Sample size for collecting germplasms – a polyploid model with ...
Indian Academy of Sciences (India)
Unknown
Conservation; diploid; exploration; germplasm; inbreeding; polyploid; seeds ... A seed factor which influences the plant sample size has also been isolated to aid the collectors in selecting the appropriate combination of number of plants and seeds per plant. ..... able saving of resources during collection and storage of.
Fan, Xitao; Wang, Lin; Thompson, Bruce
1999-01-01
A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)
Sensitivity of Mantel Haenszel Model and Rasch Model as Viewed From Sample Size
ALWI, IDRUS
2011-01-01
The aims of this research is to study the sensitivity comparison of Mantel Haenszel and Rasch Model for detection differential item functioning, observed from the sample size. These two differential item functioning (DIF) methods were compared using simulate binary item respon data sets of varying sample size, 200 and 400 examinees were used in the analyses, a detection method of differential item functioning (DIF) based on gender difference. These test conditions were replication 4 tim...
Impact of Sample Size on the Performance of Multiple-Model Pharmacokinetic Simulations▿
Tam, Vincent H.; Kabbara, Samer; Yeh, Rosa F.; Leary, Robert H.
2006-01-01
Monte Carlo simulations are increasingly used to predict pharmacokinetic variability of antimicrobials in a population. We investigated the sample size necessary to provide robust pharmacokinetic predictions. To obtain reasonably robust predictions, a nonparametric model derived from a sample population size of ≥50 appears to be necessary as the input information.
Sample Size and Item Parameter Estimation Precision When Utilizing the One-Parameter "Rasch" Model
Custer, Michael
2015-01-01
This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…
The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models
Schoeneberger, Jason A.
2016-01-01
The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…
Phylogenetic effective sample size.
Bartoszek, Krzysztof
2016-10-21
In this paper I address the question-how large is a phylogenetic sample? I propose a definition of a phylogenetic effective sample size for Brownian motion and Ornstein-Uhlenbeck processes-the regression effective sample size. I discuss how mutual information can be used to define an effective sample size in the non-normal process case and compare these two definitions to an already present concept of effective sample size (the mean effective sample size). Through a simulation study I find that the AICc is robust if one corrects for the number of species or effective number of species. Lastly I discuss how the concept of the phylogenetic effective sample size can be useful for biodiversity quantification, identification of interesting clades and deciding on the importance of phylogenetic correlations. Copyright © 2016 Elsevier Ltd. All rights reserved.
Dang, Qianyu; Mazumdar, Sati; Houck, Patricia R
2008-08-01
The generalized linear mixed model (GLIMMIX) provides a powerful technique to model correlated outcomes with different types of distributions. The model can now be easily implemented with SAS PROC GLIMMIX in version 9.1. For binary outcomes, linearization methods of penalized quasi-likelihood (PQL) or marginal quasi-likelihood (MQL) provide relatively accurate variance estimates for fixed effects. Using GLIMMIX based on these linearization methods, we derived formulas for power and sample size calculations for longitudinal designs with attrition over time. We found that the power and sample size estimates depend on the within-subject correlation and the size of random effects. In this article, we present tables of minimum sample sizes commonly used to test hypotheses for longitudinal studies. A simulation study was used to compare the results. We also provide a Web link to the SAS macro that we developed to compute power and sample sizes for correlated binary outcomes.
A new model to describe the relationship between species richness and sample size
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WenJun Zhang
2017-03-01
Full Text Available In the sampling of species richness, the number of newly found species declines as increase of sample size, and the number of distinct species tends to an upper asymptote as sample size tends to the infinity. This leads to a curve of species richness vs. sample size. In present study, I follow my principle proposed earlier (Zhang, 2016, and re-develop the model, y=K(1-e^(-rx/K, for describing the relationship between species richness (y and sample size (x, where K is the expected total number of distinct species, and r is the maximum variation of species richness per sample size (i.e., max dy/dx. Computer software and codes were given.
Desu, M M
2012-01-01
One of the most important problems in designing an experiment or a survey is sample size determination and this book presents the currently available methodology. It includes both random sampling from standard probability distributions and from finite populations. Also discussed is sample size determination for estimating parameters in a Bayesian setting by considering the posterior distribution of the parameter and specifying the necessary requirements. The determination of the sample size is considered for ranking and selection problems as well as for the design of clinical trials. Appropria
Model choice and sample size in item response theory analysis of aphasia tests.
Hula, William D; Fergadiotis, Gerasimos; Martin, Nadine
2012-05-01
The purpose of this study was to identify the most appropriate item response theory (IRT) measurement model for aphasia tests requiring 2-choice responses and to determine whether small samples are adequate for estimating such models. Pyramids and Palm Trees (Howard & Patterson, 1992) test data that had been collected from individuals with aphasia were analyzed, and the resulting item and person estimates were used to develop simulated test data for 3 sample size conditions. The simulated data were analyzed using a standard 1-parameter logistic (1-PL) model and 3 models that accounted for the influence of guessing: augmented 1-PL and 2-PL models and a 3-PL model. The model estimates obtained from the simulated data were compared to their known true values. With small and medium sample sizes, an augmented 1-PL model was the most accurate at recovering the known item and person parameters; however, no model performed well at any sample size. Follow-up simulations confirmed that the large influence of guessing and the extreme easiness of the items contributed substantially to the poor estimation of item difficulty and person ability. Incorporating the assumption of guessing into IRT models improves parameter estimation accuracy, even for small samples. However, caution should be exercised in interpreting scores obtained from easy 2-choice tests, regardless of whether IRT modeling or percentage correct scoring is used.
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Sufficient Sample Sizes for Discrete-Time Survival Analysis Mixture Models
Moerbeek, Mirjam
2014-01-01
Long-term survivors in trials with survival endpoints are subjects who will not experience the event of interest. Membership in the class of long-term survivors is unobserved and should be inferred from the data by means of a mixture model. An important question is how large the sample size should
A simulation study of sample size for multilevel logistic regression models
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Moineddin Rahim
2007-07-01
Full Text Available Abstract Background Many studies conducted in health and social sciences collect individual level data as outcome measures. Usually, such data have a hierarchical structure, with patients clustered within physicians, and physicians clustered within practices. Large survey data, including national surveys, have a hierarchical or clustered structure; respondents are naturally clustered in geographical units (e.g., health regions and may be grouped into smaller units. Outcomes of interest in many fields not only reflect continuous measures, but also binary outcomes such as depression, presence or absence of a disease, and self-reported general health. In the framework of multilevel studies an important problem is calculating an adequate sample size that generates unbiased and accurate estimates. Methods In this paper simulation studies are used to assess the effect of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and variance components of multilevel logistic regression models. In addition, the influence of prevalence of the outcome and the intra-class correlation coefficient (ICC is examined. Results The results show that the estimates of the fixed effect parameters are unbiased for 100 groups with group size of 50 or higher. The estimates of the variance covariance components are slightly biased even with 100 groups and group size of 50. The biases for both fixed and random effects are severe for group size of 5. The standard errors for fixed effect parameters are unbiased while for variance covariance components are underestimated. Results suggest that low prevalent events require larger sample sizes with at least a minimum of 100 groups and 50 individuals per group. Conclusion We recommend using a minimum group size of 50 with at least 50 groups to produce valid estimates for multi-level logistic regression models. Group size should be adjusted under conditions where the prevalence
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Shengyu eJiang
2016-02-01
Full Text Available Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item response theory model for analyzing and scoring these instruments when they provide scores on multiple scales is the multidimensional graded response model (MGRM. A simulation study was conducted to investigate the variables that might affect item parameter recovery for the MGRM. Data were generated based on different sample sizes, test lengths, and scale intercorrelations. Parameter estimates were obtained through the flexiMIRT software. The quality of parameter recovery was assessed by the correlation between true and estimated parameters as well as bias and root- mean-square-error. Results indicated that for the vast majority of cases studied a sample size of N = 500 provided accurate parameter estimates, except for tests with 240 items when 1,000 examinees were necessary to obtain accurate parameter estimates. Increasing sample size beyond N = 1,000 did not increase the accuracy of MGRM parameter estimates.
The Effect of Sterilization on Size and Shape of Fat Globules in Model Processed Cheese Samples
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B. Tremlová
2006-01-01
Full Text Available Model cheese samples from 4 independent productions were heat sterilized (117 °C, 20 minutes after the melting process and packing with an aim to prolong their durability. The objective of the study was to assess changes in the size and shape of fat globules due to heat sterilization by using image analysis methods. The study included a selection of suitable methods of preparation mounts, taking microphotographs and making overlays for automatic processing of photographs by image analyser, ascertaining parameters to determine the size and shape of fat globules and statistical analysis of results obtained. The results of the experiment suggest that changes in shape of fat globules due to heat sterilization are not unequivocal. We found that the size of fat globules was significantly increased (p < 0.01 due to heat sterilization (117 °C, 20 min, and the shares of small fat globules (up to 500 μm2, or 100 μm2 in the samples of heat sterilized processed cheese were decreased. The results imply that the image analysis method is very useful when assessing the effect of technological process on the quality of processed cheese quality.
Mixed modeling and sample size calculations for identifying housekeeping genes.
Dai, Hongying; Charnigo, Richard; Vyhlidal, Carrie A; Jones, Bridgette L; Bhandary, Madhusudan
2013-08-15
Normalization of gene expression data using internal control genes that have biologically stable expression levels is an important process for analyzing reverse transcription polymerase chain reaction data. We propose a three-way linear mixed-effects model to select optimal housekeeping genes. The mixed-effects model can accommodate multiple continuous and/or categorical variables with sample random effects, gene fixed effects, systematic effects, and gene by systematic effect interactions. We propose using the intraclass correlation coefficient among gene expression levels as the stability measure to select housekeeping genes that have low within-sample variation. Global hypothesis testing is proposed to ensure that selected housekeeping genes are free of systematic effects or gene by systematic effect interactions. A gene combination with the highest lower bound of 95% confidence interval for intraclass correlation coefficient and no significant systematic effects is selected for normalization. Sample size calculation based on the estimation accuracy of the stability measure is offered to help practitioners design experiments to identify housekeeping genes. We compare our methods with geNorm and NormFinder by using three case studies. A free software package written in SAS (Cary, NC, U.S.A.) is available at http://d.web.umkc.edu/daih under software tab. Copyright © 2013 John Wiley & Sons, Ltd.
Sample size determination and power
Ryan, Thomas P, Jr
2013-01-01
THOMAS P. RYAN, PhD, teaches online advanced statistics courses for Northwestern University and The Institute for Statistics Education in sample size determination, design of experiments, engineering statistics, and regression analysis.
Hagell, Peter; Westergren, Albert
Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N = 50 to N = 2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N less then or equal to 500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N = 250 to N = 500 may provide a good balance for the statistical interpretation of the RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined frame of reference (i.e., about 25 item parameters well targeted to the sample).
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Jamshid Jamali
2017-01-01
Full Text Available Evaluating measurement equivalence (also known as differential item functioning (DIF is an important part of the process of validating psychometric questionnaires. This study aimed at evaluating the multiple indicators multiple causes (MIMIC model for DIF detection when latent construct distribution is nonnormal and the focal group sample size is small. In this simulation-based study, Type I error rates and power of MIMIC model for detecting uniform-DIF were investigated under different combinations of reference to focal group sample size ratio, magnitude of the uniform-DIF effect, scale length, the number of response categories, and latent trait distribution. Moderate and high skewness in the latent trait distribution led to a decrease of 0.33% and 0.47% power of MIMIC model for detecting uniform-DIF, respectively. The findings indicated that, by increasing the scale length, the number of response categories and magnitude DIF improved the power of MIMIC model, by 3.47%, 4.83%, and 20.35%, respectively; it also decreased Type I error of MIMIC approach by 2.81%, 5.66%, and 0.04%, respectively. This study revealed that power of MIMIC model was at an acceptable level when latent trait distributions were skewed. However, empirical Type I error rate was slightly greater than nominal significance level. Consequently, the MIMIC was recommended for detection of uniform-DIF when latent construct distribution is nonnormal and the focal group sample size is small.
Sample size in qualitative interview studies
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Malterud, Kirsti; Siersma, Volkert Dirk; Guassora, Ann Dorrit Kristiane
2016-01-01
Sample sizes must be ascertained in qualitative studies like in quantitative studies but not by the same means. The prevailing concept for sample size in qualitative studies is “saturation.” Saturation is closely tied to a specific methodology, and the term is inconsistently applied. We propose...... the concept “information power” to guide adequate sample size for qualitative studies. Information power indicates that the more information the sample holds, relevant for the actual study, the lower amount of participants is needed. We suggest that the size of a sample with sufficient information power...... depends on (a) the aim of the study, (b) sample specificity, (c) use of established theory, (d) quality of dialogue, and (e) analysis strategy. We present a model where these elements of information and their relevant dimensions are related to information power. Application of this model in the planning...
Reliable calculation in probabilistic logic: Accounting for small sample size and model uncertainty
Energy Technology Data Exchange (ETDEWEB)
Ferson, S. [Applied Biomathematics, Setauket, NY (United States)
1996-12-31
A variety of practical computational problems arise in risk and safety assessments, forensic statistics and decision analyses in which the probability of some event or proposition E is to be estimated from the probabilities of a finite list of related subevents or propositions F,G,H,.... In practice, the analyst`s knowledge may be incomplete in two ways. First, the probabilities of the subevents may be imprecisely known from statistical estimations, perhaps based on very small sample sizes. Second, relationships among the subevents may be known imprecisely. For instance, there may be only limited information about their stochastic dependencies. Representing probability estimates as interval ranges on has been suggested as a way to address the first source of imprecision. A suite of AND, OR and NOT operators defined with reference to the classical Frochet inequalities permit these probability intervals to be used in calculations that address the second source of imprecision, in many cases, in a best possible way. Using statistical confidence intervals as inputs unravels the closure properties of this approach however, requiring that probability estimates be characterized by a nested stack of intervals for all possible levels of statistical confidence, from a point estimate (0% confidence) to the entire unit interval (100% confidence). The corresponding logical operations implied by convolutive application of the logical operators for every possible pair of confidence intervals reduces by symmetry to a manageably simple level-wise iteration. The resulting calculus can be implemented in software that allows users to compute comprehensive and often level-wise best possible bounds on probabilities for logical functions of events.
Usami, Satoshi
2014-12-01
Recent years have shown increased awareness of the importance of sample size determination in experimental research. Yet effective and convenient methods for sample size determination, especially in longitudinal experimental design, are still under development, and application of power analysis in applied research remains limited. This article presents a convenient method for sample size determination in longitudinal experimental research using a multilevel model. A fundamental idea of this method is transformation of model parameters (level 1 error variance [σ(2)], level 2 error variances [τ 00, τ 11] and its covariance [τ 01, τ 10], and a parameter representing experimental effect [δ]) into indices (reliability of measurement at the first time point [ρ 1], effect size at the last time point [Δ T ], proportion of variance of outcomes between the first and the last time points [k], and level 2 error correlation [r]) that are intuitively understandable and easily specified. To foster more convenient use of power analysis, numerical tables are constructed that refer to ANOVA results to investigate the influence on statistical power by respective indices.
Tai, Bee-Choo; Grundy, Richard; Machin, David
2011-03-15
To accurately model the cumulative need for radiotherapy in trials designed to delay or avoid irradiation among children with malignant brain tumor, it is crucial to account for competing events and evaluate how each contributes to the timing of irradiation. An appropriate choice of statistical model is also important for adequate determination of sample size. We describe the statistical modeling of competing events (A, radiotherapy after progression; B, no radiotherapy after progression; and C, elective radiotherapy) using proportional cause-specific and subdistribution hazard functions. The procedures of sample size estimation based on each method are outlined. These are illustrated by use of data comparing children with ependymoma and other malignant brain tumors. The results from these two approaches are compared. The cause-specific hazard analysis showed a reduction in hazards among infants with ependymoma for all event types, including Event A (adjusted cause-specific hazard ratio, 0.76; 95% confidence interval, 0.45-1.28). Conversely, the subdistribution hazard analysis suggested an increase in hazard for Event A (adjusted subdistribution hazard ratio, 1.35; 95% confidence interval, 0.80-2.30), but the reduction in hazards for Events B and C remained. Analysis based on subdistribution hazard requires a larger sample size than the cause-specific hazard approach. Notable differences in effect estimates and anticipated sample size were observed between methods when the main event showed a beneficial effect whereas the competing events showed an adverse effect on the cumulative incidence. The subdistribution hazard is the most appropriate for modeling treatment when its effects on both the main and competing events are of interest. Copyright © 2011 Elsevier Inc. All rights reserved.
Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models
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Sabz Ali
2016-01-01
Full Text Available For most of the time, biomedical researchers have been dealing with ordinal outcome variable in multilevel models where patients are nested in doctors. We can justifiably apply multilevel cumulative logit model, where the outcome variable represents the mild, severe, and extremely severe intensity of diseases like malaria and typhoid in the form of ordered categories. Based on our simulation conditions, Maximum Likelihood (ML method is better than Penalized Quasilikelihood (PQL method in three-category ordinal outcome variable. PQL method, however, performs equally well as ML method where five-category ordinal outcome variable is used. Further, to achieve power more than 0.80, at least 50 groups are required for both ML and PQL methods of estimation. It may be pointed out that, for five-category ordinal response variable model, the power of PQL method is slightly higher than the power of ML method.
Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models.
Ali, Sabz; Ali, Amjad; Khan, Sajjad Ahmad; Hussain, Sundas
2016-01-01
For most of the time, biomedical researchers have been dealing with ordinal outcome variable in multilevel models where patients are nested in doctors. We can justifiably apply multilevel cumulative logit model, where the outcome variable represents the mild, severe, and extremely severe intensity of diseases like malaria and typhoid in the form of ordered categories. Based on our simulation conditions, Maximum Likelihood (ML) method is better than Penalized Quasilikelihood (PQL) method in three-category ordinal outcome variable. PQL method, however, performs equally well as ML method where five-category ordinal outcome variable is used. Further, to achieve power more than 0.80, at least 50 groups are required for both ML and PQL methods of estimation. It may be pointed out that, for five-category ordinal response variable model, the power of PQL method is slightly higher than the power of ML method.
Orth, Patrick; Zurakowski, David; Alini, Mauro; Cucchiarini, Magali; Madry, Henning
2013-11-01
Advanced tissue engineering approaches for articular cartilage repair in the knee joint rely on translational animal models. In these investigations, cartilage defects may be established either in one joint (unilateral design) or in both joints of the same animal (bilateral design). We hypothesized that a lower intraindividual variability following the bilateral strategy would reduce the number of required joints. Standardized osteochondral defects were created in the trochlear groove of 18 rabbits. In 12 animals, defects were produced unilaterally (unilateral design; n=12 defects), while defects were created bilaterally in 6 animals (bilateral design; n=12 defects). After 3 weeks, osteochondral repair was evaluated histologically applying an established grading system. Based on intra- and interindividual variabilities, required sample sizes for the detection of discrete differences in the histological score were determined for both study designs (α=0.05, β=0.20). Coefficients of variation (%CV) of the total histological score values were 1.9-fold increased following the unilateral design when compared with the bilateral approach (26 versus 14%CV). The resulting numbers of joints needed to treat were always higher for the unilateral design, resulting in an up to 3.9-fold increase in the required number of experimental animals. This effect was most pronounced for the detection of small-effect sizes and estimating large standard deviations. The data underline the possible benefit of bilateral study designs for the decrease of sample size requirements for certain investigations in articular cartilage research. These findings might also be transferred to other scoring systems, defect types, or translational animal models in the field of cartilage tissue engineering.
How Sample Size Affects a Sampling Distribution
Mulekar, Madhuri S.; Siegel, Murray H.
2009-01-01
If students are to understand inferential statistics successfully, they must have a profound understanding of the nature of the sampling distribution. Specifically, they must comprehend the determination of the expected value and standard error of a sampling distribution as well as the meaning of the central limit theorem. Many students in a high…
Predicting sample size required for classification performance
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Figueroa Rosa L
2012-02-01
Full Text Available Abstract Background Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. Methods We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method. Results A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p Conclusions This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.
Sample Size Estimation: The Easy Way
Weller, Susan C.
2015-01-01
This article presents a simple approach to making quick sample size estimates for basic hypothesis tests. Although there are many sources available for estimating sample sizes, methods are not often integrated across statistical tests, levels of measurement of variables, or effect sizes. A few parameters are required to estimate sample sizes and…
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H Mohamadi Monavar
2017-10-01
Full Text Available Introduction Precision agriculture (PA is a technology that measures and manages within-field variability, such as physical and chemical properties of soil. The nondestructive and rapid VIS-NIR technology detected a significant correlation between reflectance spectra and the physical and chemical properties of soil. On the other hand, quantitatively predict of soil factors such as nitrogen, carbon, cation exchange capacity and the amount of clay in precision farming is very important. The emphasis of this paper is comparing different techniques of choosing calibration samples such as randomly selected method, chemical data and also based on PCA. Since increasing the number of samples is usually time-consuming and costly, then in this study, the best sampling way -in available methods- was predicted for calibration models. In addition, the effect of sample size on the accuracy of the calibration and validation models was analyzed. Materials and Methods Two hundred and ten soil samples were collected from cultivated farm located in Avarzaman in Hamedan province, Iran. The crop rotation was mostly potato and wheat. Samples were collected from a depth of 20 cm above ground and passed through a 2 mm sieve and air dried at room temperature. Chemical analysis was performed in the soil science laboratory, faculty of agriculture engineering, Bu-ali Sina University, Hamadan, Iran. Two Spectrometer (AvaSpec-ULS 2048- UV-VIS and (FT-NIR100N were used to measure the spectral bands which cover the UV-Vis and NIR region (220-2200 nm. Each soil sample was uniformly tiled in a petri dish and was scanned 20 times. Then the pre-processing methods of multivariate scatter correction (MSC and base line correction (BC were applied on the raw signals using Unscrambler software. The samples were divided into two groups: one group for calibration 105 and the second group was used for validation. Each time, 15 samples were selected randomly and tested the accuracy of
Smith, Philip L; Lilburn, Simon D; Corbett, Elaine A; Sewell, David K; Kyllingsbæk, Søren
2016-09-01
We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples. The model predicts the invariance of [Formula: see text] , the sum of squared sensitivities across items, for displays of different sizes. For phase discrimination, the set-size effect significantly exceeded that predicted by the sample-size model for both simultaneously and sequentially presented stimuli. Instead, the set-size effect and the serial position curves with sequential presentation were predicted by an attention-weighted version of the sample-size model, which assumes that one of the items in the display captures attention and receives a disproportionate share of resources. The choice probabilities and response time distributions from the task were well described by a diffusion decision model in which the drift rates embodied the assumptions of the attention-weighted sample-size model. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying
2011-01-01
Monte Carlo methods can be used in data analytic situations (e.g., validity studies) to make decisions about sample size and to estimate power. The purpose of using Monte Carlo methods in a validity study is to improve the methodological approach within a study where the primary focus is on construct validity issues and not on advancing…
Roslindar Yaziz, Siti; Zakaria, Roslinazairimah; Hura Ahmad, Maizah
2017-09-01
The model of Box-Jenkins - GARCH has been shown to be a promising tool for forecasting higher volatile time series. In this study, the framework of determining the optimal sample size using Box-Jenkins model with GARCH is proposed for practical application in analysing and forecasting higher volatile data. The proposed framework is employed to daily world gold price series from year 1971 to 2013. The data is divided into 12 different sample sizes (from 30 to 10200). Each sample is tested using different combination of the hybrid Box-Jenkins - GARCH model. Our study shows that the optimal sample size to forecast gold price using the framework of the hybrid model is 1250 data of 5-year sample. Hence, the empirical results of model selection criteria and 1-step-ahead forecasting evaluations suggest that the latest 12.25% (5-year data) of 10200 data is sufficient enough to be employed in the model of Box-Jenkins - GARCH with similar forecasting performance as by using 41-year data.
Basic Statistical Concepts for Sample Size Estimation
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Vithal K Dhulkhed
2008-01-01
Full Text Available For grant proposals the investigator has to include an estimation of sample size .The size of the sample should be adequate enough so that there is sufficient data to reliably answer the research question being addressed by the study. At the very planning stage of the study the investigator has to involve the statistician. To have meaningful dialogue with the statistician every research worker should be familiar with the basic concepts of statistics. This paper is concerned with simple principles of sample size calculation. Concepts are explained based on logic rather than rigorous mathematical calculations to help him assimilate the fundamentals.
Sahin, Alper; Weiss, David J.
2015-01-01
This study aimed to investigate the effects of calibration sample size and item bank size on examinee ability estimation in computerized adaptive testing (CAT). For this purpose, a 500-item bank pre-calibrated using the three-parameter logistic model with 10,000 examinees was simulated. Calibration samples of varying sizes (150, 250, 350, 500,…
Williams, David Keith; Bursac, Zoran
2014-01-01
Commonly when designing studies, researchers propose to measure several independent variables in a regression model, a subset of which are identified as the main variables of interest while the rest are retained in a model as covariates or confounders. Power for linear regression in this setting can be calculated using SAS PROC POWER. There exists a void in estimating power for the logistic regression models in the same setting. Currently, an approach that calculates power for only one variable of interest in the presence of other covariates for logistic regression is in common use and works well for this special case. In this paper we propose three related algorithms along with corresponding SAS macros that extend power estimation for one or more primary variables of interest in the presence of some confounders. The three proposed empirical algorithms employ likelihood ratio test to provide a user with either a power estimate for a given sample size, a quick sample size estimate for a given power, and an approximate power curve for a range of sample sizes. A user can specify odds ratios for a combination of binary, uniform and standard normal independent variables of interest, and or remaining covariates/confounders in the model, along with a correlation between variables. These user friendly algorithms and macro tools are a promising solution that can fill the void for estimation of power for logistic regression when multiple independent variables are of interest, in the presence of additional covariates in the model.
International Nuclear Information System (INIS)
Reiser, I; Lu, Z
2014-01-01
Purpose: Recently, task-based assessment of diagnostic CT systems has attracted much attention. Detection task performance can be estimated using human observers, or mathematical observer models. While most models are well established, considerable bias can be introduced when performance is estimated from a limited number of image samples. Thus, the purpose of this work was to assess the effect of sample size on bias and uncertainty of two channelized Hotelling observers and a template-matching observer. Methods: The image data used for this study consisted of 100 signal-present and 100 signal-absent regions-of-interest, which were extracted from CT slices. The experimental conditions included two signal sizes and five different x-ray beam current settings (mAs). Human observer performance for these images was determined in 2-alternative forced choice experiments. These data were provided by the Mayo clinic in Rochester, MN. Detection performance was estimated from three observer models, including channelized Hotelling observers (CHO) with Gabor or Laguerre-Gauss (LG) channels, and a template-matching observer (TM). Different sample sizes were generated by randomly selecting a subset of image pairs, (N=20,40,60,80). Observer performance was quantified as proportion of correct responses (PC). Bias was quantified as the relative difference of PC for 20 and 80 image pairs. Results: For n=100, all observer models predicted human performance across mAs and signal sizes. Bias was 23% for CHO (Gabor), 7% for CHO (LG), and 3% for TM. The relative standard deviation, σ(PC)/PC at N=20 was highest for the TM observer (11%) and lowest for the CHO (Gabor) observer (5%). Conclusion: In order to make image quality assessment feasible in the clinical practice, a statistically efficient observer model, that can predict performance from few samples, is needed. Our results identified two observer models that may be suited for this task
Sample size calculation for comparing two negative binomial rates.
Zhu, Haiyuan; Lakkis, Hassan
2014-02-10
Negative binomial model has been increasingly used to model the count data in recent clinical trials. It is frequently chosen over Poisson model in cases of overdispersed count data that are commonly seen in clinical trials. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation. In practice, simulation methods have been frequently used for sample size estimation. In this paper, an explicit formula is developed to calculate sample size based on the negative binomial model. Depending on different approaches to estimate the variance under null hypothesis, three variations of the sample size formula are proposed and discussed. Important characteristics of the formula include its accuracy and its ability to explicitly incorporate dispersion parameter and exposure time. The performance of the formula with each variation is assessed using simulations. Copyright © 2013 John Wiley & Sons, Ltd.
Sample size tables for clinical studies
National Research Council Canada - National Science Library
Machin, David
2009-01-01
... with sample size software S S S , which we hope will give the user even greater ﬂexibility and easy access to a wide range of designs, and allow design parameters to be tailored more readily to speciﬁc problems. Further, as some early phase designs are adaptive in nature and require knowledge of earlier patients' response to determine t...
Sample size determination for mediation analysis of longitudinal data.
Pan, Haitao; Liu, Suyu; Miao, Danmin; Yuan, Ying
2018-03-27
Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not straightforward for mediation analysis of longitudinal design. To facilitate planning the sample size for longitudinal mediation studies with a multilevel mediation model, this article provides the sample size required to achieve 80% power by simulations under various sizes of the mediation effect, within-subject correlations and numbers of repeated measures. The sample size calculation is based on three commonly used mediation tests: Sobel's method, distribution of product method and the bootstrap method. Among the three methods of testing the mediation effects, Sobel's method required the largest sample size to achieve 80% power. Bootstrapping and the distribution of the product method performed similarly and were more powerful than Sobel's method, as reflected by the relatively smaller sample sizes. For all three methods, the sample size required to achieve 80% power depended on the value of the ICC (i.e., within-subject correlation). A larger value of ICC typically required a larger sample size to achieve 80% power. Simulation results also illustrated the advantage of the longitudinal study design. The sample size tables for most encountered scenarios in practice have also been published for convenient use. Extensive simulations study showed that the distribution of the product method and bootstrapping method have superior performance to the Sobel's method, but the product method was recommended to use in practice in terms of less computation time load compared to the bootstrapping method. A R package has been developed for the product method of sample size determination in mediation longitudinal study design.
Sample size for morphological traits of pigeonpea
Directory of Open Access Journals (Sweden)
Giovani Facco
2015-12-01
Full Text Available The objectives of this study were to determine the sample size (i.e., number of plants required to accurately estimate the average of morphological traits of pigeonpea (Cajanus cajan L. and to check for variability in sample size between evaluation periods and seasons. Two uniformity trials (i.e., experiments without treatment were conducted for two growing seasons. In the first season (2011/2012, the seeds were sown by broadcast seeding, and in the second season (2012/2013, the seeds were sown in rows spaced 0.50 m apart. The ground area in each experiment was 1,848 m2, and 360 plants were marked in the central area, in a 2 m × 2 m grid. Three morphological traits (e.g., number of nodes, plant height and stem diameter were evaluated 13 times during the first season and 22 times in the second season. Measurements for all three morphological traits were normally distributed and confirmed through the Kolmogorov-Smirnov test. Randomness was confirmed using the Run Test, and the descriptive statistics were calculated. For each trait, the sample size (n was calculated for the semiamplitudes of the confidence interval (i.e., estimation error equal to 2, 4, 6, ..., 20% of the estimated mean with a confidence coefficient (1-? of 95%. Subsequently, n was fixed at 360 plants, and the estimation error of the estimated percentage of the average for each trait was calculated. Variability of the sample size for the pigeonpea culture was observed between the morphological traits evaluated, among the evaluation periods and between seasons. Therefore, to assess with an accuracy of 6% of the estimated average, at least 136 plants must be evaluated throughout the pigeonpea crop cycle to determine the sample size for the traits (e.g., number of nodes, plant height and stem diameter in the different evaluation periods and between seasons.
Mollet, Pierre; Kery, Marc; Gardner, Beth; Pasinelli, Gilberto; Royle, Andy
2015-01-01
We conducted a survey of an endangered and cryptic forest grouse, the capercaillie Tetrao urogallus, based on droppings collected on two sampling occasions in eight forest fragments in central Switzerland in early spring 2009. We used genetic analyses to sex and individually identify birds. We estimated sex-dependent detection probabilities and population size using a modern spatial capture-recapture (SCR) model for the data from pooled surveys. A total of 127 capercaillie genotypes were identified (77 males, 46 females, and 4 of unknown sex). The SCR model yielded atotal population size estimate (posterior mean) of 137.3 capercaillies (posterior sd 4.2, 95% CRI 130–147). The observed sex ratio was skewed towards males (0.63). The posterior mean of the sex ratio under the SCR model was 0.58 (posterior sd 0.02, 95% CRI 0.54–0.61), suggesting a male-biased sex ratio in our study area. A subsampling simulation study indicated that a reduced sampling effort representing 75% of the actual detections would still yield practically acceptable estimates of total size and sex ratio in our population. Hence, field work and financial effort could be reduced without compromising accuracy when the SCR model is used to estimate key population parameters of cryptic species.
Determining sample size when assessing mean equivalence.
Asberg, Arne; Solem, Kristine B; Mikkelsen, Gustav
2014-11-01
When we want to assess whether two analytical methods are equivalent, we could test if the difference between the mean results is within the specification limits of 0 ± an acceptance criterion. Testing the null hypothesis of zero difference is less interesting, and so is the sample size estimation based on testing that hypothesis. Power function curves for equivalence testing experiments are not widely available. In this paper we present power function curves to help decide on the number of measurements when testing equivalence between the means of two analytical methods. Computer simulation was used to calculate the probability that the 90% confidence interval for the difference between the means of two analytical methods would exceed the specification limits of 0 ± 1, 0 ± 2 or 0 ± 3 analytical standard deviations (SDa), respectively. The probability of getting a nonequivalence alarm increases with increasing difference between the means when the difference is well within the specification limits. The probability increases with decreasing sample size and with smaller acceptance criteria. We may need at least 40-50 measurements with each analytical method when the specification limits are 0 ± 1 SDa, and 10-15 and 5-10 when the specification limits are 0 ± 2 and 0 ± 3 SDa, respectively. The power function curves provide information of the probability of false alarm, so that we can decide on the sample size under less uncertainty.
Jacob Strunk; Hailemariam Temesgen; Hans-Erik Andersen; James P. Flewelling; Lisa Madsen
2012-01-01
Using lidar in an area-based model-assisted approach to forest inventory has the potential to increase estimation precision for some forest inventory variables. This study documents the bias and precision of a model-assisted (regression estimation) approach to forest inventory with lidar-derived auxiliary variables relative to lidar pulse density and the number of...
Correlation of Scan and Sample Measurements Using Appropriate Sample Size
International Nuclear Information System (INIS)
Lux, Jeff
2008-01-01
, gamma count rates were elevated, but samples yielded background concentrations of thorium. Gamma scans tended to correlate with gamma exposure rate measurements. The lack of correlation between scan and sample data threatened to invalidate the characterization methodology, because neither method demonstrated reliability in identifying material which required excavation, shipment, and disposal. The NRC-approved site decommissioning plan required the excavation of any material that exceeded the Criteria based on either measurement. It was necessary to resolve the differences between the various types of measurements. Health Physics technicians selected 27 locations where the relationship between scan measurements and sample counts was highly variable. To determine if 'shine' was creating this data-correlation problem, they returned to those 27 locations to collect gamma count rates with a lead shielded NaI detector. Figure 2 shows that the shielded and unshielded count rates correlated fairly well for those locations. However, the technicians also noted the presence of 'tar balls' in this area. These small chunks of tarry material typically varied in size from 2 - 10 mm in diameters. Thorium-contaminated tars had apparently been disked into the soil to biodegrade the tar. The technicians evaluated the samples, and determined that the samples yielding higher activity contained more tar balls, and the samples yielding near background levels had fewer or none. The tar was collected for analysis, and its thorium activity varied from 2 - 3 Bq/g (60 - 90 pCi/g) total thorium. Since the sample mass was small, these small tar balls greatly impacted the sample activity. Technicians determined that the maximum particle size was less than 20 mm in diameter. Based on this maximum 'particle size', over one kilogram of sample would be required to minimize the impact of the tar balls on sample results. They returned to the same 27 locations and collected soil samples containing at least
White, Simon R; Muniz-Terrera, Graciela; Matthews, Fiona E
2018-05-01
Many medical (and ecological) processes involve the change of shape, whereby one trajectory changes into another trajectory at a specific time point. There has been little investigation into the study design needed to investigate these models. We consider the class of fixed effect change-point models with an underlying shape comprised two joined linear segments, also known as broken-stick models. We extend this model to include two sub-groups with different trajectories at the change-point, a change and no change class, and also include a missingness model to account for individuals with incomplete follow-up. Through a simulation study, we consider the relationship of sample size to the estimates of the underlying shape, the existence of a change-point, and the classification-error of sub-group labels. We use a Bayesian framework to account for the missing labels, and the analysis of each simulation is performed using standard Markov chain Monte Carlo techniques. Our simulation study is inspired by cognitive decline as measured by the Mini-Mental State Examination, where our extended model is appropriate due to the commonly observed mixture of individuals within studies who do or do not exhibit accelerated decline. We find that even for studies of modest size ( n = 500, with 50 individuals observed past the change-point) in the fixed effect setting, a change-point can be detected and reliably estimated across a range of observation-errors.
Sample size estimation and sampling techniques for selecting a representative sample
Directory of Open Access Journals (Sweden)
Aamir Omair
2014-01-01
Full Text Available Introduction: The purpose of this article is to provide a general understanding of the concepts of sampling as applied to health-related research. Sample Size Estimation: It is important to select a representative sample in quantitative research in order to be able to generalize the results to the target population. The sample should be of the required sample size and must be selected using an appropriate probability sampling technique. There are many hidden biases which can adversely affect the outcome of the study. Important factors to consider for estimating the sample size include the size of the study population, confidence level, expected proportion of the outcome variable (for categorical variables/standard deviation of the outcome variable (for numerical variables, and the required precision (margin of accuracy from the study. The more the precision required, the greater is the required sample size. Sampling Techniques: The probability sampling techniques applied for health related research include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. These are more recommended than the nonprobability sampling techniques, because the results of the study can be generalized to the target population.
Sample size re-estimation in a breast cancer trial.
Hade, Erinn M; Jarjoura, David; Lai Wei
2010-06-01
During the recruitment phase of a randomized breast cancer trial, investigating the time to recurrence, we found a strong suggestion that the failure probabilities used at the design stage were too high. Since most of the methodological research involving sample size re-estimation has focused on normal or binary outcomes, we developed a method which preserves blinding to re-estimate sample size in our time to event trial. A mistakenly high estimate of the failure rate at the design stage may reduce the power unacceptably for a clinically important hazard ratio. We describe an ongoing trial and an application of a sample size re-estimation method that combines current trial data with prior trial data or assumes a parametric model to re-estimate failure probabilities in a blinded fashion. Using our current blinded trial data and additional information from prior studies, we re-estimate the failure probabilities to be used in sample size re-calculation. We employ bootstrap re-sampling to quantify uncertainty in the re-estimated sample sizes. At the time of re-estimation data from 278 patients were available, averaging 1.2 years of follow up. Using either method, we estimated a sample size increase of zero for the hazard ratio because the estimated failure probabilities at the time of re-estimation differed little from what was expected. We show that our method of blinded sample size re-estimation preserves the type I error rate. We show that when the initial guess of the failure probabilities are correct, the median increase in sample size is zero. Either some prior knowledge of an appropriate survival distribution shape or prior data is needed for re-estimation. In trials when the accrual period is lengthy, blinded sample size re-estimation near the end of the planned accrual period should be considered. In our examples, when assumptions about failure probabilities and HRs are correct the methods usually do not increase sample size or otherwise increase it by very
Estimation of individual reference intervals in small sample sizes
DEFF Research Database (Denmark)
Hansen, Ase Marie; Garde, Anne Helene; Eller, Nanna Hurwitz
2007-01-01
of that order of magnitude for all topics in question. Therefore, new methods to estimate reference intervals for small sample sizes are needed. We present an alternative method based on variance component models. The models are based on data from 37 men and 84 women taking into account biological variation...... presented in this study. The presented method enables occupational health researchers to calculate reference intervals for specific groups, i.e. smokers versus non-smokers, etc. In conclusion, the variance component models provide an appropriate tool to estimate reference intervals based on small sample...
Optimal sample size for probability of detection curves
International Nuclear Information System (INIS)
Annis, Charles; Gandossi, Luca; Martin, Oliver
2013-01-01
Highlights: • We investigate sample size requirement to develop probability of detection curves. • We develop simulations to determine effective inspection target sizes, number and distribution. • We summarize these findings and provide guidelines for the NDE practitioner. -- Abstract: The use of probability of detection curves to quantify the reliability of non-destructive examination (NDE) systems is common in the aeronautical industry, but relatively less so in the nuclear industry, at least in European countries. Due to the nature of the components being inspected, sample sizes tend to be much lower. This makes the manufacturing of test pieces with representative flaws, in sufficient numbers, so to draw statistical conclusions on the reliability of the NDT system under investigation, quite costly. The European Network for Inspection and Qualification (ENIQ) has developed an inspection qualification methodology, referred to as the ENIQ Methodology. It has become widely used in many European countries and provides assurance on the reliability of NDE systems, but only qualitatively. The need to quantify the output of inspection qualification has become more important as structural reliability modelling and quantitative risk-informed in-service inspection methodologies become more widely used. A measure of the NDE reliability is necessary to quantify risk reduction after inspection and probability of detection (POD) curves provide such a metric. The Joint Research Centre, Petten, The Netherlands supported ENIQ by investigating the question of the sample size required to determine a reliable POD curve. As mentioned earlier manufacturing of test pieces with defects that are typically found in nuclear power plants (NPPs) is usually quite expensive. Thus there is a tendency to reduce sample sizes, which in turn increases the uncertainty associated with the resulting POD curve. The main question in conjunction with POS curves is the appropriate sample size. Not
Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests
Duncanson, L.; Rourke, O.; Dubayah, R.
2015-11-01
Accurate quantification of forest carbon stocks is required for constraining the global carbon cycle and its impacts on climate. The accuracies of forest biomass maps are inherently dependent on the accuracy of the field biomass estimates used to calibrate models, which are generated with allometric equations. Here, we provide a quantitative assessment of the sensitivity of allometric parameters to sample size in temperate forests, focusing on the allometric relationship between tree height and crown radius. We use LiDAR remote sensing to isolate between 10,000 to more than 1,000,000 tree height and crown radius measurements per site in six U.S. forests. We find that fitted allometric parameters are highly sensitive to sample size, producing systematic overestimates of height. We extend our analysis to biomass through the application of empirical relationships from the literature, and show that given the small sample sizes used in common allometric equations for biomass, the average site-level biomass bias is ~+70% with a standard deviation of 71%, ranging from -4% to +193%. These findings underscore the importance of increasing the sample sizes used for allometric equation generation.
Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests
Duncanson, L.; Rourke, O.; Dubayah, R.
2015-01-01
Accurate quantification of forest carbon stocks is required for constraining the global carbon cycle and its impacts on climate. The accuracies of forest biomass maps are inherently dependent on the accuracy of the field biomass estimates used to calibrate models, which are generated with allometric equations. Here, we provide a quantitative assessment of the sensitivity of allometric parameters to sample size in temperate forests, focusing on the allometric relationship between tree height a...
Aerosol Sampling Bias from Differential Electrostatic Charge and Particle Size
Jayjock, Michael Anthony
Lack of reliable epidemiological data on long term health effects of aerosols is due in part to inadequacy of sampling procedures and the attendant doubt regarding the validity of the concentrations measured. Differential particle size has been widely accepted and studied as a major potential biasing effect in the sampling of such aerosols. However, relatively little has been done to study the effect of electrostatic particle charge on aerosol sampling. The objective of this research was to investigate the possible biasing effects of differential electrostatic charge, particle size and their interaction on the sampling accuracy of standard aerosol measuring methodologies. Field studies were first conducted to determine the levels and variability of aerosol particle size and charge at two manufacturing facilities making acrylic powder. The field work showed that the particle mass median aerodynamic diameter (MMAD) varied by almost an order of magnitude (4-34 microns) while the aerosol surface charge was relatively stable (0.6-0.9 micro coulombs/m('2)). The second part of this work was a series of laboratory experiments in which aerosol charge and MMAD were manipulated in a 2('n) factorial design with the percentage of sampling bias for various standard methodologies as the dependent variable. The experiments used the same friable acrylic powder studied in the field work plus two size populations of ground quartz as a nonfriable control. Despite some ill conditioning of the independent variables due to experimental difficulties, statistical analysis has shown aerosol charge (at levels comparable to those measured in workroom air) is capable of having a significant biasing effect. Physical models consistent with the sampling data indicate that the level and bipolarity of the aerosol charge are determining factors in the extent and direction of the bias.
Effects of sample size on the second magnetization peak in ...
Indian Academy of Sciences (India)
the sample size decreases – a result that could be interpreted as a size effect in the order– disorder vortex matter phase transition. However, local magnetic measurements trace this effect to metastable disordered vortex states, revealing the same order–disorder transition induction in samples of different size. Keywords.
Sample size determination in clinical trials with multiple endpoints
Sozu, Takashi; Hamasaki, Toshimitsu; Evans, Scott R
2015-01-01
This book integrates recent methodological developments for calculating the sample size and power in trials with more than one endpoint considered as multiple primary or co-primary, offering an important reference work for statisticians working in this area. The determination of sample size and the evaluation of power are fundamental and critical elements in the design of clinical trials. If the sample size is too small, important effects may go unnoticed; if the sample size is too large, it represents a waste of resources and unethically puts more participants at risk than necessary. Recently many clinical trials have been designed with more than one endpoint considered as multiple primary or co-primary, creating a need for new approaches to the design and analysis of these clinical trials. The book focuses on the evaluation of power and sample size determination when comparing the effects of two interventions in superiority clinical trials with multiple endpoints. Methods for sample size calculation in clin...
Simple and multiple linear regression: sample size considerations.
Hanley, James A
2016-11-01
The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright Â© 2016 Elsevier Inc. All rights reserved.
Kühberger, Anton; Fritz, Astrid; Scherndl, Thomas
2014-01-01
Background The p value obtained from a significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias. Methods We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values. Results We found a negative correlation of r = −.45 [95% CI: −.53; −.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings. Conclusion The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology. PMID:25192357
The influence of sample size on the determination of population ...
African Journals Online (AJOL)
Reliable measures of population sizes of endangered and vulnerable species are difficult to achieve because of high variability in population sizes and logistic constraints on sample sizes, yet such measures are crucial for the determination of the success of conservation and management strategies aimed at curbing ...
Estimating population size with correlated sampling unit estimates
David C. Bowden; Gary C. White; Alan B. Franklin; Joseph L. Ganey
2003-01-01
Finite population sampling theory is useful in estimating total population size (abundance) from abundance estimates of each sampled unit (quadrat). We develop estimators that allow correlated quadrat abundance estimates, even for quadrats in different sampling strata. Correlated quadrat abundance estimates based on markârecapture or distance sampling methods occur...
Approaches to sample size determination for multivariate data
Saccenti, Edoardo; Timmerman, Marieke E.
2016-01-01
Sample size determination is a fundamental step in the design of experiments. Methods for sample size determination are abundant for univariate analysis methods, but scarce in the multivariate case. Omics data are multivariate in nature and are commonly investigated using multivariate statistical
Sample size computation for association studies using case–parents ...
Indian Academy of Sciences (India)
sample size for case–control association studies is discussed. Materials and methods. Parameter settings. We consider a candidate locus with two alleles A and a where. A is putatively associated with the disease status (increasing. Keywords. sample size; association tests; genotype relative risk; power; autism. Journal of ...
Optimal Sample Size for Probability of Detection Curves
International Nuclear Information System (INIS)
Annis, Charles; Gandossi, Luca; Martin, Oliver
2012-01-01
The use of Probability of Detection (POD) curves to quantify NDT reliability is common in the aeronautical industry, but relatively less so in the nuclear industry. The European Network for Inspection Qualification's (ENIQ) Inspection Qualification Methodology is based on the concept of Technical Justification, a document assembling all the evidence to assure that the NDT system in focus is indeed capable of finding the flaws for which it was designed. This methodology has become widely used in many countries, but the assurance it provides is usually of qualitative nature. The need to quantify the output of inspection qualification has become more important, especially as structural reliability modelling and quantitative risk-informed in-service inspection methodologies become more widely used. To credit the inspections in structural reliability evaluations, a measure of the NDT reliability is necessary. A POD curve provides such metric. In 2010 ENIQ developed a technical report on POD curves, reviewing the statistical models used to quantify inspection reliability. Further work was subsequently carried out to investigate the issue of optimal sample size for deriving a POD curve, so that adequate guidance could be given to the practitioners of inspection reliability. Manufacturing of test pieces with cracks that are representative of real defects found in nuclear power plants (NPP) can be very expensive. Thus there is a tendency to reduce sample sizes and in turn reduce the conservatism associated with the POD curve derived. Not much guidance on the correct sample size can be found in the published literature, where often qualitative statements are given with no further justification. The aim of this paper is to summarise the findings of such work. (author)
[Effect sizes, statistical power and sample sizes in "the Japanese Journal of Psychology"].
Suzukawa, Yumi; Toyoda, Hideki
2012-04-01
This study analyzed the statistical power of research studies published in the "Japanese Journal of Psychology" in 2008 and 2009. Sample effect sizes and sample statistical powers were calculated for each statistical test and analyzed with respect to the analytical methods and the fields of the studies. The results show that in the fields like perception, cognition or learning, the effect sizes were relatively large, although the sample sizes were small. At the same time, because of the small sample sizes, some meaningful effects could not be detected. In the other fields, because of the large sample sizes, meaningless effects could be detected. This implies that researchers who could not get large enough effect sizes would use larger samples to obtain significant results.
Sample size and power calculation for univariate case in quantile regression
Yanuar, Ferra
2018-01-01
The purpose of this study is to calculate the statistical power and sample size in simple linear regression model based on quantile approach. The statistical theoretical framework isthen implemented to generate data using R. For any given covariate and regression coefficient, we generate a random variable and error. There are two conditions for error distributions here; normal and nonnormal distribution. This study resulted that for normal error term, sample size is large if the effect size is small. Meanwhile, the level of statistical power is also affected by effect size, the more effect size the more level of power. For nonnormal error terms, it isn’t recommended using small effect size, moderate effect size unless sample size more than 320 and large effect size unless sample size more than 160 because it resulted in lower statistical power.
Fearon, Elizabeth; Chabata, Sungai T; Thompson, Jennifer A; Cowan, Frances M; Hargreaves, James R
2017-09-14
While guidance exists for obtaining population size estimates using multiplier methods with respondent-driven sampling surveys, we lack specific guidance for making sample size decisions. To guide the design of multiplier method population size estimation studies using respondent-driven sampling surveys to reduce the random error around the estimate obtained. The population size estimate is obtained by dividing the number of individuals receiving a service or the number of unique objects distributed (M) by the proportion of individuals in a representative survey who report receipt of the service or object (P). We have developed an approach to sample size calculation, interpreting methods to estimate the variance around estimates obtained using multiplier methods in conjunction with research into design effects and respondent-driven sampling. We describe an application to estimate the number of female sex workers in Harare, Zimbabwe. There is high variance in estimates. Random error around the size estimate reflects uncertainty from M and P, particularly when the estimate of P in the respondent-driven sampling survey is low. As expected, sample size requirements are higher when the design effect of the survey is assumed to be greater. We suggest a method for investigating the effects of sample size on the precision of a population size estimate obtained using multipler methods and respondent-driven sampling. Uncertainty in the size estimate is high, particularly when P is small, so balancing against other potential sources of bias, we advise researchers to consider longer service attendance reference periods and to distribute more unique objects, which is likely to result in a higher estimate of P in the respondent-driven sampling survey. ©Elizabeth Fearon, Sungai T Chabata, Jennifer A Thompson, Frances M Cowan, James R Hargreaves. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.09.2017.
Sampling strategies for estimating brook trout effective population size
Andrew R. Whiteley; Jason A. Coombs; Mark Hudy; Zachary Robinson; Keith H. Nislow; Benjamin H. Letcher
2012-01-01
The influence of sampling strategy on estimates of effective population size (Ne) from single-sample genetic methods has not been rigorously examined, though these methods are increasingly used. For headwater salmonids, spatially close kin association among age-0 individuals suggests that sampling strategy (number of individuals and location from...
Determination of the optimal sample size for a clinical trial accounting for the population size.
Stallard, Nigel; Miller, Frank; Day, Simon; Hee, Siew Wan; Madan, Jason; Zohar, Sarah; Posch, Martin
2017-07-01
The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflect the size of the population under consideration. Incorporation of the population size is possible in a decision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N1/2) or O(N∗1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Sample size for estimating average productive traits of pigeon pea
Directory of Open Access Journals (Sweden)
Giovani Facco
2016-04-01
Full Text Available ABSTRACT: The objectives of this study were to determine the sample size, in terms of number of plants, needed to estimate the average values of productive traits of the pigeon pea and to determine whether the sample size needed varies between traits and between crop years. Separate uniformity trials were conducted in 2011/2012 and 2012/2013. In each trial, 360 plants were demarcated, and the fresh and dry masses of roots, stems, and leaves and of shoots and the total plant were evaluated during blossoming for 10 productive traits. Descriptive statistics were calculated, normality and randomness were checked, and the sample size was calculated. There was variability in the sample size between the productive traits and crop years of the pigeon pea culture. To estimate the averages of the productive traits with a 20% maximum estimation error and 95% confidence level, 70 plants are sufficient.
