Correlation Time Research Articles

Characterization of myocardial ischemia-reperfusion injury in a pig model with novel CW T1r and RAFF2 MRI methods in 1.5T

Abstract Introduction Myocardial ischemia-reperfusion injury (IRI) occurs after myocardial infarction when reperfusion aggravates the initial damage in myocardium by vast infiltration of inflammatory cells and elevated oxidative stress in myocardium that may lead to cardiac remodeling, heart failure and even death [1]. Magnetic resonance imaging (MRI) can accurately determine the anatomy of the heart and to characterize myocardial tissue. Novel endogenous rotating frame MRI methods, including continuous wave (CW) T1r and relaxation along a fictitious field with rank 2 (RAFF2), have been shown to characterize MI and other CVD in mouse and human myocardium with high sensitivity and specificity [2,3]. CW T1r and RAFF2 contrasts are occurring during radio frequency (RF) pulse (in kHz range) making them to be sensitive to slow molecular motions [2]. Comparing to conventional endogenous T1 and T2 methods, these are occurring after the RF pulse (in Larmor frequency (MHz) range) and therefore, these are non-selectively sensitive to different correlation times [4]. Golden standard to image MI is to use contrast agent (CA) in late gadolinium enhancement (LGE) method [2,4]. Using CA has drawbacks and therefore, there is an urgent need for optimizing the endogenous MRI methods in clinical practice. Purpose To characterize myocardial IRI in a pig model and to determine the sensitivity of both novel and conventional cardiac MRI methods. Methods IRI operation was done in 7 pig (30-35 kg) by occluding the LAD with a balloon catheter for 50 minutes. Imaging was done with 1.5T (MAGNETOM Aera) at 3 days and 28 days after the operation. Pig hearts were imaged with conventional native T1, T2, LGE, and with novel CW T1r and RAFF2 methods in both time points. These were imaged before CA injection and LGE was done 25 minutes after CA injection. Relaxation time mappings were done in a pixel-by-pixel manner with monoexponentially fitting. Region-of-interest (ROI) analysis was done to determine the relaxation times in IRI and remote areas. LGE was used to confirm the IRI areas in the myocardium. Relative relaxation time difference (RRTD)-values were calculated with the function of (((Relaxation Time of IRI area) – (Relaxation Time of Remote Area))/ (Relaxation Time of Remote Area)) to determine the contrast difference between IRI and remote areas. Results Relaxation times were elevated in IRI areas compared to remote areas (Figure 1A). Novel rotating frame RRTD-values were significantly higher than RRTD-values in conventional methods. This means that novel methods have larger contrast difference between IRI and remote areas compared to conventional methods, which indicates that novel methods are sensitive to characterize IRI area (Figure 1B). The location of MI areas was similar in both novel rotating frame and conventional methods compared to LGE (Figure 2). Conclusion Novel rotating frame relaxation time methods are sensitive to characterize the IRI area in pig model.Relaxation times, RRTD-valuesRelaxation times maps and LGE

Versatile VCSEL source of thermal and super-thermal light.

We have built highly controllable sources of thermal and super-thermal states on the basis of a single-mode (SM) and a multi-mode (MM) vertical cavity surface emitting laser (VCSEL) with noised driving current operating above the threshold. Varying the average driving current and the amplitude and bandwidth of noise, one can robustly obtain light with the temporal second-order intensity correlation function reaching 2.5 and correlation times from 1 µs to 10 ns. For the SM VCSEL, generated thermal lights retain its single-mode quality negating a need for a spectral filtering. For the MM VCSEL, one can still find a near-threshold regime when the generated thermal radiation is close to a single-mode one.

Molecular dynamics insights into the dynamical behavior of structurally modified water in aqueous deep eutectic solvents (ADES).

