Confidence intervals for a proportion using a fixed-inverse double sampling scheme when the data are subject to false-positive misclassification

Thermal-pulse method is a powerful tool for measuring space charge distributions in polymer films. The data analysis for thermal-pulse method involves the Fredholm integral equation of the first kind, which requires an appropriate numerical procedure to obtain a solution. Various numerical techniques, including scale transformation and regulation method, are proposed. Of those numerical methods, the scale transformation (ST) is the simplest and the most widely used method. However, it presents a high spatial resolution only near the sample surface. Monte Carlo (MC) method is one of the recently proposed ways to solve the equation numerically and has been successfully applied to the analysis of laser intensity modulation method data, which also involves the Fredholm integral equation of the first kind. In this paper we attempt to analyze thermal-pulse data in frequency domain with the MC method and discuss its effectiveness based on some numerical simulations. The simulation results indicate that the electric field profiles can be effectively extracted by the MC method. The computed profiles by the MC method consist well with the supposed distributions in the entire thickness of the sample, while the profiles reconstructed by the ST method fit very well to the supposed one at the vicinity of the target surface and distort sharply along the direction of the thermal pulse propagation in the sample bulk. On the other hand, the oscillations in the computed results by the MC method could deteriorate its accuracy in this study. The influence of noise level on the analysis based on the MC method is also tested by the use of the simulated data. The results show that the computed profiles would become more fluctuant as the noise level increases. This problem can be solved by selecting a larger value of tolerance during the singular value decomposition procedure. Thus, the value of tolerance is considered to be one of the key parameters in this algorithm, which is actually hard to determine. Additionally, the experimental data obtained from a polypropylene film under applied electric field are analyzed to illustrate the feasibility of MC method to be applied to the thermal-pulse experimental data. The results also show that the spatial accuracy by the MC method in the entire sample thickness is higher than by the ST method, which verifies that the MC method is more suitable for detecting the electric field distribution in the deep bulk of the sample. Owing to noise and error, the accuracy of MC calculation depends on the chosen tolerance value, which is now considered to be an obstacle in applying this method to the practical thermal-pulse measurement.

The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x-ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self-teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs.

Research in disease diagnosis is a challenging task due to inconsistent, class imbalance, conflicting, and the high dimensionality of medical data sets. The excellent features of each data set play an important role in improving performance of classifiers that may follow either iterative or non-iterative approaches. In the present study, a comparative study is carried out to show the performance of iterative and non-iterative classifiers in combination with genetic algorithm (GA)-based feature selection approach over some widely used medical data sets. The experiment assists to identify the clinical data sets for which feature reduction is necessary for improving performance of classifiers. For iterative approaches, two popular classifiers, namely C4.5 and RIPPER, are chosen, whereas k-NN and naïve Bayes are taken as non-iterative learners. Fourteen real-world medical domain data sets are selected from the University of California, Irvine (UCI Repository) for conducting experiment over the learners.

The current study utilises Monte Carlo simulation and Mie scattering theory to estimate the reflectance spectra of fabric coated with titanium dioxide nanopigments of various diameters and concentrations. Image processing was carried out and experimental data were gathered to evaluate the performance of Monte Carlo simulation. The distribution and location of the nanopigments on the surface of fabric were determined using the Monte Carlo method. Reflection of the fabric was calculated based on Monte Carlo simulation with the partitive mixing method and Mie theory. According to the experimental and simulation results, the reflectance of coated samples was increased by increasing the concentration and number of titanium dioxide nanoparticles. There was a good match between the results obtained by Monte Carlo simulation and the experimental results. For coated samples (dTiO2: 500 nm), the root mean square error between measured and predicted reflectance by the Monte Carlo and partitive mixing method and by Monte Carlo and Mie theory was 0.022 and 0.0078, respectively. The results indicate that the performance of the Monte Carlo and Mie method was better than that of the Monte Carlo and partitive mixing method. According to t‐test analysis, there was no statistically significant difference between the experimental data and Monte Carlo simulation.

