Correlation Time Research Articles

In situ combustion performance in heavy oil carbonate reservoirs: A triple-porosity numerical model

This study aims to address the challenges of oil recovery in highly heterogeneous carbonate reservoirs by employing a novel triple-porosity numerical model approach for in-situ combustion (ISC). The primary focus is on the performance of ISC within isolated vuggy pore spaces, a characteristic feature of such reservoirs. By simulating triple-porosity through artificially induced vuggs, core matrix, and fractures, we evaluated the feasibility of the ISC technique for heavy oil recovery in a dolomite reservoir using a combustion tube setup.The numerical model, calibrated through history matching, incorporates an extended kinetic reaction scheme to better represent high-temperature oxidation processes. Experimental and simulation results show a strong correlation in temperature profiles, ignition timing, and fluid production, effectively reproducing the observed gas compositions. This integrated experimental and numerical approach demonstrates the potential for applying ISC in triple-porosity systems, offering valuable insights for field-scale implementations in similarly complex carbonate reservoirs. The novelty of this research lies in its comprehensive modeling of the first ISC experiment in a triple-porosity framework, providing a deeper understanding of the interactions between matrix, fracture, and vugg networks, and paving the way for enhanced oil recovery (EOR) strategies in challenging reservoir environments.

Multi-timescale inconsistency evaluation and data-driven state of health prediction for circulating water-cooled series battery pack

Inter-cell inconsistency significantly challenges battery pack lifetime, performance, and safety. Current research is deficient in evaluation across multiple time scales and correlation analysis with the state of health (SOH) of batteries. In this paper, we innovatively propose a multi-timescale evaluation method using the correlation coefficient to quantify the inconsistency during long-term aging while employing the coefficient of variation to accurately capture short-term inconsistency fluctuations within a single cycle. We extracted 29 health factors from cyclic data to accurately predict SOH and proposed an improved sparrow search algorithm combined with a support vector regression (ISSA-SVR) model. The model optimizes the sparrow position update strategy by introducing a chicken flock optimization algorithm and incorporating a stochastic function mechanism. This improvement effectively reduces the error and uncertainty in the traditional search process. The experiments are carried out on a 1P36 circulating water-cooled series battery pack, verified by rigorous aging tests. The results show that the correlation coefficient is excellent in assessing long-term inconsistency. In contrast, the coefficient of variation accurately maps the subtle differences in short-term variations and is highly correlated with changes in battery capacity. The ISSA-SVR model demonstrates significantly improved accuracy and robustness in SOH prediction compared to other methods.

Slow Conformational Exchange between Partially Folded and Near-Native States of Ubiquitin: Evidence for a Multistate Folding Model.

The mechanism by which small proteins fold, i.e., via intermediates or via a two-state mechanism, is a subject of intense investigation. Intermediate states in the folding pathways of these proteins are sparsely populated due to transient lifetimes under normal conditions rendering them transparent to a majority of the biophysical methods employed for structural, thermodynamic, and kinetic characterization, which attributes are essential for understanding the cooperative folding/unfolding of such proteins. Dynamic NMR spectroscopy has enabled the characterization of folding intermediates of ubiquitin that exist in equilibrium under conditions of low pH and denaturants. At low pH, an unlocked state defined as N' is in fast exchange with an invisible state, U″, as observed by CEST NMR. Addition of urea to ubiquitin at pH 2 creates two new states F' and U', which are in slow exchange (kF'→U' = 0.14 and kU'→F' = 0.28 s-1) as indicated by longitudinal ZZ-magnetization exchange spectroscopy. High-resolution solution NMR structures of F' show it to be in an "unlocked" conformation with measurable changes in rotational diffusion, translational diffusion, and rotational correlational times. U' is characterized by the presence of just the highly conserved N-terminal β1-β2 hairpin. The folding of ubiquitin is cooperative and is nucleated by the formation of an N-terminal β-hairpin followed by significant hydrophobic collapse of the protein core resulting in the formation of bulk of the secondary structural elements stabilized by extensive tertiary contacts. U' and F' may thus be described as early and late folding intermediates in the ubiquitin folding pathway.