Effects of sample size on the second magnetization peak in ...
Indian Academy of Sciences (India)
8+ crystals are observed at low temperatures, above the temperature where the SMP totally disappears. In particular, the onset of the SMP shifts to lower fields as the sample size decreases - a result that could be interpreted as a size effect in ...
Sample size computation for association studies using case–parents ...
Indian Academy of Sciences (India)
ple size needed to reach a given power (Knapp 1999; Schaid. 1999; Chen and Deng 2001; Brown 2004). In their seminal paper, Risch and Merikangas (1996) showed that for a mul- tiplicative mode of inheritance (MOI) for the susceptibility gene, sample size depends on two parameters: the frequency of the risk allele at the ...
Four equivalent lot-sizing models
W. van den Heuvel (Wilco); A.P.M. Wagelmans (Albert)
2007-01-01
textabstractWe study the following lot-sizing models that recently appeared in the literature: a lot-sizing model with a remanufacturing option, a lot-sizing model with production time windows, and a lot-sizing model with cumulative capacities. We show the equivalence of these models with a
Sample size determination for equivalence assessment with multiple endpoints.
Sun, Anna; Dong, Xiaoyu; Tsong, Yi
2014-01-01
Equivalence assessment between a reference and test treatment is often conducted by two one-sided tests (TOST). The corresponding power function and sample size determination can be derived from a joint distribution of the sample mean and sample variance. When an equivalence trial is designed with multiple endpoints, it often involves several sets of two one-sided tests. A naive approach for sample size determination in this case would select the largest sample size required for each endpoint. However, such a method ignores the correlation among endpoints. With the objective to reject all endpoints and when the endpoints are uncorrelated, the power function is the production of all power functions for individual endpoints. With correlated endpoints, the sample size and power should be adjusted for such a correlation. In this article, we propose the exact power function for the equivalence test with multiple endpoints adjusted for correlation under both crossover and parallel designs. We further discuss the differences in sample size for the naive method without and with correlation adjusted methods and illustrate with an in vivo bioequivalence crossover study with area under the curve (AUC) and maximum concentration (Cmax) as the two endpoints.
Bergtold, Jason S.; Yeager, Elizabeth A.; Featherstone, Allen M.
2011-01-01
The logistic regression models has been widely used in the social and natural sciences and results from studies using this model can have significant impact. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this study is to examine the impact of sample size on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model. A numbe...
Bayesian Sample Size Determination For The Accurate Identification ...
African Journals Online (AJOL)
Background & Aim: Sample size estimation is a major component of the design of virtually every experiment in biosciences. Microbiologists face a challenge when allocating resources to surveys designed to determine the sampling unit of bacterial strains of interest. In this study we derived a Bayesian approach with a ...
Directory of Open Access Journals (Sweden)
R. Eric Heidel
2016-01-01
Full Text Available Statistical power is the ability to detect a significant effect, given that the effect actually exists in a population. Like most statistical concepts, statistical power tends to induce cognitive dissonance in hepatology researchers. However, planning for statistical power by an a priori sample size calculation is of paramount importance when designing a research study. There are five specific empirical components that make up an a priori sample size calculation: the scale of measurement of the outcome, the research design, the magnitude of the effect size, the variance of the effect size, and the sample size. A framework grounded in the phenomenon of isomorphism, or interdependencies amongst different constructs with similar forms, will be presented to understand the isomorphic effects of decisions made on each of the five aforementioned components of statistical power.
Current sample size conventions: Flaws, harms, and alternatives
Directory of Open Access Journals (Sweden)
Bacchetti Peter
2010-03-01
Full Text Available Abstract Background The belief remains widespread that medical research studies must have statistical power of at least 80% in order to be scientifically sound, and peer reviewers often question whether power is high enough. Discussion This requirement and the methods for meeting it have severe flaws. Notably, the true nature of how sample size influences a study's projected scientific or practical value precludes any meaningful blanket designation of value of information methods, simple choices based on cost or feasibility that have recently been justified, sensitivity analyses that examine a meaningful array of possible findings, and following previous analogous studies. To promote more rational approaches, research training should cover the issues presented here, peer reviewers should be extremely careful before raising issues of "inadequate" sample size, and reports of completed studies should not discuss power. Summary Common conventions and expectations concerning sample size are deeply flawed, cause serious harm to the research process, and should be replaced by more rational alternatives.
Impact of shoe size in a sample of elderly individuals
Directory of Open Access Journals (Sweden)
Daniel López-López
Full Text Available Summary Introduction: The use of an improper shoe size is common in older people and is believed to have a detrimental effect on the quality of life related to foot health. The objective is to describe and compare, in a sample of participants, the impact of shoes that fit properly or improperly, as well as analyze the scores related to foot health and health overall. Method: A sample of 64 participants, with a mean age of 75.3±7.9 years, attended an outpatient center where self-report data was recorded, the measurements of the size of the feet and footwear were determined and the scores compared between the group that wears the correct size of shoes and another group of individuals who do not wear the correct size of shoes, using the Spanish version of the Foot Health Status Questionnaire. Results: The group wearing an improper shoe size showed poorer quality of life regarding overall health and specifically foot health. Differences between groups were evaluated using a t-test for independent samples resulting statistically significant (p<0.05 for the dimension of pain, function, footwear, overall foot health, and social function. Conclusion: Inadequate shoe size has a significant negative impact on quality of life related to foot health. The degree of negative impact seems to be associated with age, sex, and body mass index (BMI.
Sample Size Calculations for Precise Interval Estimation of the Eta-Squared Effect Size
Shieh, Gwowen
2015-01-01
Analysis of variance is one of the most frequently used statistical analyses in the behavioral, educational, and social sciences, and special attention has been paid to the selection and use of an appropriate effect size measure of association in analysis of variance. This article presents the sample size procedures for precise interval estimation…
Approximate sample size calculations with microarray data: an illustration
Ferreira, José A.; Zwinderman, Aeilko
2006-01-01
We outline a method of sample size calculation in microarray experiments on the basis of pilot data and illustrate its practical application with both simulated and real data. The method was shown to be consistent (as the number of 'probed genes' tends to infinity) under general conditions in an
Sample size computation for association studies using case–parents ...
Indian Academy of Sciences (India)
jgen/085/03/0187-0191. Keywords. sample size; association tests; genotype relative risk; power; autism. Author Affiliations. Najla Kharrat1 Imen Ayadi1 Ahmed Rebaï1. Unité de Biostatistique et de Bioinformatique, Centre de Biotechnologie de ...
Estimating wildlife activity curves: comparison of methods and sample size.
Lashley, Marcus A; Cove, Michael V; Chitwood, M Colter; Penido, Gabriel; Gardner, Beth; DePerno, Chris S; Moorman, Chris E
2018-03-08
Camera traps and radiotags commonly are used to estimate animal activity curves. However, little empirical evidence has been provided to validate whether they produce similar results. We compared activity curves from two common camera trapping techniques to those from radiotags with four species that varied substantially in size (~1 kg-~50 kg), diet (herbivore, omnivore, carnivore), and mode of activity (diurnal and crepuscular). Also, we sub-sampled photographs of each species with each camera trapping technique to determine the minimum sample size needed to maintain accuracy and precision of estimates. Camera trapping estimated greater activity during feeding times than radiotags in all but the carnivore, likely reflective of the close proximity of foods readily consumed by all species except the carnivore (i.e., corn bait or acorns). However, additional analyses still indicated both camera trapping methods produced relatively high overlap and correlation to radiotags. Regardless of species or camera trapping method, mean overlap increased and overlap error decreased rapidly as sample sizes increased until an asymptote near 100 detections which we therefore recommend as a minimum sample size. Researchers should acknowledge that camera traps and radiotags may estimate the same mode of activity but differ in their estimation of magnitude in activity peaks.
Determining sample size for assessing species composition in ...
African Journals Online (AJOL)
Species composition is measured in grasslands for a variety of reasons. Commonly, observations are made using the wheel-point apparatus, but the problem of determining optimum sample size has not yet been satisfactorily resolved. In this study the wheel-point apparatus was used to record 2 000 observations in each of ...
Research Note Pilot survey to assess sample size for herbaceous ...
African Journals Online (AJOL)
A pilot survey to determine sub-sample size (number of point observations per plot) for herbaceous species composition assessments, using a wheel-point apparatus applying the nearest-plant method, was conducted. Three plots differing in species composition on the Zululand coastal plain were selected, and on each plot ...
Blinded sample size re-estimation in crossover bioequivalence trials.
Golkowski, Daniel; Friede, Tim; Kieser, Meinhard
2014-01-01
In drug development, bioequivalence studies are used to indirectly demonstrate clinical equivalence of a test formulation and a reference formulation of a specific drug by establishing their equivalence in bioavailability. These studies are typically run as crossover studies. In the planning phase of such trials, investigators and sponsors are often faced with a high variability in the coefficients of variation of the typical pharmacokinetic endpoints such as the area under the concentration curve or the maximum plasma concentration. Adaptive designs have recently been considered to deal with this uncertainty by adjusting the sample size based on the accumulating data. Because regulators generally favor sample size re-estimation procedures that maintain the blinding of the treatment allocations throughout the trial, we propose in this paper a blinded sample size re-estimation strategy and investigate its error rates. We show that the procedure, although blinded, can lead to some inflation of the type I error rate. In the context of an example, we demonstrate how this inflation of the significance level can be adjusted for to achieve control of the type I error rate at a pre-specified level. Furthermore, some refinements of the re-estimation procedure are proposed to improve the power properties, in particular in scenarios with small sample sizes. Copyright © 2014 John Wiley & Sons, Ltd.
Sample size and power calculation for molecular biology studies.
Jung, Sin-Ho
2010-01-01
Sample size calculation is a critical procedure when designing a new biological study. In this chapter, we consider molecular biology studies generating huge dimensional data. Microarray studies are typical examples, so that we state this chapter in terms of gene microarray data, but the discussed methods can be used for design and analysis of any molecular biology studies involving high-dimensional data. In this chapter, we discuss sample size calculation methods for molecular biology studies when the discovery of prognostic molecular markers is performed by accurately controlling false discovery rate (FDR) or family-wise error rate (FWER) in the final data analysis. We limit our discussion to the two-sample case.
Lee, Eun Gyung; Lee, Taekhee; Kim, Seung Won; Lee, Larry; Flemmer, Michael M; Harper, Martin
2014-01-01
This second, and concluding, part of this study evaluated changes in sampling efficiency of respirable size-selective samplers due to air pulsations generated by the selected personal sampling pumps characterized in Part I (Lee E, Lee L, Möhlmann C et al. Evaluation of pump pulsation in respirable size-selective sampling: Part I. Pulsation measurements. Ann Occup Hyg 2013). Nine particle sizes of monodisperse ammonium fluorescein (from 1 to 9 μm mass median aerodynamic diameter) were generated individually by a vibrating orifice aerosol generator from dilute solutions of fluorescein in aqueous ammonia and then injected into an environmental chamber. To collect these particles, 10-mm nylon cyclones, also known as Dorr-Oliver (DO) cyclones, were used with five medium volumetric flow rate pumps. Those were the Apex IS, HFS513, GilAir5, Elite5, and Basic5 pumps, which were found in Part I to generate pulsations of 5% (the lowest), 25%, 30%, 56%, and 70% (the highest), respectively. GK2.69 cyclones were used with the Legacy [pump pulsation (PP) = 15%] and Elite12 (PP = 41%) pumps for collection at high flows. The DO cyclone was also used to evaluate changes in sampling efficiency due to pulse shape. The HFS513 pump, which generates a more complex pulse shape, was compared to a single sine wave fluctuation generated by a piston. The luminescent intensity of the fluorescein extracted from each sample was measured with a luminescence spectrometer. Sampling efficiencies were obtained by dividing the intensity of the fluorescein extracted from the filter placed in a cyclone with the intensity obtained from the filter used with a sharp-edged reference sampler. Then, sampling efficiency curves were generated using a sigmoid function with three parameters and each sampling efficiency curve was compared to that of the reference cyclone by constructing bias maps. In general, no change in sampling efficiency (bias under ±10%) was observed until pulsations exceeded 25% for the
Sample size for detecting differentially expressed genes in microarray experiments
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Li Jiangning
2004-11-01
Full Text Available Abstract Background Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human. Results We hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile. Conclusions Factors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in
Development of sample size allocation program using hypergeometric distribution
International Nuclear Information System (INIS)
Kim, Hyun Tae; Kwack, Eun Ho; Park, Wan Soo; Min, Kyung Soo; Park, Chan Sik
1996-01-01
The objective of this research is the development of sample allocation program using hypergeometric distribution with objected-oriented method. When IAEA(International Atomic Energy Agency) performs inspection, it simply applies a standard binomial distribution which describes sampling with replacement instead of a hypergeometric distribution which describes sampling without replacement in sample allocation to up to three verification methods. The objective of the IAEA inspection is the timely detection of diversion of significant quantities of nuclear material, therefore game theory is applied to its sampling plan. It is necessary to use hypergeometric distribution directly or approximate distribution to secure statistical accuracy. Improved binomial approximation developed by Mr. J. L. Jaech and correctly applied binomial approximation are more closer to hypergeometric distribution in sample size calculation than the simply applied binomial approximation of the IAEA. Object-oriented programs of 1. sample approximate-allocation with correctly applied standard binomial approximation, 2. sample approximate-allocation with improved binomial approximation, and 3. sample approximate-allocation with hypergeometric distribution were developed with Visual C ++ and corresponding programs were developed with EXCEL(using Visual Basic for Application). 8 tabs., 15 refs. (Author)
Lachin, John M
2008-06-30
The conditional logistic regression model (Biometrics 1982; 38:661-672) provides a convenient method for the assessment of qualitative or quantitative covariate effects on risk in a study with matched sets, each containing a possibly different number of cases and controls. The conditional logistic likelihood is identical to the stratified Cox proportional hazards model likelihood, with an adjustment for ties (J. R. Stat. Soc. B 1972; 34:187-220). This likelihood also applies to a nested case-control study with multiply matched cases and controls, selected from those at risk at selected event times. Herein the distribution of the score test for the effect of a covariate in the model is used to derive simple equations to describe the power of the test to detect a coefficient theta (log odds ratio or log hazard ratio) or the number of cases (or matched sets) and controls required to provide a desired level of power. Additional expressions are derived for a quantitative covariate as a function of the difference in the assumed mean covariate values among cases and controls and for a qualitative covariate in terms of the difference in the probabilities of exposure for cases and controls. Examples are presented for a nested case-control study and a multiply matched case-control study.
Sample size of the reference sample in a case-augmented study.
Ghosh, Palash; Dewanji, Anup
2017-05-01
The case-augmented study, in which a case sample is augmented with a reference (random) sample from the source population with only covariates information known, is becoming popular in different areas of applied science such as pharmacovigilance, ecology, and econometrics. In general, the case sample is available from some source (for example, hospital database, case registry, etc.); however, the reference sample is required to be drawn from the corresponding source population. The required minimum size of the reference sample is an important issue in this regard. In this work, we address the minimum sample size calculation and discuss related issues. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Sample size for monitoring sirex populations and their natural enemies
Directory of Open Access Journals (Sweden)
Susete do Rocio Chiarello Penteado
2016-09-01
Full Text Available The woodwasp Sirex noctilio Fabricius (Hymenoptera: Siricidae was introduced in Brazil in 1988 and became the main pest in pine plantations. It has spread to about 1.000.000 ha, at different population levels, in the states of Rio Grande do Sul, Santa Catarina, Paraná, São Paulo and Minas Gerais. Control is done mainly by using a nematode, Deladenus siricidicola Bedding (Nematoda: Neothylenchidae. The evaluation of the efficiency of natural enemies has been difficult because there are no appropriate sampling systems. This study tested a hierarchical sampling system to define the sample size to monitor the S. noctilio population and the efficiency of their natural enemies, which was found to be perfectly adequate.
Luo, Maoyi; Xing, Shan; Yang, Yonggang; Song, Lijuan; Ma, Yan; Wang, Yadong; Dai, Xiongxin; Happel, Steffen
2018-07-01
There is a growing demand for the determination of actinides in soil and sediment samples for environmental monitoring and tracing, radiological protection, and nuclear forensic reasons. A total sample dissolution method based on lithium metaborate fusion, followed by sequential column chromatography separation, was developed for simultaneous determination of Pu, Am and Cm isotopes in large-size environmental samples by alpha spectrometry and mass spectrometric techniques. The overall recoveries of both Pu and Am for the entire procedure were higher than 70% for large-size soil samples. The method was validated using 20 g of soil samples spiked with known amounts of 239 Pu and 241 Am as well as the certified reference materials IAEA-384 (Fangataufa Lagoon sediment) and IAEA-385 (Irish Sea sediment). All the measured results agreed very well with the expected values. Copyright © 2018 Elsevier Ltd. All rights reserved.
Comparing Server Energy Use and Efficiency Using Small Sample Sizes
Energy Technology Data Exchange (ETDEWEB)
Coles, Henry C.; Qin, Yong; Price, Phillip N.
2014-11-01
This report documents a demonstration that compared the energy consumption and efficiency of a limited sample size of server-type IT equipment from different manufacturers by measuring power at the server power supply power cords. The results are specific to the equipment and methods used. However, it is hoped that those responsible for IT equipment selection can used the methods described to choose models that optimize energy use efficiency. The demonstration was conducted in a data center at Lawrence Berkeley National Laboratory in Berkeley, California. It was performed with five servers of similar mechanical and electronic specifications; three from Intel and one each from Dell and Supermicro. Server IT equipment is constructed using commodity components, server manufacturer-designed assemblies, and control systems. Server compute efficiency is constrained by the commodity component specifications and integration requirements. The design freedom, outside of the commodity component constraints, provides room for the manufacturer to offer a product with competitive efficiency that meets market needs at a compelling price. A goal of the demonstration was to compare and quantify the server efficiency for three different brands. The efficiency is defined as the average compute rate (computations per unit of time) divided by the average energy consumption rate. The research team used an industry standard benchmark software package to provide a repeatable software load to obtain the compute rate and provide a variety of power consumption levels. Energy use when the servers were in an idle state (not providing computing work) were also measured. At high server compute loads, all brands, using the same key components (processors and memory), had similar results; therefore, from these results, it could not be concluded that one brand is more efficient than the other brands. The test results show that the power consumption variability caused by the key components as a
Hu, Youna; Song, Peter X-K
2012-04-13
Quadratic inference functions (QIF) methodology is an important alternative to the generalized estimating equations (GEE) method in the longitudinal marginal model, as it offers higher estimation efficiency than the GEE when correlation structure is misspecified. The focus of this paper is on sample size determination and power calculation for QIF based on the Wald test in a marginal logistic model with covariates of treatment, time, and treatment-time interaction. We have made three contributions in this paper: (i) we derived formulas of sample size and power for QIF and compared their performance with those given by the GEE; (ii) we proposed an optimal scheme of sample size determination to overcome the difficulty of unknown true correlation matrix in the sense of minimal average risk; and (iii) we studied properties of both QIF and GEE sample size formulas in relation to the number of follow-up visits and found that the QIF gave more robust sample sizes than the GEE. Using numerical examples, we illustrated that without sacrificing statistical power, the QIF design leads to sample size saving and hence lower study cost in comparison with the GEE analysis. We conclude that the QIF analysis is appealing for longitudinal studies. Copyright © 2012 John Wiley & Sons, Ltd.
Collins, Steven D; Abbott, John C; McIntyre, Nancy E
2017-08-01
Citizen-science databases have been used to develop species distribution models (SDMs), although many taxa may be only georeferenced to county. It is tacitly assumed that SDMs built from county-scale data should be less precise than those built with more accurate localities, but the extent of the bias is currently unknown. Our aims in this study were to illustrate the effects of using county-scale data on the spatial extent and accuracy of SDMs relative to true locality data and to compare potential compensatory methods (including increased sample size and using overall county environmental averages rather than point locality environmental data). To do so, we developed SDMs in maxent with PRISM-derived BIOCLIM parameters for 283 and 230 species of odonates (dragonflies and damselflies) and butterflies, respectively, for five subsets from the OdonataCentral and Butterflies and Moths of North America citizen-science databases: (1) a true locality dataset, (2) a corresponding sister dataset of county-centroid coordinates, (3) a dataset where the average environmental conditions within each county were assigned to each record, (4) a 50/50% mix of true localities and county-centroid coordinates, and (5) a 50/50% mix of true localities and records assigned the average environmental conditions within each county. These mixtures allowed us to quantify the degree of bias from county-scale data. Models developed with county centroids overpredicted the extent of suitable habitat by 15% on average compared to true locality models, although larger sample sizes (>100 locality records) reduced this disparity. Assigning county-averaged environmental conditions did not offer consistent improvement, however. Because county-level data are of limited value for developing SDMs except for species that are widespread and well collected or that inhabit regions where small, climatically uniform counties predominate, three means of encouraging more accurate georeferencing in citizen
Sample size reduction in groundwater surveys via sparse data assimilation
Hussain, Z.
2013-04-01
In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.
Bayesian sample size determination for cost-effectiveness studies with censored data.
Directory of Open Access Journals (Sweden)
Daniel P Beavers
Full Text Available Cost-effectiveness models are commonly utilized to determine the combined clinical and economic impact of one treatment compared to another. However, most methods for sample size determination of cost-effectiveness studies assume fully observed costs and effectiveness outcomes, which presents challenges for survival-based studies in which censoring exists. We propose a Bayesian method for the design and analysis of cost-effectiveness data in which costs and effectiveness may be censored, and the sample size is approximated for both power and assurance. We explore two parametric models and demonstrate the flexibility of the approach to accommodate a variety of modifications to study assumptions.
Blinded sample size re-estimation for recurrent event data with time trends.
Schneider, S; Schmidli, H; Friede, T
2013-12-30
The use of an internal pilot study for blinded sample size re-estimation (BSSR) allows to reduce uncertainty on the appropriate sample size compared with conventional fixed sample size designs. Recently BSSR procedures for recurrent event data were proposed and investigated. These approaches assume treatment-specific constant event rates that might not always be appropriate as found in relapsing multiple sclerosis. On the basis of a proportional intensity frailty model, we propose methods for BSSR in situations where a time trend of the event rates is present. For the sample size planning and the final analysis standard negative binomial methods can be used, as long as the patient follow-up time is approximately equal in the treatment groups. To re-estimate the sample size at interim, however, a full likelihood analysis is necessary. Operating characteristics such as rejection probabilities and sample size distribution are evaluated in a simulation study motivated by a systematic review in relapsing multiple sclerosis. The key factors affecting the operating characteristics are the study duration and the length of the recruitment period. The proposed procedure for BSSR controls the type I error rate and maintains the desired power against misspecifications of the nuisance parameters. Copyright © 2013 John Wiley & Sons, Ltd.
Two-Stage Adaptive Optimal Design with Fixed First-Stage Sample Size
Directory of Open Access Journals (Sweden)
Adam Lane
2012-01-01
Full Text Available In adaptive optimal procedures, the design at each stage is an estimate of the optimal design based on all previous data. Asymptotics for regular models with fixed number of stages are straightforward if one assumes the sample size of each stage goes to infinity with the overall sample size. However, it is not uncommon for a small pilot study of fixed size to be followed by a much larger experiment. We study the large sample behavior of such studies. For simplicity, we assume a nonlinear regression model with normal errors. We show that the distribution of the maximum likelihood estimates converges to a scale mixture family of normal random variables. Then, for a one parameter exponential mean function we derive the asymptotic distribution of the maximum likelihood estimate explicitly and present a simulation to compare the characteristics of this asymptotic distribution with some commonly used alternatives.
A normative inference approach for optimal sample sizes in decisions from experience
Ostwald, Dirk; Starke, Ludger; Hertwig, Ralph
2015-01-01
“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the “sampling paradigm,” which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE. PMID:26441720
Kikuchi, Takashi; Gittins, John
2011-08-01
The behavioural Bayes approach to sample size determination for clinical trials assumes that the number of subsequent patients switching to a new drug from the current drug depends on the strength of the evidence for efficacy and safety that was observed in the clinical trials. The optimal sample size is the one which maximises the expected net benefit of the trial. The approach has been developed in a series of papers by Pezeshk and the present authors (Gittins JC, Pezeshk H. A behavioral Bayes method for determining the size of a clinical trial. Drug Information Journal 2000; 34: 355-63; Gittins JC, Pezeshk H. How Large should a clinical trial be? The Statistician 2000; 49(2): 177-87; Gittins JC, Pezeshk H. A decision theoretic approach to sample size determination in clinical trials. Journal of Biopharmaceutical Statistics 2002; 12(4): 535-51; Gittins JC, Pezeshk H. A fully Bayesian approach to calculating sample sizes for clinical trials with binary responses. Drug Information Journal 2002; 36: 143-50; Kikuchi T, Pezeshk H, Gittins J. A Bayesian cost-benefit approach to the determination of sample size in clinical trials. Statistics in Medicine 2008; 27(1): 68-82; Kikuchi T, Gittins J. A behavioral Bayes method to determine the sample size of a clinical trial considering efficacy and safety. Statistics in Medicine 2009; 28(18): 2293-306; Kikuchi T, Gittins J. A Bayesian procedure for cost-benefit evaluation of a new drug in multi-national clinical trials. Statistics in Medicine 2009 (Submitted)). The purpose of this article is to provide a rationale for experimental designs which allocate more patients to the new treatment than to the control group. The model uses a logistic weight function, including an interaction term linking efficacy and safety, which determines the number of patients choosing the new drug, and hence the resulting benefit. A Monte Carlo simulation is employed for the calculation. Having a larger group of patients on the new drug in general
Sample Size of One: Operational Qualitative Analysis in the Classroom
Directory of Open Access Journals (Sweden)
John Hoven
2015-10-01
Full Text Available Qualitative analysis has two extraordinary capabilities: first, finding answers to questions we are too clueless to ask; and second, causal inference – hypothesis testing and assessment – within a single unique context (sample size of one. These capabilities are broadly useful, and they are critically important in village-level civil-military operations. Company commanders need to learn quickly, "What are the problems and possibilities here and now, in this specific village? What happens if we do A, B, and C?" – and that is an ill-defined, one-of-a-kind problem. The U.S. Army's Eighty-Third Civil Affairs Battalion is our "first user" innovation partner in a new project to adapt qualitative research methods to an operational tempo and purpose. Our aim is to develop a simple, low-cost methodology and training program for local civil-military operations conducted by non-specialist conventional forces. Complementary to that, this paper focuses on some essential basics that can be implemented by college professors without significant cost, effort, or disruption.
Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning
Li, Zhushan
2014-01-01
Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…
Sample Size Requirements for Assessing Statistical Moments of Simulated Crop Yield Distributions
Lehmann, N.; Finger, R.; Klein, T.; Calanca, P.
2013-01-01
Mechanistic crop growth models are becoming increasingly important in agricultural research and are extensively used in climate change impact assessments. In such studies, statistics of crop yields are usually evaluated without the explicit consideration of sample size requirements. The purpose of
Constrained statistical inference: sample-size tables for ANOVA and regression.
Vanbrabant, Leonard; Van De Schoot, Rens; Rosseel, Yves
2014-01-01
Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than β2 and β3. The corresponding hypothesis is H: β1 > {β2, β3} and this is known as an (order) constrained hypothesis. A major advantage of testing such a hypothesis is that power can be gained and inherently a smaller sample size is needed. This article discusses this gain in sample size reduction, when an increasing number of constraints is included into the hypothesis. The main goal is to present sample-size tables for constrained hypotheses. A sample-size table contains the necessary sample-size at a pre-specified power (say, 0.80) for an increasing number of constraints. To obtain sample-size tables, two Monte Carlo simulations were performed, one for ANOVA and one for multiple regression. Three results are salient. First, in an ANOVA the needed sample-size decreases with 30-50% when complete ordering of the parameters is taken into account. Second, small deviations from the imposed order have only a minor impact on the power. Third, at the maximum number of constraints, the linear regression results are comparable with the ANOVA results. However, in the case of fewer constraints, ordering the parameters (e.g., β1 > β2) results in a higher power than assigning a positive or a negative sign to the parameters (e.g., β1 > 0).
Sample size re-estimation in paired comparative diagnostic accuracy studies with a binary response.
McCray, Gareth P J; Titman, Andrew C; Ghaneh, Paula; Lancaster, Gillian A
2017-07-14
The sample size required to power a study to a nominal level in a paired comparative diagnostic accuracy study, i.e. studies in which the diagnostic accuracy of two testing procedures is compared relative to a gold standard, depends on the conditional dependence between the two tests - the lower the dependence the greater the sample size required. A priori, we usually do not know the dependence between the two tests and thus cannot determine the exact sample size required. One option is to use the implied sample size for the maximal negative dependence, giving the largest possible sample size. However, this is potentially wasteful of resources and unnecessarily burdensome on study participants as the study is likely to be overpowered. A more accurate estimate of the sample size can be determined at a planned interim analysis point where the sample size is re-estimated. This paper discusses a sample size estimation and re-estimation method based on the maximum likelihood estimates, under an implied multinomial model, of the observed values of conditional dependence between the two tests and, if required, prevalence, at a planned interim. The method is illustrated by comparing the accuracy of two procedures for the detection of pancreatic cancer, one procedure using the standard battery of tests, and the other using the standard battery with the addition of a PET/CT scan all relative to the gold standard of a cell biopsy. Simulation of the proposed method illustrates its robustness under various conditions. The results show that the type I error rate of the overall experiment is stable using our suggested method and that the type II error rate is close to or above nominal. Furthermore, the instances in which the type II error rate is above nominal are in the situations where the lowest sample size is required, meaning a lower impact on the actual number of participants recruited. We recommend multinomial model maximum likelihood estimation of the conditional
Species-genetic diversity correlations in habitat fragmentation can be biased by small sample sizes.
Nazareno, Alison G; Jump, Alistair S
2012-06-01
Predicted parallel impacts of habitat fragmentation on genes and species lie at the core of conservation biology, yet tests of this rule are rare. In a recent article in Ecology Letters, Struebig et al. (2011) report that declining genetic diversity accompanies declining species diversity in tropical forest fragments. However, this study estimates diversity in many populations through extrapolation from very small sample sizes. Using the data of this recent work, we show that results estimated from the smallest sample sizes drive the species-genetic diversity correlation (SGDC), owing to a false-positive association between habitat fragmentation and loss of genetic diversity. Small sample sizes are a persistent problem in habitat fragmentation studies, the results of which often do not fit simple theoretical models. It is essential, therefore, that data assessing the proposed SGDC are sufficient in order that conclusions be robust.
Foley, Brett Patrick
2010-01-01
The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using…
Sample-size calculations for multi-group comparison in population pharmacokinetic experiments.
Ogungbenro, Kayode; Aarons, Leon
2010-01-01
This paper describes an approach for calculating sample size for population pharmacokinetic experiments that involve hypothesis testing based on multi-group comparison detecting the difference in parameters between groups under mixed-effects modelling. This approach extends what has been described for generalized linear models and nonlinear population pharmacokinetic models that involve only binary covariates to more complex nonlinear population pharmacokinetic models. The structural nonlinear model is linearized around the random effects to obtain the marginal model and the hypothesis testing involving model parameters is based on Wald's test. This approach provides an efficient and fast method for calculating sample size for hypothesis testing in population pharmacokinetic models. The approach can also handle different design problems such as unequal allocation of subjects to groups and unbalanced sampling times between and within groups. The results obtained following application to a one compartment intravenous bolus dose model that involved three different hypotheses under different scenarios showed good agreement between the power obtained from NONMEM simulations and nominal power. Copyright © 2009 John Wiley & Sons, Ltd.
Shieh, Gwowen
2013-01-01
The a priori determination of a proper sample size necessary to achieve some specified power is an important problem encountered frequently in practical studies. To establish the needed sample size for a two-sample "t" test, researchers may conduct the power analysis by specifying scientifically important values as the underlying population means…
DEFF Research Database (Denmark)
Ravn-Jonsen, Lars
Ecosystem Management requires models that can link the ecosystem level to the operation level. This link can be created by an ecosystem production model. Because the function of the individual fish in the marine ecosystem, seen in trophic context, is closely related to its size, the model groups...... fish according to size. The model summarises individual predation events into ecosystem level properties, and thereby uses the law of conversation of mass as a framework. This paper provides the background, the conceptual model, basic assumptions, integration of fishing activities, mathematical...... the predator--prey interaction, (ii) mass balance in the predator--prey allocation, and (iii) mortality and somatic growth as a consequence of the predator--prey allocation. By incorporating additional assumptions, the model can be extended to other dimensions of the ecosystem, for example, space or species...
Threshold-dependent sample sizes for selenium assessment with stream fish tissue
Hitt, Nathaniel P.; Smith, David R.
2015-01-01
Natural resource managers are developing assessments of selenium (Se) contamination in freshwater ecosystems based on fish tissue concentrations. We evaluated the effects of sample size (i.e., number of fish per site) on the probability of correctly detecting mean whole-body Se values above a range of potential management thresholds. We modeled Se concentrations as gamma distributions with shape and scale parameters fitting an empirical mean-to-variance relationship in data from southwestern West Virginia, USA (63 collections, 382 individuals). We used parametric bootstrapping techniques to calculate statistical power as the probability of detecting true mean concentrations up to 3 mg Se/kg above management thresholds ranging from 4 to 8 mg Se/kg. Sample sizes required to achieve 80% power varied as a function of management thresholds and Type I error tolerance (α). Higher thresholds required more samples than lower thresholds because populations were more heterogeneous at higher mean Se levels. For instance, to assess a management threshold of 4 mg Se/kg, a sample of eight fish could detect an increase of approximately 1 mg Se/kg with 80% power (given α = 0.05), but this sample size would be unable to detect such an increase from a management threshold of 8 mg Se/kg with more than a coin-flip probability. Increasing α decreased sample size requirements to detect above-threshold mean Se concentrations with 80% power. For instance, at an α-level of 0.05, an 8-fish sample could detect an increase of approximately 2 units above a threshold of 8 mg Se/kg with 80% power, but when α was relaxed to 0.2, this sample size was more sensitive to increasing mean Se concentrations, allowing detection of an increase of approximately 1.2 units with equivalent power. Combining individuals into 2- and 4-fish composite samples for laboratory analysis did not decrease power because the reduced number of laboratory samples was compensated for by increased
Modeling abundance using hierarchical distance sampling
Royle, Andy; Kery, Marc
2016-01-01
In this chapter, we provide an introduction to classical distance sampling ideas for point and line transect data, and for continuous and binned distance data. We introduce the conditional and the full likelihood, and we discuss Bayesian analysis of these models in BUGS using the idea of data augmentation, which we discussed in Chapter 7. We then extend the basic ideas to the problem of hierarchical distance sampling (HDS), where we have multiple point or transect sample units in space (or possibly in time). The benefit of HDS in practice is that it allows us to directly model spatial variation in population size among these sample units. This is a preeminent concern of most field studies that use distance sampling methods, but it is not a problem that has received much attention in the literature. We show how to analyze HDS models in both the unmarked package and in the BUGS language for point and line transects, and for continuous and binned distance data. We provide a case study of HDS applied to a survey of the island scrub-jay on Santa Cruz Island, California.
Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.
Algina, James; Olejnik, Stephen
2000-01-01
Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)
Bhandari, Mohit; Tornetta, Paul; Rampersad, Shelly-Ann; Sprague, Sheila; Heels-Ansdell, Diane; Sanders, David W.; Schemitsch, Emil H.; Swiontkowski, Marc; Walter, Stephen; Guyatt, Gordon; Buckingham, Lisa; Leece, Pamela; Viveiros, Helena; Mignott, Tashay; Ansell, Natalie; Sidorkewicz, Natalie; Agel, Julie; Bombardier, Claire; Berlin, Jesse A.; Bosse, Michael; Browner, Bruce; Gillespie, Brenda; O'Brien, Peter; Poolman, Rudolf; Macleod, Mark D.; Carey, Timothy; Leitch, Kellie; Bailey, Stuart; Gurr, Kevin; Konito, Ken; Bartha, Charlene; Low, Isolina; MacBean, Leila V.; Ramu, Mala; Reiber, Susan; Strapp, Ruth; Tieszer, Christina; Kreder, Hans; Stephen, David J. G.; Axelrod, Terry S.; Yee, Albert J. M.; Richards, Robin R.; Finkelstein, Joel; Holtby, Richard M.; Cameron, Hugh; Cameron, John; Gofton, Wade; Murnaghan, John; Schatztker, Joseph; Bulmer, Beverly; Conlan, Lisa; Laflamme, Yves; Berry, Gregory; Beaumont, Pierre; Ranger, Pierre; Laflamme, Georges-Henri; Jodoin, Alain; Renaud, Eric; Gagnon, Sylvain; Maurais, Gilles; Malo, Michel; Fernandes, Julio; Latendresse, Kim; Poirier, Marie-France; Daigneault, Gina; McKee, Michael M.; Waddell, James P.; Bogoch, Earl R.; Daniels, Timothy R.; McBroom, Robert R.; Vicente, Milena R.; Storey, Wendy; Wild, Lisa M.; McCormack, Robert; Perey, Bertrand; Goetz, Thomas J.; Pate, Graham; Penner, Murray J.; Panagiotopoulos, Kostas; Pirani, Shafique; Dommisse, Ian G.; Loomer, Richard L.; Stone, Trevor; Moon, Karyn; Zomar, Mauri; Webb, Lawrence X.; Teasdall, Robert D.; Birkedal, John Peter; Martin, David Franklin; Ruch, David S.; Kilgus, Douglas J.; Pollock, David C.; Harris, Mitchel Brion; Wiesler, Ethan Ron; Ward, William G.; Shilt, Jeffrey Scott; Koman, Andrew L.; Poehling, Gary G.; Kulp, Brenda; Creevy, William R.; Stein, Andrew B.; Bono, Christopher T.; Einhorn, Thomas A.; Brown, T. Desmond; Pacicca, Donna; Sledge, John B.; Foster, Timothy E.; Voloshin, Ilva; Bolton, Jill; Carlisle, Hope; Shaughnessy, Lisa; Ombremsky, William T.; LeCroy, C. Michael; Meinberg, Eric G.; Messer, Terry M.; Craig, William L.; Dirschl, Douglas R.; Caudle, Robert; Harris, Tim; Elhert, Kurt; Hage, William; Jones, Robert; Piedrahita, Luis; Schricker, Paul O.; Driver, Robin; Godwin, Jean; Hansley, Gloria; Obremskey, William Todd; Kregor, Philip James; Tennent, Gregory; Truchan, Lisa M.; Sciadini, Marcus; Shuler, Franklin D.; Driver, Robin E.; Nading, Mary Alice; Neiderstadt, Jacky; Vap, Alexander R.; Vallier, Heather A.; Patterson, Brendan M.; Wilber, John H.; Wilber, Roger G.; Sontich, John K.; Moore, Timothy Alan; Brady, Drew; Cooperman, Daniel R.; Davis, John A.; Cureton, Beth Ann; Mandel, Scott; Orr, R. Douglas; Sadler, John T. S.; Hussain, Tousief; Rajaratnam, Krishan; Petrisor, Bradley; Drew, Brian; Bednar, Drew A.; Kwok, Desmond C. H.; Pettit, Shirley; Hancock, Jill; Cole, Peter A.; Smith, Joel J.; Brown, Gregory A.; Lange, Thomas A.; Stark, John G.; Levy, Bruce; Swiontkowski, Marc F.; Garaghty, Mary J.; Salzman, Joshua G.; Schutte, Carol A.; Tastad, Linda Toddie; Vang, Sandy; Seligson, David; Roberts, Craig S.; Malkani, Arthur L.; Sanders, Laura; Gregory, Sharon Allen; Dyer, Carmen; Heinsen, Jessica; Smith, Langan; Madanagopal, Sudhakar; Coupe, Kevin J.; Tucker, Jeffrey J.; Criswell, Allen R.; Buckle, Rosemary; Rechter, Alan Jeffrey; Sheth, Dhiren Shaskikant; Urquart, Brad; Trotscher, Thea; Anders, Mark J.; Kowalski, Joseph M.; Fineberg, Marc S.; Bone, Lawrence B.; Phillips, Matthew J.; Rohrbacher, Bernard; Stegemann, Philip; Mihalko, William M.; Buyea, Cathy; Augustine, Stephen J.; Jackson, William Thomas; Solis, Gregory; Ero, Sunday U.; Segina, Daniel N.; Berrey, Hudson B.; Agnew, Samuel G.; Fitzpatrick, Michael; Campbell, Lakina C.; Derting, Lynn; McAdams, June; Goslings, J. Carel; Ponsen, Kees Jan; Luitse, Jan; Kloen, Peter; Joosse, Pieter; Winkelhagen, Jasper; Duivenvoorden, Raphaël; Teague, David C.; Davey, Joseph; Sullivan, J. Andy; Ertl, William J. J.; Puckett, Timothy A.; Pasque, Charles B.; Tompkins, John F.; Gruel, Curtis R.; Kammerlocher, Paul; Lehman, Thomas P.; Puffinbarger, William R.; Carl, Kathy L.; Weber, Donald W.; Jomha, Nadr M.; Goplen, Gordon R.; Masson, Edward; Beaupre, Lauren A.; Greaves, Karen E.; Schaump, Lori N.; Jeray, Kyle J.; Goetz, David R.; Westberry, Davd E.; Broderick, J. Scott; Moon, Bryan S.; Tanner, Stephanie L.; Powell, James N.; Buckley, Richard E.; Elves, Leslie; Connolly, Stephen; Abraham, Edward P.; Eastwood, Donna; Steele, Trudy; Ellis, Thomas; Herzberg, Alex; Brown, George A.; Crawford, Dennis E.; Hart, Robert; Hayden, James; Orfaly, Robert M.; Vigland, Theodore; Vivekaraj, Maharani; Bundy, Gina L.; Miclau, Theodore; Matityahu, Amir; Coughlin, R. Richard; Kandemir, Utku; McClellan, R. Trigg; Lin, Cindy Hsin-Hua; Karges, David; Cramer, Kathryn; Watson, J. Tracy; Moed, Berton; Scott, Barbara; Beck, Dennis J.; Orth, Carolyn; Puskas, David; Clark, Russell; Jones, Jennifer; Egol, Kenneth A.; Paksima, Nader; France, Monet; Wai, Eugene K.; Johnson, Garth; Wilkinson, Ross; Gruszczynski, Adam T.; Vexler, Liisa
2013-01-01
Inadequate sample size and power in randomized trials can result in misleading findings. This study demonstrates the effect of sample size in a large clinical trial by evaluating the results of the Study to Prospectively evaluate Reamed Intramedullary Nails in Patients with Tibial fractures (SPRINT)
Modeling and optimization of wet sizing process
International Nuclear Information System (INIS)
Thai Ba Cau; Vu Thanh Quang and Nguyen Ba Tien
2004-01-01
Mathematical simulation on basis of Stock law has been done for wet sizing process on cylinder equipment of laboratory and semi-industrial scale. The model consists of mathematical equations describing relations between variables, such as: - Resident time distribution function of emulsion particles in the separating zone of the equipment depending on flow-rate, height, diameter and structure of the equipment. - Size-distribution function in the fine and coarse parts depending on resident time distribution function of emulsion particles, characteristics of the material being processed, such as specific density, shapes, and characteristics of the environment of classification, such as specific density, viscosity. - Experimental model was developed on data collected from an experimental cylindrical equipment with diameter x height of sedimentation chamber equal to 50 x 40 cm for an emulsion of zirconium silicate in water. - Using this experimental model allows to determine optimal flow-rate in order to obtain product with desired grain size in term of average size or size distribution function. (author)
Chang, Yu-Wei; Tsong, Yi; Zhao, Zhigen
2017-01-01
Assessing equivalence or similarity has drawn much attention recently as many drug products have lost or will lose their patents in the next few years, especially certain best-selling biologics. To claim equivalence between the test treatment and the reference treatment when assay sensitivity is well established from historical data, one has to demonstrate both superiority of the test treatment over placebo and equivalence between the test treatment and the reference treatment. Thus, there is urgency for practitioners to derive a practical way to calculate sample size for a three-arm equivalence trial. The primary endpoints of a clinical trial may not always be continuous, but may be discrete. In this paper, the authors derive power function and discuss sample size requirement for a three-arm equivalence trial with Poisson and negative binomial clinical endpoints. In addition, the authors examine the effect of the dispersion parameter on the power and the sample size by varying its coefficient from small to large. In extensive numerical studies, the authors demonstrate that required sample size heavily depends on the dispersion parameter. Therefore, misusing a Poisson model for negative binomial data may easily lose power up to 20%, depending on the value of the dispersion parameter.
A Descriptive Evaluation of Software Sizing Models
1987-09-01
compensate for a lack of understanding of a software job to be done. 1.3 REPORT OUTLINE The guiding principle for model selection for this paper was...MODEL SIZE ESTIMATES FOR THE CAiSS SENSITIVITY MODEL MODEL SLOC ESD 37,600+ SPQR 35,910 BYL 22,402 PRICE SZ 21,410 ASSET-R 11,943 SSM 11,700 ASSET-R...disk. ?. Date LS, De fault current date, Re quire ] - ,, ... perffr: an,- 1 ,’ e e st i ma t e. Quantitative inputs Note- Each of the nine quantitative
A neutral model with fluctuating population size and its effective size.
Iizuka, Masaru; Tachida, Hidenori; Matsuda, Hirotsugu
2002-01-01
We consider a diffusion model with neutral alleles whose population size is fluctuating randomly. For this model, the effects of fluctuation of population size on the effective size are investigated. The effective size defined by the equilibrium average heterozygosity is larger than the harmonic mean of population size but smaller than the arithmetic mean of population size. To see explicitly the effects of fluctuation of population size on the effective size, we investigate a special case wh...
Designing image segmentation studies: Statistical power, sample size and reference standard quality.
Gibson, Eli; Hu, Yipeng; Huisman, Henkjan J; Barratt, Dean C
2017-12-01
Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
Bill, Anthony; Henderson, Sally; Penman, John
2010-01-01
Two test items that examined high school students' beliefs of sample size for large populations using the context of opinion polls conducted prior to national and state elections were developed. A trial of the two items with 21 male and 33 female Year 9 students examined their naive understanding of sample size: over half of students chose a…
Determination of boron concentration in biopsy-sized tissue samples
International Nuclear Information System (INIS)
Hou, Yougjin; Fong, Katrina; Edwards, Benjamin; Autry-Conwell, Susan; Boggan, James
2000-01-01
Inductively coupled plasma mass spectrometry (ICP-MS) is the most sensitive analytical method for boron determination. However, because boron is volatile and ubiquitous in nature, low-concentration boron sample measurement remains a challenge. In this study, an improved ICP-MS method was developed for quantitation of tissue samples with low (less than 10 ppb) and high (100 ppb) boron concentrations. The addition of an ammonia-mannitol solution converts volatile boric acid to the non-volatile ammonium borate in the spray chamber and with the formation of a boron-mannitol complex, the boron memory effect and background are greatly reduced. This results in measurements that are more accurate, repeatable, and efficient. This improved analysis method has facilitated rapid and reliable tissue biodistribution analyses of newly developed boronated compounds for potential use in neutron capture therapy. (author)
Sample Size Estimation for Detection of Splicing Events in Transcriptome Sequencing Data.
Kaisers, Wolfgang; Schwender, Holger; Schaal, Heiner
2017-09-05
Merging data from multiple samples is required to detect low expressed transcripts or splicing events that might be present only in a subset of samples. However, the exact number of required replicates enabling the detection of such rare events often remains a mystery but can be approached through probability theory. Here, we describe a probabilistic model, relating the number of observed events in a batch of samples with observation probabilities. Therein, samples appear as a heterogeneous collection of events, which are observed with some probability. The model is evaluated in a batch of 54 transcriptomes of human dermal fibroblast samples. The majority of putative splice-sites (alignment gap-sites) are detected in (almost) all samples or only sporadically, resulting in an U-shaped pattern for observation probabilities. The probabilistic model systematically underestimates event numbers due to a bias resulting from finite sampling. However, using an additional assumption, the probabilistic model can predict observed event numbers within a events (mean 7122 in alignments from TopHat alignments and 86,215 in alignments from STAR). We conclude that the probabilistic model provides an adequate description for observation of gap-sites in transcriptome data. Thus, the calculation of required sample sizes can be done by application of a simple binomial model to sporadically observed random events. Due to the large number of uniquely observed putative splice-sites and the known stochastic noise in the splicing machinery, it appears advisable to include observation of rare splicing events into analysis objectives. Therefore, it is beneficial to take scores for the validation of gap-sites into account.
Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient !
van Breukelen, Gerard J.P.; Candel, Math J.J.M.
2012-01-01
Objective: Simple guidelines for efficient sample sizes in cluster randomized trials with unknown intraclass correlation and varying cluster sizes. Methods: A simple equation is given for the optimal number of clusters and sample size per cluster. Here, optimal means maximizing power for a given
STANDARD ERRORS AND SAMPLE SIZES FOR 2-LEVEL RESEARCH
SNIJDERS, TAB; BOSKER, RJ
1993-01-01
The hierarchical linear model approach to a two-level design is considered, some variables at the lower level having fixed and others having random regression coefficients. An approximation is derived to the covariance matrix of the estimators of the fixed regression coefficients (for variables at
Structured estimation - Sample size reduction for adaptive pattern classification
Morgera, S.; Cooper, D. B.
1977-01-01
The Gaussian two-category classification problem with known category mean value vectors and identical but unknown category covariance matrices is considered. The weight vector depends on the unknown common covariance matrix, so the procedure is to estimate the covariance matrix in order to obtain an estimate of the optimum weight vector. The measure of performance for the adapted classifier is the output signal-to-interference noise ratio (SIR). A simple approximation for the expected SIR is gained by using the general sample covariance matrix estimator; this performance is both signal and true covariance matrix independent. An approximation is also found for the expected SIR obtained by using a Toeplitz form covariance matrix estimator; this performance is found to be dependent on both the signal and the true covariance matrix.
Evaluation of Approaches to Analyzing Continuous Correlated Eye Data When Sample Size Is Small.
Huang, Jing; Huang, Jiayan; Chen, Yong; Ying, Gui-Shuang
2018-02-01
To evaluate the performance of commonly used statistical methods for analyzing continuous correlated eye data when sample size is small. We simulated correlated continuous data from two designs: (1) two eyes of a subject in two comparison groups; (2) two eyes of a subject in the same comparison group, under various sample size (5-50), inter-eye correlation (0-0.75) and effect size (0-0.8). Simulated data were analyzed using paired t-test, two sample t-test, Wald test and score test using the generalized estimating equations (GEE) and F-test using linear mixed effects model (LMM). We compared type I error rates and statistical powers, and demonstrated analysis approaches through analyzing two real datasets. In design 1, paired t-test and LMM perform better than GEE, with nominal type 1 error rate and higher statistical power. In design 2, no test performs uniformly well: two sample t-test (average of two eyes or a random eye) achieves better control of type I error but yields lower statistical power. In both designs, the GEE Wald test inflates type I error rate and GEE score test has lower power. When sample size is small, some commonly used statistical methods do not perform well. Paired t-test and LMM perform best when two eyes of a subject are in two different comparison groups, and t-test using the average of two eyes performs best when the two eyes are in the same comparison group. When selecting the appropriate analysis approach the study design should be considered.
Sample size reassessment for a two-stage design controlling the false discovery rate.
Zehetmayer, Sonja; Graf, Alexandra C; Posch, Martin
2015-11-01
Sample size calculations for gene expression microarray and NGS-RNA-Seq experiments are challenging because the overall power depends on unknown quantities as the proportion of true null hypotheses and the distribution of the effect sizes under the alternative. We propose a two-stage design with an adaptive interim analysis where these quantities are estimated from the interim data. The second stage sample size is chosen based on these estimates to achieve a specific overall power. The proposed procedure controls the power in all considered scenarios except for very low first stage sample sizes. The false discovery rate (FDR) is controlled despite of the data dependent choice of sample size. The two-stage design can be a useful tool to determine the sample size of high-dimensional studies if in the planning phase there is high uncertainty regarding the expected effect sizes and variability.
