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Polymer models describing the statistics of biomolecules under confinement have applications to a wide range of single-molecule experimental techniques and give insight into biologically relevant processes in vivo. In this paper, we determine the transverse position and bending correlation functions for a wormlike chain confined within slits and cylinders (with one and two confined dimensions, respectively) using a mean-field approach that enforces rigid constraints on average. We show the theoretical predictions accurately capture the statistics of a wormlike chain from Monte Carlo simulations in both confining geometries for both weak and strong confinement. We also show that the longitudinal correlation function is accurately computed for a chain confined to a slit and leverages the accuracy of the model to suggest an experimental technique to infer the (often unobservable) transverse statistics from the (directly observable) longitudinal end-to-end distance. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the properties of transition metal (TM)-doped α - G a 2 O 3 using first-principles calculations and Monte Carlo simulations. α - G a 2 O 3 is a wide-bandgap semiconductor material with enhanced performance and lower fabrication costs on sapphire substrates compared to β - G a 2 O 3 . Doping with TMs can modify electrical transport, optical absorption, and magnetic properties, yet theoretical studies on this are scarce. Our study focused on V, Cr, Mn, and Fe impurities. We introduced a newly proposed scheme for efficiently determining the ground-state defect configuration during structural relaxation. We adopt a recent, novel image charge correction method to accurately calculate formation enthalpy and thermodynamic transition levels for spin-polarized transition metal ion doping, without employing the empirical dielectric constant. Results showed Cr ions tend to neutral substitutional Ga, while V, Mn, and Fe impurity ions tend to carry a negative charge in common n-type α - G a 2 O 3 . Magnetic moments and spin-splitting impurity levels primarily arise from transition metal impurities and their d orbitals. We used the generalized four-state method to calculate exchange interaction constants between substitution lattice sites and identified (anti) ferromagnetic couplings at specific distances in a 120-atom supercell, which are negligible in total energy calculations. Monte Carlo simulations indicated a Curie temperature of 360 K in n-type α - G a 2 O 3 : Mn system with 12.5% doping, suggesting intrinsic ferromagnetic ordering based on the Heisenberg model. Our study contributes to understanding TM-doped α - G a 2 O 3 electronic structure and magnetic properties through improved methodologies. The approach can be applied in research involving other TM-doped oxides or wide-bandgap semiconductors. [ABSTRACT FROM AUTHOR]

In a binary mixture, stripes refer to a one-dimensional periodicity of the composition, namely, a regular alternation of layers filled with particles of mostly one species. We have recently introduced [Munaò et al., Phys. Chem. Chem. Phys. 25, 16227 (2023)] a model that possibly provides the simplest binary mixture endowed with stripe order. The model consists of two species of identical hard spheres with equal concentration, which mutually interact through a square-well potential. In that paper, we have numerically shown that stripes are present in both liquid and solid phases when the attraction range is rather long. Here, we study the phase behavior of the model in terms of a density functional theory capable to account for the existence of stripes in the dense mixture. Our theory is accurate in reproducing the phases of the model, at least insofar as the composition inhomogeneities occur on length scales quite larger than the particle size. Then, using Monte Carlo simulations, we prove the existence of solid stripes even when the square well is much thinner than the particle diameter, making our model more similar to a real colloidal mixture. Finally, when the width of the attractive well is equal to the particle diameter, we observe a different and more complex form of compositional order in the solid, where each species of particle forms a regular porous matrix holding in its holes the other species, witnessing a surprising variety of emergent behaviors for a very basic model of interaction. [ABSTRACT FROM AUTHOR]

This paper focuses on phase and aggregation behavior for linear chains composed of blocks of hydrophilic and hydrophobic segments. Phase and conformational transitions of patterned chains are relevant for understanding liquid–liquid separation of biomolecular condensates, which play a prominent role in cellular biophysics and for surfactant and polymer applications. Previous studies of simple models for multiblock chains have shown that, depending on the sequence pattern and chain length, such systems can fall into one of two categories: displaying either phase separation or aggregation into finite-size clusters. The key new result of this paper is that both formation of finite-size aggregates and phase separation can be observed for certain chain architectures at appropriate conditions of temperature and concentration. For such systems, a bulk dense liquid condenses from a dilute phase that already contains multi-chain finite-size aggregates. The computational approach used in this study involves several distinct steps using histogram-reweighting grand canonical Monte Carlo simulations, which are described in some level of detail. [ABSTRACT FROM AUTHOR]

