Correlation Time Research Articles

Interplay of ethaline and water dynamics in a hydrated eutectic solvent: Deuteron and oxygen magnetic resonance studies of aqueous ethaline.

For many technological processes, the impact of water addition on the properties of deep eutectic solvents is of central importance. In this context, the impact of hydration on the reorientational dynamics of the deep eutectic solvent (DES) ethaline, a 2:1 molar mixture of ethylene glycol and choline chloride, was studied. Its overall response was explored by means of shear mechanical rheology. To achieve component-selective insights into the dynamics of this material, isotope-edited deuteron and oxygen spin-lattice and spin-spin relaxometry, as well as stimulated-echo spectroscopy, were applied and yielded motional correlation times from above room temperature down to the highly viscous regime. For all temperatures, the cholinium anion was found to reorient about two times slower than ethylene glycol, while the water and the ethylene glycol molecules display very similar mobilities. While hydration enhances the component dynamics with respect to that of dry ethaline, the present findings reveal that it does not detectably increase the heterogeneity of the solvent. Merely, the time scale similarity that is found for the hydrogen bond donor and the water molecules over a particularly wide temperature range impressively attests to the stability of the native solvent structure in the "water-in-DES" regime.

Effects of customized corneal cross-linking on higher-order aberrations in progressive keratoconus and low-grade myopia.

To evaluate the effects of customized corneal collagen cross-linking (CXL) on higher-order aberrations (HOAs) in keratoconus (KC): vertical coma (VC), horizontal coma (HC), spherical aberration (SA), trefoil (TF) and astigmatism, compared with the same effects in healthy eyes undergoing CXL for low-grade myopia. This mixed-designed study included 38 eyes of 38 patients with KC, treated and followed prospectively, who received customized epi-on CXL in high oxygen, and a retrospective control group of 23 eyes from 23 patients who underwent central 4-mm CXL treatment for low-grade myopia. VC, HC, SA, TF and keratometry values were obtained from Pentacam HR® measurements at baseline and at 1, 6, 12 and 24 months post-treatment. Statistical analyses included paired T-tests for changes over time and Pearson correlation tests to assess relationships between aberrations, best spectacle-corrected and low-contrast visual acuities (BSCVA and LCVA, respectively) and CXL parameters. Reduced HOAs and improved visual acuities were observed in KC. A 20% reduction in VC was observed at 24 months (from -1.82 ± 1.15 μm to -1.46 ± 1.01 μm; 95% CI: [0.155, 0.629], p = 0.002), while a 17% reduction in HC was observed at 12 months (from -0.35 ± 0.56 μm to -0.29 ± 0.62 μm; 95% CI: [0.003, 0.096], p = 0.037). A positive correlation was found between baseline VC and the level of improvement in VC at 24 months (R2 = 0.200, p = 0.015). SA increased by 126% at 24 months (from -0.21 ± 0.62 μm to 0.054 ± 0.52 μm; 95% CI: [0.143, 0.347], p ≤ 0.001). TF and astigmatism did not alter from the treatment. In myopia, the natural positive SA increased by 57% post-treatment (from 0.14 ± 0.061 μm to 0.22 ± 0.076 μm at 24 months; 95% CI: [0.067, 0.098], p ≤ 0.001), while changes in VC and HC were minor and BSCVA remained stable. Customized CXL effectively reduces HOAs in KC. For VC the improvement is larger in cases with higher preoperative VC, indicating that the concept of customization has its intended effect. Accordingly, SA and visual acuities improve in KC whereas CXL for low-grade myopia tends to increase corneal SA unfavourably.

Quantifying the Driving Effect of Colored Noise in Brownian Motors

Abstract The colored noise disrupts detailed balance, enabling directed transport in ratchet systems without additional driving forces. Despite a well-established understanding of the conditions for Brownian motors, the quantification of the driving effect of colored noise, and its connection to deviations from equilibrium, remains unexplored. In this study, we examine the directed motion of an overdamped Brownian motor, within an asymmetric traveling potential, subjected to the Ornstein-Uhlenbeck colored noise. We propose a scheme to quantify the driving effect of colored noise by defining an effective force, based on the stopping velocity of the traveling potential, where directed transport halts. Our results reveal a non-monotonic dependence of the effective force on the noise correlation time and a monotonic dependence on noise strength, corresponding to double and single current reversals, respectively. The non-monotonicity of the current is attributed to the interplay between the fluctuating characteristics and the driving effects of colored noise.