We combine molecular dynamics (MD) and Monte Carlo (MC) simulations to study the properties of gas molecules confined between two hard walls of a microchannel or nanochannel. The coupling between MD and MC simulations is introduced by performing MD near the boundaries for accuracy and MC in the bulk because of the low computational cost. We characterize the influence of different densities and molecule sizes on the equilibrium properties of the gas in the microchannel. The effect of the particle size on the simulation results is very small in the case of a dilute gas and increases with the density. The hybrid MD-MC simulation method is validated by comparing the results for density and temperature profiles with those of pure MD and pure MC simulations. These results compare well for pure MD and pure MC, as well as hybrid MD-MC, both in the bulk and near the boundaries, when hard-sphere interactions are used. When Lennard-Jones potentials are used to accurately model the interactions between the gas and wall molecules instead, the results of pure MD simulations differ significantly from the pure MC simulations near the boundaries, but the results of the hybrid method compare well with the pure MD results near the wall, and with the pure MC and pure MD results in the middle of the channel. The hybrid method also very accurately simulates the interface between the MD and MC simulation domains. Comparisons between MD, MC, and hybrid MD-MC computational costs are outlined. The speedup when using 50% of the domain for MD simulations and 50% for MC simulations is very small compared to pure MD simulations times, but this speedup increases drastically for more realistic situations where the region near the wall is small compared to the bulk region.

Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict "normal" sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Finally, learning-based methods are trained to statistically identify classes of faults. We find that these three classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. We apply these techniques to four real-world sensor data sets and find that the prevalence of faults as well as their type varies with data sets. All three methods are qualitatively consistent in identifying sensor faults in real world data sets, lending credence to our observations. Our work is a first-step towards automated on-line fault detection and classification.

The stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model by the use of a maximum entropy model to differentially control the intensity of the stochastic perturbation in the spatial domain. We use the kappa coefficient, figure of merit, and landscape metrics to evaluate the accuracy of the simulated results. Through the experimental results obtained for Wuhan, China, the effectiveness of the optimization is proved. The results show that, after the optimization, the kappa coefficient and figure of merit of the simulated results are significantly improved when using the stochastic variable, slightly improved when using Monte Carlo methods. The landscape metrics for the simulated results and actual data are much closer when using the stochastic variable, and slightly closer when using the Monte Carlo method, but the difference between the simulated results is narrowed, reflecting the fact that the results are more reliable.

Comprehensive evaluation methods are generally used to assess the population data. When we need to estimate the sample data in special situations, the impacts brought by the sampling error should be considered. Due to lack of the accurate measurement for the sensitivity and stability in the comprehensive evaluation methods, sampling errors usually cannot be estimated in the sampling research. Monte Carlo simulation was used in this article to solve the probability of the ordering results, and the matlab programs were presented. Based on the simulated results, we change the conventional "absolute conclusion" of comprehensive evaluation methods to "probability results" for the sample data, and put forward a new sorting and ranking method for the results of comprehensive evaluation.

Surface segregation in Pt 25Rh 75 alloys studied by Monte Carlo simulations and the modified embedded atom method

To estimate the detection rates (DRs) and false-positive rates (FPRs) in the incidental identification of trisomy 18 (T18) and trisomy 13 (T13) as part of antenatal screening for Down's syndrome (DS) using the Combined, Quadruple and Integrated test markers. Screening marker levels on 224 T18 and 67 T13 pregnancies screened for DS were evaluated. Estimated means, standard deviations and correlation coefficients were used with published estimates for unaffected pregnancies to derive detection algorithms for the two disorders. DRs and FPRs of the algorithms were estimated using Monte Carlo simulation. In T18 and T13 pregnancies first trimester nuchal translucency was raised, free β-human chorionic gonadotrophin (hCG) and pregnancy associated plasma protein-A reduced. In T18 pregnancies second trimester alphafetoprotein, unconjugated oestriol and free β-hCG were reduced. In T13 pregnancies second trimester inhibin-A was raised. These markers specified T18 and T13 algorithms. The DS Combined test algorithm detected 42% of T18 and 59% of T13 (2.00% FPR); 88% and 74% by adding the T18 Combined test algorithm (2.17% FPR) and 89% and 75% by further adding the T13 Combined test algorithm (2.19% FPR). The corresponding detection rates for the Quadruple test were: 5% and 21% (2.00% FPR), 59% and 21% (2.16% FPR) and 59% and 24% (2.28% FPR), and for the Integrated test were: 40% and 60% (2.00% FPR), 92% and 68% (2.12% FPR) and 92% and 74% (2.18% FPR).[Corrected] Antenatal screening for DS detects about 40% of T18 and about 60% of T13 pregnancies. The addition of a T18 algorithm substantially increases the detection of both trisomies with a small increase in the FPR. The further addition of a T13 algorithm results in a small increase in the detection of T13.