The exponential-6 (exp-6) potential is commonly used to model fluids at high densities. In this paper, I propose a new equation of state (EOS) in the form of an analytical expression for the excess Helmholtz free energy of an exp-6 fluid. The EOS is based on extensive Monte Carlo simulations and therefore combines the excellent accuracy of the simulations with the numerical efficiency of a polynomial expression. The mean relative error in compressibility factor and internal energy is 0.14% and 0.25% respectively, which is a significant improvement over statistical mechanical theories. The EOS was implemented into a thermochemical code in order to optimize gas parameters and evaluate its performance on pure gas data, shock compression and detonation properties. Predicted gas densities, heat capacities and speed of sound for pure gases were generally within experimental uncertainties at pressures up to 1 GPa and temperatures above 300 K. For polar molecules, a simple free energy correction was introduced which greatly improved accuracy at low temperature. Calculated shock Hugoniots showed excellent agreement with experimental values up to 150 GPa and 10 000 K, and the detonation performance was accurately predicted for a number of different types of explosives. [ABSTRACT FROM AUTHOR]

Polycrystalline thin films are widely used for devices and energy-related applications, such as power electronics, solar cells, and thermal management of devices. In many cases, large-scale crystallization during thin-film growth is challenging, so columnar grains are often found in metal and semiconductor thin films. These rough columnar grain boundaries may also have different phonon specularities from that for typically smoother top/bottom film surfaces. A simple analytical model to separately treat these boundaries and interfaces for phonon scattering is currently unavailable, although the in-plane thermal transport is critical to heat spreading within thin-film devices. In this paper, we extend the effective medium formulation from three-dimensional polycrystalline bulk materials to columnar-grained thin films. The model predictions agree well with those given by frequency-dependent phonon Monte Carlo simulations, considering varied phonon specularity at top/bottom film surfaces and grain-boundary phonon transmissivity. The analytical model is further used to analyze the existing data on polycrystalline ZnO thin films with columnar grains. [ABSTRACT FROM AUTHOR]

This paper is concerned with a theoretical analysis of the behavior of optically excited spin currents in bilayer and multilayer systems of ferromagnetic and normal metals. As the propagation, control, and manipulation of the spin currents created in ferromagnets by femtosecond optical pulses is of particular interest, we examine the influence of different thicknesses of the constituent layers for the case of electrons excited several electronvolts above the Fermi level. Using a Monte-Carlo simulation framework for such highly excited electrons, we first examine the spatiotemporal characteristics of the spin current density driven in a Fe layer, where the absorption profile of the light pulse plays an important role. Further, we examine how the combination of light absorption profile, spin-dependent transmission probabilities, and iron layer thickness affects spin current density in a Fe/Au bilayer system. For high-energy electrons studied here, the interface and secondary electron generation have a small influence on spin transport in the bilayer system. However, we find that spin injection from one layer to another is most effective within a certain range of iron layer thicknesses. [ABSTRACT FROM AUTHOR]

Inelastic collisions of OH with an inert liquid perfluoropolyether (PFPE) surface have been studied experimentally. A pulsed molecular beam of OH with a kinetic energy distribution peaking at 35 kJ mol−1 was directed at a continually refreshed PFPE surface. OH molecules were detected state-selectively with spatial and temporal resolution by pulsed, planar laser-induced fluorescence. The scattered speed distributions were confirmed to be strongly superthermal, regardless of the incidence angle (0° or 45°). Angular scattering distributions were measured for the first time; their reliability was confirmed through extensive Monte Carlo simulations of experimental averaging effects, described in Paper II [A. G. Knight et al., J. Chem. Phys. 158, 244705 (2023)]. The distributions depend markedly on the incidence angle and are correlated with scattered OH speed, consistent with predominantly impulsive scattering. For 45° incidence, the angular distributions are distinctly asymmetric to the specular side but peak at sub-specular angles. This, along with the breadth of the distributions, is incompatible with scattering from a surface that is flat on a molecular scale. New molecular dynamics simulations corroborate the roughness of the PFPE surface. A subtle but unexpected systematic dependence of the angular distribution on the OH rotational state was found, which may be dynamical in origin. The OH angular distributions are similar to those for kinematically similar Ne scattering from PFPE and hence not strongly perturbed by OH being a linear rotor. The results here are broadly compatible with prior predictions from independent quasiclassical trajectory simulations of OH scattering from a model-fluorinated self-assembled monolayer surface. [ABSTRACT FROM AUTHOR]

Proton radiography is a widely used experimental method to diagnose the electric and magnetic (EM) fields in high-energy-density plasmas. In proton radiography, the probe protons are typically assumed to be deflected only by the EM fields, whereas the Coulomb scattering caused by the charged particles in the target plasmas is generally ignored. However, at high plasma densities, the presence of Coulomb scattering could reduce the proton flux perturbations recorded on the detector and influence the inversion of the EM fields from experiments. In this paper, a theoretical model is developed for the first time to describe the proton flux distribution on the detector when the EM field deflections and Coulomb scattering coexist in deflecting the probe proton trajectories. Our theory indicates that the Coulomb scattering could decrease the signal contrast of the probed EM fields, which is determined not only by the strengths of the EM field deflections and Coulomb scattering but also by the spatial gradient of the EM fields. Monte Carlo simulations are also conducted to validate our theoretical model. It would be helpful to interpret the proton radiography experiments quantitatively. [ABSTRACT FROM AUTHOR]

