Bootstrap Statistical Method Research Articles

A step toward calculating the uncertainties in combined GIXRF‐XRR

AbstractThe combination of X‐ray reflectivity (XRR) and grazing incidence X‐ray fluorescence (GIXRF) is a surface sensitive analytical method, which can be used for the characterization of thin films and multilayered materials. Both of these techniques are implemented on the same experimental setup and make use of similar mechanical processes and the same fundamental physical concept required for a combined data analysis. The combination of these techniques removes ambiguous results for the characterization of nanometer layers, as well as nanometer depth profiles, resulting in more accurate characterization of thickness, roughness, density, and elemental composition. Due to the vast number of fitting parameters, the estimation of the thin film sample structure is a challenging task. In this paper, we propose a recursive method for estimating the uncertainties of data from GIXRF‐XRR analysis, based on a Bootstrap statistical method. This approach relies on re‐sampling a dataset to estimate statistics on a population by applying random weights. We applied this method on an as‐deposited chalcogenide germanium, antimony, and tellurium (GST) thin film with a carbon‐capping layer. We found good agreement between the experimental and the theoretical XRR‐GIXRF values for a sample structure model, of which the parameters were determined within a confidence interval using the bootstrap method. We also propose an approach for calculating the uncertainty on the solid angle of detection based on Monte Carlo simulations.

Cycle Registration in Persistent Homology With Applications in Topological Bootstrap.

We propose a novel approach for comparing the persistent homology representations of two spaces (or filtrations). Commonly used methods are based on numerical summaries such as persistence diagrams and persistence landscapes, along with suitable metrics (e.g., Wasserstein). These summaries are useful for computational purposes, but they are merely a marginal of the actual topological information that persistent homology can provide. Instead, our approach compares between two topological representations directly in the data space. We do so by defining a correspondence relation between individual persistent cycles of two different spaces, and devising a method for computing this correspondence. Our matching of cycles is based on both the persistence intervals and the spatial placement of each feature. We demonstrate our new framework in the context of topological inference, where we use statistical bootstrap methods in order to differentiate between real features and noise in point cloud data.

Operational Efficiency and Debt Cost: The Mediating Effect of Carbon Information Disclosure in Chinese Listed Companies

Voluntary corporate carbon information disclosure not only meets the carbon information needs of investors, but also enhances the legitimacy of organizations. Building a green image may increase the public consumption of products and be conducive to reducing the cost of debt. As a part of creditors’ assessment of debtors’ solvency, operational efficiency is the basic factor of debt cost reduction. By constructing the correlation between carbon information disclosure and operational efficiency, this paper attempts to test the mediating effect of carbon information disclosure in the relationship between operational efficiency and debt cost, so as to increase the understanding of the mechanism of action between carbon information disclosure and debt cost. Stepwise regression method and Bootstrap statistical method were used to test. The results show that the higher the operational efficiency, the lower the debt cost; the carbon information disclosure of enterprises in low-carbon industries has a significant partial mediating effect on the relationship between operational efficiency and debt cost while that of carbon-intensive industries is not significant. It verifies that the operating efficiency of low-carbon industry can affect the cost of debt through carbon information disclosure, and finds a significant correlation between carbon information disclosure and operating efficiency. This study broadens ways for enterprises in low-carbon industries to reduce debt costs, highlights the role of operational efficiency in various industries, and reveals favorable evidence in the positive value of carbon disclosure in low-carbon industries which in essence can better reflect the enthusiasm of enterprises in their own financing or the restrictive channels of enterprise carbon performance evaluation. This has implications for the research on how to promote the link between carbon information disclosure and debt cost of enterprises in high-carbon industries, which will help enterprises in the future to make carbon information transparency or voluntarily disclose carbon information. It is of great significance for regional and industrial enterprises to choose the disclosure system of voluntary or mandatory carbon information disclosure.

Assessment of Physiological Responses During Field Science Task Performance: Feasibility and Future Needs.

