Search Results (404)

The driving force behind protein translocation across the cell membrane is not yet fully understood. In bacteria, there is an electrochemical potential across the cell membrane, which can interact with charged residues in the translocation substrate. In this study, the protonation states of lysine and glutamate, serving as test residues in a peptide translocating across the bacterial channel SecYEG, are investigated by applying Poisson–Boltzmann continuum electrostatic free energy calculations and Monte Carlo titrations to snapshots of molecular dynamics (MD) simulations. A clear shift in protonation probability towards the uncharged state is found for both test residues as they move deeper into the channel. Thus, charge neutralization occurs irrespective of whether the original charge of the test residue is positive (lysine) or negative (glutamate). Electrostatic interactions of acidic and basic residues of SecYEG with the peptide cancel out. The main determinants of the test residue’s protonation state are the dielectric properties of its surroundings and interactions with non-titrating charges in the channel. Crucially, the membrane protein—including its water-filled pore—is assigned a low dielectric constant. The results are discussed in the context of the limitations inherent to continuum electrostatics and MD simulations with fixed protonation states. Full article

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the $H_0$ tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted. Full article

The fast and accurate calculation of the internal temperature rise in the oil-immersed transformer is the premise to realize the thermal health management and load energy evaluation of the in-service transformer. In view of the influence of nanofluids on the heat transfer process of transformer, a numerical simulation algorithm based on lattice Boltzmann method (LBM) and finite difference method (FDM) is proposed to study the heat and mass transfer process inside nano-modified oil-immersed transformer. Firstly, the D2Q9 lattice model is used to solve the fluid and thermal lattice Boltzmann equations inside the oil-immersed transformer at the mesoscopic scale, and the temperature field and velocity field are obtained by macroscopic transformation. Secondly, the electric field distribution inside the oil-immersed transformer is calculated by FDM. The viscous resistance in LBM analysis and the electric field force in FDM analysis, as well as the gravity and buoyancy of particles, are used to explore the motion characteristics of nanoparticles and metal particles. Finally, compared with the thermal ring method and the finite volume method (FVM), the relative error is less than 5%, which verifies the effectiveness of the numerical model and provides a method for studying the internal electrothermal convection of nano-modified oil-immersed transformers. Full article

We report calculations for electron–radon scattering using a complex relativistic optical potential method. The energy range of this study is 0–1000 eV, with results for the elastic (total, momentum-transfer and viscosity-transfer) cross section, summed discrete electronic-state integral excitation cross sections and electron-impact ionization cross sections presented. Here, we obtain our cross sections from a single theoretical relativistic calculation. Since radon is a heavy element, a relativistic treatment is very desirable. The electron transport coefficients are subsequently calculated for reduced electric fields ranging from 0.01 to 1000 Td, using a multi-term solution of Boltzmann’s equation. Full article

Resonance self-shielding in multi-resonant nuclide media is a dominant physical process in reactor neutronics analysis. This study proposes an improved subgroup method (ISM) based on Padé rational approximation, constructing a high-order rational function mapping between effective and background cross-sections to overcome the precision bottleneck of traditional DSMs and BIMs in nonlinear resonance interference scenarios. The method first generates cross-section relation data via ultra-fine group calculations, then solves subgroup parameters using a positive definite system, with a Spatial Homogenization (SPH) factor introduced for reaction rate conservation. Validation results show that ISM + SPH reduces k-infinity errors from −708 pcm (DSM) to +5 pcm for UO₂ fuel, and from −269 pcm to +45 pcm for MOX fuel with ²³⁹Pu, significantly enhancing neutron transport accuracy in complex fuel systems. This work provides a theoretically rigorous and practically applicable approach for efficient resonance modeling in advanced reactor fuel design. Full article

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps. Full article

The soft-reset process, or a sequence of charge emissions from a floating storage node through a transistor biased in a subthreshold bias condition, is modeled by a master (Kolmogorov–Bateman) equation. The Coulomb interaction energy after each one-charge emission leads to a stepwise potential increase, giving correlated emission rates represented by Boltzmann factors. The governing probability distribution function is a hypoexponential type, and its cumulants describe characteristics of the single-charge Coulomb interaction at room temperature on a mesoscopic scale. The cumulants are further extended into a complex domain. Starting from three fundamental assumptions, i.e., the generation of non-degenerated states due to single-charge Coulomb energy, the Markovian property of each emission event, and the independence of each state, a moment function is identified as a product of mutually prime elements (algebraically termed as prime ideals) comprising the eigenvalues or the lifetimes of the emission states. Then, the algebraic structure of the moment function is found to be highly analogous to that of an integer uniquely factored into prime numbers. Treating the lifetimes as analogs of the prime numbers, two types of zeta functions are constructed. Standard analyses of the zeta functions analogous to the prime number problem or the Riemann Hypothesis are performed. For the zeta functions, the analyticity and poles are specified, and the functional equations are derived. Also, the zeta functions are found to be equivalent to the analytic extension of the cumulants. Finally, between the number of emitted charges and the lifetime, a logarithmic relation analogous to the prime number theorem is derived. Full article

