Simulation Study Research Articles

Simulation Study on Optically Designed Refractive Beam Expander for Nd: YAG Laser Harmonics for 7 Km Detection Range

The simulation study has been conducted for the harmonics of Nd: YAG laser, namely the second harmonic generation SHG, the third harmonic generation THG, and the fourth harmonic generation FHG. Determination of beam expander's expansion ratio for specific wavelength and given detection range is the key in beam expander design for determining minimum laser spot size at the target. Knowing optimum expansion ratio decreases receiving unit dimensions and increases its performance efficiency. Simulation of the above mentioned parameters is conducted for the two types of refractive beam expander, Keplerian and Galilean. Ideal refractive indices for the lenses are chosen adequately for Nd: YAG laser harmonics wavelengths, so that increasing transmission of laser beam, consequently the received power to the detector for practical convenience.

Is exploratory factor analysis always to be preferred? A systematic comparison of factor analytic techniques throughout the confirmatory-exploratory continuum.

The number of available factor analytic techniques has been increasing in the last decades. However, the lack of clear guidelines and exhaustive comparison studies between the techniques might hinder that these valuable methodological advances make their way to applied research. The present paper evaluates the performance of confirmatory factor analysis (CFA), CFA with sequential model modification using modification indices and the Saris procedure, exploratory factor analysis (EFA) with different rotation procedures (Geomin, target, and objectively refined target matrix), Bayesian structural equation modeling (BSEM), and a new set of procedures that, after fitting an unrestrictive model (i.e., EFA, BSEM), identify and retain only the relevant loadings to provide a parsimonious CFA solution (ECFA, BCFA). By means of an exhaustive Monte Carlo simulation study and a real data illustration, it is shown that CFA and BSEM are overly stiff and, consequently, do not appropriately recover the structure of slightly misspecified models. EFA usually provides the most accurate parameter estimates, although the rotation procedure choice is of major importance, especially depending on whether the latent factors are correlated or not. Finally, ECFA might be a sound option whenever an a priori structure cannot be hypothesized and the latent factors are correlated. Moreover, it is shown that the pattern of the results of a factor analytic technique can be somehow predicted based on its positioning in the confirmatory-exploratory continuum. Applied recommendations are given for the selection of the most appropriate technique under different representative scenarios by means of a detailed flowchart. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Data-Driven Internal Model Learning Control for Nonlinear Systems.

A novel data-driven internal model learning control (DIMLC) strategy is developed for a nonlinear nonaffine system subject to unknown nonrepetitive uncertainties. At first, an iterative dynamic linearization (IDL) approach is employed for reformulating the nonlinear plant to an iterative linear data model (iLDM). Then, the nominal form of the IDL-based iLDM is used as an internal model of the nonlinear plant whose parameters are estimated by an iterative adaptive updating mechanism using only input-output (I/O) data. The equivalent feedback-principle-based internal model inversion is further applied to the subsequent controller design and analysis. The proposed DIMLC contains two parts. One is a nominal controller designed by the inversion of the internal model which achieves a perfect tracking of the target output; the other is a compensatory controller which offsets the uncertainties. The novel DIMLC is data-driven and does not require an explicit model. It can deal with model-plant mismatch and disturbances, enhancing the robustness against uncertainties. The theoretical results are verified by simulation study.

The Molecular Docking and Molecular Dynamic Effects of Omeprazole on CDKs 2, 4, and 6 as a Potential CDK Inhibitor in Cancer Treatment

Introduction: Cyclin-dependent kinases (CDKs) are serine/threonine kinase proteins that regulate the cell cycle through phosphorylation and dephosphorylation. These proteins are a main target in cancer therapy. This study investigated the effect of omeprazole on CDK2, CDK4, and CDK6 through simulation studies. Methods: To investigate the interaction between omeprazole and CDK2, 4, and 6, the threedimensional structure of omeprazole was obtained from PubChem, and the structures of CDK2, 4, and 6 were acquired from RCSB servers. The proteins were then simulated for 50 nanoseconds using the GROMACS 2021 platform before the docking process. Next, Autodock v.4.2.6 software was used to bind omeprazole as a ligand to these proteins, and a molecular dynamics simulation of the resulting protein-ligand complex was conducted using GROMACS after the docking process. Results: Omeprazole exhibited a high affinity for interacting with CDK2, 4, and 6, mainly occurring in the ATP binding site of CDK4. However, the docking of omeprazole in the CDKs induced conformational changes in their structures, which could potentially affect their function and lead to cell cycle arrest. Conclusion: Omeprazole, which is a proton pump inhibitor, can induce cell cycle arrest by interacting with the ATP-binding site of CDK4. Moreover, it can also induce conformational changes in CDK2, CDK4, and CDK6 through high-affinity interactions with specific amino acid residues.

