Extensive Simulations Research Articles

Multi-frequency weak signal detection based on Liu-like chaotic synchronization system and its hardware circuit implementation

Considering the shortcomings of traditional chaotic systems in weak signal detection methods, such as the high threshold sensitivity requirement and the narrow detection frequency domain. This study proposes a novel three-dimensional chaotic synchronization system, and the dynamical of the system are exhaustively characterized using equilibrium points, phase diagrams, Lyapunov exponential spectra, and bifurcation diagrams. This method involves weak signal detection by means of chaotic synchronization control. Synchronization of a chaotic system using a backstepping synchronization method is used to detect weak signals by analyzing the synchronization error after the introduction of weak signals in a strong noise background. The chaotic system is implemented by hardware circuits, and the simulation of chaotic synchronization control and detection of weak signals from the perspective of circuits is carried out by circuit simulation software. Additionally, the frequency range within which the system is capable of weak signal detection is tested through extensive simulation experiments. Finally, multi-frequency signals detection experiments are performed. The experimental results demonstrate that the system can accurately detect the frequency of weak signals address the limitations of narrow-band detection and multi-frequency signal detection is possible. Meanwhile, the circuit structure proposed in this paper is simple and has some value for engineering applications.

Tolerance Intervals Under a Class of Unbalanced Linear Mixed Models

The study of tolerance intervals for linear mixed models under unbalanced data has been mainly focused on one-way random-effects models. In this article, we present a method to compute both one- and two-sided ( β , γ ) -tolerance intervals that is applicable to a more general class of unbalanced linear mixed models. The proposed method is based on the concept of generalized pivotal quantities and thus relies on the derivation of pivotal quantities that are shown to be independent within this particular class of models. The method employs Monte Carlo sampling methods to obtain realizations of the quantities needed to compute the tolerance intervals of interest. Extensive simulation studies confirm that coverage probabilities of the computed tolerance intervals are consistently close to their nominal levels. Furthermore, to showcase the application of our method in real-world scenarios, we provide illustrative examples of case studies that examine models falling within the specified class. Lastly, motivated by the prospective application of our method in drug development, we implement the proposed procedures to estimate shelf life of a drug product through tolerance intervals and unbalanced stability data.

Quantum-based PRNG for image encryption: a comprehensive study

Quantum computing, grounded in the principles of quantum mechanics, has ushered in a new era of intriguing possibilities and fresh scientific insights. In this document, we present an innovative algorithm tailored for the encryption and decryption of images. This algorithm harnesses the quasi-probability values extracted from each state within the quantum system, represented as | ψ 〉 . These numerical values are seamlessly integrated with conventional data to facilitate image encryption and decryption processes. Our methodology undergoes comprehensive validation and evaluation through extensive simulations performed on the IBM Qiskit Aer simulator. We subject the generated numerical sequence to rigorous statistical scrutiny using the NIST SP 800-22 suite, ensuring its authenticity and reliability. Employing a quantum system comprising 3 qubits and uniform rotation gates, our algorithm produces a dataset consisting of 41,943,040 binary digits. Additionally, we conduct a thorough assessment encompassing both visual and statistical analyses to ascertain the effectiveness and robustness of our proposed algorithm. Furthermore, we emphasize the critical importance of achieving a quasi-probability error rate of 10 − 6 for real-world implementation, highlighting the practical viability and scalability of our approach. Through comparative analysis and meticulous examination of correlation patterns, our study provides invaluable insights into the distinctive characteristics and efficacy of our encryption scheme.

Optimization of Sparse Camera Array Arrangement using Grid-based Method

—This study explores the application of multi-camera array technology in super-resolution (SR) imaging system. We propose an innovative method based on grid representation to optimize camera arrangements using sparse Gaussian processes and variational inference. This approach significantly enhances the image reconstruction quality of sparse camera arrays. Unlike conventional techniques that provide only qualitative conclusions, our grid-based method precisely optimizes camera positions and effectively simulates the impact of assembly errors. The optimized camera arrangement substantially improves the SR reconstruction quality of sparse array imaging systems, reducing contour jaggedness and high-frequency aliasing. Extensive simulations and experimental validations confirm the robustness and practical applicability of our method. The results demonstrate that the optimized camera arrangements can achieve high-resolution imaging with enhanced robustness against assembly errors, indicating their potential for various applications such as biological microscopy, remote sensing, and smartphone photography. This work presents a new and effective strategy for designing sparse camera array imaging systems, offering significant improvements in both image quality and system reliability.