ObjectiveBy understanding the physiological demands of different types of tasks that will be performed during extravehicular activity (EVA) on Mars, human performance safety risks can be mitigated. In addition, such understanding can assist in planning EVAs with an appropriate balance of human health and safety with scientific mission return.BackgroundThis paper describes the results of a study of technical feasibility performed within a Mars human research analog, with participants conducting scientifically relevant planetary science sample analysis and return tasks in two distinct field locations.MethodsThe authors collected heart rate, respiration rate, and heart rate variability (HRV) data, using commercial off-the-shelf hardware and software from study participants as they performed field science tasks within a concept of operations for a Mars science return human expedition mission. These data were remotely monitored, shared in real time, and later analyzed to identify different responses to different tasks in order to determine if there were any predictable or consistent patterns among participants.ResultsIt was ultimately determined that, while differences exist between responses to tasks, they are highly subject to multiple sources of individual variability, dynamics of evolving field science tasks, and demands of a demanding physical environment. Further, distributional analyses of participants do not support parametric statistical analysis techniques.ConclusionThe authors conclude that the physiology of individual astronauts should be extensively studied and modeled to support individualized automated monitoring tools for each crew member that is sent to Mars. Application: Physiological monitoring for specialized populations will require significant individual-level analysis, baselining, and bootstrap statistical methods to enable appropriate human performance determinations.

Investigate the relationship between social and emotional loneliness, fear of negative evaluation, thought suppression, and mindfulness with mediating self-concealment

Introduction: Loneliness is the most painful experience of all human experiences that leads to severe psychological and physical problems. Aim: The aim of this study was to investigate the relationship between social and emotional loneliness, fear of negative evaluation, thought Suppression, and mindfulness with mediating self-concealment in students of the Shahid Charmin University of Ahvaz. Method: The research design is correlational and structural equations. The study population included all students of Shahid Charmin University of Ahvaz in the academic year of 2016-2017, 200 of them were selected as the research sample and answered the Mindfulness Attention Awareness Scale (MAAS), White Bear Suppression Inventory (WBSI), the brief version of the fear of negative evaluation scale (FNES), the short version of the social and emotional loneliness scale (SELSA-S) and self-conceal scale (SCS). Data analysis was performed using AMOS and SPSS software version 20 and Pearson correlation, path analysis, and bootstrap statistical methods. Results: The fit indices of the model including RMSEA=0.01, NFI=0.99, CFI=0.99, IFI=0.99, AGFI=0.99, GFI=0.99, which indicate good fit the proposed model. All the indirect effects of fear of negative evaluation with mindfulness and thought Suppression and also social and emotional loneliness with mindfulness and thought Suppression with the mediation of selfconcealment became significant (p≤0.001). Conclusion: Mindfulness reduces social and emotional loneliness, the fear of negative evaluation, thought suppression and self-concealment by accepting feelings and thoughts.

Optimization of a leaf wetness duration model

Characterization of LWD (Leaf Wetness Duration) is essential when simulating the spread of phytopathogenic fungi. No standard for leaf wetness measurement exists and a wide range of sensors and models are currently available for estimating LWD. While the vast majority of models use hourly climate data (temperature, relative humidity, etc.) this time step is not widely available in historical records. It is also seldom available in spatially disaggregated projected (21st century) climatic data. Our study aims to develop a new method for using temperature and humidity on a daily time step to simulate leaf wetness duration. The principle behind our model consists in reconstructing hourly temperature and relative humidity based on optimized equations with the bootstrap statistical method for the studied area. Then hourly simulated data are used to estimate the daily duration of leaf wetness. The sensitivity of this process to daily and hourly temperature and relative humidity is assessed. The impact of this model on simulations of potential infections of grapevine (Vitis vinifera L.) by powdery mildew, using a key equation within a mechanistic model that simulates the pathogen's (Erysiphe necator) primary contaminations was evaluated. Results show that the error in simulating LWD and its impact on the potential intensity of the primary contamination of powdery mildew with daily climate data is low. This study has enabled us to set up an optimized modelling chain, which could be readily reproduced in different regions provided existing observational datasets enable suitable parameterization.