In modeling the charged alpha particle transport in hot-spot plasmas of inertial confinement fusion, the energy-losing rate is a major concern in the Monte Carlo simulations of alpha particle transport of a radiative-hydrodynamic code. However, the traditionally used energy stopping-power only describes the averaged energy-losing rate of the incident charged particles, whereas the variance of the energy exchange with the background particles is generally ignored. In this paper, the variance of charged particle collisions is studied by both analytical derivation and Monte Carlo simulations. An expression of the divergence of the charged particle energy-losing rate is given for the first time, which can be directly used for practical estimations. It indicates that when the areal density of the target particles along the incident particle path length is low, the divergence of the lost energy would be much larger than the average value, and the traditionally used energy stopping-power would be no longer sufficient to describe the charged particle Coulomb collisions. It helps to obtain a more comprehensive understanding about the charged particle transport in plasmas. [ABSTRACT FROM AUTHOR]

This paper proposed a Legendre-based multi-element probabilistic collocation method for time-dependent stochastic differential equations, used for density estimation of unknown functions. This method involves discretizing the stochastic space, and on each element, constructing Lagrange interpolation basis functions based on Legendre–Gauss–Lobatto collocation/quadrature nodes. The proposed method is applied to approximate one-dimensional/two-dimensional smooth/non-smooth functions and is tested for accuracy in approximating random function values, density estimations, and mathematical expectations. This method is applied to stochastic nonlinear Schrödinger equations and coupled stochastic nonlinear Schrödinger equations, and all numerical results are compared with Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

In recent years, composite structures have been used in a large number of applications in aerospace, machinery, marine, and civil engineering. However, there are inevitably many uncertainties in the whole life cycle of composite structures, which can easily lead to structural damage and failure. Therefore, it is important to analyze the reliability and sensitivity of composite structures. At present, most of the contributions use the first-order reliability method (FORM) and the second-order reliability method (SORM) to study the reliability of composite structures and compare them with the results of the Monte Carlo simulation. However, both methods have their limitations. FORM cannot guarantee the calculation accuracy for the highly nonlinear limit state equation, and the calculation efficiency of SORM is too low. Therefore, this paper proposes to use importance sampling (IS) and backpropagation neural network-based Monte Carlo (MC-BPNN) to study the reliability, sensitivity, and dispersion of delamination growth of composite laminates. The results show that compared with FORM and SORM, IS and MC-BPNN have higher calculation accuracy and efficiency and can more accurately evaluate the failure degree of composite structures and ensure their safe operation in the field of aerospace equipment. The universality of this method can make it being widely used in the reliability and sensitivity analysis of different composite materials as well as dispersion analysis. [ABSTRACT FROM AUTHOR]

Performing full-resolution atomistic simulations of nucleic acid folding has remained a challenge for biomolecular modeling. Understanding how nucleic acids fold and how they transition between different folded structures as they unfold and refold has important implications for biology. This paper reports a theoretical model and computer simulation of the ab initio folding of DNA inverted repeat sequences. The formulation is based on an all-atom conformational model of the sugar-phosphate backbone via chain closure, and it incorporates three major molecular-level driving forces—base stacking, counterion-induced backbone self-interactions, and base pairing—via separate analytical theories designed to capture and reproduce the effects of the solvent without requiring explicit water and ions in the simulation. To accelerate computational throughput, a mixed numerical/analytical algorithm for the calculation of the backbone conformational volume is incorporated into the Monte Carlo simulation, and special stochastic sampling techniques were employed to achieve the computational efficiency needed to fold nucleic acids from scratch. This paper describes implementation details, benchmark results, and the advantages and technical challenges with this approach. [ABSTRACT FROM AUTHOR]

We obtain the phase diagram of the hard core lattice gas with third nearest neighbor exclusion on the triangular lattice using Monte Carlo simulations that are based on a rejection-free flat histogram algorithm. In a recent paper [Darjani et al., J. Chem. Phys. 151, 104702 (2019)], it was claimed that the lattice gas with third nearest neighbor exclusion undergoes two phase transitions with increasing density with the phase at intermediate densities exhibiting hexatic order with continuously varying exponents. Although a hexatic phase is expected when the exclusion range is large, it has not been seen earlier in hard core lattice gases with short range exclusion. In this paper, by numerically determining the entropies for all densities, we show that there is only a single phase transition in the system between a low-density fluid phase and a high density ordered sublattice phase and that a hexatic phase is absent. The transition is shown to be first order in nature, and the critical parameters are determined accurately. [ABSTRACT FROM AUTHOR]