Evaluation of design flood estimates with respect to sample size
Kobierska, Florian; Engeland, Kolbjorn
2016-04-01
Estimation of design floods forms the basis for hazard management related to flood risk and is a legal obligation when building infrastructure such as dams, bridges and roads close to water bodies. Flood inundation maps used for land use planning are also produced based on design flood estimates. In Norway, the current guidelines for design flood estimates give recommendations on which data, probability distribution, and method to use dependent on length of the local record. If less than 30 years of local data is available, an index flood approach is recommended where the local observations are used for estimating the index flood and regional data are used for estimating the growth curve. For 30-50 years of data, a 2 parameter distribution is recommended, and for more than 50 years of data, a 3 parameter distribution should be used. Many countries have national guidelines for flood frequency estimation, and recommended distributions include the log Pearson II, generalized logistic and generalized extreme value distributions. For estimating distribution parameters, ordinary and linear moments, maximum likelihood and Bayesian methods are used. The aim of this study is to r-evaluate the guidelines for local flood frequency estimation. In particular, we wanted to answer the following questions: (i) Which distribution gives the best fit to the data? (ii) Which estimation method provides the best fit to the data? (iii) Does the answer to (i) and (ii) depend on local data availability? To answer these questions we set up a test bench for local flood frequency analysis using data based cross-validation methods. The criteria were based on indices describing stability and reliability of design flood estimates. Stability is used as a criterion since design flood estimates should not excessively depend on the data sample. The reliability indices describe to which degree design flood predictions can be trusted.
Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide.
de Bekker-Grob, Esther W; Donkers, Bas; Jonker, Marcel F; Stolk, Elly A
2015-10-01
Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care.
Issues of sample size in sensitivity and specificity analysis with special reference to oncology
Directory of Open Access Journals (Sweden)
Atul Juneja
2015-01-01
Full Text Available Sample size is one of the basics issues, which medical researcher including oncologist faces with any research program. The current communication attempts to discuss the computation of sample size when sensitivity and specificity are being evaluated. The article intends to present the situation that the researcher could easily visualize for appropriate use of sample size techniques for sensitivity and specificity when any screening method for early detection of cancer is in question. Moreover, the researcher would be in a position to efficiently communicate with a statistician for sample size computation and most importantly applicability of the results under the conditions of the negotiated precision.
Model catalysis by size-selected cluster deposition
Energy Technology Data Exchange (ETDEWEB)
Anderson, Scott [Univ. of Utah, Salt Lake City, UT (United States)
2015-11-20
This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.
Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size
Directory of Open Access Journals (Sweden)
Zhihua Wang
2014-01-01
Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
CT dose survey in adults: what sample size for what precision?
Energy Technology Data Exchange (ETDEWEB)
Taylor, Stephen [Hopital Ambroise Pare, Department of Radiology, Mons (Belgium); Muylem, Alain van [Hopital Erasme, Department of Pneumology, Brussels (Belgium); Howarth, Nigel [Clinique des Grangettes, Department of Radiology, Chene-Bougeries (Switzerland); Gevenois, Pierre Alain [Hopital Erasme, Department of Radiology, Brussels (Belgium); Tack, Denis [EpiCURA, Clinique Louis Caty, Department of Radiology, Baudour (Belgium)
2017-01-15
To determine variability of volume computed tomographic dose index (CTDIvol) and dose-length product (DLP) data, and propose a minimum sample size to achieve an expected precision. CTDIvol and DLP values of 19,875 consecutive CT acquisitions of abdomen (7268), thorax (3805), lumbar spine (3161), cervical spine (1515) and head (4106) were collected in two centers. Their variabilities were investigated according to sample size (10 to 1000 acquisitions) and patient body weight categories (no weight selection, 67-73 kg and 60-80 kg). The 95 % confidence interval in percentage of their median (CI95/med) value was calculated for increasing sample sizes. We deduced the sample size that set a 95 % CI lower than 10 % of the median (CI95/med ≤ 10 %). Sample size ensuring CI95/med ≤ 10 %, ranged from 15 to 900 depending on the body region and the dose descriptor considered. In sample sizes recommended by regulatory authorities (i.e., from 10-20 patients), mean CTDIvol and DLP of one sample ranged from 0.50 to 2.00 times its actual value extracted from 2000 samples. The sampling error in CTDIvol and DLP means is high in dose surveys based on small samples of patients. Sample size should be increased at least tenfold to decrease this variability. (orig.)
Distribution of the two-sample t-test statistic following blinded sample size re-estimation.
Lu, Kaifeng
2016-05-01
We consider the blinded sample size re-estimation based on the simple one-sample variance estimator at an interim analysis. We characterize the exact distribution of the standard two-sample t-test statistic at the final analysis. We describe a simulation algorithm for the evaluation of the probability of rejecting the null hypothesis at given treatment effect. We compare the blinded sample size re-estimation method with two unblinded methods with respect to the empirical type I error, the empirical power, and the empirical distribution of the standard deviation estimator and final sample size. We characterize the type I error inflation across the range of standardized non-inferiority margin for non-inferiority trials, and derive the adjusted significance level to ensure type I error control for given sample size of the internal pilot study. We show that the adjusted significance level increases as the sample size of the internal pilot study increases. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
45 CFR Appendix C to Part 1356 - Calculating Sample Size for NYTD Follow-Up Populations
2010-10-01
... 45 Public Welfare 4 2010-10-01 2010-10-01 false Calculating Sample Size for NYTD Follow-Up Populations C Appendix C to Part 1356 Public Welfare Regulations Relating to Public Welfare (Continued) OFFICE... REQUIREMENTS APPLICABLE TO TITLE IV-E Pt. 1356, App. C Appendix C to Part 1356—Calculating Sample Size for NYTD...
Sample Size Requirements for Accurate Estimation of Squared Semi-Partial Correlation Coefficients.
Algina, James; Moulder, Bradley C.; Moser, Barry K.
2002-01-01
Studied the sample size requirements for accurate estimation of squared semi-partial correlation coefficients through simulation studies. Results show that the sample size necessary for adequate accuracy depends on: (1) the population squared multiple correlation coefficient (p squared); (2) the population increase in p squared; and (3) the…
Post-stratified estimation: with-in strata and total sample size recommendations
James A. Westfall; Paul L. Patterson; John W. Coulston
2011-01-01
Post-stratification is used to reduce the variance of estimates of the mean. Because the stratification is not fixed in advance, within-strata sample sizes can be quite small. The survey statistics literature provides some guidance on minimum within-strata sample sizes; however, the recommendations and justifications are inconsistent and apply broadly for many...
Implications of sampling design and sample size for national carbon accounting systems.
Köhl, Michael; Lister, Andrew; Scott, Charles T; Baldauf, Thomas; Plugge, Daniel
2011-11-08
Countries willing to adopt a REDD regime need to establish a national Measurement, Reporting and Verification (MRV) system that provides information on forest carbon stocks and carbon stock changes. Due to the extensive areas covered by forests the information is generally obtained by sample based surveys. Most operational sampling approaches utilize a combination of earth-observation data and in-situ field assessments as data sources. We compared the cost-efficiency of four different sampling design alternatives (simple random sampling, regression estimators, stratified sampling, 2-phase sampling with regression estimators) that have been proposed in the scope of REDD. Three of the design alternatives provide for a combination of in-situ and earth-observation data. Under different settings of remote sensing coverage, cost per field plot, cost of remote sensing imagery, correlation between attributes quantified in remote sensing and field data, as well as population variability and the percent standard error over total survey cost was calculated. The cost-efficiency of forest carbon stock assessments is driven by the sampling design chosen. Our results indicate that the cost of remote sensing imagery is decisive for the cost-efficiency of a sampling design. The variability of the sample population impairs cost-efficiency, but does not reverse the pattern of cost-efficiency of the individual design alternatives. Our results clearly indicate that it is important to consider cost-efficiency in the development of forest carbon stock assessments and the selection of remote sensing techniques. The development of MRV-systems for REDD need to be based on a sound optimization process that compares different data sources and sampling designs with respect to their cost-efficiency. This helps to reduce the uncertainties related with the quantification of carbon stocks and to increase the financial benefits from adopting a REDD regime.
Sexual maturity, fecundity and egg size of wild and cultured samples of Bagrus bayad macropterus
Tsadu, S.M.; Lamai, S.L.; Oladimeji, A.A.
2003-01-01
Twenty four matured samples of Bagrus bayad macropterus from the wild (Shiroro Lake, Nigeria) and under captivity, size ranging from 412.69-3300.00 g total body weight, were analysed for sexual maturity,fecundity and egg size. The average fecundity obtained were 53352.59 and 21028.32 eggs for the wild and cultured fish respectively.Positive relationship was observed between fecundity, body size and gonad weight. Fecundity increased as body size increased. A more positive and linear relatio...
Bouman, A C; ten Cate-Hoek, A J; Ramaekers, B L T; Joore, M A
2015-01-01
Non-inferiority trials are performed when the main therapeutic effect of the new therapy is expected to be not unacceptably worse than that of the standard therapy, and the new therapy is expected to have advantages over the standard therapy in costs or other (health) consequences. These advantages however are not included in the classic frequentist approach of sample size calculation for non-inferiority trials. In contrast, the decision theory approach of sample size calculation does include these factors. The objective of this study is to compare the conceptual and practical aspects of the frequentist approach and decision theory approach of sample size calculation for non-inferiority trials, thereby demonstrating that the decision theory approach is more appropriate for sample size calculation of non-inferiority trials. The frequentist approach and decision theory approach of sample size calculation for non-inferiority trials are compared and applied to a case of a non-inferiority trial on individually tailored duration of elastic compression stocking therapy compared to two years elastic compression stocking therapy for the prevention of post thrombotic syndrome after deep vein thrombosis. The two approaches differ substantially in conceptual background, analytical approach, and input requirements. The sample size calculated according to the frequentist approach yielded 788 patients, using a power of 80% and a one-sided significance level of 5%. The decision theory approach indicated that the optimal sample size was 500 patients, with a net value of €92 million. This study demonstrates and explains the differences between the classic frequentist approach and the decision theory approach of sample size calculation for non-inferiority trials. We argue that the decision theory approach of sample size estimation is most suitable for sample size calculation of non-inferiority trials.
Placzek, Marius; Friede, Tim
2017-01-01
The importance of subgroup analyses has been increasing due to a growing interest in personalized medicine and targeted therapies. Considering designs with multiple nested subgroups and a continuous endpoint, we develop methods for the analysis and sample size determination. First, we consider the joint distribution of standardized test statistics that correspond to each (sub)population. We derive multivariate exact distributions where possible, providing approximations otherwise. Based on these results, we present sample size calculation procedures. Uncertainties about nuisance parameters which are needed for sample size calculations make the study prone to misspecifications. We discuss how a sample size review can be performed in order to make the study more robust. To this end, we implement an internal pilot study design where the variances and prevalences of the subgroups are reestimated in a blinded fashion and the sample size is recalculated accordingly. Simulations show that the procedures presented here do not inflate the type I error significantly and maintain the prespecified power as long as the sample size of the smallest subgroup is not too small. We pay special attention to the case of small sample sizes and attain a lower boundary for the size of the internal pilot study.
Two-sample binary phase 2 trials with low type I error and low sample size.
Litwin, Samuel; Basickes, Stanley; Ross, Eric A
2017-04-30
We address design of two-stage clinical trials comparing experimental and control patients. Our end point is success or failure, however measured, with null hypothesis that the chance of success in both arms is p 0 and alternative that it is p 0 among controls and p 1 > p 0 among experimental patients. Standard rules will have the null hypothesis rejected when the number of successes in the (E)xperimental arm, E, sufficiently exceeds C, that among (C)ontrols. Here, we combine one-sample rejection decision rules, E⩾m, with two-sample rules of the form E - C > r to achieve two-sample tests with low sample number and low type I error. We find designs with sample numbers not far from the minimum possible using standard two-sample rules, but with type I error of 5% rather than 15% or 20% associated with them, and of equal power. This level of type I error is achieved locally, near the stated null, and increases to 15% or 20% when the null is significantly higher than specified. We increase the attractiveness of these designs to patients by using 2:1 randomization. Examples of the application of this new design covering both high and low success rates under the null hypothesis are provided. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Chaibub Neto, Elias
2015-01-01
In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson's sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling.
On sample size estimation and re-estimation adjusting for variability in confirmatory trials.
Wu, Pei-Shien; Lin, Min; Chow, Shein-Chung
2016-01-01
Sample size estimation (SSE) is an important issue in the planning of clinical studies. While larger studies are likely to have sufficient power, it may be unethical to expose more patients than necessary to answer a scientific question. Budget considerations may also cause one to limit the study to an adequate size to answer the question at hand. Typically at the planning stage, a statistically based justification for sample size is provided. An effective sample size is usually planned under a pre-specified type I error rate, a desired power under a particular alternative and variability associated with the observations recorded. The nuisance parameter such as the variance is unknown in practice. Thus, information from a preliminary pilot study is often used to estimate the variance. However, calculating the sample size based on the estimated nuisance parameter may not be stable. Sample size re-estimation (SSR) at the interim analysis may provide an opportunity to re-evaluate the uncertainties using accrued data and continue the trial with an updated sample size. This article evaluates a proposed SSR method based on controlling the variability of nuisance parameter. A numerical study is used to assess the performance of proposed method with respect to the control of type I error. The proposed method and concepts could be extended to SSR approaches with respect to other criteria, such as maintaining effect size, achieving conditional power, and reaching a desired reproducibility probability.
Sampling and chemical analysis by TXRF of size-fractionated ambient aerosols and emissions
International Nuclear Information System (INIS)
John, A.C.; Kuhlbusch, T.A.J.; Fissan, H.; Schmidt, K.-G-; Schmidt, F.; Pfeffer, H.-U.; Gladtke, D.
2000-01-01
Results of recent epidemiological studies led to new European air quality standards which require the monitoring of particles with aerodynamic diameters ≤ 10 μm (PM 10) and ≤ 2.5 μm (PM 2.5) instead of TSP (total suspended particulate matter). As these ambient air limit values will be exceeded most likely at several locations in Europe, so-called 'action plans' have to be set up to reduce particle concentrations, which requires information about sources and processes of PMx aerosols. For chemical characterization of the aerosols, different samplers were used and total reflection x-ray fluorescence analysis (TXRF) was applied beside other methods (elemental and organic carbon analysis, ion chromatography, atomic absorption spectrometry). For TXRF analysis, a specially designed sampling unit was built where the particle size classes 10-2.5 μm and 2.5-1.0 μm were directly impacted on TXRF sample carriers. An electrostatic precipitator (ESP) was used as a back-up filter to collect particles <1 μm directly on a TXRF sample carrier. The sampling unit was calibrated in the laboratory and then used for field measurements to determine the elemental composition of the mentioned particle size fractions. One of the field campaigns was carried out at a measurement site in Duesseldorf, Germany, in November 1999. As the composition of the ambient aerosols may have been influenced by a large construction site directly in the vicinity of the station during the field campaign, not only the aerosol particles, but also construction material was sampled and analyzed by TXRF. As air quality is affected by natural and anthropogenic sources, the emissions of particles ≤ 10 μm and ≤ 2.5 μm, respectively, have to be determined to estimate their contributions to the so called coarse and fine particle modes of ambient air. Therefore, an in-stack particle sampling system was developed according to the new ambient air quality standards. This PM 10/PM 2.5 cascade impactor was
Size distribution of mineral aerosol: using light-scattering models in laser particle sizing.
Veihelmann, B.; Konert, M.; van der Zande, W.J.
2006-01-01
The size distribution of semitransparent irregularly shaped mineral dust aerosol samples is determined using a commonly used laser particle-sizing technique. The size distribution is derived from intensity measurements of singly scattered light at various scattering angles close to the
Size distribution of mineral aerosol: using light-scattering models in laser particle sizing
Veihelmann, B.; Konert, M.; Zande, W.J. van der
2006-01-01
The size distribution of semitransparent irregularly shaped mineral dust aerosol,samples is determined using a commonly used laser particle-sizing technique. The size distribution is derived from intensity measurements of singly scattered light at various scattering angles close to the
The PowerAtlas: a power and sample size atlas for microarray experimental design and research
Directory of Open Access Journals (Sweden)
Wang Jelai
2006-02-01
Full Text Available Abstract Background Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. Results To address this challenge, we have developed a Microrarray PowerAtlas 1. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO. The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC. Conclusion This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.
The Sample Size Influence in the Accuracy of the Image Classification of the Remote Sensing
Directory of Open Access Journals (Sweden)
Thomaz C. e C. da Costa
2004-12-01
Full Text Available Landuse/landcover maps produced by classification of remote sensing images incorporate uncertainty. This uncertainty is measured by accuracy indices using reference samples. The size of the reference sample is defined by approximation by a binomial function without the use of a pilot sample. This way the accuracy are not estimated, but fixed a priori. In case of divergency between the estimated and a priori accuracy the error of the sampling will deviate from the expected error. The size using pilot sample (theorically correct procedure justify when haven´t estimate of accuracy for work area, referent the product remote sensing utility.
The endothelial sample size analysis in corneal specular microscopy clinical examinations.
Abib, Fernando C; Holzchuh, Ricardo; Schaefer, Artur; Schaefer, Tania; Godois, Ronialci
2012-05-01
To evaluate endothelial cell sample size and statistical error in corneal specular microscopy (CSM) examinations. One hundred twenty examinations were conducted with 4 types of corneal specular microscopes: 30 with each BioOptics, CSO, Konan, and Topcon corneal specular microscopes. All endothelial image data were analyzed by respective instrument software and also by the Cells Analyzer software with a method developed in our lab. A reliability degree (RD) of 95% and a relative error (RE) of 0.05 were used as cut-off values to analyze images of the counted endothelial cells called samples. The sample size mean was the number of cells evaluated on the images obtained with each device. Only examinations with RE 0.05); customized sample size, 336 ± 131 cells. Topcon: sample size, 87 ± 17 cells; RE, 10.1 ± 2.52; none of the examinations had sufficient endothelial cell quantity (RE > 0.05); customized sample size, 382 ± 159 cells. A very high number of CSM examinations had sample errors based on Cells Analyzer software. The endothelial sample size (examinations) needs to include more cells to be reliable and reproducible. The Cells Analyzer tutorial routine will be useful for CSM examination reliability and reproducibility.
Sample size adjustment designs with time-to-event outcomes: A caution.
Freidlin, Boris; Korn, Edward L
2017-12-01
Sample size adjustment designs, which allow increasing the study sample size based on interim analysis of outcome data from a randomized clinical trial, have been increasingly promoted in the biostatistical literature. Although it is recognized that group sequential designs can be at least as efficient as sample size adjustment designs, many authors argue that a key advantage of these designs is their flexibility; interim sample size adjustment decisions can incorporate information and business interests external to the trial. Recently, Chen et al. (Clinical Trials 2015) considered sample size adjustment applications in the time-to-event setting using a design (CDL) that limits adjustments to situations where the interim results are promising. The authors demonstrated that while CDL provides little gain in unconditional power (versus fixed-sample-size designs), there is a considerable increase in conditional power for trials in which the sample size is adjusted. In time-to-event settings, sample size adjustment allows an increase in the number of events required for the final analysis. This can be achieved by either (a) following the original study population until the additional events are observed thus focusing on the tail of the survival curves or (b) enrolling a potentially large number of additional patients thus focusing on the early differences in survival curves. We use the CDL approach to investigate performance of sample size adjustment designs in time-to-event trials. Through simulations, we demonstrate that when the magnitude of the true treatment effect changes over time, interim information on the shape of the survival curves can be used to enrich the final analysis with events from the time period with the strongest treatment effect. In particular, interested parties have the ability to make the end-of-trial treatment effect larger (on average) based on decisions using interim outcome data. Furthermore, in "clinical null" cases where there is no
Mixture model analysis of complex samples
Wedel, M; ter Hofstede, F; Steenkamp, JBEM
1998-01-01
We investigate the effects of a complex sampling design on the estimation of mixture models. An approximate or pseudo likelihood approach is proposed to obtain consistent estimates of class-specific parameters when the sample arises from such a complex design. The effects of ignoring the sample
A merchant ship size optimization model
Choi, Ki-Chul
1983-01-01
Approved for public release; distribution in unlimited. This paper analyzes how a shipowner or charterer may determine the specification of optimal ship size for a given route with respect to certain market requirements . The theory of optimal ship size, a methodology for estimating scale economics, and the various factors affecting ship size are examined using a typical conventional cargo ship and bulk cargo carriers based on shipowners ' cost data. http://archi...
Size and complexity in model financial systems.
Arinaminpathy, Nimalan; Kapadia, Sujit; May, Robert M
2012-11-06
The global financial crisis has precipitated an increasing appreciation of the need for a systemic perspective toward financial stability. For example: What role do large banks play in systemic risk? How should capital adequacy standards recognize this role? How is stability shaped by concentration and diversification in the financial system? We explore these questions using a deliberately simplified, dynamic model of a banking system that combines three different channels for direct transmission of contagion from one bank to another: liquidity hoarding, asset price contagion, and the propagation of defaults via counterparty credit risk. Importantly, we also introduce a mechanism for capturing how swings in "confidence" in the system may contribute to instability. Our results highlight that the importance of relatively large, well-connected banks in system stability scales more than proportionately with their size: the impact of their collapse arises not only from their connectivity, but also from their effect on confidence in the system. Imposing tougher capital requirements on larger banks than smaller ones can thus enhance the resilience of the system. Moreover, these effects are more pronounced in more concentrated systems, and continue to apply, even when allowing for potential diversification benefits that may be realized by larger banks. We discuss some tentative implications for policy, as well as conceptual analogies in ecosystem stability and in the control of infectious diseases.
Bice, K.; Clement, S. C.
1981-01-01
X-ray diffraction and spectroscopy were used to investigate the mineralogical and chemical properties of the Calvert, Ball Old Mine, Ball Martin, and Jordan Sediments. The particle size distribution and index of refraction of each sample were determined. The samples are composed primarily of quartz, kaolinite, and illite. The clay minerals are most abundant in the finer particle size fractions. The chemical properties of the four samples are similar. The Calvert sample is most notably different in that it contains a relatively high amount of iron. The dominant particle size fraction in each sample is silt, with lesser amounts of clay and sand. The indices of refraction of the sediments are the same with the exception of the Calvert sample which has a slightly higher value.
Small sample sizes in the study of ontogenetic allometry; implications for palaeobiology.
Brown, Caleb Marshall; Vavrek, Matthew J
2015-01-01
Quantitative morphometric analyses, particularly ontogenetic allometry, are common methods used in quantifying shape, and changes therein, in both extinct and extant organisms. Due to incompleteness and the potential for restricted sample sizes in the fossil record, palaeobiological analyses of allometry may encounter higher rates of error. Differences in sample size between fossil and extant studies and any resulting effects on allometric analyses have not been thoroughly investigated, and a logical lower threshold to sample size is not clear. Here we show that studies based on fossil datasets have smaller sample sizes than those based on extant taxa. A similar pattern between vertebrates and invertebrates indicates this is not a problem unique to either group, but common to both. We investigate the relationship between sample size, ontogenetic allometric relationship and statistical power using an empirical dataset of skull measurements of modern Alligator mississippiensis. Across a variety of subsampling techniques, used to simulate different taphonomic and/or sampling effects, smaller sample sizes gave less reliable and more variable results, often with the result that allometric relationships will go undetected due to Type II error (failure to reject the null hypothesis). This may result in a false impression of fewer instances of positive/negative allometric growth in fossils compared to living organisms. These limitations are not restricted to fossil data and are equally applicable to allometric analyses of rare extant taxa. No mathematically derived minimum sample size for ontogenetic allometric studies is found; rather results of isometry (but not necessarily allometry) should not be viewed with confidence at small sample sizes.
Sample Size Determination for Estimation of Sensor Detection Probabilities Based on a Test Variable
National Research Council Canada - National Science Library
Oymak, Okan
2007-01-01
.... Army Yuma Proving Ground. Specifically, we evaluate the coverage probabilities and lengths of widely used confidence intervals for a binomial proportion and report the required sample sizes for some specified goals...
Sample size determination for logistic regression on a logit-normal distribution.
Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance
2017-06-01
Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.
Estimating sample size for a small-quadrat method of botanical ...
African Journals Online (AJOL)
... in eight plant communities in the Nylsvley Nature Reserve. Illustrates with a table. Keywords: Botanical surveys; Grass density; Grasslands; Mixed Bushveld; Nylsvley Nature Reserve; Quadrat size species density; Small-quadrat method; Species density; Species richness; botany; sample size; method; survey; south africa
Sample size for equivalence trials: a case study from a vaccine lot consistency trial.
Ganju, Jitendra; Izu, Allen; Anemona, Alessandra
2008-08-30
For some trials, simple but subtle assumptions can have a profound impact on the size of the trial. A case in point is a vaccine lot consistency (or equivalence) trial. Standard sample size formulas used for designing lot consistency trials rely on only one component of variation, namely, the variation in antibody titers within lots. The other component, the variation in the means of titers between lots, is assumed to be equal to zero. In reality, some amount of variation between lots, however small, will be present even under the best manufacturing practices. Using data from a published lot consistency trial, we demonstrate that when the between-lot variation is only 0.5 per cent of the total variation, the increase in the sample size is nearly 300 per cent when compared with the size assuming that the lots are identical. The increase in the sample size is so pronounced that in order to maintain power one is led to consider a less stringent criterion for demonstration of lot consistency. The appropriate sample size formula that is a function of both components of variation is provided. We also discuss the increase in the sample size due to correlated comparisons arising from three pairs of lots as a function of the between-lot variance.
Grulke, Eric A.; Wu, Xiaochun; Ji, Yinglu; Buhr, Egbert; Yamamoto, Kazuhiro; Song, Nam Woong; Stefaniak, Aleksandr B.; Schwegler-Berry, Diane; Burchett, Woodrow W.; Lambert, Joshua; Stromberg, Arnold J.
2018-04-01
Size and shape distributions of gold nanorod samples are critical to their physico-chemical properties, especially their longitudinal surface plasmon resonance. This interlaboratory comparison study developed methods for measuring and evaluating size and shape distributions for gold nanorod samples using transmission electron microscopy (TEM) images. The objective was to determine whether two different samples, which had different performance attributes in their application, were different with respect to their size and/or shape descriptor distributions. Touching particles in the captured images were identified using a ruggedness shape descriptor. Nanorods could be distinguished from nanocubes using an elongational shape descriptor. A non-parametric statistical test showed that cumulative distributions of an elongational shape descriptor, that is, the aspect ratio, were statistically different between the two samples for all laboratories. While the scale parameters of size and shape distributions were similar for both samples, the width parameters of size and shape distributions were statistically different. This protocol fulfills an important need for a standardized approach to measure gold nanorod size and shape distributions for applications in which quantitative measurements and comparisons are important. Furthermore, the validated protocol workflow can be automated, thus providing consistent and rapid measurements of nanorod size and shape distributions for researchers, regulatory agencies, and industry.
Liu, P T
2001-04-01
The conventional sample-size equations based on either the precision of estimation or the power of testing a hypothesis may not be appropriate to determine sample size for a "diagnostic" testing problem, such as the eye irritant Draize test. When the animals' responses to chemical compounds are relatively uniform and extreme and the objective is to classify a compound as either irritant or nonirritant, the test using just two or three animals may be adequate.
Sample size re-estimation incorporating prior information on a nuisance parameter.
Mütze, Tobias; Schmidli, Heinz; Friede, Tim
2017-11-27
Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter-based sample size re-estimation in a design with an internal pilot study. We focus on trials with continuous endpoints in which the outcome variance is the nuisance parameter. For planning and analyzing the trial, frequentist methods are considered. Moreover, the external information on the variance is summarized by the Bayesian meta-analytic-predictive approach. To incorporate external information into the sample size re-estimation, we propose to update the meta-analytic-predictive prior based on the results of the internal pilot study and to re-estimate the sample size using an estimator from the posterior. By means of a simulation study, we compare the operating characteristics such as power and sample size distribution of the proposed procedure with the traditional sample size re-estimation approach that uses the pooled variance estimator. The simulation study shows that, if no prior-data conflict is present, incorporating external information into the sample size re-estimation improves the operating characteristics compared to the traditional approach. In the case of a prior-data conflict, that is, when the variance of the ongoing clinical trial is unequal to the prior location, the performance of the traditional sample size re-estimation procedure is in general superior, even when the prior information is robustified. When considering to include prior information in sample size re-estimation, the potential gains should be balanced against the risks. Copyright © 2017 John Wiley & Sons, Ltd.
Simulation of finite size effects of the fiber bundle model
Hao, Da-Peng; Tang, Gang; Xun, Zhi-Peng; Xia, Hui; Han, Kui
2018-01-01
In theory, the macroscopic fracture of materials should correspond with the thermodynamic limit of the fiber bundle model. However, the simulation of a fiber bundle model with an infinite size is unrealistic. To study the finite size effects of the fiber bundle model, fiber bundle models of various size are simulated in detail. The effects of system size on the constitutive behavior, critical stress, maximum avalanche size, avalanche size distribution, and increased step number of external load are explored. The simulation results imply that there is no feature size or cut size for macroscopic mechanical and statistical properties of the model. The constitutive curves near the macroscopic failure for various system size can collapse well with a simple scaling relationship. Simultaneously, the introduction of a simple extrapolation method facilitates the acquisition of more accurate simulation results in a large-limit system, which is better for comparison with theoretical results.
Size Evolution and Stochastic Models: Explaining Ostracod Size through Probabilistic Distributions
Krawczyk, M.; Decker, S.; Heim, N. A.; Payne, J.
2014-12-01
The biovolume of animals has functioned as an important benchmark for measuring evolution throughout geologic time. In our project, we examined the observed average body size of ostracods over time in order to understand the mechanism of size evolution in these marine organisms. The body size of ostracods has varied since the beginning of the Ordovician, where the first true ostracods appeared. We created a stochastic branching model to create possible evolutionary trees of ostracod size. Using stratigraphic ranges for ostracods compiled from over 750 genera in the Treatise on Invertebrate Paleontology, we calculated overall speciation and extinction rates for our model. At each timestep in our model, new lineages can evolve or existing lineages can become extinct. Newly evolved lineages are assigned sizes based on their parent genera. We parameterized our model to generate neutral and directional changes in ostracod size to compare with the observed data. New sizes were chosen via a normal distribution, and the neutral model selected new sizes differentials centered on zero, allowing for an equal chance of larger or smaller ostracods at each speciation. Conversely, the directional model centered the distribution on a negative value, giving a larger chance of smaller ostracods. Our data strongly suggests that the overall direction of ostracod evolution has been following a model that directionally pushes mean ostracod size down, shying away from a neutral model. Our model was able to match the magnitude of size decrease. Our models had a constant linear decrease while the actual data had a much more rapid initial rate followed by a constant size. The nuance of the observed trends ultimately suggests a more complex method of size evolution. In conclusion, probabilistic methods can provide valuable insight into possible evolutionary mechanisms determining size evolution in ostracods.
Sample size choices for XRCT scanning of highly unsaturated soil mixtures
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Smith Jonathan C.
2016-01-01
Full Text Available Highly unsaturated soil mixtures (clay, sand and gravel are used as building materials in many parts of the world, and there is increasing interest in understanding their mechanical and hydraulic behaviour. In the laboratory, x-ray computed tomography (XRCT is becoming more widely used to investigate the microstructures of soils, however a crucial issue for such investigations is the choice of sample size, especially concerning the scanning of soil mixtures where there will be a range of particle and void sizes. In this paper we present a discussion (centred around a new set of XRCT scans on sample sizing for scanning of samples comprising soil mixtures, where a balance has to be made between realistic representation of the soil components and the desire for high resolution scanning, We also comment on the appropriateness of differing sample sizes in comparison to sample sizes used for other geotechnical testing. Void size distributions for the samples are presented and from these some hypotheses are made as to the roles of inter- and intra-aggregate voids in the mechanical behaviour of highly unsaturated soils.
Exact Power and Sample Size Calculations for the Two One-Sided Tests of Equivalence.
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Gwowen Shieh
Full Text Available Equivalent testing has been strongly recommended for demonstrating the comparability of treatment effects in a wide variety of research fields including medical studies. Although the essential properties of the favorable two one-sided tests of equivalence have been addressed in the literature, the associated power and sample size calculations were illustrated mainly for selecting the most appropriate approximate method. Moreover, conventional power analysis does not consider the allocation restrictions and cost issues of different sample size choices. To extend the practical usefulness of the two one-sided tests procedure, this article describes exact approaches to sample size determinations under various allocation and cost considerations. Because the presented features are not generally available in common software packages, both R and SAS computer codes are presented to implement the suggested power and sample size computations for planning equivalence studies. The exact power function of the TOST procedure is employed to compute optimal sample sizes under four design schemes allowing for different allocation and cost concerns. The proposed power and sample size methodology should be useful for medical sciences to plan equivalence studies.
Exact Power and Sample Size Calculations for the Two One-Sided Tests of Equivalence
Shieh, Gwowen
2016-01-01
Equivalent testing has been strongly recommended for demonstrating the comparability of treatment effects in a wide variety of research fields including medical studies. Although the essential properties of the favorable two one-sided tests of equivalence have been addressed in the literature, the associated power and sample size calculations were illustrated mainly for selecting the most appropriate approximate method. Moreover, conventional power analysis does not consider the allocation restrictions and cost issues of different sample size choices. To extend the practical usefulness of the two one-sided tests procedure, this article describes exact approaches to sample size determinations under various allocation and cost considerations. Because the presented features are not generally available in common software packages, both R and SAS computer codes are presented to implement the suggested power and sample size computations for planning equivalence studies. The exact power function of the TOST procedure is employed to compute optimal sample sizes under four design schemes allowing for different allocation and cost concerns. The proposed power and sample size methodology should be useful for medical sciences to plan equivalence studies. PMID:27598468
A margin based approach to determining sample sizes via tolerance bounds.
Energy Technology Data Exchange (ETDEWEB)
Newcomer, Justin T.; Freeland, Katherine Elizabeth
2013-09-01
This paper proposes a tolerance bound approach for determining sample sizes. With this new methodology we begin to think of sample size in the context of uncertainty exceeding margin. As the sample size decreases the uncertainty in the estimate of margin increases. This can be problematic when the margin is small and only a few units are available for testing. In this case there may be a true underlying positive margin to requirements but the uncertainty may be too large to conclude we have sufficient margin to those requirements with a high level of statistical confidence. Therefore, we provide a methodology for choosing a sample size large enough such that an estimated QMU uncertainty based on the tolerance bound approach will be smaller than the estimated margin (assuming there is positive margin). This ensures that the estimated tolerance bound will be within performance requirements and the tolerance ratio will be greater than one, supporting a conclusion that we have sufficient margin to the performance requirements. In addition, this paper explores the relationship between margin, uncertainty, and sample size and provides an approach and recommendations for quantifying risk when sample sizes are limited.
Guo, Jiin-Huarng; Chen, Hubert J; Luh, Wei-Ming
2011-11-01
The allocation of sufficient participants into different experimental groups for various research purposes under given constraints is an important practical problem faced by researchers. We address the problem of sample size determination between two independent groups for unequal and/or unknown variances when both the power and the differential cost are taken into consideration. We apply the well-known Welch approximate test to derive various sample size allocation ratios by minimizing the total cost or, equivalently, maximizing the statistical power. Two types of hypotheses including superiority/non-inferiority and equivalence of two means are each considered in the process of sample size planning. A simulation study is carried out and the proposed method is validated in terms of Type I error rate and statistical power. As a result, the simulation study reveals that the proposed sample size formulas are very satisfactory under various variances and sample size allocation ratios. Finally, a flowchart, tables, and figures of several sample size allocations are presented for practical reference. ©2011 The British Psychological Society.
A type of sample size design in cancer clinical trials for response rate estimation.
Liu, Junfeng
2011-01-01
During the early stage of cancer clinical trials, when it is not convenient to construct an explicit hypothesis testing, a study on a new therapy often calls for a response rate (p) estimation concurrently with or right before a typical phase II study. We consider a two-stage process, where the acquired information from Stage I (with a small sample size (m)) would be utilized for sample size (n) recommendation for Stage II study aiming for a more accurate estimation. Once a sample size design and a parameter estimation protocol are applied, we study the overall utility (cost-effectiveness) in connection with the cost due to patient recruitment and treatment as well as the loss due to mean squared error from parameter estimation. Two approaches will be investigated including the posterior mixture method (a Bayesian approach) and the empirical variance method (a frequentist approach). We also discuss response rate estimation under truncated parameter space using maximum likelihood estimation with regard to sample size and mean squared error. The profiles of p-specific expected sample size, mean squared error and risk under different approaches motivate us to introduce the concept of "admissible sample size (design)". Copyright © 2010 Elsevier Inc. All rights reserved.
Sample Size for Tablet Compression and Capsule Filling Events During Process Validation.
Charoo, Naseem Ahmad; Durivage, Mark; Rahman, Ziyaur; Ayad, Mohamad Haitham
2017-12-01
During solid dosage form manufacturing, the uniformity of dosage units (UDU) is ensured by testing samples at 2 stages, that is, blend stage and tablet compression or capsule/powder filling stage. The aim of this work is to propose a sample size selection approach based on quality risk management principles for process performance qualification (PPQ) and continued process verification (CPV) stages by linking UDU to potential formulation and process risk factors. Bayes success run theorem appeared to be the most appropriate approach among various methods considered in this work for computing sample size for PPQ. The sample sizes for high-risk (reliability level of 99%), medium-risk (reliability level of 95%), and low-risk factors (reliability level of 90%) were estimated to be 299, 59, and 29, respectively. Risk-based assignment of reliability levels was supported by the fact that at low defect rate, the confidence to detect out-of-specification units would decrease which must be supplemented with an increase in sample size to enhance the confidence in estimation. Based on level of knowledge acquired during PPQ and the level of knowledge further required to comprehend process, sample size for CPV was calculated using Bayesian statistics to accomplish reduced sampling design for CPV. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
Duncanson, L.; Dubayah, R.
2015-12-01
Lidar remote sensing is widely applied for mapping forest carbon stocks, and technological advances have improved our ability to capture structural details from forests, even resolving individual trees. Despite these advancements, the accuracy of forest aboveground biomass models remains limited by the quality of field estimates of biomass. The accuracies of field estimates are inherently dependent on the accuracy of the allometric equations used to relate measurable attributes to biomass. These equations are calibrated with relatively small samples of often spatially clustered trees. This research focuses on one of many issues involving allometric equations - understanding how sensitive allometric parameters are to the sample sizes used to fit them. We capitalize on recent advances in lidar remote sensing to extract individual tree structural information from six high-resolution airborne lidar datasets in the United States. We remotely measure millions of tree heights and crown radii, and fit allometric equations to the relationship between tree height and radius at a 'population' level, in each site. We then extract samples from our tree database, and build allometries on these smaller samples of trees, with varying sample sizes. We show that for the allometric relationship between tree height and crown radius, small sample sizes produce biased allometric equations that overestimate height for a given crown radius. We extend this analysis using translations from the literature to address potential implications for biomass, showing that site-level biomass may be greatly overestimated when applying allometric equations developed with the typically small sample sizes used in popular allometric equations for biomass.
Optimum sample size to estimate mean parasite abundance in fish parasite surveys
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Shvydka S.
2018-03-01
Full Text Available To reach ethically and scientifically valid mean abundance values in parasitological and epidemiological studies this paper considers analytic and simulation approaches for sample size determination. The sample size estimation was carried out by applying mathematical formula with predetermined precision level and parameter of the negative binomial distribution estimated from the empirical data. A simulation approach to optimum sample size determination aimed at the estimation of true value of the mean abundance and its confidence interval (CI was based on the Bag of Little Bootstraps (BLB. The abundance of two species of monogenean parasites Ligophorus cephali and L. mediterraneus from Mugil cephalus across the Azov-Black Seas localities were subjected to the analysis. The dispersion pattern of both helminth species could be characterized as a highly aggregated distribution with the variance being substantially larger than the mean abundance. The holistic approach applied here offers a wide range of appropriate methods in searching for the optimum sample size and the understanding about the expected precision level of the mean. Given the superior performance of the BLB relative to formulae with its few assumptions, the bootstrap procedure is the preferred method. Two important assessments were performed in the present study: i based on CIs width a reasonable precision level for the mean abundance in parasitological surveys of Ligophorus spp. could be chosen between 0.8 and 0.5 with 1.6 and 1x mean of the CIs width, and ii the sample size equal 80 or more host individuals allows accurate and precise estimation of mean abundance. Meanwhile for the host sample size in range between 25 and 40 individuals, the median estimates showed minimal bias but the sampling distribution skewed to the low values; a sample size of 10 host individuals yielded to unreliable estimates.
Blinded sample size re-estimation in three-arm trials with 'gold standard' design.
Mütze, Tobias; Friede, Tim
2017-10-15
In this article, we study blinded sample size re-estimation in the 'gold standard' design with internal pilot study for normally distributed outcomes. The 'gold standard' design is a three-arm clinical trial design that includes an active and a placebo control in addition to an experimental treatment. We focus on the absolute margin approach to hypothesis testing in three-arm trials at which the non-inferiority of the experimental treatment and the assay sensitivity are assessed by pairwise comparisons. We compare several blinded sample size re-estimation procedures in a simulation study assessing operating characteristics including power and type I error. We find that sample size re-estimation based on the popular one-sample variance estimator results in overpowered trials. Moreover, sample size re-estimation based on unbiased variance estimators such as the Xing-Ganju variance estimator results in underpowered trials, as it is expected because an overestimation of the variance and thus the sample size is in general required for the re-estimation procedure to eventually meet the target power. To overcome this problem, we propose an inflation factor for the sample size re-estimation with the Xing-Ganju variance estimator and show that this approach results in adequately powered trials. Because of favorable features of the Xing-Ganju variance estimator such as unbiasedness and a distribution independent of the group means, the inflation factor does not depend on the nuisance parameter and, therefore, can be calculated prior to a trial. Moreover, we prove that the sample size re-estimation based on the Xing-Ganju variance estimator does not bias the effect estimate. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Rambo, Robert P
2017-01-01
The success of a SAXS experiment for structural investigations depends on two precise measurements, the sample and the buffer background. Buffer matching between the sample and background can be achieved using dialysis methods but in biological SAXS of monodisperse systems, sample preparation is routinely being performed with size exclusion chromatography (SEC). SEC is the most reliable method for SAXS sample preparation as the method not only purifies the sample for SAXS but also almost guarantees ideal buffer matching. Here, I will highlight the use of SEC for SAXS sample preparation and demonstrate using example proteins that SEC purification does not always provide for ideal samples. Scrutiny of the SEC elution peak using quasi-elastic and multi-angle light scattering techniques can reveal hidden features (heterogeneity) of the sample that should be considered during SAXS data analysis. In some cases, sample heterogeneity can be controlled using a small molecule additive and I outline a simple additive screening method for sample preparation.
Modelling the effect of size-asymmetric competition on size inequality
DEFF Research Database (Denmark)
Rasmussen, Camilla Ruø; Weiner, Jacob
2017-01-01
Abstract The concept of size asymmetry in resource competition among plants, in which larger individuals obtain a disproportionate share of contested resources, appears to be very straightforward, but the effects of size asymmetry on growth and size variation among individuals have proved...... to be controversial. It has often been assumed that competition among individual plants in a population has to be size-asymmetric to result in higher size inequality than in the absence of competition, but here we question this inference. Using very simple, individual-based models, we investigate how size symmetry...... of competition affects the development in size inequality between two competing plants and show that increased size inequality due to competition is not always strong evidence for size-asymmetric competition. Even absolute symmetric competition, in which all plants receive the same amount of resources...
SMALL SAMPLE SIZE IN 2X2 CROSS OVER DESIGNS: CONDITIONS OF DETERMINATION
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B SOLEYMANI
2001-09-01
Full Text Available Introduction. Determination of small sample size in some clinical trials is a matter of importance. In cross-over studies which are one types of clinical trials, the matter is more significant. In this article, the conditions in which determination of small sample size in cross-over studies are possible were considered, and the effect of deviation from normality on the matter has been shown. Methods. The present study has been done on such 2x2 cross-over studies that variable of interest is quantitative one and is measurable by ratio or interval scale. The method of consideration is based on use of variable and sample mean"s distributions, central limit theorem, method of sample size determination in two groups, and cumulant or moment generating function. Results. In normal variables or transferable to normal variables, there is no restricting factors other than significant level and power of the test for determination of sample size, but in the case of non-normal variables, it should be determined such large that guarantee the normality of sample mean"s distribution. Discussion. In such cross over studies that because of existence of theoretical base, few samples can be computed, one should not do it without taking applied worth of results into consideration. While determining sample size, in addition to variance, it is necessary to consider distribution of variable, particularly through its skewness and kurtosis coefficients. the more deviation from normality, the more need of samples. Since in medical studies most of the continuous variables are closed to normal distribution, a few number of samples often seems to be adequate for convergence of sample mean to normal distribution.
Page sample size in web accessibility testing: how many pages is enough?
Velleman, Eric Martin; van der Geest, Thea
2013-01-01
Various countries and organizations use a different sampling approach and sample size of web pages in accessibility conformance tests. We are conducting a systematic analysis to determine how many pages is enough for testing whether a website is compliant with standard accessibility guidelines. This
Norm Block Sample Sizes: A Review of 17 Individually Administered Intelligence Tests
Norfolk, Philip A.; Farmer, Ryan L.; Floyd, Randy G.; Woods, Isaac L.; Hawkins, Haley K.; Irby, Sarah M.
2015-01-01
The representativeness, recency, and size of norm samples strongly influence the accuracy of inferences drawn from their scores. Inadequate norm samples may lead to inflated or deflated scores for individuals and poorer prediction of developmental and academic outcomes. The purpose of this study was to apply Kranzler and Floyd's method for…
Sample sizes to control error estimates in determining soil bulk density in California forest soils
Youzhi Han; Jianwei Zhang; Kim G. Mattson; Weidong Zhang; Thomas A. Weber
2016-01-01
Characterizing forest soil properties with high variability is challenging, sometimes requiring large numbers of soil samples. Soil bulk density is a standard variable needed along with element concentrations to calculate nutrient pools. This study aimed to determine the optimal sample size, the number of observation (n), for predicting the soil bulk density with a...
Generating Random Samples of a Given Size Using Social Security Numbers.
Erickson, Richard C.; Brauchle, Paul E.
1984-01-01
The purposes of this article are (1) to present a method by which social security numbers may be used to draw cluster samples of a predetermined size and (2) to describe procedures used to validate this method of drawing random samples. (JOW)
A Bayesian predictive sample size selection design for single-arm exploratory clinical trials.
Teramukai, Satoshi; Daimon, Takashi; Zohar, Sarah
2012-12-30
The aim of an exploratory clinical trial is to determine whether a new intervention is promising for further testing in confirmatory clinical trials. Most exploratory clinical trials are designed as single-arm trials using a binary outcome with or without interim monitoring for early stopping. In this context, we propose a Bayesian adaptive design denoted as predictive sample size selection design (PSSD). The design allows for sample size selection following any planned interim analyses for early stopping of a trial, together with sample size determination before starting the trial. In the PSSD, we determine the sample size using the method proposed by Sambucini (Statistics in Medicine 2008; 27:1199-1224), which adopts a predictive probability criterion with two kinds of prior distributions, that is, an 'analysis prior' used to compute posterior probabilities and a 'design prior' used to obtain prior predictive distributions. In the sample size determination of the PSSD, we provide two sample sizes, that is, N and N(max) , using two types of design priors. At each interim analysis, we calculate the predictive probabilities of achieving a successful result at the end of the trial using the analysis prior in order to stop the trial in case of low or high efficacy (Lee et al., Clinical Trials 2008; 5:93-106), and we select an optimal sample size, that is, either N or N(max) as needed, on the basis of the predictive probabilities. We investigate the operating characteristics through simulation studies, and the PSSD retrospectively applies to a lung cancer clinical trial. (243) Copyright © 2012 John Wiley & Sons, Ltd.
Directory of Open Access Journals (Sweden)
Stefanović Milena
2013-01-01
Full Text Available In studies of population variability, particular attention has to be paid to the selection of a representative sample. The aim of this study was to assess the size of the new representative sample on the basis of the variability of chemical content of the initial sample on the example of a whitebark pine population. Statistical analysis included the content of 19 characteristics (terpene hydrocarbons and their derivates of the initial sample of 10 elements (trees. It was determined that the new sample should contain 20 trees so that the mean value calculated from it represents a basic set with a probability higher than 95 %. Determination of the lower limit of the representative sample size that guarantees a satisfactory reliability of generalization proved to be very important in order to achieve cost efficiency of the research. [Projekat Ministarstva nauke Republike Srbije, br. OI-173011, br. TR-37002 i br. III-43007
Sampling, Probability Models and Statistical Reasoning Statistical ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Sampling, Probability Models and Statistical Reasoning Statistical Inference. Mohan Delampady V R Padmawar. General Article Volume 1 Issue 5 May 1996 pp 49-58 ...
Sampling, Probability Models and Statistical Reasoning Statistical
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Sampling, Probability Models and Statistical Reasoning Statistical Inference. Mohan Delampady V R Padmawar. General Article Volume 1 Issue 5 May 1996 pp 49-58 ...
Functional Error Models to Accelerate Nested Sampling
Josset, L.; Elsheikh, A. H.; Demyanov, V.; Lunati, I.
2014-12-01
The main challenge in groundwater problems is the reliance on large numbers of unknown parameters with wide rage of associated uncertainties. To translate this uncertainty to quantities of interest (for instance the concentration of pollutant in a drinking well), a large number of forward flow simulations is required. To make the problem computationally tractable, Josset et al. (2013, 2014) introduced the concept of functional error models. It consists in two elements: a proxy model that is cheaper to evaluate than the full physics flow solver and an error model to account for the missing physics. The coupling of the proxy model and the error models provides reliable predictions that approximate the full physics model's responses. The error model is tailored to the problem at hand by building it for the question of interest. It follows a typical approach in machine learning where both the full physics and proxy models are evaluated for a training set (subset of realizations) and the set of responses is used to construct the error model using functional data analysis. Once the error model is devised, a prediction of the full physics response for a new geostatistical realization can be obtained by computing the proxy response and applying the error model. We propose the use of functional error models in a Bayesian inference context by combining it to the Nested Sampling (Skilling 2006; El Sheikh et al. 2013, 2014). Nested Sampling offers a mean to compute the Bayesian Evidence by transforming the multidimensional integral into a 1D integral. The algorithm is simple: starting with an active set of samples, at each iteration, the sample with the lowest likelihood is kept aside and replaced by a sample of higher likelihood. The main challenge is to find this sample of higher likelihood. We suggest a new approach: first the active set is sampled, both proxy and full physics models are run and the functional error model is build. Then, at each iteration of the Nested
Power and sample size calculations for Mendelian randomization studies using one genetic instrument.
Freeman, Guy; Cowling, Benjamin J; Schooling, C Mary
2013-08-01
Mendelian randomization, which is instrumental variable analysis using genetic variants as instruments, is an increasingly popular method of making causal inferences from observational studies. In order to design efficient Mendelian randomization studies, it is essential to calculate the sample sizes required. We present formulas for calculating the power of a Mendelian randomization study using one genetic instrument to detect an effect of a given size, and the minimum sample size required to detect effects for given levels of significance and power, using asymptotic statistical theory. We apply the formulas to some example data and compare the results with those from simulation methods. Power and sample size calculations using these formulas should be more straightforward to carry out than simulation approaches. These formulas make explicit that the sample size needed for Mendelian randomization study is inversely proportional to the square of the correlation between the genetic instrument and the exposure and proportional to the residual variance of the outcome after removing the effect of the exposure, as well as inversely proportional to the square of the effect size.
GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING
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Christopher Ouma Onyango
2010-09-01
Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.
Scott, Neil W; Fayers, Peter M; Aaronson, Neil K; Bottomley, Andrew; de Graeff, Alexander; Groenvold, Mogens; Gundy, Chad; Koller, Michael; Petersen, Morten A; Sprangers, Mirjam A G
2009-03-01
Differential item functioning (DIF) analyses are increasingly used to evaluate health-related quality of life (HRQoL) instruments, which often include relatively short subscales. Computer simulations were used to explore how various factors including scale length affect analysis of DIF by ordinal logistic regression. Simulated data, representative of HRQoL scales with four-category items, were generated. The power and type I error rates of the DIF method were then investigated when, respectively, DIF was deliberately introduced and when no DIF was added. The sample size, scale length, floor effects (FEs) and significance level were varied. When there was no DIF, type I error rates were close to 5%. Detecting moderate uniform DIF in a two-item scale required a sample size of 300 per group for adequate (>80%) power. For longer scales, a sample size of 200 was adequate. Considerably larger sample sizes were required to detect nonuniform DIF, when there were extreme FEs or when a reduced type I error rate was required. The impact of the number of items in the scale was relatively small. Ordinal logistic regression successfully detects DIF for HRQoL instruments with short scales. Sample size guidelines are provided.
