Extensive Simulations Research Articles

Relaxation dynamics of a liquid in the vicinity of an attractive surface: The process of escaping from the surface.

We analyze the displacements of the particles of a glass-forming molecular liquid perpendicular to a confining solid surface using extensive molecular dynamics simulations with atomistic models. In the vicinity of an attractive surface, the liquid molecules are trapped. Transient localization of liquid molecules near the surface introduces a relaxation process related to the escape of molecules from the surface into the dynamics of the interfacial liquid layer. To describe this process, we analyze several dynamical observables of the confined liquid. The self-intermediate scattering function and the mean-squared displacement of the particles located in the interfacial layer are dominated by the process of escaping from the surface. This relaxation process is also associated with a strong heterogeneity in the mobility of the interfacial particles. The studied model liquid is hydrogenated methyl methacrylate. For the confining wall, we consider different models, namely a periodic single layer of graphene and a frozen amorphous configuration of the bulk liquid (frozen wall). Near graphene, where the liquid molecules form a layered structure and adopt parallel-to-surface orientation, a clear separation between small-scale movements of the molecules near the surface and the process of escaping from the surface is observed. This is reflected in the three-step relaxation of the interfacial layer. However, near the frozen wall, where the liquid molecules do not have a preferential alignment, a clear three-step relaxation is not seen, even though the dynamical quantities are controlled by the process of escaping from the surface.

Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction.

In this article, we present a joint modeling approach for zero-inflated longitudinal count measurements and time-to-event outcomes. For the longitudinal sub-model, a mixed effects Hurdle model is utilized, incorporating various distributional assumptions such as zero-inflated Poisson, zero-inflated negative binomial, or zero-inflated generalized Poisson. For the time-to-event sub-model, a Cox proportional hazard model is applied. For the functional form linking the longitudinal outcome history to the hazard of the event, a linear combination is used. This combination is derived from the current values of the linear predictors of Hurdle mixed effects. Some other forms are also considered, including a linear combination of the current slopes of the linear predictors of Hurdle mixed effects as well as the shared random effects. A Markov chain Monte Carlo method is implemented for Bayesian parameter estimation. Dynamic prediction using joint modeling is highly valuable in personalized medicine, as discussed here for joint modeling of zero-inflated longitudinal count measurements and time-to-event outcomes. We assess and demonstrate the effectiveness of the proposed joint models through extensive simulation studies, with a specific emphasis on parameter estimation and dynamic predictions for both over-dispersed and under-dispersed data. We finally apply the joint model to longitudinal microbiome pregnancy and HIV data sets.

Efficient Distributed Transfer Learning for Large‐Scale Gaussian Graphic Models

ABSTRACTA transfer learning for the large‐scale Gaussian graphical models (GGMs) is considered in a distributed architecture for fitting these target and auxiliary studies scattered across multiple sites. A distributed transfer learning algorithm, DisPower‐Trans‐CLIME, is proposed to learn the target GGMs by incorporating the data from similar and related auxiliary studies. The algorithm is communication efficient via the distributed power method for matrix decomposition. We show that DisPower‐Trans‐CLIME has a fast convergence rate comparable to the centred Trans‐CLIME algorithm. A debiased DisPower‐Trans‐CLIME is constructed and is proved to be element‐wise asymptotically normal for statistical inference. Thus, a multiple testing procedure is developed to detect edge of GGMs with false discovery rate (FDR) control. Extensive simulation experiments have been conducted to demonstrate superior numerical performance of our proposed learning algorithm on estimation and edge detection. It is also applied to infer the regional connection networks in brain regions of interest (ROIs) based on a target hospital site by leveraging the graph from multiple other hospital sites for autism spectrum disorder. We observe that the degrees of regional connectivity in the right brain are balanced, while the ones of the left brain region are extremely uneven because of the presence of multiple strong connections. However, the specific association, between the degrees of connectivity of these regions and ASD disease, is unknown.

Pd-anchored VSe2 for glucose sensing: Prediction from first principle simulations