Estimation of power function distribution based on selective order statistic

In this article, we present the selective order statistic sampling scheme as a promising approach to estimate the parameter of the univariate power function distribution. We derive the maximum likelihood estimator and the method of moments estimator of the power function distribution parameter as well as the reliability parameter and the ratio of two means. Moreover, we derive the asymptotic properties of the proposed estimators. Finally, we conduct simulation studies to investigate the performance of the selective order statistic scheme and concluded that it suits the power function distribution and we found that the maximum likelihood estimator is better than the method of moments estimator under the selective order statistic sampling scheme.

Gumbel GARCH model with stock application

The paper proposes a new GARCH model with Gumbel conditional distribution. Several statistical properties of the model are established, like autocorrelation function and stationarity. We consider two methods for estimating the unknown parameters of the model and investigate properties of the estimators. The performances of the estimators are checked by a simulation study. We investigate the application of the process using a real stock data.

Approximate confidence interval for the reciprocal of a normal mean with a known coefficient of variation

An approximate confidence interval for the reciprocal of a normal population mean with a known coefficient of variation is proposed. This has applications in the area of nuclear physics, agriculture and economic when the researcher knows the coefficient of variation. The proposed confidence interval is based on the approximate expectation and variance of the estimator by Taylor series expansion. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence interval with the existing confidence interval. Simulation results show that the proposed confidence interval performs as well as the existing one in terms of coverage probability. However, the approximate confidence interval is very easy to calculate compared with the exact confidence interval.

In silico Designing of a Multi-epitope-based Subunit Vaccine against SARS-CoV-2 (Delta Variant) by Exploiting Its Structural Proteins: A Reverse Vaccinomics and Immunoinformatics Approach

Background: The continuously emerging novel strains of SARS-CoV-2 remain a menace to the global population. The vicious delta variant (originated in India) is considered one of the most infectious/contagious variants of SARS-CoV-2. The transmission frequency of this variant is 225% higher than other variants, extending its prevalence and causing a massive surge in the COVID-19 pandemic. It is also the most ravenous variant among others. Objective: Though the delta variant has already disappeared, it could re-emerge/come out at any time with a more powerful strike than earlier. Therefore, to tackle such ferocity, this research is undertaken with a next-generation vaccine development strategy to design a multi-epitope-based subunit vaccine against the delta variant of SARS-CoV-2, which might boost the body's immunity. Materials and Methods: In the present investigation, reverse vaccinomics and immunoinformatics approaches were adopted to create an immune-stimulating prospective vaccine candidate having B cell, helper T cell (Th)/helper T lymphocyte (HTL), cytotoxic T cell (Tc)/cytotoxic T lymphocyte (CTL), and interferon-gamma (IFN-γ) inducing epitopes by exploiting the SARS-CoV-2 (delta variant) (GenBank: MZ724536.1) structural proteins: envelope glycoprotein (E), nucleocapsid phosphoprotein (N), surface glycoprotein (S), and membrane glycoprotein (M). The established vaccine construct was then completed by combining antigenic epitopes with adjuvants and linkers. Subsequently, the 3D model of the suggested vaccine was created and docked with an immune receptor (Toll-Like Receptor-4). A molecular dynamics (MD) simulation study was performed to confirm the binding stability between the vaccine conjugate and TLR4. Later, an immune simulation study was carried out to predict the in silico immune response of the vaccine candidate. To effectively express the developed vaccine in a bacterial system (E. coli), in silico codon optimization and cloning were done in an expression vector to manufacture it on a large scale. Results: According to the computational analysis, the vaccine candidate was found to be highly antigenic while maintaining favorable properties for the human body. Molecular docking and dynamics simulation study between the suggested vaccine construct and TLR4 immune receptor depicted it as extremely efficient and stable, ensuring a proper immunological response within the host cell. Eventually, an in silico immune simulation study of the vaccine candidate demonstrated a robust immune response to vaccine administration. Conclusion: We have hypothesized that the constructed vaccine model is benign, stable, and immunogenic, making it a promising/potent candidate for immune system stimulation against SARSCOV- 2 (DV). Hereof, wet lab-based investigations are needed to justify the competence of the novel vaccine candidate towards the delta variant along with other variants of SARS-CoV-2.

