Simulation Study Research Articles

Self-weighted quantile regression estimation for diffusion parameter in jump-diffusion models

The self-weighted quantile regression estimator for the diffusion parameter in diffusion models with jumps is proposed. The consistency of the underlying estimator is obtained. Moreover, the better finite-sample properties are verified through the Monte-Carlo simulation study and empirical analysis.

Adaptive Optimal Control of Networked Nonlinear Systems With Stochastic Sensor and Actuator Dropouts Based on Reinforcement Learning.

This article investigates the adaptive optimal control problem for networked discrete-time nonlinear systems with stochastic packet dropouts in both controller-to-actuator and sensor-to-controller channels. A Bernoulli model-based Hamilton-Jacobi-Bellman (BHJB) equation is first developed to deal with the corresponding nonadaptive optimal control problem with known system dynamics and probability models of packet dropouts. The solvability of the nonadaptive optimal control problem is analyzed, and the stability and optimality of the resulting closed-loop system are proven. Two reinforcement learning (RL)-based policy iteration (PI) and value iteration (VI) algorithms are further developed to obtain the solution to the BHJB equation, and their convergence analysis is also provided. Furthermore, in the absence of a priori knowledge of partial system dynamics and probabilities of packet dropouts, two more online RL-based PI and VI algorithms are developed by using critic-actor approximators and packet dropout probability estimator. It is shown that the concerned adaptive optimal control problem can be solved by the proposed online RL-based PI and VI algorithms. Finally, simulation studies of a single-link manipulator are provided to illustrate the effectiveness of the proposed approaches.

Identification of Convective Boundary Layer Depth over the Great Lakes Region Using Aircraft Observations: A Comparison of Various Methods

Abstract This study evaluates the methods of identifying the height zi of the top of the convective boundary layer (CBL) during winter (December and January) over the Great Lakes and nearby land areas using observations taken by the University of Wyoming King Air research aircraft during the Lake-Induced Convection Experiment (1997/98) and Ontario Winter Lake-effect Systems (2013/14) field campaigns. Since CBLs facilitate vertical mixing near the surface, the most direct measurement of zi is that above which the vertical velocity turbulent fluctuations are weak or absent. Thus, we use zi from the turbulence method as the “reference value” to which zi from other methods, based on bulk Richardson number (Rib), liquid water content, and vertical gradients of potential temperature, relative humidity, and water vapor mixing ratio, are compared. The potential temperature gradient method using a threshold value of 0.015 K m−1 for soundings over land and 0.011 K m−1 for soundings over lake provided the estimates of zi that are most consistent with the turbulence method. The Rib threshold-based method, commonly used in numerical simulation studies, underestimated zi. Analyzing the methods’ performance on the averaging window zavg we recommend using zavg = 20 or 50 m for zi estimations for lake-effect boundary layers. The present dataset consists of both cloudy and cloud-free boundary layers, some having decoupled boundary layers above the inversion top. Because cases of decoupled boundary layers appear to be formed by nearby synoptic storms, we recommend use of the more general term, elevated mixed layers. Significance Statement The depth zi of the convective atmospheric boundary layer (CBL) strongly influences precipitation rates during lake-effect snowstorms (LES). However, various zi approximation methods produce significantly different results. This study utilizes extensive concurrently collected observations by project aircraft during two LES field studies [Lake-Induced Convection Experiment (Lake-ICE) and OWLeS] to assess how zi from common estimation methods compare with “reference” zi derived from turbulent fluctuations, a direct measure of CBL mixing. For soundings taken both over land and lake; with cloudy or cloud-free conditions, potential temperature gradient (PTG) methods provided the best agreement with the reference zi. A method commonly employed in numerical simulations performed relatively poorly. Interestingly, the PTG method worked equally well for “coupled” and elevated decoupled CBLs, commonly associated with nearby cyclones.

