Relevant bibliographies by topics / Sample size determination

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sample size determination'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Sample size determination"

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Muralidharan, K. "On Sample Size Determination." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 3, no. 1 (2014): 55–64. http://dx.doi.org/10.15415/mjis.2014.31005.

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G, Ajithakumari. "Sample Size Determination and Sampling Technique." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 1432–40. http://dx.doi.org/10.21275/es24924103353.

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Chun, Byoung Ryul. "Sample Size Determination." Journal of Korean Society for Clinical Pharmacology and Therapeutics 2, no. 2 (1994): 209. http://dx.doi.org/10.12793/jkscpt.1994.2.2.209.

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Clark, Virginia. "Sample Size Determination." Plastic and Reconstructive Surgery 87, no. 3 (1991): 569–73. http://dx.doi.org/10.1097/00006534-199103000-00030.

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Dell, R. B., S. Holleran, and R. Ramakrishnan. "Sample Size Determination." ILAR Journal 43, no. 4 (2002): 207–13. http://dx.doi.org/10.1093/ilar.43.4.207.

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Wang, Yu, Zheng Guan, and Tengyuan Zhao. "Sample size determination in geotechnical site investigation considering spatial variation and correlation." Canadian Geotechnical Journal 56, no. 7 (2019): 992–1002. http://dx.doi.org/10.1139/cgj-2018-0474.

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Site investigation is a fundamental element in geotechnical engineering practice, but only a small portion of geomaterials is sampled and tested during site investigation. This leads to a question of sample size determination: how many samples are needed to achieve a target level of accuracy for the results inferred from the samples? Sample size determination is a well-known topic in statistics and has many applications in a wide variety of areas. However, conventional statistical methods, which mainly deal with independent data, only have limited applications in geotechnical site investigation because geotechnical data are not independent, but spatially varying and correlated. Existing design codes around the world (e.g., Eurocode 7) only provide conceptual principles on sample size determination. No scientific or quantitative method is available for sample size determination in site investigation considering spatial variation and correlation of geotechnical properties. This study performs an extensive parametric study and develops a statistical chart for sample size determination with consideration of spatial variation and correlation using Bayesian compressive sensing or sampling. Real cone penetration test data and real laboratory test data are used to illustrate application of the proposed statistical chart, and the method is shown to perform well.
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Adcock, C. J. "Sample size determination: a review." Journal of the Royal Statistical Society: Series D (The Statistician) 46, no. 2 (1997): 261–83. http://dx.doi.org/10.1111/1467-9884.00082.

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Van Mullekom, Jennifer H. "Sample Size and Power Determination." Journal of Quality Technology 47, no. 2 (2015): 205–6. http://dx.doi.org/10.1080/00224065.2015.11918126.

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Rao, UmadeviK. "Concepts in sample size determination." Indian Journal of Dental Research 23, no. 5 (2012): 660. http://dx.doi.org/10.4103/0970-9290.107385.

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Kim, Hyungjin Myra. "Sample Size Determination and Power." International Statistical Review 83, no. 1 (2015): 168–69. http://dx.doi.org/10.1111/insr.12095_8.

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Dissertations / Theses on the topic "Sample size determination"

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Islam, A. F. M. Saiful. "Loss functions, utility functions and Bayesian sample size determination." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1259.