Randomized Phase II Cancer Clinical Trials to Validate Predictive Biomarkers.

The design of cancer clinical trials incorporating biomarkers depends on various factors, including the trial phase, the type of biomarker and whether its role has been validated. This article aims to present a method for designing and analyzing phase II cancer clinical trials that validate predictive biomarkers. We propose a randomized trial design where patients are allocated between a targeted therapy and a non-targeted therapy stratified by biomarker status. Tumor response is used as the primary endpoint to validate the biomarker through interaction testing between treatment and biomarker positivity. Additionally we propose a sample size calculation method for this design, considering two types of interaction: one based on logit-transformed response rates and the other on raw response rates. The proposed sample size method is applied to the design of a real randomized phase II trial. Extensive simulations are conducted to evaluate the performance of the test statistic and the sample size method under different scenarios. Our method provides a practical approach to validating predictive biomarkers in phase II cancer trials. The simulations demonstrate robust performance for both interaction models, offering guidance for the sample size selection and analysis strategy in biomarker-stratified trials.

Microscopic Origins of Flow Activation Energy in Biomolecular Condensates.

Material properties of biomolecular condensates dictate their form and function, influencing the diffusion of regulatory molecules and the dynamics of biochemical reactions. The increasing quality and quantity of microrheology experiments on biomolecular condensates necessitate a deeper understanding of the molecular grammar that encodes their material properties. Recent reports have identified a characteristic timescale related to network relaxation dynamics in condensates, which governs their temperature-dependent viscoelastic properties. This timescale is intimately connected to an activated process involving the dissociation of sticker regions, with the energetic barrier referred to as flow activation energy. The microscopic origin of activation energy is a complex function of sequence patterns, component stoichiometry, and external conditions. This study elucidates the microscopic origins of flow activation energy in single and multicomponent condensates composed of model peptide sequences with varying sticker and spacer motifs, with RNA as a secondary component. We dissected the effects of condensate density, RNA stoichiometry, and peptide sequence patterning using extensive sequence-resolved coarse-grained simulations. We found that flow activation energy is closely linked to the lifetime of sticker-sticker pairs under certain conditions, though the presence of multiple competing stickers further complicates this relationship. The insights gained in this study should help establish predictive multiscale models for the material properties and serve as a valuable guide for the programmable design of condensates.

Percentage of variance accounted for in structural equation models: The rediscovery of the goodness of fit index.

This article delves into the often-overlooked metric of percentage of variance accounted for in structural equation models (SEM). The goodness of fit index (GFI) provides the percentage of variance of the sum of squared covariances explained by the model. Despite being introduced over four decades ago, the GFI has been overshadowed in favor of fit indices that prioritize distinctions between close and nonclose fitting models. Similar to R² in regression, the GFI should not be used to this aim but rather to quantify the model's utility. The central aim of this study is to reintroduce the GFI, introducing a novel approach to computing the GFI using mean and mean-and-variance corrected test statistics, specifically designed for nonnormal data. We use an extensive simulation study to evaluate the precision of inferences on the GFI, including point estimates and confidence intervals. The findings demonstrate that the GFI can be very accurately estimated, even with nonnormal data, and that confidence intervals exhibit reasonable accuracy across diverse conditions, including large models and nonnormal data scenarios. The article provides methods and code for estimating the GFI in any SEM, urging researchers to reconsider the reporting of the percentage of variance accounted for as an essential tool for model assessment and selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimizing personalized treatments for targeted patient populations across multiple domains.

Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population. Second, for mental disorders, a patient's underlying mental state is not observed but can be inferred from measures of high-dimensional combinations of symptomatology. Treatment mechanisms are unknown and can be complex, and thus treatment effect moderation can take complicated forms. To address these challenges, we propose a novel method that connects measurement models, efficient weighting schemes, and flexible neural network architecture through latent variables to tailor treatments for a target population. Patients' underlying mental states are represented by a compact set of latent state variables while preserving interpretability. Weighting schemes are designed based on lower-dimensional latent variables to efficiently balance population differences so that biases in learning the latent structure and treatment effects are mitigated. Extensive simulation studies demonstrated consistent superiority of the proposed method and the weighting approach. Applications to two real-world studies of patients with major depressive disorder have shown a broad utility of the proposed method in improving treatment outcomes in the target population.