Recent studies have demonstrated that the presence of water in deep eutectic solvents (DESs) significantly affects their dynamics, structure, and physical properties. Although the structural changes due to the addition of water are well understood, the microscopic dynamics of these changes have been rarely studied. Here, we performed molecular dynamics simulation of 30% (v/v) (∼0.57 molar fraction) water mixture of DES containing CH3CONH2 and NaSCN/KSCN at various salt fractions to understand the microscopic structure and dynamics of water. The simulated results reveal a heterogeneous environment for water molecules in aqueous DES (ADES), which is influenced by the nature of the cation. The diffusion coefficients of water in ADESs are significantly lower than that in neat water and concentrated aqueous NaSCN/KSCN solution. When Na+ ions are replaced by K+ ions in the ADES system, the diffusion coefficient increases, which is consistent with the measured nuclear magnetic resonance data. Self-dynamic structure factor for water and other simulated dynamic quantities, such as reorientation, hydrogen-bond, and residence time correlation functions, show markedly slower dynamics inside ADES than in the neat water and aqueous salt solution. Moreover, these dynamics become faster when Na+ ions in ADES are replaced by K+ ions. The results suggest that the structural environment of water in Na+-rich ADES is rigid due to the presence of cation-bound water and geometrically constrained water. The medium becomes less rigid as the KSCN fraction increases due to the relatively weaker interaction of K+ ions with water than Na+ ions, which accelerates the dynamical processes.

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. (DSMAGNN) soft sensor model. Firstly, MI is used to selected quality-related variables, and the data stacking strategy is employed to convert 3D batch data into a time-delay sequence suitable for input to the soft sensor model. A multiscale pyramid network is utilized to preserve the underlying time dependence across different time scales. Secondly, since the dependence relationship between variables may vary across different time scales, the adaptive graph learning modules deduce scale-specific variable dependencies without relying on predefined priors. Thirdly, given the multiscale feature representation and the inferred dependency relationships between scale-specific variables, we introduce a multiscale temporal graph neural network to jointly model dependencies within and between variables. Building upon this foundation, the scale fusion module is deployed to facilitate collaboration across different temporal scales and automatically capture the significance of contributing temporal patterns. We validate the feasibility and effectiveness of the proposed model through experiments conducted in industrial-scale penicillin fermentation and hot continuous rolling processes, the results show that the proposed model is superior to several compared models, thus confirming its validity.

Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

A One-Dimensional Model for Investigating Scale-separated Approaches to the Interaction of Oceanic Internal Waves

Abstract High-frequency wave propagation in near-inertial wave shear has, for four decades, been considered fundamental in setting the spectral character of the oceanic internal wave continuum and for transporting energy to wave-breaking. We compare idealized ray tracing numerical results with metrics derived using a wave turbulence derivation for the kinetic equation and a path integral to study this specific process. Statistical metrics include the changing ensemble mean vertical wavenumber, referred to as a mean drift, dispersion about the mean drift, time lagged correlation estimates of wavenumber and phase locking of the wave packets with the background. The path integral permits us to identify the mean drift as a resonant process and dispersion about that mean drift as non-resonant. At small inertial wave amplitudes; ray tracing, wave turbulence and the path integral provide consistent descriptions for the mean drift of wavepackets in the spectral domain and dispersion about the mean drift. Extrapolating these results to the background internal wavefield over-predicts downscale energy transports by an order of magnitude. At oceanic amplitudes, however, the numerics support diminished transport and dispersion that coincide with the mean drift time scale becoming similar to the lagged correlation time scale. We parse this as the transition to a non-Markovian process. Despite this decrease, numerical estimates of downscale energy transfer are still too large. We argue that residual differences result from an unwarranted discard of Bragg scattering resonances. Our results support replacing the long standing interpretive paradigm of extreme scale-separated interactions with a more nuanced slate of ‘local’ interactions in the kinetic equation.

A Gaussian process model for stellar activity in 2D line profile time-series

ABSTRACT Stellar active regions like spots and faculae can distort the shapes of spectral lines, inducing variations in the radial velocities that are often orders of magnitude larger than the signals from Earth-like planets. Efforts to mitigate these activity signals have hitherto focused on either the time or the velocity (wavelength) domains. We present a physics-driven Gaussian process (GP) framework to model activity signals directly in time series of line profiles or cross-correlation functions (CCFs). Unlike existing methods that correct activity signals in line profile time series, our approach exploits the time correlation between velocity (wavelength) bins in the line profile variations, and is based on a simplified but physically motivated model for the origin of these variations. When tested on both synthetic and real data sets with signal-to-noise ratios down to ∼100, our method was able to separate the planetary signal from the activity signal, even when their periods were identical. We also conducted injection/recovery tests using two years of realistically sampled HARPS-N solar data, demonstrating the ability of the method to accurately recover a signal induced by a 1.5-Earth mass planet with a semi-amplitude of 0.3 m s−1 and a period of 33 d during high solar activity.