This paper investigates statistical inferences for product lifetimes following the inverted modified Lindley distribution, utilizing progressive Type-II censored data. The estimation of model parameters employs the maximum likelihood method, complemented by the construction of approximate confidence intervals. Bayesian estimates are also explored, incorporating squared error and linear exponential loss functions with noninformative priors. To approximate Bayes estimates, the proposal presents Gibbs sampling based on the MCMC algorithm. This results in the generation of the greatest posterior density credible intervals for the parameters. A real data analysis is conducted to validate the accuracy of all the models and methods discussed. Finally, computational studies using Monte Carlo simulations are presented to compare the suggested estimators. [ABSTRACT FROM AUTHOR]

The pseudo intensity vector (PIV) is often used to analyze the directional properties of spatial room impulse responses. In the early part of the response, it is capable of estimating the directions of individual reflections. However, thus far, its behaviour in the late field is unclear. Specifically, it is unknown whether anisotropy, i.e., a direction-dependent energy distribution, is captured by the directional estimates. In this study, a closed-form expression of the directional distribution of the pressure-normalized pseudo intensity vector contingent on a general stochastical model of anisotropic fields was analytically derived. This paper shows that the probability density function of this PIV is a multivariate Cauchy distribution, which does indeed depend on the energy distribution of the field, yet the directional distribution has very limited degrees of freedom. The derived distribution is compared to the results of Monte Carlo simulations and fields captured with a microphone array in a real room. These results facilitate better understanding of the behaviour of parametric spatial room impulse response methods and may enable improved directional estimators for anisotropic fields. [ABSTRACT FROM AUTHOR]

This paper centers on the examination of the Lomax distribution in the context of randomly censored data. Our primary objectives include deriving maximum likelihood estimators and constructing confidence intervals based on the Fisher information matrix for the unknown parameters in the context of randomly censored data. Furthermore, we develop Bayes estimators utilizing gamma priors, considering both squared error and general entropy loss functions. We also calculate Bayesian credible intervals for the parameters. To offer practical insights, we apply these methods to a real-world dataset subject to random censorship. Finally, for comparative purposes, we conduct a Monte Carlo simulation to assess the various estimation techniques introduced in this study. [ABSTRACT FROM AUTHOR]

Inevitable process variations (PVT) brought by both the magnetic tunneling junction (MTJ) and MOSFET based on the complementary metal-oxide semiconductor (CMOS) technology become a major obstacle for the mass production of spin transfer torque magnetic random access memory (STT-MRAM). The detriment of the process variations leads to a serious degradation in the fundamental yield with the shrinkage of the technology nodes. However, the conventional data-cell-variation-tolerant (DCVT) sense scheme cannot get the target read yield due to the limited sense margin (SM). To resolve this problem, a DCVT triple sampling non-destructive self-reference sensing scheme (TSNS) is proposed in the paper, which doubles the SM, with lower power consumption and better SM compared with the conventional DCVT sense scheme. Monte Carlo simulation with industry-compatible 65-nm model parameters results show that the proposed sensing scheme shows over 2.5 times higher SM and less power consumption compared to the previous self-reference circuit. The proposed sensing scheme can get the target read yield with lower power consumption. [ABSTRACT FROM AUTHOR]

Proton irradiation of lithium fluoride (LiF) crystals and thin films causes the formation of electronic defects, known as color centers, in the crystal lattice, some of which show photoluminescence in the visible range under blue-light excitation. With a suitable irradiation geometry, the energy density that protons deposit in the material can be recorded as a spatial distribution of these light-emitting color centers, from which a luminous replica of the proton Bragg curve can be thereafter extracted and analyzed in a fluorescence microscope. In this paper, the cases of two LiF thin films deposited on silicon substrates and of a LiF crystal, all of them transversally irradiated with a nominal 35 MeV proton beam, are considered. A comparison of the measured photoluminescent Bragg curves with Monte Carlo simulations demonstrates (i) that the Bragg peaks in the films are located at the very same positions that would be expected in the underlying silicon substrates rather than in LiF and (ii) that an even small grazing angle of the impinging proton beam is able to significantly modify the shape of the Bragg curve in the films. Both of these findings are ascribed to the effects of multiple Coulomb scattering in both the film and the substrate. The coincidence of the Bragg peak positions with those expected in the silicon substrates and the possibility of visualizing such peaks in the Bragg curves stored as latent fluorescence images in the LiF films allow one to regard them as information transducers for proton beam diagnostics and dosimetry. [ABSTRACT FROM AUTHOR]

