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Fig. 10 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description

Fig. 10. Drawing of the oral field of a larva of Gracixalus quangi from Hoa Binh Province (IEBR 4333b) in development stage 35: natural view on top, schematic drawing below). Drawing by C. Niggemann.

Fig. 9 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description

Fig. 9. Drawing of a larva of Gracixalus quangi from Hoa Binh Province (IEBR 4333b) in development stage 35 in dorsal and lateral view. Drawing by C. Niggemann.

Fig. 8 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description

Fig. 8. Advanced developmental stages of Gracixalus quangi from Hoa Binh Province (development stages indicated). Photo credit T. Ziegler and T.D. Tran.

Fig. 6 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description

Fig. 6. Early developmental stages of Gracixalus quangi from Hoa Binh Province (development stages indicated). Photo credit C.T. Pham.

Fig. 3 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description

Fig. 3. Adult couple of Gracixalus quangi from Hoa Binh Province (male above, female below). Photo credit C.T. Pham.

Fig. 11 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description

Fig. 11. Larva of Gracixalus quangi from Hoa Binh Province (IEBR 4333b) in development stage 35 in life (photo taken 4 May 2014). Photo credit T. Ziegler.

opencc-by-4.0Feb 2023View details →

Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)

Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A Spherical trochophore ca. 48–72-h post-fertilization. B Elongated embryo at ca. 5 days post-fertilization (dpf) with four segments and rudiments of parapodia, Roman numerals refer to segment numbers. C Dorsal view of larva/juvenile at 6–7 dpf showing parapodia formation; note first cirruslike appendage. Nuchal organs (no) visible as cilia semicircles in front of the prototroch (pt). D Ventral view of larva/juvenile at ca. 8–10 dpf. E

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 2. Stages of software implementation

The algorithm has two stages, first is pre-processing of given MRI image and after that segmentation and then perform morphological operations.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 20. Overview about which Binding Mechanisms Work at what Hierarchical Levels and Development Stages of the Brain

As a result of our research, in [60], a solution to the binding problem for perception was suggested by combining the already existing binding hypotheses in a conclusive way, supplementing them with other insights about the perceptual system of the brain, and translating them into a technically implementable model. It was demonstrated via computational simulations that different binding mechanisms proposed in literature are not mutually inclusive. On the contrary! At different hierarchical levels and in different development stages, different binding mechanisms are acting in perception. An overview about these circumstances is given in Figure 20. A detailed description can be found in.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Principal stages of image classification system

In computer vision, images or objects are recognised by machine going through two phases shown in the figure 4. First the system is trained with features extracted from sample images in training stage then they are tested on input images in testing stage. The performance of the classifier depends on features extracted from the image. This research work is carried out in three different experiments, first experiment is performed on data set containing the original images of sixteen categories, noisy images are classified in second experiment, and third experiment detects the type of noise affected the image followed by filtering through appropriate filter, then filtered images are classified. The performance of each of the experiment is measured with two approaches. First approach extracts the statistical texture features of the whole image, and the original image of size 128x128 is divided into sixteen blocks of size 32x32 pixels in second approach. Then six statistical texture features discussed in second section are extracted from each of the block producing 96 features from each of the images are used for training and testing stage.

opencc-by-4.0Jan 2016View details →

The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 4. The artefact during the initial structuring stage

The IT developer elaborates the detailed structure of the artefact, while considering several aspects: — The resources that are necessary to the artefact in order to accomplish its mission; — The artefact’s resistance to the changes regarding the functional requirements; — The artefact’s resistance to the technological changes; — The reasonably priced integration of the artefact in the structure of the host system; — The assurance of a reasonable reusability coefficient of the artefact during the struc- turing process of other artefacts; — The flexibility of the relations that exist among the components of the artefact; — The flexibility of the artefact’s connections with the host system.

opencc-by-4.0Mar 2018View details →

Preprints Servers as a Hub for Early-Stage Research Outputs

Data for "Preprints as a Hub for Early-Stage Research Outputs" This data set contains three documents results from a survey focused on how  preprint servers relate to open science. See http://researchpreprints.com/2017/12/18/a-short-research-project-where-do-preprints-fit-in/ for the research plan.  - submission systems and websites.xlsx A check of submission pages for preprint servers. Opportunities to link to other early-stage research outputs were recorded. Information displayed on websites was also checked on an  ad hoc basis.  - preprint abstracts.xlsx   A check of 25 or 50 preprints for selected preprint servers, to see what information linking to other early-stage research outputs was visible.  - preprint operator survey   Individual responses from a survey of those operating preprint servers. Names and email addresses were collected but are not reported.

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 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 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 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students

It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues  value is located within the 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 12. Drawing of autocorrelation function and partial correlation for females primary stage students

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 11. Drawing the time series for males and females primary stage after the First difference

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of autocorrelation function and partial correlation for Males and Females primary stage students

The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).