Quantumness of electron transport in quantum dot devices through Leggett–Garg inequalities: A non-equilibrium Green’s function approach

Although coherent manipulation of electronic states can be achieved in quantum dot (QD) devices by harnessing nanofabrication tools, it is often hard to fathom the extent to which these nanoelectronic devices can behave quantum mechanically. Witnessing their nonclassical nature would thus remain of paramount importance in the emerging world of quantum technologies, since the coherent dynamics of electronic states plays there a crucial role. Against this backdrop, we resort to the general framework of Leggett-Garg inequalities (LGI) as it allows for distinguishing the classical and quantum transport through nanostructures by way of various two-time correlation functions. Using the local charge detection at two different time, we investigate here theoretically whether any quantum violation of the original LGI exists with varying device configurations and parameters under both Markovian and non-Markovian dynamics. Two-time correlators within LGI are derived in terms of the non-equilibrium Green’s functions (NEGFs) by exactly solving the quantum Langevin equations. The present study of non-Markovian dynamics of quantum systems interacting with reservoirs is significant for understanding the relaxation phenomenon in the ultrafast transient regime to especially mimic what happens to high-speed quantum devices. We can potentially capture the effect of finite reservoir correlation time by accounting for level-broadening at the electrodes along with non-Markovian memory effects. Furthermore, the large bias restriction is no longer imposed in our calculations so that we can safely consider a finite bias between the electronic reservoirs. Our approach is likely to open up new possibilities of witnessing the quantumness for other quantum many-body systems as well that are driven out of the equilibrium.

Rapid Interpretation of Protein Backbone Rotation Dynamics Directly from Spin Relaxation Data.

Besides their structure, dynamics is pivotal for protein functions, particularly for intrinsically disordered proteins (IDPs) that do not fold into a fixed 3D structure. However, the detection of protein dynamics is difficult for IDPs and other disordered biomolecules. NMR spin relaxation rates are sensitive to the rapid rotations of chemical bonds, but their interpretation is arduous for IDPs or molecular assemblies with a complex dynamic landscape. Here we demonstrate numerically that the dynamics of a wide range of proteins, from short peptides to partially disordered proteins and peptides in micelles, can be characterized by calculating the total effective correlation times of protein backbone N-H bond rotations, τeff, from experimentally measured transverse 15N spin relaxation rates, R2, using a linear relation. Our results enable the determination of magnetic-field-independent and intuitively understandable parameters describing protein dynamics at different regions of the sequence directly from experiments. A practical advance of the approach is demonstrated by analyzing partially disordered proteins in which rotations of disordered regions occur with timescales of 1-2 ns, independent of their size, suggesting that rotations of disordered and folded regions are uncoupled in these proteins.

The infrared spectrum of the protic ionic liquid 1-methylimidazolium nitrate: An ab initio molecular dynamics simulation study

Complex features observed in the infrared (IR) spectrum of the protic ionic liquid 1-methylimidazolium nitrate, [C1im][NO3], are assigned using ab initio molecular dynamics (AIMD) simulation. AIMD is used at the same level of theory to simulate the known X-ray crystal structure of [C1im][NO3] and a cluster of ionic pairs representing the liquid phase. The calculation of several single particle and collective time correlation functions of velocity forms the basis for the AIMD simulation assignment of the infrared spectra. The AIMD simulation demonstrates that the lift of the degeneracy of the anion asymmetric stretching mode, νas(NO3), due to symmetry breaking and coupling to external modes result in multiple band splitting in the crystal spectrum in the region of the νas(NO3) mode. Owing of the intrinsic anharmonic characteristic of the AIMD simulation, the calculation accounts for the broad band in the high-frequency range of the IR spectrum caused by the cation ν(NH) stretching mode in strong cation–anion hydrogen bond. The AIMD simulation of the liquid captures spectral changes upon melting of [C1im][NO3] due to loosening of the NH···O hydrogen bond and changes in the [NO3]- interaction with other hydrogen atoms of the imidazolium ring. The AIMD simulation assigns the intermolecular stretching mode of the hydrogen bond, ν(NH…O), as the band at 183 cm−1 in the far infrared spectrum of the [C1im][NO3] crystal.

Modified stochastic diffusion particle-tracking model (MSDPTM) incorporating energy cascade theory and eddy intermittency for suspended sediment transport in open channel flow.

This study presents a modified stochastic diffusion particle tracking model (MSDPTM) that incorporates energy cascade theory to more accurately simulate suspended sediment transport. The impact of turbulent eddies on sediment particles is an intermittent process, which is also considered in this study. The study examines the time correlation between eddies using eddy turnover time and finds that closer-scale eddies exhibit higher correlations than those farther apart. The statistical properties of particle movement, such as the ensemble mean and variance of particle trajectories, have been calculated and compared with the stochastic diffusion particle tracking model (SDPTM) results. Notably, MSDPTM with intermittency demonstrates a significantly larger ensemble mean of particle trajectories in the streamwise direction than other particle tracking models. The proposed model is validated through comparison with available data, showing its enhanced performance. The results of the simulation indicate that MSDPTM outperforms SDPTM, especially when the intermittency effect of eddies is considered.