A simple nomogram for sample size for estimating sensitivity and specificity of medical tests
Directory of Open Access Journals (Sweden)
Malhotra Rajeev
2010-01-01
Full Text Available Sensitivity and specificity measure inherent validity of a diagnostic test against a gold standard. Researchers develop new diagnostic methods to reduce the cost, risk, invasiveness, and time. Adequate sample size is a must to precisely estimate the validity of a diagnostic test. In practice, researchers generally decide about the sample size arbitrarily either at their convenience, or from the previous literature. We have devised a simple nomogram that yields statistically valid sample size for anticipated sensitivity or anticipated specificity. MS Excel version 2007 was used to derive the values required to plot the nomogram using varying absolute precision, known prevalence of disease, and 95% confidence level using the formula already available in the literature. The nomogram plot was obtained by suitably arranging the lines and distances to conform to this formula. This nomogram could be easily used to determine the sample size for estimating the sensitivity or specificity of a diagnostic test with required precision and 95% confidence level. Sample size at 90% and 99% confidence level, respectively, can also be obtained by just multiplying 0.70 and 1.75 with the number obtained for the 95% confidence level. A nomogram instantly provides the required number of subjects by just moving the ruler and can be repeatedly used without redoing the calculations. This can also be applied for reverse calculations. This nomogram is not applicable for testing of the hypothesis set-up and is applicable only when both diagnostic test and gold standard results have a dichotomous category.
Building predictive models of soil particle-size distribution
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Alessandro Samuel-Rosa
2013-04-01
Full Text Available Is it possible to build predictive models (PMs of soil particle-size distribution (psd in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index. The PMs explained more than half of the data variance. This performance is similar to (or even better than that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd of soils in regions of complex geology.
Sample size for estimating average trunk diameter and plant height in eucalyptus hybrids
Directory of Open Access Journals (Sweden)
Alberto Cargnelutti Filho
2016-01-01
Full Text Available ABSTRACT: In eucalyptus crops, it is important to determine the number of plants that need to be evaluated for a reliable inference of growth. The aim of this study was to determine the sample size needed to estimate average trunk diameter at breast height and plant height of inter-specific eucalyptus hybrids. In 6,694 plants of twelve inter-specific hybrids it was evaluated trunk diameter at breast height at three (DBH3 and seven years (DBH7 and tree height at seven years (H7 of age. The statistics: minimum, maximum, mean, variance, standard deviation, standard error, and coefficient of variation were calculated. The hypothesis of variance homogeneity was tested. The sample size was determined by re sampling with replacement of 10,000 re samples. There was an increase in the sample size from DBH3 to H7 and DBH7. A sample size of 16, 59 and 31 plants is adequate to estimate DBH3, DBH7 and H7 means, respectively, of inter-specific hybrids of eucalyptus, with amplitude of confidence interval of 95% equal to 20% of the estimated mean.
Sample Size Induced Brittle-to-Ductile Transition of Single-Crystal Aluminum Nitride
2015-08-01
Aluminum Nitride by GA Gazonas and JW McCauley Weapons and Materials Research Directorate, ARL JJ Guo, KM Reddy, A Hirata, T Fujita, and MW Chen...Sample Size Induced Brittle-to-Ductile Transition of Single-Crystal Aluminum Nitride 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...their microscopic structure. In this study, we report a size induced brittle-to-ductile transition in single-crystal aluminum nitride (AlN). When the
[On the impact of sample size calculation and power in clinical research].
Held, Ulrike
2014-10-01
The aim of a clinical trial is to judge the efficacy of a new therapy or drug. In the planning phase of the study, the calculation of the necessary sample size is crucial in order to obtain a meaningful result. The study design, the expected treatment effect in outcome and its variability, power and level of significance are factors which determine the sample size. It is often difficult to fix these parameters prior to the start of the study, but related papers from the literature can be helpful sources for the unknown quantities. For scientific as well as ethical reasons it is necessary to calculate the sample size in advance in order to be able to answer the study question.
Information-based sample size re-estimation in group sequential design for longitudinal trials.
Zhou, Jing; Adewale, Adeniyi; Shentu, Yue; Liu, Jiajun; Anderson, Keaven
2014-09-28
Group sequential design has become more popular in clinical trials because it allows for trials to stop early for futility or efficacy to save time and resources. However, this approach is less well-known for longitudinal analysis. We have observed repeated cases of studies with longitudinal data where there is an interest in early stopping for a lack of treatment effect or in adapting sample size to correct for inappropriate variance assumptions. We propose an information-based group sequential design as a method to deal with both of these issues. Updating the sample size at each interim analysis makes it possible to maintain the target power while controlling the type I error rate. We will illustrate our strategy with examples and simulations and compare the results with those obtained using fixed design and group sequential design without sample size re-estimation. Copyright © 2014 John Wiley & Sons, Ltd.
Bi, Ran; Liu, Peng
2016-03-31
RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments. In this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method. Our proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network ( http://cran.r-project.org ).
van Hassel, Daniël; van der Velden, Lud; de Bakker, Dinny; van der Hoek, Lucas; Batenburg, Ronald
2017-12-04
Our research is based on a technique for time sampling, an innovative method for measuring the working hours of Dutch general practitioners (GPs), which was deployed in an earlier study. In this study, 1051 GPs were questioned about their activities in real time by sending them one SMS text message every 3 h during 1 week. The required sample size for this study is important for health workforce planners to know if they want to apply this method to target groups who are hard to reach or if fewer resources are available. In this time-sampling method, however, standard power analyses is not sufficient for calculating the required sample size as this accounts only for sample fluctuation and not for the fluctuation of measurements taken from every participant. We investigated the impact of the number of participants and frequency of measurements per participant upon the confidence intervals (CIs) for the hours worked per week. Statistical analyses of the time-use data we obtained from GPs were performed. Ninety-five percent CIs were calculated, using equations and simulation techniques, for various different numbers of GPs included in the dataset and for various frequencies of measurements per participant. Our results showed that the one-tailed CI, including sample and measurement fluctuation, decreased from 21 until 3 h between one and 50 GPs. As a result of the formulas to calculate CIs, the increase of the precision continued and was lower with the same additional number of GPs. Likewise, the analyses showed how the number of participants required decreased if more measurements per participant were taken. For example, one measurement per 3-h time slot during the week requires 300 GPs to achieve a CI of 1 h, while one measurement per hour requires 100 GPs to obtain the same result. The sample size needed for time-use research based on a time-sampling technique depends on the design and aim of the study. In this paper, we showed how the precision of the
Overestimation of test performance by ROC analysis: Effect of small sample size
International Nuclear Information System (INIS)
Seeley, G.W.; Borgstrom, M.C.; Patton, D.D.; Myers, K.J.; Barrett, H.H.
1984-01-01
New imaging systems are often observer-rated by ROC techniques. For practical reasons the number of different images, or sample size (SS), is kept small. Any systematic bias due to small SS would bias system evaluation. The authors set about to determine whether the area under the ROC curve (AUC) would be systematically biased by small SS. Monte Carlo techniques were used to simulate observer performance in distinguishing signal (SN) from noise (N) on a 6-point scale; P(SN) = P(N) = .5. Four sample sizes (15, 25, 50 and 100 each of SN and N), three ROC slopes (0.8, 1.0 and 1.25), and three intercepts (0.8, 1.0 and 1.25) were considered. In each of the 36 combinations of SS, slope and intercept, 2000 runs were simulated. Results showed a systematic bias: the observed AUC exceeded the expected AUC in every one of the 36 combinations for all sample sizes, with the smallest sample sizes having the largest bias. This suggests that evaluations of imaging systems using ROC curves based on small sample size systematically overestimate system performance. The effect is consistent but subtle (maximum 10% of AUC standard deviation), and is probably masked by the s.d. in most practical settings. Although there is a statistically significant effect (F = 33.34, P<0.0001) due to sample size, none was found for either the ROC curve slope or intercept. Overestimation of test performance by small SS seems to be an inherent characteristic of the ROC technique that has not previously been described
Mesh-size effects on drift sample composition as determined with a triple net sampler
Slack, K.V.; Tilley, L.J.; Kennelly, S.S.
1991-01-01
Nested nets of three different mesh apertures were used to study mesh-size effects on drift collected in a small mountain stream. The innermost, middle, and outermost nets had, respectively, 425 ??m, 209 ??m and 106 ??m openings, a design that reduced clogging while partitioning collections into three size groups. The open area of mesh in each net, from largest to smallest mesh opening, was 3.7, 5.7 and 8.0 times the area of the net mouth. Volumes of filtered water were determined with a flowmeter. The results are expressed as (1) drift retained by each net, (2) drift that would have been collected by a single net of given mesh size, and (3) the percentage of total drift (the sum of the catches from all three nets) that passed through the 425 ??m and 209 ??m nets. During a two day period in August 1986, Chironomidae larvae were dominant numerically in all 209 ??m and 106 ??m samples and midday 425 ??m samples. Large drifters (Ephemerellidae) occurred only in 425 ??m or 209 ??m nets, but the general pattern was an increase in abundance and number of taxa with decreasing mesh size. Relatively more individuals occurred in the larger mesh nets at night than during the day. The two larger mesh sizes retained 70% of the total sediment/detritus in the drift collections, and this decreased the rate of clogging of the 106 ??m net. If an objective of a sampling program is to compare drift density or drift rate between areas or sampling dates, the same mesh size should be used for all sample collection and processing. The mesh aperture used for drift collection should retain all species and life stages of significance in a study. The nested net design enables an investigator to test the adequacy of drift samples. ?? 1991 Kluwer Academic Publishers.
Predictors of Citation Rate in Psychology: Inconclusive Influence of Effect and Sample Size.
Hanel, Paul H P; Haase, Jennifer
2017-01-01
In the present article, we investigate predictors of how often a scientific article is cited. Specifically, we focus on the influence of two often neglected predictors of citation rate: effect size and sample size, using samples from two psychological topical areas. Both can be considered as indicators of the importance of an article and post hoc (or observed) statistical power, and should, especially in applied fields, predict citation rates. In Study 1, effect size did not have an influence on citation rates across a topical area, both with and without controlling for numerous variables that have been previously linked to citation rates. In contrast, sample size predicted citation rates, but only while controlling for other variables. In Study 2, sample and partly effect sizes predicted citation rates, indicating that the relations vary even between scientific topical areas. Statistically significant results had more citations in Study 2 but not in Study 1. The results indicate that the importance (or power) of scientific findings may not be as strongly related to citation rate as is generally assumed.
Richter, Veronika; Muche, Rainer; Mayer, Benjamin
2018-01-26
Statistical sample size calculation is a crucial part of planning nonhuman animal experiments in basic medical research. The 3R principle intends to reduce the number of animals to a sufficient minimum. When planning experiments, one may consider the impact of less rigorous assumptions during sample size determination as it might result in a considerable reduction in the number of required animals. Sample size calculations conducted for 111 biometrical reports were repeated. The original effect size assumptions remained unchanged, but the basic properties (type 1 error 5%, two-sided hypothesis, 80% power) were varied. The analyses showed that a less rigorous assumption on the type 1 error level (one-sided 5% instead of two-sided 5%) was associated with a savings potential of 14% regarding the original number of required animals. Animal experiments are predominantly exploratory studies. In light of the demonstrated potential reduction in the numbers of required animals, researchers should discuss whether less rigorous assumptions during the process of sample size calculation may be reasonable for the purpose of optimizing the number of animals in experiments according to the 3R principle.
Morgera, S. D.; Cooper, D. B.
1976-01-01
The experimental observation that a surprisingly small sample size vis-a-vis dimension is needed to achieve good signal-to-interference ratio (SIR) performance with an adaptive predetection filter is explained. The adaptive filter requires estimates as obtained by a recursive stochastic algorithm of the inverse of the filter input data covariance matrix. The SIR performance with sample size is compared for the situations where the covariance matrix estimates are of unstructured (generalized) form and of structured (finite Toeplitz) form; the latter case is consistent with weak stationarity of the input data stochastic process.
Treatment Trials for Neonatal Seizures: The Effect of Design on Sample Size.
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Nathan J Stevenson
Full Text Available Neonatal seizures are common in the neonatal intensive care unit. Clinicians treat these seizures with several anti-epileptic drugs (AEDs to reduce seizures in a neonate. Current AEDs exhibit sub-optimal efficacy and several randomized control trials (RCT of novel AEDs are planned. The aim of this study was to measure the influence of trial design on the required sample size of a RCT. We used seizure time courses from 41 term neonates with hypoxic ischaemic encephalopathy to build seizure treatment trial simulations. We used five outcome measures, three AED protocols, eight treatment delays from seizure onset (Td and four levels of trial AED efficacy to simulate different RCTs. We performed power calculations for each RCT design and analysed the resultant sample size. We also assessed the rate of false positives, or placebo effect, in typical uncontrolled studies. We found that the false positive rate ranged from 5 to 85% of patients depending on RCT design. For controlled trials, the choice of outcome measure had the largest effect on sample size with median differences of 30.7 fold (IQR: 13.7-40.0 across a range of AED protocols, Td and trial AED efficacy (p<0.001. RCTs that compared the trial AED with positive controls required sample sizes with a median fold increase of 3.2 (IQR: 1.9-11.9; p<0.001. Delays in AED administration from seizure onset also increased the required sample size 2.1 fold (IQR: 1.7-2.9; p<0.001. Subgroup analysis showed that RCTs in neonates treated with hypothermia required a median fold increase in sample size of 2.6 (IQR: 2.4-3.0 compared to trials in normothermic neonates (p<0.001. These results show that RCT design has a profound influence on the required sample size. Trials that use a control group, appropriate outcome measure, and control for differences in Td between groups in analysis will be valid and minimise sample size.
Beerli, Peter
2004-04-01
Current estimators of gene flow come in two methods; those that estimate parameters assuming that the populations investigated are a small random sample of a large number of populations and those that assume that all populations were sampled. Maximum likelihood or Bayesian approaches that estimate the migration rates and population sizes directly using coalescent theory can easily accommodate datasets that contain a population that has no data, a so-called 'ghost' population. This manipulation allows us to explore the effects of missing populations on the estimation of population sizes and migration rates between two specific populations. The biases of the inferred population parameters depend on the magnitude of the migration rate from the unknown populations. The effects on the population sizes are larger than the effects on the migration rates. The more immigrants from the unknown populations that are arriving in the sample populations the larger the estimated population sizes. Taking into account a ghost population improves or at least does not harm the estimation of population sizes. Estimates of the scaled migration rate M (migration rate per generation divided by the mutation rate per generation) are fairly robust as long as migration rates from the unknown populations are not huge. The inclusion of a ghost population does not improve the estimation of the migration rate M; when the migration rates are estimated as the number of immigrants Nm then a ghost population improves the estimates because of its effect on population size estimation. It seems that for 'real world' analyses one should carefully choose which populations to sample, but there is no need to sample every population in the neighbourhood of a population of interest.
A size-structured model of bacterial growth and reproduction.
Ellermeyer, S F; Pilyugin, S S
2012-01-01
We consider a size-structured bacterial population model in which the rate of cell growth is both size- and time-dependent and the average per capita reproduction rate is specified as a model parameter. It is shown that the model admits classical solutions. The population-level and distribution-level behaviours of these solutions are then determined in terms of the model parameters. The distribution-level behaviour is found to be different from that found in similar models of bacterial population dynamics. Rather than convergence to a stable size distribution, we find that size distributions repeat in cycles. This phenomenon is observed in similar models only under special assumptions on the functional form of the size-dependent growth rate factor. Our main results are illustrated with examples, and we also provide an introductory study of the bacterial growth in a chemostat within the framework of our model.
Wan, Xiang; Wang, Wenqian; Liu, Jiming; Tong, Tiejun
2014-12-19
In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.'s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different
Sample Size Bounding and Context Ranking as Approaches to the Human Error Quantification Problem
International Nuclear Information System (INIS)
Reer, B.
2004-01-01
The paper describes a technique denoted as Sub-Sample-Size Bounding (SSSB), which is useable for the statistical derivation of context-specific probabilities from data available in existing reports on operating experience. Applications to human reliability analysis (HRA) are emphasised in the presentation of this technique. Exemplified by a sample of 180 abnormal event sequences, the manner in which SSSB can provide viable input for the quantification of errors of commission (EOCs) are outlined. (author)
Study of Radon and Thoron exhalation from soil samples of different grain sizes.
Chitra, N; Danalakshmi, B; Supriya, D; Vijayalakshmi, I; Sundar, S Bala; Sivasubramanian, K; Baskaran, R; Jose, M T
2018-03-01
The exhalation of radon ( 222 Rn) and thoron ( 220 Rn) from a porous matrix depends on the emanation of them from the grains by the recoil effect. The emanation factor is a quantitative estimate of the emanation phenomenon. The present study is to investigate the effect of grain size of the soil matrix on the emanation factor. Soil samples from three different locations were fractionated into different grain size categories ranging from <0.1 to 2mm. The emanation factors of each of the grain size range were estimated by measuring the mass exhalation rates of radon and thoron and the activity concentrations of 226 Ra and 232 Th. The emanation factor was found to increase with decrease in grain size. This effect was made evident by keeping the parent radium concentration constant for all grain size fractions. The governing factor is the specific surface area of the soil samples which increases with decrease in grain size. Copyright © 2017 Elsevier Ltd. All rights reserved.
How to use COSMIC Functional Size in Effort Estimation Models?
Gencel, Cigdem
2008-01-01
Although Functional Size Measurement (FSM) methods have become widely used by the software organizations, the functional size based effort estimation still needs further investigation. Most of the studies on effort estimation consider total functional size of the software as the primary input to estimation models and they mostly focus on identifying the project parameters which might have a significant effect on the size-effort relationship. This study brings suggestions on how to use COSMIC ...
Umesh P. Agarwal; Sally A. Ralph; Carlos Baez; Richard S. Reiner; Steve P. Verrill
2017-01-01
Although X-ray diffraction (XRD) has been the most widely used technique to investigate crystallinity index (CrI) and crystallite size (L200) of cellulose materials, there are not many studies that have taken into account the role of sample moisture on these measurements. The present investigation focuses on a variety of celluloses and cellulose...
Introduction to Sample Size Choice for Confidence Intervals Based on "t" Statistics
Liu, Xiaofeng Steven; Loudermilk, Brandon; Simpson, Thomas
2014-01-01
Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis…
Size-Resolved Penetration Through High-Efficiency Filter Media Typically Used for Aerosol Sampling
Czech Academy of Sciences Publication Activity Database
Zíková, Naděžda; Ondráček, Jakub; Ždímal, Vladimír
2015-01-01
Roč. 49, č. 4 (2015), s. 239-249 ISSN 0278-6826 R&D Projects: GA ČR(CZ) GBP503/12/G147 Institutional support: RVO:67985858 Keywords : filters * size-resolved penetration * atmospheric aerosol sampling Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.953, year: 2015
B-graph sampling to estimate the size of a hidden population
Spreen, M.; Bogaerts, S.
2015-01-01
Link-tracing designs are often used to estimate the size of hidden populations by utilizing the relational links between their members. A major problem in studies of hidden populations is the lack of a convenient sampling frame. The most frequently applied design in studies of hidden populations is
Bolton tooth size ratio among Sudanese Population sample: A preliminary study.
Abdalla Hashim, Ala'a Hayder; Eldin, Al-Hadi Mohi; Hashim, Hayder Abdalla
2015-01-01
The study of the mesiodistal size, the morphology of teeth and dental arch may play an important role in clinical dentistry, as well as other sciences such as Forensic Dentistry and Anthropology. The aims of the present study were to establish tooth-size ratio in Sudanese sample with Class I normal occlusion, to compare the tooth-size ratio between the present study and Bolton's study and between genders. The sample consisted of dental casts of 60 subjects (30 males and 30 females). Bolton formula was used to compute the overall and anterior ratio. The correlation coefficient between the anterior ratio and overall ratio was tested, and Student's t-test was used to compare tooth-size ratios between males and females, and between the present study and Bolton's result. The results of the overall and anterior ratio was relatively similar to the mean values reported by Bolton, and there were no statistically significant differences between the mean values of the anterior ratio and the overall ratio between males and females. The correlation coefficient was (r = 0.79). The result obtained was similar to the Caucasian race. However, the reality indicates that the Sudanese population consisted of different racial groups; therefore, the firm conclusion is difficult to draw. Since this sample is not representative for the Sudanese population, hence, a further study with a large sample collected from the different parts of the Sudan is required.
How Big Is Big Enough? Sample Size Requirements for CAST Item Parameter Estimation
Chuah, Siang Chee; Drasgow, Fritz; Luecht, Richard
2006-01-01
Adaptive tests offer the advantages of reduced test length and increased accuracy in ability estimation. However, adaptive tests require large pools of precalibrated items. This study looks at the development of an item pool for 1 type of adaptive administration: the computer-adaptive sequential test. An important issue is the sample size required…
International Nuclear Information System (INIS)
Sampson, T.E.
1991-01-01
Recent advances in segmented gamma scanning have emphasized software corrections for gamma-ray self-adsorption in particulates or lumps of special nuclear material in the sample. another feature of this software is an attenuation correction factor formalism that explicitly accounts for differences in sample container size and composition between the calibration standards and the individual items being measured. Software without this container-size correction produces biases when the unknowns are not packaged in the same containers as the calibration standards. This new software allows the use of different size and composition containers for standards and unknowns, as enormous savings considering the expense of multiple calibration standard sets otherwise needed. This paper presents calculations of the bias resulting from not using this new formalism. These calculations may be used to estimate bias corrections for segmented gamma scanners that do not incorporate these advanced concepts
Directory of Open Access Journals (Sweden)
Cynthia Stretch
Full Text Available Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65 ♀ in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5 ♂, 5 ♀ to n = 120 (60 ♂, 60 ♀ thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n = 134, we identified 717 differentially expressed transcripts (p<0.0001 and we were able predict sex with ~90% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n = 10 (5 ♂, 5 ♀, no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ~50% (no better than chance. We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex.
Jarvis, Nicholas; Larsbo, Mats; Koestel, John; Keck, Hannes
2017-04-01
The long-range connectivity of macropore networks may exert a strong control on near-saturated and saturated hydraulic conductivity and the occurrence of preferential flow through soil. It has been suggested that percolation concepts may provide a suitable theoretical framework to characterize and quantify macropore connectivity, although this idea has not yet been thoroughly investigated. We tested the applicability of percolation concepts to describe macropore networks quantified by X-ray scanning at a resolution of 0.24 mm in eighteen cylinders (20 cm diameter and height) sampled from the ploughed layer of four soils of contrasting texture in east-central Sweden. The analyses were performed for sample sizes ("regions of interest", ROI) varying between 3 and 12 cm in cube side-length and for minimum pore thicknesses ranging between image resolution and 1 mm. Finite sample size effects were clearly found for ROI's of cube side-length smaller than ca. 6 cm. For larger sample sizes, the results showed the relevance of percolation concepts to soil macropore networks, with a close relationship found between imaged porosity and the fraction of the pore space which percolated (i.e. was connected from top to bottom of the ROI). The percolating fraction increased rapidly as a function of porosity above a small percolation threshold (1-4%). This reflects the ordered nature of the pore networks. The percolation relationships were similar for all four soils. Although pores larger than 1 mm appeared to be somewhat better connected, only small effects of minimum pore thickness were noted across the range of tested pore sizes. The utility of percolation concepts to describe the connectivity of more anisotropic macropore networks (e.g. in subsoil horizons) should also be tested, although with current X-ray scanning equipment it may prove difficult in many cases to analyze sufficiently large samples that would avoid finite size effects.
A Unimodal Model for Double Observer Distance Sampling Surveys.
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Earl F Becker
Full Text Available Distance sampling is a widely used method to estimate animal population size. Most distance sampling models utilize a monotonically decreasing detection function such as a half-normal. Recent advances in distance sampling modeling allow for the incorporation of covariates into the distance model, and the elimination of the assumption of perfect detection at some fixed distance (usually the transect line with the use of double-observer models. The assumption of full observer independence in the double-observer model is problematic, but can be addressed by using the point independence assumption which assumes there is one distance, the apex of the detection function, where the 2 observers are assumed independent. Aerially collected distance sampling data can have a unimodal shape and have been successfully modeled with a gamma detection function. Covariates in gamma detection models cause the apex of detection to shift depending upon covariate levels, making this model incompatible with the point independence assumption when using double-observer data. This paper reports a unimodal detection model based on a two-piece normal distribution that allows covariates, has only one apex, and is consistent with the point independence assumption when double-observer data are utilized. An aerial line-transect survey of black bears in Alaska illustrate how this method can be applied.
Size and shape characteristics of drumlins, derived from a large sample, and associated scaling laws
Clark, Chris D.; Hughes, Anna L. C.; Greenwood, Sarah L.; Spagnolo, Matteo; Ng, Felix S. L.
2009-04-01
Ice sheets flowing across a sedimentary bed usually produce a landscape of blister-like landforms streamlined in the direction of the ice flow and with each bump of the order of 10 2 to 10 3 m in length and 10 1 m in relief. Such landforms, known as drumlins, have mystified investigators for over a hundred years. A satisfactory explanation for their formation, and thus an appreciation of their glaciological significance, has remained elusive. A recent advance has been in numerical modelling of the land-forming process. In anticipation of future modelling endeavours, this paper is motivated by the requirement for robust data on drumlin size and shape for model testing. From a systematic programme of drumlin mapping from digital elevation models and satellite images of Britain and Ireland, we used a geographic information system to compile a range of statistics on length L, width W, and elongation ratio E (where E = L/ W) for a large sample. Mean L, is found to be 629 m ( n = 58,983), mean W is 209 m and mean E is 2.9 ( n = 37,043). Most drumlins are between 250 and 1000 metres in length; between 120 and 300 metres in width; and between 1.7 and 4.1 times as long as they are wide. Analysis of such data and plots of drumlin width against length reveals some new insights. All frequency distributions are unimodal from which we infer that the geomorphological label of 'drumlin' is fair in that this is a true single population of landforms, rather than an amalgam of different landform types. Drumlin size shows a clear minimum bound of around 100 m (horizontal). Maybe drumlins are generated at many scales and this is the minimum, or this value may be an indication of the fundamental scale of bump generation ('proto-drumlins') prior to them growing and elongating. A relationship between drumlin width and length is found (with r2 = 0.48) and that is approximately W = 7 L 1/2 when measured in metres. A surprising and sharply-defined line bounds the data cloud plotted in E- W
A random energy model for size dependence : recurrence vs. transience
Külske, Christof
1998-01-01
We investigate the size dependence of disordered spin models having an infinite number of Gibbs measures in the framework of a simplified 'random energy model for size dependence'. We introduce two versions (involving either independent random walks or branching processes), that can be seen as
Size selective isocyanate aerosols personal air sampling using porous plastic foams
Khanh Huynh, Cong; Duc, Trinh Vu
2009-02-01
As part of a European project (SMT4-CT96-2137), various European institutions specialized in occupational hygiene (BGIA, HSL, IOM, INRS, IST, Ambiente e Lavoro) have established a program of scientific collaboration to develop one or more prototypes of European personal samplers for the collection of simultaneous three dust fractions: inhalable, thoracic and respirable. These samplers based on existing sampling heads (IOM, GSP and cassettes) use Polyurethane Plastic Foam (PUF) according to their porosity to support sampling and separator size of the particles. In this study, the authors present an original application of size selective personal air sampling using chemical impregnated PUF to perform isocyanate aerosols capturing and derivatizing in industrial spray-painting shops.
10Be measurements at MALT using reduced-size samples of bulk sediments
Horiuchi, Kazuho; Oniyanagi, Itsumi; Wasada, Hiroshi; Matsuzaki, Hiroyuki
2013-01-01
In order to establish 10Be measurements on reduced-size (1-10 mg) samples of bulk sediments, we investigated four different pretreatment designs using lacustrine and marginal-sea sediments and the AMS system of the Micro Analysis Laboratory, Tandem accelerator (MALT) at The University of Tokyo. The 10Be concentrations obtained from the samples of 1-10 mg agreed within a precision of 3-5% with the values previously determined using corresponding ordinary-size (∼200 mg) samples and the same AMS system. This fact demonstrates reliable determinations of 10Be with milligram levels of recent bulk sediments at MALT. On the other hand, a clear decline of the BeO- beam with tens of micrograms of 9Be carrier suggests that the combination of ten milligrams of sediments and a few hundred micrograms of the 9Be carrier is more convenient at this stage.
{sup 10}Be measurements at MALT using reduced-size samples of bulk sediments
Energy Technology Data Exchange (ETDEWEB)
Horiuchi, Kazuho, E-mail: kh@cc.hirosaki-u.ac.jp [Graduate School of Science and Technology, Hirosaki University, 3, Bunkyo-chou, Hirosaki, Aomori 036-8561 (Japan); Oniyanagi, Itsumi [Graduate School of Science and Technology, Hirosaki University, 3, Bunkyo-chou, Hirosaki, Aomori 036-8561 (Japan); Wasada, Hiroshi [Institute of Geology and Paleontology, Graduate school of Science, Tohoku University, 6-3, Aramaki Aza-Aoba, Aoba-ku, Sendai 980-8578 (Japan); Matsuzaki, Hiroyuki [MALT, School of Engineering, University of Tokyo, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032 (Japan)
2013-01-15
In order to establish {sup 10}Be measurements on reduced-size (1-10 mg) samples of bulk sediments, we investigated four different pretreatment designs using lacustrine and marginal-sea sediments and the AMS system of the Micro Analysis Laboratory, Tandem accelerator (MALT) at University of Tokyo. The {sup 10}Be concentrations obtained from the samples of 1-10 mg agreed within a precision of 3-5% with the values previously determined using corresponding ordinary-size ({approx}200 mg) samples and the same AMS system. This fact demonstrates reliable determinations of {sup 10}Be with milligram levels of recent bulk sediments at MALT. On the other hand, a clear decline of the BeO{sup -} beam with tens of micrograms of {sup 9}Be carrier suggests that the combination of ten milligrams of sediments and a few hundred micrograms of the {sup 9}Be carrier is more convenient at this stage.
Size selective isocyanate aerosols personal air sampling using porous plastic foams
International Nuclear Information System (INIS)
Cong Khanh Huynh; Trinh Vu Duc
2009-01-01
As part of a European project (SMT4-CT96-2137), various European institutions specialized in occupational hygiene (BGIA, HSL, IOM, INRS, IST, Ambiente e Lavoro) have established a program of scientific collaboration to develop one or more prototypes of European personal samplers for the collection of simultaneous three dust fractions: inhalable, thoracic and respirable. These samplers based on existing sampling heads (IOM, GSP and cassettes) use Polyurethane Plastic Foam (PUF) according to their porosity to support sampling and separator size of the particles. In this study, the authors present an original application of size selective personal air sampling using chemical impregnated PUF to perform isocyanate aerosols capturing and derivatizing in industrial spray-painting shops.
A COMPUTATIONAL TOOL TO EVALUATE THE SAMPLE SIZE IN MAP POSITIONAL ACCURACY
Directory of Open Access Journals (Sweden)
Marcelo Antonio Nero
Full Text Available Abstract: In many countries, the positional accuracy control by points in Cartography or Spatial data corresponds to the comparison between sets of coordinates of well-defined points in relation to the same set of points from a more accurate source. Usually, each country determines a maximum number of points which could present error values above a pre-established threshold. In many cases, the standards define the sample size as 20 points, with no more consideration, and fix this threshold in 10% of the sample. However, the sampling dimension (n, considering the statistical risk, especially when the percentages of outliers are around 10%, can lead to a producer risk (to reject a good map and a user risk (to accept a bad map. This article analyzes this issue and allows defining the sampling dimension considering the risk of the producer and of the user. As a tool, a program developed by us allows defining the sample size according to the risk that the producer / user can or wants to assume. This analysis uses 600 control points, each of them with a known error. We performed the simulations with a sample size of 20 points (n and calculate the associated risk. Then we changed the value of (n, using smaller and larger sizes, calculating for each situation the associated risk both for the user and for the producer. The computer program developed draws the operational curves or risk curves, which considers three parameters: the number of control points; the number of iterations to create the curves; and the percentage of control points above the threshold, that can be the Brazilian standard or other parameters from different countries. Several graphs and tables are presented which were created with different parameters, leading to a better decision both for the user and for the producer, as well as to open possibilities for other simulations and researches in the future.
PIXE–PIGE analysis of size-segregated aerosol samples from remote areas
Energy Technology Data Exchange (ETDEWEB)
Calzolai, G., E-mail: calzolai@fi.infn.it [Department of Physics and Astronomy, University of Florence and National Institute of Nuclear Physics (INFN), Via G. Sansone 1, 50019 Sesto Fiorentino (Italy); Chiari, M.; Lucarelli, F.; Nava, S.; Taccetti, F. [Department of Physics and Astronomy, University of Florence and National Institute of Nuclear Physics (INFN), Via G. Sansone 1, 50019 Sesto Fiorentino (Italy); Becagli, S.; Frosini, D.; Traversi, R.; Udisti, R. [Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino (Italy)
2014-01-01
The chemical characterization of size-segregated samples is helpful to study the aerosol effects on both human health and environment. The sampling with multi-stage cascade impactors (e.g., Small Deposit area Impactor, SDI) produces inhomogeneous samples, with a multi-spot geometry and a non-negligible particle stratification. At LABEC (Laboratory of nuclear techniques for the Environment and the Cultural Heritage), an external beam line is fully dedicated to PIXE–PIGE analysis of aerosol samples. PIGE is routinely used as a sidekick of PIXE to correct the underestimation of PIXE in quantifying the concentration of the lightest detectable elements, like Na or Al, due to X-ray absorption inside the individual aerosol particles. In this work PIGE has been used to study proper attenuation correction factors for SDI samples: relevant attenuation effects have been observed also for stages collecting smaller particles, and consequent implications on the retrieved aerosol modal structure have been evidenced.
Sample size calculation for microarray experiments with blocked one-way design
Directory of Open Access Journals (Sweden)
Jung Sin-Ho
2009-05-01
Full Text Available Abstract Background One of the main objectives of microarray analysis is to identify differentially expressed genes for different types of cells or treatments. Many statistical methods have been proposed to assess the treatment effects in microarray experiments. Results In this paper, we consider discovery of the genes that are differentially expressed among K (> 2 treatments when each set of K arrays consists of a block. In this case, the array data among K treatments tend to be correlated because of block effect. We propose to use the blocked one-way ANOVA F-statistic to test if each gene is differentially expressed among K treatments. The marginal p-values are calculated using a permutation method accounting for the block effect, adjusting for the multiplicity of the testing procedure by controlling the false discovery rate (FDR. We propose a sample size calculation method for microarray experiments with a blocked one-way design. With FDR level and effect sizes of genes specified, our formula provides a sample size for a given number of true discoveries. Conclusion The calculated sample size is shown via simulations to provide an accurate number of true discoveries while controlling the FDR at the desired level.
Dong, Nianbo; Maynard, Rebecca
2013-01-01
This paper and the accompanying tool are intended to complement existing supports for conducting power analysis tools by offering a tool based on the framework of Minimum Detectable Effect Sizes (MDES) formulae that can be used in determining sample size requirements and in estimating minimum detectable effect sizes for a range of individual- and…
Significance, Errors, Power, and Sample Size: The Blocking and Tackling of Statistics.
Mascha, Edward J; Vetter, Thomas R
2018-02-01
Inferential statistics relies heavily on the central limit theorem and the related law of large numbers. According to the central limit theorem, regardless of the distribution of the source population, a sample estimate of that population will have a normal distribution, but only if the sample is large enough. The related law of large numbers holds that the central limit theorem is valid as random samples become large enough, usually defined as an n ≥ 30. In research-related hypothesis testing, the term "statistically significant" is used to describe when an observed difference or association has met a certain threshold. This significance threshold or cut-point is denoted as alpha (α) and is typically set at .05. When the observed P value is less than α, one rejects the null hypothesis (Ho) and accepts the alternative. Clinical significance is even more important than statistical significance, so treatment effect estimates and confidence intervals should be regularly reported. A type I error occurs when the Ho of no difference or no association is rejected, when in fact the Ho is true. A type II error occurs when the Ho is not rejected, when in fact there is a true population effect. Power is the probability of detecting a true difference, effect, or association if it truly exists. Sample size justification and power analysis are key elements of a study design. Ethical concerns arise when studies are poorly planned or underpowered. When calculating sample size for comparing groups, 4 quantities are needed: α, type II error, the difference or effect of interest, and the estimated variability of the outcome variable. Sample size increases for increasing variability and power, and for decreasing α and decreasing difference to detect. Sample size for a given relative reduction in proportions depends heavily on the proportion in the control group itself, and increases as the proportion decreases. Sample size for single-group studies estimating an unknown parameter
Sample size requirements to detect gene-environment interactions in genome-wide association studies.
Murcray, Cassandra E; Lewinger, Juan Pablo; Conti, David V; Thomas, Duncan C; Gauderman, W James
2011-04-01
Many complex diseases are likely to be a result of the interplay of genes and environmental exposures. The standard analysis in a genome-wide association study (GWAS) scans for main effects and ignores the potentially useful information in the available exposure data. Two recently proposed methods that exploit environmental exposure information involve a two-step analysis aimed at prioritizing the large number of SNPs tested to highlight those most likely to be involved in a GE interaction. For example, Murcray et al. ([2009] Am J Epidemiol 169:219–226) proposed screening on a test that models the G-E association induced by an interaction in the combined case-control sample. Alternatively, Kooperberg and LeBlanc ([2008] Genet Epidemiol 32:255–263) suggested screening on genetic marginal effects. In both methods, SNPs that pass the respective screening step at a pre-specified significance threshold are followed up with a formal test of interaction in the second step. We propose a hybrid method that combines these two screening approaches by allocating a proportion of the overall genomewide significance level to each test. We show that the Murcray et al. approach is often the most efficient method, but that the hybrid approach is a powerful and robust method for nearly any underlying model. As an example, for a GWAS of 1 million markers including a single true disease SNP with minor allele frequency of 0.15, and a binary exposure with prevalence 0.3, the Murcray, Kooperberg and hybrid methods are 1.90, 1.27, and 1.87 times as efficient, respectively, as the traditional case-control analysis to detect an interaction effect size of 2.0.
DEFF Research Database (Denmark)
Shetty, Nisha; Min, Tai-Gi; Gislum, René
2011-01-01
The effects of the number of seeds in a training sample set on the ability to predict the viability of cabbage or radish seeds are presented and discussed. The supervised classification method extended canonical variates analysis (ECVA) was used to develop a classification model. Calibration sub...... and radish data. The misclassification rates at optimal sample size were 8%, 6% and 7% for cabbage and 3%, 3% and 2% for radish respectively for random method (averaged for 10 iterations), DUPLEX and CADEX algorithms. This was similar to the misclassification rate of 6% and 2% for cabbage and radish obtained...
Galbraith, Niall D; Manktelow, Ken I; Morris, Neil G
2010-11-01
Previous studies demonstrate that people high in delusional ideation exhibit a data-gathering bias on inductive reasoning tasks. The current study set out to investigate the factors that may underpin such a bias by examining healthy individuals, classified as either high or low scorers on the Peters et al. Delusions Inventory (PDI). More specifically, whether high PDI scorers have a relatively poor appreciation of sample size and heterogeneity when making statistical judgments. In Expt 1, high PDI scorers made higher probability estimates when generalizing from a sample of 1 with regard to the heterogeneous human property of obesity. In Expt 2, this effect was replicated and was also observed in relation to the heterogeneous property of aggression. The findings suggest that delusion-prone individuals are less appreciative of the importance of sample size when making statistical judgments about heterogeneous properties; this may underpin the data gathering bias observed in previous studies. There was some support for the hypothesis that threatening material would exacerbate high PDI scorers' indifference to sample size.
Assumptions behind size-based ecosystem models are realistic
DEFF Research Database (Denmark)
Andersen, Ken Haste; Blanchard, Julia L.; Fulton, Elizabeth A.
2016-01-01
A recent publication about balanced harvesting (Froese et al., ICES Journal of Marine Science; doi:10.1093/icesjms/fsv122) contains several erroneous statements about size-spectrum models. We refute the statements by showing that the assumptions pertaining to size-spectrum models discussed by Fro...... that there is indeed a constructive role for a wide suite of ecosystem models to evaluate fishing strategies in an ecosystem context...
Streamed Sampling on Dynamic data as Support for Classification Model
Directory of Open Access Journals (Sweden)
Heru Sukoco
2013-11-01
Full Text Available Data mining process on dynamically changing data have several problems, such as unknown data size and skew of the data is always changing. Random sampling method commonly applied for extracting general synopsis from very large database. In this research, Vitterâ€™s reservoir algorithm is used to retrieve k records of data from the database and put into the sample. Sample is used as input for classification task in data mining. Sample type is backing sample and it saved as table contains value of id and priority. Priority indicates the probability of how long data retained in the sample. Kullback-Leibler divergence applied to measure the similarity between population and sample distribution. Result of this research is showed that continuously taken samples randomly is possible when transaction occurs. Kullback-Leibler divergence is a very good measure to maintain similar distribution between the population and the sample with interval from 0 to 0.0001. Sample results are always up to date on new transactions with similar skewnes. In purpose of classification task, decision tree model is improved significantly when the changing occurred.
Kikuchi, Takashi; Gittins, John
2009-08-15
It is necessary for the calculation of sample size to achieve the best balance between the cost of a clinical trial and the possible benefits from a new treatment. Gittins and Pezeshk developed an innovative (behavioral Bayes) approach, which assumes that the number of users is an increasing function of the difference in performance between the new treatment and the standard treatment. The better a new treatment, the more the number of patients who want to switch to it. The optimal sample size is calculated in this framework. This BeBay approach takes account of three decision-makers, a pharmaceutical company, the health authority and medical advisers. Kikuchi, Pezeshk and Gittins generalized this approach by introducing a logistic benefit function, and by extending to the more usual unpaired case, and with unknown variance. The expected net benefit in this model is based on the efficacy of the new drug but does not take account of the incidence of adverse reactions. The present paper extends the model to include the costs of treating adverse reactions and focuses on societal cost-effectiveness as the criterion for determining sample size. The main application is likely to be to phase III clinical trials, for which the primary outcome is to compare the costs and benefits of a new drug with a standard drug in relation to national health-care. Copyright 2009 John Wiley & Sons, Ltd.
Crystallite size variation of TiO2 samples depending time heat treatment
International Nuclear Information System (INIS)
Galante, A.G.M.; Paula, F.R. de; Montanhera, M.A.; Pereira, E.A.; Spada, E.R.
2016-01-01
Titanium dioxide (TiO 2 ) is an oxide semiconductor that may be found in mixed phase or in distinct phases: brookite, anatase and rutile. In this work was carried out the study of the residence time influence at a given temperature in the TiO 2 powder physical properties. After the powder synthesis, the samples were divided and heat treated at 650 °C with a ramp up to 3 °C/min and a residence time ranging from 0 to 20 hours and subsequently characterized by x-ray diffraction. Analyzing the obtained diffraction patterns, it was observed that, from 5-hour residence time, began the two-distinct phase coexistence: anatase and rutile. It also calculated the average crystallite size of each sample. The results showed an increase in average crystallite size with increasing residence time of the heat treatment. (author)
A contemporary decennial global Landsat sample of changing agricultural field sizes
White, Emma; Roy, David
2014-05-01
Agriculture has caused significant human induced Land Cover Land Use (LCLU) change, with dramatic cropland expansion in the last century and significant increases in productivity over the past few decades. Satellite data have been used for agricultural applications including cropland distribution mapping, crop condition monitoring, crop production assessment and yield prediction. Satellite based agricultural applications are less reliable when the sensor spatial resolution is small relative to the field size. However, to date, studies of agricultural field size distributions and their change have been limited, even though this information is needed to inform the design of agricultural satellite monitoring systems. Moreover, the size of agricultural fields is a fundamental description of rural landscapes and provides an insight into the drivers of rural LCLU change. In many parts of the world field sizes may have increased. Increasing field sizes cause a subsequent decrease in the number of fields and therefore decreased landscape spatial complexity with impacts on biodiversity, habitat, soil erosion, plant-pollinator interactions, and impacts on the diffusion of herbicides, pesticides, disease pathogens, and pests. The Landsat series of satellites provide the longest record of global land observations, with 30m observations available since 1982. Landsat data are used to examine contemporary field size changes in a period (1980 to 2010) when significant global agricultural changes have occurred. A multi-scale sampling approach is used to locate global hotspots of field size change by examination of a recent global agricultural yield map and literature review. Nine hotspots are selected where significant field size change is apparent and where change has been driven by technological advancements (Argentina and U.S.), abrupt societal changes (Albania and Zimbabwe), government land use and agricultural policy changes (China, Malaysia, Brazil), and/or constrained by
Media Exposure: How Models Simplify Sampling
DEFF Research Database (Denmark)
Mortensen, Peter Stendahl
1998-01-01
In media planning, the distribution of exposures to more ad spots in more media (print, TV, radio) is crucial to the evaluation of the campaign. If such information should be sampled, it would only be possible in expensive panel-studies (eg TV-meter panels). Alternatively, the distribution of exp...... of exposures may be modelled statistically, using the Beta distribution combined with the Binomial Distribution. Examples are given....
Evaluating the performance of species richness estimators: sensitivity to sample grain size
DEFF Research Database (Denmark)
Hortal, Joaquín; Borges, Paulo A. V.; Gaspar, Clara
2006-01-01
and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar....... Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using...
Sample sizing of biological materials analyzed by energy dispersion X-ray fluorescence
International Nuclear Information System (INIS)
Paiva, Jose D.S.; Franca, Elvis J.; Magalhaes, Marcelo R.L.; Almeida, Marcio E.S.; Hazin, Clovis A.
2013-01-01
Analytical portions used in chemical analyses are usually less than 1g. Errors resulting from the sampling are barely evaluated, since this type of study is a time-consuming procedure, with high costs for the chemical analysis of large number of samples. The energy dispersion X-ray fluorescence - EDXRF is a non-destructive and fast analytical technique with the possibility of determining several chemical elements. Therefore, the aim of this study was to provide information on the minimum analytical portion for quantification of chemical elements in biological matrices using EDXRF. Three species were sampled in mangroves from the Pernambuco, Brazil. Tree leaves were washed with distilled water, oven-dried at 60 deg C and milled until 0.5 mm particle size. Ten test-portions of approximately 500 mg for each species were transferred to vials sealed with polypropylene film. The quality of the analytical procedure was evaluated from the reference materials IAEA V10 Hay Powder, SRM 2976 Apple Leaves. After energy calibration, all samples were analyzed under vacuum for 100 seconds for each group of chemical elements. The voltage used was 15 kV and 50 kV for chemical elements of atomic number lower than 22 and the others, respectively. For the best analytical conditions, EDXRF was capable of estimating the sample size uncertainty for further determination of chemical elements in leaves. (author)
Sample sizing of biological materials analyzed by energy dispersion X-ray fluorescence
Energy Technology Data Exchange (ETDEWEB)
Paiva, Jose D.S.; Franca, Elvis J.; Magalhaes, Marcelo R.L.; Almeida, Marcio E.S.; Hazin, Clovis A., E-mail: dan-paiva@hotmail.com, E-mail: ejfranca@cnen.gov.br, E-mail: marcelo_rlm@hotmail.com, E-mail: maensoal@yahoo.com.br, E-mail: chazin@cnen.gov.b [Centro Regional de Ciencias Nucleares do Nordeste (CRCN-NE/CNEN-PE), Recife, PE (Brazil)
2013-07-01
Analytical portions used in chemical analyses are usually less than 1g. Errors resulting from the sampling are barely evaluated, since this type of study is a time-consuming procedure, with high costs for the chemical analysis of large number of samples. The energy dispersion X-ray fluorescence - EDXRF is a non-destructive and fast analytical technique with the possibility of determining several chemical elements. Therefore, the aim of this study was to provide information on the minimum analytical portion for quantification of chemical elements in biological matrices using EDXRF. Three species were sampled in mangroves from the Pernambuco, Brazil. Tree leaves were washed with distilled water, oven-dried at 60 deg C and milled until 0.5 mm particle size. Ten test-portions of approximately 500 mg for each species were transferred to vials sealed with polypropylene film. The quality of the analytical procedure was evaluated from the reference materials IAEA V10 Hay Powder, SRM 2976 Apple Leaves. After energy calibration, all samples were analyzed under vacuum for 100 seconds for each group of chemical elements. The voltage used was 15 kV and 50 kV for chemical elements of atomic number lower than 22 and the others, respectively. For the best analytical conditions, EDXRF was capable of estimating the sample size uncertainty for further determination of chemical elements in leaves. (author)
Czech Academy of Sciences Publication Activity Database
Duintjer Tebbens, Jurjen; Schlesinger, P.
2007-01-01
Roč. 52, č. 1 (2007), s. 423-437 ISSN 0167-9473 R&D Projects: GA AV ČR 1ET400300415; GA MŠk LC536 Institutional research plan: CEZ:AV0Z10300504 Keywords : linear discriminant analysis * numerical aspects of FLDA * small sample size problem * dimension reduction * sparsity Subject RIV: BA - General Mathematics Impact factor: 1.029, year: 2007
Dziak, John J.; Nahum-Shani, Inbal; Collins, Linda M.
2012-01-01
Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions, by helping investigators to screen several candidate intervention components simultaneously and decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the populatio...
Robust inference in sample selection models
Zhelonkin, Mikhail
2015-11-20
The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.
An open-population hierarchical distance sampling model
Sollmann, Rachel; Beth Gardner,; Richard B Chandler,; Royle, J. Andrew; T Scott Sillett,
2015-01-01
Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for direct estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for island scrub-jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying number of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring.
Improved Mathematical Models for Particle-Size Distribution Data
African Journals Online (AJOL)
BirukEdimon
four existing curve fitting models common to geotechnical applications are reviewed and presented first. Definitions of Important Parameters and. Variables. A given soil will be made up of grains of many different sizes and described by the grain size distribution. The main variables are % Clay, %. Silt, % Sand, % of fine and ...
Influence of horizontal resolution and ensemble size on model performance
CSIR Research Space (South Africa)
Dalton, A
2014-10-01
Full Text Available Computing costs increase with an increase in global model resolution and ensemble size. This paper strives to determine the extent to which resolution and ensemble size affect seasonal forecast skill when simulating mid-summer rainfall totals over...
Improved mathematical models for particle-size distribution data ...
African Journals Online (AJOL)
Prior studies have suggested that particle-size distribution data of soils is central and helpful in this regard. This study proposes two improved mathematical models to describe and represent the varied particle-size distribution (PSD) data for tropically weathered residual (TWR) soils. The theoretical analysis and the ...
A Markov decision model for optimising economic production lot size ...
African Journals Online (AJOL)
Abstract. Traditional approaches towards determining the economic production lot (EPL) size in man- ufacturing applications assume deterministic demand, often at a constant rate. In this paper, an optimisation model is developed for determining the EPL size that minimises production and inventory costs of a periodic ...
Multiscale sampling model for motion integration.
Sherbakov, Lena; Yazdanbakhsh, Arash
2013-09-30
Biologically plausible strategies for visual scene integration across spatial and temporal domains continues to be a challenging topic. The fundamental question we address is whether classical problems in motion integration, such as the aperture problem, can be solved in a model that samples the visual scene at multiple spatial and temporal scales in parallel. We hypothesize that fast interareal connections that allow feedback of information between cortical layers are the key processes that disambiguate motion direction. We developed a neural model showing how the aperture problem can be solved using different spatial sampling scales between LGN, V1 layer 4, V1 layer 6, and area MT. Our results suggest that multiscale sampling, rather than feedback explicitly, is the key process that gives rise to end-stopped cells in V1 and enables area MT to solve the aperture problem without the need for calculating intersecting constraints or crafting intricate patterns of spatiotemporal receptive fields. Furthermore, the model explains why end-stopped cells no longer emerge in the absence of V1 layer 6 activity (Bolz & Gilbert, 1986), why V1 layer 4 cells are significantly more end-stopped than V1 layer 6 cells (Pack, Livingstone, Duffy, & Born, 2003), and how it is possible to have a solution to the aperture problem in area MT with no solution in V1 in the presence of driving feedback. In summary, while much research in the field focuses on how a laminar architecture can give rise to complicated spatiotemporal receptive fields to solve problems in the motion domain, we show that one can reframe motion integration as an emergent property of multiscale sampling achieved concurrently within lamina and across multiple visual areas.
Estimating the Effective Sample Size of Tree Topologies from Bayesian Phylogenetic Analyses
Lanfear, Robert; Hua, Xia; Warren, Dan L.
