Chain Simulation Research Articles

TopoLoop: A new tool for chromatin loop detection in live cells via single-particle tracking.

We present a novel method for identifying topological features of chromatin domains in live cells using single-particle tracking and topological data analysis (TDA). By applying TDA to particle trajectories, we can effectively detect complex spatial patterns, such as loops, that are often missed by traditional time series analysis. Using simulations of polymer bead-spring chains, we have validated the accuracy of our method and determined its limitations for detecting loops. Our approach offers a promising avenue for exploring the topological complexity of chromatin in living cells using TDA techniques.

Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers.

We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces by using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed length bonds, with configurations updated through adaptive nonlocal Monte Carlo moves. This proposed method enables precise calculation of a polymer's response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing the polymer conformation.

QPOs from charged particles around magnetized black holes in braneworlds

Quasiperiodic oscillations (QPOs) are a powerful tool for testing gravity theories, probing gravitational and electromagnetic field properties, and obtaining constraints on the black hole and field parameters. This work considers charged particle dynamics near uniformly magnetized black holes in braneworlds. First, we obtain the solution of the Maxwell equation for magnetic fields and calculate the radial and angular magnetic field components. We derive and analyze the effective potential of charged particles for circular orbits and investigate the energy and angular momentum for the circular orbits. We also analyze the combined effects of magnetic interaction and braneworlds on the charged particles’ innermost stable circular orbits (ISCOs). We calculate the angular momentum of charged particles in Keplerian orbits in the presence of an external magnetic field and braneworlds. Also, we investigate frequencies of the particle oscillations along vertical and angular directions. We applied our studies on particle oscillations to the QPO studies in the relativistic precession model. Finally, we obtain constraints on magnetic interaction and braneworld parameters together with the black hole mass and QPO orbits using Monte Carlo Markov Chain (MCMC) simulation in the four-dimensional parameter space for the QPOs observed in the microquasars XTE J1550-564, GRO J1655-40 & GRS 1915-105, and at the center of galaxies M82 and Milky Way.

Simulation of Spin Chains with Off-Diagonal Coupling Using the Inchworm Method.

We study the dynamical simulation of an open quantum spin chain with nearest neighboring coupling, where each spin in the chain is associated with a harmonic bath. This is an extension of our previous work (Wang, G.; Cai, Z. J. Chem. Theory Comput. 2023, 19, 8523-8540) by generalizing the application of the inchworm method and the technique of modular path integrals from diagonally coupled cases to off-diagonally coupled cases. Additionally, to reduce computational and memory cost in long time simulation, we apply tensor-train representation to efficiently represent the reduced density matrix of the spin chains, and employ the transfer tensor method (TTM) to avoid exponential growth of computational cost with respect to time. Abundant numerical experiments are performed to validate our method.

Development of a Residual Biomass Supply Chain Simulation Model Using AnyLogistix: A Methodical Approach

Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m3. CO2 emissions were measured at 487.7 kg/m3. The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production.

Bayesian Updating for Prediction of Scour Depth Using Natural Frequency of Monopiles

Scour is a non-negligible issue of monopiles that profoundly threatens the safety of monopile for offshore wind turbines (OWTs). Accurately predicting the scour depth is essential for the design and operation of OWTs. This study introduces a model aimed at predicting scour depth from the aspect of the natural frequency of monopile. The model is developed using uniform design samples to ensure its applicability across a wider range of OWT monopiles and soil properties. To enhance the model accuracy, a Bayesian framework is employed, incorporating prior information. The three main model coefficients are updated iteratively, allowing the predicted scour depth to converge with the observed values. The Monte Carlo Markov chain (MCMC) simulation is utilized to generate the posterior distribution. The model accuracy is validated through 48 representative samples, and the effectiveness of Bayesian updating in improving the model precision is demonstrated by comparing the results prior to and following Bayesian updating. Additionally, the numerical simulations and monitored data confirm the validity of the proposed prediction model.