The Correlation of Podcast Storytelling Durationwith Discussion Timing in Enhancing EFLLearners' Listening Skills

The Correlation of Podcast Storytelling Durationwith Discussion Timing in Enhancing EFLLearners' Listening Skills

Damage and Failure Modeling of Composite Material Structures Using the Pam-Crash Code

Simulating composite material structures requires complex constitutive models, which normally require fine meshes to obtain an accurate prediction of their behavior. Pam-Crash software has been used for several years in the automotive industry and has been proved to be an efficient tool for simulating metallic structures, returning good correlations in a fast computational time. However, constitutive models for composite materials in Pam-Crash present some difficulties: some materials are not able to be suitably modeled and the predictive results depend on the mesh refinement. This work proposes a solution for predicting the progressive damage of composite materials in Pam-Crash, which scales the energy dissipated by the damage mechanisms and checks the viability of modeling the material behavior, taking into account the recommended size of finite elements in the automotive industry. The proposed solution is applied for the simulation of Open Hole specimens to evaluate the ultimate strength consistency. After this, it is applied for the simulation of Compact Tension specimens to check the consistency of crack propagation behavior. By considering the target size of the finite elements in the material card definition, the predictions demonstrate great improvement in the equivalence in results between different mesh refinements. Finally, the solution is applied to simulate impact tests on large structures. Good correlations with experimental data are obtained in fast computational times, making this methodology a candidate for application in composite-related automotive simulations.

EVALUASI CYCLE TIME TERHADAP KONDISI MEDAN PADA PROYEK PEMBANGUNAN AKSES JALAN SAMOTA LANJUTAN KABUPATEN SUMBAWA

The Samota Road access development project continued in Sumbawa Regency is one part of the Samota area development project which acts as access to mobilization and supports the acceleration of development, economy, social and culture. In large-scale road works, heavy equipment is needed to facilitate work in a relatively shorter time. Errors in the operating time of heavy equipment and differences in the work field will affect cycle time and have an impact on productivity. This study aims to determine the amount of heavy equipment cycle time, heavy equipment productivity, and the effect of cycle time on heavy equipment productivity. The methods used in this research are time study and correlation methods. The time study method is a measurement of work by collecting data based on the time needed to complete a job so that productivity can be calculated, while the correlation method is an analysis to determine the level of relationship between the independent variable (X) and the dependent variable (Y).Based on data analysis, the value of cycle time, productivity and the effect of cycle time on heavy equipment productivity were obtained. In segment 1, the cycle time of the motor grader is 303,247 seconds with a productivity of 916,037 m2/hour and the cycle time of the vibratory roller is 612,345 seconds with a productivity of 1061,400 m3/hour. For the effect of cycle time on the productivity of the motor grader, the correlation test result is 0,986 and the vibratory roller is 0,999. In segment 4, the motor grader cycle time is 316,107 seconds with a productivity of 831,112 m2/ hour and the vibratory roller cycle time is 655,370 seconds with a productivity of 988,200 m3/hour. For the effect of cycle time on motor grader productivity, the correlation test results were obtained at 0,975 and vibratory roller at 0,999. In both segments, there is a very strong influence and relationship between variable X (cycle time) and variable Y (productivity).

Time-Lag Transiograms and Their Implications for Landscape Change Characterization

Markov chain transition probability matrices (TPMs) have traditionally been used to characterize land use and land cover (LULC) changes and species succession. However, previous studies relied solely on TPMs or transition area matrices to describe overall class area/proportion changes, overlooking important time correlation features. This study introduces the concept of idealized time-lag transiograms and demonstrates how they can be computed from temporal TPMs, using illustrative examples. The primary objective is to explore the potential value and utility of idealized time-lag transiograms in revealing additional characteristics of landscape change. Specifically, we focus on computing idealized time-lag transiograms with a fixed starting point and highlighting their fundamental features, such as sills, practical correlation ranges, and curve shapes, along with peak positions and peak height ratios of peaked cross-transiograms. These features are identified and discussed in terms of their potential implications for characterizing LULC changes. While idealized time-lag transiograms with a fixed starting point may not precisely predict future LULC changes due to the assumptions of the Markov property and time homogeneity (i.e., stationarity), they provide new insights into future LULC dynamics, revealing aspects that traditional Markov chain analysis has overlooked.