2016-01-01
Bayesian phylogenetic analyses estimate posterior distributions of phylogenetic tree topologies and other parameters using Markov chain Monte Carlo (MCMC) methods. Before making inferences from these distributions, it is important to assess their adequacy. To this end, the effective sample size (ESS) estimates how many truly independent samples of a given parameter the output of the MCMC represents. The ESS of a parameter is frequently much lower than the number of samples taken from the MCMC because sequential samples from the chain can be non-independent due to autocorrelation. Typically, phylogeneticists use a rule of thumb that the ESS of all parameters should be greater than 200. However, we have no method to calculate an ESS of tree topology samples, despite the fact that the tree topology is often the parameter of primary interest and is almost always central to the estimation of other parameters. That is, we lack a method to determine whether we have adequately sampled one of the most important parameters in our analyses. In this study, we address this problem by developing methods to estimate the ESS for tree topologies. We combine these methods with two new diagnostic plots for assessing posterior samples of tree topologies, and compare their performance on simulated and empirical data sets. Combined, the methods we present provide new ways to assess the mixing and convergence of phylogenetic tree topologies in Bayesian MCMC analyses. PMID:27435794
Sample size and power determination when limited preliminary information is available
Directory of Open Access Journals (Sweden)
Christine E. McLaren
2017-04-01
Full Text Available Abstract Background We describe a novel strategy for power and sample size determination developed for studies utilizing investigational technologies with limited available preliminary data, specifically of imaging biomarkers. We evaluated diffuse optical spectroscopic imaging (DOSI, an experimental noninvasive imaging technique that may be capable of assessing changes in mammographic density. Because there is significant evidence that tamoxifen treatment is more effective at reducing breast cancer risk when accompanied by a reduction of breast density, we designed a study to assess the changes from baseline in DOSI imaging biomarkers that may reflect fluctuations in breast density in premenopausal women receiving tamoxifen. Method While preliminary data demonstrate that DOSI is sensitive to mammographic density in women about to receive neoadjuvant chemotherapy for breast cancer, there is no information on DOSI in tamoxifen treatment. Since the relationship between magnetic resonance imaging (MRI and DOSI has been established in previous studies, we developed a statistical simulation approach utilizing information from an investigation of MRI assessment of breast density in 16 women before and after treatment with tamoxifen to estimate the changes in DOSI biomarkers due to tamoxifen. Results Three sets of 10,000 pairs of MRI breast density data with correlation coefficients of 0.5, 0.8 and 0.9 were simulated and generated and were used to simulate and generate a corresponding 5,000,000 pairs of DOSI values representing water, ctHHB, and lipid. Minimum sample sizes needed per group for specified clinically-relevant effect sizes were obtained. Conclusion The simulation techniques we describe can be applied in studies of other experimental technologies to obtain the important preliminary data to inform the power and sample size calculations.
Optimum workforce-size model using dynamic programming approach
African Journals Online (AJOL)
This paper presents an optimum workforce-size model which determines the minimum number of excess workers (overstaffing) as well as the minimum total recruitment cost during a specified planning horizon. The model is an extension of other existing dynamic programming models for manpower planning in the sense ...
optimum workforce-size model using dynamic programming approach
African Journals Online (AJOL)
DJFLEX
This paper presents an optimum workforce-size model which determines the minimum number of excess workers (overstaffing) as well as the minimum total recruitment cost during a specified planning horizon. The model is an extension of other existing dynamic programming models for manpower planning in the sense ...
The theoretical foundations for size spectrum models of fish communities
DEFF Research Database (Denmark)
Andersen, Ken Haste; Jacobsen, Nis Sand; Farnsworth, K.D.
2016-01-01
assessment of fisheries. We describe the fundamental concepts in size-based models about food encounter and the bioenergetics budget of individuals. Within the general framework three model types have emerged that differs in their degree of complexity: the food-web, the trait-based and the community model...
Li, Zipeng; Lai, Kelvin Yi-Tse; Chakrabarty, Krishnendu; Ho, Tsung-Yi; Lee, Chen-Yi
2017-12-01
Sample preparation in digital microfluidics refers to the generation of droplets with target concentrations for on-chip biochemical applications. In recent years, digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation. However, there remain two major problems associated with sample preparation on a conventional DMFB. First, only a (1:1) mixing/splitting model can be used, leading to an increase in the number of fluidic operations required for sample preparation. Second, only a limited number of sensors can be integrated on a conventional DMFB; as a result, the latency for error detection during sample preparation is significant. To overcome these drawbacks, we adopt a next generation DMFB platform, referred to as micro-electrode-dot-array (MEDA), for sample preparation. We propose the first sample-preparation method that exploits the MEDA-specific advantages of fine-grained control of droplet sizes and real-time droplet sensing. Experimental demonstration using a fabricated MEDA biochip and simulation results highlight the effectiveness of the proposed sample-preparation method.
SIMPLIFIED MATHEMATICAL MODEL OF SMALL SIZED UNMANNED AIRCRAFT VEHICLE LAYOUT
Directory of Open Access Journals (Sweden)
2016-01-01
Full Text Available Strong reduction of new aircraft design period using new technology based on artificial intelligence is the key problem mentioned in forecasts of leading aerospace industry research centers. This article covers the approach to devel- opment of quick aerodynamic design methods based on artificial intelligence neural system. The problem is being solved for the classical scheme of small sized unmanned aircraft vehicle (UAV. The principal parts of the method are the mathe- matical model of layout, layout generator of this type of aircraft is built on aircraft neural networks, automatic selection module for cleaning variety of layouts generated in automatic mode, robust direct computational fluid dynamics method, aerodynamic characteristics approximators on artificial neural networks.Methods based on artificial neural networks have intermediate position between computational fluid dynamics methods or experiments and simplified engineering approaches. The use of ANN for estimating aerodynamic characteris-tics put limitations on input data. For this task the layout must be presented as a vector with dimension not exceeding sev-eral hundred. Vector components must include all main parameters conventionally used for layouts description and com- pletely replicate the most important aerodynamics and structural properties.The first stage of the work is presented in the paper. Simplified mathematical model of small sized UAV was developed. To estimate the range of geometrical parameters of layouts the review of existing vehicle was done. The result of the work is the algorithm and computer software for generating the layouts based on ANN technolo-gy. 10000 samples were generated and the dataset containig geometrical and aerodynamic characteristics of layoutwas created.
Waif goodbye! Average-size female models promote positive body image and appeal to consumers.
Diedrichs, Phillippa C; Lee, Christina
2011-10-01
Despite consensus that exposure to media images of thin fashion models is associated with poor body image and disordered eating behaviours, few attempts have been made to enact change in the media. This study sought to investigate an effective alternative to current media imagery, by exploring the advertising effectiveness of average-size female fashion models, and their impact on the body image of both women and men. A sample of 171 women and 120 men were assigned to one of three advertisement conditions: no models, thin models and average-size models. Women and men rated average-size models as equally effective in advertisements as thin and no models. For women with average and high levels of internalisation of cultural beauty ideals, exposure to average-size female models was associated with a significantly more positive body image state in comparison to exposure to thin models and no models. For men reporting high levels of internalisation, exposure to average-size models was also associated with a more positive body image state in comparison to viewing thin models. These findings suggest that average-size female models can promote positive body image and appeal to consumers.
Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A
2017-06-30
Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Zeestraten, Eva; Lambert, Christian; Chis Ster, Irina; Williams, Owen A; Lawrence, Andrew J; Patel, Bhavini; MacKinnon, Andrew D; Barrick, Thomas R; Markus, Hugh S
2016-01-01
Detecting treatment efficacy using cognitive change in trials of cerebral small vessel disease (SVD) has been challenging, making the use of surrogate markers such as magnetic resonance imaging (MRI) attractive. We determined the sensitivity of MRI to change in SVD and used this information to calculate sample size estimates for a clinical trial. Data from the prospective SCANS (St George’s Cognition and Neuroimaging in Stroke) study of patients with symptomatic lacunar stroke and confluent leukoaraiosis was used (n = 121). Ninety-nine subjects returned at one or more time points. Multimodal MRI and neuropsychologic testing was performed annually over 3 years. We evaluated the change in brain volume, T2 white matter hyperintensity (WMH) volume, lacunes, and white matter damage on diffusion tensor imaging (DTI). Over 3 years, change was detectable in all MRI markers but not in cognitive measures. WMH volume and DTI parameters were most sensitive to change and therefore had the smallest sample size estimates. MRI markers, particularly WMH volume and DTI parameters, are more sensitive to SVD progression over short time periods than cognition. These markers could significantly reduce the size of trials to screen treatments for efficacy in SVD, although further validation from longitudinal and intervention studies is required. PMID:26036939
Methodology for sample preparation and size measurement of commercial ZnO nanoparticles
Directory of Open Access Journals (Sweden)
Pei-Jia Lu
2018-04-01
Full Text Available This study discusses the strategies on sample preparation to acquire images with sufficient quality for size characterization by scanning electron microscope (SEM using two commercial ZnO nanoparticles of different surface properties as a demonstration. The central idea is that micrometer sized aggregates of ZnO in powdered forms need to firstly be broken down to nanosized particles through an appropriate process to generate nanoparticle dispersion before being deposited on a flat surface for SEM observation. Analytical tools such as contact angle, dynamic light scattering and zeta potential have been utilized to optimize the procedure for sample preparation and to check the quality of the results. Meanwhile, measurements of zeta potential values on flat surfaces also provide critical information and save lots of time and efforts in selection of suitable substrate for particles of different properties to be attracted and kept on the surface without further aggregation. This simple, low-cost methodology can be generally applied on size characterization of commercial ZnO nanoparticles with limited information from vendors. Keywords: Zinc oxide, Nanoparticles, Methodology
International Nuclear Information System (INIS)
Sampson, T.E.
1991-01-01
Recent advances in segmented gamma scanning have emphasized software corrections for gamma-ray self-absorption in particulates or lumps of special nuclear material in the sample. Another feature of this software is an attenuation correction factor formalism that explicitly accounts for differences in sample container size and composition between the calibration standards and the individual items being measured. Software without this container-size correction produces biases when the unknowns are not packaged in the same containers as the calibration standards. This new software allows the use of different size and composition containers for standards and unknowns, an enormous savings considering the expense of multiple calibration standard sets otherwise needed. This report presents calculations of the bias resulting from not using this new formalism. The calculations may be used to estimate bias corrections for segmented gamma scanners that do not incorporate these advanced concepts. This paper describes this attenuation-correction-factor formalism in more detail and illustrates the magnitude of the biases that may arise if it is not used. 5 refs., 7 figs
Hemery, Lenaïg G; Politano, Kristin K; Henkel, Sarah K
2017-08-01
With increasing cascading effects of climate change on the marine environment, as well as pollution and anthropogenic utilization of the seafloor, there is increasing interest in tracking changes to benthic communities. Macrofaunal surveys are traditionally conducted as part of pre-incident environmental assessment studies and post-incident monitoring studies when there is a potential impact to the seafloor. These surveys usually characterize the structure and/or spatiotemporal distribution of macrofaunal assemblages collected with sediment cores; however, many different sampling protocols have been used. An assessment of the comparability of past and current survey methods was in need to facilitate future surveys and comparisons. This was the aim of the present study, conducted off the Oregon coast in waters 25-35 m deep. Our results show that the use of a sieve with a 1.0-mm mesh size gives results for community structure comparable to results obtained from a 0.5-mm mesh size, which allows reliable comparisons of recent and past spatiotemporal surveys of macroinfauna. In addition to our primary objective of comparing methods, we also found interacting effects of seasons and depths of collection. Seasonal differences (summer and fall) were seen in infaunal assemblages in the wave-induced sediment motion zone but not deeper. Thus, studies where wave-induced sediment motion can structure the benthic communities, especially during the winter months, should consider this effect when making temporal comparisons. In addition, some macrofauna taxa-like polychaetes and amphipods show high interannual variabilities, so spatiotemporal studies should make sure to cover several years before drawing any conclusions.
Statistical characterization of a large geochemical database and effect of sample size
Zhang, C.; Manheim, F. T.; Hinde, J.; Grossman, J.N.
2005-01-01
smaller numbers of data points showed that few elements passed standard statistical tests for normality or log-normality until sample size decreased to a few hundred data points. Large sample size enhances the power of statistical tests, and leads to rejection of most statistical hypotheses for real data sets. For large sample sizes (e.g., n > 1000), graphical methods such as histogram, stem-and-leaf, and probability plots are recommended for rough judgement of probability distribution if needed. ?? 2005 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Sebastian Wilhelm
2015-12-01
Full Text Available The production of silica is performed by mixing an inorganic, silicate-based precursor and an acid. Monomeric silicic acid forms and polymerizes to amorphous silica particles. Both further polymerization and agglomeration of the particles lead to a gel network. Since polymerization continues after gelation, the gel network consolidates. This rather slow process is known as “natural syneresis” and strongly influences the product properties (e.g., agglomerate size, porosity or internal surface. “Enforced syneresis” is the superposition of natural syneresis with a mechanical, external force. Enforced syneresis may be used either for analytical or preparative purposes. Hereby, two open key aspects are of particular interest. On the one hand, the question arises whether natural and enforced syneresis are analogous processes with respect to their dependence on the process parameters: pH, temperature and sample size. On the other hand, a method is desirable that allows for correlating natural and enforced syneresis behavior. We can show that the pH-, temperature- and sample size-dependency of natural and enforced syneresis are indeed analogous. It is possible to predict natural syneresis using a correlative model. We found that our model predicts maximum volume shrinkages between 19% and 30% in comparison to measured values of 20% for natural syneresis.
Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong
2016-05-30
Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.
Sample size effect on the determination of the irreversibility line of high-Tc superconductors
International Nuclear Information System (INIS)
Li, Q.; Suenaga, M.; Li, Q.; Freltoft, T.
1994-01-01
The irreversibility lines of a high-J c superconducting Bi 2 Sr 2 Ca 2 Cu 3 O x /Ag tape were systematically measured upon a sequence of subdivisions of the sample. The irreversibility field H r (T) (parallel to the c axis) was found to change approximately as L 0.13 , where L is the effective dimension of the superconducting tape. Furthermore, it was found that the irreversibility line for a grain-aligned Bi 2 Sr 2 Ca 2 Cu 3 O x specimen can be approximately reproduced by the extrapolation of this relation down to a grain size of a few tens of micrometers. The observed size effect could significantly obscure the real physical meaning of the irreversibility lines. In addition, this finding surprisingly indicated that the Bi 2 Sr 2 Ca 2 Cu 2 O x /Ag tape and grain-aligned specimen may have similar flux line pinning strength
Multi-Criteria Model for Determining Order Size
Directory of Open Access Journals (Sweden)
Katarzyna Jakowska-Suwalska
2013-01-01
Full Text Available A multi-criteria model for determining the order size for materials used in production has been presented. It was assumed that the consumption rate of each material is a random variable with a known probability distribution. Using such a model, in which the purchase cost of materials ordered is limited, three criteria were considered: order size, probability of a lack of materials in the production process, and deviations in the order size from the consumption rate in past periods. Based on an example, it has been shown how to use the model to determine the order sizes for polyurethane adhesive and wood in a hard-coal mine. (original abstract
Influence of secular trends and sample size on reference equations for lung function tests.
Quanjer, P H; Stocks, J; Cole, T J; Hall, G L; Stanojevic, S
2011-03-01
The aim of our study was to determine the contribution of secular trends and sample size to lung function reference equations, and establish the number of local subjects required to validate published reference values. 30 spirometry datasets collected between 1978 and 2009 provided data on healthy, white subjects: 19,291 males and 23,741 females aged 2.5-95 yrs. The best fit for forced expiratory volume in 1 s (FEV(1)), forced vital capacity (FVC) and FEV(1)/FVC as functions of age, height and sex were derived from the entire dataset using GAMLSS. Mean z-scores were calculated for individual datasets to determine inter-centre differences. This was repeated by subdividing one large dataset (3,683 males and 4,759 females) into 36 smaller subsets (comprising 18-227 individuals) to preclude differences due to population/technique. No secular trends were observed and differences between datasets comprising >1,000 subjects were small (maximum difference in FEV(1) and FVC from overall mean: 0.30- -0.22 z-scores). Subdividing one large dataset into smaller subsets reproduced the above sample size-related differences and revealed that at least 150 males and 150 females would be necessary to validate reference values to avoid spurious differences due to sampling error. Use of local controls to validate reference equations will rarely be practical due to the numbers required. Reference equations derived from large or collated datasets are recommended.
Walton, Emily; Casey, Christy; Mitsch, Jurgen; Vázquez-Diosdado, Jorge A.; Yan, Juan; Dottorini, Tania; Ellis, Keith A.; Winterlich, Anthony
2018-01-01
Automated behavioural classification and identification through sensors has the potential to improve health and welfare of the animals. Position of a sensor, sampling frequency and window size of segmented signal data has a major impact on classification accuracy in activity recognition and energy needs for the sensor, yet, there are no studies in precision livestock farming that have evaluated the effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8, 16 and 32 Hz) of a triaxial accelerometer and gyroscope sensor and window size (3, 5 and 7 s) on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with 44 feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F-score 91%–97%) was obtained using combination of 32 Hz, 7 s and 32 Hz, 5 s for both ear and collar sensors, although, results obtained with 16 Hz and 7 s window were comparable with accuracy of 91%–93% and F-score 88%–95%. Energy efficiency was best at a 7 s window. This suggests that sampling at 16 Hz with 7 s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs. PMID:29515862
Holonic Business Process Modeling in Small to Medium Sized Enterprises
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Nur Budi Mulyono
2012-01-01
Full Text Available Holonic modeling analysis which is the application of system thinking in design, manage, and improvement, is used in a novel context for business process modeling. An approach and techniques of holon and holarchies is presented specifically for small and medium sized enterprise process modeling development. The fitness of the approach is compared with well known reductionist or task breakdown approach. The strength and weaknesses of the holonic modeling is discussed with illustrating case example in term of its suitability for an Indonesia’s small and medium sized industry. The novel ideas in this paper have great impact on the way analyst should perceive business process. Future research is applying the approach in supply chain context.Key words: Business process, holonic modeling, operations management, small to medium sized enterprise
Flaw-size measurement in a weld samples by ultrasonic frequency analysis
International Nuclear Information System (INIS)
Adler, L.; Cook, K.V.; Whaley, H.L. Jr.; McClung, R.W.
1975-01-01
An ultrasonic frequency-analysis technique was developed and applies to characterize flaws in an 8-in. (203-mm) thick heavy-section steel weld specimen. The technique applies a multitransducer system. The spectrum of the received broad-band signal is frequency analyzed at two different receivers for each of the flaws. From the two spectra, the size and orientation of the flaw are determined by the use of an analytic model proposed earlier. (auth)
Soybean yield modeling using bootstrap methods for small samples
Energy Technology Data Exchange (ETDEWEB)
Dalposso, G.A.; Uribe-Opazo, M.A.; Johann, J.A.
2016-11-01
One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters. (Author)
Holonic Business Process Modeling in Small to Medium Sized Enterprises
Nur Budi Mulyono; Tezar Yuliansyah Saputra; Nur Arief Rahmatsyah
2012-01-01
Holonic modeling analysis which is the application of system thinking in design, manage, and improvement, is used in a novel context for business process modeling. An approach and techniques of holon and holarchies is presented specifically for small and medium sized enterprise process modeling development. The fitness of the approach is compared with well known reductionist or task breakdown approach. The strength and weaknesses of the holonic modeling is discussed with illustrating case exa...
Shrinking microbubbles with microfluidics: mathematical modelling to control microbubble sizes.
Salari, A; Gnyawali, V; Griffiths, I M; Karshafian, R; Kolios, M C; Tsai, S S H
2017-11-29
Microbubbles have applications in industry and life-sciences. In medicine, small encapsulated bubbles (methods are distinguished due to their precise control and ease-of-fabrication. Nevertheless, sub-10 μm diameter bubble generation using microfluidics remains challenging, and typically requires expensive equipment and cumbersome setups. Recently, our group reported a microfluidic platform that shrinks microbubbles to sub-10 μm diameters. The microfluidic platform utilizes a simple microbubble-generating flow-focusing geometry, integrated with a vacuum shrinkage system, to achieve microbubble sizes that are desirable in medicine, and pave the way to eventual clinical uptake of microfluidically generated microbubbles. A theoretical framework is now needed to relate the size of the microbubbles produced and the system's input parameters. In this manuscript, we characterize microbubbles made with various lipid concentrations flowing in solutions that have different interfacial tensions, and monitor the changes in bubble size along the microfluidic channel under various vacuum pressures. We use the physics governing the shrinkage mechanism to develop a mathematical model that predicts the resulting bubble sizes and elucidates the dominant parameters controlling bubble sizes. The model shows a good agreement with the experimental data, predicting the resulting microbubble sizes under different experimental input conditions. We anticipate that the model will find utility in enabling users of the microfluidic platform to engineer bubbles of specific sizes.
Sample size for estimating average trunk diameter and plant height in eucalyptus hybrids
Alberto Cargnelutti Filho; Rafael Beltrame; Dilson Antônio Bisognin; Marília Lazarotto; Clovis Roberto Haselein; Darci Alberto Gatto; Gleison Augusto dos Santos
2016-01-01
ABSTRACT: In eucalyptus crops, it is important to determine the number of plants that need to be evaluated for a reliable inference of growth. The aim of this study was to determine the sample size needed to estimate average trunk diameter at breast height and plant height of inter-specific eucalyptus hybrids. In 6,694 plants of twelve inter-specific hybrids it was evaluated trunk diameter at breast height at three (DBH3) and seven years (DBH7) and tree height at seven years (H7) of age. The ...
Magnetic response and critical current properties of mesoscopic-size YBCO superconducting samples
Energy Technology Data Exchange (ETDEWEB)
Lisboa-Filho, P N [UNESP - Universidade Estadual Paulista, Grupo de Materiais Avancados, Departamento de Fisica, Bauru (Brazil); Deimling, C V; Ortiz, W A, E-mail: plisboa@fc.unesp.b [Grupo de Supercondutividade e Magnetismo, Departamento de Fisica, Universidade Federal de Sao Carlos, Sao Carlos (Brazil)
2010-01-15
In this contribution superconducting specimens of YBa{sub 2}Cu{sub 3}O{sub 7-{delta}} were synthesized by a modified polymeric precursor method, yielding a ceramic powder with particles of mesoscopic-size. Samples of this powder were then pressed into pellets and sintered under different conditions. The critical current density was analyzed by isothermal AC-susceptibility measurements as a function of the excitation field, as well as with isothermal DC-magnetization runs at different values of the applied field. Relevant features of the magnetic response could be associated to the microstructure of the specimens and, in particular, to the superconducting intra- and intergranular critical current properties.
An Updated Survey on Statistical Thresholding and Sample Size of fMRI Studies
Directory of Open Access Journals (Sweden)
Andy W. K. Yeung
2018-01-01
Full Text Available Background: Since the early 2010s, the neuroimaging field has paid more attention to the issue of false positives. Several journals have issued guidelines regarding statistical thresholds. Three papers have reported the statistical analysis of the thresholds used in fMRI literature, but they were published at least 3 years ago and surveyed papers published during 2007–2012. This study revisited this topic to evaluate the changes in this field.Methods: The PubMed database was searched to identify the task-based (not resting-state fMRI papers published in 2017 and record their sample sizes, inferential methods (e.g., voxelwise or clusterwise, theoretical methods (e.g., parametric or non-parametric, significance level, cluster-defining primary threshold (CDT, volume of analysis (whole brain or region of interest and software used.Results: The majority (95.6% of the 388 analyzed articles reported statistics corrected for multiple comparisons. A large proportion (69.6% of the 388 articles reported main results by clusterwise inference. The analyzed articles mostly used software Statistical Parametric Mapping (SPM, Analysis of Functional NeuroImages (AFNI, or FMRIB Software Library (FSL to conduct statistical analysis. There were 70.9%, 37.6%, and 23.1% of SPM, AFNI, and FSL studies, respectively, that used a CDT of p ≤ 0.001. The statistical sample size across the articles ranged between 7 and 1,299 with a median of 33. Sample size did not significantly correlate with the level of statistical threshold.Conclusion: There were still around 53% (142/270 studies using clusterwise inference that chose a more liberal CDT than p = 0.001 (n = 121 or did not report their CDT (n = 21, down from around 61% reported by Woo et al. (2014. For FSL studies, it seemed that the CDT practice had no improvement since the survey by Woo et al. (2014. A few studies chose unconventional CDT such as p = 0.0125 or 0.004. Such practice might create an impression that the
Estimating required information size by quantifying diversity in random-effects model meta-analyses
DEFF Research Database (Denmark)
Wetterslev, Jørn; Thorlund, Kristian; Brok, Jesper
2009-01-01
BACKGROUND: There is increasing awareness that meta-analyses require a sufficiently large information size to detect or reject an anticipated intervention effect. The required information size in a meta-analysis may be calculated from an anticipated a priori intervention effect or from...... an intervention effect suggested by trials with low-risk of bias. METHODS: Information size calculations need to consider the total model variance in a meta-analysis to control type I and type II errors. Here, we derive an adjusting factor for the required information size under any random-effects model meta......-trial variability and a sampling error estimate considering the required information size. D2 is different from the intuitively obvious adjusting factor based on the common quantification of heterogeneity, the inconsistency (I2), which may underestimate the required information size. Thus, D2 and I2 are compared...
Strategies for informed sample size reduction in adaptive controlled clinical trials
Arandjelović, Ognjen
2017-12-01
Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. The main goal is to make the process of introducing new medical interventions to patients more efficient. The principal challenge, which is an outstanding research problem, is to be found in the question of how adaptation should be performed so as to minimize the chance of distorting the outcome of the trial. In this paper, we propose a novel method for achieving this. Unlike most of the previously published work, our approach focuses on trial adaptation by sample size adjustment, i.e. by reducing the number of trial participants in a statistically informed manner. Our key idea is to select the sample subset for removal in a manner which minimizes the associated loss of information. We formalize this notion and describe three algorithms which approach the problem in different ways, respectively, using (i) repeated random draws, (ii) a genetic algorithm, and (iii) what we term pair-wise sample compatibilities. Experiments on simulated data demonstrate the effectiveness of all three approaches, with a consistently superior performance exhibited by the pair-wise sample compatibilities-based method.
Elemental analysis of size-fractionated particulate matter sampled in Goeteborg, Sweden
Energy Technology Data Exchange (ETDEWEB)
Wagner, Annemarie [Department of Chemistry, Atmospheric Science, Goeteborg University, SE-412 96 Goeteborg (Sweden)], E-mail: wagnera@chalmers.se; Boman, Johan [Department of Chemistry, Atmospheric Science, Goeteborg University, SE-412 96 Goeteborg (Sweden); Gatari, Michael J. [Institute of Nuclear Science and Technology, University of Nairobi, P.O. Box 30197-00100, Nairobi (Kenya)
2008-12-15
The aim of the study was to investigate the mass distribution of trace elements in aerosol samples collected in the urban area of Goeteborg, Sweden, with special focus on the impact of different air masses and anthropogenic activities. Three measurement campaigns were conducted during December 2006 and January 2007. A PIXE cascade impactor was used to collect particulate matter in 9 size fractions ranging from 16 to 0.06 {mu}m aerodynamic diameter. Polished quartz carriers were chosen as collection substrates for the subsequent direct analysis by TXRF. To investigate the sources of the analyzed air masses, backward trajectories were calculated. Our results showed that diurnal sampling was sufficient to investigate the mass distribution for Br, Ca, Cl, Cu, Fe, K, Sr and Zn, whereas a 5-day sampling period resulted in additional information on mass distribution for Cr and S. Unimodal mass distributions were found in the study area for the elements Ca, Cl, Fe and Zn, whereas the distributions for Br, Cu, Cr, K, Ni and S were bimodal, indicating high temperature processes as source of the submicron particle components. The measurement period including the New Year firework activities showed both an extensive increase in concentrations as well as a shift to the submicron range for K and Sr, elements that are typically found in fireworks. Further research is required to validate the quantification of trace elements directly collected on sample carriers.
Directory of Open Access Journals (Sweden)
Ya Li
2012-12-01
Full Text Available In order to prepare a high capacity packing material for solid-phase extraction with specific recognition ability of trace ractopamine in biological samples, uniformly-sized, molecularly imprinted polymers (MIPs were prepared by a multi-step swelling and polymerization method using methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a cross-linker, and toluene as a porogen respectively. Scanning electron microscope and specific surface area were employed to identify the characteristics of MIPs. Ultraviolet spectroscopy, Fourier transform infrared spectroscopy, Scatchard analysis and kinetic study were performed to interpret the specific recognition ability and the binding process of MIPs. The results showed that, compared with other reports, MIPs synthetized in this study showed high adsorption capacity besides specific recognition ability. The adsorption capacity of MIPs was 0.063Â mmol/g at 1Â mmol/L ractopamine concentration with the distribution coefficient 1.70. The resulting MIPs could be used as solid-phase extraction materials for separation and enrichment of trace ractopamine in biological samples. Keywords: Ractopamine, Uniformly-sized molecularly imprinted polymers, Solid-phase extraction, Multi-step swelling and polymerization, Separation and enrichment
Dziak, John J.; Nahum-Shani, Inbal; Collins, Linda M.
2012-01-01
Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions, by helping investigators to screen several candidate intervention components simultaneously and decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or employees within organizations). In this article we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements such as the number of clusters, the number of lower-level units, and the intraclass correlation affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes, because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation. PMID:22309956
Dziak, John J; Nahum-Shani, Inbal; Collins, Linda M
2012-06-01
Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or when employees are nested within organizations). In this article, we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel, multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements-such as the number of clusters, the number of lower-level units, and the intraclass correlation-affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation. (c) 2012 APA, all rights reserved
Modeling the Effect of Tumor Size in Early Breast Cancer
Verschraegen, Claire; Vinh-Hung, Vincent; Cserni, Gábor; Gordon, Richard; Royce, Melanie E.; Vlastos, Georges; Tai, Patricia; Storme, Guy
2005-01-01
Summary Background Data: The purpose of this study was to determine the type of relationship between tumor size and mortality in early breast carcinoma. Methods: The data was abstracted from 83,686 cases registered in the Surveillance, Epidemiology, and End Results Program of women diagnosed with primary breast carcinoma between 1988 and 1997 presenting with a T1–T2 lesion and no metastasis in whom axillary node dissection was performed: 58,070 women were node-negative (N0) and 25,616 were node-positive (N+). End point was death from any cause. Tumor size was modeled as a continuous variable by proportional hazards using a generalized additive models procedure. Results: Functionally, a Gompertzian expression exp(-exp(-(size-15)/10)) provided a good fit to the effect of tumor size (in millimeters) on mortality, irrespective of nodal status. Quantitatively, for tumor size between 3 and 50 mm, the increase of crude cumulative death rate (number of observed deaths divided by the number of patients at risk) increased with size from 10% to 25% for N0 and from 20% to 40% for N+. Conclusions: The functional relationship of tumor size with mortality is concordant with current knowledge of tumor growth. However, its qualitative and quantitative independence of nodal status is in contradiction with the prevailing concept of sequential disease progression from primary tumor to regional nodes. This argues against the perception that nodal metastases are caused by the primary tumor. PMID:15650642
Friede, Tim; Kieser, Meinhard
2013-01-01
The internal pilot study design allows for modifying the sample size during an ongoing study based on a blinded estimate of the variance thus maintaining the trial integrity. Various blinded sample size re-estimation procedures have been proposed in the literature. We compare the blinded sample size re-estimation procedures based on the one-sample variance of the pooled data with a blinded procedure using the randomization block information with respect to bias and variance of the variance estimators, and the distribution of the resulting sample sizes, power, and actual type I error rate. For reference, sample size re-estimation based on the unblinded variance is also included in the comparison. It is shown that using an unbiased variance estimator (such as the one using the randomization block information) for sample size re-estimation does not guarantee that the desired power is achieved. Moreover, in situations that are common in clinical trials, the variance estimator that employs the randomization block length shows a higher variability than the simple one-sample estimator and in turn the sample size resulting from the related re-estimation procedure. This higher variability can lead to a lower power as was demonstrated in the setting of noninferiority trials. In summary, the one-sample estimator obtained from the pooled data is extremely simple to apply, shows good performance, and is therefore recommended for application. Copyright © 2013 John Wiley & Sons, Ltd.
On population size estimators in the Poisson mixture model.
Mao, Chang Xuan; Yang, Nan; Zhong, Jinhua
2013-09-01
Estimating population sizes via capture-recapture experiments has enormous applications. The Poisson mixture model can be adopted for those applications with a single list in which individuals appear one or more times. We compare several nonparametric estimators, including the Chao estimator, the Zelterman estimator, two jackknife estimators and the bootstrap estimator. The target parameter of the Chao estimator is a lower bound of the population size. Those of the other four estimators are not lower bounds, and they may produce lower confidence limits for the population size with poor coverage probabilities. A simulation study is reported and two examples are investigated. © 2013, The International Biometric Society.
Patel, Nitin R; Ankolekar, Suresh
2007-11-30
Classical approaches to clinical trial design ignore economic factors that determine economic viability of a new drug. We address the choice of sample size in Phase III trials as a decision theory problem using a hybrid approach that takes a Bayesian view from the perspective of a drug company and a classical Neyman-Pearson view from the perspective of regulatory authorities. We incorporate relevant economic factors in the analysis to determine the optimal sample size to maximize the expected profit for the company. We extend the analysis to account for risk by using a 'satisficing' objective function that maximizes the chance of meeting a management-specified target level of profit. We extend the models for single drugs to a portfolio of clinical trials and optimize the sample sizes to maximize the expected profit subject to budget constraints. Further, we address the portfolio risk and optimize the sample sizes to maximize the probability of achieving a given target of expected profit.
Dong, H.; Zhang, H.; Zuo, Y.; Gao, P.; Ye, G.
2018-01-01
Mercury intrusion porosimetry (MIP) measurements are widely used to determine pore throat size distribution (PSD) curves of porous materials. The pore throat size of porous materials has been used to estimate their compressive strength and air permeability. However, the effect of sample size on
Candel, Math J J M; Van Breukelen, Gerard J P
2010-06-30
Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.
Heo, Moonseong; Kim, Namhee; Rinke, Michael L; Wylie-Rosett, Judith
2018-02-01
Stepped-wedge (SW) designs have been steadily implemented in a variety of trials. A SW design typically assumes a three-level hierarchical data structure where participants are nested within times or periods which are in turn nested within clusters. Therefore, statistical models for analysis of SW trial data need to consider two correlations, the first and second level correlations. Existing power functions and sample size determination formulas had been derived based on statistical models for two-level data structures. Consequently, the second-level correlation has not been incorporated in conventional power analyses. In this paper, we derived a closed-form explicit power function based on a statistical model for three-level continuous outcome data. The power function is based on a pooled overall estimate of stratified cluster-specific estimates of an intervention effect. The sampling distribution of the pooled estimate is derived by applying a fixed-effect meta-analytic approach. Simulation studies verified that the derived power function is unbiased and can be applicable to varying number of participants per period per cluster. In addition, when data structures are assumed to have two levels, we compare three types of power functions by conducting additional simulation studies under a two-level statistical model. In this case, the power function based on a sampling distribution of a marginal, as opposed to pooled, estimate of the intervention effect performed the best. Extensions of power functions to binary outcomes are also suggested.
Feng, Hao-chuan; Zhang, Wei; Zhu, Yu-liang; Lei, Zhi-yi; Ji, Xiao-mei
2017-06-01
Particle size distributions (PSDs) of bottom sediments in a coastal zone are generally multimodal due to the complexity of the dynamic environment. In this paper, bottom sediments along the deep channel of the Pearl River Estuary (PRE) are used to understand the multimodal PSDs' characteristics and the corresponding depositional environment. The results of curve-fitting analysis indicate that the near-bottom sediments in the deep channel generally have a bimodal distribution with a fine component and a relatively coarse component. The particle size distribution of bimodal sediment samples can be expressed as the sum of two lognormal functions and the parameters for each component can be determined. At each station of the PRE, the fine component makes up less volume of the sediments and is relatively poorly sorted. The relatively coarse component, which is the major component of the sediments, is even more poorly sorted. The interrelations between the dynamics and particle size of the bottom sediment in the deep channel of the PRE have also been investigated by the field measurement and simulated data. The critical shear velocity and the shear velocity are calculated to study the stability of the deep channel. The results indicate that the critical shear velocity has a similar distribution over large part of the deep channel due to the similar particle size distribution of sediments. Based on a comparison between the critical shear velocities derived from sedimentary parameters and the shear velocities obtained by tidal currents, it is likely that the depositional area is mainly distributed in the northern part of the channel, while the southern part of the deep channel has to face higher erosion risk.
Saccenti, Edoardo; Timmerman, Marieke E.
2016-01-01
Sample size determination is a fundamental step in the design of experiments. Methods for sample size determination are abundant for univariate analysis methods, but scarce in the multivariate case. Omics data are multivariate in nature and are commonly investi-gated using multivariate statistical
Cloud Computing Adoption Business Model Factors: Does Enterprise Size Matter?
Bogataj Habjan, Kristina; Pucihar, Andreja
2017-01-01
This paper presents the results of research investigating the impact of business model factors on cloud computing adoption. The introduced research model consists of 40 cloud computing business model factors, grouped into eight factor groups. Their impact and importance for cloud computing adoption were investigated among enterpirses in Slovenia. Furthermore, differences in opinion according to enterprise size were investigated. Research results show no statistically significant impacts of in...
An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies
DEFF Research Database (Denmark)
Thompson, Wesley K.; Wang, Yunpeng; Schork, Andrew J.
2015-01-01
minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local...... for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome...... analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While...
McClure, Leslie A; Szychowski, Jeff M; Benavente, Oscar; Hart, Robert G; Coffey, Christopher S
2016-10-01
The use of adaptive designs has been increasing in randomized clinical trials. Sample size re-estimation is a type of adaptation in which nuisance parameters are estimated at an interim point in the trial and the sample size re-computed based on these estimates. The Secondary Prevention of Small Subcortical Strokes study was a randomized clinical trial assessing the impact of single- versus dual-antiplatelet therapy and control of systolic blood pressure to a higher (130-149 mmHg) versus lower (size re-estimation was performed during the Secondary Prevention of Small Subcortical Strokes study resulting in an increase from the planned sample size of 2500-3020, and we sought to determine the impact of the sample size re-estimation on the study results. We assessed the results of the primary efficacy and safety analyses with the full 3020 patients and compared them to the results that would have been observed had randomization ended with 2500 patients. The primary efficacy outcome considered was recurrent stroke, and the primary safety outcomes were major bleeds and death. We computed incidence rates for the efficacy and safety outcomes and used Cox proportional hazards models to examine the hazard ratios for each of the two treatment interventions (i.e. the antiplatelet and blood pressure interventions). In the antiplatelet intervention, the hazard ratio was not materially modified by increasing the sample size, nor did the conclusions regarding the efficacy of mono versus dual-therapy change: there was no difference in the effect of dual- versus monotherapy on the risk of recurrent stroke hazard ratios (n = 3020 HR (95% confidence interval): 0.92 (0.72, 1.2), p = 0.48; n = 2500 HR (95% confidence interval): 1.0 (0.78, 1.3), p = 0.85). With respect to the blood pressure intervention, increasing the sample size resulted in less certainty in the results, as the hazard ratio for higher versus lower systolic blood pressure target approached, but did not
Effects of sample size on estimation of rainfall extremes at high temperatures
Boessenkool, Berry; Bürger, Gerd; Heistermann, Maik
2017-09-01
High precipitation quantiles tend to rise with temperature, following the so-called Clausius-Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.
Effects of sample size on estimation of rainfall extremes at high temperatures
Directory of Open Access Journals (Sweden)
B. Boessenkool
2017-09-01
Full Text Available High precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.
Stochastic Modeling of Bacteria Cell Size Control and Homeostasis
Chen, Yanyan; Buceta, Javier
Besides recent breakthroughs, there is a gap of knowledge about the mechanisms underlying cell size control and homeostasis. In this context, recent studies support the incremental rule in rod-shaped bacteria: cells add a constant length to their size before dividing which is independent of their size at birth. This growing pattern, when coupled with the mid-cell division mechanism, leads to size convergence and homeostasis. However, some aberrantly long mutant strains of E. coli, e.g. ΔFtsW, do not typically divide at the middle. Whether cell size control and homeostasis apply to those mutant backgrounds, or the role played by biomechanical cues, remain open questions. Here we present a combination of theoretical, experimental, and computational approaches to address these questions. First, we introduce a Markov chain model that describes either wild-type (wt) strains or growth-defective strains. Second, we propose a polymer-like model to account for the mechanical inputs. Finally, we test experimentally some of our predictions by using wt and conditional mutant (ΔFtsW) strains. Altogether, our preliminary studies suggest a way to unify the principles of cell size control and homeostasis of wt and growth-defective cell strains.
Wellek, Stefan
2017-09-10
In clinical trials using lifetime as primary outcome variable, it is more the rule than the exception that even for patients who are failing in the course of the study, survival time does not become known exactly since follow-up takes place according to a restricted schedule with fixed, possibly long intervals between successive visits. In practice, the discreteness of the data obtained under such circumstances is plainly ignored both in data analysis and in sample size planning of survival time studies. As a framework for analyzing the impact of making no difference between continuous and discrete recording of failure times, we use a scenario in which the partially observed times are assigned to the points of the grid of inspection times in the natural way. Evaluating the treatment effect in a two-arm trial fitting into this framework by means of ordinary methods based on Cox's relative risk model is shown to produce biased estimates and/or confidence bounds whose actual coverage exhibits marked discrepancies from the nominal confidence level. Not surprisingly, the amount of these distorting effects turns out to be the larger the coarser the grid of inspection times has been chosen. As a promising approach to correctly analyzing and planning studies generating discretely recorded failure times, we use large-sample likelihood theory for parametric models accommodating the key features of the scenario under consideration. The main result is an easily implementable representation of the expected information and hence of the asymptotic covariance matrix of the maximum likelihood estimators of all parameters contained in such a model. In two real examples of large-scale clinical trials, sample size calculation based on this result is contrasted with the traditional approach, which consists of applying the usual methods for exactly observed failure times. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Bayesian assurance and sample size determination in the process validation life-cycle.
Faya, Paul; Seaman, John W; Stamey, James D
2017-01-01
Validation of pharmaceutical manufacturing processes is a regulatory requirement and plays a key role in the assurance of drug quality, safety, and efficacy. The FDA guidance on process validation recommends a life-cycle approach which involves process design, qualification, and verification. The European Medicines Agency makes similar recommendations. The main purpose of process validation is to establish scientific evidence that a process is capable of consistently delivering a quality product. A major challenge faced by manufacturers is the determination of the number of batches to be used for the qualification stage. In this article, we present a Bayesian assurance and sample size determination approach where prior process knowledge and data are used to determine the number of batches. An example is presented in which potency uniformity data is evaluated using a process capability metric. By using the posterior predictive distribution, we simulate qualification data and make a decision on the number of batches required for a desired level of assurance.
A bootstrap test for comparing two variances: simulation of size and power in small samples.
Sun, Jiajing; Chernick, Michael R; LaBudde, Robert A
2011-11-01
An F statistic was proposed by Good and Chernick ( 1993 ) in an unpublished paper, to test the hypothesis of the equality of variances from two independent groups using the bootstrap; see Hall and Padmanabhan ( 1997 ), for a published reference where Good and Chernick ( 1993 ) is discussed. We look at various forms of bootstrap tests that use the F statistic to see whether any or all of them maintain the nominal size of the test over a variety of population distributions when the sample size is small. Chernick and LaBudde ( 2010 ) and Schenker ( 1985 ) showed that bootstrap confidence intervals for variances tend to provide considerably less coverage than their theoretical asymptotic coverage for skewed population distributions such as a chi-squared with 10 degrees of freedom or less or a log-normal distribution. The same difficulties may be also be expected when looking at the ratio of two variances. Since bootstrap tests are related to constructing confidence intervals for the ratio of variances, we simulated the performance of these tests when the population distributions are gamma(2,3), uniform(0,1), Student's t distribution with 10 degrees of freedom (df), normal(0,1), and log-normal(0,1) similar to those used in Chernick and LaBudde ( 2010 ). We find, surprisingly, that the results for the size of the tests are valid (reasonably close to the asymptotic value) for all the various bootstrap tests. Hence we also conducted a power comparison, and we find that bootstrap tests appear to have reasonable power for testing equivalence of variances.
Lejoly, Cassandra; Howell, Ellen S.; Taylor, Patrick A.; Springmann, Alessondra; Virkki, Anne; Nolan, Michael C.; Rivera-Valentin, Edgard G.; Benner, Lance A. M.; Brozovic, Marina; Giorgini, Jon D.
2017-10-01
The Near-Earth Asteroid (NEA) population ranges in size from a few meters to more than 10 kilometers. NEAs have a wide variety of taxonomic classes, surface features, and shapes, including spheroids, binary objects, contact binaries, elongated, as well as irregular bodies. Using the Arecibo Observatory planetary radar system, we have measured apparent rotation rate, radar reflectivity, apparent diameter, and radar albedos for over 350 NEAs. The radar albedo is defined as the radar cross-section divided by the geometric cross-section. If a shape model is available, the actual cross-section is known at the time of the observation. Otherwise we derive a geometric cross-section from a measured diameter. When radar imaging is available, the diameter was measured from the apparent range depth. However, when radar imaging was not available, we used the continuous wave (CW) bandwidth radar measurements in conjunction with the period of the object. The CW bandwidth provides apparent rotation rate, which, given an independent rotation measurement, such as from lightcurves, constrains the size of the object. We assumed an equatorial view unless we knew the pole orientation, which gives a lower limit on the diameter. The CW also provides the polarization ratio, which is the ratio of the SC and OC cross-sections.We confirm the trend found by Benner et al. (2008) that taxonomic types E and V have very high polarization ratios. We have obtained a larger sample and can analyze additional trends with spin, size, rotation rate, taxonomic class, polarization ratio, and radar albedo to interpret the origin of the NEAs and their dynamical processes. The distribution of radar albedo and polarization ratio at the smallest diameters (≤50 m) differs from the distribution of larger objects (>50 m), although the sample size is limited. Additionally, we find more moderate radar albedos for the smallest NEAs when compared to those with diameters 50-150 m. We will present additional trends we
Lai, Xiaoming; Zhu, Qing; Zhou, Zhiwen; Liao, Kaihua
2017-12-01
In this study, seven random combination sampling strategies were applied to investigate the uncertainties in estimating the hillslope mean soil water content (SWC) and correlation coefficients between the SWC and soil/terrain properties on a tea + bamboo hillslope. One of the sampling strategies is the global random sampling and the other six are the stratified random sampling on the top, middle, toe, top + mid, top + toe and mid + toe slope positions. When each sampling strategy was applied, sample sizes were gradually reduced and each sampling size contained 3000 replicates. Under each sampling size of each sampling strategy, the relative errors (REs) and coefficients of variation (CVs) of the estimated hillslope mean SWC and correlation coefficients between the SWC and soil/terrain properties were calculated to quantify the accuracy and uncertainty. The results showed that the uncertainty of the estimations decreased as the sampling size increasing. However, larger sample sizes were required to reduce the uncertainty in correlation coefficient estimation than in hillslope mean SWC estimation. Under global random sampling, 12 randomly sampled sites on this hillslope were adequate to estimate the hillslope mean SWC with RE and CV ≤10%. However, at least 72 randomly sampled sites were needed to ensure the estimated correlation coefficients with REs and CVs ≤10%. Comparing with all sampling strategies, reducing sampling sites on the middle slope had the least influence on the estimation of hillslope mean SWC and correlation coefficients. Under this strategy, 60 sites (10 on the middle slope and 50 on the top and toe slopes) were enough to ensure the estimated correlation coefficients with REs and CVs ≤10%. This suggested that when designing the SWC sampling, the proportion of sites on the middle slope can be reduced to 16.7% of the total number of sites. Findings of this study will be useful for the optimal SWC sampling design.
Linear Model for Optimal Distributed Generation Size Predication
Directory of Open Access Journals (Sweden)
Ahmed Al Ameri
2017-01-01
Full Text Available This article presents a linear model predicting optimal size of Distributed Generation (DG that addresses the minimum power loss. This method is based fundamentally on strong coupling between active power and voltage angle as well as between reactive power and voltage magnitudes. This paper proposes simplified method to calculate the total power losses in electrical grid for different distributed generation sizes and locations. The method has been implemented and tested on several IEEE bus test systems. The results show that the proposed method is capable of predicting approximate optimal size of DG when compared with precision calculations. The method that linearizes a complex model showed a good result, which can actually reduce processing time required. The acceptable accuracy with less time and memory required can help the grid operator to assess power system integrated within large-scale distribution generation.
Hua, Xue; Hibar, Derrek P; Ching, Christopher R K; Boyle, Christina P; Rajagopalan, Priya; Gutman, Boris A; Leow, Alex D; Toga, Arthur W; Jack, Clifford R; Harvey, Danielle; Weiner, Michael W; Thompson, Paul M
2013-02-01
Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. Copyright © 2012 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Daniel Vasiliu
Full Text Available Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED. Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
The economic production lot size model with several production rates
DEFF Research Database (Denmark)
Larsen, Christian
We study an extension of the economic production lot size model, where more than one production rate can be used during a cycle. The production rates and their corresponding runtimes are decision variables. We decompose the problem into two subproblems. First, we show that all production rates...
Determining the optimum cell size of digital elevation model for ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 120; Issue 4. Determining the optimum cell size of digital elevation model for hydrologic ... Technology, Bahal 127 028, Bhiwani, Haryana, India. Agricultural & Food Engineering Department, Indian Institute of Technology, Kharagpur-721302, West Bengal, India.
Finite-Size Effects for Some Bootstrap Percolation Models
Enter, A.C.D. van; Adler, Joan; Duarte, J.A.M.S.
The consequences of Schonmann's new proof that the critical threshold is unity for certain bootstrap percolation models are explored. It is shown that this proof provides an upper bound for the finite-size scaling in these systems. Comparison with data for one case demonstrates that this scaling
A dynamic lot-sizing model with demand time windows
C.Y. Lee (Chung-Yee); S. Cetinkaya; A.P.M. Wagelmans (Albert)
1999-01-01
textabstractOne of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed then the demand of a given period cannot be delivered earlier or later than the
A Dynamic Lot-Sizing Model with Demand Time Windows
C.Y. Lee (Chung-Yee); S. Çetinkaya; A.P.M. Wagelmans (Albert)
1999-01-01
textabstractOne of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed then the demand of a given period cannot be delivered earlier or later than the
Determining the optimum cell size of digital elevation model for ...
Indian Academy of Sciences (India)
obtained DEMs were explored for their intrinsic quality using four different methods, i.e., sink analy- sis, fractal dimension of derived stream network, entropy measurement and ... ters decrease, and many delicate landscape fea- tures are lost. However, as one can understand, it is not enough to model the cell size effects.
Discriminating Models of Different Sized Color Geometric Figures
Directory of Open Access Journals (Sweden)
Y A Chudina
2013-12-01
Full Text Available The data obtained by us in two experiments, where the different sized color geometric figures were used as stimuli, are described in the article. We have built configurative and categorical discriminating models differing by formal and intensional characteristics. The models reflect the different ways of visual gestalt generation: additive and non-additive principles of consolidation of neural mechanisms analyzing the visual features of the picture.
Parabolic Free Boundary Price Formation Models Under Market Size Fluctuations
Markowich, Peter A.
2016-10-04
In this paper we propose an extension of the Lasry-Lions price formation model which includes uctuations of the numbers of buyers and vendors. We analyze the model in the case of deterministic and stochastic market size uctuations and present results on the long time asymptotic behavior and numerical evidence and conjectures on periodic, almost periodic, and stochastic uctuations. The numerical simulations extend the theoretical statements and give further insights into price formation dynamics.
Directory of Open Access Journals (Sweden)
Shaukat S. Shahid
2016-06-01
Full Text Available In this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50 on the eigenvalues and eigenvectors resulting from principal component analysis (PCA. For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22 of a small data set comprising of 55 samples (stations from where water samples were collected. Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.