Curvature fluctuations of fluid vesicles reveal hydrodynamic dissipation within the bilayer

The biological function of membranes is closely related to their softness, which is often studied through the membranes' thermally driven fluctuations. Typically, the analysis assumes that the relaxation rate of a pure bending deformation is determined by the competition between membrane bending rigidity and viscous dissipation in the surrounding medium. Here, we reexamine this assumption and demonstrate that viscous flows within the membrane dominate the dynamics of bending fluctuations of nonplanar membranes with a radius of curvature smaller than the Saffman-Delbrück length. Using flickering spectroscopy of giant vesicles made of dipalmitoylphosphatidylcholine, DPPC:cholesterol mixtures and pure diblock-copolymer membranes, we experimentally detect the signature of membrane dissipation in curvature fluctuations. We show that membrane viscosity can be reliably obtained from the short time behavior of the shape time correlations. The results indicate that the DPPC:cholesterol membranes behave as a Newtonian fluid, while the polymer membranes exhibit more complex rheology. Our study provides physical insights into the time scales of curvature remodeling of biological and synthetic membranes.

Markers of low field NMR relaxation features of tissues

This work presents an approach to exploiting Nuclear Magnetic Resonance (NMR) relaxometry data (1H spin-lattice relaxation rates covering the frequency range from below 1 kHz to 10 MHz) for the purpose of differentiating between pathological and reference tissues. Characteristic quantities (markers) that can be obtained in a straightforward manner, not resorting to an advanced analysis of 1H spin-lattice relaxation data, have been identified and compared for pathological and reference colon tissues. Moreover, the relaxation data have been parametrised in terms of Lorentzian spectral densities and the possibility of using the obtained dipolar relaxation constants and correlation times as biomarkers to assess the state of tissues has been discussed. It has also been demonstrated that the relaxation data for the reference and the pathological tissues can be attributed to two groups (for each case). The studies are a step towards exploiting the potential of NMR relaxometry for characterisation of pathological changes in tissues.

Capturing Long-Range Memory Structures with Tree-Geometry Process Tensors

We introduce a class of quantum non-Markovian processes—dubbed process trees—that exhibit polynomially decaying temporal correlations and memory distributed across timescales. This class of processes is described by a tensor network with treelike geometry whose component tensors are (1) causality-preserving maps (superprocesses) and (2) locality-preserving temporal change-of-scale transformations. We show that the long-range correlations in this class of processes tends to originate almost entirely from memory effects and can accommodate genuinely quantum power-law correlations in time. Importantly, this class allows efficient computation of multitime correlation functions. To showcase the potential utility of this model-agnostic class for numerical simulation of physical models, we show how it can efficiently approximate the strong memory dynamics of the paradigmatic spin-boson model, in terms of arbitrary multitime features. In contrast to an equivalently costly matrix-product-operator representation, the ansatz produces a fiducial characterization of the relevant physics. Finally, leveraging 2D tensor-network renormalization-group methods, we detail an algorithm for deriving a process tree from an underlying Hamiltonian via the Feynmann-Vernon influence functional. Our work lays the foundation for the development of more efficient numerical techniques in the field of strongly interacting open quantum systems, as well as the theoretical development of a temporal renormalization-group scheme. Published by the American Physical Society 2024

Comparison of daytime and nighttime supine gastroesophageal reflux by pH monitoring of individuals with clinical suspicion of gastroesophageal reflux disease.

