Search Results (4,838)

Modeling carbonate growth in fractures and pores is important for understanding carbon sequestration in the environment or when supersaturated solutions are injected into rocks. Here, we study the simple but nontrivial problem of calcite growth on fractures with rough walls of the same mineral using kinetic Monte Carlo simulations of attachment and detachment of molecules and scaling approaches. First, we consider wedge-shaped fracture walls whose upper terraces are in the same low-energy planes and show that the valleys are slowly filled by the propagation of parallel monolayer steps in the wedge sides. The growth ceases when the walls reach these low-energy configurations so that a gap between the walls may not be filled. Second, we consider fracture walls with equally separated monolayer steps (vicinal surfaces with roughness below 1 nm) and show that growth by step propagation will eventually clog the fracture gap. In both cases, scaling approaches predict the times to attain the final configurations as a function of the initial geometry and the step-propagation velocity, which is set by the saturation index. The same reasoning applied to a random wall geometry shows that step propagation leads to lateral filling of surface valleys until the wall reaches the low-energy crystalline plane that has the smallest initial density of molecules. Thus, the final configurations of the fracture walls are much more sensitive to the crystallography than to the roughness or the local curvature. The framework developed here may be used to determine those configurations, the times to reach them, and the mass of deposited mineral. Effects of transport limitations are discussed when the fracture gap is significantly narrowed. Full article

The direct detection of singlet-state oxygen (¹O₂) constitutes the holy grail dosimetric method for type-II photodynamic therapy (PDT), a goal that can be quantified using multispectral singlet oxygen near-infrared luminescence dosimetry (MSOLD). The optical properties of tissues, specifically their scattering and absorption coefficients, play a crucial role in determining how the treatment and luminescence light are attenuated. Variations in these properties can significantly impact the spatial distribution of the treatment light and hence the generation of singlet oxygen and the detection of singlet oxygen luminescence signals. In this study, we investigated the impact of varying optical properties on the detection of ¹O₂ luminescence signals during Photofrin-mediated PDT in tissue-mimicking phantoms. For comparison, we also conducted Monte Carlo (MC) simulations under the same conditions. The experimental and simulations are substantially equivalent. This study advances the understanding of MSOLD during PDT. Full article

This study addresses the challenges of estimating decay times for chemical components, focusing on hydroxylated fullerene $C_{60}{\left(OH\right)}_{29}$, which poses potential environmental risks due to its persistence and transformation in soil. Given the complexities of real-world experiments such as limited sample availability, time constraints, and the need for efficient resource use, a framework using the Gamma distribution based on hybrid Type-II censoring schemes was developed to model the decay time. The Gamma distribution’s flexibility and mathematical properties make it well-suited for reliability and decay analysis, capturing variable hazard rates and accommodating different censoring structures. We employ maximum likelihood estimation (MLE) and Bayesian methods to estimate the model’s parameters, consequently estimating the reliability and hazard functions. The large sample theory for MLE is used to approximate variances for constructing asymptotic confidence intervals. Additionally, we utilize the Markov chain Monte Carlo technique within the Bayesian framework to ensure robust parameter estimation. Through simulation studies and statistical tests—such as Chi-Square, Kolmogorov–Smirnov, and others—we assess the Gamma distribution’s fit and compare its performance with other distributions, validating the proposed model’s effectiveness. Full article

The reliability assesment of large power systems, particularly when considering both generation and transmission facilities, is a computationally demanding and complex problem. The sequential Monte Carlo simulation is arguably the most versatile approach for tackling this problem. However, assessing sampled states in the sequential Monte Carlo simulation is time-intensive, rendering its use less appealing, particularly if nonlinear network representation must be deployed. In this context, this paper introduces a tensor-based predictor–corrector approach to reduce the burden of state evaluations in power generation and transmission reliability assessments. The approach allows for searching for sequences of operation points which can be assigned as success states within the sequential Monte Carlo simulation. If required, failure states are evaluated using a cross-entropy optimization algorithm designed to minimize load curtailments taking into account discrete variables. Numerical results emphasize the applicability of the developed algorithms using a small test system and the IEEE-RTS79 test system. Full article

Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This paper introduces a detailed MATLAB (R2024a)/Simulink-based simulation platform that models a benchmark low-voltage industrial installation, including a six-pulse controlled rectifier, linear loads, and a capacitor bank for power factor correction. Systematic simulations are performed with the platform to examine the sensitivity of notch characteristics to key parameters within plausible ranges, such as short-circuit power at the point of common coupling, commutation reactance, firing angle, snubber circuits, and rated power of the rectifier. In addition, parameters such as the rated power of linear loads and the compensation power of the capacitor bank are examined. Other influencing parameters including background voltage unbalance and distortion are also modeled and considered. A comparative analysis with field measurements from German industrial LV networks validates the plausibility and suitability of the simulations. Building upon this platform, a Monte Carlo simulation approach is adopted to generate extensive datasets of realistic voltage notch waveforms by randomly varying these key parameters. A case study conducted under conditions typical of German LV networks demonstrates the applicability of the simulations. To support further research, the simulation platform and exemplary synthetic waveforms are provided alongside the paper, serving as a valuable tool for testing and designing strategies for analysis, detection, and monitoring of voltage notches. Full article

