Autocorrelation Research Articles

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

Forecasting the Volatility of Tuna Fish Prices in North Sumatra using the ARCH Method in the Period January - April 2024

Tuna (Euthynnus affinis) is one of the most important fisheries commodities in Indonesia with significant economic value, especially in its contribution to fisheries export revenue. However, the price of tuna experiences significant fluctuations that can affect local and national economic stability. This study analyzes the daily price fluctuations of tuna in the North Sumatra market from January 1, 2024 to April 29, 2024 using a time series analysis approach. Daily price data were collected and analyzed to identify existing price patterns and volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) model was selected to address the heteroscedasticity in the data, which suggests that the volatility of tuna prices can be well predicted based on past price behavior. The analysis steps include identifying the optimal ARCH model using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), as well as testing parameter significance and normality assumptions to validate the model fit. The results show that the ARMA (1,0,0) model is the optimal one to model the price volatility of yellow tuna with the MAPE obtained of 2.382. compared to the ARMA-ARCH method with the MAPE value obtained of 2,747. Because it still contains heteroskedasticity effects, even though the results are good, the prediction results do not closely match the original data. The model is effective in improving price forecasting accuracy, which is important to support decision-making in risk management and economic planning in the fisheries sector. The findings contribute to understanding the dynamics of the yellowtail market and optimizing strategies for fisheries management.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p < 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

Cognitive correlates of circadian rhythm and sleep-wake behaviour in chronic obstructive pulmonary disease patients

ABSTRACT Chronic obstructive pulmonary disease (COPD) patients often experience reduced physical activity, sleep disturbances, and cognitive impairment. However, reports on measurement of rest-activity rhythm and sleep-wake behavior and their impact on cognitive functions in COPD patients are limited. This study aimed to objectively measure circadian rhythms (rest-activity and ambient illuminance) and sleep behaviors in clinically stable COPD patients and their relationship with cognitive functions. The study involved 65 male COPD patients and 50 age-matched controls, monitored over 3–7 days using actigraphy. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA) followed by short interbal time estimation via time production and reproduction with reaction time measurement using TimeProd software. Findings indicated significant disruptions in circadian rhythms in COPD patients, characterized by lower mesor, amplitude, and autocorrelation coefficients compared to controls. Patients also reported poorer sleep quality and higher sleep fragmentation, with 85.7% displaying cognitive impairment. Notably, longer time estimations, increased variability in task performance, and slower reaction times suggested cognitive deterioration. Positive correlations emerged between rhythm parameters (amplitude and circadian quotient) and cognitive performance metrics. This highlights the relevance of circadian and sleep disturbances in COPD, suggesting that addressing these rhythms could help mitigate cognitive decline, potentially through chronotherapeutic strategies.

Extracting parity-violating gravitational waves from projected tidal force tensor in three dimensions

Gravitational waves (GWs) may be produced by various mechanisms in the early universe. In particular, if parity is violated, it may lead to the production of parity-violating GWs. In this paper, we focus on GWs on the scale of the large-scale structure. Since GWs induce tidal deformations of the shape of galaxies, one can extract such GW signals by observing images of galaxies in galaxy surveys. Conventionally the detection of such signals is discussed by considering the three-dimensional power spectra of the E/B-modes. Here, we develop a complementary new technique to estimate the contribution of GWs to the tidal force tensor field projected on the celestial sphere, which is a directly observable quantity. We introduce two two-dimensional vector fields constructed by taking the divergence and curl of the projected tidal field in three dimensions. Their auto-correlation functions naturally contain contributions of the scalar-type tidal field. However, we find that the divergence of the curl of the projected tidal field, which is a pseudo-scalar quantity, is free from the scalar contribution and thus enables us to extract GW signals. We also find that we can detect parity-violating signals in the GWs by observing the nonzero cross-correlation between the divergence of the projected tidal field and the curl of it. It roughly corresponds to measuring the cross-power spectrum of E and B-modes, but these are complementary to each other in the sense that our estimator can be naturally defined locally in position space. Finally we present expressions of the correlation functions in the form of Fourier integrals, and discuss the properties of the kernels specific to the GW case, which we call the overlap reduction function, borrowing the terminology used in the pulsar timing array experiments.

