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Sampling strategies
Published in Andrew Metcalfe, David Green, Tony Greenfield, Mahayaudin Mansor, Andrew Smith, Jonathan Tuke, Statistics in Engineering, 2019
Andrew Metcalfe, David Green, Tony Greenfield, Mahayaudin Mansor, Andrew Smith, Jonathan Tuke
In large studies it is usual to sample in stages (multi-stage sampling). For example, consider the estimation of the capacity of farm dams in the Murray-Darling basin (see Example 14.7). The area of the Murray-Darling basin is around 106 km2. This could be divided into one hundred 100 km by 100 km squares. We could take an SRS of 5 of these large squares. We then consider more detailed maps for these 5 squares, sub-divide them into 100 squares of size 10 km by 10 km, and undertake a detailed field investigation of 5 from these 100. The final sample size would be 25 10 km2 squares. The sampling units at the first stage are 100 km by 100 km squares, which each contain 100 second stage sampling units (10 km by 10 km squares). The 100 km by 100 km squares are examples of clusters.
Simulation of Cumulative Absolute Velocity Consistent Endurance Time Excitations
Published in Journal of Earthquake Engineering, 2021
Mohammadreza Mashayekhi, Homayoon E. Estekanchi, Abolhassan Vafai, Seyyed Ali Mirfarhadi
In this study, IDA analysis is used as a benchmark for verification of the proposed method. Figure 16 compares the IDA curves of these structures and the ET method results. This figure shows excellent compatibility of the ET method results and IDA analysis. In order to quantify the accuracy of the ET method, a parameter is proposed; this parameter as brought in Equation (20) integrates the absolute error over all intensity measures and represents the error in percentage. Accuracy percentage is calculated by subtracting the error percentage from one hundred. In this equation, EDP stands for engineering demand parameter and IM is representative of intensity measure. In this study, acceleration spectra at first mode period is used as intensity measure and maximum interstory drift ratio is employed as engineering demand parameter.
Atoms and molecules in soft confinement potentials
Published in Molecular Physics, 2020
L. F. Pašteka, T. Helgaker, T. Saue, D. Sundholm, H.-J. Werner, M. Hasanbulli, J. Major, P. Schwerdtfeger
After Sommerfeld and Welker's [2] reformulation of the problem, many authors revisited this model. Suryanarayana and Weil [7] formulated the wave functions in terms of the confluent hypergeometric functions and calculated energy eigenvalues numerically to a few significant figures. Both Goldman and Joslin [34] and Chuu et al. [35] used a modified version of Kummer's differential equation and represented the formal solutions in terms of Whittaker functions, computing energy eigenvalues from a truncated series expansion of the Whittaker functions with many terms. In 2005, Burrows and Cohen [36, 37] investigated the model using a combination of group theory and algebraic methods. Recently, in 2007, Aquino et al. [38] showed that it is possible to obtain the energy for the confined hydrogen atom to a very high accuracy, with up to hundred decimal digits. In addition to energy eigenvalues for the ground state and many excited states, they computed expectation values , and , hyperfine splitting and magnetic screening constants, polarisabilities in the Kirkwood approximation, and pressure as a function of the confinement radius.
Revisiting Online Video Popularity: A Sentimental Analysis
Published in Cybernetics and Systems, 2019
Wei-Lun Chang, Li-Ming Chen, Alexey Verkholantsev
Except comment, YouTube allows user to click like or dislike to simply express own feelings. Although many users ignore like and dislike, this is still an important factor to influence them (Sorensen, Pusz, and Brietzke 2014). This research names Enjoyment Index (EI) that includes the concepts of number of likes and number of views. In reality, users may not click dislike if they do not like the video. Therefore, the number of dislikes usually is zero or very small. That is, the ratio of number of likes to dislikes reflects people the percentage of users who are willing to comment (Primack et al. 2015). This research also proposes the concept of enjoyment index (EI) that enfolds number of views (V) and number of likes (L). The reason to divide number of likes by number of views is to understand how many likes per hundred views; that is, the unit is a percentage. The higher number of EI, more enjoyment of the video from users. It is objective that number of likes and dislikes only considers users who evaluated the video. Eq. (4) indicates the calculation of enjoyment index.