The present work analyzes the combined viscoelectric and steric effects on electroosmotic flow in a soft channel with polyelectrolyte coating. The structured channel surface, which controls the electric potential, creates two different flow regions: the electrolyte flow within the permeable polyelectrolyte layer (PEL) and the bulk electrolyte. Thus, this study discusses the interaction of various electrostatic effects to predict the electroosmotic flow field. The nonlinear governing equations describing the fluid flow are the modified Poisson–Boltzmann equation for the electric potential distribution, the mass conservation equation, and the modified Navier–Stokes equations for the flow field, which are solved numerically using a one-dimensional (1D) scheme. The results indicate that the flow enhances when increasing the electric potential magnitude across the channel cross-section via the rise in different dimensionless parameters, such as the PEL thickness, the steric factor, and the ratio of the electrokinetic parameter of the PEL to that of the electrolyte layer. This research demonstrates that the PEL significantly enhances control over electroosmotic flow. However, it is crucial to consider that viscoelectric effects at high electric fields and the friction generated by the grafted polymer brushes of the PEL can reduce these benefits. Full article

Micro-scale lubrication flow of a gas between eccentric circular cylinders with an arbitrary temperature difference is studied on the basis of the Navier–Stokes set of equations and the velocity slip and temperature jump boundary conditions. The dimensionless curvature, which is defined as the mean clearance divided by the radius of the inner cylinder, is small, the Knudsen number and the Reynolds number based on the mean clearance are small, and the temperature ratio is arbitrary. The Reynolds-type lubrication equation is derived analytically. For a verification of the equation, an assessment is conducted against the solution of the direct numerical analysis of the Bhatnagar–Gross–Krook–Welander (BGKW) model of the Boltzmann equation in the author’s previous work [Doi, T. Phys. Fluids 2024, 36, 042016]. The solution of the lubrication equation agrees with that of the Boltzmann equation satisfactorily well over the slip flow regime, not only in the eccentric force and the torque but also in the local distribution of the temperature, flow velocity, and the normal stress. A superiority of the lubrication equation over the lubrication model proposed in the author’s previous work is also discussed. Full article

Accurate evaluation of the thermal conductivity of UO₂ with defects is very significant for optimizing fuel performance and enhancing the safety design of reactors. We employed a method that combines the Boltzmann transport equation with DFT+U to calculate the thermal conductivity of UO₂ containing fission products and irradiation-induced point defects. Our investigation reveals that the thermal conductivity of UO₂ is influenced by defect concentration, defect type, and temperature. Fission products and irradiation defects result in a decrease in thermal conductivity, but they have markedly different impacts on phonon scattering mechanisms. Metal cations tend to scatter low-frequency phonons (less than 5.8 THz), while the fission gas xenon scatters both low-frequency and high-frequency phonons (greater than 5.8 THz), depending on its occupancy at lattice sites. Uranium vacancies scatter low-frequency phonons, while oxygen vacancies scatter high-frequency phonons. When uranium and oxygen vacancies coexist, they scatter phonons across the entire frequency spectrum, which further results in a significant reduction in the thermal conductivity of UO₂. Our calculated results align well with experimental data across a wide temperature range and provide fundamental insights into the heat transfer mechanisms in irradiated UO₂. These findings are essential for establishing a thermal conductivity database for UO₂ under various irradiation conditions and benefit the development of advanced high-performance UO₂ fuel. Full article