This study aims to compare pHmetric characteristics of diurnal and nocturnal supine reflux. We studied 500 consecutive individuals with clinical suspicion of gastroesophageal reflux disease (GERD) who underwent esophageal high-resolution manometry and prolonged ambulatory pH monitoring. Patients were classified with supine GERD pattern when the percentage of acid reflux time in the supine position was equal to or greater than 2.2%. Data on acid reflux in the supine position during daytime and nighttime recumbency were: (i) acid exposure time, (ii) number of reflux episodes, (iii) longest reflux episode, (iv) interval between the last meal and the supine position, (v) interval between the supine position and the first acid reflux episode, and (vi) reported symptoms. Of the 500 evaluated patients, 238 (48%) had GERD. Among these, supine pattern was observed in 134 (56%) patients, bipositional in 53 (23%), and orthostatic in 51 (21%). In patients with daytime recumbency, 112 (51% of 217) were pathological refluxers, with a mean DeMeester score of 45 ± 26. Total acid exposure time (P = 0.8) and reported Symptom Index (P = 0.2) did not differ depending on the period, whether diurnal or nocturnal. All other pHmetric parameters were lower during daytime recumbency. In conclusion, diurnal supine reflux has similar acid exposure time and temporal correlation between symptoms and reflux episodes as nocturnal supine reflux. Other pHmetric parameters are lower for diurnal supine reflux.

Bayesian inference for link travel time correlation of a bus route

Estimation of link travel time correlation of a bus route is essential to many bus operation applications. Link travel time on a bus route could exhibit complex correlation structures, such as long-range correlations, negative correlations, and time-varying correlations. This paper develops a Bayesian Gaussian model to estimate the link travel time correlation matrix of a bus route using smart-card-like data. Our method overcomes the small-sample-size problem in correlation matrix estimation by borrowing/integrating those incomplete observations from other bus routes. We first conduct a synthetic experiment and results show that the proposed method produces an accurate estimation for correlations with credible intervals. Next, we perform experiments on a real-world bus route with in-out-stop record data; results show that both local and long-range correlations exist on this bus route. Finally, we demonstrate an application of using the estimated covariance matrix to make probabilistic forecasting of link and trip travel time.

Pain Fluctuations in Parkinson's Disease and Their Association with Motor and Non-Motor Fluctuations.

Pain fluctuations are a characteristic phenomenon in advanced Parkinson's disease (PD), but their temporal association with motor and non-motor symptom (NMS) fluctuations remains largely enigmatic. Moreover, data on their importance for disease severity perception and health-related quality-of-life (hr-QoL) is limited. To dissect pain fluctuations with respect to pain type and frequency patterns, and their association with motor and non-motor fluctuations. Prospective observational cohort study in advanced PD assessing symptom fluctuations by simultaneous hourly ratings using the PD Home diary (Off, On, Dyskinetic state), a pain diary (assessing 9 pain types) and a non-motor diary (10 key NMS) based on validated instruments. Forty-seven out of 55 eligible participants with fluctuating PD (51% men, median age 65, median disease duration 10 years) had sufficient datasets (>95% of hours) from 2 consecutive days. Pain was reported in 35% of waking hours with clear circadian rhythm peaking in early morning Off periods and clustering during motor Off state (49% of Off state hours with pain). Main NMS co-fluctuating with pain were "Fatigue" and "Inner Restlessness". Simultaneous assessment of global disease severity by participants revealed that pain was associated with worse disease severity only in motor On and Dyskinetic state but not in Off state, which translated into significant correlations of daily pain times with hr-QoL only during motor On and Dyskinetic state. Aside from treating motor Off periods, specific recognition of pain particularly during motor On and Dyskinetic state comprises an important aspect for disease management in advanced PD.

Spin relaxation dynamics with a continuous spin environment: The dissipaton equation of motion approach.

We investigate the quantum dynamics of a spin coupling to a bath of independent spins via the dissipaton equation of motion (DEOM) approach. The bath, characterized by a continuous spectral density function, is composed of spins that are independent level systems described by the su(2) Lie algebra, representing an environment with a large magnitude of anharmonicity. Based on the previous work by Suarez and Silbey [J. Chem. Phys. 95, 9115 (1991)] and by Makri [J. Chem. Phys. 111, 6164 (1999)] that the spin bath can be mapped to a Gaussian environment under its linear response limit, we use the time-domain Prony fitting decomposition scheme to the bare-bath time correlation function (TCF) given by the bosonic fluctuation-dissipation theorem to generate the exponential decay basis (or pseudo modes) for DEOM construction. The accuracy and efficiency of this strategy have been explored by a variety of numerical results. We envision that this work provides new insights into extending the hierarchical equations of motion and DEOM approach to certain types of anharmonic environments with arbitrary TCF or spectral density.

