Monte Carlo Sampling Technique Research Articles

Exploring the deceleration parameter in f(T) gravity: A comprehensive analysis using parametrization techniques and observational data

In this paper, we employ parametrization techniques within the framework of [Formula: see text] gravity to investigate the deceleration parameter (DP), a key quantity characterizing the universe’s expansion dynamics. By analyzing the DP, we gain valuable insights into the nature of cosmic constituents and their impact on the universe’s evolution. We utilize a combination of observational data, including 31 Cosmic Chronometers (CC) measurements, 1048 Type Ia Supernovae (SNIa), 17 Baryon Acoustic Oscillation (BAO) measurements, Cosmic Microwave Background (CMB), 162 Gamma Ray Bursts (GRB), and 24 observations of compact radio quasars (Q). We employ the Markov chain Monte Carlo (MCMC) sampling technique to estimate the best-fit range of model parameters. Cosmological and cosmographic parameters are investigated, and their implications in the context of cosmology have been discussed. We also analyze the statefinder and [Formula: see text] diagnostic to gain deeper insights into the universe’s behavior. Furthermore, we conduct statistical analyses to compare our model with the standard [Formula: see text]CDM model. Our investigation also includes the study of physical parameters, providing comprehensive insights into the cosmological behavior within the [Formula: see text] gravity framework. The results and comparisons presented in this work contribute to a deeper understanding of the universe’s dynamics and provide valuable implications for the cosmological model under consideration.

Auxiliary two-filter particle smoothing for one generalized hidden Markov model

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data.

Stochastic Optimization of an Electric Bus Dynamic Wireless Charging System

The Electric Bus Dynamic Wireless Charging (EB-DWC) system is a bus charging system that enables electric buses to receive power wirelessly from ground-based electromagnetic induction devices. In this system, how to optimally configure the charging infrastructures while considering the unpredictable nature of bus movement is a great challenge. This paper presents an optimization problem for an EB-DWC system in urban settings, addressing stochastic elements inherent in the vehicle speed, initial charging state, and dwell time at bus stops. We formulate a stochastic planning problem for the EB-DWC system by integrating these uncertainties and apply Monte Carlo sampling techniques to effectively solve this problem. The proposed method can improve the system’s robustness effectively.

Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction

In this paper, we present a novel self-supervised framework for Sentiment Cue Extraction (SCE) aimed at enhancing the interpretability of text sentiment analysis models. Our approach leverages self-supervised learning to identify and highlight key textual elements that significantly influence sentiment classification decisions. Central to our framework is the development of an innovative Mask Sequence Interpretation Score (MSIS), a bespoke metric designed to assess the relevance and coherence of identified sentiment cues within binary text classification tasks. By employing Monte Carlo Sampling techniques optimized for computational efficiency, our framework demonstrates exceptional effectiveness in processing large-scale text data across diverse datasets, including English and Chinese, thus proving its versatility and scalability. The effectiveness of our approach is validated through extensive experiments on several benchmark datasets, including SST-2, IMDb, Yelp, and ChnSentiCorp. The results indicate a substantial improvement in the interpretability of the sentiment analysis models without compromising their predictive accuracy. Furthermore, our method stands out for its global interpretability, offering an efficient solution for analyzing new data compared to traditional techniques focused on local explanations.

TEA+ : A Novel Temporal Graph Random Walk Engine With Hybrid Storage Architecture

Many real-world networks are characterized by being temporal and dynamic, wherein the temporal information signifies the changes in connections, such as the addition or removal of links between nodes. Employing random walks on these temporal networks is a crucial technique for understanding the structural evolution of such graphs over time. However, existing state-of-the-art sampling methods are designed for traditional static graphs, and as such, they struggle to efficiently handle the dynamic aspects of temporal networks. This deficiency can be attributed to several challenges, including increased sampling complexity, extensive index space, limited programmability, and a lack of scalability. In this paper, we introduce TEA+ , a robust, fast, and scalable engine for conducting random walks on temporal graphs. Central to TEA+ is an innovative hybrid sampling method that amalgamates two Monte Carlo sampling techniques. This fusion significantly diminishes space complexity while maintaining a fast sampling speed. Additionally, TEA+ integrates a range of optimizations that significantly enhance sampling efficiency. This is further supported by an effective graph updating strategy, skilled in managing dynamic graph modifications and adeptly handling the insertion and deletion of both edges and vertices. For ease of implementation, we propose a temporal-centric programming model, designed to simplify the development of various random walk algorithms on temporal graphs. To ensure optimal performance across storage constraints, TEA+ features a degree-aware hybrid storage architecture, capable of adeptly scaling in different memory environments. Experimental results showcase the prowess of TEA+ , as it attains up to three orders of magnitude speedups compared to current random walk engines on extensive temporal graphs.