We investigate how collisional interactions between the condensate and the thermal cloud influence the distinct dynamical regimes (Josephson plasma, phase-slip-induced dissipative regime, and macroscopic quantum self-trapping) emerging in ultracold atomic Josephson junctions at non-zero subcritical temperatures. Specifically, we discuss how the self-consistent dynamical inclusion of collisional processes facilitating the exchange of particles between the condensate and the thermal cloud impacts both the condensate and the thermal currents, demonstrating that their relative importance depends on the system’s dynamical regime. Our study is performed within the full context of the Zaremba–Nikuni–Griffin (ZNG) formalism, which couples a dissipative Gross–Pitaevskii equation for the condensate dynamics to a quantum Boltzmann equation with collisional terms for the thermal cloud. In the Josephson plasma oscillation and vortex-induced dissipative regimes, collisions markedly alter dynamics at intermediate-to-high temperatures, amplifying damping in the condensate imbalance mode and inducing measurable frequency shifts. In the self-trapping regime, collisions destabilize the system even at low temperatures, prompting a transition to Josephson-like dynamics on a temperature-dependent timescale. Our results show the interplay between coherence, dissipation, and thermal effects in a Bose–Einstein condensate at a finite temperature, providing a framework for tailoring Josephson junction dynamics in experimentally accessible regimes. Full article

Gas voids inevitably form during the solidification of phase change materials (PCMs) due to volumetric contraction and thus deteriorate the thermal conductivity of solidified PCMs. In this work, the gas void morphology and distribution in solidified pure paraffin within a cubic thermal energy storage unit are experimentally studied. The three-dimensional structure of the solidified pure paraffin is reconstructed via computed tomography (CT) scanning with a resolution of up to 25 µm. Four distinct morphological types of gas voids are found, including irregular elliptical gas voids, elongated “needle-like” gas voids, micro gas voids, and large circular gas voids. The formation mechanisms of each type are analyzed. The morphology and distribution of gas voids indicate that the solidified pure paraffin structure is anisotropic. The effective thermal conductivity (ETC) of this solid–gas structure is numerically evaluated using lattice Boltzmann simulations, and a two-term power equation is fitted. The results show that the ETC in the vertical direction is significantly lower than in the horizontal direction and the ETC could be reduced by as much as 31.5% due to the presence of gas voids. Full article

Although the lattice Boltzmann method (LBM) is relatively straightforward, it demands a well-crafted framework to handle the complex partial differential equations involved in multiphase flow simulations. For the first time to our knowledge, this work proposes a novel LBM framework to solve Eulerian–Eulerian multiphase flow equations without any finite difference correction, including very-large-density ratios and also a realistic relation for the drag coefficient. The proposed methodology and all reported LBM formulas can be applied to any dimension. This opens a promising venue for simulating multiphase flows in large High Performance Computing (HPC) facilities and on novel parallel hardware. This LBM framework consists of six coupled LBM schemes—running on the same lattice—ensuring an efficient implementation in large codes with minimum effort. The preliminary numeral results agree in an excellent way with the reference numerical solution obtained by a traditional finite difference solver. Full article

A mesoscopic lattice Boltzmann method based on the BGK model is proposed to solve a class of two-dimensional second-order nonlinear partial differential equations by incorporating an amending function. The model provides an efficient and stable framework for simulating initial value problems of second-order nonlinear partial differential equations and is adaptable to various nonlinear systems, including strongly nonlinear cases. The numerical characteristics and evolution patterns of these nonlinear equations are systematically investigated. A D2Q4 lattice model is employed, and the kinetic moment constraints for both local equilibrium and correction distribution functions are derived in the four velocity directions. Explicit analytical expressions for these distribution functions are presented. The model is verified to recover the target macroscopic equations in the continuous limit via Chapman–Enskog analysis. Numerical experiments using exact solutions are performed to assess the model’s accuracy and stability. The results show excellent agreement with exact solutions and demonstrate the model’s robustness in capturing nonlinear dynamics. Full article

We investigate the thermal conductivity of graphene Moiré superlattices formed by twisting bilayer graphene (TBG) at small angles, employing approach-to-equilibrium molecular dynamics and lattice dynamics calculations based on the Boltzmann Transport Equation. Our simulations reveal a non-monotonic dependence of the thermal conductivity on the twisting angle, with a local minimum near the first magic angle ($\theta \sim$1.1°). This behavior is attributed to the evolution of local stacking configurations—AA, AB, and saddle-point (SP)—across the Moiré superlattice, which strongly affect phonon transport. A detailed analysis of phonon mean free paths (MFP) and mode-resolved thermal conductivity shows that AA stacking suppresses thermal transport, while AB and SP stackings exhibit enhanced conductivity owing to more efficient low-frequency phonon transport. Furthermore, we establish a direct correlation between the thermal conductivity of twisted structures and the relative abundance of stacking domains within the Moiré supercell. Our results demonstrate that even very small changes in twisting angle (<2°) can lead to thermal conductivity variations of over 30%, emphasizing the high tunability of thermal transport in TBG. Full article