A Machine Learning Classifier Based on Statistical Method for Small Number of Samples

Due to the small number of train samples, the classifier with ideal generalization ability for small sample size is not easy to obtain. However, the problem of constructing small sample classifier exists widely in the real world, especially in the field of biological medicine. Therefore, building a classifier based on small number of samples has become a research hotspot. In this study, we proposed a machine learning classifier based on statistical methods to solve this problem. In this method, Bootstrap, chi-square test and other statistical methods were firstly combined to solve the performance evaluation of multiple machine learning classifiers under small sample sizes dataset. Then, the application of Youden index in machine learning classifiers was optimized to meet the requirements of clinical application. The simulation experiment on the UCI breast cancer data set shows that this method is more stable and accurate for the performance evaluation, and at the same time，optimized Youden index in machine learning classifier is able to flexibly meet the application requirements of small number of samples in the medical research field.

Statistical bootstrap in the problem of availability factor estimating: example of SRs 1200 excavator reliability

It is difficult sometimes to estimate the confidence intervals for the particular statistics, because there is no so far for some of them mathematical expression for their variance. That is true for availability factor—complex characteristic of restorable items reliability. Statistical bootstrap, as one of resampling methods, has been applied to this problem. Availability of subsystems of rotary excavator has been considered using the proposed method. The data of reliability were collected in Serbia, at open coal development. The proposed model is based on statistical bootstrap method. On the basis of proposed method, the availability factor confidence intervals and histograms for excavator subsystems were built. The availability factor of the system in a whole was also evaluated. The results, as well as comparison with existing approaches, are presented. The attempt has been made to estimate the risk factor of subsystems using the data of availability.

The number of measurements of a surface’s topography and the expected variation in adhesion to the surface

Variations in roughness on a surface spawn variations in adhesion force between the surface and any particles that contact the surface. To fully characterize the adhesion that will be exhibited when a particle contacts any location on the surface, it is desirable to map the surface with nanoscale detail. Since it is impractical to make nanoscale roughness measurements over the entirety of a surface with a characteristic dimension on the order of centimeters, a relationship between the number of surface measurements and the likely variation in the expected adhesion force is similarly desirable. In this work, the predicted van der Waals force was used to describe the particle adhesion force. The bootstrap statistical method was employed to estimate the error associated with the predicted mean adhesion force between a smooth spherical particle and a rough surface as a function of the number of locations on the surface where the roughness was measured. Specifically, 40 atomic force microscope (AFM) topographical scans (5 × 5 μm) were taken of three different surfaces and used as model surface inputs to an existing van der Waals adhesion force simulator. The simulator described the expected adhesion force resulting from 1200 contacts between the smooth, spherical particle (10 μm diameter) and random locations on each scanned area. After analyzing the results using the bootstrap method, it was determined that the adhesion between the particle and 10–15 scanned areas (out of 40) optimizes the accuracy of the predicted adhesion with respect to the researcher’s labor.

Understanding Phenotypic Plasticity Induced by Environmental Factors Using Computational Markov Modeling

Cancer plasticity contributes to tumorigenesis, tumor progression, and drug resistance. Plasticity has traditionally been attributed to stem‐like cells, but recent studies used markers of stemness and differentiation to purify specific phenotypes from a mixed population of cancer cells by flow cytometry, and found that both differentiated and dedifferentiated phenotypes could revert over time to a heterogeneous equilibrium mixture, through stochastic transitions. A given instance of heterogeneity can in theory arise either from multiple static phenotypes or from transitions between non‐static phenotypes, and therefore, understanding plasticity requires measuring transition rates.We developed a Markov‐modeling computational approach to analyze time‐series intensity data from flow cytometry experiments, and to quantify phenotypic change in terms of cell state transitions. The software employs optimization and novel statistical bootstrap methods to compute best‐fit transition probabilities (and associated dispersion measures or confidence intervals) for stochastic transitions between multiple cell states.Applying this method to the MCF10A model of breast cancer provided quantification of stochastic transitions as follows. Cells were grown in different culture conditions: basal medium supplemented with the components of MCF10a complete growth medium (insulin, EGF, hydrocortisone, cholera toxin), given individually or together. For each condition, cells were purified into populations of epithelial‐like CD44+CD24+ cells and stem‐like CD44+CD24− cells. The purified populations were cultured for 12 days, and the population fraction of each state was measured using flow cytometry on days 4, 8 and 12 post‐sorting. Analysis using Markov models showed that Hydrocortisone (HC), and Cholera toxin (CTX) cause the largest changes in transition rates, compared with control (basal media). CTX drove the equilibrium toward a more differentiated CD24+ phenotype. In contrast, HC supplementation drove toward the CD24− phenotype, which showed increased mammosphere formation and an aggressive mRNA profile. The bootstrapping method showed these transition rate changes to be statistically significant.In conclusion, we developed a novel algorithm to estimate transition probabilities from FACS data, and to make statistical comparisons between transition probabilities. Applying our method to a cell culture model of triple‐negative breast cancer showed that exposure to hydrocortisone increased the rate of cell transitions toward a more malignant phenotype, suggesting that chronic exposure to the stress hormone cortisol might be particularly deleterious. This software will be released open‐source and is currently available by request.Support or Funding Information Singapore Ministry of Health's National Medical Research Council (NMRC) under its Open Fund Individual Research Grant scheme (OFIRG15nov062). National Research Foundation (NRF), Prime Minister's Office, Singapore, under its CREATE programme, Singapore‐MIT Alliance for Research and Technology (SMART) BioSystems and Micromechanics (BioSyM) IRG This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Prevalence of Physical Inactivity and its Effects on Blood Pressure and Metabolic Parameters in a Brazilian Urban Population