Optimal fibre length and maximum isometric force are the most influential parameters when modelling muscular adaptations to unloading using Hill-type muscle models.

Introduction: Spaceflight is associated with severe muscular adaptations with substantial inter-individual variability. A Hill-type muscle model is a common method to replicate muscle physiology in musculoskeletal simulations, but little is known about how the underlying parameters should be adjusted to model adaptations to unloading. The aim of this study was to determine how Hill-type muscle model parameters should be adjusted to model disuse muscular adaptations. Methods: Isokinetic dynamometer data were taken from a bed rest campaign and used to perform tracking simulations at two knee extension angular velocities (30°·s-1 and 180°·s-1). The activation and contraction dynamics were solved using an optimal control approach and direct collocation method. A Monte Carlo sampling technique was used to perturb muscle model parameters within physiological boundaries to create a range of theoretical and feasible parameters to model muscle adaptations. Results: Optimal fibre length could not be shortened by more than 67% and 61% for the knee flexors and non-knee muscles, respectively. Discussion: The Hill-type muscle model successfully replicated muscular adaptations due to unloading, and recreated salient features of muscle behaviour associated with spaceflight, such as altered force-length behaviour. Future researchers should carefully adjust the optimal fibre lengths of their muscle-models when trying to model adaptations to unloading, particularly muscles that primarily operate on the ascending and descending limbs of the force-length relationship.

Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations

AbstractBank vegetation plays a key role in both hydrodynamics and morphodynamics of natural rivers; however, these effects are often unaccounted for in the computational flow dynamics of natural waterways. Recent studies using the large‐eddy simulation (LES), however, have attempted to gain insights into the impacts of bank vegetation on the mean flow field of the natural rivers using a vegetation model, which applies a sink term to the momentum equations of motion. This approach accounts for the effects of the vegetation and provides a practical approach to account for the complex patches of bank vegetation in large‐scale rivers. To implement the vegetation model, a drag coefficient reflecting the overall resistance of vegetal structures to the flow is needed, but due to the lack of calibrated data and range of size, density and type of vegetation, this parameter can be a significant source of uncertainty in the model results. In this study, we use uncertainty quantification (UQ) to investigate the hydrodynamics and bed shear results when a bank vegetation is incorporated in an LES model. To this end, we used the polynomial chaos expansion and Monte Carlo sampling techniques to determine the uncertainties associated with the drag coefficient in the vegetation model and from uncertainties in the bed roughness and inflow discharge. The UQ analysis provided spatially varying confidence levels for the spanwise and vertical distribution of velocity magnitude and for the bed shear stress distributions. In addition, Sobol indices were computed to indicate the relative influence that each parameter had on the overall uncertainty. In general, it was found that uncertainty in flow discharge was the dominant source of uncertainty; however, the drag coefficient in the vegetation model and the bed roughness parameter also made significant contribution to the uncertainty near the banks and bed, respectively.

A Bayesian approach to modeling topic-metadata relationships

AbstractThe objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically relevant metadata. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself in an unsupervised fashion, usually by common topic models. A frequently used procedure to achieve this is the method of composition, a Monte Carlo sampling technique performing multiple repeated linear regressions of sampled topic proportions on metadata covariates. In this paper, we propose two modifications of this approach: First, we substantially refine the existing implementation of the method of composition from the package by replacing linear regression with the more appropriate Beta regression. Second, we provide a fundamental enhancement of the entire estimation framework by substituting the current blending of frequentist and Bayesian methods with a fully Bayesian approach. This allows for a more appropriate quantification of uncertainty. We illustrate our improved methodology by investigating relationships between Twitter posts by German parliamentarians and different metadata covariates related to their electoral districts, using the structural topic model to estimate topic proportions.