A new extension of Burr-Hatke exponential distribution with engineering and biomedical applications

In this study, the Topp-Leone family of distribution approach was used to modify the Burr Hatke Exponential distribution to provide adequate fits for some engineering and health data which previous existing distributions in the family of Burr Hatke Exponential have failed to do. The new distribution improves the robustness of Burr Hatke Exponential distribution by making it capable of modeling emerging new world complex data with varying features, possesses greater capacity and flexibility to model lifetime data, has better goodness of fit. Some mathematical properties of the derived distribution such as quantile function, moments, order statistics, entropies, etc were obtained and discussed. Some non-Bayesian estimation approaches like maximum likelihood estimation (ML), maximum Product spacing estimation (MPSE), Least squares estimation (LS), weighted least squares estimation (WLS), Cramer-von-Mises estimation (CVM), Anderson-Darling estimation (AD), and right-tailed Anderson Darling estimation (RTAD), as well as Bayesian method under independent gamma priors were adopted to estimate the parameters of the model and the various methods proved efficient. From the simulation results, the bias and root mean squared error for the parameters are relatively small and the become smaller as the sample size becomes larger. This shows convenience and improved estimation accuracy. We further constructed a regression model using the proposed distribution. Extensive simulation studies were used to determine the efficiency of the method in both the estimation of its parameters and that of the regression model. The TL-BHE regression was fitted on censored CD4 count data of HIV/AIDs patients. The competitiveness, applicability, and usefulness of the model were demonstrated using datasets and the results validate the theoretical findings. The results indicate that the TL-BHE distribution achieved better results than the baseline distribution and other variants of the classical distributions.

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery.

This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene regulatory network information into hypothesis testing for the difference between mean vectors and linear discriminant analysis, we aim to effectively capture and utilize previously validated external gene interaction information. Our method leverages a block-coordinate descent approach which enables us to incorporate mixed graph information into linear structural equation modeling, accommodating directed/undirected edges and potential cycles in gene regulatory networks. Extensive simulations under various data scenarios have demonstrated the effectiveness of our approach with improved power for gene pathway tests and disease classification over existing methods. An application to a lung cancer dataset from the Cancer Genome Atlas Program (TCGA) further exemplifies the potential of our graph-informed approach in empowering the detection of differentially expressed gene pathways and gene expression-based classification of different lung cancer stages. Our findings underscore the potential utility of incorporating gene regulatory network information in gene pathway analysis, setting the stage for future advancements in gene pathway discovery, disease diagnosis, and treatment strategies.

TSLiNGAM: DirectLiNGAM Under Heavy Tails

One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on their causes. Possible identifiability of SCMs given data depends on assumptions made on the noise variables and the functional classes in the SCM. For instance, in the LiNGAM model, the functional class is restricted to linear functions and the disturbances have to be non-Gaussian. In this work, we propose TSLiNGAM, a new method for identifying the DAG of a causal model based on observational data. TSLiNGAM builds on DirectLiNGAM, a popular algorithm which uses simple OLS regression for identifying causal directions between variables. TSLiNGAM leverages the non-Gaussianity assumption of the error terms in the LiNGAM model to obtain more efficient and robust estimation of the causal structure. TSLiNGAM is justified theoretically and is studied empirically in an extensive simulation study. It performs significantly better on heavy-tailed and skewed data and demonstrates a high small-sample efficiency. In addition, TSLiNGAM also shows better robustness properties as it is more resilient to contamination. Supplementary materials for this article are available online.