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This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesian sample size determination (SSD) using loss or utility function with a linear cost function. A number of researchers have studied the Bayesian SSD problem. One group has considered utility (loss) functions and cost functions in the SSD problem and others not. Among the former most of the SSD problems are based on a symmetrical squared error (SE) loss function. On the other hand, in a situation when underestimation is more serious than overestimation or vice-versa, then an asymmetric loss function should be used. For such a loss function how many observations do we need to take to estimate the parameter under study? We consider different types of asymmetric loss functions and a linear cost function for sample size determination. For the purposes of comparison, firstly we discuss the SSD for a symmetric squared error loss function. Then we consider the SSD under different types of asymmetric loss functions found in the literature. We also introduce a new bounded asymmetric loss function and obtain SSD under this loss function. In addition, to estimate a parameter following a particular model, we present some theoretical results for the optimum SSD problem under a particular choice of loss function. We also develop computer programs to obtain the optimum SSD where the analytic results are not possible. In the two parameter exponential family it is difficult to estimate the parameters when both are unknown. The aim of the second part is to obtain an optimum decision for the two parameter exponential family under the two parameter conjugate utility function. In this case we discuss Lindley’s (1976) optimum decision for one 6 parameter exponential family under the conjugate utility function for the one parameter exponential family and then extend the results to the two parameter exponential family. We propose a two parameter conjugate utility function and then lay out the approximation procedure to make decisions on the two parameters. We also offer a few examples, normal distribution, trinomial distribution and inverse Gaussian distribution and provide the optimum decisions on both parameters of these distributions under the two parameter conjugate utility function.
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Gibbons, Christopher. "Determination of power and sample size for Levene's test." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447667.

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Chang, Yu-Wei. "Sample Size Determination for a Three-arm Biosimilar Trial." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/298932.

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Statistics<br>Ph.D.<br>The equivalence assessment usually consists of three tests and is often conducted through a three-arm clinical trial. The first two tests are to demonstrate the superiority of the test treatment and the reference treatment to placebo, and they are followed by the equivalence test between the test treatment and the reference treatment. The equivalence is commonly defined in terms of mean difference, mean ratio or ratio of mean differences, i.e. the ratio of the mean difference of the test and placebo to the mean difference of the reference and placebo. In this dissertation, the equivalence assessment for both continuous data and discrete data are discussed. For the continuous case, the test of the ratio of mean differences is applied. The advantage of this test is that it combines a superiority test of the test treatment over the placebo and an equivalence test through one hypothesis. For the discrete case, the two-step equivalence assessment approach is studied for both Poisson and negative binomial data. While a Poisson distribution implies that population mean and variance are the same, the advantage of applying a negative binomial model is that it accounts for overdispersion, which is a common phenomenon of count medical endpoints. The test statistics, power function, and required sample size examples for a three-arm equivalence trial are given for both continuous and discrete cases. In addition, discussions on power comparisons are complemented with numerical results.<br>Temple University--Theses
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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.

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Meganathan, Karthikeyan. "Sample Size Determination in Simple Logistic Regression: Formula versus Simulation." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663458916666.

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Banton, Dwaine Stephen. "A BAYESIAN DECISION THEORETIC APPROACH TO FIXED SAMPLE SIZE DETERMINATION AND BLINDED SAMPLE SIZE RE-ESTIMATION FOR HYPOTHESIS TESTING." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/369007.

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Statistics<br>Ph.D.<br>This thesis considers two related problems that has application in the field of experimental design for clinical trials: • fixed sample size determination for parallel arm, double-blind survival data analysis to test the hypothesis of no difference in survival functions, and • blinded sample size re-estimation for the same. For the first problem of fixed sample size determination, a method is developed generally for testing of hypothesis, then applied particularly to survival analysis; for the second problem of blinded sample size re-estimation, a method is developed specifically for survival analysis. In both problems, the exponential survival model is assumed. The approach we propose for sample size determination is Bayesian decision theoretical, using explicitly a loss function and a prior distribution. The loss function used is the intrinsic discrepancy loss function introduced by Bernardo and Rueda (2002), and further expounded upon in Bernardo (2011). We use a conjugate prior, and investigate the sensitivity of the calculated sample sizes to specification of the hyper-parameters. For the second problem of blinded sample size re-estimation, we use prior predictive distributions to facilitate calculation of the interim test statistic in a blinded manner while controlling the Type I error. The determination of the test statistic in a blinded manner continues to be nettling problem for researchers. The first problem is typical of traditional experimental designs, while the second problem extends into the realm of adaptive designs. To the best of our knowledge, the approaches we suggest for both problems have never been done hitherto, and extend the current research on both topics. The advantages of our approach, as far as we see it, are unity and coherence of statistical procedures, systematic and methodical incorporation of prior knowledge, and ease of calculation and interpretation.<br>Temple University--Theses
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Pedersen, Kristen E. "Sample Size Determination in Auditing Accounts Receivable Using a Zero-Inflated Poisson Model." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/421.