Comprehensive capacitance sensor calibration for high-speed fluid-machinery tip clearance characterization

This paper aims to lay a foundation for high-fidelity tip clearance measurements with capacitance sensors that can be adapted to any highspeed fluid machine, such as high-pressure turbines and compressors. A novel calibration procedure that does not involve rotating components is investigated and compared against established methods. First, a calibration bench is developed to demonstrate the static and dynamic performance of the sensors and perform quasi-static and dynamic calibrations in a controlled environment. Second, an in-situ methodology is developed to calibrate the sensors once installed in a turbine rig without complex positioning systems. A comprehensive uncertainty quantification analysis based on Markov Chain Monte Carlo sampling techniques shows that combined standard uncertainties as low as uc = 2.40 μm and uc = 4.69 μm could be achieved with the proposed bench and in-situ methodologies, respectively.

Robust optimization for functional multiresponse in 3D printing process

Computer models are commonly used to simulate the functional relationships between inputs and outputs for quality design in 3D printing. However, the high-dimensional outputs of functional multiresponse make it challenging to develop the simulation model and perform robust optimization. This paper proposes a novel optimization method with an additive multiresponse Gaussian process model for dealing with functional multiresponse optimization problems. First, an additive covariance function is constructed to capture the correlation of the temporal inputs. Second, the Markov Chain Monte Carlo sampling technique is adopted to determine the simulation model and quantify the uncertainty. Finally, the optimization model is constructed by integrating the quality loss function and interval analysis method, and the Bayesian optimization algorithm is used to obtain the optimal solution. A numerical simulation example and a 3D printing case study are used to illustrate the effectiveness of the proposed method. The comparison results show that the responses of the proposed method are closer to the targets than the current ones, and all fall within the specified interval.

A new framework for the assessment of model probabilities of the different crystal plasticity models for lamellar grains in Titanium alloys

This paper presents a novel framework for assessing the relative accuracy of the different slip transfer criterion in modeling the constitutive response of the lamellar morphology grains in Titanium alloys. The main steps involved in the proposed framework include: (i) rigorous statistical evaluation of the salient morphological characteristics of the lamellar grains using image processing techniques, (ii) modeling the effects of dislocation slip transfer as additive penalties to the critical resolved shear strengths of the different slip systems in each phase, (iii) consideration of multiple geometric slip transfer criteria for the numerical evaluation of the different penalty terms, and (iv) the Bayesian calibration of the material parameters in the constitutive descriptions using spherical nanoindentation experimental data on a polycrystalline Ti-6Al-4V sample. Step (iv) described above involves the construction and refinement of a Gaussian process (GP) surrogate model for the indentation measurements, which is trained on crystal plasticity finite element simulations incorporating the constitutive descriptions selected in Steps (ii) and (iii). The GP surrogates are then combined with Markov Chain Monte Carlo sampling techniques to calibrate the parameters in the various constitutive models studied in this work. Additionally, we have computed the posterior model probabilities for all the constitutive models considered and identified the most plausible slip transfer criterion. This specific constitutive model is then utilized to present a homogenized crystal plasticity model for the lamellar morphology. The proposed framework presents a robust scale-bridging protocol for incorporating the uncertain information obtained from lower scales.

Demand response model by locational marginal electricity–carbon price considering wind power uncertainty and energy storage systems

With the increasing prominence of global carbon emissions, how to achieve carbon emission reduction in power systems is an urgent issue. Early research introduced carbon cost in the objective functions or constraints in an optimization model. However, the carbon cost cannot account for the time-varying carbon emissions caused by load electricity consumption. To address this challenge, this paper proposes a demand response (DR) program based on the locational marginal prices on both electricity consumption and carbon emission to reshape the load consumption pattern, which guide the load from both “electricity perspective” and “carbon perspective”. This model can reduce the carbon emissions of the power system while considering the economics of power purchase and the traditional DR to peak load shifting. Meanwhile, historical data fitting, Monte Carlo sampling and scenario reduction techniques are used to take full account of wind power uncertainties. The energy storage (ES) systems are introduced to simultaneously arbitrage from both electricity and carbon markets by appropriate charging strategies. The simulation results using PJM 5-bus system show that the locational marginal electricity–carbon price proposed in this paper can reduce the carbon emissions of the power system while ensuring an economic operation. By adding the carbon emission factor to the price, the load demand not only follows the fluctuation of electricity price, but also shows the trend of following the change of carbon emission.

Elucidation of the mechanism and the free energy of a Morita-Baylis-Hillman type reaction catalyzed by enzymes using transition path sampling.