Enhancing MPPT efficiency in PV systems under partial shading: A hybrid POA&PO approach for rapid and accurate energy harvesting

Partial shading in operational environments introduces multiple peaks in the output characteristics of photovoltaic (PV) systems, presenting significant challenges to energy harvesting. This study introduces a novel meta-heuristic algorithm, termed POA&PO, which aims to address the maximum power point tracking (MPPT) issues in PV systems. The algorithm capitalizes on the global search capability of the POA method to quickly pinpoint the range with the maximum power, followed by the fast convergence of the PO method to ensure both rapidity and accuracy of the solution. Extensive simulation tests, conducted in MATLAB/SIMULINK, have demonstrated the efficacy of the POA&PO algorithm, achieving an average tracking efficiency of 99.97 % with a convergence time of 0.3 s in step response tests; under the EN50530 test standard, the algorithm also showed sustained and stable tracking of ramp signals. Moreover, practical testing utilizing a new, low-cost indoor PV simulator confirmed the algorithm’s high performance under controlled conditions, yielding an average tracking efficiency of 97.03 % and a convergence time of 0.18 s. This paper highlights the capacity of the developed algorithm to reliably, accurately, and swiftly achieve high energy transfer efficiency. Additionally, the innovative and economical experimental testing methods employed are emphasized, contributing to the practical applicability and cost-effectiveness of the proposed solution.

Renewable estimation in expectile regression model with streaming data sets

Streaming data continually expands over time, making it challenging to apply traditional expectile regression methods due to memory limitations. Expectile regression is advantageous for studying the entire conditional distribution of the response to a predictor, but handling streaming data requires innovative approaches. This paper proposes an online renewable expectile regression strategy, which updates the estimator using both the current data and summary statistics from historical data. An ADMM algorithm is employed to efficiently compute the proposed estimator. We establish the consistency and asymptotic normality of this renewable estimator under regular conditions. Extensive simulations and real-data experiments demonstrate that our method performs comparably to the oracle estimator while maintaining computational efficiency and low storage requirements.

Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis

This research examines how degrees of freedom and covariance matrix configurations affect Wishart distributed matrix eigenvalue distributions. We use the energy distance metric to compare eigenvalue distributions in Identity, Diagonal, and Structured covariance matrices. Through extensive simulations, we show that degrees of freedom and covariance matrix architectures greatly affect eigenvalue dispersion and energy distance distributions. Statistical models are more reliable with smaller, more stable distributions from higher degrees of freedom. We analyze the eigenvalue distributions of NextEra Energy (NEE), Enphase Energy (ENPH), First Solar (FSLR), SunPower (SPWR), and Brookfield Renewable Partners (BEP) stocks using this analytical methodology. Daily returns and covariance matrices were calculated using daily closing prices from January 1, 2018, to January 1, 2023. Our findings support simulation studies indicating larger degrees of freedom lead to more stable energy distance distributions.The findings of this study are useful in finance, genetics, and environmental studies, where stability and variability of the covariance structure are important. The requirement for higher-dimensional settings and real-world datasets to validate the theoretical framework are our research’s constraints. This research expands Wishart distribution knowledge and provides a solid analytical foundation for data analysis and model fitting in numerous scientific fields.

Statistical Inference for Networks of High-Dimensional Point Processes

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes processes. The key ingredient for the inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarize the entire history of the process. Combining recent martingale central limit theorem with the new concentration inequality, we then characterize the convergence rate of the test statistics in a continuous time domain. Finally, to account for potential non-stationarity of the process in practice, we extend our statistical inference procedure to a flexible class of Hawkes processes with time-varying background intensities and unknown transition functions. The finite sample validity of the inferential tools is illustrated via extensive simulations and further applied to a neuron spike train dataset. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

ANFIS-RTSMC based MPPT for a hybrid PV-diesel-battery water pumping system

This paper introduces a novel hybrid method for achieving Maximum Power Point Tracking (MPPT) in a photovoltaic-diesel-battery hybrid water pumping system driven by an induction motor drive (IMD). The innovative MPPT control strategy uniquely combines the rapid response and minimal oscillations of the Adaptive Neuro Fuzzy Inference System with the high robustness of Robust Terminal Sliding Mode Control (RTSMC). This combination ensures unprecedented steady-state resilience to changes in solar irradiance, enhancing system stability across a variety of IMD speeds, and making the system robust to parameter variations. Unlike traditional approaches, this two-stage photovoltaic (PV) setup integrates a regulated boost converter for MPPT control and a three-phase inverter using Space Vector Pulse Width Modulation (SVPWM) to regulate the DC link voltage (Vdc). Extensive MATLAB Simulink simulations demonstrate the proposed method’s superior efficiency in power extraction and its resilience under varying operating conditions, showcasing its originality and effectiveness.