In this paper, we report grand canonical Monte Carlo simulations performed to characterize the adsorption of four linear alcohol molecules, comprising between two and five carbon atoms (namely, ethanol, n-propanol, n-butanol, and n-pentanol) on crystalline ice in a temperature range typical of the Earth's troposphere. The adsorption details analyzed at 228 K show that, at low coverage of the ice surface, the polar head of the adsorbed molecules tends to optimize its hydrogen bonding with the surrounding water, whereas the aliphatic chain lies more or less parallel to the ice surface. With increasing coverage, the lateral interactions between the adsorbed alcohol molecules lead to the reorientation of the aliphatic chains that tend to become perpendicular to the surface; the adsorbed molecules pointing thus their terminal methyl group up to the gas phase. When compared to the experimental data, the simulated and measured isotherms show a very good agreement, although a small temperature shift between simulations and experiments could be inferred from simulations at various temperatures. In addition, this agreement appears to be better for ethanol and n-propanol than for n-butanol and n-pentanol, especially at the highest pressures investigated, pointing to a possible slight underestimation of the lateral interactions between the largest alcohol molecules by the interaction potential model used. Nevertheless, the global accuracy of the approach used, as tested under tropospheric conditions, opens the way for its use in modeling studies also relevant to another (e.g., astrophysical) context. [ABSTRACT FROM AUTHOR]

This paper aims to address the challenges of short optical communication distances and high Bit Error Rates (BERs) caused by seawater absorption, scattering, and underwater turbulence. This paper establishes an optical communication channel model based on the Monte Carlo method and simulates and analyzes the process of received optical power attenuation when light is transmitted underwater due to absorption and scattering. The simulation model focuses on the influence of seawater type, transmission distance, receiver position, and angle on the received optical power attenuation. The results of this simulation will provide theoretical support for the development of prototype underwater optical communication systems and experiments in complex environments. To solve the long-range weak signal reception and processing issues in underwater optical wireless communication systems, this paper proposes the design and implementation of an underwater optical wireless communication system using optimized on-off keying high-power light-emitting diode/laser diode modulation technology and avalanche photodiode (APD)/photomultiplier (PMT) receiver modules. In addition, this paper describes the design of the underwater optical communication host computer software, which provides functions such as mode selection, BER monitoring, and file transfer. To test the performance of the proposed system, this paper conducts experiments in a 35 m long pool, fixed platform, and underwater mobile platform, as well as cross-air-water interface optical communication performance test experiments in a complex hydrological environment. The results show that the system achieved a 35 m underwater medium communication distance, transmission rate of 5–20 Mbps, and cross-domain wireless optical communication function under the condition of a BER of less than 10−6. The experiments also revealed that the communication distance decreases as the turbidity of the water body increases and the PMT receiver module selected at the receiver end has a longer communication distance than the APD receiver module. Overall, this paper demonstrates the feasibility of the proposed technology for underwater optical communication systems in complex waters, providing theoretical and data support for the next engineering practice. [ABSTRACT FROM AUTHOR]

In the alpha particle transport in ICF hotspot, previous models focus mainly on how the incident particles lose their energy but lost sight of how the target particles will respond to this lost energy. In this paper, we developed a novel single-scattering model based on the Monte Carlo method, which abandons the stopping-power and models every single-scattering event in the alpha particle life. It enables to describe both the energy stopping of the incident alpha particle and the target particles response to the collisions. With this model, it shows that the target DT-ions at the ICF hotspot boundary will be non-Maxwellian distributed after colliding with the high-energy alpha particles, which refers to a much higher fusion reactivity compared with a Maxwellian one. At the same time, this model gives a longer and dispersed alpha particle range in hotspot plasmas and suggests that the traditionally used stopping power models would overestimate the stopping ability of the target particles. [ABSTRACT FROM AUTHOR]

This paper proposes a nested generalized sidelobe canceller (NGSC) for typical array signal processing tasks, including source counting, localization, and signal separation. Multiple blocking matrices are arranged in a nested structure to successively eliminate dominant sources until the remaining signal is predominantly incoherent noise. The microphone signals are dereverberated using a multichannel weighted prediction error algorithm. In source counting, the number of sound sources is determined by tracking the average power of the blocked signal. In source localization, the direction-of-arrival (DOA) is estimated via cosine similarity in conjunction with the golden section search. In signal separation, the estimated DOA enables speech separation in a linearly constrained minimum variance beamformer with postfiltering (LCMV-PF). Monte Carlo simulations are performed to compare the proposed NGSC approach with four baselines, minimum description length, second order statistic of the eigenvalue, multistage Wiener filtering, and multiple signal classification. The results show that NGSC achieves at least 28.80% higher source counting accuracy with a 1.04° lower root mean square degree error than the baselines. The signal-to-distortion ratio achieved by LCMV-PF is 1.52 dB higher than that achieved by the linearly constrained minimum power beamformer and the multichannel Wiener filter. [ABSTRACT FROM AUTHOR]