Physical simulation study on production characteristics and mechanism of connate water in gas reservoirs

The late-stage development of gas reservoirs often encounters the paradox of significant remaining formation pressure coupled with low wellhead pressure, which indicates small drainage volume, low gas production rate, and low recovery efficiency, reducing gas supply and economic benefit. Owing to the lack of experimental research, the reasons behind this contradiction between gas production and producing pressure differential are unclear. The key factors affecting the development outcomes are reservoir permeability and initial water saturation, while the evaluation parameters include gas and water production rates, reservoir pressure, and recovery efficiency. Based on the characteristic properties of typical gas fields, physical simulation experiments of constant-rate gas production are conducted on spliced long cores with average permeabilities of 2.300, 0.486, and 0.046 millidarcy (mD). Furthermore, leveraging the multi-point embedded pressure measurement technique, the pressure drawdown propagations and the macroscopic and microscopic characteristics of connate water production at the initial water saturations of 0%, 20%, 40%, and 55% are investigated. By connate water, we mean water that occurs naturally within the pores of rock. Pre- and post-experiment core weighing and nuclear magnetic resonance testing are performed. In addition to the mercury injection tests, the results indicate that during gas reservoir depletion, connate water primarily stems from macropores and mesopores, with micropores and nanopores capturing water through capillary imbibition. Moreover, lower permeability and higher initial water saturation lead to greater pressure gradients, increased connate water production, and reduced recovery efficiency. Reservoirs with permeabilities below 0.1 mD are significantly affected by connate water, exhibiting steep pressure profiles. Owing to connate water, the near-wellbore pressure quickly decreases, while distant reservoir pressure barely decreases, implying a limited drainage area. To enhance the recovery efficiency, measures like infill drilling and reservoir stimulation are recommended for low-permeability gas reservoirs.

Multi-objective optimization of heat charging performance of phase change materials in tree-shaped perforated fin heat exchangers

To enhance the convective heat transfer and overall thermal performance of horizontal latent heat storage (LHS) units, an innovative tree-shaped fin structure with perforations was introduced. A numerical simulation study examined the impact of perforation layers on the unit's overall performance. For a comprehensive analysis of the perforations' cumulative effect on the LHS unit, the Response Surface Methodology (RSM) was utilized to assess the influence of variations in perforation diameter and number on material consumption and heat exchange targets, deriving predictive correlations. The results indicated that compared to a non-perforated structure, the maximum improvement was observed with three layers of perforations, reducing material usage by 0.93%, while increasing the Nusselt number (Nu) and heat exchange by 10.19% and 36.51%, respectively. Moreover, the average temperature of the phase change material (PCM) in one and three perforated layers was higher than in the non-perforated tree-shaped fins, with complete melting times reduced by 5.37% and 2.51%, respectively. RSM results showed a negative linear correlation between increased perforation diameter and number with reduced material consumption, with the number of perforations having a more significant impact on heat exchange than their diameter. The Pareto optimal points obtained using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) demonstrated lower material usage and higher heat exchange. Compared to the non-perforated tree-shaped fins, the Pareto-optimized solutions reduced material consumption by 2%–2.68% and increased heat exchange by 79.42%–94.45%. Analysis of the flow field, temperature field, and phase change process revealed that perforations significantly enhanced the mixing of PCM in different areas of the tree-like fins and secondary flow, with temperatures near the perforations increasing by 5–7K and flow velocity by 3–4 times, significantly promoting convective heat transfer.

Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data

Abstract We present several algorithms designed to learn a pattern of correspondence between 2 data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model. In the motivating case study, the challenge is to better understand micro-RNA regulation in the striatum of Huntington’s disease model mice. The algorithms unfold in 2 stages. First, an optimal transport plan P and an optimal affine transformation are learned, using the Sinkhorn–Knopp algorithm and a mini-batch gradient descent. Second, P is exploited to derive either several co-clusters or several sets of matched elements. A simulation study illustrates how the algorithms work and perform. The real data application further illustrates their applicability and interest.

