Pair Correlation Research Articles

Miscibility prediction of polylactic acid/polyhydroxyalkanoate blends by molecular dynamics simulations

Blending polylactic acid (PLA) with polyhydroxyalkanoates (PHAs) represents a compelling approach to achieve fully bio-based blends with favorable properties. This work employed molecular dynamics (MD) simulations to study the miscibility of PLA and PHA with different side chain functional groups. The miscibility of the blends was assessed through analyzing snapshots of the blends, pair correlation function, radius of gyration, interaction energy, solubility parameter, and Flory−Huggins interaction parameter. Results revealed that the miscibility with PLA is predominantly affected by the polarity of the side chain functional group of PHA. To achieve effective blending with PLA, it is advisable to use PHAs with non-polar side chain groups. Examination of the glass transition temperature (Tg) for the miscible blends reveals that the rigidity of PLA can be reduced by blending with PHA bearing non-polar side chain functional groups.

Enhancing the reliability of Reverse Monte Carlo simulations of metallic glass structure by imposing strict constraints from partial pair correlation functions

Reverse Monte Carlo (RMC) simulations are widely used to reconstruct 3-dimensional (3D) metallic glass (MG) structure from neutron/X-ray diffraction data, but their reliability is debated. Here, we employ RMC to simulate a Zr50Cu50 MG’s structure using two types of “experimental” inputs as constraints, one with a total pair correlation function (TPCF) and the other with 3 partial PCFs (PPCFs), all of which were generated from a 3D glass structure obtained from molecular dynamics (MD) simulations of the same alloy. This MD-generated structure is then used as a benchmark to evaluate RMC’s accuracy. Notably, both methods can replicate the MG’s TPCFs, and total bond-angle/bond-length distributions. However, only the PPCFs-constrained RMC is able to re-produce the local structure, as manifested by nearly replicated partial bond-length/bond-angle distributions, partial coordination numbers, atomic volume, etc. This work demonstrates that PPCFs can impose stricter constraints than a TPCF does, significantly improving the reliability of RMC simulations.

Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core

In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of spins N, leading to significant computational effort. The GEMM subroutine can be adopted to accelerate the calculation of the dynamical spin–spin correlation function, but the computational cost of simulating large spin systems (>40000 spins) on CPUs remains expensive. In this work, we perform the simulation on a graphics processing unit (GPU), a hardware solution widely used as an accelerator for scientific computing and deep learning. We show that GPUs can accelerate the simulation up to 25-fold compared to multi-core CPUs when using the GEMM subroutine on both. To hide memory latency, we fuse the element-wise operation into the GEMM kernel using CUTLASS which can improve the performance by 26% ∼ 33% compared to the implementation based on cuBLAS. Furthermore, we perform the ‘on-the-fly’ calculation in the epilogue of the GEMM subroutine to avoid saving intermediate results on global memory, which makes large-scale atomistic spin dynamics simulations feasible and affordable.

A multivariate process quality correlation diagnosis method based on grouping technique

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new diagnostic method based on quality component grouping is proposed. Firstly, three theorems describing the properties of the covariance matrix of multivariate process quality are established based on the statistical viewpoint of product quality, to prove the correlation decomposition theorem, which decomposes the correlation of all the quality components into a series of correlations of components pairs, and then by using the factor analysis method, all quality components are grouped in order to maximize the correlations in the same groups and minimize the ones between different groups. Finally, on the basis of correlations between different groups are ignored, T2 control charts of component pairs in the same groups are established to form the diagnostic model. Theoretical analysis and practice prove that for the multivariate process quality whose the correlations between different components vary considerably, the grouping technique enables the size of the correlation diagnostic model to be drastically reduced, thus allowing the proposed method can be used as a generalized theoretical model for the correlation diagnosis.

Unified Understanding of the Structure, Thermodynamics, and Diffusion of Single-Chain Nanoparticle Fluids.