Hydrogen Bond Network Shaping Proton Penetration Behavior across Two-Dimensional Nanoporous Materials.

In this study, we investigate aqueous proton penetration behavior across four types of two-dimensional (2D) nanoporous materials with similar pore sizes using extensive ReaxFF molecular dynamics simulations. The results reveal significant differences in proton penetration energy barriers among the four kinds of 2D materials, despite their comparable pore sizes. Our analysis indicates that these variations in energy barriers stem from differences in the hydrogen bond (HB) network formed between the 2D nanoporous materials and the aqueous environment. The HB network can be classified into two categories: those formed between the surface of the 2D nanoporous materials and the aqueous environment, and those formed between the edge atoms of the nanopores and the water molecules inside the pores. A strong HB network formed between the surface of the 2D nanoporous materials and the aqueous environment induces an orientational preference of water molecules, resulting in an aggregated water layer with high density. This high-density water region traps protons, making it difficult for them to escape and penetrate the nanopores. On the other hand, a strong HB network formed between the edge atoms of the nanopores and the water molecules inside the pores impedes the rotation and migration of water molecules, further inhibiting proton penetration behavior. To facilitate the proton penetration process, in addition to a sufficiently large pore size, a weak HB network between the 2D nanoporous material and the aqueous environment is necessary.

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method, called FAStEN, combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. In addition, we derive asymptotic oracle properties, which guarantee estimation and selection consistency for the proposed FAStEN estimator. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance, without sacrificing the quality of the coefficients’ estimation. The theoretical derivations and the simulation study provide a strong motivation for our approach. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study. Complete FAStEN code is provided at https://github.com/IBM/funGCN. Supplementary materials for this article are available online.

Reliability inference for dual constant-stress accelerated life test with exponential distribution and progressively Type-II censoring

Accelerated life test provides a feasible and effective way to rapidly derive lifetime information by exposing products to higher-than-normal operating conditions. However, most of the previous research on accelerated life test has focused on the application of a single stress factor and a traditional censoring scheme. This article considers the reliability inference for a dual constant-stress accelerated life test model with exponential distribution and progressively Type-II censoring. Point estimates for model parameters are provided using maximum likelihood estimation and the weighted least squares method based on random variable transformation. In addition, we construct asymptotic confidence intervals, approximate confidence intervals, and bootstrap confidence intervals for the parameters of interest. Finally, extensive simulation studies and an illustrative example are presented to investigate the performance of the proposed methods.

Transformer-aided dynamic causal model for scalable estimation of effective connectivity

Abstract Dynamic Causal Models (DCMs) in functional Magnetic Resonance Imaging (fMRI) decipher causal interactions, known as Effective Connectivity, among neuronal populations. However, their utility is often constrained by computational limitations, restricting analysis to a small subset of interacting brain areas, typically fewer than 10, thus lacking scalability. While the regression DCM (rDCM) has emerged as a faster alternative to traditional DCMs, it is not without its limitations, including the linearization of DCM terms, reliance on a fixed Hemodynamic Response Function (HRF), and an inability to accommodate modulatory influences. In response to these challenges, we propose a novel hybrid approach named Transformer encoder DCM decoder (TREND), which combines a Transformer encoder with state-of-the-art physiological DCM (P-DCM) as decoder. This innovative method addresses the scalability issue while preserving the nonlinearities inherent in DCM equations. Through extensive simulations, we validate TREND’s efficacy by demonstrating its ability to accurately predict effective connectivity values with dramatically reduced computational time relative to original P-DCM even in networks comprising up to, for instance, 100 interacting brain regions. Furthermore, we showcase TREND on an empirical fMRI dataset demonstrating the superior accuracy and/or speed of TREND compared with other DCM variants. In summary, by amalgamating P-DCM with Transformer, we introduce and validate a pioneering approach for determining effective connectivity values among brain regions, extending its applicability seamlessly to large-scale brain networks.