Accurate detection of glucose molecule is clinically important for treatment of diabetes. The sensor should efficiently detect the glucose molecule with suitable response time. The conductivity of the sensor material thus plays a vital role in fast and precise sensing. Layered vanadium di-selenide (VSe2) is a transition metal dichalcogenide (TMDC) with metallic characteristics. The current study is to portray a theoretical exhaustive investigation about enhancement in effectual detection of glucose using VSe2 decorated with transition metals (TM) such as Ag and Pd. We have performed extensive DFT simulations to relate adsorption energy of glucose with pristine and decorated VSe2. Introduction of TM to pristine VSe2 was found to increase the adsorption energy of glucose which increases the sensitivity of metal- anchored VSe2. Comprehensive partial density of states (PDOS) and total density of states (TDOS) analysis has been carried out to analyse orbital interaction and qualitative charge transfer which has also been measured using Bader charge analysis. In addition to that, permanency of the best decorated system has been verified through molecular dynamics calculation and modality of recurrent serviceability has been determined by means of recovery time estimation. This is crucial since as on today, most of the available glucose detectors are meant for single usage. Our theoretically derived findings clearly point to the prediction that TM-decorated layered VSe2 can be an efficient glucose sensor, if practically developed in forthcoming times. The obtained results will thus potentially be beneficial for experimentalists for scheming and evolving VSe2-based glucose sensors.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.

Investigations on Millimeter-Wave Indoor Channel Simulations for 5G Networks

Due to the extensively accessible bandwidth of many tens of GHz, millimeter-wave (mmWave) and sub-terahertz (THz) frequencies are anticipated to play a significant role in 5G and 6G wireless networks and beyond. This paper presents investigations on mmWave bands within the indoor environment based on extensive simulations; the study considers the behavior of the omnidirectional and directional propagation characteristics, including path loss exponents (PLE) delay spread (DS), the number of clusters, and the number of rays per cluster at different frequencies (28 GHz, 39 GHz, 60 GHz and 73 GHz) in both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios. This study finds that the PLE and DS show dependency on frequency; it was also found that, in NLOS scenarios, the number of clusters follows a Poisson distribution, while, in LOS, it follows a decaying exponential distribution. This study enhances understanding of the indoor channel behavior at different frequency bands within the same environment, as many research papers focus on single or two bands; this paper considers four frequency bands. The simulation is important as it provides insights into omnidirectional channel behavior at different frequencies, essential for indoor channel planning.

Transient behavior of the DYNASWIRL® phase separator during cryogenics tank-to-tank transfer operations

In order to separate gas and liquid from a two-phase mixture in space or earth applications, one can generate a strong artificial acceleration field instead of relying on gravity. This can be achieved by generating a swirl flow in a separator. The DYNASWIRL® phase separator is such a passive device, which had been demonstrated in air/water mixtures in the laboratory and on five reduced gravity NASA parabolic flights. In this work, extensive laboratory testing and numerical simulations are conducted to demonstrate the validity of the DYNASWIRL for phase separation with cryogenics. Liquid nitrogen (LN2) is used for extensive testing involving unsteady tank-to-tank transfer with quenching of separator and piping, liquid boil-off and vaporization, phase separation and recovery. This paper describes the separator and testing setups used. It examines the effects of the liquid (water and LN2), geometric parameters, and their effects on the separation and on the pressure loss across the separator, and analyzes the flow dynamics of the gas removal process. Validated numerical simulations support the experimental results and help explain the effects of the design parameters on the results.

Realization of global audio telepresence via a learning-based model-matching approach with an acoustic array system

A Global Audio Telepresence (GOAT) system requires a microphone array to capture the spatial audio signals in the far end and a loudspeaker array to reconstruct the sound field in the near end. This seamlessly immerses near-end users in remote audio scenes with full ambience. In this paper, we use a learning-based GOAT system (L-GOAT) based on the model-matching principle, where a deep neural network (DNN) acts as non-linear filters for the GOAT system. The network training attempts to minimize the matching error between the signals reproduced by the DNN and the desired signals filtered by the far-end acoustic transfer functions (ATFs). Extensive simulations were carried out for multi-source scenarios in two different rooms with different reverberation times. To implement the L-GOAT system, a five-microphone linear array was adopted in the far-end room, while a six-loudspeaker array was utilized in the near-end room. The objective evaluation matrices, including the Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and the matching errors, were conducted to validate the efficacy of the GOAT systems. The proposed learning-based approach has demonstrated superior performance compared to a conventional digital signal processing (DSP)-based method.

On application of sound power radiation of rolling tyre measured using CPX-based methodology

The present paper provides a comprehensive account of sound power radiation measurement from a rolling tyre, utilising a methodology that employs an advanced PolyU Mark III Close-Proximity (CPX) trailer enclosure which is aimed to provide CPX measurement per relevant ISO stsandard with suppressed background noise within the interior. The interior walls and ceiling of the enclosure are equipped with diffuser panels, creating a reverberant environment suitable for sound power measurement using the comparison method. The optimal design of the enclosure, which minimizes acoustic resonance and ensures a uniform distribution of acoustic energy, is determined through extensive numerical simulations. During tyre/road sound power measurements, the spatially averaged sound pressure level (SPL) distributions within the enclosure are obtained with a reference sound source (RSS) of known sound power. The relationships between the RSS SWLs and the corresponding SPLs are established at all vehicle speeds. These relationships can then be utilised to deduce the total SWL, as well as its 1/3-octave spectrum, from the captured averaged SPL radiated by a tyre rolling at any vehicle speed. The effectiveness of the CPX-based sound power measurement are illustrated. Additionally, the potential of utilising the measured SWLs for predicting pass-by noise radiation to roadside is discussed.