Circular motion and QPOs near black holes in Kalb–Ramond gravity

General relativity (GR) theory modifications include different scalar, vector, and tensor fields with non-minimal gravitational coupling. Kalb–Ramond (KR) gravity is a modified theory formulated based on the presence of the bosonic field. One astrophysical way to test gravity is by studying the motion of test particles in the spacetime of black holes (BHs) using observational data. In the present work, we aimed to test KR gravity through theoretical studies of epicyclic frequencies of particle oscillations using quasi-periodic oscillation (QPO) frequency data from microquasars. First, we derive equations of motion and analyze the effective potential for circular orbits. Also, we studied the energy and angular momentum of particles corresponding to circular orbits. In addition, we analyze the stability of circular orbits. It is shown that the radius of the innermost stable circular orbits is inversely proportional to the KR parameter. We are also interested in how the energy and angular momentum of test particles at ISCO behave around the KR BHs. We found that the Keplerian frequency for the test particles in KR gravity is the same as that in GR. Finally, we study the QPOs by applying epicyclic oscillations in the relativistic precession (RP), warped disc (WD), and epicyclic resonance (ER) models. We also analyze QPO orbits in the resonance cases of upper and lower frequencies 3:2, 4:3, and 5:4 in the QPO as mentioned above models. We obtain constraints on the KR gravity parameter and BH mass using a Monte Carlo Markov Chain simulation in the multidimensional parameter space for the microquasars GRO J1655-40 & XTE J1550-564, M82 X-1, and Sgr A*.

Closed-Form Formula for the Conditional Moment-Generating Function Under a Regime-Switching, Nonlinear Drift CEV Process, with Applications to Option Pricing

An analytical derivation of the conditional moment-generating function (MGF) for a regime-switching nonlinear drift constant elasticity of variance process is established. The proposed model incorporates both regime-switching mechanisms and nonlinear drift components to better capture market phenomena such as volatility smiles and leverage effects. Regime-switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. Closed-form formulas for the MGF under various conditions, which are then applied for option pricing, are also derived. The efficacy and accuracy of the results are validated through a discrete Markov chain simulation. The results obtained from the proposed formulas completely match with those from MC simulations, while requiring significantly less computational time.

Simulation of Kitaev chain using one-dimensional chain of superconducting qubits and environmental effects on topological states

Simulation of Kitaev chain using one-dimensional chain of superconducting qubits and environmental effects on topological states

Benchmarking building energy consumption for space heating using an empirical Bayesian approach with urban-scale energy model

Calibration of an urban-scale building energy model is crucial for advancing building energy efficiency codes and policies to reduce carbon dioxide emissions whereas the accuracy of the model is challenged by performance gaps resulting from sparse data and uncertainties in modeling. An empirical Bayesian (EB) approach, incorporated a probability-based mixed-effects energy model and Monte Carlo Markov chain simulation, was developed to predict and benchmark building energy use intensity (EUI) for space heating, using the metering data of 2020 stochastically sampled district heating substations from a municipal utility database. The limitations, implementations and performances of different modeling strategy, including EB as well as Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP) and Fully Bayes (FB) were compared. The results show that EB strategy achieved superior prediction performance than other strategies on the novel test dataset. The preparation, calibration, and validation efforts for implementing the EB approach were demonstrated. The proposed methodology is recommended as a robust approach for benchmarking urban-scale building EUI, distinguished from other machine learning approaches by requiring fewer training samples, better interpreting uncertainties in the data used, and exhibiting superior generalization capabilities. The proposed methodology was successfully demonstrated through the urban-scale benchmarking of space heating EUI in Beijing.

The Conformation of Glycosidic Linkages According to Various Force Fields: Monte Carlo Modeling of Polysaccharides Based on Extrapolation of Short-Chain Properties.