Suppression of core temperature fluctuations by edge cooling in the J-TEXT tokamak

Abstract The suppression of core temperature fluctuations due to edge cooling in the J-TEXT tokamak is presented. The supersonic molecular beam is injected into the low density plasmas and the core temperature rise appears. The staggered time correlation technique is utilized to infer the temperature fluctuations from an electron cyclotron emission radiometer (ECE). The existing correlation ECE (CECE) also enables the observation of the temperature fluctuations simultaneously. A significant flattening of the density gradients appears almost in the whole plasmas after the cold pulse injection. The behaviors of the temperature fluctuations from both ECE and CECE show a good agreement, except for the fluctuation level, which exhibits a relative variation of approximately 10% − 50%. The relative intensity of temperature fluctuations decreases within the surface of r / a ∼ 0.8 due to the edge cooling. The cross-power analysis and the agreement in measurements suggest that temperature fluctuations from turbulence inferred from a single ECE channel may be possible. Linear simulations suggest the drop in temperature fluctuation intensity may relate to the trapped electron mode turbulence stabilization. This paper also demonstrates the evolution of both gradients and fluctuations before and after supersonic molecular beam injection with density scans.

Stocks, Gold and Crude Oil: How Valuable are Volatility and Correlation Timing?

Abstract We study the sources of portfolio returns under parameter uncertainty arising from allocation strategies based on holdings of the NASDAQ index, WTI crude oil and gold. We investigate the contribution of volatility and correlation forecasts for dynamic portfolio allocations in the model of Brandt, Goyal, Clara, and Stroud (2005). We estimate alternative forecasting models using Bayesian methods, and evaluate investor’s utility under the Bayesian predictive density. We then compare the performance of different portfolio strategies through both Sharpe ratios and utility-based metrics. We show that dynamic strategies based on timing volatilities and correlations can add positively to the economic gains generated by non-diversified portfolios involving holdings of either crude oil or gold only. Hence, the economic benefits generated by holding crude oil and gold in asset allocations with stocks arise from the predictability of their volatilities and correlations. JEL classification numbers: C22, C52, G11, G23. Keywords: Bayesian models, dynamic asset allocation, multivariate correlation models, crude oil, gold.

New strokes to the portrait of inactivated actin

In addition to the well-known monomeric and polymeric forms of actin there is another unique thermodynamically stable state of this protein, called "inactivated actin" (I-actin). I-actin is formed at moderate concentration of a denaturant, release of Ca2+ ions and /or ATP, or after heating. This state is a monodisperse associate and it has the same spectral characteristics regardless of the method of preparation. The interest in I-actin arises from the discovery of similar-sized short oligomers of actin in the cell nucleus, which structurally differ from polymeric actin. In this work, we investigated the intramolecular mobility of I-actin using the time-resolved anisotropy method. Our findings indicate that its tryptophan residues participate in structural oscillations, although their correlation time is significantly longer than that of native actin. Using the dynamic light scattering, we demonstrated that I-actin obtained by heating possesses the same dimensions as I-actin in 1.8 M GdnHCl. Using the fluorescent probe ANS, it was shown that I-actin has a unique structure with hydrophobic pockets on the surface and tryptophan residues in the polar internal regions of the structure.

Age and serum Anti-Mullerian Hormone (AMH) levels as predictors of time to return of menses after chemotherapy.