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In the practice of auditing, a sample of accounts is chosen to verify if the accounts are materially misstated, as opposed to auditing all accounts; it would be too expensive to audit all acounts. This paper seeks to find a method for choosing a sample size of accounts that will give a more accurate estimate than the current methods for sample size determination that are currently being used. A review of methods to determine sample size will be investigated under both the frequentist and Bayesian settings, and then our method using the Zero-Inflated Poisson (ZIP) model will be introduced which explicitly considers zero versus non-zero errors. This model is favorable due to the excess zeros that are present in auditing data which the standard Poisson model does not account for, and this could easily be extended to data similar to accounting populations.
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Chen, Yanran. "Influence of Correlation and Missing Data on Sample Size Determination in Mixed Models." Bowling Green State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1370448410.

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Huh, Seungho. "SAMPLE SIZE DETERMINATION AND STATIONARITY TESTING IN THE PRESENCE OF TREND BREAKS." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010222-121906.

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<p>Traditionally it is believed that most macroeconomic time series represent stationary fluctuations around a deterministic trend. However, simple applications of the Dickey-Fuller test have, in many cases, been unable to show that major macroeconomic variables are stationary univariate time series structure. One possible reason for non-rejection of unit roots is that the simple mean or linear trend function used by the tests are not sufficient to describe the deterministic part of the series. To address this possibility, unit root tests in the presence of trend breaks have been studied by several researchers.In our work, we deal with some issues associated with unit root testing in time series with a trend break.The performance of various unit root test statistics is compared with respect to the break induced size distortion problem. We examine the effectiveness of tests based on symmetric estimators as compared to those based on the least squares estimator.In particular, we show that tests based on the weighted symmetric estimator not only eliminate thespurious rejection problem but also have reasonably good power properties when modified to allow for a break.We suggest alternative test statistics for testing the unit root null hypothesis in the presence of a trend break. Our new test procedure, which we call the ``bisection'' method, is based on the idea of subgrouping. This is simpler than other methods since the necessity of searching for the break is avoided.Using stream flow data from the US Geological Survey, we perform a temporal analysis of some hydrologicvariables. We first show that the time series for the target variables are stationary, then focus on finding the sample size necessary to detect a mean change if one occurs. Three different approaches are used to solve this problem: OLS, GLS and a frequency domain method. A cluster analysis of stations is also performed using these sample sizes as data.We investigate whether available geographic variables can be used to predict cluster membership. <P>
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Safaie, Nasser. "A fully Bayesian approach to sample size determination for verifying process improvement." Diss., Wichita State University, 2010. http://hdl.handle.net/10057/3656.

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There has been significant growth in the development and application of Bayesian methods in industry. The Bayes’ theorem describes the process of learning from experience and shows how knowledge about the state of nature is continually modified as new data become available. This research is an effort to introduce the Bayesian approach as an effective tool for evaluating process adjustments aimed at causing a change in a process parameter. This is usually encountered in scenarios where the process is found to be stable but operating away from the desired level. In these scenarios, a number of changes are proposed and tested as part of the improvement efforts. Typically, it is desired to evaluate the effect of these changes as soon as possible and take appropriate actions. Despite considerable research efforts to utilize the Bayesian approach, there are few guidelines for loss computation and sample size determination. This research proposed a fully Bayesian approach for determining the maximum economic number of measurements required to evaluate and verify such efforts. Mathematical models were derived and used to establish implementation boundaries from economic and technical viewpoints. In addition, numerical examples were used to illustrate the steps involved and highlight the economic advantages of the proposed procedures.<br>Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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Books on the topic "Sample size determination"

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Daplyn, M. G. Sample size determination for formal surveys. Pakistan Agricultural Research Council, 1994.

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1938-, Herrendörfer Günter, ed. Experimental design: Sample size determination and block designs. D. Reidel Pub. Co., 1986.

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Sozu, Takashi, Tomoyuki Sugimoto, Toshimitsu Hamasaki, and Scott R. Evans. Sample Size Determination in Clinical Trials with Multiple Endpoints. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22005-5.