A jet that emerges from a nozzle and attaches to an inclined, setback wall is studied. The reattaching jet is the canonical element of most fluidic devices, and the aim of this paper is to improve the understanding of its dynamics. An analytical model is developed that describes the dynamic behavior of the jet position in response to acoustic excitation. A novel, unsteady jet curvature equation is derived from first principles, which forms the basis of the model. The Görtler velocity profile for a plane, turbulent jet is assumed, and an alternative approach to determining its virtual origin is used. A novel approach to modeling the momentum balance at the jet reattachment point is demonstrated to predict the observed behavior in response to acoustic excitation. The resulting model is linearized at a series of operating points informed by data from the literature at a range of flow rates, wall setback distances, and gas types. A Monte Carlo analysis is conducted to quantify the model sensitivity to parameter uncertainty. The bandwidth of the jet response to acoustic excitation is demonstrated to depend linearly on jet velocity. [ABSTRACT FROM AUTHOR]

Hybrid methods that combine molecular dynamics methods capable of analyzing dynamics with Monte Carlo (MC) methods that can efficiently treat thermodynamically stable states are valuable for understanding complex chemical processes in which an equilibrium state is reached through many elementary processes. The hybrid MC (HMC) method is one such promising method; however, it often fails to sample configurations properly from the canonical multimodal distribution due to the rugged potential energy surfaces. In this paper, we extend the HMC method to overcome this difficulty. The new method, which is termed potential scaling HMC (PS-HMC), makes use of an artificially modulated trajectory to propose a new configuration. The trajectory is generated from Hamilton's equations, but the potential energy surface is scaled to be gradually flattened and then recovered to the original surface, which facilitates barrier-crossing processes. We apply the PS-HMC method to three kinds of molecular processes: the thermal motion of argon particles, butane isomerization, and an atom transfer chemical reaction. These applications demonstrate that the PS-HMC method is capable of correctly constructing the canonical ensemble with a multimodal distribution. The sampling efficiency and accepted trajectories are examined to clarify the features of the PS-HMC method. Despite the potential scaling, many reactive atom transfer trajectories (elementary processes) pass through the vicinity of the minimum energy path. Furthermore, we demonstrate that the method can properly imitate the relaxation process owing to the inherent configurational continuity. By comparing the PS-HMC method with other relevant methods, we can conclude that the new method is a unique approach for studying both the dynamic and thermodynamic aspects of chemical processes. [ABSTRACT FROM AUTHOR]

There are currently no models to fully predict the effects of internal wave induced scattering on acoustic pulses. Existing models, which predict time domain statistics, either use the ray-based path integral method or Monte Carlo type simulations. The path integral method fails to accurately predict all of the effects of scattering. The Monte Carlo methods base the statistics on ensemble averages and are not physics-based models. This paper overcomes these limitations by using the modes of the waveguide in a transport theory application. The transport theory equations have, thus far, been used only to explain diffusion of mode intensities and decorrelation due to internal waves at individual frequencies. This paper extends the current narrowband application predict mode correlations across different frequencies and, from that, the broadband time-front, time wander, travel time bias, and the amount of spread in intensity across time and depth. To validate these predictions, this paper uses separate parabolic equation simulations. The comparisons between the two are good, suggesting a success for the mode-based transport theory approach. [ABSTRACT FROM AUTHOR]

Small systems have higher surface area-to-volume ratios than macroscopic systems. The thermodynamics of small systems therefore deviates from the description of classical thermodynamics. One consequence of this is that properties of small systems can be dependent on the system's ensemble. By comparing the properties in grand canonical (open) and canonical (closed) systems, we investigate how a small number of particles can induce an ensemble dependence. Emphasis is placed on the insight that can be gained by investigating ideal gases. The ensemble equivalence of small ideal gas systems is investigated by deriving the properties analytically, while the ensemble equivalence of small systems with particles interacting via the Lennard-Jones or the Weeks–Chandler–Andersen potential is investigated through Monte Carlo simulations. For all the investigated small systems, we find clear differences between the properties in open and closed systems. For systems with interacting particles, the difference between the pressure contribution to the internal energy, and the difference between the chemical potential contribution to the internal energy, are both increasing with the number density. The difference in chemical potential is, with the exception of the density dependence, qualitatively described by the analytic formula derived for an ideal gas system. The difference in pressure, however, is not captured by the ideal gas model. For the difference between the properties in the open and closed systems, the response of increasing the particles' excluded volume is similar to the response of increasing the repulsive forces on the system walls. This indicates that the magnitude of the difference between the properties in open and closed systems is related to the restricted movement of the particles in the system. The work presented in this paper gives insight into the mechanisms behind ensemble in-equivalence in small systems, and illustrates how a simple statistical mechanical model, such as the ideal gas, can be a useful tool in these investigations. [ABSTRACT FROM AUTHOR]

