process, which is classified as fractional Brownian motion. More than 50 \% of the trajectories along the x-axis follow superdiffusion, while this amount drops to 30 \% for motion along the y-axis as a result of the asymmetry of the field of view.

Correlation study between structural parameters of droplets and rheological properties of O/W high internal phase Pickering emulsions above the closest packing density

The correlation between the structural parameters of droplets and rheological properties of O/W high internal phase Pickering emulsions (HIPPEs) stabilized by soy protein isolate (SPI) nanoparticles was elucidated, with a volume fraction of dispersed phase (ϕ) above the closest packing density (74%). The structural basis of SPI nanoparticles was confirmed to be spherical heat-induced protein aggregates, which had moderate wettability and excellent interfacial activity. The structural parameters of emulsion droplets (contact length L, diameter D, length d1, width d2, the number of adjacent contact droplets Z), and the stability and rheological properties of HIPPEs were characterized by the confocal laser scanning microscopy (CLSM), diffusing wave spectroscopy (DWS), and mechanical rheometer respectively. The results of CLSM indicated the transition of droplets from spherical to polyhedral shapes as increased ϕ, accompanied by an increased L and Z. The rheological and stability analysis confirmed the widened linear viscoelastic range, elevated gel point (εc), and the extended half−life of the intensity autocorrelation function of HIPPEs as ϕ increased. Moreover, the loss factor (tan δ) remained constant at 0.1 within the ϕ of 74%–86%, revealing the solid−like nature of HIPPEs. The correlation between ϕ, structural parameters of droplets, and rheological properties of HIPPEs was further analyzed. The nonlinear regression fitting between the L/D and G' (elastic modulus) yielded a better fitting effect than d1/d2 and Z, suggesting an intrinsic correlation between emulsion structure and rheology. In summary, this study elucidated the intricate relationship between ϕ, droplet structure, and rheological properties of HIPPEs, offering a research foundation and theoretical basis for the development and application of protein particle−stabilized HIPPEs.

Enhancing the thermal conductivity and viscosity of ethylene glycol-based single-walled carbon nanotube (SWCNT) nanofluid: An investigation utilizing equilibrium molecular dynamics simulation

In this work, thermal conductivity (TC), viscosity, and rheological properties of an ethylene glycol (EG) based single-walled carbon nanotube (SWCNT) nanofluid (NF) have been computed using equilibrium molecular dynamics (EMD) simulation. In SWCNT, for the interaction between carbon atoms, Tersoff potential is used. Results indicate that TC and viscosity increase in nonlinear fashion with volume fraction. However, with temperature, TC increases but viscosity decreases. Increased interaction between CNT and liquid atoms of EG, and the high heat conductance ability of SWCNT nanoparticles enhance the effective conductivity and viscosity of NFs. Longer CNTs provide more efficient heat transfer pathways and more interactions between CNT & base fluid molecules, which contribute to enhanced TC and viscosity of NFs. Weakening of intermolecular forces within the NF with increasing temperature decreases viscosity. To validate the results, radial distribution function (RDF) and stress autocorrelation function (SACF) have been estimated. Mean square displacement (MSD) investigation demonstrates that the diffusion of liquid atoms (or molecules) serves as the fundamental mechanism for heat conduction in nanofluid. The results have been compared with experimental findings for analogous dispersive medium. Broadly, an attempt has been made to explore how interactions between the base fluid and nanoparticles (NPs) can enhance the thermal and rheological efficiencies of nanofluids.

ARIMA-Based forecasting of monthly rainfall in Mandi district, Himachal Pradesh

ABSTRACT To anticipate monthly rainfall time series, autoregressive integrated moving average (ARIMA) modelling was performed using R studio. For a period of 30 years (in mm), the monthly rainfall data were gathered for eight localities in the Mandi district of Himachal Pradesh: Bharol, Chachiyot, Jogindernagar, Karsog, Kataula, Mandi, Sarkaghat, and Sundernagar. The augmented Dickey–Fuller test was performed to determine whether the rainfall data were steady before applying the ARIMA model. The minimal values of the Akaike information criterion, Bayesian information criterion, autocorrelation function, and partial autocorrelation function were used to select the best models. The best fit was determined to be ARIMA (0,0,2) (2,1,0)12 for five stations and ARIMA (0,0,0) (1,1,0)12 for the remaining three stations. A box test is performed to validate the ARIMA model and verify the autocorrelation in the data. Using the ARIMA model, forecasts are made for the 10 years (2021–2030). The validity and performance of the ARIMA models were evaluated using error statistics (mean absolute error = 34.65, root-mean-square error = 33.58, and R2 = 0.93). The projected data's mean and standard deviation were close to the actual data. As a result, the ARIMA model's monthly rainfall prediction parameters were satisfactory.