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Lwanga, S. Kaggwa. Sample size determination in health studies: A practical manual. World Health Organization, 1991.

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Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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S, Lin Jin-Mann, Teply John, and Rocky Mountain Research Station (Fort Collins, Colo.), eds. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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Lin, Nancy Pei-ching. A new approach to sample size determination of replicated Latin square designs and analysis of multiple comparison procedures. Ching sheng wen wu kung ying kung ssu, 1985.

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Carlile, David W. An assessment of age determination needs and samples sizes for groundfish fisheries managed by the State of Alaska. Alaska Dept. of Fish and Game, Division of Sport Fish, Research and Technical Services, 2005.

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Book chapters on the topic "Sample size determination"

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Cohen, Michael P. "Sample Size Determination." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_500.

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Muralidharan, K. "Sample Size Determination." In Six Sigma for Organizational Excellence. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2325-2_6.

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Miah, Abdul Quader. "Determination of Sample Size." In Applied Statistics for Social and Management Sciences. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0401-8_14.

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Verma, J. P., and Priyam Verma. "Introduction to Sample Size Determination." In Determining Sample Size and Power in Research Studies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5204-5_1.

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Sozu, Takashi, Tomoyuki Sugimoto, Toshimitsu Hamasaki, and Scott R. Evans. "Convenient Sample Size Formula." In Sample Size Determination in Clinical Trials with Multiple Endpoints. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22005-5_4.

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Valliant, Richard, Jill A. Dever, and Frauke Kreuter. "Power Calculations and Sample Size Determination." In Statistics for Social and Behavioral Sciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93632-1_4.

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Moore, Dirk F. "Sample Size Determination for Survival Studies." In Use R! Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31245-3_11.

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Valliant, Richard, Jill A. Dever, and Frauke Kreuter. "Power Calculations and Sample Size Determination." In Practical Tools for Designing and Weighting Survey Samples. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6449-5_4.

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Chen, Ding-Geng, and Yiu-Fai Yung. "Sample Size Determination and Power Analysis." In Structural Equation Modeling Using R/SAS. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003365860-12.

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Elashoff, Janet D., and Stanley Lemeshow. "Sample Size Determination in Epidemiological Studies." In Handbook of Epidemiology. Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-09834-0_15.

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Conference papers on the topic "Sample size determination"

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Keo, Van Dong, Xuan Tran Hiep, Quoc Nguyen Banh, Tran Anh Son, and Duong Huyen Lynh. "Determination of Geometrical Parameters to Balance the Pressure Drop of Channels on a Microfluidic Chip." In 2024 International Conference on Machining, Materials and Mechanical Technologies. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-me24oh.

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In the past few years, micro-droplets have been widely used in diverse fields of biological and chemical research, spanning from drug delivery and material synthesis to point-of-care diagnostics, digital PCR, and single-cell analysis. Droplet-based microfluidics offers a powerful platform for conducting complex experiments, screening processes, and analyses with enhanced precision, efficiency, and versatility. While creating droplets with uniform sizes is a common objective of microfluidics, it is not limited to producing droplets of a single size per chip. Creating microdroplets with different sizes on a microfluidic chip holds significant importance in various applications. This can provide flexibility in controlling chemical processes, biological reactions, or product quality. By controlling the size of the microdroplets, researchers can precisely regulate the release kinetics of the encapsulated substances, leading to improved therapeutic outcomes and reduced side effects for patients. In chemical analysis, microfluidic platforms can produce microdroplets of different sizes to enable high-throughput screening of chemical reactions or biological assays. By manipulating the droplet size, researchers can enhance reaction efficiency, increase sample throughput, and reduce reagent consumption, making the analysis process more cost-effective and time-efficient. To create microdroplets with different sizes on a microfluidic chip, adjusting process parameters such as pressure, flow rate, and channel design is an approach. In this research, geometrical parameters of the channel such as shape, size, and length are calculated to ensure the pressure drop from the inlet to the creation point droplets of each branch is the same, ensuring the stable operation of the system. The input solution in the research is glucose, which fully exhibits the behavior of a non-Newtonian liquid under defined conditions. The power law viscosity model is used to describe the rheological behavior of glucose liquids.
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Laue, C., P. Vater, R. Brandt, J. Roesch та F. Schleker. "α-Emitters and Corrosion Products in the Primary Coolant of German Nuclear Power Plants Studied by Nuclear Track Micro Filters". У CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96108.