The conformational features of the glycosidic linkage are the most important variable to consider when studying di-, oligo-, and polysaccharide molecules using molecular dynamics (MD) simulations. The accuracy of the theoretical model describing this degree of freedom influences the quality of the results obtained from MD calculations based on this model. This article focuses on the following two issues related to the conformation of the glycosidic linkage. First, we describe the results of a comparative analysis of the predictions of three carbohydrate-dedicated classical force fields for MD simulations, namely, CHARMM, GLYCAM, and GROMOS, in the context of different parameters of structural and energetic nature related to the conformation of selected types of glycosidic linkages, α(1 → 4), β(1 → 3), and β(1 → 4), connecting glucopyranose units. This analysis revealed several differences, mainly concerning the energy levels of the secondary and tertiary conformers and the linkage flexibility within the dominant exo-syn conformation for α(1 → 4) and β(1 → 3) linkages. Some aspects of the comparative analysis also included the newly developed, carbohydrate-dedicated Martini 3 coarse-grained force field. Second, to overcome the time-scale problem associated with sampling slow degrees of freedom in polysaccharide chains during MD simulations, we developed a coarse-grained (CG) model based on the data from MD simulations and designed for Monte Carlo modeling. This model (CG MC) is based on information from simulations of short saccharide chains, effectively sampled in atomistic MD simulations, and is capable of extrapolating local conformational properties to the case of polysaccharides of arbitrary length. The CG MC model has the potential to estimate the conformations of very long polysaccharide chains, taking into account the influence of secondary and tertiary conformations of glycosidic linkages. With respect to the comparative analysis of force fields, the application of CG MC modeling showed that relatively small differences in the predictions of individual force fields with respect to a single glycosidic linkage accumulate when considering their effect on the structure of longer chains, leading to drastically different predictions with respect to parameters describing the polymer conformation, such as the persistence length.

Observational test of ℛ2 spacetimes with the S2 star in the Milky Way galactic center

A novel class of vacuum metrics expressible in analytical form was recently found for pure ℛ2 gravity, based on a groundwork put forth by Buchdahl in 1962. These Buchdahl-inspired solutions offer a practical framework for testing ℛ2 gravity through empirical observations. Within a subclass of asymptotically flat Buchdahl-inspired vacuum spacetimes, we identified a parameter ϵ measuring the deviation from the classic Schwarzschild metric, which corresponds to ϵ=0. In this paper, we employ observational data from the S2 star's orbit around Sgr A* in the Milky Way galactic center and perform Monte Carlo Markov Chain simulations to probe the effects of the new metrics on the orbit of the S2 star. Our analysis presented herein reports a range at 95% confidence level on the deviation parameter as ϵ ∈ (-0.6690, 0.4452). While no decisive evidence either in favor or in disfavor of the asymptotically flat Buchdahl-inspired spacetimes has been achieved, the obtained bound is compatible with the tighter results using other data of different nature as recently reported in Eur. Phys. J. C 84 (2024) 330. As a meaningful test probing into a strong-field regime, our present study calls for further observations with prolonged period and improved accuracy in order to tighten the bound for ϵ using the S2 star orbit.

Charged particles and quasiperiodic oscillations in Black–bounce–Reissner–Nordström geometry in braneworlds

We study the dynamics and oscillations of electrically charged particles along stable circular orbits around a nonrotating electrically charged spacetime. In this work, we consider a regularized Reissner–Nordström (RN) spacetime under the transformation r2→r2+l2, known as the black-bounce-RN (or Simpson–Visser RN) spacetime, which can represent both a black hole and a wormhole depending on the values of l, in the presence of braneworld effects. In our analyses, we introduce a new parameter that shows a similar gravitational effect, which includes electric (Q) and tidal charges (B) of the spacetime as b=Q2+B. We investigate the horizon properties of the spacetime and estimate the parametric distributions that describe a black hole and a wormhole. Furthermore, we analyze the effective potential of the particles and the critical angular momentum for the cases dominated by the tidal charge b<0 and the electric charge b>0, respectively. Moreover, we apply the epicyclic frequencies in the relativistic precession model to fit twin high-frequency (HF) quasiperiodic oscillations (QPOs) observed in microquasars and active galactic nuclei. Finally, we apply a Monte Carlo Markov Chain (MCMC) simulation to constrain the multidimensional parameter space for the microquasars GRO J1655-40 & GRS 1915-105 and the galactic center, for being a black hole and wormhole candidate using observational data from QPOs. Our findings suggest that the central object of the Milky Way and the microquasars GRS 1915-105 may be a black hole and a wormhole with certain parameters. However, there are no constraints for the microquasar GRO J1655-40 obtained regarding the wormhole case. It implies that the central object fails to be a candidate for being a wormhole in the black-bounce charged spacetimes in the braneworlds model.