Instabilities and Pattern Formation in Epidemic Spread Induced by Nonlinear Saturation Effects and Ornstein–Uhlenbeck Noise

Abstract We analytically study the emergence of instabilities and the consequent steady-state pattern formation in a stochastic partial differential equation (PDE) based, compartmental model of spatiotemporal epidemic spread. The model is characterized by: (1) strongly nonlinear forces representing the infection transmission mechanism and (2) random environmental forces represented by the Ornstein–Uhlenbeck (O–U) stochastic process which better approximates real-world uncertainties. Employing second-order perturbation analysis and computing the local Lyapunov exponent, we find the emergence of diffusion-induced instabilities and analyze the effects of O–U noise on these instabilities. We obtain a range of values of the diffusion coefficient and correlation time in parameter space that support the onset of instabilities. Notably, the stability and pattern formation results depend critically on the correlation time of the O–U stochastic process; specifically, we obtain lower values of steady-state infection density for higher correlation times. Also, for lower correlation times the results approach those obtained in the white noise case. The analytical results are valid for lower-order correlation times. In summary, the results provide insights into the onset of noise-induced, and Turing-type instabilities in a stochastic PDE epidemic model in the presence of strongly nonlinear deterministic infection forces and stochastic environmental forces represented by Ornstein–Uhlenbeck noise.

Ultralight dark matter explanation of NANOGrav observations

The angular correlation of pulsar residuals observed by NANOGrav and other pulsar timing array collaborations show evidence in support of the Hellings-Downs correlation expected from stochastic gravitational wave background (SGWB). In this paper, we offer a nongravitational wave explanation of the observed pulsar timing correlations as caused by an ultralight Lμ−Lτ gauge boson dark matter (ULDM). ULDM can affect the pulsar correlations in two ways. The gravitational potential of vector ULDM gives rise to a Shapiro time delay of the pulsar signals and a nontrivial angular correlation (as compared to the scalar ULDM case). In addition, if the pulsars have a nonzero charge of the dark matter gauge group, then the electric field of the local dark matter causes an oscillation of the pulsar and a corresponding Doppler shift of the pulsar signal. We point out that pulsars carry a significant charge of muons, and thus the Lμ−Lτ vector dark matter contributes to both the Doppler oscillations and the time delay of the pulsar signals. The synergy between these two effects provides a better fit to the shape of the angular correlation function, as observed by the NANOGrav collaboration, compared to the standard SGWB explanation or the SGWB combined with time delay explanations. Our analysis shows that, in addition to the SGWB signal, there may potentially be excess timing residuals attributable to the Lμ−Lτ ULDM. Published by the American Physical Society 2024

Heat transfer enhancement of PCM in the triple-pipe helical-coiled latent heat thermal energy storage unit and complete melting time correlation

Secondary flows induced in helical-coiled pipes significantly enhance the thermal storage performance of latent heat thermal energy storage units. This paper presents a three-dimensional model of a triple-pipe helical-coiled system to investigate the melting characteristics of phase change material within it. A correlation for the complete melting time was developed based on the simulation results. The findings indicate that the thermal-hydraulic fields of the heat transfer fluids and phase change material deflect from the vertical central axis at cross-sections. The secondary flows within the inner pipe are more effective in enhancing melting than those in the outer pipe. Melting performance improves with increasing inner pipe diameter, coil diameter (from 100 mm to 160 mm), inclination angle, or heat transfer fluid temperature, or decreasing helical pitch. The inner pipe diameter, helical pitch and heat transfer fluid temperature exhibit more pronounced influences. An inner pipe diameter of 30 mm, a coil diameter of 160 mm, a helical pitch of 80 mm, an inclination angle of 90°, a heat transfer fluid temperature of 360 K, and a heat transfer fluid Reynolds number of 2000, respectively produce a faster melting process. The developed correlation for the triple-pipe helical-coiled pipe thermal storage unit accurately predicts 96 % of the 45 data within ±11 %.