Extensive study of droplet dynamics with end-grafted polymer chains in Pickering emulsions: Molecular Dynamics simulation and a generic model derived from the generalized Langevin equation

This paper investigates Pickering emulsions (PEs), formed by suspending oil droplets as the dispersed phase in water as the continuous phase using uncharged particles at their interface. To prevent coalescence, the particles are grafted with polymer chains, as emulsifier, not only to provide steric repulsion forces through the excluded volume between monomers floating in water, compensating for the absence of Coulombic repulsive forces but also control emulsion stabilization/destabilization. We combined molecular dynamics (MD) simulations and a generalized Langevin equation (GLE) model to study the dynamics of hairy-droplets using two dynamic quantities: mean square displacement (MSD) and velocity autocorrelation function (VACF). The number of grafted-polymer-chains, f, was the main parameter of interest, while the other parameters of the system were kept constant. A statistical approach was used to estimate theoretical GLE-based model parameters along with their uncertainties, providing insight into the diffusion behavior of these hairy-droplets and specifically addressing the transition between different observed regimes. we capture three specific values of grafted-polymer-chains as, fb=47 and ft=185 describe the transitions of mushroom-brush conformation of grafted-polymer-chains and viscous-viscoelastic behavior of hairy-droplets, respectively, and fg=257 representing the gel-like state number.

Leveraging AI for a Greener Future: Exploring the Economic and Financial Impacts on Sustainable Environment in the United States

In response to increasing environmental challenges, the United States has deliberately adopted technical advancements to promote sustainable development. This includes efforts to decrease pollution, improve energy efficiency, and encourage the use of environmentally friendly technology in different industries. This study investigates the role of Artificial Intelligence (AI) technology in promoting environmental sustainability in the United States from 1990 to 2019. It also examines the impacts of financial development, ICT use, and economic growth on the Load Capacity Factor (LCF). Various unit root tests revealed no unit root issues and mixed integration orders among variables. The Autoregressive Distributive Lag (ARDL) model explored cointegration, indicating long-run relationships among the variables. The ARDL findings confirm the Load Capacity Curve hypothesis for the United States, with AI technology and ICT use positively correlating with LCF in both the short and long run. Conversely, financial development and population growth significantly reduce LCF. Robustness checks using FMOLS, DOLS, and CCR estimation approaches align with the ARDL results. Granger causality tests reveal unidirectional causality from economic growth, AI, financial development, and ICT use to LCF and bidirectional causality between population and LCF. Diagnostic tests confirm the results are free from heterogeneity, serial correlation, and specification errors. This study underscores the importance of AI and ICT in enhancing environmental sustainability while highlighting the adverse impacts of financial development and population growth on LCF.

A Synchronization Algorithm for MBOC Signal Based on Reconstructed Correlation Function

In order to address the ambiguous synchronization problem caused by the multi-peak nature of the autocorrelation function of the modulated signal of the Multiplexed Binary Offset Carrier (MBOC) in the Global Navigation Satellite System (GNSS), a new synchronization algorithm for MBOC signals is presented in this paper, which uses a reconstruction correlation function to effectively handle synchronization ambiguities associated with multi-peak signals. The paper proposes an algorithm for reconstructing the correlation function of the MBOC signal by analyzing its characteristics. The algorithm generates three local auxiliary signals, namely, pseudo-random codes (PRN), BOC(1,1) signals, and MBOC signals, which are correlated with the received signal. By combining the three correlation functions, the algorithm produces a reconstructed correlation function based on reconstruction rules, eliminating side peaks and achieving unambiguous synchronization. Simulation results show that the proposed algorithm in this paper eliminates all side peaks while maintaining a high detection probability, and its deblurring capability is optimal compared to other algorithms. In addition, the discriminant curve shows that the algorithm in this paper successfully eliminates all the mis-locked points, and the slope gain is improved by more than 2.5 dB compared with other algorithms, and the anti-multipath performance of the algorithm in this paper is better than that of other traditional algorithms, such as ASPeCT (Autocorrelation Side-Peak Cancellation Technique).