Active Brownian information engine: Self-propulsion induced colossal performance.

The information engine is a feedback mechanism that extorts work from a single heat bath using the mutual information earned during the measurement. We consider an overdamped active Ornstein-Uhlenbeck particle trapped in a 1D harmonic oscillator. The particle experiences fluctuations from an inherent thermal bath with a diffusion coefficient (D) and an active reservoir, with characteristic correlation time (τa) and strength (Da). We design a feedback-driven active Brownian information engine (ABIE) and analyze its best performance criteria. The optimal functioning criteria, the information gained during measurement, and the excess output work are reliant on the dispersion of the steady-state distribution of the particle's position. The extent of enhanced performance of such ABIE depends on the relative values of two underlying time scales of the process, namely, thermal relaxation time (τr) and the characteristic correlation time (τa). In the limit of τa/τr → 0, one can achieve the upper bound on colossal work extraction as ∼0.202γ(D+Da) (γ is the friction coefficient). The excess amount of extracted work reduces and converges to its passive counterpart (∼0.202γD) in the limit of τa/τr → high. Interestingly, when τa/τr = 1, half the upper bound of excess work is achieved irrespective of the strength of either reservoirs, thermal or active. Finally, we look into the average displacement of active Brownian particles in each feedback cycle, which surpasses its thermal analog due to the broader marginal probability distribution.

THE ROLE OF THE GREEN ECONOMY IN SHAPING ECONOMIC GROWTH IN POLAND

The aim of this paper is to examine and understand the impact of various green economy indicators on economic growth in Poland from 2010 to 2021. The study focused on a select number of indicators, including the final energy intensity of the economy, expenditures on environmental fixed assets in relation to gross domestic product (GDP), the share of renewable electricity in gross final electricity consumption, primary energy intensity of GDP, and the percentage of urban green areas. The relationship between green economy indicators and economic growth, expressed as gross domestic product per capita, was examined using regression analysis, time series analysis and correlation analysis. Data for the analysis were taken from the Central Statistical Office. Time trend analysis showed that the economy’s final energy intensity and the share of renewable energy improved steadily over the period under review, with a positive impact on GDP per capita. Correlation analysis demonstrated a robust inverse correlation between energy intensity and GDP per capita, along with a positive association between the share of renewable energy and economic growth. In linear and multiple regressions, the economy’s final energy intensity, expenditures on environmental fixed assets, and the share of renewable energy exhibited a significant impact on GDP per capita. The study’s conclusions underscore the necessity of sustained and intensified efforts towards a green economy in Poland, which is pivotal for ensuring long-term economic growth and sustainable development.

Compact Electron Paramagnetic Resonance on a Chip Spectrometer Using a Single Sided Permanent Magnet.

Electron paramagnetic resonance (EPR) spectroscopy provides information about the physical and chemical properties of materials by detecting paramagnetic states. Conventional EPR measurements are performed in high Q resonator using large electromagnets which limits the available space for operando experiments. Here we present a solution toward a portable EPR sensor based on the combination of the EPR-on-a-Chip (EPRoC) and a single-sided permanent magnet. This device can be placed directly into the sample environment (i.e., catalytic reaction vessels, ultrahigh vacuum deposition chambers, aqueous environments, etc.) to conduct in situ and operando measurements. The EPRoC reported herein is comprised of an array of 14 voltage-controlled oscillator (VCO) coils oscillating at 7 GHz. By using a single grain of crystalline BDPA, EPR measurements at different positions of the magnet with respect to the VCO array were performed. It was possible to create a 2D spatial map of a 1.5 mm × 5 mm region of the magnetic field with 50 μm resolution. This allowed for the determination of the magnetic field intensity and homogeneity, which are found to be 254.69 mT and 700 ppm, respectively. The magnetic field was mapped also along the vertical direction using a thin film a-Si layer. The EPRoC and permanent magnet were combined to form a miniaturized EPR spectrometer to perform experiments on tempol (4-hydroxy-2,2,6,6-teramethylpiperidin-1-oxyl) dissolved in an 80% glycerol and 20% water solution. It was possible to determine the molecular tumbling correlation time and to establish a calibration procedure to quantify the number of spins within the sample.

Space and Time Correlations in Quantum Histories

AbstractThe formalism of generalized quantum histories allows a symmetrical treatment of space and time correlations, by taking different traces of the same history density matrix. In this framework, the characterization of spatial and temporal entanglement is revisited. An operative protocol is presented, to map a history state into the ket of a static composite system. It is demonstrated, by examples, how the Leggett–Garg and the temporal Clauser‐Horne‐Shimony‐Holt (CHSH) inequalities can be violated in this approach.