EIGER. VI. The Correlation Function, Host Halo Mass, and Duty Cycle of Luminous Quasars at z ≳ 6

We expect luminous (M 1450 ≲ −26.5) high-redshift quasars to trace the highest-density peaks in the early Universe. Here, we present observations of four z ≳ 6 quasar fields using JWST/NIRCam in the imaging and wide-field slitless spectroscopy mode and report a wide range in the number of detected [O iii]-emitting galaxies in the quasars’ environments, ranging between a density enhancement of δ ≈ 65 within a 2 cMpc radius—one of the largest protoclusters during the Epoch of Reionization discovered to date—to a density contrast consistent with zero, indicating the presence of a UV-luminous quasar in a region comparable to the average density of the Universe. By measuring the two-point cross-correlation function of quasars and their surrounding galaxies, as well as the galaxy autocorrelation function, we infer a correlation length of quasars at 〈z〉 = 6.25 of r0QQ=22.0−2.9+3.0cMpch−1 , while we obtain a correlation length of the [O iii]-emitting galaxies of r0GG=4.1±0.3cMpch−1 . By comparing the correlation functions to dark-matter-only simulations we estimate the minimum mass of the quasars’ host dark matter halos to be log10(Mhalo,min/M⊙)=12.43−0.15+0.13 (and log10(Mhalo,min[OIII]/M⊙)=10.56−0.03+0.05 for the [O iii] emitters), indicating that (a) luminous quasars do not necessarily reside within the most overdense regions in the early Universe, and that (b) the UV-luminous duty cycle of quasar activity at these redshifts is f duty ≪ 1. Such short quasar activity timescales challenge our understanding of early supermassive black hole growth and provide evidence for highly dust-obscured growth phases or episodic, radiatively inefficient accretion rates.

Full-field amplitude speckle decorrelation angiography.

We propose a new simple and cost-effective optical imaging technique, full-field amplitude speckle decorrelation angiography (FASDA), capable of visualizing skin microvasculature with high resolution, and sensitive to small, superficial vessels with slow blood flow and larger, deeper vessels with faster blood flow. FASDA makes use of a laser source with limited temporal coherence, can be implemented with cameras with conventional frame rates, and does not require raster scanning. The proposed imaging technique is based on the simultaneous evaluation of two metrics: the blood flow index, a contrast-based metric used in laser speckle contrast imaging, and the adaptive speckle decorrelation index (ASDI), a new metric that we defined based on the second-order autocorrelation function that considers the limited speckle modulation that occurs in partially-coherent imaging. We demonstrate excellent delineation of small, superficial vessels with slow blood flow in skin nevi using ASDI and larger, deeper vessels with faster blood flow using BFI, providing a powerful new tool for the imaging of microvasculature with significantly lower hardware complexity and cost than other optical imaging techniques.

Design of concatenative complete complementary codes for CCC-CDMA via specific sequences and extended Boolean functions

A complete complementary code (CCC) consists of M sequence sets with size M. The sum of the auto-correlation functions of each sequence set is an impulse function, and the sum of cross-correlation functions of the different sequence sets is equal to zero. Thanks to their excellent correlation, CCCs received extensive use in engineering. In addition, they are strongly connected to orthogonal matrices. In some application scenarios, additional requirements are made for CCCs, such as recently proposed for concatenative CCC (CCCC) division multiple access (CCC-CDMA) technologies. In fact, CCCCs are a special kind of CCCs which requires that each sequence set in CCC be concatenated to form a zero-correlation-zone (ZCZ) sequence set. However, this requirement is challenging, and the literature is thin since there is only one construction in this context. We propose to go beyond the literature through this contribution to reduce the gap between their interest and our limited knowledge of CCCCs. This paper will employ novel methods for designing CCCCs and precisely derive two constructions of these objects. The first is based on perfect cross Z-complementary pair and Hadamard matrices, and the second relies on extended Boolean functions. Specifically, we highlight that optimal and asymptotic optimal CCCCs could be obtained through the proposed constructions. Besides, we shall present a comparison analysis with former structures in the literature and examples to illustrate our main results.