Simulation of the Medical Evacuation Chain: A Conceptual Model

Simulation of the Medical Evacuation Chain: A Conceptual Model

Topological charge pumping in tetramerized Kitaev chains with nodal loop in parameter space

We investigated the topological pumping charge of a tetramerized Kitaev chain with spatially modulated chemical potential, which hosts nodal loops in parameter space and violates particle number conservation. In the simplest case, with alternatively assigned hopping and pairing terms, we show that the model can be mapped into the Rice–Mele model by a partial particle-hole transformation and subsequently supports topological charge pumping as a demonstration of the Chern number for the ground state. Beyond this special case, analytic analysis shows that the nodal loops are conic curves. Numerical simulation of a finite-size chain indicates that the pumping charge is zero for a quasiadiabatic loop within the nodal loop and is ±1 for a quasiadiabatic passage enclosing the nodal loop. Our findings unveil the topology of Kitaev chains in parameter space.

Revisiting the second Vassiliev (In)variant for polymer knots

Knots in open strands such as ropes, fibers, and polymers, cannot typically be described in the language of knot theory, which characterizes only closed curves in space. Simulations of open knotted polymer chains, often parameterized to DNA, typically perform a closure operation and calculate the Alexander polynomial to assign a knot topology. This is limited in scenarios where the topology is less well-defined, for example when the chain is in the process of untying or is strongly confined. Here, we use a discretized version of the Second Vassiliev Invariant for open chains to analyze Langevin Dynamics simulations of untying and strongly confined polymer chains. We demonstrate that the Vassiliev parameter can accurately and efficiently characterize the knotted state of polymers, providing additional information not captured by a single-closure Alexander calculation. We discuss its relative strengths and weaknesses compared to standard techniques, and argue that it is a useful and powerful tool for analyzing polymer knot simulations.

Breaking the baryon-dark matter degeneracy in a model-independent way through the Sunyaev-Zeldovich effect

Context. In cosmological fits, it is common to fix the baryon density ωb via the cosmic microwave background. We here constrain ωb by means of a model-independent interpolation of the acoustic parameter from correlated baryonic acoustic oscillations. Aims. The proposed technique is used to alleviate the degeneracy between baryonic and dark matter abundances. Methods. We propose a model-independent Bézier parametric interpolation and applied it to intermediate-redshift data. We first interpolated the observational Hubble data to extract cosmic bounds over the (reduced) Hubble constant h0 and interpolated the angular diameter distances, D(z), of the galaxy clusters, inferred from the Sunyaev-Zeldovich effect, to constrain the spatial curvature, Ωk. Through the Hubble points and D(z) determined in this way, we interpolated uncorrelated data of baryonic acoustic oscillations bounding the baryon ωb and total matter ωm densities, reinforcing the constraints on h0 and Ωk with the same technique. Finally, to remove the matter sector degeneracy, we obtained ωb by interpolating the acoustic parameter from correlated baryonic acoustic oscillations. Results. Monte Carlo Markov chain simulations agree at 1σ confidence level with the flat ΛCDM model and are roughly suitable at 1σ with its nonflat extension, while the Hubble constant appears in tension up to the 2σ confidence levels. Conclusions. Our method excludes very small extensions of the standard cosmological model, and on the Hubble tension side, seems to match local constraints slightly.