Confined fluid dynamics in a viscoelastic, amorphous, and microporous medium: Study of a kerogen by molecular simulations and the generalized Langevin equation.

We combine the use of molecular dynamics simulations and the generalized Langevin equation to study the diffusion of a fluid adsorbed within kerogen, the main organic phase of shales. As a class of microporous and amorphous materials that can exhibit significant adsorption-induced swelling, the dynamics of the kerogen's microstructure is expected to play an important role in the confined fluid dynamics. This role is investigated by conducting all-atom simulations with or without solid dynamics. Whenever the dynamics coupling between the fluid and solid is accounted for, we show that the fluid dynamics displays some qualitative differences compared to bulk fluids, which can be modulated by the amount of adsorbed fluid owing to adsorption-induced swelling. We highlight that working with the memory kernel, the central time correlation function of the generalized Langevin equation, allows the fingerprint of the dynamics of the solid to appear on that of the fluid. Interestingly, we observe that the memory kernels of fluid diffusion in kerogen qualitatively behave as those of tagged particles in supercooled liquids. We emphasize the importance of reproducing the velocity-force correlation function to validate the memory kernel numerically obtained as confinement enhances the numerical instabilities. This route is interesting as it opens the way for modeling the impact of fluid concentration on the diffusion coefficient in such ultra-confining cases.

Assessing anticancer properties of PEGylated platinum nanoparticles on human breast cancer cell lines using in-vitro assays

This study describes the in-vitro cytotoxic effects of PEG-400 (Polyethylene glycol-400)-capped platinum nanoparticles (PEGylated Pt NPs) on both normal and cancer cell lines. Structural characterization was carried out using x-ray diffraction and Raman spectroscopy with an average crystallite size 5.7 nm, and morphological assessment using Scanning electron microscopy (SEM) revealed the presence of spherical platinum nanoparticles. The results of energy-dispersive x-ray spectroscopy (EDX) showed a higher percentage fraction of platinum content by weight, along with carbon and oxygen, which are expected from the capping agent, confirming the purity of the platinum sample. The dynamic light scattering experiment revealed an average hydrodynamic diameter of 353.6 nm for the PEGylated Pt NPs. The cytotoxicity profile of PEGylated Pt NPs was assessed on a normal cell line (L929) and a breast cancer cell line (MCF-7) using the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. The results revealed an IC50 of 79.18 μg ml−1 on the cancer cell line and non-toxic behaviour on the normal cell line. In the dual staining apoptosis assay, it was observed that the mortality of cells cultured in conjunction with platinum nanoparticles intensified and the proliferative activity of MCF-7 cells gradually diminished over time in correlation with the increasing concentration of the PEGylated Pt NPs sample. The in vitro DCFH-DA assay for oxidative stress assessment in nanoparticle-treated cells unveiled the mechanistic background of the anticancer activity of PEGylated platinum nanoparticles as ROS-assisted mitochondrial dysfunction.

Revealing the molecular structures of α-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy.

Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures and distinct interfacial water structures, theoretical calculations using molecular dynamics (MD) simulations are required. These MD simulations typically need relatively long trajectories (a few nanoseconds) to achieve reliable SFG response function calculations via the dipole moment-polarizability time correlation function. However, the requirement for long trajectories limits the use of computationally expensive techniques, such as abinitio MD (AIMD) simulations, particularly for complex solid-water interfaces. In this work, we present a pathway for calculating vibrational spectra (IR, Raman, and SFG) of solid-water interfaces using machine learning (ML)-accelerated methods. We employ both the dipole moment-polarizability correlation function and the surface-specific velocity-velocity correlation function approaches to calculate SFG spectra. Our results demonstrate the successful acceleration of AIMD simulations and the calculation of SFG spectra using ML methods. This advancement provides an opportunity to calculate SFG spectra for complicated solid-water systems more rapidly and at a lower computational cost with the aid of ML.

Confinement Effects on Reorientation Dynamics of Water Confined within Graphite Nanoslits.