Transition path sampling (TPS) is a Monte-Carlo importance sampling technique in the space of trajectories which is used to elucidate transition states, reaction coordinates and kinetic parameters of complex biomolecular reactions. Within TPS an ensemble of truly dynamic reactive trajectories are obtained with a Monte-Carlo type acceptance criterion in combination with the shooting and shifting algorithms. To sample the equilibrium properties such as free energies, the inclusion of non-reactive trajectories in the TPS ensemble is essential since these trajectories sample the long lived reactant and product states. With a modified acceptance criterion and in combination with a window based sampling algorithm, TPS can be used to obtain unbiased free energy profiles for chemical reactions that are catalyzed by enzymes. Here we use TPS to study the proton transfer mechanism and the corresponding reaction free energy of enzymes (MBHases) that catalyze a Morita-Baylis-Hillman (MBH) type reaction. These enzymes were developed through directed evolution and we aim to map the quantum mechanical tunneling (QMT) contribution in the proton transfer mechanism to laboratory evolution of the MBHases to determine the role played by QMT to rate improvements of evolved variants. The calculated free energies would also provide insights into the role of QMT in the reaction mechanism as the width and height of the free energy barrier determines the importance of the QMT contributions.

Spectroastrometric Reverberation Mapping of Broad-line Regions

Spectroastrometry measures source astrometry as a function of wavelength/velocity. Reverberations of spectroastrometric signals naturally arise in broad-line regions (BLRs) of active galactic nuclei (AGNs) as a result of the continuum variations that drive responses of the broad emission lines with time delays. Such signals provide a new diagnostic for mapping BLR kinematics and geometry, complementary to the traditional intensity reverberation mapping (RM) technique. We present a generic mathematical formalism for spectroastrometric RM and show that under realistic parameters of a phenomenological BLR model, the spectroastrometric reverberation signals vary on a level of several to tens of microarcseconds, depending on the BLR size, continuum variability, and angular-size distance. We also derive the analytical expressions of spectroastrometric RM for an inclined ring-like BLR. We develop a Bayesian framework with a sophisticated Monte Carlo sampling technique to analyze spectroastrometric data and infer the BLR properties, including the central black hole mass and angular-size distance. We demonstrate the potential of spectroastrometric RM in spatially resolving BLR kinematics and geometry through a suite of simulation tests. The application to realistic observation data of 3C 273 obtains tentative, but enlightening results, reinforcing the practical feasibility of conducting spectroastrometric RM experiments on bright AGNs with the operating Very Large Telescope Interferometer as well as possibly with the planned next-generation 30 m class telescopes.

Adaptive optimal control of pneumatic suspensions for comfort improvement of flexible railway vehicles using Monte Carlo simulations

ABSTRACT This paper presents an adaptive optimal control strategy focused on improving ride comfort by minimising the frequency-weighted acceleration measured at three locations on a flexible carbody floor. Front and rear suspensions adapt the restriction orifice, which connects each pneumatic spring to its auxiliary air reservoir, thus conferring different dynamic stiffness and damping depending on train speed and track quality. In this work, as opposed to previous publications by our group, the optimal control problem aims at the particular frequency range which affects passenger comfort in accordance to standard UNE-EN-12299. In addition, a rigorous statistical analysis based on Monte Carlo and Bootstrap sampling techniques has been used to deal with the stochastic nature of track irregularities. Results prove that focusing on the comfort frequency range improves the performance of the adaptive suspension, and that the proposed statistical analysis of optimisation results guarantees the appropriate selection of optimal diameters.

The Performances of Gelman-Rubin and Geweke's Convergence Diagnostics of Monte Carlo Markov Chains in Bayesian Analysis

Bayesian statistics have been widely used given the development of Markov chain Monte Carlo sampling techniques and the growth of computational power. A major challenge of Bayesian methods that has not yet been fully addressed is how we can appropriately evaluate the convergence of the random samples to the target posterior distributions. In this paper, we focus on Gelman and Rubin's diagnostic (PSRF), Brooks and Gleman's diagnostic (MPSRF), and Geweke's diagnostics, and compare the Type I error rate and Type II error rate of seven convergence criteria: MPSRF>1.1, any upper bound of PSRF is larger than 1.1, more than 5% of the upper bounds of PSRFs are larger than 1.1, any PSRF is larger than 1.1, more than 5% of PSRFs are larger than 1.1, any Geweke test statistic is larger than 1.96 or smaller than -1.96, and more than 5% of Geweke test statistics are larger than 1.96 or smaller than -1.96. Based on the simulation results, we recommend the upper bound of PSRF if we only can choose one diagnostic. When the number of estimated parameters is large, between the diagnostic per parameter (i.e., PSRF) or the multivariate diagnostic (i.e., MPSRF), we recommend the upper bound of PSRF over MPSRF. Additionally, we do not suggest claiming convergence at the analysis level while allowing a small proportion of the parameters to have significant convergence diagnosis results.

Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

The LisbOn KInetics Monte Carlo solver

The LisbOn KInetics Monte Carlo (LoKI-MC) is an open-source simulation tool that solves the electron kinetics for low-temperature plasmas excited by uniform DC electric fields from different gas mixtures, using Monte Carlo techniques. Following the strategy and data organization of the LisbOn KInetics Boltzmann (LoKI-B) solver, the program easily addresses any complex mixture of atomic/molecular species, describing electron collisions with any target state (electronic, vibrational and rotational), characterized by any user-prescribed population. LoKI-MC is written in C++, benefiting from a highly-efficient object-oriented structure. On input, the code requires the working conditions, the gas-mixture composition, the distributions of populations for the levels of the atomic/molecular gases considered, and the relevant sets of electron-scattering cross sections obtained from the open-access website LXCat. On output, it yields the electron energy and velocity distribution functions, the electron swarm parameters, the collision rate-coefficients, and the electron power absorbed from the electric field and transferred to the different collisional channels. This paper presents LoKI-MC and gives examples of results obtained for different model and real gases, verifying and benchmarking the simulation tool against analytical solutions and other available codes, and assesses its computational performance. Moreover, the effects of the gas temperature, anisotropic ionization scattering (induced by momentum conservation) and superelastic collisions are analyzed in real gases. Program summaryProgram title: LoKI-MCCPC Library link to program files:https://doi.org/10.17632/n7t6ctp2st.1Developer's repository link:https://github.com/IST-Lisbon/LoKI-MCLicensing provisions: GPLv3Programming language: C++17Nature of problem: Simulation of the electron kinetics in non-equilibrium low-temperature plasmas, excited from arbitrarily complex gas-mixtures, under static and uniform electric fields.Solution method: Monte Carlo sampling techniques.

Fragility analysis of existing prestressed concrete bridges under traffic loads according to new Italian guidelines

AbstractAfter the 2018 collapse of Morandi highway bridge in Genova, new Italian guidelines (GL) were issued for prioritization of safety evaluations and retrofit interventions on existing bridges. Among several novelties, GL introduced new traffic load models (TLMs) which can be adopted for existing bridges in case of noncompliant safety checks according to the Italian building code (NTC) provisions. In this study, a class of simply supported, beam‐type, prestressed concrete bridge decks built between 1970 and 1980 in Italy is considered. Assuming geometric, material, and load random variables together with consideration of capacity model uncertainty, Monte Carlo sampling technique was implemented in MATLAB in order to randomly generate deck models and to evaluate their traffic‐load fragility. Fragility analysis was carried out to compare vulnerability levels of existing Italian bridges under different load patterns provided by GL and NTC. In addition to a prescribed TLM, the sensitivity of fragility to bridge usage limitations, such as reduced distance of external load lane from kerb or reduced number of lanes, was also quantified to support decision‐making by road management companies. In the final part of the study, the annual failure probability of selected bridges was estimated using a European weigh‐in‐motion database and convolution of fragility and hazard. Analysis results show that structural fragility significantly depends on the load pattern, indicating that more realistic vehicle models should be developed to achieve a target safety level conforming to modern codes for constructions.

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis.

With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of data-driven methods and approaches for aquaponic cultivation remains a challenge. The objective of this research is to investigate statistical methods to make inferences using small datasets for nutrient control in aquaponics to optimize yield. In this work, we employed the Density-Based Synthetic Minority Over-sampling TEchnique (DB-SMOTE) to address dataset imbalance, and ExtraTreesClassifer and Recursive Feature Elimination (RFE) to choose the relevant features. Synthetic data generation techniques such as the Monte-Carlo (MC) sampling techniques were used to generate enough data points and different feature engineering techniques were used on the predictors before evaluating the performance of kernel-based classifiers with the goal of controlling nutrients in the aquaponic solution for optimal growth.[27–35]