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opencc-by-4.0Mar 2023View details →

Cryo-ET tilt series of human GBP1 coatomers assembled on SUVs

Uncorrected frame stacks of cryo-tomographic tilt series acquired for human GBP1 coaotomers assembled on small unilamellar vesicles.

Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 5 we can see that the mse curve reaches the In performance goal but it does not decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these results one can say that RNN is better choice than Feedforward MLP in prediction purpose.

Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can say that Y=T regression is not so good.

Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can say that Y=T regression is totally fit. This paper also comprises of comparative study of performance(mse) plot of both network.

Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Methodology This paper develops an ANN based comparative predictive model for NASDAQ stock prediction. The first ANN model is developed with Multi-Layer Feed forward Network Architecture & the second model is developed with Recurrent Neural Network Architecture. In this paper gradient descent based back propagation learning algorithm is used for the supervised learning of the predictive network.

opencc-by-sa-4.0Apr 2018View details →

Bedload rate time series

Files R1qsraw.txt to R7qsraw.txt: Bedload rate (g/s) time series for total bedload (column 1) and 14 grain size fractions over seven experimental runs (R1 to R7). Fractional bedload rates go from column 2 to 15 as follows:  0.5-0.7 mm (column 2) 0.7-1  mm (column 3) 1-1.4  mm (column 4) 1.4-2  mm  (column 5) 2-2.8  mm (column 6) 2.8-4  mm (column 7) 4-5.6  mm (column 8) 5.6-8  mm (column 9) 8-11   mm  (column 10) 11-16 mm   (column 11) 16-22 mm  (column 12) 22-32 mm (column 13) 32-45 mm  (column 14) 45-64 mm  (column 15) File SurfaceDg.txt: Contains the geometric mean size for the bed surface in mm (column 2) for different experimental times (time in h, column 1) File BedloadDg.txt: Contains the geometric mean size for the bedload in mm (column 2) every 1 h (time in h, column 1) File slope.txt: Contains the bed slope at the thalweg in m/m (column 2) for different experimental times (time in h, column 1)

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 18. Drawing of the time series for males and females of primary stage students and its prediction)

Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 17. Drawing of the time series for females of primary stage students and its prediction

It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 16. Drawing of the time series for males of primary stage students and its prediction

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 11. Drawing the time series for males and females primary stage after the First difference

We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 9. Drawing the time series for males and females primary stage students

The Unit Radix Dickey-Fuller Test is used to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.736458 , Statistic Test = 0.380545 , P-Value  = 0.794 We notice from the values above P-Value = 0.794on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis ( The Nonexistence of a Radix Unit) implies that the time series is stable. Figure (9) represents the time series of females and males in the primary stage students.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 5. Drawing the time series for primary stage female students

We use the Unit Radix Dickey-Fuller Test to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.233403, Statistic Test = 0.125769, P-Value = 0.6405 The values above P-Value = 0.6405 is noted on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis (The Nonexistence of a Radix Unit) implies that the time series is stable. Figure 5 represents the Time series of Female Primary Stage Students.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 3. Drawing the time series for Males primary stage students after getting the first difference

The Unit Radix Dickey-Fuller Test is used to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.276151, Statistic Test = 0.87796, P-Value = 0.8984 We get to notice from the values above P-Value = 0.8984 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is observed that the stability of the Time Series has been accomplished . See figure 3.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 1. Drawing the time series for males primary stage

After collecting all the students’ dropout proportion for both males and females in the primary stage, the first step of the Box-Jenkins is to draw the time chain data to understand the chain's attitude.

opencc-by-4.0Jun 2018View details →

Time series of detonation velocity for Fickett's model for various values of activation energy

This dataset contains several time series of detonation velocity for Fickett's model. Parameters are: q=4, resolution per unit lenth is 1280. Activation energies (theta) are 0.95, 1, 1.004, 1.055, 1.065, 1.089.

opencc-by-4.0Oct 2018View details →

Time series of Inland Surface Water Dataset in China (ISWDC)

The Inland Surface Water Dataset in China (ISWDC) maps the water body larger than 0.0625 km2 in the terrestrial land of China for the period 2000–2016, in 8-day temporal and 250 m spatial resolution. It is closely correlated with the national reference data with the determinant coefficients (R2) greater than 0.99 in 2000, 2005, and 2010, and possess very good consistency, very similar change dynamics, and similar spatial patterns in different regions with the GSW dataset. The ISWDC data set can be used for studies on the inter-annual and seasonal variation of the surface water systems. It can also be used as reference data for other surface water data set verification and as input parameter for regional and global hydro-climatic models.

opencc-by-4.0Nov 2018View details →

A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys

This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017. The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.

opencc-by-4.0Dec 1902View details →

PLATE XX. Fig. 1 in New genera and species from the Belly River Series (mid-Cretaceous)

PLATE XX. Fig. 1. Monoclonius belli, Lambe, parietal element of posterior crest, superior view; one-third the natural size. Page 66. Fig. 2. The same, inferior view. Fig. 3. Monoclonius dawsoni, Lambe, rostrai bone, side view; one-half the natural size. (Provisionaily associated with M. dawsoni). Page 63. Fig. 4. Outline of section of at ". " same c Fig. 5. Horn core with an asymmetrical base. Kot specifically determined. Fig. G. Sectional outlines of same at a and h" g, groove for premaxilla