Chemotherapeutic agents result in the loss of growing follicles, which can manifest as amenorrhoea. Alkylating chemotherapy (AC) is known to be more gonadotoxic than non-alkylating chemotherapy (NAC). Anti-Mullerian Hormone (AMH), an indirect marker of ovarian reserve, and age have been investigated as predictors of ovarian function after chemotherapy, however little is known about the time to return of menses. This study aimed to assess how patient age and baseline serum AMH levels at cancer diagnosis affect the time to return of menses post-chemotherapy. This retrospective cohort study examined oncology patients (n=67) who underwent chemotherapy and were treated through the Reproductive Services Unit of two institutions in Melbourne, Australia. Primary outcomes included correlation between age and baseline AMH with time to return of menses after chemotherapy. Secondary outcomes include the change in AMH levels at 6- and 12-months post-completion of chemotherapy. Pairwise correlation of pre-chemotherapy AMH level and time to return of menses demonstrated statistical significance (Spearman's coefficient, ρ = -0.40) for patients who underwent AC. This analysis in breast cancer patients who underwent AC displayed a negative correlation, however, was not statistically significant. No association was found between age and time to return of menses for all cancer (NAC or AC) or breast cancer patients who underwent AC. Higher AMH level prior to AC was associated with an earlier return of menses after chemotherapy. Age at the commencement of chemotherapy was not associated with return of menses. Further prospective research is required to assess post-chemotherapy recovery of AMH.

Fourier transforms of time correlation functions using Hermite functions

We present an alternative to standard Fourier transform methods in order to obtain the power spectrum from a time correlation function. Our approach involves fitting the correlation function with a sum of Hermite functions, and recombining these to obtain the power spectrum directly. Although Fourier transform methods have been used for many decades, our approach avoids some ambiguities and uncertainties that face the user, and also allow for a more flexible form of the power spectrum to be obtained. We present a few examples to show the quality of the method.

An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure. Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size. We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low-rank core matrix and convolutional long short-term memory (ConvLSTM) unit. We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state-of-the-art approaches, our approach achieves higher peak signal-to-noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters. The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction.

Theory of spin center sensing of diffusion and other surface electric dynamics

Surface electric dynamics influences the quantum coherence of near-surface spin centers (i.e., T1 and T2 times) through spatial and temporal fluctuations of the surface charge density and the electrostatic potential at the crystal surface that are described by the spectral noise density S(ω). We find that the electric noise's S(ω) dependence on the depth d of a spin center below the surface follows a power law d−γ not solely determined by whether the surface charges move as localized pointlike charges (monopoles) or dipoles. The exponent γ instead reveals the structure of the surface charge and surface potential correlation functions. For surface dynamics originating from diffusion, the spin center's relaxation and decoherence times exhibit a temporal crossover near the correlation time of the fluctuators and thus provide a quantitative fingerprint of the diffusive behavior of charged particles at surfaces. Published by the American Physical Society 2024

Correlation times of velocity and kinetic helicity fluctuations in non-helical hydrodynamic turbulence

Non-helical turbulence within a linear shear flow has demonstrated efficient amplification of large-scale magnetic fields in numerical simulations, but its precise mechanism remains elusive. The incoherent $\alpha$ mechanism proposes that a zero-mean fluctuating transport coefficient $\alpha$ (linked to kinetic helicity) in the shear flow is a candidate driver. Previous renovating-flow models have proposed that the correlation time of helicity fluctuations must be sufficiently extended to overcome turbulent magnetic diffusivity, yet only empirical validation of this concept has been obtained. In this study, we conduct direct numerical simulations of weakly compressible non-helical hydrodynamic turbulence. We scrutinize the correlation times of velocity and kinetic helicity fluctuations in distinct flow configurations, including rotation, shearing and Keplerian flows, as well as the shearing burgulence counterpart. Our findings indicate that rotation contributes to a prolonged correlation time of helicity compared with velocity, particularly notable in auto-correlations of both volume-averaged quantities and individual Fourier modes due to the formation of large-scale vortices. In contrast, moderate shear strength does not exhibit significant scale separation, with shear flows elongating vortices in the shear direction. Shearing burgulence, characterized by shorter helicity correlation times, appears less conducive to hosting the incoherent $\alpha$ effect. Notably, at modest shear rates, only Keplerian flows exhibit sufficiently coherent helicity fluctuations, in contrast to shearing flows. However, the relative strength of helicity fluctuations compared with turbulent diffusivity is significantly lower, raising doubts about the viability of the incoherent $\alpha$ effect as a potential dynamo driver in the subsonic flows examined in this study.