Single-chain nanoparticles (SCNPs) are a fascinating class of soft nano-objects with promising properties and relevance to protein condensates, polymer nanocomposites, nanomedicine, bioimaging, catalysis, and drug delivery. We combine molecular dynamics simulations and equilibrium and time-dependent statistical mechanical theory to construct a unified understanding of how the internal conformational structure of SCNPs, of both a simple fractal globule-like form and more complex objects with multiple internal intermediate length scales, determines nm-scale intermolecular packing correlations, thermodynamic properties, and center-of-mass diffusion over a wide range of concentrations up to dense melts. The intermolecular pair correlations generically exhibit a distinctive deep correlation hole form due to SCNP internal connectivity structure and repulsive interparticle interactions associated with a globular-like conformation on the macromolecular scale, with concentration-dependent deviations at small separations. Unanticipated exponential-like dependences of the equation-of-state, osmotic compressibility, and center-of-mass diffusion constant on SCNP macromolecular packing fraction are theoretically predicted and confirmed via simulations. System-specific behaviors are found associated with SCNP internal structure, but overarching regularities are identified and understood based on a generalized effective globule conformation on macromolecular scales. Diffusivity slows down by 2-3 decades with increasing concentration and is understood as a consequence of a nonactivated excluded volume-driven weak-caging process associated with space-time correlated intermolecular forces experienced by the SCNP. Good agreement between the theory and simulations is established, testable predictions are made, and a quantitative comparison with viscosity measurements on a specific SCNP fluid is carried out. The basic theoretical approach can potentially be extended to treat the chemical and physical consequences of varying the structure of other classes of soft nanoparticles with distinctive internal nanoscale organization relevant in nanotechnology and nanomedicine, and the possible emergence of macromolecular kinetically arrested glasses.

Availability and stability of palliative care for family members of terminally ill patients in an integrated model of health and social care

BackgroundPalliative care and the integration of health and social care have gradually become the key direction of development to address the aging of the population and the growing burden of multimorbidity at the end of life in the elderly.AimsTo explore the benefits/effectiveness of the availability and stability of palliative care for family members of terminally ill patients in an integrated institution for health and social care.MethodsThis prospective observational study was conducted at an integrated institution for health and social care. 230 patients with terminal illness who received palliative care and their family members were included. Questionnaires and scales were administered to the family members of patients during the palliative care process, including quality-of-life (SF-8), family burden (FBSD, CBI), anxiety (HAMA), and distress (DT). We used paired t-tests and correlation analyses to analyze the data pertaining to our research questions.ResultsIn the integrated institution for health and social care, palliative care can effectively improve quality of life, reduce the family’s burden and relieve psychological impact for family members of terminally ill patients. Palliative care was an independent influencing factor on the quality of life, family burden, and psychosocial status. Independently of patient-related and family-related factors, the results are stable and widely applicable.ConclusionThe findings underline the availability and stability of palliative care and the popularization of an integrated service model of health and social care for elder adults.

Anisotropic remixing of a phase separated binary colloidal system with particles of different sizes in an external modulation.

We explore the phase behavior of a binary colloidal system under external spatially periodic modulation. We perform Monte Carlo simulations on a binary mixture of big and small repulsive Lennard-Jones particles with a diameter ratio of 2:1. We characterize structure by isotropic and anisotropic pair correlation functions, cluster size distribution, bond angle distribution, order parameter, and specific heat. We observe the demixing of the species in the absence of external modulation. However, the mixing of the species gets enhanced with increasing potential strength along with the alignment of the particles transverse to the modulation. The de-mixing order parameter shows discontinuity with increasing modulation strength, characterizing a first order phase transition. The peak in specific heat increases linearly with the size of the system. We also look into the dynamical behavior of the system via computing Mean Square Displacement (MSD) along both parallel and perpendicular directions to the modulation. We observe a decrease in the diffusion coefficient for both types of particles as we increase the strength of the modulation.

Multi-mode multiple wave scattering in suspensions of solid particles in viscous liquids: part 1 asymptotic results

The propagation of coherent longitudinal waves in a viscous liquid containing a random distribution of spherical elastic particles is analysed using a model based on multiple scattering theory. This model, initially developed to describe the propagation of coupled longitudinal and transverse elastic waves in isotropic solid materials, takes into account wave conversions at the particle surface as well as positional correlations between particles via the pair correlation function. By modelling a Newtonian viscous liquid through complex frequency-dependent elastic moduli (i.e. using an imaginary shear modulus), the model is adapted to the case of viscous fluids and we show that conversions of longitudinal waves into transverse waves are important to take into account. We present asymptotic results from the model for case of long wavelength for the longitudinal waves, without assumptions on the shear wavelength. For high concentrations, these wave conversions are reinforced by the contribution of position correlations between the particles. In a subsequent paper, we present numerical validation of the asymptotic approximations and compare them with experimental results for solid particles in water.