Effect of Hydrophobic Side-Group Chemistry on Conformation, Intermolecular Structure and Dynamics of Cationic Poly(Vinyl Amine) in Aqueous Solution

Atomistic molecular dynamics simulations of the single chains of cationic polyelectrolytes of atactic poly(vinyl amines) (PVAm) modified by hydrophobic side-groups, poly(N-ethyl-n-vinyl amine) (ethyl-PVAm) and poly(N-benzyl-n-vinyl amine) (benzyl-PVAm), in aqueous solution, were carried out over the entire range of degree-of-ionization, f. These polymers, to our knowledge, were studied here by molecular simulations for the first time. The interplay of electrostatic interactions, excluded volume, hydrophobic effect, hydrogen bonding and counter-ion interaction with hydrophobic-polyions, is analyzed. The conformational transition of the polyelectrolyte chain from a coiled to an extended state was gradual and was observed for 0.3 < f < 0.7. Ethyl-PVAm and benzyl-PVAm exhibited similar values of radius-of-gyration Rg and end-to-end distance R for f > 0.5, in agreement with available experimental data. The more hydrophobic benzyl-PVAm showed a more extended conformation with a preference for trans states in the backbone. The change in chain expansion with respect to f was more for benzyl-PVAm as compared to ethyl-PVAm. The relaxation time of polymer-water H-bonds increased with f and the hydrophobicity of the polymer chain, with faster decay for ethyl-PVAm. The water molecules solvating the hydrophobic PVAm’s showed dynamical restriction due to bulky hydrophobic side-groups resulting in long-lived H-bonds. The number of polymer-water H-bonds showed a transition at a certain charge, which influenced the complex interplay of steric effect, hydrophobic effect and solvation.

System-Level Offshore Wind Energy and Hydrogen Generation Availability and Operations and Maintenance Costs

With the current trends of wind energy already playing a major part in the Scottish energy supply, the capacity of wind farms is predicted to grow exponentially and reach further depths offshore. However, a key challenge that presents itself is the integration of large producing assets into the current UK grid. One potential solution to this is green hydrogen production, which is being heavily researched in industry, with many concepts being investigated for large-scale purposes. However, the operations and maintenance (O&amp;M) costs and availability of green hydrogen systems need to be quantified to ensure economical and technical viability, which is sparse in the available literature. The study presented in this paper investigated the availability and O&amp;M costs of coupled wind–hydrogen systems by attempting to quantify the failure rates, repair times, repair costs and number of technicians required for key green hydrogen components. This study also utilised an O&amp;M model created by the University of Strathclyde, which uses Monte Carlo Markov chain simulations to produce the O&amp;M outputs. A number of assumptions were made throughout the study in relation to the O&amp;M model inputs, and the baseline availability for the coupled wind–hydrogen system was 85.24%. Whilst the wind turbine still contributed a major part to the downtime seen in the simulations, the combined hydrogen system also contributed a significant amount, a total of 37%, which could have been due to the technology readiness levels of some the components included in the hydrogen system.

Random density matrices: Closed form expressions for the variance of squared Hilbert-Schmidt distance

One of the fundamental aspects of quantum information theory is the study of distance measures between quantum states. Hilbert-Schmidt distance serves as a convenient choice for exploring a wide range of quantum information-related phenomena. This study contributes by deriving precise closed-form expressions for the variance of the squared Hilbert-Schmidt distance, both between one fixed and one random density matrix and between two independent random density matrices. Notably, our findings go beyond recently acquired results concerning the mean-square Hilbert-Schmidt distance. By incorporating both mean and variance, we extend our analysis to provide a gamma-distribution-based approximation for the probability density function of the squared Hilbert-Schmidt distance. The validity of these analytical results is confirmed through Monte Carlo simulations. Additionally, we perform a comparative analysis, aligning our analytical predictions with the mean and variance of the squared Hilbert-Schmidt distance calculated in correlated spin chain simulations, revealing a satisfactory agreement.