Objective Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors

We develop Bayesian model selection via fractional Bayes factors to simultaneously assess spatial dependence and select regressors in Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) spatial random effects. Selection of covariates and spatial model structure is difficult, as spatial confounding creates a tension between fixed and spatial random effects. Researchers have commonly performed selection separately for fixed and random effects in spatial hierarchical models. Simultaneous selection methods relieve the researcher from arbitrarily fixing one of these types of effects while selecting the other. Notably, Bayesian approaches to simultaneously select covariates and spatial model structure are limited. Our use of fractional Bayes factors allows for selection of fixed effects and spatial model structure under automatic reference priors for model parameters, which obviates the need to specify hyperparameters for priors. We also show the equivalence between two ICAR specifications and derive the minimal training size for the fractional Bayes factor applied to the ICAR model under the reference prior. We perform a simulation study to assess the performance of our approach and we compare results to the Deviance Information Criterion and Widely Applicable Information Criterion. We demonstrate that our fractional Bayes factor approach assigns low posterior model probability to spatial models when data is truly independent and reliably selects the correct covariate structure with highest probability within the model space. Finally, we demonstrate our Bayesian model selection approach with applications to county-level median household income in the contiguous United States and residential crime rates in the neighborhoods of Columbus, Ohio.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

An Insight into the Repurposing of Phytoconstituents obtained from Delhi's Aravalli Biodiversity Park as Antifungal Agents.

The global prevalence of fungal infections is alarming in both the pre- and post- COVID period. Due to a limited number of antifungal drugs, there are hurdles in treatment strategies for fungal infections due to toxic potential, drug interactions, and the development of fungal resistance. All the antifungal targets (existing and newer) and pipeline molecules showing promise against these targets are reviewed. The objective was to predict or repurpose phyto-based antifungal compounds based on a dual target inhibition approach (Sterol-14-α- demethylase and HSP-90) using a case study. In pursuit of repurposing the phytochemicals as antifungal agents, a team of researchers visited Aravalli Biodiversity Park (ABP), Delhi, India, to collect information on available medicinal plants. From 45 plants, a total of 1149 ligands were collected, and virtual screening was performed using Schrodinger Suite 2016 software to get 83 hits against both the target proteins: Sterol-14-α-demethylase and HSP-90. After analysis of docking results, ligands were selected based on their interaction against both the target proteins and comparison with respective standard ligands (fluconazole and ganetespib). We have selected Isocarthamidin, Quercetin and Boeravinone B based on their docking score and binding interaction against the HSP-90 (Docking Score -9.65, -9.22 and -9.21, respectively) and 14-α-demethylase (Docking Score -9.19, -10.76 and -9.74 respectively). The docking protocol was validated and MM/GBSA studies depicted better stability of selected three ligands (Isocarthamidin, Quercetin, Boeravinone B) complex as compared to standard complex. Further, MD simulation studies were performed using the Desmond (67) software package version 2018-4. All the findings are presented as a case study for the prediction of dual targets for the repurposing of certain phytochemicals as antifungal agents.

Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods.

In genome-wide association studies (GWAS), logistic regression is one of the most popular analytics methods for binary traits. Multinomial regression is an extension of binary logistic regression that allows for multiple categories. However, many GWAS methods have been limited application to binary traits. These methods have improperly often been used to account for ordinal traits, which causes inappropriate type I error rates and poor statistical power. Owing to the lack of analysis methods, GWAS of ordinal traits has been known to be problematic and gaining attention. In this paper, we develop a general framework for identifying ordinal traits associated with genetic variants in pedigree-structured samples by collapsing and kernel methods. We use the local odds ratios GEE technology to account for complicated correlation structures between family members and ordered categorical traits. We use the retrospective idea to treat the genetic markers as random variables for calculating genetic correlations among markers. The proposed genetic association method can accommodate ordinal traits and allow for the covariate adjustment. We conduct simulation studies to compare the proposed tests with the existing models for analyzing the ordered categorical data under various configurations. We illustrate application of the proposed tests by simultaneously analyzing a family study and a cross-sectional study from the Genetic Analysis Workshop 19 (GAW19) data.