Theoretical investigation of collective motion in some liquid metals using pseudopotential approach

The collective motions are studied using a recently created pseudopotential and some elastic constants. This paper involves the calculations of the collective motions like phonon dispersion curves (PDCs) for longitudinal and transverse frequencies ([Formula: see text] and [Formula: see text]), longitudinal sound velocity ([Formula: see text]), transverse sound velocity ([Formula: see text]), isothermal bulk modulus (B), modulus of rigidity (G), Young’s modulus (Y), Poisson’s ratio [Formula: see text], Debye temperature ([Formula: see text]) of some monovalent (Na), divalent (Mg) and polyvalent liquid metals (Al, Pb, Bi). This investigation is based upon the theory of pseudopotential of second-order approach via the equations of Hubbard and Beeby (HB). [Formula: see text] The pair correlation function is calculated from the different mean spherical approximation (MSA), one-component plasma (OCP) and optimized random phase approximation (ORPA) structure factors employed in this work. Three different types of local field correction (screening) functions proposed by Hartree (H), Taylor (T) and Nagy (N) are utilized in this paper. The current findings are found to be in qualitative accord with experimental and theoretical data.

Microscopic understanding of NMR signals by dynamic mean-field theory for spins

A recently developed dynamic mean-field theory for disordered spins (spinDMFT) is shown to capture the spin dynamics of nuclear spins very well. The key quantities are the spin autocorrelations. In order to compute the free induction decay (FID), pair correlations are needed in addition. They can be computed on spin clusters of moderate size which are coupled to the dynamic mean fields determined in a first step by spinDMFT. We dub this versatile approach non-local spinDMFT (nl-spinDMFT). It is a particular asset of nl-spinDMFT that one knows from where the contributions to the FID stem. We illustrate the strengths of nl-spinDMFT in comparison to experimental data for CaF2. Furthermore, spinDMFT provides the dynamic mean fields explaining the FID of the nuclear spins of 13C in adamantane up to some static noise. The spin Hahn echo in adamantane is free from effects of static noise and agrees excellently with the spinDMFT results without further fitting.

An incremental energy stimulation test to predict success in left bundle branch capture

Abstract Introduction Left bundle branch (LBB) stimulation is guided by fluoroscopic projections and electrocardiographic criteria limited by positional variations. Purpose To evaluate an incremental energy stimulation test (IEST) to optimize success. Methods Prospective, non-randomized experimental study for proof-of-concept analysis (N=23), consisting on: (i) stimulation at three locations at the high, middle and low right mid-ventricular septum (right oblique 30o); and (ii) continuous unipolar stimulation (lumenless fixed helix electrode) at each location with increasing outputs (2 volt x 0.5 ms up to 10 vol x 1.5 ms). The final position for lead penetration was made by the operator blinded to the results of the IEST. Results IEST shortened the S-LVAT (δ-S-LVAT) up to 6.4±5 ms (range 0 to 18 ms), 8±8 ms (range 0 to 36 ms) and 5.5±5 ms (range 0 to 20 ms) respectively for high, mid, and low sites (Figure, panel A and B). There was not observed any paired correlation between the magnitude of the δ-S-LVAT at the three different positions (Pearson 0.36, 0.12 and 0.22; p= 0.1, 0.6 and 0.4 respectively), suggesting that the δ-S-LVAT is site specific. The final position for lead penetration was medium in 17 cases vs low in 6 cases. The low-output S-LVAT interval at the final position before lead penetration was positively correlated with the final S-LVAT achieved after lead penetration (Pearson 0.44; p=0.04), but the magnitude of the correlation increased with the high-output S-LVAT interval (Pearson 0.57; p=0.006) suggesting a better prediction performance (Figure, panel C). A complex relationship was observed where higher δ-S-LVAT values were associated with shorter S-LVAT after penetration and final position of the lead. In fact, for cases with low-output S-LVAT interval > 100 ms, the δ-S-LVAT significantly correlated with S-LVAT after penetration and final position of the lead (Pearson -0.61; p=0.026). Conclusions IEST lead to a site-specific shortening of the S-LVAT, pointing to optimized positions for LBB pacing. The technique provides with additional diagnostic criteria to determine the better site for lead penetration.

