Correlation Time Research Articles

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.

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.

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.

Optimizing Agricultural Outcomes: Machine Learning in Crop Yield Prediction

Digital agriculture is becoming more and more in demand as a result of the quick advancement of information technology. Crop yield has always garnered a lot of interest as a significant problem in agricultural production. At the moment, machine learning and artificial intelligence in general are the most popular methods for predicting agricultural productivity. Consequently, one of the main problems in digital agriculture is creating a machine learning technique that can reliably forecast crop yield. Crop yield prediction has a strong time correlation, in contrast to conventional regression prediction problems. For instance, there are significant temporal correlations in the weather data for every county. Furthermore, crop yield is somewhat impacted by geographic data from various places. For instance, a county is likely to have large yields if its neighbouring countries have a strong harvest. We used models like random forest, decision tree classifier, support vector machine, KNN, and logic regression in this study. With an accuracy score of 99.77%, random forest produced the greatest results out of all of them.

Determination of prothrombin time by the Ovren method in recipients after liver transplantation from a donor diagnosed with brain death

Objective. To evaluate a new method for diagnosing the state of the hemostatic system in patients with hepatobiliary pathology who underwent liver transplantation from a donor diagnosed with brain death. Materials and Methods. A pilot prospective study was conducted involving 10 patients with liver cirrhosis of various etiologies who underwent liver transplantation from a donor diagnosed with brain death. Laboratory parameters of liver function were analyzed perioperatively, during the first 5 days, on the 10th and 14th day after surgery and before discharge or death. The correlation of the prothrombin time by Ovren with the prothrombin time by Quick and with the international normalization ratio was determined using the Spearman rank correlation coefficient. Values of p ˂0.05 were considered statistically significant. Results. The mean number of points on the scale for assessing the severity of liver failure (MELD) before surgery was 27.7 ± 6 (minimum – 15, maximum – 36), which corresponded to severe class C liver failure. The correlation between the value of prothrombin time according to Quick and according to Ovren, as well as between the values of the international normalized ratio and prothrombin time according to Ovren was statistically significantly negative: rs = –0.85315, p (two–sided test) = 0.00042 and rs = –0.90527, p (two–sided test) ˂0.05, respectively. The Ovren prothrombin time was 48% on the 5th day after surgery, after which the functions and reserves of coagulation normalized starting from the 10th day, which is conveniently assessed by an increase in this indicator to 74.5% and above. Conclusions. Determination of the prothrombin time by Ovren can improve the diagnosis of liver failure and monitoring of liver function recovery in patients after liver transplantation from a donor diagnosed with brain death.

Numerical Simulation of the Melting of Solid Particles in Thermal Convection with a Modified Immersed Boundary Method

A new immersed boundary method is proposed for the numerical simulation of the melting of solid particles in its own liquid at a high temperature. The main feature of the new method is the use of the modified direct-forcing immersed boundary method for the solution of the flow field and the sharp-interface immersed boundary method for the temperature field. The accuracy of the proposed method is validated via three problems: the sedimentation of a non-melting particle, the melting of a fixed particle under mixed thermal convection, and the sedimentation of a melting particle. The method is then applied to the investigation of the effects of various parameters, the particle interactions and the particle shape on the particle melting time. A correlation for the melting time of a circular particle in forced thermal convection is established as a function of the Reynolds, Prandtl, and Stefan numbers. The melting time of a particle in mixed thermal convection first increases and then decreases, as the Grashof number increases. The effects of the particle interactions on the melting time are complicated due to the natural convection between two particles. The sufficiently strong natural convection can even render the downstream particle melt faster than the single particle. For the same particle area, the elliptic particle with the aspect ratio being around 1.4 melts most slowly.

Batch process soft sensing based on data-stacking multiscale adaptive graph neural network

Abstract Soft sensing technology has found extensive application in predicting key quality variables in batch processes. However, its application in batch process is limited by the uneven batch length, the correlation of data and the difficulty in extracting the dependencies between variables and within variables. To address these issues, we propose a data-stacking multiscale adaptive graph neural network (DSMAGNN) soft sensor model. Firstly, Mutual information (MI) is used to selected quality-related variables, the 3D batch data is converted into a time-delay sequence suitable for input to the soft sensor model through the data stacking strategy, and the underlying time correlation at different time scales is preserved by incorporating the multi-scale pyramid network. Secondly, the dependencies between variables are inferred by the adaptive graph learning module, while the dependencies both within and between variables are modeled by the multi-scale temporal graph neural network. Thirdly, collaborative work across different time scales is further facilitated by the scale fusion module. Finally, the feasibility and effectiveness of the model are verified through experiments in the industrial-scale penicillin fermentation process and hot rolling process.

MSDG: Multi-Scale Dynamic Graph Neural Network for Industrial Time Series Anomaly Detection.

A large number of sensors are typically installed in industrial plants to collect real-time operational data. These sensors monitor data with time series correlation and spatial correlation over time. In previous studies, GNN has built many successful models to deal with time series data, but most of these models have fixed perspectives and struggle to capture the dynamic correlations in time and space simultaneously. Therefore, this paper constructs a multi-scale dynamic graph neural network (MSDG) for anomaly detection in industrial sensor data. First, a multi-scale sliding window mechanism is proposed to input different scale sensor data into the corresponding network. Then, a dynamic graph neural network is constructed to capture the spatial-temporal dependencies of multivariate sensor data. Finally, the model comprehensively considers the extracted features for sequence reconstruction and utilizes the reconstruction errors for anomaly detection. Experiments have been conducted on three real public datasets, and the results show that the proposed method outperforms the mainstream methods.

SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.