Globalization, Urbanization, Energy Consumption, Economic Expansion and Industrialization: Pathway to Tanzania’s Environmental Sustainability Agenda 2030

This study delves into a comprehensive analysis of interconnectedness between globalization, urbanization, energy consumption, industrialization, and economic expansion as the pathway towards Tanzania’s 2030 environmental sustainability agenda utilizing time series data spanning from 1990 to 2022. This data was sourced from the World Bank and KOF Swiss Institute. The autoregressive distributed lag (ARDL) model was utilized in this study. Unit root test, ARDL bound cointegration test, Akaike Information Criterion, Serial correlation test, Heteroscedasticity test, and Jarque-Bera test were used for data analysis. The study revealed that the lag of environmental sustainability, industrialization, and economic expansion affects CO2 emissions in the short term by 0.516%, 0.153%, and -0.179% respectively. The study further shows that urbanization increases CO2 emissions by 1.02 percent in the long run, while globalization reduces CO2 by 0.298 percent, which consequently improves environmental sustainability. ECM (Error Correction Mechanisms) suggests 54% speed adjustment of environmental sustainability within one year after shocks on explanatory variables. The study further recommends an appropriate policy that will encourage conservation of the environment, monitoring and evaluation, technology and innovation of green-friendly energy sources, workshops, and community inclusion on matters concerning the environment

A post-processing method called Fourier transform based on local maxima of autocorrelation function for extracting fault feature of bearings

Accurate extraction of fault features from signals containing weak repetitive pulses is a critical issue in fault diagnosis of bearings. To address this challenge, a novel solution termed Fourier transform based on Local Maxima (FT-LM) is proposed in this paper. In the FT-LM method, local maxima are first extracted from signals, followed by the application of a Fourier transform to this sequence. Furthermore, to enhance the detection of repetitive pulses, the Short-Time Fourier Transform (STFT) and the unbiased autocorrelation function are integrated into the FT-LM, developing a post-processing algorithm called Fourier Transform based on Local Maxima of Autocorrelation Function (FT-LMACF). By employing a series of simulated signals and two sets of public experimental data, comparisons are made among the FT-LMACF, mainstream post-processing methods, the Kurtogram method and the spectral coherence based on the STFT. The results indicate that the FT-LMACF outperforms other algorithms in extracting fault features inherent in repetitive pulses.

Timing and Scintillation Studies of Pulsars in Globular Cluster M3 (NGC 5272) with FAST

We present the phase-connected timing solutions of all five pulsars in globular cluster M3 (NGC 5272), namely PSRs M3A to F (PSRs J1342+2822A to F), with the exception of PSR M3C, from FAST archival data. In these timing solutions, those of PSRs M3E and F are obtained for the first time. We find that PSRs M3E and F have low-mass companions and are in circular orbits with periods of 7.1 and 3.0 days, respectively. For PSR M3C, we have not detected its signal in all 41 observations. We found no X-ray counterparts for these pulsars in archival Chandra images in the band of 0.2–20 keV. From the autocorrelation function analysis of M3A and M3B’s dynamic spectra, the scintillation timescale ranges from 7.0 ± 0.3 to 60.0 ± 0.6 minutes, and the scintillation bandwidth ranges from 4.6 ± 0.2 to 57.1 ± 1.1 MHz. The measured scintillation bandwidths from the dynamic spectra indicate strong scintillation, and the scattering medium is anisotropic. From the secondary spectra, we captured a scintillation arc only for PSR M3B with a curvature of 649 ± 23 m−1 mHz−2.

Effective detection of early warning signal with power spectrum in climate change system

The development of early warning signals (EWS) before abrupt changes can help prevent system collapse. Current EWS, such as increasing autocorrelation (AC) and variance, provide general indicators of impending tipping points by detecting the slowing down of dynamics near transitions. However, these conventional EWS often fail to distinguish between oscillatory behavior (e.g., Hopf bifurcation) and shifts to a distant attractor (e.g., Fold bifurcation). Additionally, traditional EWS are less reliable in systems affected by density-dependent noise. To address these limitations, alternative EWS based on power spectrum analysis, known as spectral EWS, have been proposed. In this study, we apply analytical approximations for EWS as systems approach different types of local bifurcations. This novel method allows us to use spectral EWS that offer enhanced sensitivity to approaching transitions and increased robustness to density-dependent noise. We demonstrate the application of spectral EWS as robust indicators across three general models with different bifurcations. Our analysis also reveals distinct signals preceding transitions in data from sea ice loss. This combined approach underscores the advantages of incorporating spectral EWS into existing methodologies, providing a more comprehensive toolkit for anticipating critical transitions in real-world systems.