Plasmonic MIM waveguide based FR sensors for refractive index sensing of human hemoglobin

Fano resonance (FR) is a universal phenomenon that is used to attain electromagnetic-induced transparency (EIT), high absorption and sensitivity, and low-power photonic devices. This work presents dual FR refractive index (RI) sensor models on a plasmonic metal-insulator-metal (MIM) waveguide system. The FR phenomenon is attained by including circular and elliptic nanorod defects in the bus waveguides. The resonances originate from the defect's narrow discreteness and the rectangular resonator's broad state. Analytical methods such as finite difference time domain (FDTD) and multimode interference coupled mode theory are adopted to analyze the FRs. The shapes of the Fano line and resonance peak amplitude can be tuned independently by controlling the diameter of the defects, the separation between the defects, and the coupling (between the resonator and the bus waveguide) distance. Moreover, the proposed structures detect the RI (human hemoglobin) variation in the bus waveguide and resonator. The obtained results with circular nanorod defect verify the autocorrelation coefficient of 99.92 %, ensuring the device's linearity and high performance. However, an autocorrelation of 99.7 % is attained by using two elliptic nanorod defects.

Aerodynamic optimization design and experimental verification of a high-load axial flow compressor

The performance and stable operating range of compressors are critical to the efficient operation of various turbomachinery systems. This paper proposes a multi-level optimization strategy combining the uni-uniform direct free deformation method, Linux partitioned CPU accelerated parallel computing technology, multi-objective particle swarm optimization algorithm and downhill simplex algorithm, which improves design efficiency. Numerical results show that after optimization design, the average value of peak efficiency and stall margin increases. The flow mechanism of compressor performance improvement after optimization lies in the reduction of a low-velocity separation zone in stator hub region. Moreover, the experiment study confirms the reliability and accuracy of the optimization design method and found that flow instability triggering mode, propagation characteristics of the stall cell, and surge frequency are changed after optimization design. Setting a probability distribution threshold for autocorrelation coefficient and cross-correlation coefficients can be used to predict the arrival of the surge condition.

Temperature behavior of the velocity autocorrelation function in large MD models of water.

Velocity autocorrelation functions (VACFs) were calculated using the molecular dynamics method in the TIP4P/2005 and SPC/E water models of 157 464 molecules at temperatures ranging from 250 to 370K. The large size of the models and the high accuracy of the calculations allow us to reliably compute the long-time tails of the VACFs, showing that they systematically change shape from hydrodynamic (argon-like) at high temperatures to that typical of supercooled liquids at low temperatures. These tails in the range of 2-10ps can be well fitted by a combination of two power functions: At-3/2 - Bt-β (A, B > 0, β ≈ 2). It is found that the amplitude of the hydrodynamic asymptote, A, approaches zero as the temperature decreases, thereby rendering the negative power-law decay,-Bt-2, the dominant term within the specified time interval. The presence of a negative -Bt-2 decay in the time interval of 2-10ps determines the specific shape of the VACF long-time tail of water, distinguishing it from ordinary simple liquids. The amplitude B, which is always non-zero, demonstrates a slight increase with rising temperature. At medium temperatures, weak but well-defined damped oscillations are observed on the VACF in the 0.5-2ps interval.