A resolution-improving method for multiband imaging based on an extrapolated RELAX algorithm (ERA) is proposed. The proposed method improves image resolution by reconstructing a wideband signal through nonadjacent narrow subbands. The key points of this method are the coherent compensation of the subbands and the accurate extraction of target point scatters’ parameters from each subband. For coherent compensation, a two-step phase compensation (TSPC) is used to precisely compensate for the phase incoherence terms between the subbands. In order to accurately extract the parameters of point scatters (PSs), the ERA is proposed, which establishes a cost function between the extrapolated subbands and the PSs’ parameters. Furthermore, to enhance the robustness of PSs’ parameters extraction, a local band purification (LBP) process is proposed to resist the non-Gaussian clutter in the subbands. To validate the performance of the proposed method, Monte Carlo experiments using simulated data are carried out, whose results show that the proposed method can successfully improve the image resolution through the subbands under low signal-to-clutter ratio (SCR). Moreover, experiments with real measured data are conducted, using two 0.75 GHz subbands to reconstruct a 5 GHz signal, whose 2D image result is approximated to that based on the real 5 GHz signal. Image parameters are also compared, whose results demonstrate that the proposed method has satisfactory accuracy and robustness on improving image resolution. Full article

We explore the application of integrated nested Laplace approximations for the Bayesian estimation of stochastic volatility models characterized by long memory. The logarithmic variance persistence in these models is represented by a Fractional Gaussian Noise process, which we approximate as a linear combination of independent first-order autoregressive processes, lending itself to a Gaussian Markov Random Field representation. Our results from Monte Carlo experiments indicate that this approach exhibits small sample properties akin to those of Markov Chain Monte Carlo estimators. Additionally, it offers the advantages of reduced computational complexity and the mitigation of posterior convergence issues. We employ this methodology to estimate volatility dependency patterns for both the SP&500 index and major cryptocurrencies. We thoroughly assess the in-sample fit and extend our analysis to the construction of out-of-sample forecasts. Furthermore, we propose multi-factor extensions and apply this method to estimate volatility measurements from high-frequency data, underscoring its exceptional computational efficiency. Our simulation results demonstrate that the INLA methodology achieves comparable accuracy to traditional MCMC methods for estimating latent parameters and volatilities in LMSV models. The proposed model extensions show strong in-sample fit and out-of-sample forecast performance, highlighting the versatility of the INLA approach. This method is particularly advantageous in high-frequency contexts, where the computational demands of traditional posterior simulations are often prohibitive. Full article

Accurate luminance-based image generation is critical in physically based simulations, as even minor inaccuracies in radiative transfer calculations can introduce noise or artifacts, adversely affecting image quality. The radiative transfer simulator, SWEET, uses a backward Monte Carlo approach, and its performance is analyzed alongside other simulators to assess how Monte Carlo-induced biases vary with parameters like optical thickness and medium anisotropy. This work details the advancements made to SWEET since the previous publication, with a specific focus on a more comprehensive comparison with other simulators such as Mitsuba. The core objective is to evaluate the precision of SWEET by comparing radiometric quantities like luminance, which serves as a method for validating the simulator. This analysis is particularly important in contexts such as automotive camera imaging, where accurate scene representation is crucial to reducing noise and ensuring the reliability of image-based systems in autonomous driving. By focusing on detailed radiometric comparisons, this study underscores SWEET’s ability to minimize noise, thus providing high-quality imaging for advanced applications. Full article

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications. Full article

To address the uncertainty in the statistical distribution model of positioning error for the accuracy test of long-endurance inertial navigation systems, a probability distribution model adheres to the statistical rule of the radial positioning error of inertial navigation systems. The probability distribution density function (PDF), cumulative density function (CDF), and characteristic numbers (mean, standard deviation, root-mean-square) of the radial positioning error are derived based on the static-base positioning error of the long-range inertial navigation systems. Methods are provided for estimating the parameters of the probability distribution of the radial positioning errors. The theoretical derivation results demonstrate that the radial positioning error follows the Hoyt distribution. The distribution parameters and the number of features grow linearly with time, while the mean and standard deviation converge to 60% and 80% of the root-mean-square, respectively. Through a large-sample Monte Carlo simulation, the experimental results were consistent with the theoretical derivation results. These results indicate that the theoretical derivation results can be used to optimize the design of the long-endurance rotary modulation inertial navigation system’s accuracy test. Full article