Quantitative upper bounds related to an isogeny criterion for elliptic curves

AbstractFor and elliptic curves defined over a number field , without complex multiplication, we consider the function counting nonzero prime ideals of the ring of integers of , of good reduction for and , of norm at most , and for which the Frobenius fields and are equal. Motivated by an isogeny criterion of Kulkarni, Patankar, and Rajan, which states that and are not potentially isogenous if and only if , we investigate the growth in of . We prove that if and are not potentially isogenous, then there exist positive constants , , and such that the following bounds hold: (i) ; (ii) under the Generalized Riemann Hypothesis for Dedekind zeta functions (GRH); (iii) under GRH, Artin's Holomorphy Conjecture for the Artin ‐functions of number field extensions, and a Pair Correlation Conjecture for the zeros of the Artin ‐functions of number field extensions.

Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.

Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classification methods, using diverse characteristic parameters of EEG signals in the time, frequency, and statistical domains. This paper explores the results of an experiment that aimed to highlight arithmetic mental tasks contained in the PhysioNet database, performed on a group of 36 subjects. The majority of publications on this topic deal with machine learning (ML)-based classification methods with supervised learning support vector machine (SVM) algorithms, K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Decision Trees (DTs). Also, there are frequent approaches based on the analysis of EEG data as time series and their classification with Recurrent Neural Networks (RNNs), as well as with improved algorithms such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BLSTM), and Gated Recurrent Units (GRUs). In the present work, we evaluate the classification method based on the comparison of domain limits for two specific characteristics of EEG signals: the statistical correlation of pairs of signals and the size of the spectral peak detected in theta, alpha, and beta bands. This study provides some interpretations regarding the electrical activity of the brain, consolidating and complementing the results of similar research. The classification method used is simple and easy to apply and interpret. The analysis of EEG data showed that the theta and beta frequency bands were the only discriminators between the relaxation and arithmetic calculation states. Notably, the F7 signal, which used the spectral peak criterion, achieved the best classification accuracy (100%) in both theta and beta bands for the subjects with the best results in performing calculations. Also, our study found the Fz signal to be a good sensor in the theta band for mental task discrimination for all subjects in the group with 90% accuracy.

Study of Raman scattering in As–Se chalcogenide glasses by applying of partial pair correlation functions

Study of Raman scattering in As–Se chalcogenide glasses by applying of partial pair correlation functions

Navigating the Cascading Ripples: A Study on the Interplay between the COVID-19 Recession and the Financial Fortunes of Micro Finance Institutions (MFIs)

Purpose: The success of Micro Finance Institutions (MFIs) hinges on effective decision-making amidst challenging economic circumstances, necessitating the ability to anticipate and navigate future business conditions. This research aims to explore the relationship between financial performance and the COVID-19 pandemic's impact on MFIs listed on the National Stock Exchange (NSE) in India. By analyzing financial data and employing statistical techniques, the study seeks to elucidate the resilience of MFIs during the pandemic and the role of financial ratios in predicting earnings per share (EPS). Additionally, the research aims to contribute to a deeper understanding of the interplay between financial indicators and organizational outcomes within the microfinance sector. Design/Methodology/Approach: This research adopts a quantitative approach, utilizing secondary data obtained from quarterly financial reports of selected MFIs listed on the NSE from 2018 to 2022. Statistical analyses such as paired t-tests, correlation, and regression are employed to assess the impact of the COVID-19 pandemic recession on MFIs' profitability and to investigate the relationship between financial ratios and earnings per share (EPS). Findings/Result: Despite the COVID-19 pandemic recession, the profitability of Indian Micro Finance Institutions (MFIs) remained largely unaffected, as indicated by non-significant differences in earnings before interest and taxes (EBIT) before and after the pandemic. Regression analysis highlighted significant influences of financial ratios like the current ratio, total asset ratio, net profit ratio, and gross profit ratio on earnings per share (EPS), underscoring the sector's resilience and the importance of financial analysis in predicting MFI performance. Originality/Value: This research offers novel insights into the resilience of Indian Micro Finance Institutions (MFIs) during the COVID-19 pandemic and underscores the significance of financial analysis in guiding strategic decision-making processes within the microfinance sector. Paper Type: Empirical Research.