Noise resilient and accurate target detection using fractional-order Fourier domain correlation

Quantum enhanced optical target detection provides a unique route to increased noise resilience of classical LiDARs (laser imaging, detection, and ranging) by using time correlation of non-classical photon pairs. Such enhancement is dictated by the detector temporal uncertainty that is typically orders of magnitude larger than the intrinsic correlation time. To circumvent such detector limitation, we explore the possibility of measuring correlation in the fractional-order Fourier domain (FrFD), which can be realized with the non-local dispersion cancellation. Experimentally, we verify this principle using a fiber-coupled waveguide source of photon pairs, showing enhanced noise rejection as compared with conventional time-domain coincidence detection and classical intensity detection. For false alarm rates of 10−9, an 89 dB improved detection rate is measured using receiver operating characteristics when comparing our FrFD protocol with classical intensity detection. Additionally, we discussed the resilience of FrFD correlation against intentionally prepared counterfeit signal photons. The possibility enabled by measuring correlation in the FrFD should also provide potential benefit for various sensing and communication protocols that relies on coincidence detection.

Semi-supervised convolutional generative adversarial networks for dynamic fault classification with manifold regularization

Data-driven fault classification identifies and categorizes faults or anomalies in industrial systems, to ensure efficient and safe operations. This paper addresses the challenges of semi-supervised learning for dynamic fault classification in industrial processes. The key problems involve utilizing three unique characteristics of typical industrial process dataset: 1) the presence of unlabeled samples due to high labeling costs and required expert knowledge, 2) the local manifold structure arising from strong variable coupling, and 3) time correlation inherent in time-series sensing data of dynamic processes. To tackle these challenges, a semi-supervised fault classification model, SSDCGAN, is proposed, based on deep convolutional Generative Adversarial Networks. The model captures dynamic temporal information through a moving window technique and leverages manifold regularization to maintain classifier consistency along the manifold’s tangent direction. Evaluations on the Tennessee Eastman (TE) benchmark demonstrate that SSDCGAN enhances fault classification accuracy, outperforming current methods.

Machine learning for real-time reservoir operation simulation: comparing input variables and algorithms for the Sirikit Reservoir, Thailand

ABSTRACT Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations.

Multi-parameter correlation analysis and multi-step prediction of seawater quality based on graph spatio-temporal analysis network

Abstract In recent years, with the increasing pollution of near-shore waters, the water quality pollution incidents have been aggravated, which seriously threatens many aspects of coastal economic development, ecological environment and living health. Therefore, there is an urgent need for an effective method to predict the water quality of near-shore waters. However, due to seasonal changes, ocean currents, biological activities and other factors, the marine environment has strong complexity and uncertainty, which leads to the monitoring data of seawater quality parameters are unstable, non-linear and other characteristics. At the same time, there are interactions between different parameters, so it is not easy to dig deeper into the information in the data, and the accuracy of the existing prediction methods for multi-parameter multi-step prediction of seawater quality is generally low. To solve the above problems, a new graph neural network model is proposed in this paper. The model can effectively extract the local time correlation, global time correlation and spatial correlation in non-Euclidean space of seawater quality parameter data from multiple dimensions. Finally, this paper evaluates the model performance using the seawater parameter data from the near-shore waters of Beibu Gulf, and compared with the five baseline models, the model proposed in this paper shows the best performance in all the defined evaluation indexes.

Power spectral density imaging for polymeric films using ex-situ solid-state magnetic resonance with needlelike ferromagnet.

An ex-situ solid-state nuclear magnetic resonance (NMR) method employing localized magnetic field gradients generated by a needlelike ferromagnet is described. The depth profiling of a multilayer polymeric film using the proposed method and spin density imaging is successfully acquired. The imaging of the variable-frequency spinlattice relaxation rate in the depth direction reveals differences in the spectral density function for molecular dynamics between the surface and interior of the film. This may be due to the differences in the correlation time distribution of the molecular dynamics between the surfaces and interior of the film.