Care of the dying – medical student confidence and preparedness: mixed-methods simulation study

ObjectivesOf all doctors, Foundation Year 1 trainees spend the most time caring for dying patients yet report poor preparation and low confidence in providing this care. Despite documented effectiveness of...

What measures are needed to achieve a tobacco endgame target? A Singapore-based simulation study

BackgroundAn increasing number of countries are pursuing a tobacco ‘endgame’. We sought to determine the combination of measures it would take to achieve a tobacco endgame in the city-state of...

Extending the actor-partner interdependence model to accommodate multivariate dyadic data using latent variables.

This study extends the traditional Actor-Partner Interdependence model (APIM; Kenny, 1996) to incorporate dyadic data with multiple indicators reflecting latent constructs. Although the APIM has been widely used to model interdependence in dyads, the method and its applications have largely been limited to single sets of manifest variables. This article presents three extensions of the APIM that can be applied to multivariate dyadic data; a manifest APIM linking multiple indicators as manifest variables, a composite-score APIM relating univariate sums of multiple variables, and a latent APIM connecting underlying constructs of multiple indicators. The properties of the three methods in analyzing data with various dyadic patterns are investigated through a simulation study. It is found that the latent APIM adequately estimates dyadic relationships and holds reasonable power when measurement reliability is not too low, whereas the manifest APIM yields poor power and high type I error rates in general. The composite-score APIM, even though it is found to be a better alternative to the manifest APIM, fails to correctly reflect latent dyadic interdependence, raising inferential concerns. We illustrate the APIM extensions for multivariate dyadic data analysis by an example study on relationship commitment and happiness among married couples in Wisconsin. In cases where the measures are reliable reflections of psychological constructs, we suggest using the latent APIM for examining research hypotheses that discuss implications beyond observed variables. We conclude with stressing the importance of carefully examining measurement models when designing and conducting dyadic data analyses. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Data-driven covariate selection for confounding adjustment by focusing on the stability of the effect estimator.

Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset are truly confounders, is known to yield potentially inefficient and unstable estimators. In this article, we introduce a data-driven confounder selection strategy that focuses on stable estimation of the treatment effect. The approach exploits the causal knowledge that after adjusting for confounders to eliminate all confounding biases, adding any remaining non-confounding covariates associated with only treatment or outcome, but not both, should not systematically change the effect estimator. The strategy proceeds in two steps. First, we prioritize covariates for adjustment by probing how strongly each covariate is associated with treatment and outcome. Next, we gauge the stability of the effect estimator by evaluating its trajectory adjusting for different covariate subsets. The smallest subset that yields a stable effect estimate is then selected. Thus, the strategy offers direct insight into the (in)sensitivity of the effect estimator to the chosen covariates for adjustment. The ability to correctly select confounders and yield valid causal inferences following data-driven covariate selection is evaluated empirically using extensive simulation studies. Furthermore, we compare the introduced method empirically with routine variable selection methods. Finally, we demonstrate the procedure using two publicly available real-world datasets. A step-by-step practical guide with user-friendly R functions is included. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE allele, a commonly associated variant of Alzheimer's. Thus, we show that BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver.

In machine learning and statistics, the penalized regression methods are the main tools for variable selection (or feature selection) in high-dimensional sparse data analysis. Due to the nonsmoothness of the associated thresholding operators of commonly used penalties such as the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD), and the minimax concave penalty (MCP), the classical Newton-Raphson algorithm cannot be used. In this article, we propose a cubic Hermite interpolation penalty (CHIP) with a smoothing thresholding operator. Theoretically, we establish the nonasymptotic estimation error bounds for the global minimizer of the CHIP penalized high-dimensional linear regression. Moreover, we show that the estimated support coincides with the target support with a high probability. We derive the Karush-Kuhn-Tucker (KKT) condition for the CHIP penalized estimator and then develop a support detection-based Newton-Raphson (SDNR) algorithm to solve it. Simulation studies demonstrate that the proposed method performs well in a wide range of finite sample situations. We also illustrate the application of our method with a real data example.

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.