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Abstract Micro-particles (of μm size) are contaminants in the primary coolant of light water reactors and have their origin in corrosion processes. These micro-particles can be separated by filtration through Nuclear Track Micro Filters (NTMF) with pores having well-defined sizes (e.g. 2 μm). A rapid method for determining the α-activity in the coolant was developed. The α-sources are mostly adsorbed on micro-particles and can be separated by filtration. Afterwards, the activity can be directly measured α-spectrometrically. (90 ± 10) % of the total α-activity of the coolant is retained on the surface of the NTMF. Detailed investigations show differences of the measured α-activity in duplicate samples by a factor of two. Differences of one order of magnitude in the determination of α-activity by the filtration method and by conventional analytical chemistry are also observed. The first difference is caused by inhomogeneity of the coolant itself, and the latter by adsorption of actinides on the sample container surface. Correlations between α-activity due to transuranium elements and γ-activity due to corrosion products were studied over long periods. However, no relationship could be found. The solid particles separated by NTMF were analyzed by scanning electron microscopy (SEM) coupled with energy-dispersive X-ray analysis (EDXA). The chemical composition of these particles shows the usual chemical composition of primary circuit structural materials, but some particles seem to be simple conventional dust (Si, O, Ca. ….). The distribution of α-activity on loaded NTMF samples was investigated by α-autoradiography. Results of these investigations show that the α-emitters are geometrically concentrated, i. c., they form either conglomerates (e.g. colloids) or they arc adsorbed on the surface of micro-particles. Particles identified by autoradiography as carrier of α-emitters were analyzed by EDXA, too.
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Alghamdi, Talal M. "On-Site Sampling and Determination of Aliphatic Amines in Industrial Waste Water Using Solid Phase Microextraction (SPME)." In CORROSION 2014. NACE International, 2014. https://doi.org/10.5006/c2014-3935.

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Abstract Amine-based corrosion inhibitors are added to the crude oil streams to protect the pipelines from corrosive attacks by oxygen. Amine residuals should be monitored in the system and this is classically done by liquid-liquid extraction (LLE) of amines followed by colorimetric determination using spectrophotometry. Solid phase microextraction (SPME), a novel extraction technique, was used as a sample preparation technique to isolate the amines from the complex sample matrix and gas chromatography (GC) system was used for separation and quantification of amines. A flow-through system was used to simulate the process of flowing streams in pipelines during oil production. A method was developed to determine amine residuals at the lowest detection limit possible at the optimized conditions, such as solution pH and sand contents. It was found that residuals can be determined as low as 1 ng/mL using the GC/FID system. This method was developed in the lab using the flow-through system and synthesized waste water. This method is an environmental friendly technique that will minimize the use of hazardous solvents and hence the need for the proper disposal of them. Also it does not require pretreatment steps of samples and many reagents to be used in the extraction processes of amine residuals. Finally, it will provide more precise results in timely manner for better and quicker actions.
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Dharmarajan, Sai, Satabdi Saha, Xinying Fang, and Jaejoon Song. "Sample Size Determination for Electronic Phenotyping." In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI). IEEE, 2023. http://dx.doi.org/10.1109/ichi57859.2023.00084.

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Chang, Ernie, Muhammad Hassan Rashid, Pin-Jie Lin, et al. "Revisiting Sample Size Determination in Natural Language Understanding." In Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.419.

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Jin, X., P. Woytowitz, and T. Tan. "On Determination of Sample Size to Evaluate Reliability Growth Plans." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62189.