The study of alloys using computational methods has been a difficult task due to the usually unknown stoichiometry and local atomic ordering of the different structures experimentally. In order to combat this, first-principles methods have been coupled with statistical methods such as the cluster expansion formalism in order to construct the energy hull diagram, which helps to determine if an alloyed structure can exist in nature. Traditionally, density functional theory (DFT) has been used in such workflows. In this paper, we propose to use chemically accurate many-body variational Monte Carlo (VMC) and diffusion Monte Carlo (DMC) methods to construct the energy hull diagram of an alloy system due to the fact that such methods have a weaker dependence on the starting wavefunction and density functional, scale similarly to DFT with the number of electrons, and have had demonstrated success for a variety of materials. To carry out these simulations in a high-throughput manner, we propose a method called Jastrow sharing, which involves recycling the optimized Jastrow parameters between alloys with different stoichiometries. We show that this eliminates the need for extra VMC Jastrow optimization calculations and results in significant computational cost savings (on average 1/4 savings of total computational time). Since it is a novel post-transition metal chalcogenide alloy series that has been synthesized in its few-layer form, we used monolayer GaSxSe1−x as a case study for our workflow. By extensively testing our Jastrow sharing procedure for monolayer GaSxSe1−x and quantifying the cost savings, we demonstrate how a pathway toward chemically accurate high-throughput simulations of alloys can be achieved using many-body VMC and DMC methods. [ABSTRACT FROM AUTHOR]

In a recent paper, Lucco Castello et al. (arXiv:2107.03537) provided an accurate parameterization of classical one-component plasma bridge functions that was embedded in a novel dielectric scheme for strongly coupled electron liquids. Here, this approach is rigorously formulated, its set of equations is formally derived, and its numerical algorithm is scrutinized. A systematic comparison with available and new path integral Monte Carlo simulations reveals a rather unprecedented agreement especially in terms of the interaction energy and the long wavelength limit of the static local field correction. [ABSTRACT FROM AUTHOR]

In this paper, the occupancy of sidewall surface states having a clear signature in the performance of AlGaN/GaN-based self-switching diodes (SSDs) is analyzed using a semi-classical Monte Carlo (MC) simulator in a wide temperature (T) range, from 100 to 300 K. Experimental I – V curves show an unusual current decrease at low temperature attributed to surface trapping. The dependence on T of the negative surface charge density σ at the etched sidewalls of the SSDs is essential to explain the measurements. Two devices with different widths (80 and 150 nm) have been characterized and simulated in detail paying especial attention to the modeling of the surface states. At room temperature, MC simulations with a position-independent value of σ are able to qualitatively reproduce the I – V curves. However, a more complex approach is required to correctly replicate the values and shape of the DC experimental curves at low temperature, below 220 K. An algorithm where σ depends not only on T but also on the applied bias V is proposed to successfully fit the current values at every bias point. The model is able to explain the physics of the unexpected dependence of the resistance with the channel width and the sign change in the bowing coefficient, the parameters that govern the detection capabilities of the diodes. [ABSTRACT FROM AUTHOR]

Background: Of all measures of variability, the variance is by far the most popular. This paper argues an alternative index of variability, shares many properties with the variance, but can be more informative about the properties of distributions that depart from normality. Procedures: An estimator for the population variance is proposed. The proposed estimator is based, in its calculations, on the actual variable values and their ranks. A simulation study is employed to compare the performance of the proposed estimator with that of eight other estimators appearing in the literature using the mean square error criterion. The influence of sample size, normality, and non-normality is investigated. We suggest a new measure of skewness based on the new proposed estimator for the population variance. The mean value, median, and the new estimator for the population variance are the only three summary statistics that may be effectively used to derive the suggested coefficient of skewness. A limited Monte Carlo simulation is used to examine the potential of the suggested statistic to identify skewness. The covariance between a random variable and its rankings is how the new estimator is stated. This format is used to derive new correlation coefficients. Results and conclusion: The simulation study shows that the proposed estimator, under conditions of normality, performs better than most estimators considered, whereas it fails to give better results than some others. In cases of non-normality considered, the proposed estimator was superior to eight competitors. According to preliminary results, the new coefficient of skewness performs well overall in a limited simulation. The new correlation coefficient outperforms Pearson's correlation for the non-normal model, as demonstrated by simulation analysis. Furthermore, it has been demonstrated that the new estimator is a useful tool for extracting a straightforward method to obtain novel skewness and correlation coefficients. [ABSTRACT FROM AUTHOR]