Gas Reservoir Identification Based on Nonstationary and Anisotropic FFT-MA Stochastic Modeling

Summary High-resolution reservoir modeling is a crucial technique for the precise identification of gas reservoirs, holding significant importance in guiding natural gas development. However, the nonstationarity and statistical anisotropy of subsurface media present immense challenges to the reliable implementation of high-resolution reservoir modeling. In response to the nonstationarity and anisotropy of complex reservoirs, we propose a novel stochastic modeling method based on fast Fourier transform moving average (FFT-MA). In this method, variational mode decomposition (VMD) is introduced to decompose logging curves into a series of sparse components with specific center frequencies and narrow bandwidths. Subsequently, the autocorrelation functions of each component are computed and synthesized, thereby inferring the nonstationary vertical autocorrelation functions of logging curves. In addition, to characterize the anisotropic and lateral nonstationary features of the reservoir, angle parameters and lateral autocorrelation functions are extracted from seismic records. Lastly, considering the high sensitivity of Lamé constants (λ and μ) and their density-combined counterparts (λρ and μρ) to gas-bearing reservoirs, FFT-MA stochastic modeling is applied to λρ, μρ, and ρ. Gas identification is then performed based on the joint probability distribution extracted from logging data. The proposed method is tested in the sand-shale reservoirs of the Yinggehai Basin, China. The results indicate that the stochastic models for λρ, μρ, and ρ effectively characterize the nonstationary and anisotropic features of complex reservoirs, significantly enhancing the resolution and accuracy of gas identification.

A Cumulant-Based Method for Acquiring GNSS Signals.

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo-developed and maintained by the European Union-utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs.

Periodic INAR(1) model with Bell innovations distribution

Abstract In this paper, we introduce a class of periodic integer-valued autoregressive BL-PINAR(1) models with Bell innovations distribution based on the binomial thinning operator. The basic probabilistic and statistical properties of this class are studied. Indeed, the first and the second moment periodically stationary conditions are established. The closed forms of these moments are, under the obtained conditions, derived. Furthermore, the periodic autocovariance structure is also considered while providing the closed form of the periodic autocorrelation function. The conditional least squares (CLS), Yule–Walker (YW), weighted conditional least squares (WCLS), and conditional maximum likelihood (CML) methods are applied to estimate the underlying parameters. The asymptotic properties of the CLS and the YW estimators are obtained. The performances of these methods are compared through a simulation study. An application on a real data set is provided.

Embracing Technological Advancement: Assessing the Impact of Innovation on Sectorial Employment in Tanzania

Technological innovation is one of the most rapidly advancing and dynamic sector today. It drives growth across all sectors of the economy. However, this rapid growth presents both opportunities and challenges. Taking innovation into account, this study aims to assess the impact of technological innovation on three sectors which are agriculture, industrial, and service sectors. The study employed the autoregressive distributed lag model (ARDL), with data from 2000 to 2022 obtained from the World Bank (WB) and the World Intellectual Property Organization (WIPO). Three ARDL models were used to estimate employment in the agriculture sector, employment in the industrial sector and employment in the service sector with inflation (CPI) and interest rate (INT) as the control variables. The ADF (Augmented Dickey-Fuller) test, PP(Philip-Perron) test, Serial Correlation LM test, BG Heteroscedasticity, and Jarque Bera Normality test were utilized to ensure robust estimates of the models. The findings revealed that innovation has a significant positive influence on employment in the agriculture sector and service sector whereas the industrial sector is negatively influenced by innovation. Nevertheless, inflation has a significant positive effect on employment in the agriculture and service sectors but negatively affects employment in the industrial sector, where a lag in interest rate influence negatively employment in the agricultural sector. Relying on these findings, the study recommends an increase in expenditure on research and development, formulation of policies that will mainstream innovation in all sectors of the economy, and investing more in education and skills development

Influence of Asset Quality on the Financial Performance of Insurance Companies in Kenya