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The reliability performance of Semiconductor Manufacturing Equipments (SME) is very important for both equipment manufacturers and customers. However, the response variables are random in nature and can significantly change due to many factors. In order to track the equipment reliability performance with certain confidence, this paper proposes an efficient methodology to calculate the number of samples needed to measure the reliability performance of the SME tools. This paper presents a frequency-based Statistics methodology to calculate the number of sampled tools to evaluate the SME reliability field performance based on certain confidence levels and error margins. One example case has been investigated to demonstrate the method. We demonstrate that the multiple weeks accumulated average reliability metrics of multiple tools do not equal the average of the multiple weeks accumulated average reliability metrics of these tools. We show how the number of required sampled tools increases when the reliability performance is improved and quantify the larger number of sampled tools required when a tighter margin of error or higher confidence level is needed.
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Wilson, K. J., and M. Farrow. "Assurance for sample size determination in binomial reliability demonstration testing." In 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability. Institute of Mathematics and its Applications, 2018. http://dx.doi.org/10.19124/ima.2018.001.30.

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Rongfeng Li, Beining Huang, Rongfeng Li, and Wenxin Li. "Test sample size determination for biometric systems based on confidence elasticity." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252517.

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Melinscak, Filip, and Luis Montesano. "Sample size determination for BCI studies: How many subjects and trials?" In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591000.

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Dong, Guangling, Fenghua He, Yu Yao, Hui Zhao, and Chi He. "Sample size determination method for credibility evaluation based on statistical power analysis." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053546.

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Reports on the topic "Sample size determination"

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W Djimeu, Eric, and Deo-Gracias Houndolo. Power calculation for causal inference in social science: sample size and minimum detectable effect determination. International Initiative for Impact Evaluation (3ie), 2015. http://dx.doi.org/10.23846/wp0026.

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Schreuder, Hans T., Jin-Mann S. Lin, and John Teply. Annual design-based estimation for the annualized inventories of forest inventory and analysis: sample size determination. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2000. http://dx.doi.org/10.2737/rmrs-gtr-66.

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Zhao, Bei, James Laszlo, and Heather Kaminsky. PR-461-14602-R02 Sample Mixing and Centrifugal Forces on Density and Sediment-Water Results. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011492.

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The crude oil being transported by pipeline varies widely in density, viscosity, sulfur content, and other fluid properties. In a custody transfer, both parties are concerned with the quantity and the quality of the crude oil since this determines the monetary value of the transaction. The objective of this project is to investigate various factors that can impact accurate determination of the density, sediment and water content of crude. The findings will be critical to improve current procedures, including proper sample mixing and handling, analytical methodologies, and operation parameters, so that reliable and accurate custody transfer can be provided to all involved parties internationally. This project investigated the impact of mixing and analytical methodologies on crude density and S and W determination. Operating parameters tested included mixing time (3 or 5 minutes), mixing system (new or old) and sample size (3 or 7 liters). Density determination was conducted by a thermos-hydrometer test and a digital density meter and the results were compared. S and W content was quantified by the centrifuge method and Karl Fischer titration and the results were compared. For the centrifuge method, the impact of two centrifugal forces on S and W results was also studied. There is a related webinar.
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Głąb, Tomasz, Jarosław Knaga, Tomasz Zaleski, Paweł Dziwisz, Jan Gluza, and Dariusz Glanas. Determination of soil particle size distribution using computer analysis of microscopic images. Publishing House of the University of Agriculture in Krakow, 2025. https://doi.org/10.15576/repourk/2025.1.3.