Molecular dynamics simulations were used to investigate the reorientation dynamics of water confined within graphite nanoslits of size less than 2 nm, where molecules formed inner and interfacial layers parallel to the confining walls. Significantly related to molecular reorientations, the hydrogen-bond (HB) network of nanoconfined water therein was scrutinized by HB configuration fractions compared to those of bulk water and the influences on interfacial-molecule orientations relative to a nearby C atom plate. The second-rank orientation time correlation functions (OTCFs) of nanoconfined water were calculated and found to follow stretched-exponential, power-law, and power-law decays in a time series. To understand this naïve behavior of reorientation relaxation, the approach of statistical mechanics was adopted in our studies. In terms of the orientation Van Hove function (OVHF), an alternative meaning was given to the second-rank OTCF, which is a measure of the deviation of the OVHF between a molecular system and free molecules in random orientations. Indicated by the OVHFs at related time scales, the stretched-exponential decay of the second-rank OTCF resulted from molecules evacuating out of HB cages formed by their neighbors. After the evacuations, the inner molecules relaxed at relatively fast rates toward random orientations, but the interfacial molecules reoriented at slow rates due to restrictions by hydrophobic interactions with graphite walls. The first power-law decay of the second-rank OTCF was attributed to the distinct relaxation rates of inner and interfacial molecules within a graphite nanoslit. When the inner molecules were completely random in orientation, the second-rank OTCFs changed to another power law decay with a power smaller than the first one.

Serum Alpha-synuclein in Restless Legs Syndrome.

To determine any correlation between serum alpha-synuclein (α-syn) concentrations and restless legs syndrome (RLS), and to explore the impact of intravenous iron supplementation on serum α-syn levels. We collected clinical data on 113 RLS patients in whom serum α-syn levels were quantified using an ELISA kit and compared to a group of 45 age matched controls. A subset of 9 RLS patients who received intravenous (IV) iron underwent pre- and post-treatment blood sampling to assess α-syn and ferritin response. Family history of RLS was reported by 62.8% of patients, and current dopaminergic augmentation was observed in 31.0%. Low serum ferritin levels below 75 μg/L were seen in 39.8%. Serum α-syn levels were found to be significantly decreased in RLS patients (mean: 7.7ng/ml) compared to controls (mean: 10.7ng/ml,), p<0.05. Stratification based on sex, age and age of onset, did not reveal significant differences in α-syn levels. In 9 RLS patients who received IV iron treatment, a linear correlation between fold change in α-syn and ferritin was observed (R: 0.7, p <0.05). The temporal relation between serum α-syn and IV iron treatment showed a gradual decline of α-syn and ferritin by time correlation (p = 0.023, R: -0.739). In our study of 113 RLS subjects, serum α-syn levels were decreased in RLS patients compared to healthy controls and increased in the 9 patients who received IV iron treatment in correlation with ferritin. This correlation could suggest a mechanism for reduced dopamine transmission in RLS.

Monte Carlo simulations of glass-forming liquids beyond Metropolis.

Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction

Gold price and foreign exchange price prediction are usually regarded as serialized data with strong time correlation. It is precisely because of this feature that the performance of traditional convolutional neural networks is often not as good as that of recurrent neural networks. This paper fine-tunes the CNN-LSTM model in two ways. The first is to simplify the input data, convert the time series into binary data, and change the model task from predicting prices to predicting price fluctuations. The second is to add a dropout layer and use the dropout layer to improve the model's overfitting problem. The results demonstrate that the improved model outperforms the first model, with prediction accuracy rising from 54% to 57%. Furthermore, the refined model is not limited to processing the gold price series; it can also be applied to other time series, and when paired with other models, it could yield even better outcomes.

The Effect of Spin Relaxation on Magnetic Compass Sensitivity in ErCry4a.

This study explores the impact of thermal motion on the magnetic compass mechanism in migratory birds, focusing on the radical pair mechanism within cryptochrome photoreceptors. The coherence of radical pairs, crucial for magnetic field inference, is curbed by spin relaxation induced by intra-protein motion. Molecular dynamics simulations, density-functional-theory-based calculations, and spin dynamics calculations were employed, utilizing Bloch-Redfield-Wangsness (BRW) relaxation theory, to investigate compass sensitivity. Previous research hypothesized that European robin's cryptochrome 4a (ErCry4a) optimized intra-protein motion to minimize spin relaxation, enhancing magnetic sensing compared to the plant Arabidopsis thaliana's cryptochrome 1 (AtCry1). Different correlation times of the nuclear hyperfine coupling constants in AtCry1 and ErCry4a were indeed found, leading to distinct radical pair recombination yields in the two species, with ErCry4a showing optimized sensitivity. However, this optimization is likely negligible in realistic spin systems with numerous nuclear spins. Beyond insights in magnetic sensing, the study presents a detailed method employing molecular dynamics simulations to assess for spin relaxation effects on chemical reactions with realistically modelled protein motion, relevant for studying radical pair reactions at finite temperature.