Differentially Private Inference for Compositional Data

Confidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides a framework to conduct statistical analyses while mitigating the risk of leaking private information. Compositional data, which consist of vectors with positive components that add up to a constant, have received little attention in the differential privacy literature. This article proposes differentially private approaches for analyzing compositional data based on the Dirichlet distribution. We explore several methods, including Bayesian and bootstrap procedures. For the Bayesian methods, we consider posterior inference techniques based on Markov Chain Monte Carlo, Approximate Bayesian Computation, and asymptotic approximations. We conduct an extensive simulation study to compare these approaches and make evidence-based recommendations. Finally, we apply the methodology to a data set from the American Time Use Survey.

A Sparse Beta Regression Model for Network Analysis

For statistical analysis of network data, the β -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the β -model, this paper proposes the Sparse β -Regression Model (S β RM) that unites two research themes developed recently in modelling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized likelihood with an ℓ 1 penalty for parameter estimation. While our estimation method is closely related to the LASSO method for logistic regression, we develop a new theory emphasizing the use of our model for dealing with a parameter regime that can handle sparse networks usually seen in practice. More interestingly, the resulting inference on the homophily parameter demands no debiasing normally employed in LASSO type estimation. We provide extensive simulation and data analysis to illustrate the use of the model. As a special case of our model, we extend the Erdős-Rényi model by including covariates and develop the associated statistical inference for sparse networks, which may be of independent interest.

Laplacian-enhanced non-synchronous measurements method

To address the challenges of physical geometric constraints of microphone arrays and the lack of a prior information about the operating frequencies of target signals, we propose a novel method for broadband multiple-sound-source localization: a non-synchronous measurements method for broadband multiple-sound-source localization based on Laplacian-enhanced low-rank tensor completion. Our method thoroughly analyzes the tensor data structure of broadband signals and introduces a multiplier-optimized alternating direction method. Through extensive simulation studies, our approach has achieved remarkable results. We validate the outstanding performance of the Laplacian-enhanced algorithm, demonstrating its ability to generate highly accurate sound maps, capturing five distinct speech signal sources. Compared to other methods, our approach provides a more comprehensive global view. This technological advancement brings substantial hope for the precise localization of multiple broadband sound sources in critical applications, offering significant potential for future applications.

Ab Initio Kinetics of Electrochemical Reactions Using the Computational Fc0/Fc+ Electrode.

The current state-of-the-art electron-transfer modeling primarily focuses on the kinetics of charge transfer between an electroactive species and an inert electrode. Experimental studies have revealed that the existing Butler-Volmer model fails to satisfactorily replicate experimental voltammetry results for both solution-based and surface-bound redox couples. Consequently, experimentalists lack an accurate tool for predicting electron-transfer kinetics. In response to this challenge, we developed a density functional theory-based approach for accurately predicting current peak potentials by using the Marcus-Hush model. Through extensive cyclic voltammetry simulations, we conducted a thorough exploration that offers valuable insights for conducting well-informed studies in the field of electrochemistry.

Optimization of the age of correlated information in V2X networks with edge computing

Vehicle-to-Everything (V2X) communication has the potential to revolutionize the travel experience in intelligent transportation, which has received the great attention recently. However, ensuring the freshness of information from multiple sources is critical for the real-time and reliable communication in vehicular networks, especially for timely updates of service centers. To address this issue, we use a promising metric called Age of Correlated Information (AoCI), which can characterize the freshness of multi-source information. Therefore, we propose a novel model that can dynamically regulate the channel activation matching and edge computing collaboration strategy to minimize AoCI in V2X vehicular networks. Firstly, we describe the system model of a V2X network with edge computing, including definitions and assumptions for freshness of information, edge co-computing, etc. Secondly, we formulate the joint optimization problem as a source-related age minimization (SRAM) problem, which is NP-complete. A heuristic algorithm is proposed to solve it under fast-fading channel. Finally, since traditional graph models cannot capture the changing correlation between nodes in dynamic networks, we use graph convolutional networks(GCN) to extract the features of multi-source correlation. The features extracted by GCN include relevant attributes of the sources and its communication links. The features are provided as input to a double deep Q network (DDQN) for training the model that can adapt to a dynamic network environment. Extensive simulation experiments in different network scenarios validate that our proposed method can effectively and efficiently reduce the average AoCI and the computational resources.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

A new robust approach for the polytomous logistic regression model based on Rényi's pseudodistances.