Li, Ya; Fu, Qiang; Liu, Meng; Jiao, Yuan-Yuan; Du, Wei; Yu, Chong; Liu, Jing; Chang, Chun; Lu, Jian
2012-01-01
In order to prepare a high capacity packing material for solid-phase extraction with specific recognition ability of trace ractopamine in biological samples, uniformly-sized, molecularly imprinted polymers (MIPs) were prepared by a multi-step swelling and polymerization method using methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a cross-linker, and toluene as a porogen respectively. Scanning electron microscope and specific surface area were employed to identify the characteristics of MIPs. Ultraviolet spectroscopy, Fourier transform infrared spectroscopy, Scatchard analysis and kinetic study were performed to interpret the specific recognition ability and the binding process of MIPs. The results showed that, compared with other reports, MIPs synthetized in this study showed high adsorption capacity besides specific recognition ability. The adsorption capacity of MIPs was 0.063 mmol/g at 1 mmol/L ractopamine concentration with the distribution coefficient 1.70. The resulting MIPs could be used as solid-phase extraction materials for separation and enrichment of trace ractopamine in biological samples. PMID:29403774
Directory of Open Access Journals (Sweden)
Łącka Katarzyna
2016-03-01
Full Text Available Introduction: The aim of this study was to propose the optimal methodology for stallion semen morphology analysis while taking into consideration the staining method, the microscopic techniques, and the workload generated by a number of samples. Material and Methods: Ejaculates from eight pure-bred Arabian horses were tested microscopically for the incidence of morphological defects in the spermatozoa. Two different staining methods (eosin-nigrosin and eosin-gentian dye, two different techniques of microscopic analysis (1000× and 400× magnifications, and two sample sizes (200 and 500 spermatozoa were used. Results: Well-formed spermatozoa and those with major and minor defects according to Blom’s classification were identified. The applied staining methods gave similar results and could be used in stallion sperm morphology analysis. However, the eosin-nigrosin method was more recommendable, because it allowed to limit the number of visible artefacts without hindering the identification of protoplasm drops and enables the differentiation of living and dead spermatozoa. Conclusion: The applied microscopic techniques proved to be equally efficacious. Therefore, it is practically possible to opt for the simpler and faster 400x technique of analysing sperm morphology to examine stallion semen. We also found that the number of spermatozoa clearly affects the results of sperm morphology evaluation. Reducing the number of spermatozoa from 500 to 200 causes a decrease in the percentage of spermatozoa identified as normal and an increase in the percentage of spermatozoa determined as morphologically defective.
Weighted piecewise LDA for solving the small sample size problem in face verification.
Kyperountas, Marios; Tefas, Anastasios; Pitas, Ioannis
2007-03-01
A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance.
DEFF Research Database (Denmark)
Gerke, Oke; Poulsen, Mads Hvid; Bouchelouche, Kirsten
2009-01-01
/CT also performs well in adjacent areas, then sample sizes in accuracy studies can be reduced. PROCEDURES: Traditional standard power calculations for demonstrating sensitivities of both 80% and 90% are shown. The argument is then described in general terms and demonstrated by an ongoing study...... of metastasized prostate cancer. RESULTS: An added value in accuracy of PET/CT in adjacent areas can outweigh a downsized target level of accuracy in the gold standard region, justifying smaller sample sizes. CONCLUSIONS: If PET/CT provides an accuracy benefit in adjacent regions, then sample sizes can be reduced...
Size-exclusion chromatography-based enrichment of extracellular vesicles from urine samples.
Lozano-Ramos, Inés; Bancu, Ioana; Oliveira-Tercero, Anna; Armengol, María Pilar; Menezes-Neto, Armando; Del Portillo, Hernando A; Lauzurica-Valdemoros, Ricardo; Borràs, Francesc E
2015-01-01
Renal biopsy is the gold-standard procedure to diagnose most of renal pathologies. However, this invasive method is of limited repeatability and often describes an irreversible renal damage. Urine is an easily accessible fluid and urinary extracellular vesicles (EVs) may be ideal to describe new biomarkers associated with renal pathologies. Several methods to enrich EVs have been described. Most of them contain a mixture of proteins, lipoproteins and cell debris that may be masking relevant biomarkers. Here, we evaluated size-exclusion chromatography (SEC) as a suitable method to isolate urinary EVs. Following a conventional centrifugation to eliminate cell debris and apoptotic bodies, urine samples were concentrated using ultrafiltration and loaded on a SEC column. Collected fractions were analysed by protein content and flow cytometry to determine the presence of tetraspanin markers (CD63 and CD9). The highest tetraspanin content was routinely detected in fractions well before the bulk of proteins eluted. These tetraspanin-peak fractions were analysed by cryo-electron microscopy (cryo-EM) and nanoparticle tracking analysis revealing the presence of EVs.When analysed by sodium dodecyl sulphate-polyacrylamide gel electrophoresis, tetraspanin-peak fractions from urine concentrated samples contained multiple bands but the main urine proteins (such as Tamm-Horsfall protein) were absent. Furthermore, a preliminary proteomic study of these fractions revealed the presence of EV-related proteins, suggesting their enrichment in concentrated samples. In addition, RNA profiling also showed the presence of vesicular small RNA species.To summarize, our results demonstrated that concentrated urine followed by SEC is a suitable option to isolate EVs with low presence of soluble contaminants. This methodology could permit more accurate analyses of EV-related biomarkers when further characterized by -omics technologies compared with other approaches.
Size-exclusion chromatography-based enrichment of extracellular vesicles from urine samples
Directory of Open Access Journals (Sweden)
Inés Lozano-Ramos
2015-05-01
Full Text Available Renal biopsy is the gold-standard procedure to diagnose most of renal pathologies. However, this invasive method is of limited repeatability and often describes an irreversible renal damage. Urine is an easily accessible fluid and urinary extracellular vesicles (EVs may be ideal to describe new biomarkers associated with renal pathologies. Several methods to enrich EVs have been described. Most of them contain a mixture of proteins, lipoproteins and cell debris that may be masking relevant biomarkers. Here, we evaluated size-exclusion chromatography (SEC as a suitable method to isolate urinary EVs. Following a conventional centrifugation to eliminate cell debris and apoptotic bodies, urine samples were concentrated using ultrafiltration and loaded on a SEC column. Collected fractions were analysed by protein content and flow cytometry to determine the presence of tetraspanin markers (CD63 and CD9. The highest tetraspanin content was routinely detected in fractions well before the bulk of proteins eluted. These tetraspanin-peak fractions were analysed by cryo-electron microscopy (cryo-EM and nanoparticle tracking analysis revealing the presence of EVs.When analysed by sodium dodecyl sulphate–polyacrylamide gel electrophoresis, tetraspanin-peak fractions from urine concentrated samples contained multiple bands but the main urine proteins (such as Tamm–Horsfall protein were absent. Furthermore, a preliminary proteomic study of these fractions revealed the presence of EV-related proteins, suggesting their enrichment in concentrated samples. In addition, RNA profiling also showed the presence of vesicular small RNA species.To summarize, our results demonstrated that concentrated urine followed by SEC is a suitable option to isolate EVs with low presence of soluble contaminants. This methodology could permit more accurate analyses of EV-related biomarkers when further characterized by -omics technologies compared with other approaches.
Multivariate Effect Size Estimation: Confidence Interval Construction via Latent Variable Modeling
Raykov, Tenko; Marcoulides, George A.
2010-01-01
A latent variable modeling method is outlined for constructing a confidence interval (CI) of a popular multivariate effect size measure. The procedure uses the conventional multivariate analysis of variance (MANOVA) setup and is applicable with large samples. The approach provides a population range of plausible values for the proportion of…
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Matthew Nahorniak
Full Text Available In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the sampling design phase. Questions may arise requiring the use of model based statistical tools such as multiple regression, quantile regression, or regression tree analysis. However, such model based tools may require, for ensuring unbiased estimation, data from simple random samples, which can be problematic when analyzing data from unequal probability designs. Despite numerous method specific tools available to properly account for sampling design, too often in the analysis of ecological data, sample design is ignored and consequences are not properly considered. We demonstrate here that violation of this assumption can lead to biased parameter estimates in ecological research. In addition, to the set of tools available for researchers to properly account for sampling design in model based analysis, we introduce inverse probability bootstrapping (IPB. Inverse probability bootstrapping is an easily implemented method for obtaining equal probability re-samples from a probability sample, from which unbiased model based estimates can be made. We demonstrate the potential for bias in model-based analyses that ignore sample inclusion probabilities, and the effectiveness of IPB sampling in eliminating this bias, using both simulated and actual ecological data. For illustration, we considered three model based analysis tools--linear regression, quantile regression, and boosted regression tree analysis. In all models, using both simulated and actual ecological data, we
The critical domain size of stochastic population models.
Reimer, Jody R; Bonsall, Michael B; Maini, Philip K
2017-02-01
Identifying the critical domain size necessary for a population to persist is an important question in ecology. Both demographic and environmental stochasticity impact a population's ability to persist. Here we explore ways of including this variability. We study populations with distinct dispersal and sedentary stages, which have traditionally been modelled using a deterministic integrodifference equation (IDE) framework. Individual-based models (IBMs) are the most intuitive stochastic analogues to IDEs but yield few analytic insights. We explore two alternate approaches; one is a scaling up to the population level using the Central Limit Theorem, and the other a variation on both Galton-Watson branching processes and branching processes in random environments. These branching process models closely approximate the IBM and yield insight into the factors determining the critical domain size for a given population subject to stochasticity.
Clark, Timothy; Berger, Ursula; Mansmann, Ulrich
2013-03-21
To assess the completeness of reporting of sample size determinations in unpublished research protocols and to develop guidance for research ethics committees and for statisticians advising these committees. Review of original research protocols. Unpublished research protocols for phase IIb, III, and IV randomised clinical trials of investigational medicinal products submitted to research ethics committees in the United Kingdom during 1 January to 31 December 2009. Completeness of reporting of the sample size determination, including the justification of design assumptions, and disagreement between reported and recalculated sample size. 446 study protocols were reviewed. Of these, 190 (43%) justified the treatment effect and 213 (48%) justified the population variability or survival experience. Only 55 (12%) discussed the clinical importance of the treatment effect sought. Few protocols provided a reasoned explanation as to why the design assumptions were plausible for the planned study. Sensitivity analyses investigating how the sample size changed under different design assumptions were lacking; six (1%) protocols included a re-estimation of the sample size in the study design. Overall, 188 (42%) protocols reported all of the information to accurately recalculate the sample size; the assumed withdrawal or dropout rate was not given in 177 (40%) studies. Only 134 of the 446 (30%) sample size calculations could be accurately reproduced. Study size tended to be over-estimated rather than under-estimated. Studies with non-commercial sponsors justified the design assumptions used in the calculation more often than studies with commercial sponsors but less often reported all the components needed to reproduce the sample size calculation. Sample sizes for studies with non-commercial sponsors were less often reproduced. Most research protocols did not contain sufficient information to allow the sample size to be reproduced or the plausibility of the design assumptions to
Size reduction techniques for vital compliant VHDL simulation models
Rich, Marvin J.; Misra, Ashutosh
2006-08-01
A method and system select delay values from a VHDL standard delay file that correspond to an instance of a logic gate in a logic model. Then the system collects all the delay values of the selected instance and builds super generics for the rise-time and the fall-time of the selected instance. Then, the system repeats this process for every delay value in the standard delay file (310) that correspond to every instance of every logic gate in the logic model. The system then outputs a reduced size standard delay file (314) containing the super generics for every instance of every logic gate in the logic model.
Sevelius, Jae M.
2017-01-01
Background. Transgender individuals have a gender identity that differs from the sex they were assigned at birth. The population size of transgender individuals in the United States is not well-known, in part because official records, including the US Census, do not include data on gender identity. Population surveys today more often collect transgender-inclusive gender-identity data, and secular trends in culture and the media have created a somewhat more favorable environment for transgender people. Objectives. To estimate the current population size of transgender individuals in the United States and evaluate any trend over time. Search methods. In June and July 2016, we searched PubMed, Cumulative Index to Nursing and Allied Health Literature, and Web of Science for national surveys, as well as “gray” literature, through an Internet search. We limited the search to 2006 through 2016. Selection criteria. We selected population-based surveys that used probability sampling and included self-reported transgender-identity data. Data collection and analysis. We used random-effects meta-analysis to pool eligible surveys and used meta-regression to address our hypothesis that the transgender population size estimate would increase over time. We used subsample and leave-one-out analysis to assess for bias. Main results. Our meta-regression model, based on 12 surveys covering 2007 to 2015, explained 62.5% of model heterogeneity, with a significant effect for each unit increase in survey year (F = 17.122; df = 1,10; b = 0.026%; P = .002). Extrapolating these results to 2016 suggested a current US population size of 390 adults per 100 000, or almost 1 million adults nationally. This estimate may be more indicative for younger adults, who represented more than 50% of the respondents in our analysis. Authors’ conclusions. Future national surveys are likely to observe higher numbers of transgender people. The large variety in questions used to ask
EMPIRICAL MODEL FOR HYDROCYCLONES CORRECTED CUT SIZE CALCULATION
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André Carlos Silva
2012-12-01
Full Text Available Hydrocyclones are devices worldwide used in mineral processing for desliming, classification, selective classification, thickening and pre-concentration. A hydrocyclone is composed by one cylindrical and one conical section joint together, without any moving parts and it is capable of perform granular material separation in pulp. The mineral particles separation mechanism acting in a hydrocyclone is complex and its mathematical modelling is usually empirical. The most used model for hydrocyclone corrected cut size is proposed by Plitt. Over the years many revisions and corrections to Plitt´s model were proposed. The present paper shows a modification in the Plitt´s model constant, obtained by exponential regression of simulated data for three different hydrocyclones geometry: Rietema, Bradley and Krebs. To validate the proposed model literature data obtained from phosphate ore using fifteen different hydrocyclones geometry are used. The proposed model shows a correlation equals to 88.2% between experimental and calculated corrected cut size, while the correlation obtained using Plitt´s model is 11.5%.
Lihong, Huang; Jianling, Bai; Hao, Yu; Feng, Chen
2017-06-20
Sample size re-estimation is essential in oncology studies. However, the use of blinded sample size reassessment for survival data has been rarely reported. Based on the density function of the exponential distribution, an expectation-maximization (EM) algorithm of the hazard ratio was derived, and several simulation studies were used to verify its applications. The method had obvious variation in the hazard ratio estimates and overestimation for the relatively small hazard ratios. Our studies showed that the stability of the EM estimation results directly correlated with the sample size, the convergence of the EM algorithm was impacted by the initial values, and a balanced design produced the best estimates. No reliable blinded sample size re-estimation inference can be made in our studies, but the results provide useful information to steer the practitioners in this field from repeating the same endeavor..
DEFF Research Database (Denmark)
Kostoulas, P.; Nielsen, Søren Saxmose; Browne, W. J.
2013-01-01
measure of the data heterogeneity, has been used to modify formulae for individual sample size estimation. However, subgroups of animals sharing common characteristics, may exhibit excessively less or more heterogeneity. Hence, sample size estimates based on the ICC may not achieve the desired precision...... and power when applied to these groups. We propose the use of the variance partition coefficient (VPC), which measures the clustering of infection/disease for individuals with a common risk profile. Sample size estimates are obtained separately for those groups that exhibit markedly different heterogeneity......, thus, optimizing resource allocation. A VPC-based predictive simulation method for sample size estimation to substantiate freedom from disease is presented. To illustrate the benefits of the proposed approach we give two examples with the analysis of data from a risk factor study on Mycobacterium avium...
Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling
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Reinelt Gerhard
2009-12-01
Full Text Available Abstract Background The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. Results We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. Conclusions Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters
Convergence of surface diffusion parameters with model crystal size
Cohen, Jennifer M.; Voter, Arthur F.
1994-07-01
A study of the variation in the calculated quantities for adatom diffusion with respect to the size of the model crystal is presented. The reported quantities include surface diffusion barrier heights, pre-exponential factors, and dynamical correction factors. Embedded atom method (EAM) potentials were used throughout this effort. Both the layer size and the depth of the crystal were found to influence the values of the Arrhenius factors significantly. In particular, exchange type mechanisms required a significantly larger model than standard hopping mechanisms to determine adatom diffusion barriers of equivalent accuracy. The dynamical events that govern the corrections to transition state theory (TST) did not appear to be as sensitive to crystal depth. Suitable criteria for the convergence of the diffusion parameters with regard to the rate properties are illustrated.
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Elsa Tavernier
Full Text Available We aimed to examine the extent to which inaccurate assumptions for nuisance parameters used to calculate sample size can affect the power of a randomized controlled trial (RCT. In a simulation study, we separately considered an RCT with continuous, dichotomous or time-to-event outcomes, with associated nuisance parameters of standard deviation, success rate in the control group and survival rate in the control group at some time point, respectively. For each type of outcome, we calculated a required sample size N for a hypothesized treatment effect, an assumed nuisance parameter and a nominal power of 80%. We then assumed a nuisance parameter associated with a relative error at the design stage. For each type of outcome, we randomly drew 10,000 relative errors of the associated nuisance parameter (from empirical distributions derived from a previously published review. Then, retro-fitting the sample size formula, we derived, for the pre-calculated sample size N, the real power of the RCT, taking into account the relative error for the nuisance parameter. In total, 23%, 0% and 18% of RCTs with continuous, binary and time-to-event outcomes, respectively, were underpowered (i.e., the real power was 90%. Even with proper calculation of sample size, a substantial number of trials are underpowered or overpowered because of imprecise knowledge of nuisance parameters. Such findings raise questions about how sample size for RCTs should be determined.
DEFF Research Database (Denmark)
Liu, Hong-Sheng; Mishnaevsky, Leon
2013-01-01
A computational model of martensitic phase transformation in nanostructured nitinol is developed which takes into account the grain size effect. On the basis of the theoretical analysis of the thermodynamic transformation criterion and the energy barrier for phase transformation, it was demonstra......A computational model of martensitic phase transformation in nanostructured nitinol is developed which takes into account the grain size effect. On the basis of the theoretical analysis of the thermodynamic transformation criterion and the energy barrier for phase transformation...... transformation are totally suppressed. Graded and localized distributions of grain sizes of nitinol were compared with nitinol samples with homogeneous grain size distribution. In the materials with localized region of small grains, it was observed that the martensite rich regions form first on the border...
SIMPLIFIED MATHEMATICAL MODEL OF SMALL SIZED UNMANNED AIRCRAFT VEHICLE LAYOUT
2016-01-01
Strong reduction of new aircraft design period using new technology based on artificial intelligence is the key problem mentioned in forecasts of leading aerospace industry research centers. This article covers the approach to devel- opment of quick aerodynamic design methods based on artificial intelligence neural system. The problem is being solved for the classical scheme of small sized unmanned aircraft vehicle (UAV). The principal parts of the method are the mathe- matical model of layou...
A new model for the body size-metabolism relationship.
Roberts, Michael F; Lightfoot, Edwin N; Porter, Warren P
2010-01-01
The allometric 3/4 power relation, initially used for describing the relation between mammalian basal metabolic rate and body size, is often used as a general model for organismal design. The use of allometric regression as a model has important limitations: it is not mechanistic, it combines all physiological variables into one correlate of body size, and it combines data from several physiological states. In reassessing the use of allometric equations, we first describe problems with their use in studies of organismal design and then use a formulation for distributed net heat production and temperature distribution within the body to derive an alternative equation for the relation between basal metabolism and body size. Tests of the heat flow equation against data reported in the literature indicate that it is an accurate estimator of basal metabolism under thermoneutral conditions and suggest that the allometric equation is a special case of this mechanistic and more general model. We propose that our method is more meaningful and widely applicable for thermoneutral conditions than is a purely allometric approach.
Condori-Fernandez, Nelly; Daneva, Maia; Buglione, Luigi; Ormandjieva, Olga; Ormandjieva, O.; Constantinides, C.; Abran, A.; Lee, R.
2010-01-01
This paper reports on an experiment that investigates the predictability of software project size from software product size. The predictability research problem is analyzed at the stage of early requirements by accounting the size of functional requirements as well as the size of non-functional
Ghrefat, H.A.; Goodell, P.C.; Hubbard, B.E.; Langford, R.P.; Aldouri, R.E.
2007-01-01
Visible and Near-Infrared (VNIR) through Short Wavelength Infrared (SWIR) (0.4-2.5????m) AVIRIS data, along with laboratory spectral measurements and analyses of field samples, were used to characterize grain size variations in aeolian gypsum deposits across barchan-transverse, parabolic, and barchan dunes at White Sands, New Mexico, USA. All field samples contained a mineralogy of ?????100% gypsum. In order to document grain size variations at White Sands, surficial gypsum samples were collected along three Transects parallel to the prevailing downwind direction. Grain size analyses were carried out on the samples by sieving them into seven size fractions ranging from 45 to 621????m, which were subjected to spectral measurements. Absorption band depths of the size fractions were determined after applying an automated continuum-removal procedure to each spectrum. Then, the relationship between absorption band depth and gypsum size fraction was established using a linear regression. Three software processing steps were carried out to measure the grain size variations of gypsum in the Dune Area using AVIRIS data. AVIRIS mapping results, field work and laboratory analysis all show that the interdune areas have lower absorption band depth values and consist of finer grained gypsum deposits. In contrast, the dune crest areas have higher absorption band depth values and consist of coarser grained gypsum deposits. Based on laboratory estimates, a representative barchan-transverse dune (Transect 1) has a mean grain size of 1.16 ??{symbol} (449????m). The error bar results show that the error ranges from - 50 to + 50????m. Mean grain size for a representative parabolic dune (Transect 2) is 1.51 ??{symbol} (352????m), and 1.52 ??{symbol} (347????m) for a representative barchan dune (Transect 3). T-test results confirm that there are differences in the grain size distributions between barchan and parabolic dunes and between interdune and dune crest areas. The t-test results
Tang, Yongqiang
2015-01-01
A sample size formula is derived for negative binomial regression for the analysis of recurrent events, in which subjects can have unequal follow-up time. We obtain sharp lower and upper bounds on the required size, which is easy to compute. The upper bound is generally only slightly larger than the required size, and hence can be used to approximate the sample size. The lower and upper size bounds can be decomposed into two terms. The first term relies on the mean number of events in each group, and the second term depends on two factors that measure, respectively, the extent of between-subject variability in event rates, and follow-up time. Simulation studies are conducted to assess the performance of the proposed method. An application of our formulae to a multiple sclerosis trial is provided.
Directory of Open Access Journals (Sweden)
Christopher Ryan Penton
2016-06-01
Full Text Available We examined the effect of different soil sample sizes obtained from an agricultural field, under a single cropping system uniform in soil properties and aboveground crop responses, on bacterial and fungal community structure and microbial diversity indices. DNA extracted from soil sample sizes of 0.25, 1, 5 and 10 g using MoBIO kits and from 10 and 100 g sizes using a bead-beating method (SARDI were used as templates for high-throughput sequencing of 16S and 28S rRNA gene amplicons for bacteria and fungi, respectively, on the Illumina MiSeq and Roche 454 platforms. Sample size significantly affected overall bacterial and fungal community structure, replicate dispersion and the number of operational taxonomic units (OTUs retrieved. Richness, evenness and diversity were also significantly affected. The largest diversity estimates were always associated with the 10 g MoBIO extractions with a corresponding reduction in replicate dispersion. For the fungal data, smaller MoBIO extractions identified more unclassified Eukaryota incertae sedis and unclassified glomeromycota while the SARDI method retrieved more abundant OTUs containing unclassified Pleosporales and the fungal genera Alternaria and Cercophora. Overall, these findings indicate that a 10 g soil DNA extraction is most suitable for both soil bacterial and fungal communities for retrieving optimal diversity while still capturing rarer taxa in concert with decreasing replicate variation.
Shi, Guo-Liang; Tian, Ying-Ze; Ma, Tong; Song, Dan-Lin; Zhou, Lai-Dong; Han, Bo; Feng, Yin-Chang; Russell, Armistead G
2017-06-01
Long-term and synchronous monitoring of PM 10 and PM 2.5 was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models (PMF2, ME2-2way and ME2-3way). Consistent results were found: the primary source categories contributed 63.4% (PMF2), 64.8% (ME2-2way) and 66.8% (ME2-3way) to PM 10 , and contributed 60.9% (PMF2), 65.5% (ME2-2way) and 61.0% (ME2-3way) to PM 2.5 . Secondary sources contributed 31.8% (PMF2), 32.9% (ME2-2way) and 31.7% (ME2-3way) to PM 10 , and 35.0% (PMF2), 33.8% (ME2-2way) and 36.0% (ME2-3way) to PM 2.5 . The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment (SDA) was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east (ENE) contributed the highest to both PM 10 (12.7%) and PM 2.5 (9.7%) in Chengdu, followed by the crustal dust from south-east (SE) for PM 10 (9.8%) and secondary nitrate & secondary organic carbon from ENE for PM 2.5 (9.6%). Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and to develop effective pollution control strategies. Copyright © 2016. Published by Elsevier B.V.
Comparing interval estimates for small sample ordinal CFA models.
Natesan, Prathiba
2015-01-01
Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading
Reliable critical sized defect rodent model for cleft palate research.
Mostafa, Nesrine Z; Doschak, Michael R; Major, Paul W; Talwar, Reena
2014-12-01
Suitable animal models are necessary to test the efficacy of new bone grafting therapies in cleft palate surgery. Rodent models of cleft palate are available but have limitations. This study compared and modified mid-palate cleft (MPC) and alveolar cleft (AC) models to determine the most reliable and reproducible model for bone grafting studies. Published MPC model (9 × 5 × 3 mm(3)) lacked sufficient information for tested rats. Our initial studies utilizing AC model (7 × 4 × 3 mm(3)) in 8 and 16 weeks old Sprague Dawley (SD) rats revealed injury to adjacent structures. After comparing anteroposterior and transverse maxillary dimensions in 16 weeks old SD and Wistar rats, virtual planning was performed to modify MPC and AC defects dimensions, taking the adjacent structures into consideration. Modified MPC (7 × 2.5 × 1 mm(3)) and AC (5 × 2.5 × 1 mm(3)) defects were employed in 16 weeks old Wistar rats and healing was monitored by micro-computed tomography and histology. Maxillary dimensions in SD and Wistar rats were not significantly different. Preoperative virtual planning enhanced postoperative surgical outcomes. Bone healing occurred at defect margin leaving central bone void confirming the critical size nature of the modified MPC and AC defects. Presented modifications for MPC and AC models created clinically relevant and reproducible defects. Copyright © 2014 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.
Event-based total suspended sediment particle size distribution model
Thompson, Jennifer; Sattar, Ahmed M. A.; Gharabaghi, Bahram; Warner, Richard C.
2016-05-01
One of the most challenging modelling tasks in hydrology is prediction of the total suspended sediment particle size distribution (TSS-PSD) in stormwater runoff generated from exposed soil surfaces at active construction sites and surface mining operations. The main objective of this study is to employ gene expression programming (GEP) and artificial neural networks (ANN) to develop a new model with the ability to more accurately predict the TSS-PSD by taking advantage of both event-specific and site-specific factors in the model. To compile the data for this study, laboratory scale experiments using rainfall simulators were conducted on fourteen different soils to obtain TSS-PSD. This data is supplemented with field data from three construction sites in Ontario over a period of two years to capture the effect of transport and deposition within the site. The combined data sets provide a wide range of key overlooked site-specific and storm event-specific factors. Both parent soil and TSS-PSD in runoff are quantified by fitting each to a lognormal distribution. Compared to existing regression models, the developed model more accurately predicted the TSS-PSD using a more comprehensive list of key model input parameters. Employment of the new model will increase the efficiency of deployment of required best management practices, designed based on TSS-PSD, to minimize potential adverse effects of construction site runoff on aquatic life in the receiving watercourses.
A Gamma Model for Mixture STR Samples
DEFF Research Database (Denmark)
Christensen, Susanne; Bøttcher, Susanne Gammelgaard; Morling, Niels
This project investigates the behavior of the PCR Amplification Kit. A number of known DNA-profiles are mixed two by two in "known" proportions and analyzed. Gamma distribution models are fitted to the resulting data to learn to what extent actual mixing proportions can be rediscovered in the amp...
Hoefgen, Barbara; Schulze, Thomas G; Ohlraun, Stephanie; von Widdern, Olrik; Höfels, Susanne; Gross, Magdalena; Heidmann, Vivien; Kovalenko, Svetlana; Eckermann, Anita; Kölsch, Heike; Metten, Martin; Zobel, Astrid; Becker, Tim; Nöthen, Markus M; Propping, Peter; Heun, Reinhard; Maier, Wolfgang; Rietschel, Marcella
2005-02-01
Several lines of evidence indicate that abnormalities in the functioning of the central serotonergic system are involved in the pathogenesis of affective illness. A 44-base-pair insertion/deletion polymorphism in the 5' regulatory region of the serotonin transporter gene (5-HTTLPR), which influences expression of the serotonin transporter, has been the focus of intensive research since an initial report on an association between 5-HTTLPR and depression-related personality traits. Consistently replicated evidence for an involvement of this polymorphism in the etiology of mood disorders, particularly in major depressive disorder (MDD), remains scant. We assessed a potential association between 5-HTTLPR and MDD, using the largest reported sample to date (466 patients, 836 control subjects). Individuals were all of German descent. Patients were systematically recruited from consecutive inpatient admissions. Control subjects were drawn from random lists of the local Census Bureau and screened for psychiatric disorders. The short allele of 5-HTTLPR was significantly more frequent in patients than in control subjects (45.5% vs. 39.9%; p = .006; odds ratio = 1.26). These results support an involvement of 5-HTTLPR in the etiology of MDD. They also demonstrate that the detection of small genetic effects requires very large and homogenous samples.
Modeling group size and scalar stress by logistic regression from an archaeological perspective.
Directory of Open Access Journals (Sweden)
Gianmarco Alberti
Full Text Available Johnson's scalar stress theory, describing the mechanics of (and the remedies to the increase in in-group conflictuality that parallels the increase in groups' size, provides scholars with a useful theoretical framework for the understanding of different aspects of the material culture of past communities (i.e., social organization, communal food consumption, ceramic style, architecture and settlement layout. Due to its relevance in archaeology and anthropology, the article aims at proposing a predictive model of critical level of scalar stress on the basis of community size. Drawing upon Johnson's theory and on Dunbar's findings on the cognitive constrains to human group size, a model is built by means of Logistic Regression on the basis of the data on colony fissioning among the Hutterites of North America. On the grounds of the theoretical framework sketched in the first part of the article, the absence or presence of colony fissioning is considered expression of not critical vs. critical level of scalar stress for the sake of the model building. The model, which is also tested against a sample of archaeological and ethnographic cases: a confirms the existence of a significant relationship between critical scalar stress and group size, setting the issue on firmer statistical grounds; b allows calculating the intercept and slope of the logistic regression model, which can be used in any time to estimate the probability that a community experienced a critical level of scalar stress; c allows locating a critical scalar stress threshold at community size 127 (95% CI: 122-132, while the maximum probability of critical scale stress is predicted at size 158 (95% CI: 147-170. The model ultimately provides grounds to assess, for the sake of any further archaeological/anthropological interpretation, the probability that a group reached a hot spot of size development critical for its internal cohesion.
Modeling of aerosol dynamics - Aerosol size and composition
International Nuclear Information System (INIS)
Beyak, R.A.; Peterson, T.W.
1980-01-01
A mechanism for the evolution of the size and composition of an aerosol particle is investigated by developing a model that includes the transfer of gaseous pollutants to the surface of existing aerosol particles, the transfer of the pollutants across the gas-particle interface, and the reaction of dissolved species within the aerosol particle. The reaction mechanisms considered include homogeneous gas-phase and heterogeneous solution chemistry. Aerosol particles evolving within an urban plume and within power-plant and smelter plumes are studied, along with the evolution of particle size and chemical composition for regions of various relative humidities, with particular emphasis on aqueous chemistry in the low-humidity environments of the southwestern U.S. A simulation of a copper smelting complex in central Arizona indicates that all particles shrink from their original size as plume humidity approaches background relative humidity, that most secondary sulfate formation occurs in the near-plume region, and that the final particle pH is in the range 2.0-2.2 for a wide range of physical conditions
Uyaguari-Diaz, Miguel I; Slobodan, Jared R; Nesbitt, Matthew J; Croxen, Matthew A; Isaac-Renton, Judith; Prystajecky, Natalie A; Tang, Patrick
2015-04-17
Next-generation sequencing of environmental samples can be challenging because of the variable DNA quantity and quality in these samples. High quality DNA libraries are needed for optimal results from next-generation sequencing. Environmental samples such as water may have low quality and quantities of DNA as well as contaminants that co-precipitate with DNA. The mechanical and enzymatic processes involved in extraction and library preparation may further damage the DNA. Gel size selection enables purification and recovery of DNA fragments of a defined size for sequencing applications. Nevertheless, this task is one of the most time-consuming steps in the DNA library preparation workflow. The protocol described here enables complete automation of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments. In this study, we describe a high-throughput approach to prepare high quality DNA libraries from freshwater samples that can be applied also to other environmental samples. We used an indirect approach to concentrate bacterial cells from environmental freshwater samples; DNA was extracted using a commercially available DNA extraction kit, and DNA libraries were prepared using a commercial transposon-based protocol. DNA fragments of 500 to 800 bp were gel size selected using Ranger Technology, an automated electrophoresis workstation. Sequencing of the size-selected DNA libraries demonstrated significant improvements to read length and quality of the sequencing reads.
Neeson, Thomas M; Van Rijn, Itai; Mandelik, Yael
2013-07-01
Ecologists and paleontologists often rely on higher taxon surrogates instead of complete inventories of biological diversity. Despite their intrinsic appeal, the performance of these surrogates has been markedly inconsistent across empirical studies, to the extent that there is no consensus on appropriate taxonomic resolution (i.e., whether genus- or family-level categories are more appropriate) or their overall usefulness. A framework linking the reliability of higher taxon surrogates to biogeographic setting would allow for the interpretation of previously published work and provide some needed guidance regarding the actual application of these surrogates in biodiversity assessments, conservation planning, and the interpretation of the fossil record. We developed a mathematical model to show how taxonomic diversity, community structure, and sampling effort together affect three measures of higher taxon performance: the correlation between species and higher taxon richness, the relative shapes and asymptotes of species and higher taxon accumulation curves, and the efficiency of higher taxa in a complementarity-based reserve-selection algorithm. In our model, higher taxon surrogates performed well in communities in which a few common species were most abundant, and less well in communities with many equally abundant species. Furthermore, higher taxon surrogates performed well when there was a small mean and variance in the number of species per higher taxa. We also show that empirically measured species-higher-taxon correlations can be partly spurious (i.e., a mathematical artifact), except when the species accumulation curve has reached an asymptote. This particular result is of considerable practical interest given the widespread use of rapid survey methods in biodiversity assessment and the application of higher taxon methods to taxa in which species accumulation curves rarely reach an asymptote, e.g., insects.
International Nuclear Information System (INIS)
Hoo, Christopher M.; Doan, Trang; Starostin, Natasha; West, Paul E.; Mecartney, Martha L.
2010-01-01
Optimal deposition procedures are determined for nanoparticle size characterization by atomic force microscopy (AFM). Accurate nanoparticle size distribution analysis with AFM requires non-agglomerated nanoparticles on a flat substrate. The deposition of polystyrene (100 nm), silica (300 and 100 nm), gold (100 nm), and CdSe quantum dot (2-5 nm) nanoparticles by spin coating was optimized for size distribution measurements by AFM. Factors influencing deposition include spin speed, concentration, solvent, and pH. A comparison using spin coating, static evaporation, and a new fluid cell deposition method for depositing nanoparticles is also made. The fluid cell allows for a more uniform and higher density deposition of nanoparticles on a substrate at laminar flow rates, making nanoparticle size analysis via AFM more efficient and also offers the potential for nanoparticle analysis in liquid environments.
Levin, Gregory P; Emerson, Sarah C; Emerson, Scott S
2013-04-15
Adaptive clinical trial design has been proposed as a promising new approach that may improve the drug discovery process. Proponents of adaptive sample size re-estimation promote its ability to avoid 'up-front' commitment of resources, better address the complicated decisions faced by data monitoring committees, and minimize accrual to studies having delayed ascertainment of outcomes. We investigate aspects of adaptation rules, such as timing of the adaptation analysis and magnitude of sample size adjustment, that lead to greater or lesser statistical efficiency. Owing in part to the recent Food and Drug Administration guidance that promotes the use of pre-specified sampling plans, we evaluate alternative approaches in the context of well-defined, pre-specified adaptation. We quantify the relative costs and benefits of fixed sample, group sequential, and pre-specified adaptive designs with respect to standard operating characteristics such as type I error, maximal sample size, power, and expected sample size under a range of alternatives. Our results build on others' prior research by demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. We thus provide insight into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired. Copyright © 2012 John Wiley & Sons, Ltd.
Bovens, M; Csesztregi, T; Franc, A; Nagy, J; Dujourdy, L
2014-01-01
The basic goal in sampling for the quantitative analysis of illicit drugs is to maintain the average concentration of the drug in the material from its original seized state (the primary sample) all the way through to the analytical sample, where the effect of particle size is most critical. The size of the largest particles of different authentic illicit drug materials, in their original state and after homogenisation, using manual or mechanical procedures, was measured using a microscope with a camera attachment. The comminution methods employed included pestle and mortar (manual) and various ball and knife mills (mechanical). The drugs investigated were amphetamine, heroin, cocaine and herbal cannabis. It was shown that comminution of illicit drug materials using these techniques reduces the nominal particle size from approximately 600 μm down to between 200 and 300 μm. It was demonstrated that the choice of 1 g increments for the primary samples of powdered drugs and cannabis resin, which were used in the heterogeneity part of our study (Part I) was correct for the routine quantitative analysis of illicit seized drugs. For herbal cannabis we found that the appropriate increment size was larger. Based on the results of this study we can generally state that: An analytical sample weight of between 20 and 35 mg of an illicit powdered drug, with an assumed purity of 5% or higher, would be considered appropriate and would generate an RSDsampling in the same region as the RSDanalysis for a typical quantitative method of analysis for the most common, powdered, illicit drugs. For herbal cannabis, with an assumed purity of 1% THC (tetrahydrocannabinol) or higher, an analytical sample weight of approximately 200 mg would be appropriate. In Part III we will pull together our homogeneity studies and particle size investigations and use them to devise sampling plans and sample preparations suitable for the quantitative instrumental analysis of the most common illicit
Grain size of loess and paleosol samples: what are we measuring?
Varga, György; Kovács, János; Szalai, Zoltán; Újvári, Gábor
2017-04-01
Particle size falling into a particularly narrow range is among the most important properties of windblown mineral dust deposits. Therefore, various aspects of aeolian sedimentation and post-depositional alterations can be reconstructed only from precise grain size data. Present study is aimed at (1) reviewing grain size data obtained from different measurements, (2) discussing the major reasons for disagreements between data obtained by frequently applied particle sizing techniques, and (3) assesses the importance of particle shape in particle sizing. Grain size data of terrestrial aeolian dust deposits (loess and paleosoil) were determined by laser scattering instruments (Fritsch Analysette 22 Microtec Plus, Horiba Partica La-950 v2 and Malvern Mastersizer 3000 with a Hydro Lv unit), while particles size and shape distributions were acquired by Malvern Morphologi G3-ID. Laser scattering results reveal that the optical parameter settings of the measurements have significant effects on the grain size distributions, especially for the fine-grained fractions (slide with a consistent orientation with their largest area facing to the camera. However, this is only one outcome of infinite possible projections of a three-dimensional object and it cannot be regarded as a representative one. The third (height) dimension of the particles remains unknown, so the volume-based weightings are fairly dubious in the case of platy particles. Support of the National Research, Development and Innovation Office (Hungary) under contract NKFI 120620 is gratefully acknowledged. It was additionally supported (for G. Varga) by the Bolyai János Research Scholarship of the Hungarian Academy of Sciences.
DEFF Research Database (Denmark)
Chan, A.W.; Hrobjartsson, A.; Jorgensen, K.J.
2008-01-01
OBJECTIVE: To evaluate how often sample size calculations and methods of statistical analysis are pre-specified or changed in randomised trials. DESIGN: Retrospective cohort study. Data source Protocols and journal publications of published randomised parallel group trials initially approved...... in 1994-5 by the scientific-ethics committees for Copenhagen and Frederiksberg, Denmark (n=70). MAIN OUTCOME MEASURE: Proportion of protocols and publications that did not provide key information about sample size calculations and statistical methods; proportion of trials with discrepancies between...... information presented in the protocol and the publication. RESULTS: Only 11/62 trials described existing sample size calculations fully and consistently in both the protocol and the publication. The method of handling protocol deviations was described in 37 protocols and 43 publications. The method...
Large scale inference in the Infinite Relational Model: Gibbs sampling is not enough
DEFF Research Database (Denmark)
Albers, Kristoffer Jon; Moth, Andreas Leon Aagard; Mørup, Morten
2013-01-01
The stochastic block-model and its non-parametric extension, the Infinite Relational Model (IRM), have become key tools for discovering group-structure in complex networks. Identifying these groups is a combinatorial inference problem which is usually solved by Gibbs sampling. However, whether...... Gibbs sampling suffices and can be scaled to the modeling of large scale real world complex networks has not been examined sufficiently. In this paper we evaluate the performance and mixing ability of Gibbs sampling in the Infinite Relational Model (IRM) by implementing a high performance Gibbs sampler....... We find that Gibbs sampling can be computationally scaled to handle millions of nodes and billions of links. Investigating the behavior of the Gibbs sampler for different sizes of networks we find that the mixing ability decreases drastically with the network size, clearly indicating a need...
Cosmic structure sizes in generic dark energy models
Energy Technology Data Exchange (ETDEWEB)
Bhattacharya, Sourav [Indian Institute of Technology Ropar, Department of Physics, Rupnagar, Punjab (India); Tomaras, Theodore N. [ITCP and Department of Physics, University of Crete, Heraklion (Greece)
2017-08-15
The maximum allowable size of a spherical cosmic structure as a function of its mass is determined by the maximum turn around radius R{sub TA,max}, the distance from its center where the attraction on a radial test particle due to the spherical mass is balanced with the repulsion due to the ambient dark energy. In this work, we extend the existing results in several directions. (a) We first show that, for w ≠ -1, the expression for R{sub TA,max} found earlier, using the cosmological perturbation theory, can be derived using a static geometry as well. (b) In the generic dark energy model with arbitrary time dependent state parameter w(t), taking into account the effect of inhomogeneities upon the dark energy as well, it is shown that the data constrain w(t = today) > -2.3. (c) We address the quintessence and the generalized Chaplygin gas models, both of which are shown to predict structure sizes consistent with observations. (orig.)
Modeling photoacoustic spectral features of micron-sized particles.
Strohm, Eric M; Gorelikov, Ivan; Matsuura, Naomi; Kolios, Michael C
2014-10-07
The photoacoustic signal generated from particles when irradiated by light is determined by attributes of the particle such as the size, speed of sound, morphology and the optical absorption coefficient. Unique features such as periodically varying minima and maxima are observed throughout the photoacoustic signal power spectrum, where the periodicity depends on these physical attributes. The frequency content of the photoacoustic signals can be used to obtain the physical attributes of unknown particles by comparison to analytical solutions of homogeneous symmetric geometric structures, such as spheres. However, analytical solutions do not exist for irregularly shaped particles, inhomogeneous particles or particles near structures. A finite element model (FEM) was used to simulate photoacoustic wave propagation from four different particle configurations: a homogeneous particle suspended in water, a homogeneous particle on a reflecting boundary, an inhomogeneous particle with an absorbing shell and non-absorbing core, and an irregularly shaped particle such as a red blood cell. Biocompatible perfluorocarbon droplets, 3-5 μm in diameter containing optically absorbing nanoparticles were used as the representative ideal particles, as they are spherical, homogeneous, optically translucent, and have known physical properties. The photoacoustic spectrum of micron-sized single droplets in suspension and on a reflecting boundary were measured over the frequency range of 100-500 MHz and compared directly to analytical models and the FEM. Good agreement between the analytical model, FEM and measured values were observed for a droplet in suspension, where the spectral minima agreed to within a 3.3 MHz standard deviation. For a droplet on a reflecting boundary, spectral features were correctly reproduced using the FEM but not the analytical model. The photoacoustic spectra from other common particle configurations such as particle with an absorbing shell and a
Model of tooth morphogenesis predicts carabelli cusp expression, size, and symmetry in humans.
Directory of Open Access Journals (Sweden)
John P Hunter
Full Text Available BACKGROUND: The patterning cascade model of tooth morphogenesis accounts for shape development through the interaction of a small number of genes. In the model, gene expression both directs development and is controlled by the shape of developing teeth. Enamel knots (zones of nonproliferating epithelium mark the future sites of cusps. In order to form, a new enamel knot must escape the inhibitory fields surrounding other enamel knots before crown components become spatially fixed as morphogenesis ceases. Because cusp location on a fully formed tooth reflects enamel knot placement and tooth size is limited by the cessation of morphogenesis, the model predicts that cusp expression varies with intercusp spacing relative to tooth size. Although previous studies in humans have supported the model's implications, here we directly test the model's predictions for the expression, size, and symmetry of Carabelli cusp, a variation present in many human populations. METHODOLOGY/PRINCIPAL FINDINGS: In a dental cast sample of upper first molars (M1s (187 rights, 189 lefts, and 185 antimeric pairs, we measured tooth area and intercusp distances with a Hirox digital microscope. We assessed Carabelli expression quantitatively as an area in a subsample and qualitatively using two typological schemes in the full sample. As predicted, low relative intercusp distance is associated with Carabelli expression in both right and left samples using either qualitative or quantitative measures. Furthermore, asymmetry in Carabelli area is associated with asymmetry in relative intercusp spacing. CONCLUSIONS/SIGNIFICANCE: These findings support the model's predictions for Carabelli cusp expression both across and within individuals. By comparing right-left pairs of the same individual, our data show that small variations in developmental timing or spacing of enamel knots can influence cusp pattern independently of genotype. Our findings suggest that during evolution new cusps
Model of Tooth Morphogenesis Predicts Carabelli Cusp Expression, Size, and Symmetry in Humans
Hunter, John P.; Guatelli-Steinberg, Debbie; Weston, Theresia C.; Durner, Ryan; Betsinger, Tracy K.
2010-01-01
Background The patterning cascade model of tooth morphogenesis accounts for shape development through the interaction of a small number of genes. In the model, gene expression both directs development and is controlled by the shape of developing teeth. Enamel knots (zones of nonproliferating epithelium) mark the future sites of cusps. In order to form, a new enamel knot must escape the inhibitory fields surrounding other enamel knots before crown components become spatially fixed as morphogenesis ceases. Because cusp location on a fully formed tooth reflects enamel knot placement and tooth size is limited by the cessation of morphogenesis, the model predicts that cusp expression varies with intercusp spacing relative to tooth size. Although previous studies in humans have supported the model's implications, here we directly test the model's predictions for the expression, size, and symmetry of Carabelli cusp, a variation present in many human populations. Methodology/Principal Findings In a dental cast sample of upper first molars (M1s) (187 rights, 189 lefts, and 185 antimeric pairs), we measured tooth area and intercusp distances with a Hirox digital microscope. We assessed Carabelli expression quantitatively as an area in a subsample and qualitatively using two typological schemes in the full sample. As predicted, low relative intercusp distance is associated with Carabelli expression in both right and left samples using either qualitative or quantitative measures. Furthermore, asymmetry in Carabelli area is associated with asymmetry in relative intercusp spacing. Conclusions/Significance These findings support the model's predictions for Carabelli cusp expression both across and within individuals. By comparing right-left pairs of the same individual, our data show that small variations in developmental timing or spacing of enamel knots can influence cusp pattern independently of genotype. Our findings suggest that during evolution new cusps may first appear as
Cheboyina, Sreekhar; O'Haver, John; Wyandt, Christy M
2006-01-01
A mathematical model was developed based on the theory of drop formation to predict the size of the pellets formed in the freeze pelletization process. Further the model was validated by studying the effect of various parameters on the pellet size such as viscosity of the pellet forming and column liquids, surface/interfacial tension, density difference between pellet forming and column liquids; size, shape, and material of construction of the needle tips and temperatures maintained in the columns. In this study, pellets were prepared from different matrices including polyethylene glycols and waxes. The column liquids studied were silicone oils and aqueous glycerol solutions. The surface/interfacial tension, density difference between pellet forming and column liquids and needle tip size were found to be the most important factors affecting pellet size. The viscosity of the column liquid was not found to significantly affect the size of the pellets. The size of the pellets was also not affected by the pellet forming liquids of low viscosities. An increase in the initial column temperature slightly decreased the pellet size. The mathematical model developed was found to successfully predict the size of the pellets with an average error of 3.32% for different matrices that were studied.
Viswanath, Shruthi; Chemmama, Ilan E; Cimermancic, Peter; Sali, Andrej
2017-12-05
Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Simon Boitard
2016-03-01
Full Text Available Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey, PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.
ACTINIDE REMOVAL PROCESS SAMPLE ANALYSIS, CHEMICAL MODELING, AND FILTRATION EVALUATION
Energy Technology Data Exchange (ETDEWEB)
Martino, C.; Herman, D.; Pike, J.; Peters, T.
2014-06-05
Filtration within the Actinide Removal Process (ARP) currently limits the throughput in interim salt processing at the Savannah River Site. In this process, batches of salt solution with Monosodium Titanate (MST) sorbent are concentrated by crossflow filtration. The filtrate is subsequently processed to remove cesium in the Modular Caustic Side Solvent Extraction Unit (MCU) followed by disposal in saltstone grout. The concentrated MST slurry is washed and sent to the Defense Waste Processing Facility (DWPF) for vitrification. During recent ARP processing, there has been a degradation of filter performance manifested as the inability to maintain high filtrate flux throughout a multi-batch cycle. The objectives of this effort were to characterize the feed streams, to determine if solids (in addition to MST) are precipitating and causing the degraded performance of the filters, and to assess the particle size and rheological data to address potential filtration impacts. Equilibrium modelling with OLI Analyzer{sup TM} and OLI ESP{sup TM} was performed to determine chemical components at risk of precipitation and to simulate the ARP process. The performance of ARP filtration was evaluated to review potential causes of the observed filter behavior. Task activities for this study included extensive physical and chemical analysis of samples from the Late Wash Pump Tank (LWPT) and the Late Wash Hold Tank (LWHT) within ARP as well as samples of the tank farm feed from Tank 49H. The samples from the LWPT and LWHT were obtained from several stages of processing of Salt Batch 6D, Cycle 6, Batch 16.
Hamilton, A J; Waters, E K; Kim, H J; Pak, W S; Furlong, M J
2009-06-01
The combined action of two lepidoteran pests, Plutella xylostella L. (Plutellidae) and Pieris rapae L. (Pieridae),causes significant yield losses in cabbage (Brassica oleracea variety capitata) crops in the Democratic People's Republic of Korea. Integrated pest management (IPM) strategies for these cropping systems are in their infancy, and sampling plans have not yet been developed. We used statistical resampling to assess the performance of fixed sample size plans (ranging from 10 to 50 plants). First, the precision (D = SE/mean) of the plans in estimating the population mean was assessed. There was substantial variation in achieved D for all sample sizes, and sample sizes of at least 20 and 45 plants were required to achieve the acceptable precision level of D < or = 0.3 at least 50 and 75% of the time, respectively. Second, the performance of the plans in classifying the population density relative to an economic threshold (ET) was assessed. To account for the different damage potentials of the two species the ETs were defined in terms of standard insects (SIs), where 1 SI = 1 P. rapae = 5 P. xylostella larvae. The plans were implemented using different economic thresholds (ETs) for the three growth stages of the crop: precupping (1 SI/plant), cupping (0.5 SI/plant), and heading (4 SI/plant). Improvement in the classification certainty with increasing sample sizes could be seen through the increasing steepness of operating characteristic curves. Rather than prescribe a particular plan, we suggest that the results of these analyses be used to inform practitioners of the relative merits of the different sample sizes.
Diel differences in 0+ fish samples: effect of river size and habitat
Czech Academy of Sciences Publication Activity Database
Janáč, Michal; Jurajda, Pavel
2013-01-01
Roč. 29, č. 1 (2013), s. 90-98 ISSN 1535-1459 R&D Projects: GA MŠk LC522 Institutional research plan: CEZ:AV0Z60930519 Keywords : young-of-the-year fish * diurnal * nocturnal * habitat complexity * stream size Subject RIV: EG - Zoology Impact factor: 1.971, year: 2013
General power and sample size calculations for high-dimensional genomic data
van Iterson, M.; van de Wiel, M.; Boer, J.M.; Menezes, R.
2013-01-01
In the design of microarray or next-generation sequencing experiments it is crucial to choose the appropriate number of biological replicates. As often the number of differentially expressed genes and their effect sizes are small and too few replicates will lead to insufficient power to detect
Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach
Rotondi, Michael A.; Donner, Allan
2009-01-01
The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…
Queuing theory models used for port equipment sizing
Dragu, V.; Dinu, O.; Ruscă, A.; Burciu, Ş.; Roman, E. A.
2017-08-01
The significant growth of volumes and distances on road transportation led to the necessity of finding solutions to increase water transportation market share together with the handling and transfer technologies within its terminals. It is widely known that the biggest times are consumed within the transport terminals (loading/unloading/transfer) and so the necessity of constantly developing handling techniques and technologies in concordance with the goods flows size so that the total waiting time of ships within ports is reduced. Port development should be achieved by harmonizing the contradictory interests of port administration and users. Port administrators aim profit increase opposite to users that want savings by increasing consumers’ surplus. The difficulty consists in the fact that the transport demand - supply equilibrium must be realised at costs and goods quantities transiting the port in order to satisfy the interests of both parties involved. This paper presents a port equipment sizing model by using queueing theory so that the sum of costs for ships waiting operations and equipment usage would be minimum. Ship operation within the port is assimilated to a mass service waiting system in which parameters are later used to determine the main costs for ships and port equipment.