Cox processes driven by transformed Gaussian processes on linear networks—A review and new contributions

Cox processes driven by transformed Gaussian processes on linear networks—A review and new contributions

Unconventional superconductivity and charge ordering in an extended ionic Hubbard model on the triangular lattice

To address the issues of superconducting and charge properties in hydrated sodium cobalt, we investigate an extended ionic Hubbard model on the triangular lattice, which includes off-site attractions between sites with minor charges. Our quantum Monte Carlo simulations show that close to quarter filling, the off-site attractions induce a significant enhancement of d-wave and extended s-wave pairing correlations. Simultaneously, they render an increase of the charge structure factor at most of wave vectors. A combination with charge correlations in real space indicates the development of short-range finite-q charge density waves on the sites with minor charges. Our findings provide a new perspective on the superconducting mechanism in hydrated sodium cobalt based on the electronic structure which explicitly takes the effect of sodium atoms into account.

Neural-network quantum states for ultra-cold Fermi gases

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.

Statistical theory of fluids confined in quenched disordered porous media.

We develop a theory to calculate structural correlations and thermodynamic properties of a fluid confined in a random porous solid medium (matrix). We used density functional formalism to derive an annealed averaged expression for the density profile and excess free energy of fluid arising due to random fields of a particular realization of the matrix. After performing the second average over the quenched-disordered variables, the excess free energy is organized to give one- and two-body potentials for fluid particles. The average over disorder reduces the system to an effective one-component system of fluid in which particles feel one-body (external) potential and interact via effective pair potential. The effective pair potential is a sum of the bare (the one in the pure fluid) and the matrix-induced potential. The resulting partition function involves only fluid variables. Equations are derived for fluid-fluid and fluid-matrix correlation functions and for free energy, pressure, and chemical potential of the fluid. The theory is applied to a model system of hard spheres and results for the effective pair potential, correlation functions, and thermodynamic properties are reported. The effective pair potential is found to be attractive at the contact and develops a repulsive peak before decaying to zero. Results for pair correlation function and structure factor are compared with simulation results for several fluid densities at two matrix densities. In all the cases, a very good agreement has been found.

Actin cytoskeleton differently regulates cell surface organization of GPI-anchored proteins in polarized epithelial cells and fibroblasts.

The spatiotemporal compartmentalization of membrane-associated glycosylphosphatidylinositol-anchored proteins (GPI-APs) on the cell surface regulates their biological activities. These GPI-APs occupy distinct cellular functions such as enzymes, receptors, and adhesion molecules, and they are implicated in several vital cellular processes. Thus, unraveling the mechanisms and regulators of their membrane organization is essential. In polarized epithelial cells, GPI-APs are enriched at the apical surface, where they form small cholesterol-independent homoclusters and larger heteroclusters accommodating multiple GPI-AP species, all confined within areas of approximately 65-70nm in diameter. Notably, GPI-AP homoclustering occurs in the Golgi apparatus through a cholesterol- and calcium-dependent mechanism that drives their apical sorting. Despite the critical role of Golgi GPI-AP clustering in their cell surface organization and the importance of cholesterol in heterocluster formation, the regulatory mechanisms governing GPI-AP surface organization, particularly in the context of epithelial polarity, remain elusive. Given that the actin cytoskeleton undergoes substantial remodeling during polarity establishment, this study explores whether the actin cytoskeleton regulates the spatiotemporal apical organization of GPI-APs in MDCK cells. Utilizing various imaging techniques (number and brightness, FRET/FLIM, and dSTORM coupled to pair correlation analysis), we demonstrate that the apical organization of GPI-APs, at different scales, does not rely on the actin cytoskeleton, unlike in fibroblastic cells. Interestingly, calcium chelation disrupts the organization of GPI-APs at the apical surface by impairing Golgi GPI-AP clustering, emphasizing the existence of an interplay among Golgi clustering, apical sorting, and surface organization in epithelial cells. In summary, our findings unveil distinct mechanisms regulating the organization of GPI-APs in cell types of different origins, plausibly allowing them to adapt to different external signals and different cellular environments in order to achieve specialized functions.