Abstract Background: Cardiovascular disease is the leading cause of mortality in the world and physical inactivity represents an important risk factor. Objective: This study aimed to evaluate the prevalence of physical inactivity in the adult population and its effects on blood pressure, blood glucose and lipid profile. Methods: A population-based cross-sectional study with stratified simple random sampling was conducted in 1,717 adults divided by age groups: 18-39, 40-49, 50-59, 60-69 and ≥ 70 years. The participants answered the physical activity questionnaire and were classified as physically active or inactive. The bootstrap statistical method was used to assess physical activity, associated with lipid profile and blood glucose levels. The level of significance was 5%. Results: The prevalence of physical inactivity in the general population was 65.8%. There was a significant difference in the group older than 70 years. There was a significant decrease in physical activity in the group with lower educational level, with a significant difference between social classes AB and C. The prevalence of hypertension was 27.5% among physically inactive and 21.4% among active individuals (p = 0.04). The prevalence of metabolic syndrome was 26.1% in inactive and 16.7% in the active individuals (p = 0.007). Total cholesterol, low-density lipoprotein and triglycerides levels were more elevated in the physically inactive group, which was not observed with high-density lipoprotein levels. Blood glucose was also higher in the inactive group. Conclusion: This study shows a high prevalence of physical inactivity and a positive correlation between risk factors for cardiovascular disease, mainly blood pressure, glucose and lipids profiles. (Int J Cardiovasc Sci. 2018; [online].ahead print, PP.0-0)

Statistic and dosimetric criteria to assess the shift of the prescribed dose for lung radiotherapy plans when integrating point kernel models in medical physics: are we ready?

To apply the statistical bootstrap analysis and dosimetric criteria's to assess the change of prescribed dose (PD) for lung cancer to maintain the same clinical results when using new generations of dose calculation algorithms. Nine lung cancer cases were studied. For each patient, three treatment plans were generated using exactly the same beams arrangements. In plan 1, the dose was calculated using pencil beam convolution (PBC) algorithm turning on heterogeneity correction with modified batho (PBC-MB). In plan 2, the dose was calculated using anisotropic analytical algorithm (AAA) and the same PD, as plan 1. In plan 3, the dose was calculated using AAA with monitor units (MUs) obtained from PBC-MB, as input. The dosimetric criteria's include MUs, delivered dose at isocentre (Diso) and calculated dose to 95% of the target volume (D95). The bootstrap method was used to assess the significance of the dose differences and to accurately estimate the 95% confidence interval (95% CI). Wilcoxon and Spearman's rank tests were used to calculate P values and the correlation coefficient (ρ). Statistically significant for dose difference was found using point kernel model. A good correlation was observed between both algorithms types, with ρ>0.9. Using AAA instead of PBC-MB, an adjustment of the PD in the isocentre is suggested. For a given set of patients, we assessed the need to readjust the PD for lung cancer using dosimetric indices and bootstrap statistical method. Thus, if the goal is to keep on with the same clinical results, the PD for lung tumors has to be adjusted with AAA. According to our simulation we suggest to readjust the PD by 5% and an optimization for beam arrangements to better protect the organs at risks (OARs).