Effect of field viscometer dial readings on the estimation of drilling fluid frictional pressure loss using Herschel–Bulkley model

The main objective of this study is to investigate how uncertainties in dial readings would affect the frictional pressure loss estimation of the flow of drilling fluid in drill pipe. In this study, a new calculation procedure for determination of frictional pressure loss using the Herschel–Bulkley model is presented and verified with experimental data. A Monte Carlo simulation study is conducted to investigate the sensitivity of each dial reading on the estimation of frictional pressure drop for different circulation velocities. The results show that for the low yield fluids, 1o dial reading error introduces more than 6% error to the frictional pressure gradient prediction, especially at low flow velocity. While the errors in dial readings result in much smaller error in pressure gradient estimation for fluids with high yield stress, typically less than 5%. In addition, the readings at lower rotational speeds, i.e., 3 and 6 RPM, have a significant effect on the estimation of the frictional pressure loss, especially for low flow velocity and low yield stress fluids. More importantly, the uncertainty in dial readings has less effect on the frictional pressure drop estimation at moderately high flow velocity. This research also emphasizes the importance of rheological model parameter estimation using all available dial readings with the assistance of computational tools to better capture the rheological response of drilling fluids for better hydraulic calculation.

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Cervical misalignment in motorcyclists in relation to new helmet removal recommendations shown with augmented reality resources: A biomechanical analysis

ObjectiveThe aim of this study is to determine the best technique and position for helmet removal in injured motorcyclists by comparing cervical misalignment produced in the supine position and prone position. MethodComparative cross-sectional clinical simulation study to quantify CM using biomechanical analysis with the use of inertial systems. The main variable was determined for the flexion-extension motion. The extraction was tested for both positions (prone position and supine position), which were repeated 3 times for each of the 30 volunteers included, and the movement from the initial neutral position was also determined, resulting in a total of 270 biomechanical studies. ResultsA flexion was observed when moving the patient from the neutral position to the SP, due to the size of the helmet, of 1.29° ± 5.12°. Helmet removal in the supine position resulted in an average flexion-extension range of 17.51° ± 6.49°, while the same extraction in prone position recorded an average range of 10.82° ± 8.05°. For the main variable, statistically significant differences were found when comparing prone position and supine position (p = 0.0087). ConclusionsThe main conclusion of the study is that the helmet removal should be done in the position in which we find the patient, whether in prone position or supine position. Additionally, the new technique described for the prone position causes less movement of the cervical spine than the usual supine position.

Variational Estimation for Multidimensional Generalized Partial Credit Model.

Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap, this paper presents a novel Gaussian variational estimation algorithm for the multidimensional generalized partial credit model. The proposed algorithm demonstrates both fast and accurate performance, as illustrated through a series of simulation studies and two real data analyses.

Distributed Nash Equilibrium Seeking Dynamics With Discrete Communication.

In this brief, we aim to provide a distributed Nash equilibrium seeking algorithm in continuous time with discrete communications. A group of agents are considered playing a continuous-kernel noncooperative game over a network. The agents need to seek the Nash equilibrium when each player cannot get the overall action profiles in real time, rather are only able to get information from its networked neighbors. Meanwhile, a continuous-time dynamics is discussed for the players to update their variables, but the communications over the network are only assumed to allow at discrete-time instants, since continuous-time communications are prohibitive and cumbersome in practice. First, the periodic communication is considered at a fixed interval, and the solvability of Nash equilibrium seeking is shown with discrete communications. Then, an event-trigger communication scheme is proposed to further reduce the communication rounds. Nevertheless, the event-trigger communication scheme requires each player continuously monitoring its local states. To alleviate the monitoring burden, a periodic event detection mechanism is further developed. The exponential convergence of the dynamics with the three discrete communication schemes is proven. Finally, the comparative simulation studies are designed to illustrate the algorithm performance with different communication schemes and parameter settings.

Simulation Study of the Damage Effect of Tandem Warhead on Concrete Target

Simulation Study of the Damage Effect of Tandem Warhead on Concrete Target

The causality measure of partial mutual information from mixed embedding (PMIME) revisited.

The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic.