Early Detection of Myocardial Involvement in Thalassemia Intermedia Patients: Multiparametric Mapping by Magnetic Resonance Imaging.

No study has assessed myocardial T1 and T2 values in patients with beta-thalassemia intermedia (β-TI). To assess the prevalence of myocardial involvement in β-TI patients by T2* relaxometry and native T1 and T2 mapping and to determine the correlation of myocardial relaxation times with demographic and clinical parameters. Prospective matched-cohort study. 42 β-TI patients (27 females, 39.65 ± 12.32 years), enrolled in the Extension-Myocardial Iron Overload in Thalassaemia Network, and 42 age- and sex-matched healthy volunteers (27 females, 40.01 ± 11.36 years) and thalassemia major (TM) patients (27 females, 39.27 ± 11.57 years). 1.5 T/multi-echo gradient echo, modified Look-Locker inversion recovery, multi-echo fast-spin-echo, cine balanced steady-state-free precession, and late gadolinium enhancement (LGE) sequences. Hepatic, pancreatic, and left ventricular (LV) T2* values, LV native T1 and T2 values, biventricular ejection fractions and volumes, and presence and extent of replacement myocardial fibrosis. Comparisons between two groups were performed with two-sample t tests, Wilcoxon's signed rank tests, or χ2 testing. Correlation analysis was performed using Pearson's or Spearman's test. P < 0.05 was considered statistically significant. β-TI patients had significantly higher LV T2 values than healthy subjects (56.84 ± 4.03 vs. 52.46 ± 2.50 msec, P < 0.0001) and significantly higher LV T1 values than TM patients (1018.32 ± 48.94 vs. 966.66 ± 66.47 msec, P < 0.0001). In β-TI, female gender was associated with significantly increased LV T1 (P = 0.041) and T2 values (P < 0.0001), while splenectomy and presence of regular transfusions were associated with significantly lower LV T1 values (P = 0.014 and P = 0.001, respectively). In β-TI patients, all LV relaxation times were significantly correlated with each other (T2*-T1: P = 0.003; T2*-T2: P = 0.003; T1-T2: P < 0.0001). Two patients with a reduced LV T2* also had a reduced LV T1, while only one had a reduced LV T2. Three patients had a reduced LV T1 but a normal LV T2*; 66.7% of the patients had an increased LV T2. All LV relaxation times were significantly correlated with pancreas T2* values (T2*: P = 0.033; T1: P < 0.0001; T2: P = 0.014). No LV relaxation time was associated (P > 0.05) with hepatic iron concentration, biventricular function parameters, or LGE presence. The combined use of all three myocardial relaxation times has potential to improve sensitivity in the detection of early/subclinical myocardial involvement in β-Tl patients. 2 TECHNICAL EFFICACY: Stage 2.

Asymptotic decoupling of population growth rate and cell size distribution

The rate at which individual bacterial cells grow depends on the concentrations of cellular components such as ribosomes and proteins. These concentrations continuously fluctuate over time and are inherited from mother to daughter cells, leading to correlations between the growth rates of cells across generations. Division sizes of cells are also stochastic and correlated across generations due to a phenomenon known as cell size regulation. Fluctuations and correlations from both growth and division processes affect the population dynamics of an exponentially growing culture. Here, we provide analytic solutions for the population dynamics of cells with continuously fluctuating growth rates coupled with a generic model of cell-size regulation. We show that in balanced growth, the effects of growth and division processes decouple; the population growth rate only depends on the single-cell growth rate process, and the population cell size distribution only depends on the model of division and cell size regulation. The population growth rate is always higher than the average single-cell growth rate and the difference increases with growth rate variability and its correlation time. This difference also sets the timescale for the population to reach its steady state. We provide analytical solutions for oscillations in population growth rate and traveling waves in size distribution during this approach to the steady state. Published by the American Physical Society 2024

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.