Particle size - An important factor in environmental consequence modeling
International Nuclear Information System (INIS)
Yuan, Y.C.; MacFarlane, D.
1991-01-01
Most available environmental transport and dosimetry codes for radiological consequence analysis are designed primarily for estimating dose and health consequences to specific off-site individuals as well as the population as a whole from nuclear facilities operating under either normal or accident conditions. Models developed for these types of analyses are generally based on assumptions that the receptors are at great distances (several kilometers), and the releases are prolonged and filtered. This allows the use of simplified approaches such as averaged meteorological conditions and the use of a single (small) particle size for atmospheric transport and dosimetry analysis. Source depletion from particle settling, settle-out, and deposition is often ignored. This paper estimates the effects of large particles on the resulting dose consequences from an atmospheric release. The computer program AI-RISK has been developed to perform multiparticle-sized atmospheric transport, dose, and pathway analyses for estimating potential human health consequences from the accidental release of radioactive materials. The program was originally developed to facilitate comprehensive analyses of health consequences, ground contamination, and cleanup associated with possible energetic chemical reactions in high-level radioactive waste (HLW) tanks at a US Department of Energy site
Family size, birth order, and intelligence in a large South American sample.
Velandia, W; Grandon, G M; Page, E B
1978-01-01
The confluence theory, which hypothesizes a relationship between intellectual development birth order, and family size, was examined in a colombian study of more than 36,000 college applicants. The results of the study did not support the confluence theory. The confluence theory states that the intellectual development of a child is related to average mental age of the members of his family at the time of his birth. The mental age of the parents is always assigned a value of 30 and siblings are given scores equivalent to their chronological age at the birth of the subject. Therefore, the average mental age of family members for a 1st born child is 30, or 60 divided by 2. If a subject is born into a family consisting of 2 parents and a 6-year old sibling, the average mental age of family members tends, therefore, to decrease with each birth order. The hypothesis derived from the confluence theory states that there is a positive relationship between average mental age of a subject's family and the subject's performance on intelligence tests. In the Colombian study, data on family size, birth order and socioeconomic status was derived from college application forms. Intelligence test scores for each subject was obtained from college entrance exams. The mental age of each applicant's family at the time of the applicant's birth was calculated. Multiple correlation analysis and path analysis were used to assess the relationship. Results were 1) the test scores of subjects from families with 2,3,4, and 5 children were higher than test scores of the 1st born subjects; 2) the rank order of intelligence by family size was 3,4,5,2,6,1 instead of the hypothesized 1,2,3,4,5,6; and 3) only 1% of the variability in test scores was explained by the variables of birth order and family size. Further analysis indicated that socioeconomic status was a far more powerful explanatory variable than family size.
Investigation of a Gamma model for mixture STR samples
DEFF Research Database (Denmark)
Christensen, Susanne; Bøttcher, Susanne Gammelgaard; Lauritzen, Steffen L.
The behaviour of PCR Amplification Kit, when used for mixture STR samples, is investigated. A model based on the Gamma distribution is fitted to the amplifier output for constructed mixtures, and the assumptions of the model is evaluated via residual analysis.......The behaviour of PCR Amplification Kit, when used for mixture STR samples, is investigated. A model based on the Gamma distribution is fitted to the amplifier output for constructed mixtures, and the assumptions of the model is evaluated via residual analysis....
Boef, Anna G C; Dekkers, Olaf M; Vandenbroucke, Jan P; le Cessie, Saskia
2014-11-01
Instrumental variable (IV) analysis is promising for estimation of therapeutic effects from observational data as it can circumvent unmeasured confounding. However, even if IV assumptions hold, IV analyses will not necessarily provide an estimate closer to the true effect than conventional analyses as this depends on the estimates' bias and variance. We investigated how estimates from standard regression (ordinary least squares [OLS]) and IV (two-stage least squares) regression compare on mean squared error (MSE). We derived an equation for approximation of the threshold sample size, above which IV estimates have a smaller MSE than OLS estimates. Next, we performed simulations, varying sample size, instrument strength, and level of unmeasured confounding. IV assumptions were fulfilled by design. Although biased, OLS estimates were closer on average to the true effect than IV estimates at small sample sizes because of their smaller variance. The threshold sample size above which IV analysis outperforms OLS regression depends on instrument strength and strength of unmeasured confounding but will usually be large given the typical moderate instrument strength in medical research. IV methods are of most value in large studies if considerable unmeasured confounding is likely and a strong and plausible instrument is available. Copyright © 2014 Elsevier Inc. All rights reserved.
Heymann, D.; Lakatos, S.; Walton, J. R.
1973-01-01
Review of the results of inert gas measurements performed on six grain-size fractions and two single particles from four samples of Luna 20 material. Presented and discussed data include the inert gas contents, element and isotope systematics, radiation ages, and Ar-36/Ar-40 systematics.
Algina, James; Keselman, H. J.
2008-01-01
Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection. (Contains 2 tables.)
Hans T. Schreuder; Jin-Mann S. Lin; John Teply
2000-01-01
The Forest Inventory and Analysis units in the USDA Forest Service have been mandated by Congress to go to an annualized inventory where a certain percentage of plots, say 20 percent, will be measured in each State each year. Although this will result in an annual sample size that will be too small for reliable inference for many areas, it is a sufficiently large...
Tang, Yongqiang
2017-05-25
We derive the sample size formulae for comparing two negative binomial rates based on both the relative and absolute rate difference metrics in noninferiority and equivalence trials with unequal follow-up times, and establish an approximate relationship between the sample sizes required for the treatment comparison based on the two treatment effect metrics. The proposed method allows the dispersion parameter to vary by treatment groups. The accuracy of these methods is assessed by simulations. It is demonstrated that ignoring the between-subject variation in the follow-up time by setting the follow-up time for all individuals to be the mean follow-up time may greatly underestimate the required size, resulting in underpowered studies. Methods are provided for back-calculating the dispersion parameter based on the published summary results.
Wind erosion model of a multiple sized particles bed
Energy Technology Data Exchange (ETDEWEB)
Descamps, I.; Pons, A.; Harion, J.-L. [IMP-CNRS UPR 8521, Perpignan (France)
2006-07-01
A model has been developed in order to predict more accurately fugitive dust emissions by aeolian erosion on industrial sites. This model takes into account the time evolution of the bed surface features during erosion by a turbulent flow. It consists of four parts corresponding to aerodynamic entrainment and is based on the interaction between particle take-off and wall turbulence. A take-off criterion compares the lift force exerted by the flow on the particle with the sum of the weight and adhesive force. Bed pavement and saltation are also taken into account. Bed pavement is induced by the non-erodible particles. On steel plants stockpiles, ores and coals have granulometric spectra going a few microns to a few centimetres in diameter. In fact, the non-erodible particles, that cannot take-off because of their inertia, form obstacles in the finer particle take-off and lead to a time decrease in emitted mass flux. The new model has been tested for the case of a bimodal size distribution by comparison with relevant experimental data. The results demonstrate that the mode allows predicting the mass flux time decrease due to non-erodible particles. 17 refs., 6 figs.
Glottal aerodynamics in compliant, life-sized vocal fold models
McPhail, Michael; Dowell, Grant; Krane, Michael
2013-11-01
This talk presents high-speed PIV measurements in compliant, life-sized models of the vocal folds. A clearer understanding of the fluid-structure interaction of voiced speech, how it produces sound, and how it varies with pathology is required to improve clinical diagnosis and treatment of vocal disorders. Physical models of the vocal folds can answer questions regarding the fundamental physics of speech, as well as the ability of clinical measures to detect the presence and extent of disorder. Flow fields were recorded in the supraglottal region of the models to estimate terms in the equations of fluid motion, and their relative importance. Experiments were conducted over a range of driving pressures with flow rates, given by a ball flowmeter, and subglottal pressures, given by a micro-manometer, reported for each case. Imaging of vocal fold motion, vector fields showing glottal jet behavior, and terms estimated by control volume analysis will be presented. The use of these results for a comparison with clinical measures, and for the estimation of aeroacoustic source strengths will be discussed. Acknowledge support from NIH R01 DC005642.
Basic distribution free identification tests for small size samples of environmental data
International Nuclear Information System (INIS)
Federico, A.G.; Musmeci, F.
1998-01-01
Testing two or more data sets for the hypothesis that they are sampled form the same population is often required in environmental data analysis. Typically the available samples have a small number of data and often then assumption of normal distributions is not realistic. On the other hand the diffusion of the days powerful Personal Computers opens new possible opportunities based on a massive use of the CPU resources. The paper reviews the problem introducing the feasibility of two non parametric approaches based on intrinsic equi probability properties of the data samples. The first one is based on a full re sampling while the second is based on a bootstrap approach. A easy to use program is presented. A case study is given based on the Chernobyl children contamination data [it
DEFF Research Database (Denmark)
Picchini, Umberto; Forman, Julie Lyng
2016-01-01
a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An approximate Bayesian computation (ABC)-MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm...
Measuring proteins with greater speed and resolution while reducing sample size
Hsieh, Vincent H.; Wyatt, Philip J.
2017-01-01
A multi-angle light scattering (MALS) system, combined with chromatographic separation, directly measures the absolute molar mass, size and concentration of the eluate species. The measurement of these crucial properties in solution is essential in basic macromolecular characterization and all research and production stages of bio-therapeutic products. We developed a new MALS methodology that has overcome the long-standing, stubborn barrier to microliter-scale peak volumes and achieved the hi...
International Nuclear Information System (INIS)
Zorina, M.V.; Mironov, V.L.; Mironov, S.V.
2005-01-01
A model is developed, which enables computation of the the angular dependences of X-ray reflection, with taking into account the finiteness of sample sizes and the diffractometer alignment errors. It is shown that the angular dependences of refraction for glass and quartz wafers calculated with account of possible errors of the diffractometer optical system alignment are in good agreement with the experimental curves in the entire range of angles [ru
Buss, Daniel F; Borges, Erika L
2008-01-01
This study is part of the effort to test and to establish Rapid Bioassessment Protocols (RBP) using benthic macroinvertebrates as indicators of the water quality of wadeable streams in south-east Brazil. We compared the cost-effectiveness of sampling devices frequently used in RBPs, Surber and Kick-net samplers, and of three mesh sizes (125, 250 and 500 microm). A total of 126,815 benthic macroinvertebrates were collected, representing 57 families. Samples collected with Kick method had significantly higher richness and BMWP scores in relation to Surber, but no significant increase in the effort, measured by the necessary time to process samples. No significant differences were found between samplers considering the cost/effectiveness ratio. Considering mesh sizes, significantly higher abundance and time for processing samples were necessary for finer meshes, but no significant difference were found considering taxa richness or BMWP scores. As a consequence, the 500 microm mesh had better cost/effectiveness ratios. Therefore, we support the use of a kick-net with a mesh size of 500 microm for macroinvertebrate sampling in RBPs using family level in streams of similar characteristics in Brazil.
International Nuclear Information System (INIS)
John L. Bowen; Rowena Gonzalez; David S. Shafer
2001-01-01
As part of the preliminary site characterization conducted for Project 57, soils samples were collected for separation into several size-fractions using the Suspended Soil Particle Sizing System (SSPSS). Soil samples were collected specifically for separation by the SSPSS at three general locations in the deposited Project 57 plume, the projected radioactivity of which ranged from 100 to 600 pCi/g. The primary purpose in focusing on samples with this level of activity is that it would represent anticipated residual soil contamination levels at the site after corrective actions are completed. Consequently, the results of the SSPSS analysis can contribute to dose calculation and corrective action-level determinations for future land-use scenarios at the site
Olberg, Britta; Perleth, Matthias; Felgentraeger, Katja; Schulz, Sandra; Busse, Reinhard
2017-01-01
The aim of this study was to assess the quality of reporting sample size calculation and underlying design assumptions in pivotal trials of high-risk medical devices (MDs) for neurological conditions. Systematic review of research protocols for publicly registered randomized controlled trials (RCTs). In the absence of a published protocol, principal investigators were contacted for additional data. To be included, trials had to investigate a high-risk MD, registered between 2005 and 2015, with indications stroke, headache disorders, and epilepsy as case samples within central nervous system diseases. Extraction of key methodological parameters for sample size calculation was performed independently and peer-reviewed. In a final sample of seventy-one eligible trials, we collected data from thirty-one trials. Eighteen protocols were obtained from the public domain or principal investigators. Data availability decreased during the extraction process, with almost all data available for stroke-related trials. Of the thirty-one trials with sample size information available, twenty-six reported a predefined calculation and underlying assumptions. Justification was given in twenty and evidence for parameter estimation in sixteen trials. Estimates were most often based on previous research, including RCTs and observational data. Observational data were predominantly represented by retrospective designs. Other references for parameter estimation indicated a lower level of evidence. Our systematic review of trials on high-risk MDs confirms previous research, which has documented deficiencies regarding data availability and a lack of reporting on sample size calculation. More effort is needed to ensure both relevant sources, that is, original research protocols, to be publicly available and reporting requirements to be standardized.
Size selectivity of standardized multimesh gillnets in sampling coarse European species
Czech Academy of Sciences Publication Activity Database
Prchalová, Marie; Kubečka, Jan; Říha, Milan; Mrkvička, Tomáš; Vašek, Mojmír; Jůza, Tomáš; Kratochvíl, Michal; Peterka, Jiří; Draštík, Vladislav; Křížek, J.
2009-01-01
Roč. 96, č. 1 (2009), s. 51-57 ISSN 0165-7836. [Fish Stock Assessment Methods for Lakes and Reservoirs: Towards the true picture of fish stock. České Budějovice, 11.09.2007-15.09.2007] R&D Projects: GA AV ČR(CZ) 1QS600170504; GA ČR(CZ) GA206/07/1392 Institutional research plan: CEZ:AV0Z60170517 Keywords : gillnet * seine * size selectivity * roach * perch * rudd Subject RIV: EH - Ecology, Behaviour Impact factor: 1.531, year: 2009
Marvanová, Soňa; Kulich, Pavel; Skoupý, Radim; Hubatka, František; Ciganek, Miroslav; Bendl, Jan; Hovorka, Jan; Machala, Miroslav
2018-04-01
Size-segregated particulate matter (PM) is frequently used in chemical and toxicological studies. Nevertheless, toxicological in vitro studies working with the whole particles often lack a proper evaluation of PM real size distribution and characterization of agglomeration under the experimental conditions. In this study, changes in particle size distributions during the PM sample manipulation and also semiquantitative elemental composition of single particles were evaluated. Coarse (1-10 μm), upper accumulation (0.5-1 μm), lower accumulation (0.17-0.5 μm), and ultrafine (<0.17 μm) PM fractions were collected by high volume cascade impactor in Prague city center. Particles were examined using electron microscopy and their elemental composition was determined by energy dispersive X-ray spectroscopy. Larger or smaller particles, not corresponding to the impaction cut points, were found in all fractions, as they occur in agglomerates and are impacted according to their aerodynamic diameter. Elemental composition of particles in size-segregated fractions varied significantly. Ns-soot occurred in all size fractions. Metallic nanospheres were found in accumulation fractions, but not in ultrafine fraction where ns-soot, carbonaceous particles, and inorganic salts were identified. Dynamic light scattering was used to measure particle size distribution in water and in cell culture media. PM suspension of lower accumulation fraction in water agglomerated after freezing/thawing the sample, and the agglomerates were disrupted by subsequent sonication. Ultrafine fraction did not agglomerate after freezing/thawing the sample. Both lower accumulation and ultrafine fractions were stable in cell culture media with fetal bovine serum, while high agglomeration occurred in media without fetal bovine serum as measured during 24 h.
An environmental sampling model for combining judgment and randomly placed samples
Energy Technology Data Exchange (ETDEWEB)
Sego, Landon H.; Anderson, Kevin K.; Matzke, Brett D.; Sieber, Karl; Shulman, Stanley; Bennett, James; Gillen, M.; Wilson, John E.; Pulsipher, Brent A.
2007-08-23
In the event of the release of a lethal agent (such as anthrax) inside a building, law enforcement and public health responders take samples to identify and characterize the contamination. Sample locations may be rapidly chosen based on available incident details and professional judgment. To achieve greater confidence of whether or not a room or zone was contaminated, or to certify that detectable contamination is not present after decontamination, we consider a Bayesian model for combining the information gained from both judgment and randomly placed samples. We investigate the sensitivity of the model to the parameter inputs and make recommendations for its practical use.
Directory of Open Access Journals (Sweden)
Manan Gupta
Full Text Available Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates
Gupta, Manan; Joshi, Amitabh; Vidya, T N C
2017-01-01
Mark-recapture estimators are commonly used for population size estimation, and typically yield unbiased estimates for most solitary species with low to moderate home range sizes. However, these methods assume independence of captures among individuals, an assumption that is clearly violated in social species that show fission-fusion dynamics, such as the Asian elephant. In the specific case of Asian elephants, doubts have been raised about the accuracy of population size estimates. More importantly, the potential problem for the use of mark-recapture methods posed by social organization in general has not been systematically addressed. We developed an individual-based simulation framework to systematically examine the potential effects of type of social organization, as well as other factors such as trap density and arrangement, spatial scale of sampling, and population density, on bias in population sizes estimated by POPAN, Robust Design, and Robust Design with detection heterogeneity. In the present study, we ran simulations with biological, demographic and ecological parameters relevant to Asian elephant populations, but the simulation framework is easily extended to address questions relevant to other social species. We collected capture history data from the simulations, and used those data to test for bias in population size estimation. Social organization significantly affected bias in most analyses, but the effect sizes were variable, depending on other factors. Social organization tended to introduce large bias when trap arrangement was uniform and sampling effort was low. POPAN clearly outperformed the two Robust Design models we tested, yielding close to zero bias if traps were arranged at random in the study area, and when population density and trap density were not too low. Social organization did not have a major effect on bias for these parameter combinations at which POPAN gave more or less unbiased population size estimates. Therefore, the
Sampled-data L-infinity smoothing: fixed-size ARE solution with free hold function
Meinsma, Gjerrit; Mirkin, Leonid
The problem of estimating an analog signal from its noisy sampled measurements is studied in the L-infinity (induced L2-norm) framework. The main emphasis is placed on relaxing causality requirements. Namely, it is assumed that l future measurements are available to the estimator, which corresponds
Tu, Xinjun; Du, Xiaoxia; Singh, Vijay P.; Chen, Xiaohong; Du, Yiliang; Li, Kun
2017-11-01
Constructing a joint distribution of low flows between the donor and recipient basins and analyzing their joint risk are commonly required for implementing interbasin water transfer. In this study, daily streamflow data of bi-basin low flows were sampled at window sizes from 3 to183 days by using the annual minimum method. The stationarity of low flows was tested by a change point analysis and non-stationary low flows were reconstructed by using the moving mean method. Three bivariate Archimedean copulas and five common univariate distributions were applied to fit the joint and marginal distributions of bi-basin low flows. Then, by considering the window size of sampling low flows under environmental change, the change in the joint risk of interbasin water transfer was investigated. Results showed that the non-stationarity of low flows in the recipient basin at all window sizes was significant due to the regulation of water reservoirs. The general extreme value distribution was found to fit the marginal distributions of bi-basin low flows. Three Archimedean copulas satisfactorily fitted the joint distribution of bi-basin low flows and then the Frank copula was found to be the comparatively better. The moving mean method differentiated the location parameter of the GEV distribution, but did not differentiate the scale and shape parameters, and the copula parameters. Due to environmental change, in particular the regulation of water reservoirs in the recipient basin, the decrease of the joint synchronous risk of bi-basin water shortage was slight, but those of the synchronous assurance of water transfer from the donor were remarkable. With the enlargement of window size of sampling low flows, both the joint synchronous risk of bi-basin water shortage, and the joint synchronous assurance of water transfer from the donor basin when there was a water shortage in the recipient basin exhibited a decreasing trend, but their changes were with a slight fluctuation, in
In situ detection of small-size insect pests sampled on traps using multifractal analysis
Xia, Chunlei; Lee, Jang-Myung; Li, Yan; Chung, Bu-Keun; Chon, Tae-Soo
2012-02-01
We introduce a multifractal analysis for detecting the small-size pest (e.g., whitefly) images from a sticky trap in situ. An automatic attraction system is utilized for collecting pests from greenhouse plants. We applied multifractal analysis to segment action of whitefly images based on the local singularity and global image characteristics. According to the theory of multifractal dimension, the candidate blobs of whiteflies are initially defined from the sticky-trap image. Two schemes, fixed thresholding and regional minima obtainment, were utilized for feature extraction of candidate whitefly image areas. The experiment was conducted with the field images in a greenhouse. Detection results were compared with other adaptive segmentation algorithms. Values of F measuring precision and recall score were higher for the proposed multifractal analysis (96.5%) compared with conventional methods such as Watershed (92.2%) and Otsu (73.1%). The true positive rate of multifractal analysis was 94.3% and the false positive rate minimal level at 1.3%. Detection performance was further tested via human observation. The degree of scattering between manual and automatic counting was remarkably higher with multifractal analysis (R2=0.992) compared with Watershed (R2=0.895) and Otsu (R2=0.353), ensuring overall detection of the small-size pests is most feasible with multifractal analysis in field conditions.
Aznar, Ramón; Barahona, Francisco; Geiss, Otmar; Ponti, Jessica; José Luis, Tadeo; Barrero-Moreno, Josefa
2017-12-01
Single particle-inductively coupled plasma mass spectrometry (SP-ICPMS) is a promising technique able to generate the number based-particle size distribution (PSD) of nanoparticles (NPs) in aqueous suspensions. However, SP-ICPMS analysis is not consolidated as routine-technique yet and is not typically applied to real test samples with unknown composition. This work presents a methodology to detect, quantify and characterise the number-based PSD of Ag-NPs in different environmental aqueous samples (drinking and lake waters), aqueous samples derived from migration tests and consumer products using SP-ICPMS. The procedure is built from a pragmatic view and involves the analysis of serial dilutions of the original sample until no variation in the measured size values is observed while keeping particle counts proportional to the dilution applied. After evaluation of the analytical figures of merit, the SP-ICPMS method exhibited excellent linearity (r 2 >0.999) in the range (1-25) × 10 4 particlesmL -1 for 30, 50 and 80nm nominal size Ag-NPs standards. The precision in terms of repeatability was studied according to the RSDs of the measured size and particle number concentration values and a t-test (p = 95%) at the two intermediate concentration levels was applied to determine the bias of SP-ICPMS size values compared to reference values. The method showed good repeatability and an overall acceptable bias in the studied concentration range. The experimental minimum detectable size for Ag-NPs ranged between 12 and 15nm. Additionally, results derived from direct SP-ICPMS analysis were compared to the results conducted for fractions collected by asymmetric flow-field flow fractionation and supernatant fractions after centrifugal filtration. The method has been successfully applied to determine the presence of Ag-NPs in: lake water; tap water; tap water filtered by a filter jar; seven different liquid silver-based consumer products; and migration solutions (pure water and
Sample selection and taste correlation in discrete choice transport modelling
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2008-01-01
of taste correlation in willingness-to-pay estimation are presented. The first contribution addresses how to incorporate taste correlation in the estimation of the value of travel time for public transport. Given a limited dataset the approach taken is to use theory on the value of travel time as guidance...... many issues that deserve attention. This thesis investigates how sample selection can affect estimation of discrete choice models and how taste correlation should be incorporated into applied mixed logit estimation. Sampling in transport modelling is often based on an observed trip. This may cause...... a sample to be choice-based or governed by a self-selection mechanism. In both cases, there is a possibility that sampling affects the estimation of a population model. It was established in the seventies how choice-based sampling affects the estimation of multinomial logit models. The thesis examines...
An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies.
Directory of Open Access Journals (Sweden)
Wesley K Thompson
2015-12-01
Full Text Available Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD and the other for schizophrenia (SZ. A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the
An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies.
Thompson, Wesley K; Wang, Yunpeng; Schork, Andrew J; Witoelar, Aree; Zuber, Verena; Xu, Shujing; Werge, Thomas; Holland, Dominic; Andreassen, Ole A; Dale, Anders M
2015-12-01
Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of
Directory of Open Access Journals (Sweden)
John M Lachin
Full Text Available Preservation of β-cell function as measured by stimulated C-peptide has recently been accepted as a therapeutic target for subjects with newly diagnosed type 1 diabetes. In recently completed studies conducted by the Type 1 Diabetes Trial Network (TrialNet, repeated 2-hour Mixed Meal Tolerance Tests (MMTT were obtained for up to 24 months from 156 subjects with up to 3 months duration of type 1 diabetes at the time of study enrollment. These data provide the information needed to more accurately determine the sample size needed for future studies of the effects of new agents on the 2-hour area under the curve (AUC of the C-peptide values. The natural log(x, log(x+1 and square-root (√x transformations of the AUC were assessed. In general, a transformation of the data is needed to better satisfy the normality assumptions for commonly used statistical tests. Statistical analysis of the raw and transformed data are provided to estimate the mean levels over time and the residual variation in untreated subjects that allow sample size calculations for future studies at either 12 or 24 months of follow-up and among children 8-12 years of age, adolescents (13-17 years and adults (18+ years. The sample size needed to detect a given relative (percentage difference with treatment versus control is greater at 24 months than at 12 months of follow-up, and differs among age categories. Owing to greater residual variation among those 13-17 years of age, a larger sample size is required for this age group. Methods are also described for assessment of sample size for mixtures of subjects among the age categories. Statistical expressions are presented for the presentation of analyses of log(x+1 and √x transformed values in terms of the original units of measurement (pmol/ml. Analyses using different transformations are described for the TrialNet study of masked anti-CD20 (rituximab versus masked placebo. These results provide the information needed to
Margetan, Frank J.; Leckey, Cara A.; Barnard, Dan
2012-01-01
The size and shape of a delamination in a multi-layered structure can be estimated in various ways from an ultrasonic pulse/echo image. For example the -6dB contours of measured response provide one simple estimate of the boundary. More sophisticated approaches can be imagined where one adjusts the proposed boundary to bring measured and predicted UT images into optimal agreement. Such approaches require suitable models of the inspection process. In this paper we explore issues pertaining to model-based size estimation for delaminations in carbon fiber reinforced laminates. In particular we consider the influence on sizing when the delamination is non-planar or partially transmitting in certain regions. Two models for predicting broadband sonic time-domain responses are considered: (1) a fast "simple" model using paraxial beam expansions and Kirchhoff and phase-screen approximations; and (2) the more exact (but computationally intensive) 3D elastodynamic finite integration technique (EFIT). Model-to-model and model-to experiment comparisons are made for delaminations in uniaxial composite plates, and the simple model is then used to critique the -6dB rule for delamination sizing.
Margetan, Frank J.; Leckey, Cara A.; Barnard, Dan
2013-01-01
The size and shape of a delamination in a multi-layered structure can be estimated in various ways from an ultrasonic pulse/echo image. For example the -6dB contours of measured response provide one simple estimate of the boundary. More sophisticated approaches can be imagined where one adjusts the proposed boundary to bring measured and predicted UT images into optimal agreement. Such approaches require suitable models of the inspection process. In this paper we explore issues pertaining to model-based size estimation for delaminations in carbon fiber reinforced laminates. In particular we consider the influence on sizing when the delamination is non-planar or partially transmitting in certain regions. Two models for predicting broadband sonic time-domain responses are considered: (1) a fast "simple" model using paraxial beam expansions and Kirchhoff and phase-screen approximations; and (2) the more exact (but computationally intensive) 3D elastodynamic finite integration technique (EFIT). Model-to-model and model-to experiment comparisons are made for delaminations in uniaxial composite plates, and the simple model is then used to critique the -6dB rule for delamination sizing.
Mass Balance Model, A study of contamination effects in AMS 14C sample analysis
Prokopiou, Markella
2010-01-01
In this training thesis a background correction analysis, also known as mass balance model, was implemented to study the contamination effects in AMS 14C sample processing. A variety of backgrounds and standards with sizes ranging from 50 μg C to 1500 μg
Ultrasonic detection and sizing of cracks in cast stainless steel samples
International Nuclear Information System (INIS)
Allidi, F.; Edelmann, X.; Phister, O.; Hoegberg, K.; Pers-Anderson, E.B.
1986-01-01
The test consisted of 15 samples of cast stainless steel, each with a weld. Some of the specimens were provided with artificially made thermal fatique cracks. The inspection was performed with the P-scan method. The investigations showed an improvement of recognizability relative to earlier investigations. One probe, the dual type, longitudinal wave 45 degrees, low frequence 0.5-1 MHz gives the best results. (G.B.)
Portfolio size as funktion of the premium: modeling and optimization
DEFF Research Database (Denmark)
Asmussen, Søren; Christensen, Bent Jesper; Taksar, Michael I
, where r is the risk-free interest rate. Based on calculations of the customers' present values of the alternative strategies of insuring and not insuring, the portfolio size n(p) is derived, and also the rate of claims from the insured customers is given. Further, the value of p which is optimal......An insurance company has a large number N of potential customers characterized by i.i.d. r.v.'s A1,…,AN giving the arrival rates of claims. Customers are risk averse, and a customer accepts an offered premium p according to his A-value. The modeling further involves a discount rate d>r of customers...... for minimizing the ruin probability is derived in a diffusion approximation to the Cramér-Lundberg risk process with an added liability rate L of the company. The solution involves the Lambert W function. Similar discussion is given for extensions involving customers having only partial information...
Rakow, Tobias; El Deeb, Sami; Hahne, Thomas; El-Hady, Deia Abd; AlBishri, Hassan M; Wätzig, Hermann
2014-09-01
In this study, size-exclusion chromatography and high-resolution atomic absorption spectrometry methods have been developed and evaluated to test the stability of proteins during sample pretreatment. This especially includes different storage conditions but also adsorption before or even during the chromatographic process. For the development of the size exclusion method, a Biosep S3000 5 μm column was used for investigating a series of representative model proteins, namely bovine serum albumin, ovalbumin, monoclonal immunoglobulin G antibody, and myoglobin. Ambient temperature storage was found to be harmful to all model proteins, whereas short-term storage up to 14 days could be done in an ordinary refrigerator. Freezing the protein solutions was always complicated and had to be evaluated for each protein in the corresponding solvent. To keep the proteins in their native state a gentle freezing temperature should be chosen, hence liquid nitrogen should be avoided. Furthermore, a high-resolution continuum source atomic absorption spectrometry method was developed to observe the adsorption of proteins on container material and chromatographic columns. Adsorption to any container led to a sample loss and lowered the recovery rates. During the pretreatment and high-performance size-exclusion chromatography, adsorption caused sample losses of up to 33%. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Campolina, Daniel de A. M.; Lima, Claubia P. B.; Veloso, Maria Auxiliadora F.
2014-06-01
For all the physical components that comprise a nuclear system there is an uncertainty. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a best estimate calculation that has been replacing the conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using a Monte Carlo code by sampling the input parameters is recent because of the huge computational effort required. In this work a sample space of MCNPX calculations was used to propagate the uncertainty. The sample size was optimized using the Wilks formula for a 95th percentile and a two-sided statistical tolerance interval of 95%. Uncertainties in input parameters of the reactor considered included geometry dimensions and densities. It was showed the capacity of the sampling-based method for burnup when the calculations sample size is optimized and many parameter uncertainties are investigated together, in the same input.
International Nuclear Information System (INIS)
Polach, H.; Robertson, S.; Kaihola, L.
1982-01-01
Radiocarbon dating parameters, such as instrumental techniques used, dating precision achieved, sample size, cost and availability of equipment and, in more detail, the merit of small gas proportional counting systems are considered. It is shown that small counters capable of handling 10-100mg of carbon are a viable proposition in terms of achievable precision and in terms of sample turnover, if some 10 mini-counters are operated simultaneously within the same shield. After consideration of the factors affecting the performance of a small gas proportional system it is concluded that an automatic, labour saving, cost effective and efficient carbon dating system, based on some sixteen 10 ml-size counters operating in parallel, could be built using state-of-art knowledge and components
Sex determination by tooth size in a sample of Greek population.
Mitsea, A G; Moraitis, K; Leon, G; Nicopoulou-Karayianni, K; Spiliopoulou, C
2014-08-01
Sex assessment from tooth measurements can be of major importance for forensic and bioarchaeological investigations, especially when only teeth or jaws are available. The purpose of this study is to assess the reliability and applicability of establishing sex identity in a sample of Greek population using the discriminant function proposed by Rösing et al. (1995). The study comprised of 172 dental casts derived from two private orthodontic clinics in Athens. The individuals were randomly selected and all had clear medical history. The mesiodistal crown diameters of all the teeth were measured apart from those of the 3rd molars. The values quoted for the sample to which the discriminant function was first applied were similar to those obtained for the Greek sample. The results of the preliminary statistical analysis did not support the use of the specific discriminant function for a reliable determination of sex by means of the mesiodistal diameter of the teeth. However, there was considerable variation between different populations and this might explain the reason for lack of discriminating power of the specific function in the Greek population. In order to investigate whether a better discriminant function could be obtained using the Greek data, separate discriminant function analysis was performed on the same teeth and a different equation emerged without, however, any real improvement in the classification process, with an overall correct classification of 72%. The results showed that there were a considerably higher percentage of females correctly classified than males. The results lead to the conclusion that the use of the mesiodistal diameter of teeth is not as a reliable method as one would have expected for determining sex of human remains from a forensic context. Therefore, this method could be used only in combination with other identification approaches. Copyright © 2014. Published by Elsevier GmbH.
Energy Technology Data Exchange (ETDEWEB)
Plionis, Alexander A [Los Alamos National Laboratory; Peterson, Dominic S [Los Alamos National Laboratory; Tandon, Lav [Los Alamos National Laboratory; Lamont, Stephen P [Los Alamos National Laboratory
2009-01-01
Uranium particles within the respirable size range pose a significant hazard to the health and safety of workers. Significant differences in the deposition and incorporation patterns of aerosols within the respirable range can be identified and integrated into sophisticated health physics models. Data characterizing the uranium particle size distribution resulting from specific foundry-related processes are needed. Using personal air sampling cascade impactors, particles collected from several foundry processes were sorted by activity median aerodynamic diameter onto various Marple substrates. After an initial gravimetric assessment of each impactor stage, the substrates were analyzed by alpha spectrometry to determine the uranium content of each stage. Alpha spectrometry provides rapid nondestructive isotopic data that can distinguish process uranium from natural sources and the degree of uranium contribution to the total accumulated particle load. In addition, the particle size bins utilized by the impactors provide adequate resolution to determine if a process particle size distribution is: lognormal, bimodal, or trimodal. Data on process uranium particle size values and distributions facilitate the development of more sophisticated and accurate models for internal dosimetry, resulting in an improved understanding of foundry worker health and safety.
Fraley, R. Chris; Vazire, Simine
2014-01-01
The authors evaluate the quality of research reported in major journals in social-personality psychology by ranking those journals with respect to their N-pact Factors (NF)—the statistical power of the empirical studies they publish to detect typical effect sizes. Power is a particularly important attribute for evaluating research quality because, relative to studies that have low power, studies that have high power are more likely to (a) to provide accurate estimates of effects, (b) to produce literatures with low false positive rates, and (c) to lead to replicable findings. The authors show that the average sample size in social-personality research is 104 and that the power to detect the typical effect size in the field is approximately 50%. Moreover, they show that there is considerable variation among journals in sample sizes and power of the studies they publish, with some journals consistently publishing higher power studies than others. The authors hope that these rankings will be of use to authors who are choosing where to submit their best work, provide hiring and promotion committees with a superior way of quantifying journal quality, and encourage competition among journals to improve their NF rankings. PMID:25296159
Fraley, R Chris; Vazire, Simine
2014-01-01
The authors evaluate the quality of research reported in major journals in social-personality psychology by ranking those journals with respect to their N-pact Factors (NF)-the statistical power of the empirical studies they publish to detect typical effect sizes. Power is a particularly important attribute for evaluating research quality because, relative to studies that have low power, studies that have high power are more likely to (a) to provide accurate estimates of effects, (b) to produce literatures with low false positive rates, and (c) to lead to replicable findings. The authors show that the average sample size in social-personality research is 104 and that the power to detect the typical effect size in the field is approximately 50%. Moreover, they show that there is considerable variation among journals in sample sizes and power of the studies they publish, with some journals consistently publishing higher power studies than others. The authors hope that these rankings will be of use to authors who are choosing where to submit their best work, provide hiring and promotion committees with a superior way of quantifying journal quality, and encourage competition among journals to improve their NF rankings.
Directory of Open Access Journals (Sweden)
Finch Stephen J
2005-04-01
Full Text Available Abstract Background Phenotype error causes reduction in power to detect genetic association. We present a quantification of phenotype error, also known as diagnostic error, on power and sample size calculations for case-control genetic association studies between a marker locus and a disease phenotype. We consider the classic Pearson chi-square test for independence as our test of genetic association. To determine asymptotic power analytically, we compute the distribution's non-centrality parameter, which is a function of the case and control sample sizes, genotype frequencies, disease prevalence, and phenotype misclassification probabilities. We derive the non-centrality parameter in the presence of phenotype errors and equivalent formulas for misclassification cost (the percentage increase in minimum sample size needed to maintain constant asymptotic power at a fixed significance level for each percentage increase in a given misclassification parameter. We use a linear Taylor Series approximation for the cost of phenotype misclassification to determine lower bounds for the relative costs of misclassifying a true affected (respectively, unaffected as a control (respectively, case. Power is verified by computer simulation. Results Our major findings are that: (i the median absolute difference between analytic power with our method and simulation power was 0.001 and the absolute difference was no larger than 0.011; (ii as the disease prevalence approaches 0, the cost of misclassifying a unaffected as a case becomes infinitely large while the cost of misclassifying an affected as a control approaches 0. Conclusion Our work enables researchers to specifically quantify power loss and minimum sample size requirements in the presence of phenotype errors, thereby allowing for more realistic study design. For most diseases of current interest, verifying that cases are correctly classified is of paramount importance.
RNA Profiling for Biomarker Discovery: Practical Considerations for Limiting Sample Sizes
Directory of Open Access Journals (Sweden)
Danny J. Kelly
2005-01-01
Full Text Available We have compared microarray data generated on Affymetrix™ chips from standard (8 micrograms or low (100 nanograms amounts of total RNA. We evaluated the gene signals and gene fold-change estimates obtained from the two methods and validated a subset of the results by real time, polymerase chain reaction assays. The correlation of low RNA derived gene signals to gene signals obtained from standard RNA was poor for less to moderately abundant genes. Genes with high abundance showed better correlation in signals between the two methods. The signal correlation between the low RNA and standard RNA methods was improved by including a reference sample in the microarray analysis. In contrast, the fold-change estimates for genes were better correlated between the two methods regardless of the magnitude of gene signals. A reference sample based method is suggested for studies that would end up comparing gene signal data from a combination of low and standard RNA templates; no such referencing appears to be necessary when comparing fold-changes of gene expression between standard and low template reactions.
Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data
Dong, Kai
2015-09-16
DNA sequencing techniques bring novel tools and also statistical challenges to genetic research. In addition to detecting differentially expressed genes, testing the significance of gene sets or pathway analysis has been recognized as an equally important problem. Owing to the “large pp small nn” paradigm, the traditional Hotelling’s T2T2 test suffers from the singularity problem and therefore is not valid in this setting. In this paper, we propose a shrinkage-based diagonal Hotelling’s test for both one-sample and two-sample cases. We also suggest several different ways to derive the approximate null distribution under different scenarios of pp and nn for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when nn is moderate or large, but it is better when nn is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling’s test.
Sampling to estimate population size and detect trends in Tricolored Blackbirds
Meese, Robert; Yee, Julie L.; Holyoak, Marcel
2015-01-01
The Tricolored Blackbird (Agelaius tricolor) is a medium-sized passerine that nests in the largest colonies of any North American landbird since the extinction of the passenger pigeon (Ectopistes migratorius) over 100 years ago (Beedy and Hamilton 1999). The species has a restricted range that occurs almost exclusively within California, with only a few hundred birds scattered in small groups in Oregon, Washington, Nevada, and northwestern Baja California, Mexico (Beedy and Hamilton 1999). Tricolored Blackbirds are itinerant breeders (i.e., breed more than once per year in different locations) and use a wide variety of nesting substrates (Hamilton 1998), many of which are ephemeral. They are also insect dependent during the breeding season, and reproductive success is strongly correlated with relative insect abundance (Meese 2013). Researchers have noted for decades that Tricolored Blackbird’s insect prey are highly variable in space and time; Payne (1969), for example, described the species as a grasshopper follower because they are preferred food items, and high grasshopper abundance is often associated with high reproductive success (Payne 1969, Meese 2013). Thus, the species’ basic reproductive strategy is tied to rather infrequent periods of relatively high insect abundance in some locations followed by much longer periods of range -wide relatively low insect abundance and poor reproductive success. Of course, anthropogenic factors such as habitat loss and insecticide use may be at least partly responsible for these patterns (Hallman et al. 2014, Airola et al. 2014).
Dealing with large sample sizes: comparison of a new one spot dot blot method to western blot.
Putra, Sulistyo Emantoko Dwi; Tsuprykov, Oleg; Von Websky, Karoline; Ritter, Teresa; Reichetzeder, Christoph; Hocher, Berthold
2014-01-01
Western blot is the gold standard method to determine individual protein expression levels. However, western blot is technically difficult to perform in large sample sizes because it is a time consuming and labor intensive process. Dot blot is often used instead when dealing with large sample sizes, but the main disadvantage of the existing dot blot techniques, is the absence of signal normalization to a housekeeping protein. In this study we established a one dot two development signals (ODTDS) dot blot method employing two different signal development systems. The first signal from the protein of interest was detected by horseradish peroxidase (HRP). The second signal, detecting the housekeeping protein, was obtained by using alkaline phosphatase (AP). Inter-assay results variations within ODTDS dot blot and western blot and intra-assay variations between both methods were low (1.04-5.71%) as assessed by coefficient of variation. ODTDS dot blot technique can be used instead of western blot when dealing with large sample sizes without a reduction in results accuracy.
The Hierarchy Model of the Size Distribution of Centres
J. Tinbergen (Jan)
1968-01-01
textabstractWe know that human beings live in centres, that is, cities, towns and villages of different size. Both large and small centres have a number of advantages and disadvantages, different for different people and this is why we have a whole range of sizes. Statistically, we even find that
A GMM-Based Test for Normal Disturbances of the Heckman Sample Selection Model
Directory of Open Access Journals (Sweden)
Michael Pfaffermayr
2014-10-01
Full Text Available The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek (2007, the present contribution derives a GMM-based pseudo-score LM test on whether the third and fourth moments of the disturbances of the outcome equation of the Heckman model conform to those implied by the truncated normal distribution. The test is easy to calculate and in Monte Carlo simulations it shows good performance for sample sizes of 1000 or larger.
Directory of Open Access Journals (Sweden)
V. Indira
2015-03-01
Full Text Available Hydraulic brake in automobile engineering is considered to be one of the important components. Condition monitoring and fault diagnosis of such a component is very essential for safety of passengers, vehicles and to minimize the unexpected maintenance time. Vibration based machine learning approach for condition monitoring of hydraulic brake system is gaining momentum. Training and testing the classifier are two important activities in the process of feature classification. This study proposes a systematic statistical method called power analysis to find the minimum number of samples required to train the classifier with statistical stability so as to get good classification accuracy. Descriptive statistical features have been used and the more contributing features have been selected by using C4.5 decision tree algorithm. The results of power analysis have also been verified using a decision tree algorithm namely, C4.5.
MAID : An effect size based model for microarray data integration across laboratories and platforms
Directory of Open Access Journals (Sweden)
Edwards Aled M
2008-07-01
Full Text Available Abstract Background Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions. Results Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct
Directory of Open Access Journals (Sweden)
B. Kone
2009-05-01
Full Text Available Soil texture is an important property for evaluating its inherent fertility especially by using pedo-transfers functions requiring particle size data. However, there is no existing quantitative method for in situ estimation of soil particle size, delaying judgement of soil chemical properties in the field. For this purpose, laboratory particle size analyses of 1028 samples from 281 Ferralsol profiles, located between latitudes 7º N and 10º N in Côte d’Ivoire and their respective colour notation by Munsell chart were used to generate prediction models. Multiple Linear Regression Analysis by Group was processed to identify clay, sand and silt contents in the soil based on color hue (2.5YR, 5YR, 7.5YR, and 10YR and Chroma (1, 2, 3, 4, 5, 6, 7, 8. The evaluation was conducted for each horizon coded as H1 (0-20 cm, H2 (20-60 cm, H3 (60-80 cm and H4 (80-150 cm and used as grouping variables. Highly significant (P< 0.001 models were identified for clay and sand. These models were used to estimate successfully clay and sand contents for other Ferralsol samples by comparing calculated and measured mean using the null hypothesis of difference and Tukey’s tests. They were accurate for at all depths, except 80 - 150 cm, for sand in 10YR soils. The method was deemed appropriate for in situ estimation of soil particle size contents in Ferralsol environment for improving reconnaissance agricultural soil surveys.
Ruthrauff, Daniel R.; Tibbitts, T. Lee; Gill, Robert E.; Dementyev, Maksim N.; Handel, Colleen M.
2012-01-01
The Rock Sandpiper (Calidris ptilocnemis) is endemic to the Bering Sea region and unique among shorebirds in the North Pacific for wintering at high latitudes. The nominate subspecies, the Pribilof Rock Sandpiper (C. p. ptilocnemis), breeds on four isolated islands in the Bering Sea and appears to spend the winter primarily in Cook Inlet, Alaska. We used a stratified systematic sampling design and line-transect method to survey the entire breeding range of this population during springs 2001-2003. Densities were up to four times higher on the uninhabited and more northerly St. Matthew and Hall islands than on St. Paul and St. George islands, which both have small human settlements and introduced reindeer herds. Differences in density, however, appeared to be more related to differences in vegetation than to anthropogenic factors, raising some concern for prospective effects of climate change. We estimated the total population at 19 832 birds (95% CI 17 853–21 930), ranking it among the smallest of North American shorebird populations. To determine the vulnerability of C. p. ptilocnemis to anthropogenic and stochastic environmental threats, future studies should focus on determining the amount of gene flow among island subpopulations, the full extent of the subspecies' winter range, and the current trajectory of this small population.
Mikkelsen, Mark; Loo, Rachelle S; Puts, Nicolaas A J; Edden, Richard A E; Harris, Ashley D
2018-02-21
The relationships between scan duration, signal-to-noise ratio (SNR) and sample size must be considered and understood to design optimal GABA-edited magnetic resonance spectroscopy (MRS) studies. Simulations investigated the effects of signal averaging on SNR, measurement error and group-level variance against a known ground truth. Relative root mean square errors (measurement error) and coefficients of variation (group-level variance) were calculated. GABA-edited data from 18 participants acquired from five voxels were used to examine the relationships between scan duration, SNR and quantitative outcomes in vivo. These relationships were then used to determine the sample sizes required to observe different effect sizes. In both simulated and in vivo data, SNR increased with the square root of the number of averages. Both measurement error and group-level variance were shown to follow an inverse-square-root function, indicating no significant impact of cumulative artifacts. Comparisons between the first two-thirds of the data and the full dataset showed no statistical difference in group-level variance. There was, however, some variability across the five voxels depending on SNR, which impacted the sample sizes needed to detect group differences in specific brain regions. Typical scan durations can be reduced if taking into account a statistically acceptable amount of variance and the magnitudes of predicted effects. While scan duration in GABA-edited MRS has typically been considered in terms of SNR, it is more appropriate to think in terms of the amount of measurement error and group-level variance that provides sufficient statistical power. Copyright © 2018 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Akram, M.; Aftab, F.
2016-01-01
In the present study, fruits (drupes) were collected from Changa Manga Forest Plus Trees (CMF-PT), Changa Manga Forest Teak Stand (CMF-TS) and Punjab University Botanical Gardens (PUBG) and categorized into very large (= 17 mm dia.), large (12-16 mm dia.), medium (9-11 mm dia.) or small (6-8 mm dia.) fruit size grades. Fresh water as well as mechanical scarification and stratification were tested for breaking seed dormancy. Viability status of seeds was estimated by cutting test, X-rays and In vitro seed germination. Out of 2595 fruits from CMF-PT, 500 fruits were of very large grade. This fruit category also had highest individual fruit weight (0.58 g) with more number of 4-seeded fruits (5.29 percent) and fair germination potential (35.32 percent). Generally, most of the fruits were 1-seeded irrespective of size grades and sampling sites. Fresh water scarification had strong effect on germination (44.30 percent) as compared to mechanical scarification and cold stratification after 40 days of sowing. Similarly, sampling sites and fruit size grades also had significant influence on germination. Highest germination (82.33 percent) was obtained on MS (Murashige and Skoog) agar-solidified medium as compared to Woody Plant Medium (WPM) (69.22 percent). Seedlings from all the media were transferred to ex vitro conditions in the greenhouse and achieved highest survival (28.6 percent) from seedlings previously raised on MS agar-solidified medium after 40 days. There was an association between the studied parameters of teak seeds and the sampling sites and fruit size. (author)
Webb, Kristen; Allard, Marc
2010-02-01
Evolutionary and forensic studies commonly choose the mitochondrial control region as the locus for which to evaluate the domestic dog. However, the number of dogs that need to be sampled in order to represent the control region variation present in the worldwide population is yet to be determined. Following the methods of Pereira et al. (2004), we have demonstrated the importance of surveying the complete control region rather than only the popular left domain. We have also evaluated sample saturation in terms of the haplotype number and the number of polymorphisms within the control region. Of the most commonly cited evolutionary research, only a single study has adequately surveyed the domestic dog population, while all forensic studies have failed to meet the minimum values. We recommend that future studies consider dataset size when designing experiments and ideally sample both domains of the control region in an appropriate number of domestic dogs.
Model-directed sampling in Chernobyl forests: General methodology and 1994 sampling program
International Nuclear Information System (INIS)
Radiologically-contaminated forest and natural ecosystems contribute significantly to the human radiation dose in the intermediate (several years) and long (several decades) terms following the radionuclide release. As a result of the Chernobyl nuclear power plant accident in 1986, extensive forested areas in Europe were significantly contaminated with cesium, strontium, plutonium and other radionuclides. This study develops a dynamic model that describes the pathways of radionuclides which undergo complex transfer processes in forests and natural ecosystems. This generic model, FORESTPATH, calculates time-dependent radionuclide concentrations in forest compartments based on the information available on residence half-times. Because of the high complexity, traditional sampling programs often provide only limited and fragmented information for the ecosystem to be modeled. A model-directed sampling program was initiated which implies close feedback between ecosystem sampling and modeling of the radionuclide pathways using the FORESTPATH model. This program is now being applied by an international team of USA, European Union (EU) and Newly Independent States (NIS) members in the Chernobyl Nuclear Power Plant (NPP) Exclusion Zone
Optimizing incomplete sample designs for item response model parameters
van der Linden, Willem J.
Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with
Latent spatial models and sampling design for landscape genetics
Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.
2016-01-01
We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
Heo, Moonseong; Kim, Yongman; Xue, Xiaonan; Kim, Mimi Y
2010-02-10
It is often anticipated in a longitudinal cluster randomized clinical trial (cluster-RCT) that the course of outcome over time will diverge between intervention arms. In these situations, testing the significance of a local intervention effect at the end of the trial may be more clinically relevant than evaluating overall mean differences between treatment groups. In this paper, we present a closed-form power function for detecting this local intervention effect based on maximum likelihood estimates from a mixed-effects linear regression model for three-level continuous data. Sample size requirements for the number of units at each data level are derived from the power function. The power function and the corresponding sample size requirements are verified by a simulation study. Importantly, it is shown that sample size requirements computed with the proposed power function are smaller than that required when testing group mean difference using data only at the end of trial and ignoring the course of outcome over the entire study period. (c) 2009 John Wiley & Sons, Ltd.
Particle size distribution models of small angle neutron scattering pattern on ferro fluids
International Nuclear Information System (INIS)
Sistin Asri Ani; Darminto; Edy Giri Rachman Putra
2009-01-01
The Fe 3 O 4 ferro fluids samples were synthesized by a co-precipitation method. The investigation of ferro fluids microstructure is known to be one of the most important problems because the presence of aggregates and their internal structure influence greatly the properties of ferro fluids. The size and the size dispersion of particle in ferro fluids were determined assuming a log normal distribution of particle radius. The scattering pattern of the measurement by small angle neutron scattering were fitted by the theoretical scattering function of two limitation models are log normal sphere distribution and fractal aggregate. Two types of particle are detected, which are presumably primary particle of 30 Armstrong in radius and secondary fractal aggregate of 200 Armstrong with polydispersity of 0.47 up to 0.53. (author)
Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions.