Grain characteristics of copper filler have an important influence on physical properties of through silicon vias (TSVs) in three-dimensional (3D) packaging. Due to the mismatch of coefficients of thermal expansion (CTE) between the copper and silicon, there exist obvious thermal stresses when the TSV structure is bearing thermal load. However, the elastic response characteristics of copper can be affected distinctly by the size, shape and orientation of copper grains in TSVs. These effects can not be ignored, especially for 3D ICs undergoing continuous miniaturization and increasing integration. In this study, the grain growth and evolution of a typical copper filler in the TSV structure is dynamically simulated and investigated by using Monte Carlo method (MCM), then the thermo-mechanical behavior of TSVs with considering the grain characteristics of copper filler is studied with combined MCM and finite element method (FEM), i.e., MC-FE method. Besides, the effects of grain evolution on the protrusion of copper filler in the TSV is also investigated. Simulation results are presented for providing more quantitative description and comprehensive understanding of the influences of microstructure evolution of copper filler on the thermo-mechanical behavior of Cu-filled TSVs.

Polya tree Monte Carlo method

Sensitivity study of C/O logging measurements by Monte Carlo method

In the social computing environment, the complete information about an individual is usually distributed in heterogeneous social networks, which are presented as linked data. Synthetically recognizing and integrating these distributed and heterogeneous data for efficiently information searching is an important but challenging work. In this paper, a dynamic weight (DW)-based similarity calculation is proposed to recognize and integrate similar individuals from distributed data environments. First, each link of an individual is weighted by applying DW. Then, a semantic similarity metric is proposed to combine the DW into similarity calculation. Then, a searching system framework for a similarity-based individual is designed and tested in real-world data sets. Finally, massive experiments are conducted both in benchmark and real-world social community data sets. The results show that our approach can produce a good result in similar individual searching in social networks. In addition, it performs significantly better than the existing state-of-the-art approaches in similar individual searching.

A new three-parameter (valency, ion size, and charge asymmetry) model, the asymmetric restricted primitive model (ARPM) of ionic liquids, has recently been proposed. Given that ionic liquids generally are composed of monovalent species, the ARPM effectively reduces to a two-parameter model. Monte Carlo (MC) simulations have demonstrated that the ARPM is able to reproduce key properties of room temperature ionic liquids (RTILs) in bulk and at charged surfaces. The relatively modest complexity of the model raises the possibility, which is explored here, that a classical density functional theory (DFT) could resolve its properties. This is relevant because it might generate great improvements in terms of both numerical efficiency and understanding in the continued research of RTILs and their applications. In this report, a DFT for rod-like molecules is proposed as an approximate theoretical tool for an ARPM fluid. Borrowing data on the ion pair fraction from a single bulk simulation, the ARPM is modelled as a mixture of dissociated ions and connected ion pairs. We have specifically studied an ARPM where the hard-sphere diameter is 5 Å, with the charge located 1 Å from the hard-sphere centre. We focus on fluid structure and electrochemical behaviour of this ARPM fluid, into which a model electrode is immersed. The latter is modelled as a perfect conductor, and surface polarization is handled by the method of image charges. Approximate methods, which were developed in an earlier study, to take image interactions into account, are also incorporated in the DFT. We make direct numerical comparisons between DFT predictions and corresponding simulation data. The DFT theory is implemented both in the normal mean field form with respect to the electrostatic interactions and in a correlated form based on hole formation by both steric repulsions and ion-ion Coulomb interactions. The results clearly show that ion-ion correlations play a very important role in the screening of the charged surfaces by our ARPM ionic liquid. We have studied electrostatic potentials and ion density profiles as well the differential capacitance. The mean-field DFT fails to reproduce these properties, but the inclusion of ion-ion correlation by a simple approximate treatment yields quite reasonable agreement with the corresponding simulation results. An interesting finding is that there appears to be a surface phase transition at relatively low surface charge which is readily explored by DFT, but seen also in the MC simulations at somewhat higher asymmetry.