The goal of the study presented in this work is to evaluate the performance of a proposed adaptive beamforming approach when combined with non-orthogonal multiple access (NOMA) in cell-free massive multiple input multiple output (CF m-MIMO) orientations. In this context, cooperative beamforming is employed taking into consideration the geographically adjacent access points (APs) of a virtual cell, aiming to minimize co-channel interference (CCI) among mobile stations (MSs) participating in NOMA transmission. Performance is evaluated statistically via extensive Monte Carlo (MC) simulations in a two-tier wireless orientation. As the results indicate, for high data rate services, various key performance indicators (KPIs) can be improved compared to orthogonal multiple access, such as the minimum number of users in the topology as well as the available PRBs for downlink transmission. Although in NOMA transmission more directional beamforming configurations are required to compensate for the increased CCI levels, the increase in the number of hardware elements is reduced compared to the corresponding gain in the considered KPIs. Full article

Data-driven decision-making has become crucial across various domains. Randomization and re-randomization are standard techniques employed in controlled experiments to estimate causal effects in the presence of numerous pre-treatment covariates. This paper quantifies the worst-case mean squared error of the difference-in-means estimator as a generalized discrepancy of covariates between treatment and control groups. We demonstrate that existing randomized or re-randomized experiments utilizing Monte Carlo methods are sub-optimal in minimizing this generalized discrepancy. To address this limitation, we introduce a novel optimal deterministic experiment based on quasi-Monte Carlo techniques, which effectively minimizes the generalized discrepancy in a model-independent manner. We provide a theoretical proof indicating that the difference-in-means estimator derived from the proposed experiment converges more rapidly than those obtained from completely randomized or re-randomized experiments using Mahalanobis distance. Simulation results illustrate that the proposed experiment significantly reduces covariate imbalances and estimation uncertainties when compared to existing randomized and deterministic approaches. In summary, the proposed experiment serves as a reliable and effective framework for controlled experimentation in causal inference. Full article

This paper investigates statistical methods for estimating unknown lifetime parameters using a progressive first-failure censoring dataset. The failure mode’s lifetime distribution is modeled by the odd-generalized-exponential–inverse-Weibull distribution. Maximum-likelihood estimators for the model parameters, including the survival, hazard, and inverse hazard rate functions, are obtained, though they lack closed-form expressions. The Newton–Raphson method is used to compute these estimations. Confidence intervals for the parameters are approximated via the normal distribution of the maximum-likelihood estimation. The Fisher information matrix is derived using the missing information principle, and the delta method is applied to approximate the confidence intervals for the survival, hazard rate, and inverse hazard rate functions. Bayes estimators were calculated with the squared error, linear exponential, and general entropy loss functions, utilizing independent gamma distributions for informative priors. Markov-chain Monte Carlo sampling provides the highest-posterior-density credible intervals and Bayesian point estimates for the parameters and reliability characteristics. This study evaluates these methods through Monte Carlo simulations, comparing Bayes and maximum-likelihood estimates based on mean squared errors for point estimates, average interval widths, and coverage probabilities for interval estimators. A real dataset is also analyzed to illustrate the proposed methods. Full article

Shale gas, a low-permeability, unconventional resource, requires horizontal drilling and multi-stage fracturing for commercial production. This study develops a trilinear flow model for fractured horizontal wells in shale gas formations, incorporating key mechanisms such as adsorption, desorption, diffusion, wellbore storage, and skin effects. The model delineates seven distinct flow regimes, providing insights into gas migration processes and the factors controlling production. Sensitivity analyses reveal that desorption plays a critical role under low-pressure and low-production conditions, significantly enhancing gas transfer rates from the matrix to the fracture network and contributing to overall production. Monte Carlo simulations further highlight the variability in pressure responses under different input conditions, offering a comprehensive understanding of the model’s behavior in complex reservoir environments. These findings advance the characterization of shale gas flow dynamics and inform the optimization of production strategies. Full article

Multicomponent gas mixture diffusion in a microscale confined flow in the transition gas regime at Knudsen numbers (Kn) above 0.1 has potential engineering applications in gas-phase microfluidics. Although the calculation of the diffusion coefficient accounts for the influence of the concentration of other species in a multicomponent gas mixture, the higher rate of gas-wall collision at 0.1 < Kn ≤ 10 introduces additional complications not predicted by conventional calculation methods. Thus, simultaneous measurement of diffusion coefficients for multiple gas species ensures accurate estimation of the diffusion coefficient of a particular species that includes the effect of interactions with other species and wall surface conditions in a multicomponent gas mixture at Kn > 0.1. However, most experimental methods for measuring the diffusion coefficient are not species-specific and therefore cannot directly differentiate between the species diffusing in a gas mixture. Thus, this paper demonstrates a new experiment methodology consisting of a two-bulb diffusion configuration accompanied by a tunable diode laser absorption spectroscopy detection technique for species-specific, in-situ, simultaneous measurement of the effective diffusion coefficient for a CO₂-N₂O gas mixture in the transition gas regime. The experimental results are compared against direct simulation Monte Carlo calculations and the Bosanquet approximation showing a deviation that has not been reported in the literature before. Full article