The research aimed to investigate the influence of asset quality on the financial performance of insurance companies. The study was anchored on the efficiency structure theory. Positivism research philosophy was employed with a descriptive research design that targeted a population of 56 general insurance and life insurance firms in Kenya. Secondary balanced panel data spanning 15 years from 2008 to 2022 was also used in the study. The secondary data was sourced from the financial statements of the firms and industry publications from the Insurance Regulatory Authority (IRA). The quantitative data collected was analyzed using descriptive statistics inform of percentages, frequencies, mean, and standard deviation. Additionally, a balanced dynamic panel data regression model was performed. Prior to fitting the panel regression model, diagnostic tests were undertaken to evaluate linearity, stationarity, normality of residuals, serial correlation, and homoscedasticity. The research included 41 insurance firms that met the inclusion criteria. The study results determined that asset quality has a statistically significant and negative influence on the financial performance of insurance companies in Kenya (β = -0.1956, t = -2.93, p = 0.003). The study concluded that asset quality is a collective consideration of diversification, liquidity, risk and liability profiles and thus plays a crucial role in influencing profitability. The study recommends that additional regulations should be instituted by the IRA that entail developing a range of targets on each of the aspects of asset quality.

Characterizing biomolecular structure features through an innovative elliptical dichroism spectrometry for cancer detection

This research introduces a novel method for evaluating the structural features of biomolecules, utilizing our innovative Elliptical Dichroism (ED) spectrometer specifically designed for stereochemical analysis. By integrating ED spectrometry with autocorrelation (AC) analysis, we investigate the conformational characteristics of biological molecules such as amino acids, proteins, and extracellular vesicles (EVs) induced by elliptically polarized UV absorption. Our streamlined approach offers a cost-effective and portable solution with minimal sample consumption and supports multiple working modes to efficiently characterize biomolecular structures. The insight from this new approach demonstrates potential applications in using biomolecular characterization for cancer detection.

Stock Return Autocorrelations and Expected Option Returns

We show that the return autocorrelation of underlying stock is an important determinant of expected equity option returns. Using an extended Black-Scholes model incorporating the presence of stock return autocorrelation, we demonstrate that expected returns of both call and put options are increasing in the return autocorrelation coefficient of the underlying stock. Consistent with this insight, we find strong empirical support in the cross-section of average returns of equity options. Our paper highlights the necessity to control for stock return autocorrelation when studying option return predictability. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03071 .

Stress Engineering of Magnetization Fluctuation and Noise Spectra in Low-Barrier Nanomagnets Used as Analog and Binary Stochastic Neurons.

A single-domain nanomagnet, shaped like a thin elliptical disk with small eccentricity, has a double-well potential profile with two degenerate energy minima separated by a small barrier of a few kT (k = Boltzmann constant and T = absolute temperature). The two minima correspond to the magnetization pointing along the two mutually anti-parallel directions along the major axis of the ellipse. At room temperature, the magnetization fluctuates randomly between the two minima, mimicking telegraph noise. This makes the nanomagnet act as a "binary" stochastic neuron (BSN) with the neuronal state encoded in the magnetization orientation. If the nanomagnet is magnetostrictive, then the barrier can be depressed further by applying (electrically generated) uniaxial stress along the ellipse's major axis, thereby gradually eroding the double-well shape. When the barrier almost vanishes, the magnetization begins to randomly assume any arbitrary orientation (not just along the major axis), making the nanomagnet act as an "analog" stochastic neuron (ASN). The magnetization fluctuation then begins to increasingly resemble white noise. The full width at half maximum (FWHM) of the noise auto-correlation function decreases with increasing stress, as the fluctuation gradually transforms from telegraph noise to white noise. Consistent with this trend, the noise spectral density exhibits a 1/fβ spectrum (at high frequencies) with β decreasing from 2.00 to 1.88 with increasing stress. Stress can thus not only reconfigure a BSN to an ASN, which has its own applications, but it can also perform "noise engineering", i.e., tune the auto-correlation function and power spectral density, having applications in signal processing.