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The project aims to develop a prototype device for determining the texture of soils and mineral deposits. The innovation of the designed solution consists in a significant reduction in the time of composition analysis with the possibility of any division into granulometric groups and the complete automation of the measurement from the moment the sample is introduced into the apparatus until the result is obtained. As part of the project, industrial research and experimental development are planned to be divided into the following stages: 1. Development of the measuring system. 2. Development of the structure and construction of device prototypes. 3. Development of the construction of the measuring system 4. Development of a mathematical model for processing data from the measuring system. 5. Preparation of software for device control and data recording. 6. Making the final prototype of the device. 7. Test tests of the final version of the device. The research will be conducted by a research consortium consisting of the project leader, i.e. Aumatic sp. z o.o. and the University of Agriculture in Krakow. The developed product will be intended for sale both on the domestic market and for export. Due to the number of entities potentially interested in the apparatus and the financial possibilities of potential recipients, the largest market should be developed countries (e.g. EU countries, USA, Canada, etc.). The main target groups of clients were: 1. Scientific institutions (universities, research institutes). 2. Institutions and enterprises performing analyzes for the needs of precision farming. 3. Chemical and Agricultural Stations. 4. Ceramic clay mining plants. 5. Manufacturers of ceramic products. 6. Laboratories conducting geotechnical tests for the needs of construction. 7. Laboratories carrying out environmental tests in the field of soil quality. 8. Provincial Inspectorates for Environmental Protection, Regional Directorates for Environmental Protection
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Mejía, Luis Fernando, Alejandro Izquierdo, and Guillermo A. Calvo. On the Empirics of Sudden Stops: The Relevance of Balance-Sheet Effects. Inter-American Development Bank, 2004. http://dx.doi.org/10.18235/0010819.

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Using a sample of 32 developed and developing countries we analyze the empirical characteristics of Sudden Stops in capital flows and the relevance of balance-sheet effects in the likelihood of their occurrence. We find that large real exchange rate (RER) fluctuations accompanied by Sudden Stops are basically an emerging market (EM) phenomenon. Sudden Stops seem to come in bunches, grouping together countries that are different in many respects. However, countries are similar in that they remain vulnerable to large RER fluctuations. This may be the case because countries are forced to make large adjustments in the absorption of tradable goods, and/or because the size of dollar liabilities in the banking system (i.e., domestic liability dollarization, or DLD) is large. Openness, understood as a large supply of tradable goods that reduces leverage over the current account deficit, in combination with DLD, is a key determinant of the probability of Sudden Stops. The relationship between Openness and DLD in the determination of the probability of Sudden Stops is highly non-linear, implying that the interaction of high current account leverage and high dollarization may be a dangerous cocktail.
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Bourdeau, J. E., and R. D. Dyer. Regional-scale lake-sediment sampling and analytical protocols with examples from the Geological Survey of Canada. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331911.

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Regional-scale lake sediment surveys have been successfully used since the 1970s as a means for reconnaissance geochemical exploration. Lake sediment sampling is typically performed in areas with a lack of streams and an overabundance of small-sized (=5 km across) lakes. Lake sediments are known to have major, minor and trace element concentrations that reflect the local geology. Overall, lake sediment surveys are planned and conducted following four distinct stages: 1) background research, 2) orientation survey, 3) regional survey, and 4) detailed survey. At the Geological Survey of Canada, samples are usually collected from a helicopter with floats. Sample density ranges from 1 sample per 6 - 13 km2. Samples are collected from the centre of the lake using a gravity torpedo sampler which corresponds to a hollow-pipe, butterfly bottom-valved sampler attached by a rope to the helicopter. Collected sediment samples are then placed in labelled bags and left to air dry. Detailed field notes and additional samples (field duplicates), for the purpose of an adequate quality assurance and quality control program, are also taken. Samples are then milled and sent to analytical laboratories for element determination. Commonly used analytical methods include: X-ray fluorescence (XRF), atomic absorption spectroscopy (AAS), inductively coupled plasma-atomic emission spectrometry (ICP-AES) and -mass spectrometry (ICP-MS), instrumental neutron activation analysis (INAA), and/or determination of volatile compounds and organic carbon using Loss on Ignition (LOI). Analytical data is first evaluated for quality (contamination, accuracy and precision). Numerous options for the analysis of lake sediment data exist, ranging from simple basic element concentration maps and statistical graphical displays together with summary statistics, to employing multivariate methodologies, and, more recently, using machine learning algorithms. By adopting the set of guidelines and examples presented in this manual, scientific researchers, exploration geologists, geochemists and citizen scientists will be able to directly compare lake sediment datasets from anywhere in Canada.
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Bigl, Matthew, Samuel Beal, and Charles Ramsey. Determination of residual low-order detonation particle characteristics from Composition B mortar rounds. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45260.