In recent years, there has been a lot of interest in the research of cooperative censoring schemes. In this work, we compared the relative benefits of two competing length-of-life products using inverse Weibull lifetime products with a joint type-II hybrid censoring scheme (JHC-Type II). We initially examined the maximum likelihood estimators and their confidence intervals (CIs) for the unknown parameters based on JHC-Type II. Then, under the premise of independent gamma priors, we offer Bayes estimates of the parameters using squared error loss and LINEX loss functions. We used the Markov chain Monte Carlo method to create credible intervals and Bayesian estimates. Based on the parametric bootstrapping techniques known as Boot-p and Boot-t, we create two bootstrapping CIs. In addition, we do a Monte Carlo simulation experiment to track how well the aforementioned approaches work and to determine the corresponding confidence and credible intervals. Finally, to show how the approaches covered in this paper might be used, we consider a real physical dataset. [ABSTRACT FROM AUTHOR]

In this paper, a new flexible two-parameter family of distributions is introduced. Some mathematical properties, such as asymptotic behavior, moments, stochastic order, entropy, and quantile function, are examined. A special case of this family is introduced called Gemeay–Zeghdoudi distribution. The unknown parameters of the Gemeay–Zeghdoudi distribution are estimated via many estimators. The efficiency of the estimators is compared with a Monte Carlo simulation. The applicability of the new distribution is illustrated by the annual maximum flood and survival times of patients with breast cancer real data analyses. [ABSTRACT FROM AUTHOR]

When simulating a charged particle trajectory in plasmas with Monte Carlo methods, the traditional single-scattering model is accurate but computationally expensive. The traditional multiple-scattering model reduces the computation cost but assumes an exponential distribution of the scattering angle, which overestimates the cumulative scattering angle. In this paper, we develop a new Monte Carlo method for the charged particle transport simulations. The scattering angle is naturally sampled from a modified screened Rutherford scattering cross section, in which a greatly amplified minimum deflection angle is used. With this model, the simulations give the same scattering angle distribution with the single-scattering model but only take a comparable computation cost with the multiple-scattering model when the small scattering angle and Markovian approximations can be fulfilled. This model would be especially useful for the study of inertial-confinement-fusion, which is sensitive to the alpha-particle energy deposition. [ABSTRACT FROM AUTHOR]

Uncertainty evaluation for unknown distribution data is a key problem to be solved in uncertainty evaluation theory. To evaluate the measurement uncertainty of data with unknown distributions, a novel uncertainty evaluation method based on the particle filter (PF) and beta distribution is proposed in this paper. A beta distribution with wide adaptability was adopted as the distribution type of measurement results, the parameters of the beta distribution were taken as the parameters to be estimated, and a state-space model was established. The PF method, suitable for non-Gaussian data, was utilized to obtain the estimates of the parameters of the beta distribution according to the measurement results. Finally, the best estimates of the measurement results and their uncertainty were calculated using the beta distribution parameters. Simulation results show that the proposed method is adaptive to accurately evaluate the measurement uncertainties of data, especially for non-Gaussian distribution data or asymmetrically distributed data. Multiple evaluation results show that the method has good robustness. The experimental results for the drift errors of a laser interferometer show that the uncertainty result of the proposed method is consistent with the Monte Carlo method. This method is suitable for a variety of distribution types that can be characterized through beta distribution and can solve the optimal estimation and uncertainty evaluation of most measurement results with unknown distribution types. [ABSTRACT FROM AUTHOR]

The acceleration and heating of particles in gas–particle flows represents a general question in fluid mechanics and has been widely studied since the 1920s. However, studies on the gas adsorption–desorption process of a particle in a gas–particle flow are still lacking. In this paper, we aim to introduce a framework with which to explain the gas adsorption–desorption kinetic equilibrium of a spherical particle in a gas–particle flow. We also explore the potential influences of gas adsorption and desorption on the heating and acceleration of a particle in such a flow. We study the gas adsorption–desorption process using the Langmuir adsorption model and the flow method presented by Brancher in a Direct Simulation Monte Carlo simulation. We develop a new model to consider the gas adsorption–desorption kinetic equilibrium on the surfaces of spherical particles in the calculation of the Thermal Accommodation Coefficient. We observe from the simulations that the trend of gas adsorption is opposite to that of the particle surface temperature. We also find that the efficiency of the convective heat transfer and acceleration of a particle is higher for a particle with a higher adsorption probability on its surface. This paper provides a new framework with which to study the gas adsorption–desorption processes of particles in gas–particle flows. It also inspires for future work on the potential applications of the particle gas adsorption–desorption model in astrophysics. [ABSTRACT FROM AUTHOR]