Exploring MTH1 inhibitory potential of Thymoquinone and Baicalin for therapeutic targeting of breast cancer

Cancers frequently have increased ROS levels due to disrupted redox balance, leading to oxidative DNA and protein damage, mutations, and apoptosis. The MTH1 protein plays a crucial role by sanitizing the oxidized dNTP pools. Hence, cancer cells rely on MTH1 to prevent the integration of oxidized dNTPs into DNA, preventing DNA damage and allowing cancer cell proliferation. We have discovered Thymoquinone (TQ) and Baicalin (BC) as inhibitors of MTH1 using combined docking and MD simulation approaches complemented by experimental validations via assessing binding affinity and enzyme inhibition. Docking and MD simulations studies revealed an efficient binding of TQ and BC to the active site pocket of the MTH1, and the resultant complexes are appreciably stable. Fluorescence measurements estimated a strong binding affinity of TQ and BC with Ka 3.4 ×106 and 1.0 ×105, respectively. Treating breast cancer cells with TQ and BC significantly inhibited the growth and proliferation (IC50 values 28.3 µM and 34.8 µM) and induced apoptosis. TQ and BC increased the ROS production in MCF7 cells, imposing substantial oxidative stress on cancer cells and leading to cell death. Finally, TQ and BC are proven strong MTH1 inhibitors, offering promising prospects for anti-cancer therapy.

Reliability and Optimization Simulation Study of Zero-Temperature-Delay Point in Digital Circuits for Advanced Technology

Reliability and Optimization Simulation Study of Zero-Temperature-Delay Point in Digital Circuits for Advanced Technology

Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to a MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods, like least squares, minimum Hellinger distance, and optimal bounded influence function, available. This paper introduces a novel robust estimation technique, the Method of Truncated Moments (MTuM), pecifically designed to estimate the tail index of a Pareto distribution from grouped data. Inferential justification of the MTuM is established by employing the central limit theorem and validating it through a comprehensive simulation study.

Impact of Decarceration Plus Alcohol, Substance Use, and Mental Health Screening on Life Expectancies of Black Sexual Minority Men and Black Transgender Women Living With HIV in the United States: A Simulation Study Based on HPTN 061.

Given the disproportionate rates of incarceration and lower life expectancy (LE) among Black sexual minority men (BSMM) and Black transgender women (BTW) with HIV, we modeled the impact of decarceration and screening for psychiatric conditions and substance use on LE of US BSMM/BTW with HIV. We augmented a microsimulation model previously validated to predict LE and leading causes of death in the US with estimates from the HPTN 061 cohort and the Veteran's Aging Cohort Studies. We estimated independent associations among psychiatric and substance use disorders, to simulate the influence of treatment of one condition on improvement on others. We used this augmented simulation to estimate LE for BSMM/BTW with HIV with a history of incarceration under alternative policies of decarceration (ie, reducing the fraction exposed to incarceration), screening for psychiatric conditions and substance use, or both. Baseline LE was 61.3 years. Reducing incarceration by 25%, 33%, 50%, and 100% increased LE by 0.29, 0.31, 0.53, and 1.08 years, respectively, versus no reductions in incarceration. When reducing incarceration by 33% and implementing screening for alcohol, tobacco, substance use, and depression, in which a positive screen triggers diagnostic assessment for all psychiatric and substance use conditions and linkage to treatment, LE increased by 1.52 years compared with no screening or decarceration. LE among BSMM/BTW with HIV is short compared with other people with HIV. Reducing incarceration and improving screening and treatment of psychiatric conditions and substance use could substantially increase LE in this population.

Joint Random Partition Models for Multivariate Change Point Analysis

Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underlying distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of information. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our approach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.

Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany.

Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies

Abstract Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we propose a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM is replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection is used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical bayes. Finally, shared memory and parallel computing schemes were used to reduce the computation time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, FarmCPU, and the control method of BLUPmrMLM in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. According to the reanalysis of two large rice datasets, compared with the above methods, BLUPmrMLM significantly reduced the computation time and identified more previously reported genes. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).