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Empirical measurements of the spatial distribution, particle-size distribution, mass, morphology, and energetic composition of particles from low-order (LO) detonations are critical to accurately characterizing environ-mental impacts on military training ranges. This study demonstrated a method of generating and characterizing LO-detonation particles, previously applied to insensitive munitions, to 81 mm mortar rounds containing the conventional explosive formulation Composition B. The three sampled rounds had estimated detonation efficiencies ranging from 64% to 82% as measured by sampled residual energetic material. For all sampled rounds, energetic deposition rates were highest closer to the point of detonation; however, the mass per radial meter varied. The majority of particles (&gt;60%), by mass, were &lt;2 mm in size. However, the spatial distribution of the &lt;2 mm particles from the point of detonation varied be-tween the three sampled rounds. In addition to the particle-size-distribution results, several method performance observations were made, including command-detonation configurations, sampling quality control, particle-shape influence on laser-diffraction particle-size analysis (LD-PSA), and energetic purity trends. Overall, this study demonstrated the successful characterization of Composition B LO-detonation particles from command detonation through combined analysis by LD-PSA and sieving.
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Bigl, Matthew, Samuel Beal, and Charles Ramsey. Determination of residual low-order detonation particle characteristics from IMX-104 mortar rounds. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42163.

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The environmental fate and transport of energetic compounds on military training ranges are largely controlled by the particle characteristics of low-order detonations. This study demonstrated a method of command detonation, field sampling, laboratory processing, and analysis techniques for characterizing low-order detonation particles from 60 mm and 81 mm mortar rounds containing the insensitive munition formulation IMX-104. Particles deposited from three rounds of each caliber were comprehensively sampled and characterized for particle size, energetic purity, and morphology. The 60 mm rounds were command-detonated low order consistently (seven low-order detonations of seven tested rounds), with con-sumption efficiencies of 62%–80% (n = 3). The 81 mm rounds detonated low order inconsistently (three low-order detonations of ten tested rounds), possibly because the rounds were sourced from manufacturing test runs. These rounds had lower consumption efficiencies of 39%–64% (n = 3). Particle-size distributions showed significant variability between munition calibers, between rounds of the same caliber, and with distance from the detonation point. The study reviewed command-detonation configurations, particle transfer losses during sampling and particle-size analysis, and variations in the energetic purity of recovered particles. Overall, this study demonstrated the successful characterization of IMX-104 low-order detonation particles from command detonation to analysis.
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Dailami, N., M. Bhaskara Rao, and K. Subramanyam. On the Selection of the Best Gamma Population. Determination of Minimax Sample Sizes. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada166138.

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Fourqurean, James, Johannes Krause, Juan González-Corredor, Tom Frankovich, and Justin Campbell. Caricas Partner's Practical Field and Laboratory Guide. Florida International University, 2024. http://dx.doi.org/10.25148/merc_fac.2024.32.

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This field and laboratory guide describes the field and laboratory methods used to characterize blue carbon in seagrass meadows. It was developed for the Caribbean Carbon Accounting in Seagrass project and describes the protocols and methods used by the network. In brief, at each project site, seagrass abundance, species composition, canopy height, and sediment type were assessed at sixteen 0.25 m2 quadrats placed at random locations within the site. Eight 20 cm diameter cores were taken to assess seagrass biomass, shoot density, and to provide the material for assessing seagrass carbon and nutrient content. All seagrasses within each of the eight cores were separated by species and tissue type, washed and scraped to remove epiphytes, then dried and weighed. A piston core of uncompressed soils was retrieved, to a depth of 1 m or until refusal. Cores were subsampled at 5 cm depth intervals using small subcorers. All subcores were weighed wet to permit the calculation of porosity and soil dry bulk density. Seagrass tissue and sediment samples were oven-dried at 60°C, and dry weight recorded. Finally, samples were analyzed in the laboratory for determination of Loss on Ignition, total carbon content, inorganic carbon content, organic carbon content, and carbon and nitrogen content as well as stable isotope ratios. The resulting data allow for the estimation of seagrass organic carbon stocks as well as nutrient and carbonate stocks in biomass and sediment, their relationship with environmental covariates, and the contribution of seagrass material to carbon stocks.
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