This paper presents a robust alternative to the maximum likelihood estimator (MLE) for the polytomous logistic regression model, known as the family of minimum Rènyi Pseudodistance (RP) estimators. The proposed minimum RP estimators are parametrized by a tuning parameter $\alpha \ge 0$, and include the MLE as a special case when $\alpha =0$. These estimators, along with a family of RP-based Wald-type tests, are shown to exhibit superior performance in the presence of misclassification errors. The paper includes an extensive simulation study and a real data example to illustrate the robustness of these proposed statistics.

Modeling recent positive selection using identity-by-descent segments

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.

Lateral migration of a deformable fluid particle in a square channel flow of viscoelastic fluid

Cross-stream migration of a deformable fluid particle is investigated computationally in a pressure-driven channel flow of a viscoelastic fluid via interface-resolved simulations. Flow equations are solved fully coupled with the Giesekus model equations using an Eulerian–Lagrangian method and extensive simulations are performed for a wide range of flow parameters to reveal the effects of particle deformability, fluid elasticity, shear thinning and fluid inertia on the particle migration dynamics. Migration rate of a deformable particle is found to be much higher than that of a solid particle under similar flow conditions mainly due to the free-slip condition on its surface. It is observed that the direction of particle migration can be altered by varying shear thinning of the ambient fluid. With a strong shear thinning, the particle migrates towards the wall while it migrates towards the channel centre in a purely elastic fluid without shear thinning. An onset of elastic flow instability is observed beyond a critical Weissenberg number, which in turn causes a path instability even for a nearly spherical particle. An inertial path instability is also observed once particle deformation exceeds a critical value. Shear thinning is found to be suppressing the path instability in a viscoelastic fluid with a high polymer concentration whereas it reverses its role and promotes path instability in a dilute polymer solution. It is found that migration of a deformable particle towards the wall induces a secondary flow with a velocity that is approximately an order of magnitude higher than the one induced by a solid particle under similar flow conditions.

A new model of the impact of information dissemination with emotional responses on disease transmission in multilayer networks

During disease transmission, the dissemination of information about the disease prompts safety concerns among individuals. Individuals’ various emotional responses to information may affect infection prevention measures and, consequently, disease transmission. In this paper, we propose a new coupled information and disease transmission model with emotional factors in multiplex networks. Our specific focus is on the impact of diverse emotional responses on the dynamics process. We employ the Microscopic Markov Chain Approach (MMCA) to analyze the model, and obtaining state transfer equations, and deriving the disease transmission threshold. Extensive numerical simulations show that dissemination of information by relevant authorities motivates individuals to adopt scientific self-protective behaviors, thus helping to control disease transmission. Furthermore, individuals who have different emotional responses after obtaining information have varying effects on disease transmission, and reducing the anxiety response or increasing the calm response of individuals can more effectively decrease the scale of disease transmission and increase the disease transmission threshold. In summary, improving the quality of information released by the emergency management department, reducing the individual’s anxiety response, and encouraging the adoption of positive and effective protective measures are of paramount importance for epidemic prevention and control.

Semi-Supervised Triply Robust Inductive Transfer Learning

In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce target population and uses a large amount of unlabeled data simultaneously to improve the learning accuracy in the target population. Specifically, we consider a high dimensional covariate shift setting and employ two nuisance models, a density ratio model and an imputation model, to combine transfer learning and surrogate-assisted semi-supervised learning strategies effectively and achieve triple robustness. While the STRIFLE approach assumes the target and source populations to share the same conditional distribution of outcome Y given both the surrogate features S and predictors X , it allows the true underlying model of Y⏧ X to differ between the two populations due to the potential covariate shift in S and X . Different from double robustness, even if both nuisance models are misspecified or the distribution of Y⏧ S , X is not the same between the two populations when the shifted source population and the target population share enough similarities, the triply robust STRIFLE estimator can still partially use the source population when the shifted source population and the target population share enough similarities. Moreover, it is guaranteed to be no worse than the target-only surrogate-assisted semi-supervised estimator with an additional error term from transferability detection. These desirable properties of our estimator are established theoretically and verified in finite samples via extensive simulation studies. We use the STRIFLE estimator to train a Type II diabetes polygenic risk prediction model for the African American target population by transferring knowledge from electronic health records linked genomic data observed in a larger European source population. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.