Forstmann, B U; Ratcliff, R; Wagenmakers, E-J
2016-01-01
Sequential sampling models assume that people make speeded decisions by gradually accumulating noisy information until a threshold of evidence is reached. In cognitive science, one such model--the diffusion decision model--is now regularly used to decompose task performance into underlying processes such as the quality of information processing, response caution, and a priori bias. In the cognitive neurosciences, the diffusion decision model has recently been adopted as a quantitative tool to study the neural basis of decision making under time pressure. We present a selective overview of several recent applications and extensions of the diffusion decision model in the cognitive neurosciences.
Geostatistical modeling of riparian forest microclimate and its implications for sampling
Eskelson, B.N.I.; Anderson, P.D.; Hagar, J.C.; Temesgen, H.
2011-01-01
Predictive models of microclimate under various site conditions in forested headwater stream - riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (Tair). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas.
A grain size distribution model for non-catalytic gas-solid reactions
Heesink, Albertus B.M.; Prins, W.; van Swaaij, Willibrordus Petrus Maria
1993-01-01
A new model to describe the non-catalytic conversion of a solid by a reactant gas is proposed. This so-called grain size distribution (GSD) model presumes the porous particle to be a collection of grains of various sizes. The size distribution of the grains is derived from mercury porosimetry
A multidimensional stability model for predicting shallow landslide size and shape across landscapes
David G. Milledge; Dino Bellugi; Jim A. McKean; Alexander L. Densmore; William E. Dietrich
2014-01-01
The size of a shallow landslide is a fundamental control on both its hazard and geomorphic importance. Existing models are either unable to predict landslide size or are computationally intensive such that they cannot practically be applied across landscapes. We derive a model appropriate for natural slopes that is capable of predicting shallow landslide size but...
International Nuclear Information System (INIS)
Blanpain, O.
2009-10-01
The purpose of this work is the implementation of a sediment transport model in the English Channel. The design of such a model requires the identification of the physical processes, their modelling and their in-situ validation. Because of the sedimentary particularities of the study area, modelling of the mechanical behaviour of a non uniform mixture of sediments and particularly of the fine grains within a coarse matrix is required. This study focused on the characterization of the relevant processes by acquisition of experimental and in-situ data. Data acquired in hydro-sedimentary conditions comparable to those found in the English Channel are scarce. A new instrument and image processing technique were specifically conceived and implemented in-situ to observe and measure, with a high temporal resolution, the dynamics of a strongly heterogeneous mixture of particles in a grain-size scale. The data collected compared well with several existing formulations. One of these formulations was chosen to be adapted. The transfer dynamics of fine grains in coarse sediments and their depth of penetration were acquired from stratigraphic samples. The sediment transport model deals with multi-size grains and multi sedimentary layers, it is forced by swell and currents, and accounts for bead load and suspended load transports. It was applied to realistic scenarios for the English Channel. (author)
Vlašić Tanasković, Jelena; Coucke, Wim; Leniček Krleža, Jasna; Vuković Rodriguez, Jadranka
2017-03-01
Laboratory evaluation through external quality assessment (EQA) schemes is often performed as 'peer group' comparison under the assumption that matrix effects influence the comparisons between results of different methods, for analytes where no commutable materials with reference value assignment are available. With EQA schemes that are not large but have many available instruments and reagent options for same analyte, homogenous peer groups must be created with adequate number of results to enable satisfactory statistical evaluation. We proposed a multivariate analysis of variance (MANOVA)-based test to evaluate heterogeneity of peer groups within the Croatian EQA biochemistry scheme and identify groups where further splitting might improve laboratory evaluation. EQA biochemistry results were divided according to instruments used per analyte and the MANOVA test was used to verify statistically significant differences between subgroups. The number of samples was determined by sample size calculation ensuring a power of 90% and allowing the false flagging rate to increase not more than 5%. When statistically significant differences between subgroups were found, clear improvement of laboratory evaluation was assessed before splitting groups. After evaluating 29 peer groups, we found strong evidence for further splitting of six groups. Overall improvement of 6% reported results were observed, with the percentage being as high as 27.4% for one particular method. Defining maximal allowable differences between subgroups based on flagging rate change, followed by sample size planning and MANOVA, identifies heterogeneous peer groups where further splitting improves laboratory evaluation and enables continuous monitoring for peer group heterogeneity within EQA schemes.
Canepari, Silvia; Perrino, Cinzia; Olivieri, Fabio; Astolfi, Maria Luisa
A study of the elemental composition and size distribution of atmospheric particulate matter and of its spatial and temporal variability has been conducted at two traffic sites and one urban background site in the area of Rome, Italy. Chemical analysis included the fractionation of 22 elements (Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Na, Ni, Pb, S, Sb, Si, Sn, Sr, Ti, Tl, V) into a water-extractable and a residual fraction. Size distribution analysis included measurements of aerosols in twelve size classes in the range 0.03-10 μm. The simultaneous determination of PM 10 and PM 2.5 at three sites during a 2-week study allowed the necessary evaluation of space and time concentration variations. The application of a chemical fractionation procedure to size-segregated samples proved to be a valuable approach for the characterisation of PM and for discriminating different emission sources. Extractable and residual fractions of the elements showed in fact different size distributions: for almost all elements the extractable fraction was mainly distributed in the fine particle size, while the residual fraction was in general predominant in the coarse size range. For some elements (As, Cd, Sb, Sn, V) the dimensional separation between the extractable fraction, almost quantitatively present in the fine mode particles, and the residual fraction, mainly distributed in the coarse mode particles, was almost quantitative. Under these conditions, the application of the chemical fractionation procedure to PM 10 samples allows a clear distinction between contributes originating from fine and coarse particle emission sources. The results related to PM (10-2.5) and PM 2.5 daily samples confirmed that chemical fractionation analysis increases the selectivity of most elements as source tracers. Extractable and residual fractions of As, Mg, Ni, Pb, S, Sn, Tl, Sb, Cd and V showed different time patterns and different spatial and size distributions, clearly indicating that the two
DEFF Research Database (Denmark)
Kiil, Søren; Johnsson, Jan Erik; Dam-Johansen, Kim
1999-01-01
Danish limestone types with very different particle size distributions (PSDs). All limestones were of a high purity. Model predictions were found to be qualitatively in good agreement with experimental data without any use of adjustable parameters. Deviations between measurements and simulations were...... attributed primarily to the PSD measurements of the limestone particles, which were used as model inputs. The PSDs, measured using a laser diffrac-tion-based Malvern analyser, were probably not representative of the limestone samples because agglomeration phenomena took place when the particles were......In wet flue gas desulphurisation (FGD) plants, the most common sorbent is limestone. Over the past 25 years, many attempts to model the transient dissolution of limestone particles in aqueous solutions have been performed, due to the importance for the development of reliable FGD simu-lation tools...
Characteristic length scale of input data in distributed models: implications for modeling grid size
Artan, G. A.; Neale, C. M. U.; Tarboton, D. G.
2000-01-01
The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model response. The semi-variogram and the characteristic length calculated from the spatial autocorrelation were used to determine the scale of variability of the remotely sensed and GIS-generated model input data. The data were collected from two hillsides at Upper Sheep Creek, a sub-basin of the Reynolds Creek Experimental Watershed, in southwest Idaho. The data were analyzed in terms of the semivariance and the integral of the autocorrelation. The minimum characteristic length associated with the variability of the data used in the analysis was 15 m. Simulated and observed radiometric surface temperature fields at different spatial resolutions were compared. The correlation between agreement simulated and observed fields sharply declined after a 10×10 m2 modeling grid size. A modeling grid size of about 10×10 m2 was deemed to be the best compromise to achieve: (a) reduction of computation time and the size of the support data; and (b) a reproduction of the observed radiometric surface temperature.
Artan, Guleid A.; Neale, C. M. U.; Tarboton, D. G.
2000-01-01
The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model response. The semi-variogram and the characteristic length calculated from the spatial autocorrelation were used to determine the scale of variability of the remotely sensed and GIS-generated model input data. The data were collected from two hillsides at Upper Sheep Creek, a sub-basin of the Reynolds Creek Experimental Watershed, in southwest Idaho. The data were analyzed in terms of the semivariance and the integral of the autocorrelation. The minimum characteristic length associated with the variability of the data used in the analysis was 15 m. Simulated and observed radiometric surface temperature fields at different spatial resolutions were compared. The correlation between agreement simulated and observed fields sharply declined after a 10×10 m2 modeling grid size. A modeling grid size of about 10×10 m2 was deemed to be the best compromise to achieve: (a) reduction of computation time and the size of the support data; and (b) a reproduction of the observed radiometric surface temperature.
Rollinson, Njal; Edge, Christopher B; Brooks, Ronald J
2013-08-01
Smith and Fretwell's classic model predicts that parents can maximize fitness by dividing the energy available for reproduction into offspring of an optimal size. However, this model breaks down when clutch size is small (~1-10 offspring). Invariant rules are an extension of the Smith-Fretwell model, and these rules predict how offspring size will vary among and within individuals that produce small clutch sizes. Here, we provide a narrow test of invariant rules using three turtle species, then we synthesize and re-analyze existing data from 18 different species (comprising five Orders) to evaluate whether invariant rules are followed across broad taxa. We do not find support for most invariant rules in turtles, and our re-analysis demonstrates a general mismatch between observed and expected values across all taxa evaluated, suggesting that invariant rules fail to predict reproductive patterns in nature. Morphological constraints on offspring size and reproductive effort may be important reasons for disparities between theory and observation both in turtles and other taxa. Paradoxically, morphological constraints are most common in small-bodied species and individuals, but these same candidates are also those which produce the small clutch sizes that are necessary to test invariant rules, such that a fair test of invariant rules will often be challenging. Mismatches between theory and observation might also occur because theory assumes that mothers exert control over resource allocation to offspring. In fact, there is evidence of widespread genetic correlations among investment per offspring and reproductive effort, such that these traits are not independent.
Number-average size model for geological systems and its application in economic geology
Directory of Open Access Journals (Sweden)
Q. F. Wang
2011-07-01
Full Text Available Various natural objects follow a number-size relationship in the fractal domain. In such relationship, the accumulative number of the objects beyond a given size shows a power-law relationship with the size. Yet in most cases, we also need to know the relationship between the accumulative number of the objects and their average size. A generalized number-size model and a number-average size model are constructed in this paper. In the number-average size model, the accumulative number shows a power-law relationship with the average size when the given size is much less than the maximum size of the objects. When the fractal dimension D_{s} of the number-size model is smaller than 1, the fractal dimension D_{s} of the number-average size model is almost equal to 1; and when D_{s} > 1, the D_{m} is approximately equal to D_{s}. In mineral deposits, according to the number-average size model, the ore tonnage may show a fractal relationship with the grade, as the cutoff changes for a single ore deposit. This is demonstrated by a study of the relationship between tonnage and grade in the Reshuitang epithermal hot-spring gold deposit, China.
Directory of Open Access Journals (Sweden)
Paillisson J.-M.
2011-05-01
Full Text Available The ecological importance of the red-swamp crayfish (Procambarus clarkii in the functioning of freshwater aquatic ecosystems is becoming more evident. It is important to know the limitations of sampling methods targeting this species, because accurate determination of population characteristics is required for predicting the ecological success of P. clarkii and its potential impacts on invaded ecosystems. In the current study, we addressed the question of trap efficiency by comparing population structure provided by eight trap devices (varying in number and position of entrances, mesh size, trap size and construction materials in three habitats (a pond, a reed bed and a grassland in a French marsh in spring 2010. Based on a large collection of P. clarkii (n = 2091, 272 and 213 respectively in the pond, reed bed and grassland habitats, we found that semi-cylindrical traps made from 5.5 mm mesh galvanized steel wire (SCG were the most efficient in terms of catch probability (96.7–100% compared to 15.7–82.8% depending on trap types and habitats and catch-per-unit effort (CPUE: 15.3, 6.0 and 5.1 crayfish·trap-1·24 h-1 compared to 0.2–4.4, 2.9 and 1.7 crayfish·trap-1·24 h-1 by the other types of fishing gear in the pond, reed bed and grassland respectively. The SCG trap was also the most effective for sampling all size classes, especially small individuals (carapace length \\hbox{$\\leqslant 30$} ⩽ 30 mm. Sex ratio was balanced in all cases. SCG could be considered as appropriate trapping gear to likely give more realistic information about P. clarkii population characteristics than many other trap types. Further investigation is needed to assess the catching effort required for ultimately proposing a standardised sampling method in a large range of habitats.
Directory of Open Access Journals (Sweden)
Sunil Kumar C
2014-01-01
Full Text Available With number of students growing each year there is a strong need to automate systems capable of evaluating descriptive answers. Unfortunately, there aren’t many systems capable of performing this task. In this paper, we use a machine learning tool called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. Our experiments are designed to cater to our primary goal of identifying the optimum training sample size so as to get optimum auto scoring. Besides the technical overview and the experiments design, the paper also covers challenges, benefits of the system. We also discussed interdisciplinary areas for future research on this topic.
A Markov decision model for optimising economic production lot size ...
African Journals Online (AJOL)
Adopting such a Markov decision process approach, the states of a Markov chain represent possible states of demand. The decision of whether or not to produce additional inventory units is made using dynamic programming. This approach demonstrates the existence of an optimal state-dependent EPL size, and produces ...
Inoue, Akiomi; Kawakami, Norito; Tsuchiya, Masao; Sakurai, Keiko; Hashimoto, Hideki
2010-01-01
The purpose of this study was to investigate the cross-sectional association of employment contract, company size, and occupation with psychological distress using a nationally representative sample of the Japanese population. From June through July 2007, a total of 9,461 male and 7,717 female employees living in the community were randomly selected and surveyed using a self-administered questionnaire and interview including questions about occupational class variables, psychological distress (K6 scale), treatment for mental disorders, and other covariates. Among males, part-time workers had a significantly higher prevalence of psychological distress than permanent workers. Among females, temporary/contract workers had a significantly higher prevalence of psychological distress than permanent workers. Among males, those who worked at companies with 300-999 employees had a significantly higher prevalence of psychological distress than those who worked at the smallest companies (with 1-29 employees). Company size was not significantly associated with psychological distress among females. Additionally, occupation was not significantly associated with psychological distress among males or females. Similar patterns were observed when the analyses were conducted for those who had psychological distress and/or received treatment for mental disorders. Working as part-time workers, for males, and as temporary/contract workers, for females, may be associated with poor mental health in Japan. No clear gradient in mental health along company size or occupation was observed in Japan.
Venkatesan, Arjun K; Gan, Wenhui; Ashani, Harsh; Herckes, Pierre; Westerhoff, Paul
2018-04-15
Phosphorus (P) is an important and often limiting element in terrestrial and aquatic ecosystem. A lack of understanding of its distribution and structures in the environment limits the design of effective P mitigation and recovery approaches. Here we developed a robust method employing size exclusion chromatography (SEC) coupled to an ICP-MS to determine the molecular weight (MW) distribution of P in environmental samples. The most abundant fraction of P varied widely in different environmental samples: (i) orthophosphate was the dominant fraction (93-100%) in one lake, two aerosols and DOC isolate samples, (ii) species of 400-600 Da range were abundant (74-100%) in two surface waters, and (iii) species of 150-350 Da range were abundant in wastewater effluents. SEC-DOC of the aqueous samples using a similar SEC column showed overlapping peaks for the 400-600 Da species in two surface waters, and for >20 kDa species in the effluents, suggesting that these fractions are likely associated with organic matter. The MW resolution and performance of SEC-ICP-MS agreed well with the time integrated results obtained using conventional ultrafiltration method. Results show that SEC in combination with ICP-MS and DOC has the potential to be a powerful and easy-to-use method in identifying unknown fractions of P in the environment. Copyright © 2018 Elsevier Ltd. All rights reserved.
Chen, Xiao; Lu, Bin; Yan, Chao-Gan
2018-01-01
Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Josef, Noam; Berenshtein, Igal; Rousseau, Meghan; Scata, Gabriella; Fiorito, Graziano; Shashar, Nadav
2016-01-01
Camouflage is common throughout the phylogenetic tree and is largely used to minimize detection by predator or prey. Cephalopods, and in particular Sepia officinalis cuttlefish, are common models for camouflage studies. Predator avoidance behavior is particularly important in this group of soft-bodied animals that lack significant physical defenses. While previous studies have suggested that immobile cephalopods selectively camouflage to objects in their immediate surroundings, the camouflage characteristics of cuttlefish during movement are largely unknown. In a heterogenic environment, the visual background and substrate feature changes quickly as the animal swim across it, wherein substrate patch is a distinctive and high contrast patch of substrate in the animal's trajectory. In the current study, we examine the effect of substrate patch size on cuttlefish camouflage, and specifically the minimal size of an object for eliciting intensity matching response while moving. Our results indicated that substrate patch size has a positive effect on animal's reflectance change, and that the threshold patch size resulting in camouflage response falls between 10 and 19 cm (width). These observations suggest that the animal's length (7.2-12.3 cm mantle length in our case) serves as a possible threshold filter below which objects are considered irrelevant for camouflage, reducing the frequency of reflectance changes-which may lead to detection. Accordingly, we have constructed a computational model capturing the main features of the observed camouflaging behavior, provided for cephalopod camouflage during movement.
Sorption of water vapour by the Na+-exchanged clay-sized fractions of some tropical soil samples
International Nuclear Information System (INIS)
Yormah, T.B.R.; Hayes, M.H.B.
1993-09-01
Water vapour sorption isotherms at 299K for the Na + -exchanged clay-sized (≤ 2μm e.s.d.) fraction of two sets of samples taken at three different depths from a tropical soil profile have been studied. One set of samples was treated (with H 2 O 2 ) for the removal of much of the organic matter (OM); the other set (of the same samples) was not so treated. The isotherms obtained were all of type II and analyses by the BET method yielded values for the Specific Surface Areas (SSA) and for the average energy of adsorption of the first layer of adsorbate (E a ). OM content and SSA for the untreated samples were found to decrease with depth. Whereas removal of organic matter made negligible difference to the SSA of the top/surface soil, the same treatment produced a significant increase in the SSA of the samples taken from the middle and from the lower depths in the profile; the resulting increase was more pronounced for the subsoil. It has been deduced from these results that OM in the surface soil was less involved with the inorganic soil colloids than that in the subsoil. The increase in surface area which resulted from the removal of OM from the subsoil was most probably due to disaggregation. Values of E a obtained show that for all the samples the adsorption of water vapour became more energetic after the oxidative removal of organic matter; the resulting ΔE a also increased with depth. This suggests that in the dry state, the ''cleaned'' surface of the inorganic soil colloids was more energetic than the ''organic-matter-coater surface''. These data provide strong support for the deduction that OM in the subsoil was in a more ''combined'' state than that in the surface soil. (author). 21 refs, 4 figs, 2 tabs
Energy Technology Data Exchange (ETDEWEB)
Chang, Ying-jie [Department of Agricultural Chemistry, National Taiwan University, Taipei 106, Taiwan (China); Shih, Yang-hsin, E-mail: yhs@ntu.edu.tw [Department of Agricultural Chemistry, National Taiwan University, Taipei 106, Taiwan (China); Su, Chiu-Hun [Material and Chemical Research Laboratories, Industrial Technology Research Institute, Hsinchu 310, Taiwan (China); Ho, Han-Chen [Department of Anatomy, Tzu-Chi University, Hualien 970, Taiwan (China)
2017-01-15
Highlights: • Three emerging techniques to detect NPs in the aquatic environment were evaluated. • The pretreatment of centrifugation to decrease the interference was established. • Asymmetric flow field flow fractionation has a low recovery of NPs. • Hydrodynamic chromatography is recommended to be a low-cost screening tool. • Single particle ICPMS is recommended to accurately measure trace NPs in water. - Abstract: Due to the widespread application of engineered nanoparticles, their potential risk to ecosystems and human health is of growing concern. Silver nanoparticles (Ag NPs) are one of the most extensively produced NPs. Thus, this study aims to develop a method to detect Ag NPs in different aquatic systems. In complex media, three emerging techniques are compared, including hydrodynamic chromatography (HDC), asymmetric flow field flow fractionation (AF4) and single particle inductively coupled plasma-mass spectrometry (SP-ICP-MS). The pre-treatment procedure of centrifugation is evaluated. HDC can estimate the Ag NP sizes, which were consistent with the results obtained from DLS. AF4 can also determine the size of Ag NPs but with lower recoveries, which could result from the interactions between Ag NPs and the working membrane. For the SP-ICP-MS, both the particle size and concentrations can be determined with high Ag NP recoveries. The particle size resulting from SP-ICP-MS also corresponded to the transmission electron microscopy observation (p > 0.05). Therefore, HDC and SP-ICP-MS are recommended for environmental analysis of the samples after our established pre-treatment process. The findings of this study propose a preliminary technique to more accurately determine the Ag NPs in aquatic environments and to use this knowledge to evaluate the environmental impact of manufactured NPs.
mizer: an R package for multispecies, trait-based and community size spectrum ecological modelling
DEFF Research Database (Denmark)
Scott, Finlay; Blanchard, Julia L.; Andersen, Ken Haste
2014-01-01
1. Size spectrum ecological models are representations of a community of individuals which grow and change trophic level. A key emergent feature of these models is the size spectrum; the total abundance of all individuals that scales negatively with size. The models we focus on are designed...... to capture fish community dynamics useful for assessing the community impacts of fishing. 2. We present mizer, anRpackage for implementing dynamic size spectrum ecological models of an entire aquatic community subject to fishing.Multiple fishing gears can be defined and fishing mortality can change through...... time making it possible to simulate a range of exploitation strategies and management options. 3. mizer implements three versions of the size spectrum modelling framework: the communitymodel,where individuals are only characterized by their size; the trait-based model, where individuals are further...
Graphical models for inference under outcome-dependent sampling
DEFF Research Database (Denmark)
Didelez, V; Kreiner, S; Keiding, N
2010-01-01
We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in case-control studies. Graphical models represent assumptions about the conditional independencies among the variables. By including...
Modeling evaporative loss of oil mist collected by sampling filters.
Raynor, P C; Volckens, J; Leith, D
2000-01-01
Oil mists can cause respiratory distress and have been linked to skin and gastrointestinal cancers in workers. Standard concentration assessment methods call for sampling these mists with fibrous or membrane filters. Previous experimental studies using glass fiber (GF) filters and polyvinyl chloride and polytetrafluoroethylene membrane filters indicate that mist sampled onto filters may volatilize. A model has been developed to predict the evaporation of mist collected on a fibrous sampling filter. Evaporation of retained fluid from membrane filters can be modeled by treating the filter as though it is a fibrous filter. Predictions from the model exhibit good agreement with experimental results. At low mist concentrations, the model indicates that evaporation of retained mineral oil occurs readily. At high mist concentrations, significant evaporation from the filters is not expected because the vapor accompanying the airborne mist is already saturated with the compounds in the oil. The findings from this study indicate that sampling mineral oil mist with filters in accordance with standard methods can lead to estimates of worker exposure to oil mist that are too low.
Sampling and modeling riparian forest structure and riparian microclimate
Bianca N.I. Eskelson; Paul D. Anderson; Hailemariam. Temesgen
2013-01-01
Riparian areas are extremely variable and dynamic, and represent some of the most complex terrestrial ecosystems in the world. The high variability within and among riparian areas poses challenges in developing efficient sampling and modeling approaches that accurately quantify riparian forest structure and riparian microclimate. Data from eight stream reaches that are...
A binary logistic regression model with complex sampling design of ...
African Journals Online (AJOL)
A binary logistic regression model with complex sampling design of unmet need for family planning among all women aged (15-49) in Ethiopia. ... Conclusion: The key determinants of unmet need family planning in Ethiopia were residence, age, marital-status, education, household members, birth-events and number of ...
Applying Model Checking to Industrial-Sized PLC Programs
AUTHOR|(CDS)2079190; Darvas, Daniel; Blanco Vinuela, Enrique; Tournier, Jean-Charles; Bliudze, Simon; Blech, Jan Olaf; Gonzalez Suarez, Victor M
2015-01-01
Programmable logic controllers (PLCs) are embedded computers widely used in industrial control systems. Ensuring that a PLC software complies with its specification is a challenging task. Formal verification has become a recommended practice to ensure the correctness of safety-critical software but is still underused in industry due to the complexity of building and managing formal models of real applications. In this paper, we propose a general methodology to perform automated model checking of complex properties expressed in temporal logics (\\eg CTL, LTL) on PLC programs. This methodology is based on an intermediate model (IM), meant to transform PLC programs written in various standard languages (ST, SFC, etc.) to different modeling languages of verification tools. We present the syntax and semantics of the IM and the transformation rules of the ST and SFC languages to the nuXmv model checker passing through the intermediate model. Finally, two real cases studies of \\CERN PLC programs, written mainly in th...
Directory of Open Access Journals (Sweden)
Fotini Kokou
2016-05-01
Full Text Available One of the main concerns in gene expression studies is the calculation of statistical significance which in most cases remains low due to limited sample size. Increasing biological replicates translates into more effective gains in power which, especially in nutritional experiments, is of great importance as individual variation of growth performance parameters and feed conversion is high. The present study investigates in the gilthead sea bream Sparus aurata, one of the most important Mediterranean aquaculture species. For 24 gilthead sea bream individuals (biological replicates the effects of gradual substitution of fish meal by plant ingredients (0% (control, 25%, 50% and 75% in the diets were studied by looking at expression levels of four immune-and stress-related genes in intestine, head kidney and liver. The present results showed that only the lowest substitution percentage is tolerated and that liver is the most sensitive tissue to detect gene expression variations in relation to fish meal substituted diets. Additionally the usage of three independent biological replicates were evaluated by calculating the averages of all possible triplets in order to assess the suitability of selected genes for stress indication as well as the impact of the experimental set up, thus in the present work the impact of FM substitution. Gene expression was altered depending of the selected biological triplicate. Only for two genes in liver (hsp70 and tgf significant differential expression was assured independently of the triplicates used. These results underlined the importance of choosing the adequate sample number especially when significant, but minor differences in gene expression levels are observed. Keywords: Sample size, Gene expression, Fish meal replacement, Immune response, Gilthead sea bream
Tigers on trails: occupancy modeling for cluster sampling.
Hines, J E; Nichols, J D; Royle, J A; MacKenzie, D I; Gopalaswamy, A M; Kumar, N Samba; Karanth, K U
2010-07-01
Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy
Energy Technology Data Exchange (ETDEWEB)
Damiani, Rick [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2016-02-08
This manual summarizes the theory and preliminary verifications of the JacketSE module, which is an offshore jacket sizing tool that is part of the Wind-Plant Integrated System Design & Engineering Model toolbox. JacketSE is based on a finite-element formulation and on user-prescribed inputs and design standards' criteria (constraints). The physics are highly simplified, with a primary focus on satisfying ultimate limit states and modal performance requirements. Preliminary validation work included comparing industry data and verification against ANSYS, a commercial finite-element analysis package. The results are encouraging, and future improvements to the code are recommended in this manual.
Energy Technology Data Exchange (ETDEWEB)
Garino, Terry J.
2007-09-01
The sintering behavior of Sandia chem-prep high field varistor materials was studied using techniques including in situ shrinkage measurements, optical and scanning electron microscopy and x-ray diffraction. A thorough literature review of phase behavior, sintering and microstructure in Bi{sub 2}O{sub 3}-ZnO varistor systems is included. The effects of Bi{sub 2}O{sub 3} content (from 0.25 to 0.56 mol%) and of sodium doping level (0 to 600 ppm) on the isothermal densification kinetics was determined between 650 and 825 C. At {ge} 750 C samples with {ge}0.41 mol% Bi{sub 2}O{sub 3} have very similar densification kinetics, whereas samples with {le}0.33 mol% begin to densify only after a period of hours at low temperatures. The effect of the sodium content was greatest at {approx}700 C for standard 0.56 mol% Bi{sub 2}O{sub 3} and was greater in samples with 0.30 mol% Bi{sub 2}O{sub 3} than for those with 0.56 mol%. Sintering experiments on samples of differing size and shape found that densification decreases and mass loss increases with increasing surface area to volume ratio. However, these two effects have different causes: the enhancement in densification as samples increase in size appears to be caused by a low oxygen internal atmosphere that develops whereas the mass loss is due to the evaporation of bismuth oxide. In situ XRD experiments showed that the bismuth is initially present as an oxycarbonate that transforms to metastable {beta}-Bi{sub 2}O{sub 3} by 400 C. At {approx}650 C, coincident with the onset of densification, the cubic binary phase, Bi{sub 38}ZnO{sub 58} forms and remains stable to >800 C, indicating that a eutectic liquid does not form during normal varistor sintering ({approx}730 C). Finally, the formation and morphology of bismuth oxide phase regions that form on the varistors surfaces during slow cooling were studied.
Modeling postnatal exposures and their interactions with birth size.
Cole, Tim J
2004-01-01
The fetal origins of adult disease hypothesis postulates that the inverse association between birth weight and later adverse outcome reflects fetal programming that increases the risk of later disease. However, low birth weight is associated with catch-up after birth, and weight gain is itself a risk factor for later disease. It is difficult to disentangle the effects on outcome of the size and growth components of weight change through time. This paper presents the life course plot, a device to display both size and growth effects simultaneously. It is based on the multiple-regression analysis of the outcome on the various weights, expressed as z-scores, and the plot displays the coefficients plotted against the corresponding ages of measurement. Examples from Brazil (Pelotas) and the Phillippines (Cebu) relate blood pressure in adolescence to weight through childhood. They show small inverse weight effects in infancy, but early weight is less important than weight and weight gain during adolescence. In addition, birth length in the Cebu study affects the strength of the relationship between weight and blood pressure in adolescence. This suggests a fetal programming effect, with children who were relatively long at birth having a more sensitive relationship between blood pressure and weight at age 15. Whether this is a good or a bad thing is not immediately clear.
Thompson, Steven K
2012-01-01
Praise for the Second Edition "This book has never had a competitor. It is the only book that takes a broad approach to sampling . . . any good personal statistics library should include a copy of this book." —Technometrics "Well-written . . . an excellent book on an important subject. Highly recommended." —Choice "An ideal reference for scientific researchers and other professionals who use sampling." —Zentralblatt Math Features new developments in the field combined with all aspects of obtaining, interpreting, and using sample data Sampling provides an up-to-date treat
Decision Models for Determining the Optimal Life Test Sampling Plans
Nechval, Nicholas A.; Nechval, Konstantin N.; Purgailis, Maris; Berzins, Gundars; Strelchonok, Vladimir F.
2010-11-01
Life test sampling plan is a technique, which consists of sampling, inspection, and decision making in determining the acceptance or rejection of a batch of products by experiments for examining the continuous usage time of the products. In life testing studies, the lifetime is usually assumed to be distributed as either a one-parameter exponential distribution, or a two-parameter Weibull distribution with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Moreover, sampling costs, inspection costs, warranty costs, and rejection costs are all essential, and ought to be considered in choosing an appropriate sampling plan. The choice of an appropriate life test sampling plan is a crucial decision problem because a good plan not only can help producers save testing time, and reduce testing cost; but it also can positively affect the image of the product, and thus attract more consumers to buy it. This paper develops the frequentist (non-Bayesian) decision models for determining the optimal life test sampling plans with an aim of cost minimization by identifying the appropriate number of product failures in a sample that should be used as a threshold in judging the rejection of a batch. The two-parameter exponential and Weibull distributions with two unknown parameters are assumed to be appropriate for modelling the lifetime of a product. A practical numerical application is employed to demonstrate the proposed approach.
State-space modeling of population sizes and trends in Nihoa Finch and Millerbird
Gorresen, P. Marcos; Brinck, Kevin W.; Camp, Richard J.; Farmer, Chris; Plentovich, Sheldon M.; Banko, Paul C.
2016-01-01
Both of the 2 passerines endemic to Nihoa Island, Hawai‘i, USA—the Nihoa Millerbird (Acrocephalus familiaris kingi) and Nihoa Finch (Telespiza ultima)—are listed as endangered by federal and state agencies. Their abundances have been estimated by irregularly implemented fixed-width strip-transect sampling from 1967 to 2012, from which area-based extrapolation of the raw counts produced highly variable abundance estimates for both species. To evaluate an alternative survey method and improve abundance estimates, we conducted variable-distance point-transect sampling between 2010 and 2014. We compared our results to those obtained from strip-transect samples. In addition, we applied state-space models to derive improved estimates of population size and trends from the legacy time series of strip-transect counts. Both species were fairly evenly distributed across Nihoa and occurred in all or nearly all available habitat. Population trends for Nihoa Millerbird were inconclusive because of high within-year variance. Trends for Nihoa Finch were positive, particularly since the early 1990s. Distance-based analysis of point-transect counts produced mean estimates of abundance similar to those from strip-transects but was generally more precise. However, both survey methods produced biologically unrealistic variability between years. State-space modeling of the long-term time series of abundances obtained from strip-transect counts effectively reduced uncertainty in both within- and between-year estimates of population size, and allowed short-term changes in abundance trajectories to be smoothed into a long-term trend.
Sampled-data models for linear and nonlinear systems
Yuz, Juan I
2014-01-01
Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives: · the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable; · although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and · the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central. The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either p...
The genealogy of samples in models with selection.
Neuhauser, C; Krone, S M
1997-02-01
We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.
Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data.
Liu, Zitao; Hauskrecht, Milos
2016-02-01
Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy.
Chhikara, R. S.; Odell, P. L.
1973-01-01
A multichannel scanning device may fail to observe objects because of obstructions blocking the view, or different categories of objects may make up a resolution element giving rise to a single observation. Ground truth will be required on any such categories of objects in order to estimate their expected proportions associated with various classes represented in the remote sensing data. Considering the classes to be distributed as multivariate normal with different mean vectors and common covariance, maximum likelihood estimates are given for the expected proportions of objects associated with different classes, using the Bayes procedure for classification of individuals obtained from these classes. An approximate solution for simultaneous confidence intervals on these proportions is given, and thereby a sample-size needed to achieve a desired amount of accuracy for the estimates is determined.
Ataeefard, Maryam; Shadman, Alireza; Saeb, Mohammad Reza; Mohammadi, Yousef
2016-08-01
A mathematical modeling approach was proposed combining the capabilities of response surface methodology (RSM) and desirability function (DF) and implemented successfully in production of printing toner particles. Toner powders were systematically synthesized through suspension copolymerization process. Applying RSM, a series of experiments were designed and toner particles were prepared and the effects of monomer ratio, colorant and surfactant content on the particle size (PS), particle size distribution (PSD), thermal and colorimetric properties (∆ E) of the resulting toner were monitored and discussed. The second-order models corresponding to each target characteristic, i.e., PS, PSD, and ∆ E of different types of toner powders, were obtained by individual optimization to express variation of each property in terms of polymerization parameters. Applying statistical calculations, the best reduced models were identified to be fed in the second step of optimization. Since toners with appropriate PS, PSD, and CP were needed, we applied multi-objective optimization based on DF approach. The results show that exact tuning of toner properties is closely possible with the aid of hybrid mathematical model developed in this work. Noticeably, desirabilities are very close to 100 %.
Lyons, James E; Kendall, William L; Royle, J Andrew; Converse, Sarah J; Andres, Brad A; Buchanan, Joseph B
2016-03-01
We present a novel formulation of a mark-recapture-resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state-space formulation of the Jolly-Seber mark-recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state-space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers. © 2015, The International Biometric Society.
Lyons, James E.; Kendall, William L.; Royle, J. Andrew; Converse, Sarah J.; Andres, Brad A.; Buchanan, Joseph B.
2016-01-01
We present a novel formulation of a mark–recapture–resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state–space formulation of the Jolly–Seber mark–recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state–space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.
Li, Aifeng; Ma, Feifei; Song, Xiuli; Yu, Rencheng
2011-03-18
Solid-phase adsorption toxin tracking (SPATT) technology was developed as an effective passive sampling method for dissolved diarrhetic shellfish poisoning (DSP) toxins in seawater. HP20 and SP700 resins have been reported as preferred adsorption substrates for lipophilic algal toxins and are recommended for use in SPATT testing. However, information on the mechanism of passive adsorption by these polymeric resins is still limited. Described herein is a study on the adsorption of OA and DTX1 toxins extracted from Prorocentrum lima algae by HP20 and SP700 resins. The pore size distribution of the adsorbents was characterized by a nitrogen adsorption method to determine the relationship between adsorption and resin porosity. The Freundlich equation constant showed that the difference in adsorption capacity for OA and DTX1 toxins was not determined by specific surface area, but by the pore size distribution in particular, with micropores playing an especially important role. Additionally, it was found that differences in affinity between OA and DTX1 for aromatic resins were as a result of polarity discrepancies due to DTX1 having an additional methyl moiety. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.
Woo, Hyun-Kyung; Sunkara, Vijaya; Park, Juhee; Kim, Tae-Hyeong; Han, Ja-Ryoung; Kim, Chi-Ju; Choi, Hyun-Il; Kim, Yoon-Keun; Cho, Yoon-Kyoung
2017-02-28
Extracellular vesicles (EVs) are cell-derived, nanoscale vesicles that carry nucleic acids and proteins from their cells of origin and show great potential as biomarkers for many diseases, including cancer. Efficient isolation and detection methods are prerequisites for exploiting their use in clinical settings and understanding their physiological functions. Here, we presented a rapid, label-free, and highly sensitive method for EV isolation and quantification using a lab-on-a-disc integrated with two nanofilters (Exodisc). Starting from raw biological samples, such as cell-culture supernatant (CCS) or cancer-patient urine, fully automated enrichment of EVs in the size range of 20-600 nm was achieved within 30 min using a tabletop-sized centrifugal microfluidic system. Quantitative tests using nanoparticle-tracking analysis confirmed that the Exodisc enabled >95% recovery of EVs from CCS. Additionally, analysis of mRNA retrieved from EVs revealed that the Exodisc provided >100-fold higher concentration of mRNA as compared with the gold-standard ultracentrifugation method. Furthermore, on-disc enzyme-linked immunosorbent assay using urinary EVs isolated from bladder cancer patients showed high levels of CD9 and CD81 expression, suggesting that this method may be potentially useful in clinical settings to test urinary EV-based biomarkers for cancer diagnostics.
Hua, Xue; Hibar, Derrek P.; Ching, Christopher R.K.; Boyle, Christina P.; Rajagopalan, Priya; Gutman, Boris A.; Leow, Alex D.; Toga, Arthur W.; Jack, Clifford R.; Harvey, Danielle; Weiner, Michael W.; Thompson, Paul M.
2013-01-01
Various neuroimaging measures are being evaluated for tracking Alzheimer’s disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24 months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39 AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. PMID:23153970
Directory of Open Access Journals (Sweden)
Valéria Schimitz Marodim
2000-10-01
Full Text Available Este estudo visa a estabelecer o delineamento experimental e o tamanho de amostra para a cultura da alface (Lactuca sativa em hidroponia, pelo sistema NFT (Nutrient film technique. O experimento foi conduzido no Laboratório de Cultivos Sem Solo/Hidroponia, no Departamento de Fitotecnia da Universidade Federal de Santa Maria e baseou-se em dados de massa de plantas. Os resultados obtidos mostraram que, usando estrutura de cultivo de alface em hidroponia sobre bancadas de fibrocimento com seis canais, o delineamento experimental adequado é blocos ao acaso se a unidade experimental for constituída de faixas transversais aos canais das bancadas, e deve ser inteiramente casualizado se a bancada for a unidade experimental; para a variável massa de plantas, o tamanho da amostra é de 40 plantas para uma semi-amplitude do intervalo de confiança em percentagem da média (d igual a 5% e de 7 plantas para um d igual a 20%.This study was carried out to establish the experimental design and sample size for hydroponic lettuce (Lactuca sativa crop under nutrient film technique. The experiment was conducted in the Laboratory of Hydroponic Crops of the Horticulture Department of the Federal University of Santa Maria. The evaluated traits were plant weight. Under hydroponic conditions on concrete bench with six ducts, the most indicated experimental design for lettuce is randomised blocks for duct transversal plots or completely randomised for bench plot. The sample size for plant weight should be 40 and 7 plants, respectively, for a confidence interval of mean percentage (d equal to 5% and 20%.
A sample application of nuclear power human resources model
International Nuclear Information System (INIS)
Gurgen, A.; Ergun, S.
2016-01-01
One of the most important issues for a new comer country initializing the nuclear power plant projects is to have both quantitative and qualitative models for the human resources development. For the quantitative model of human resources development for Turkey, “Nuclear Power Human Resources (NPHR) Model” developed by the Los Alamos National Laboratory was used to determine the number of people that will be required from different professional or occupational fields in the planning of human resources for Akkuyu, Sinop and the third nuclear power plant projects. The number of people required for different professions for the Nuclear Energy Project Implementation Department, the regulatory authority, project companies, construction, nuclear power plants and the academy were calculated. In this study, a sample application of the human resources model is presented. The results of the first tries to calculate the human resources needs of Turkey were obtained. Keywords: Human Resources Development, New Comer Country, NPHR Model
National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents sediment size predictions from a sediment spatial model developed for the New York offshore spatial planning area. The model also includes...
Determining the optimum cell size of digital elevation model for ...
Indian Academy of Sciences (India)
obtained DEMs were explored for their intrinsic quality using four different methods, i.e., sink analy- sis, fractal dimension of derived stream network, entropy measurement and semivariogram modelling. These methods were applied to determine the level artifacts (interpolation error) in DEM surface as well as derived stream ...
Sampling from stochastic reservoir models constrained by production data
Energy Technology Data Exchange (ETDEWEB)
Hegstad, Bjoern Kaare
1997-12-31
When a petroleum reservoir is evaluated, it is important to forecast future production of oil and gas and to assess forecast uncertainty. This is done by defining a stochastic model for the reservoir characteristics, generating realizations from this model and applying a fluid flow simulator to the realizations. The reservoir characteristics define the geometry of the reservoir, initial saturation, petrophysical properties etc. This thesis discusses how to generate realizations constrained by production data, that is to say, the realizations should reproduce the observed production history of the petroleum reservoir within the uncertainty of these data. The topics discussed are: (1) Theoretical framework, (2) History matching, forecasting and forecasting uncertainty, (3) A three-dimensional test case, (4) Modelling transmissibility multipliers by Markov random fields, (5) Up scaling, (6) The link between model parameters, well observations and production history in a simple test case, (7) Sampling the posterior using optimization in a hierarchical model, (8) A comparison of Rejection Sampling and Metropolis-Hastings algorithm, (9) Stochastic simulation and conditioning by annealing in reservoir description, and (10) Uncertainty assessment in history matching and forecasting. 139 refs., 85 figs., 1 tab.
Analysis of litter size and average litter weight in pigs using a recursive model
DEFF Research Database (Denmark)
Varona, Luis; Sorensen, Daniel; Thompson, Robin
2007-01-01
An analysis of litter size and average piglet weight at birth in Landrace and Yorkshire using a standard two-trait mixed model (SMM) and a recursive mixed model (RMM) is presented. The RMM establishes a one-way link from litter size to average piglet weight. It is shown that there is a one-to-one...
Carr, Greg J; Bailer, A John; Rawlings, Jane M; Belanger, Scott E
2018-01-19
The fish acute toxicity test method is foundational to aquatic toxicity testing strategies, yet the literature lacks a concise sample size assessment. While various sources address sample size, historical precedent seems to play a larger role than objective measures. Here, a novel and comprehensive quantification of the effect of sample size on estimation of the LC 50 is presented, covering a wide range of scenarios. The results put into perspective the practical differences across a range of sample sizes, from N = 5/concentration up to N = 23/concentration. This work provides a framework for setting sample size guidance. It illustrates ways to quantify the performance of LC 50 estimation, which can be used to set sample size guidance given reasonably difficult, or worst-case scenarios. There is a clear benefit to larger sample size studies: they reduce error in the determination of LC 50 s, and lead to more robust safe environmental concentration determinations, particularly in cases likely to be called worst-case (shallow slope and true LC 50 near the edges of the concentration range). Given that the use of well-justified sample sizes is crucial to reducing uncertainty in toxicity estimates, these results lead us to recommend a reconsideration of the current de minimis 7/concentration sample size for critical studies (e.g., studies needed for a chemical registration, which are being tested for the first time, or involving difficult test substances). This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Test results of the first 50 kA NbTi full size sample for ITER
International Nuclear Information System (INIS)
Ciazynski, D.; Zani, L.; Huber, S.; Stepanov, B.; Karlemo, B.
2003-01-01
Within the framework of the research studies for the International Thermonuclear Experimental Reactor (ITER) project, the first full size NbTi conductor sample was fabricated in industry and tested in the SULTAN facility (Villigen, Switzerland). This sample (PF-FSJS), which is relevant to the Poloidal Field coils of ITER, is composed of two parallel straight bars of conductor, connected at bottom through a joint designed according to the Cea twin-box concept. The two conductor legs are identical except for the use of different strands: a nickel plated NbTi strand with a pure copper matrix in one leg, and a bare NbTi strand with copper matrix and internal CuNi barrier in the other leg. The two conductors and the joint were extensively tested regarding DC (direct current) and AC (alternative current) properties. This paper reports on the tests results and analysis, stressing the differences between the two conductor legs and discussing the impact of the test results on the ITER design criteria for conductor and joint. While joint DC resistance, conductors and joint AC losses, fulfilled the ITER requirements, neither conductor could reach its current sharing temperature at relevant ITER currents, due to instabilities. Although the drop in temperature is slight for the CuNi strand cable, it is more significant for the Ni plated strand cable. (authors)
Medical Modeling of Particle Size Effects for CB Inhalation Hazards
2015-09-01
warfare) may create adverse health effects when inhaled. Once the materials enter the respiratory tract, they may deposit on the airway surfaces...mppd.htm). New features in this version include a deposition model specifically for nanoparticles, nonuniform lung ventilation to include the effect ... mechanisms cause local lesions, but the more virulent strains may then spread throughout the body via blood or lymph (Celli 2008). The effects of
Size exclusion chromatography models and its comparison with experiment
Czech Academy of Sciences Publication Activity Database
Netopilík, Miloš
2017-01-01
Roč. 8, 4 (Suppl) (2017), s. 29 E-ISSN 2157-7064. [International Conference and Exhibition on Advances in Chromatography & HPLC Techniques /3./. 13.07.2017-14.07.2017, Berlin] R&D Projects: GA ČR(CZ) GC17-04258J Institutional support: RVO:61389013 Keywords : model of separation * flow-rate influence Subject RIV: CD - Macromolecular Chemistry
International Nuclear Information System (INIS)
Campolina, D. de A. M.; Lima, C.P.B.; Veloso, M.A.F.
2013-01-01
For all the physical components that comprise a nuclear system there is an uncertainty. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a best estimate calculation that has been replacing the conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using a Monte Carlo code by sampling the input parameters is recent because of the huge computational effort required. In this work a sample space of MCNPX calculations was used to propagate the uncertainty. The sample size was optimized using the Wilks formula for a 95. percentile and a two-sided statistical tolerance interval of 95%. Uncertainties in input parameters of the reactor considered included geometry dimensions and densities. It was showed the capacity of the sampling-based method for burnup when the calculations sample size is optimized and many parameter uncertainties are investigated together, in the same input. Particularly it was shown that during the burnup, the variances when considering all the parameters uncertainties is equivalent to the sum of variances if the parameter uncertainties are sampled separately
A Size and Boundary Effects Model for Quasi-Brittle Fracture
Directory of Open Access Journals (Sweden)
Xiaofeng Gao
2016-12-01
Full Text Available The fracture behaviors of quasi-brittle materials are commonly specimen size (size effect and crack size (boundary effect dependent. In this study, a new failure model is developed for characterizing the size and boundary effects. The derivative of the energy release rate is firstly introduced to predict the nominal strength dominated by the strength mechanism. Combined with the energy criterion for the energy mechanism, an asymptotic model is developed to capture the effect of any crack size on the nominal strength, and its expression for geometrically similar specimens is also established, which is able to characterize the size effect. Detailed comparisons of the proposed model with the size effect law and the boundary effect model are performed, respectively. The nominal strength predictions based on the proposed model are validated with the experimental results of cracked three-point bending beam specimens made of concrete, of limestone and of hardened cement paste and compared with the model predictions given by the size effect law and the boundary effect model.
Alves, Adriana; Pereira, Giovanna de Souza; Janasi, Valdecir de Assis; Higgins, Michael; Polo, Liza Angelica; Juriaans, Orlando Stanley; Ribeiro, Bruno Vieira
2015-12-01
Magma mixing is widely recognized in contemporary petrology as one of the primary igneous processes. Microgranitoid enclaves (MEs) are considered to be remnants of such mixing processes, and the term has a well-established genetic implication. However, microgranitoid enclaves span a wide range of compositions, and felsic varieties are also frequently reported. Nd-Sr isotope and textural data from felsic microgranitoid enclaves (FMEs), mafic microgranitoid enclaves (MMEs) and host granites from the Salto pluton, Itu Granitic Province, show that the cm-sized MMEs are dioritic, have medium-grained igneous textures and xenocrysts of alkali feldspar and quartz. The FMEs are cm- to meter-sized, have spheric shapes, show corrugated contacts with the host granites, and have resorbed feldspars and deformed quartz crystals interpreted as xenocrysts set in a fine-grained groundmass. Compared to the host granites, both MME and FME samples have increased FeO, MgO, TiO2, P2O5 and Zr contents, but their Sr and Nd isotope signatures are identical: FME 87Sr/86Sri = 0.7088-0.7063, εNdi = - 10.0 to - 10.2; MME 87Sr/86Sri = 0.7070, εNdi = - 10.5; host granite 87Sr/86Sri 0.7056-0.7060, εNdi = - 10.2 to - 10.3. These indicate that the enclaves derive from a similar source, although the melts from which they formed were probably hotter and chemically more primitive than their host granites. Crystal size distributions (CSDs) of plagioclase in samples drilled from rinds and cores of three FMEs show that the rind samples are systematically finer-grained than the samples from the cores, which indicates that the FMEs cooled inwards and contradict interpretations that the FMEs are autoliths. Thermal modeling suggests that a slightly more primitive, hotter magma would be thermally equilibrated with an evolved resident melt within weeks after mixing/mingling. Upon thermal equilibrium, the FMEs would have an increased crystal cargo, and the resulting touching framework would impart a solid
Sampling algorithms for validation of supervised learning models for Ising-like systems
Portman, Nataliya; Tamblyn, Isaac
2017-12-01
In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).
Testing exclusion restrictions and additive separability in sample selection models
DEFF Research Database (Denmark)
Huber, Martin; Mellace, Giovanni
2014-01-01
Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction of these......Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction...... of these assumptions by applying the approach of Huber and Mellace (Testing instrument validity for LATE identification based on inequality moment constraints, 2011) (for testing instrument validity under treatment endogeneity) to the sample selection framework. We show that the exclusion restriction and additive...... separability imply two testable inequality constraints that come from both point identifying and bounding the outcome distribution of the subpopulation that is always selected/observed. We apply the tests to two variables for which the exclusion restriction is frequently invoked in female wage regressions: non...
Wu, Hao-Yi; Hahn, Oliver; Wechsler, Risa H.; Mao, Yao-Yuan; Behroozi, Peter S.
2013-02-01
We present the first results from the RHAPSODY cluster re-simulation project: a sample of 96 "zoom-in" simulations of dark matter halos of 1014.8 ± 0.05 h -1 M ⊙, selected from a 1 h -3 Gpc3 volume. This simulation suite is the first to resolve this many halos with ~5 × 106 particles per halo in the cluster mass regime, allowing us to statistically characterize the distribution of and correlation between halo properties at fixed mass. We focus on the properties of the main halos and how they are affected by formation history, which we track back to z = 12, over five decades in mass. We give particular attention to the impact of the formation history on the density profiles of the halos. We find that the deviations from the Navarro-Frenk-White (NFW) model and the Einasto model depend on formation time. Late-forming halos tend to have considerable deviations from both models, partly due to the presence of massive subhalos, while early-forming halos deviate less but still significantly from the NFW model and are better described by the Einasto model. We find that the halo shapes depend only moderately on formation time. Departure from spherical symmetry impacts the density profiles through the anisotropic distribution of massive subhalos. Further evidence of the impact of subhalos is provided by analyzing the phase-space structure. A detailed analysis of the properties of the subhalo population in RHAPSODY is presented in a companion paper.
Noise-Induced Transitions in a Population Growth Model Based on Size-Dependent Carrying Capacity
Directory of Open Access Journals (Sweden)
Neeme Lumi
2014-01-01
Full Text Available The stochastic dynamics of a population growth model with size-dependent carrying capacity is considered. The effect of a fluctuating environment on population growth is modeled as a multiplicative dichotomous noise. At intermediate values of population size the deterministic counterpart of the model behaves similarly to the Von Foerster model for human population, but at small and very large values of population size substantial differences occur. In the stochastic case, an exact analytical solution for the stationary probability distribution is found. It is established that variation of noise correlation time can cause noise-induced transitions between three different states of the system characterized by qualitatively different behaviors of the probability distributions of the population size. Also, it is shown that, in some regions of the system parameters, variatio