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opencc-by-4.0May 2009View details →

Fig. 2 in An Evaluation Of Stone Marten (Martes Foina) Records In The City Of Budapest, Hungary

Fig. 2. The number of yearly topographical records of stone martens in Budapest (n = 303) during the 13 years of monitoring

opencc-by-4.0May 2009View details →

Fig. 1 in An Evaluation Of Stone Marten (Martes Foina) Records In The City Of Budapest, Hungary

Fig. 1. Numbering and location of the 23 districts in Budapest. The grey patches represent the DESERT (≤ 50% green) and the striped patches represent the GREEN (≥ 50% green) type districts according to the records on the stone marten. There were no topographical data available for districts

opencc-by-4.0Jan 2022View details →

Fabrication, Characterization and Evaluation of an Alginate–Lignin Composite for Rare-Earth Elements Recovery

<p>This dataset contains the raw data for the publication "Fabrication, Characterization and Evaluation of an Alginate–Lignin Composite for Rare-Earth Elements Recovery" by Fila et al, published in Materials. The upload includes raw data of physicochemical characterizations of alginate-based composite, i.e. alginate-lignin, including BET, SEM, TG, XPS and XRD analyses. </p>

opencc-by-4.0Jun 2024View details →

Fig. 1 in Different staining techniques evaluation for the study of sperm morphology and morphometry in bats (Mammalia: Chiroptera)

Fig. 1. Sperm morphology and morphometric variables. (a) Part of the spermatozoa, (b) morphometric variables measured in this study: head length (HL), head width (HW), middle piece length (MPL) and tail length (TL).

opencc-by-4.0Jun 2024View details →

Fig. 2 in Different staining techniques evaluation for the study of sperm morphology and morphometry in bats (Mammalia: Chiroptera)

Fig. 2. Microscopic images of spermatozoa stained with (a) Toluidine Blue (TB), (b) Giemsa (G), (c) May Grünwald-Giemsa (MG-G), (d) GRAM (Gr), (e) Hematoxylin-Eosin (HE), (f) DAPI, (g) Janus Green and (JG) (h) Basic Fuchsin (BF), b=10 µm.

Figure 2 in EVALUATION OF THE EFFECTIVENESS OF USING A UNIVERSAL METHOD FOR ISOLATING GENOMIC dsDNA BY SALTING OUT TECHNIQUE ACCORDING TO THE S.M. ALJANABI AND I. MARTINEZ PROTOCOL FOR YEAST SACCHAROMYCES CEREVISIAE

Figure 2. Quantification of extracted Saccharomyces cerevisiae DNA by UVVis Spectrophotometry (DNA samples: 1–15).

Fig. 3 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 3. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polysterene) smoke and larvae age (hours after eggs are laid)

Fig. 4 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 4. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polysterene) smoke and pupae age (hours after eggs are laid)

Fig. 9 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 9. Abdominal tergite anomalies in wild type Drosophila melanogaster in the result of treating pupae with the smoke of burning polysterene; 1 – females, 2 – males

Fig. 2 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 2. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polyethyleneterephtalane) smoke and pupae age (hours after eggs are laid)

Fig. 1 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 1. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polyethyleneterephtalane) smoke and larvae age (hours after eggs are laid)

opencc-by-4.0Dec 2011View details →

Fig. 2 in Evaluation Of Winter Hardiness In Different Cultivated Tilia Taxa - Experience Of Some Most Valuable Dendrological Plantations In Central Latvia (Vidzeme) After Extremely Hard Winter In Year 2009/2010

Fig. 2. Long-term average temperatures of January in Latvia (by Turlajs 2007) with inventoried objects in central part of Latvia.

opencc-by-4.0Jul 2024View details →

Data from: Evaluating Window Size Effects on Univariate Time Series Forecasting with Machine Learning

<p>In the realm of time series prediction modeling, the window size (w) is a critical hyperparameter that determines the number of time units included in each example provided to a learning model. This hyperparameter is crucial because it allows the learning model to recognize both long-term and short-term trends, as well as seasonal patterns, while reducing sensitivity to random noise. This study aims to elucidate the impact of window size on the performance of machine learning algorithms in univariate time series forecasting tasks. To achieve this, we employed 40 time series from two different domains, conducting experiments with varying window sizes using four types of machine learning algorithms: Bagging, Boosting, Stacking, and a Recurrent Neural Network (RNN) architecture. The results reveal that increasing the window size generally enhances the evaluation metric values up to a stabilization point, beyond which further increases do not significantly improve predictive accuracy. This stabilization effect was observed in both domains when w values exceeded 100 time steps. Moreover, the study found that RNN architectures do not consistently outperform ensemble models in various univariate time series forecasting scenarios.</p>

Figure 8 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 8. Representation of complex isosurface (A, C, and E) and 2D interactions within the enzyme active site (B, D, and F): AG/5TIQ (A, B); AE/5TIQ (C, D); PU/5TIQ (E, F).

Figure 7 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 7. Representation of complex ribbons (A, C, and E) and 2D interactions within the enzyme active site (B, D, and F): AG/5TIQ (A, B); AE/5TIQ (C, D); PU/5TIQ (E, F).

Figure 4 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 4. Spectra of 1H-NMR (A) and J-resolved 1H-1H (B) expanded on the region of aromatic signals of the acetanolic subfraction - washing water - acids (88.82%) of L. pacari obtained in methanol-d4.

Figure 3 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 3. (A) Scores of PCA (71.5% PC1 and 21.3% PC2) of 6 subfractions obtained from the ethanolic extract of L. pacari classified by (RT%) activity of each subfraction. (B) Loadings from PC1 (71.5%) with NMR signals discriminating between subfractions.

Figure 5. Spectrum region HSQC 1H-13C in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 5. Spectrum region HSQC 1H-13C (A) and HMBC 1H-13C (B) with region of aromatics compounds of the acid wash water acetate subfraction (88.83%) of L. pacari obtained in methanol-d4.

Figure 2 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 2. Scores of PCA of obtained subfractions from the ethanolic extract of L.pacari classified by the (RT%) activity of each subfraction.

Figure 1 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 1. Profile of 1H-NMR spectra of subfractions obtained from the ethanolic extract of L. pacari classified by (RT%) activity of each subfraction.