Assessment of the Online Core Power Measured by a Boron Chamber in a Pool-Type Research Reactor Using a Nonlinear Calibration Model

The CABRI facility is an experimental nuclear reactor of the French Atomic Energy Commission (CEA) designed to study the behavior of fuel rods at high burnup under reactivity initiated accident conditions, such as a control rod ejection. The distinctive feature of this reactor is its reactivity injection system. The power can rise from 100 kW to 25 GW in a few milliseconds. To know the energy released into a test rod, it is necessary to access the driver core power online. The neutron flux is measured online by compensated boron chambers. These neutron detectors are calibrated during the commissioning phase thanks to standards given by a conventional heat balance. The boron chamber signal depends on the temperature of the pool and the magnitude of the core power according to a nonlinear multivariate model. The uncertainties of the standards and those of the neutron chamber signal cannot be neglected. Moreover, the size of the sample is very small due to the operational constraints. A classic regression method does not take into account all these parameters. In such a situation, we show how the statistical bootstrap method can prove to be a useful and easy tool in tackling this issue. This paper describes first the adjustment of the calibration model that will be used for the prediction during the core power transient and second how we take into account both the uncertainties of the physical variables and the small size of the experimental sample.

Determining Selected Diesel Engine Combustion Descriptors Using the Bootstrap Method

This paper presents the method that uses the coefficient of variation (COV) of combustion chamber pressure to determine the crank angle degree (CAD) at which the rate of heat release (HRR) reaches its maximum. The results were obtained for a diesel engine running at full load on diesel. The bootstrap statistical method was used to find the crank angle degree of the peak rate of heat release. The uncertainty of the COV for the combustion chamber pressure was calculated. A good agreement between the experimental results and the literature data shows the validity of the analysis.

Measurement of cardiac troponin I in healthy lactating dairy cows using a point of care analyzer (i-STAT-1)

BackgroundCardiac troponin I (cTnI) has been shown to be an accurate predictor of myocardial injury in cattle. The point-of-care i-STAT 1 immunoassay can be used to quantify blood cTnI in cattle. However, the cTnI reference interval in whole blood of healthy early lactating dairy cows remains unknown. ObjectiveTo determine a blood cTnI reference interval in healthy early lactating Holstein dairy cows using the analyzer i-STAT 1. MethodsForty healthy lactating dairy Holstein cows (0–60 days in milk) were conveniently selected from four commercial dairy farms. Each selected cow was examined by a veterinarian and transthoracic echocardiography was performed. A cow-side blood cTnI dosage was measured at the same time. A bootstrap statistical analysis method using unrestricted resampling was used to determine a reference interval for blood cTnI values. ResultsForty healthy cows were recruited in the study. Median blood cTnI was 0.02 ng/mL (minimum: 0.00, maximum: 0.05). Based on the bootstrap analysis method with 40 cases, the 95th percentile of cTnI values in healthy cows was 0.036 ng/mL (90% CI: 0.02–0.05 ng/mL). ConclusionsA reference interval for blood cTnI values in healthy lactating cows was determined. Further research is needed to determine whether cTnI blood values could be used to diagnose and provide a prognosis for cardiac and noncardiac diseases in lactating dairy cows.

Methods for determining the uncertainty of population estimates derived from satellite imagery and limited survey data: a case study of Bo city, Sierra Leone.

This study demonstrates the use of bootstrap methods to estimate the total population of urban and periurban areas using satellite imagery and limited survey data. We conducted complete household surveys in 20 neighborhoods in the city of Bo, Sierra Leone, which collectively were home to 25,954 persons living in 1,979 residential structures. For five of those twenty sections, we quantized the rooftop areas of structures extracted from satellite images. We used bootstrap statistical methods to estimate the total population of the pooled sections, including the associated uncertainty intervals, as a function of sample size. Evaluations based either on rooftop area per person or on the mean number of occupants per residence both converged on the true population size. We demonstrate with this simulation that demographic surveys of a relatively small proportion of residences can provide a foundation for accurately estimating the total population in conjunction with aerial photographs.

Investigation of the characteristics of Portuguese regular moment-frame RC buildings and development of a vulnerability model

A vulnerability model capable of providing the probabilistic distribution of loss ratio for a set of intensity measure levels is a fundamental tool to perform earthquake loss estimation and seismic risk assessment. The aim of the study presented herein is to develop a set of vulnerability functions for 48 reinforced concrete building typologies, categorized based on the date of construction (which has a direct relation with the design code level), number of storeys (height of the building) and seismic zonation (which affects the design of the buildings). An analytical methodology was adopted, in which thousands of nonlinear dynamic analyses were performed on 2D moment resisting frames with masonry infills, using one hundred ground motion records that are compatible, to the extent possible, with the Portuguese tectonic environment. The generation of the structural models was carried out using the probabilistic distribution of a set of geometric and material properties, compiled based on information gathered from a large sample of drawings and technical specifications of typical Portuguese reinforced concrete buildings, located in various regions in the country. Various key aspects in the development of the vulnerability model are investigated herein, such as the selection of the ground motion records, the modelling of the infilled frames, the definition of the damage criterion and the evaluation of dynamic (i.e. period of vibration) and structural (i.e. displacement and base shear capacity) parameters of the frames. A statistical bootstrap method is demonstrated to estimate the variability of the loss ratio at each intensity measure level, allowing the estimation of the mean, as well as 10 and 90 % percentile vulnerability curves.

Use of the Bootstrap Method to Develop a Physical Fitness Test for Public Safety Officers Who Serve as Both Police Officers and Firefighters

Physical fitness testing is a common tool for motivating employees with strenuous occupations to reach and maintain a minimum level of fitness. Nevertheless, the use of such tests can be hampered by several factors, including required compliance with US antidiscrimination laws. The Highland Park (Texas) Department of Public Safety implemented testing in 1991, but no single test adequately evaluated its sworn employees, who are cross-trained and serve as police officers and firefighters. In 2010, the department's fitness experts worked with exercise physiologists from Baylor Heart and Vascular Hospital to develop and evaluate a single test that would be equitable regardless of race/ethnicity, disability, sex, or age >50 years. The new test comprised a series of exercises to assess overall fitness, followed by two sequences of job-specific tasks related to firefighting and police work, respectively. The study group of 50 public safety officers took the test; raw data (e.g., the number of repetitions performed or the time required to complete a task) were collected during three quarterly testing sessions. The statistical bootstrap method was then used to determine the levels of performance that would correlate with 0, 1, 2, or 3 points for each task. A sensitivity analysis was done to determine the overall minimum passing score of 17 points. The new physical fitness test and scoring system have been incorporated into the department's policies and procedures as part of the town's overall employee fitness program.

Multivariate polynomial regression regularized via fit function uncertainty

This article describes a multivariate polynomial regression method where the uncertainty of the input parameters are approximated with Gaussian distributions, derived from the central limit theorem for large weighted sums, directly from the training sample. The estimated uncertainties can be propagated into the optimal fit function, as an alternative to the statistical bootstrap method. This uncertainty can be propagated further into a loss function like quantity, with which it is possible to calculate the expected loss function, and allows to select the optimal polynomial degree with statistical significance. Combined with simple phase space splitting methods, it is possible to model most features of the training data even with low degree polynomials or constants.

Folding and unfolding thermodynamics of the TC10b Trp-cage miniprotein

We examine the folding-unfolding of a variant of the Trp-cage, known as TC10b, and compare structural stability, dynamics, and thermodynamics with that of the TC5b variant, using replica exchange molecular dynamics (REMD). The TC10b variant was designed to have larger helical stability by the substitution of amino acids with greater alpha helical propensities in the N-terminal region. Experiments have shown TC10b to possess larger overall stability than TC5b. Simulations starting from unbiased, unfolded initial conditions are run for 1 μs per replica. The calculations show a higher melting temperature for TC10b than TC5b, and suggest a more ordered folded structure through the elimination of a substate found in the folded ensemble of TC5b. We model the difference in Gibbs free energy, ΔG(P,T), of folding using the bootstrap statistical method, which is used to calculate uncertainties associated with the thermodynamic parameters for both variants of the Trp-cage. We find that while the shape of the area for which the protein is stability folded is elliptical for TC5b, there is a degree of uncertainty associated with that of TC10b, with one model suggesting elliptical and another suggesting hyperbolic. This model suggests that at high pressures, TC5b can experience pressure denaturation, but TC10b may not.