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175 results for “GRAPHICS”
Figure 1 in A graphically illustrated glossary of polychaete terminology: invasive species of Sabellidae, Serpulidae and Spionidae
Figure 1. (a) Bispira manicata with abdomen region highlighted red. (b) Posterior notopodia of Boccardiella bihamata stained with methyl green; arrow points to acicular spine. (c) Acicular thoracic uncini of Euchone limnicola. (d) Ventral anal depression in Euchone variabilis (left, stained with methylene blue) with lateral flanges, and in Euchone limnicola, without flanges. (e) Collar region/base of radiolar crown in Myxicola infundibulum stained with methylene blue; anterior peristomial ring highlighted red. (f) Lateral view of Spirobranchus tetraceros (left) and ventral view of Spirobranchus kraussii (right), both stained with methylene blue; arrows point to apron. (g) Z-shaped avicular thoracic uncini of Laonome triangularis (above) and Bispira manicata (below, stained with methyl green). (h) Bayonet collar chaetae in Serpula jukesi, stained with methyl green. (i) Bayonet thoracic chaetae in Jasmineira sp. (j) Arrows point to branchiae in Boccardia chilensis (left and right specimens stained with methylene blue and methyl green, respectively). All scales in mm.
Figure 2 in A graphically illustrated glossary of polychaete terminology: invasive species of Sabellidae, Serpulidae and Spionidae
Figure 2. (a) Thoracic uncini of Bispira manicata, stained with methyl green; arrow points to breast of uncinus. (b) Broadly hooded thoracic chaetae of Euchone limnicola and collar chaetae of Laonome calida. (c) Capillary chaetae from collar of Spirobranchus taeniatus (on left, stained with methyl green) and chaetiger 1 of Boccardia proboscidea (on right). (d) Dorsal view of anterior ends of Polydora haswelli (on left, stained with methyl green) and Boccardia proboscidea (on right); arrows point to caruncle. (e) Arrows point to thoracic chaetae of (left to right, respectively) Bispira porifera, Boccardiella bihamata (stained with methyl green) and Spirobranchus cariniferus (stained with methylene blue). (f) Arrows demonstrate chaetal inversion in Branchiomma bairdi (left) and Spirobranchus cariniferus (right). (g) Lateral views of thoracic regions of Bispira porifera (left) and Polydora haswelli (right, stained with methyl green); each bracket indicates 1 chaetiger. All scales in mm.
FlowPhotoChem graphical abstract
Open the record for dataset details and reuse information.
Figure 3. Graphical representation using a in Investigating the risk of non-indigenous species introduction through ship hulls in Chile
Figure 3. Graphical representation using a nMDS based on Bray-Curtis distances, of the taxa assemblages in each sampling type (ships, settlement plates in the Talcahuano port, and natural substrates at El Manzano pier). Stress value is 0.17.
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 10. Graphic containing the results for 1000 bits
<p>The distribution obtained depending on the length of the input string shows that time depends on the input length, but for lengths even closer together, the times are also close (this can be seen in Figure 8 for 20 bits inputs). Time does not change so much depending on which of the 14 different 16th degree primitive polynomials has been used. </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 9. Graphic containing the results for 1000 bits
<p>The next two graphics show the obtained results from the execution of the main program for each of the 14 degrees, 16th primitive polynomials for three different situations depending on the lengths of the entrance data polynomial. The lengths of the input polynomials were 20. 30. 40, 50, 100 and 1000 bits. The maximum number of sequences is 216-1(Solomon, 1967). </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 8. Graphic containing the results for 20 bits
<p>The next two graphics show the obtained results from the execution of the main program for each of the 14 degrees, 16th primitive polynomials for three different situations depending on the lengths of the entrance data polynomial. The lengths of the input polynomials were 20. 30. 40, 50, 100 and 1000 bits. The maximum number of sequences is 216-1(Solomon, 1967). </p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 1. The graphical representation of the morphism
<p>A morphism of partial algebras such that (30) and if (31), then (32) (see Figure 1). We obtain f(L) = T which means that “for every element of L the associated element of T is computed by the morphism f” (Ţăndăreanu, 2000).</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing
<p>To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 3. The graphic stack of X3DOM (Havele 2011)
<p>The current release of X3DOM supports native implementations (iOS8, Chrome, and Firefox for Android), with fallback to WebGL API, and partially to X3D/SAI plugins (INSTANTREALITY 2017). X3DOM is above WebGL, OpenGL and DirectX, and subsequently has less complexity (in Figure 3 is shown the graphical stack). Integrated into the HTML DOM, X3DOM allows web programmers to continue their experience, based on known web technologies such as CSS, Java Script, JQuery or Ajax. Standard technologies can streamline a VR or AR application development, by hiding the low-level complex tasks, and allow the access to device sensors and video camera via high-level API functions. X3DOM supports embedded X3D-XML files references using inline nodes, i.e. an X3D- XML file can reference other X3D-XML files and build a hierarchy of assets (X3DOM 2017) which can be loaded in the background with a higher throughput.</p>
FIGURE 8 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 8. Graphic correlation of the three Compte subfacies area sections. The columns show the observed first occurrences of important taxa in the studied sections, indicated by their projected position on the Pyrenean CS. The numbers represent the taxa listed in the range chart (Figures 5 and 6) and in the Appendix. (CSU: Composite Standard Unit).
FIGURE 10 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 10. Graphic correlation of four composite standard (CS) databases, three from NW-Gondwana (Anti-Atlas, Pyrenees and Montagne Noire) and one from S-Laurussia (Ardenne) (Golonka, 2000). The standard reference section for the Middle Devonian CS and the Anti-Atlas CS is the Jebel Ou Driss section in the Eastern Anti-Atlas (Morocco) (Belka et al., 1997). The standard reference sections for the Ardenne and Montagne Noire CSs are the Couvin-Givet section and the Pic de Vissou section, respectively. The columns show the observed first occurrences of important taxa in the regional CS's, indicated by their projected position on the Middle Devonian CS. The numbers represent the taxa listed in the range chart (Figures 5–6) and in the Appendix.
FIGURE 9 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 9. Correlation between the Pyrenean composite standard and the Middle Devonian composite standard. The numbers represent the taxa listed in the range chart (Figures 5 and 6) and Appendix. See also legend in Figure 4.
FIGURE 1. 1 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 1. 1. Middle Devonian paleogeographic situation with indication of the study area (courtesy of R. Blakey, NAU Geology), 2. Map of Paleozoic rocks on the Iberian Peninsula and position of figure 1.3, 3. Location of the studied Devonian outcrops.
FIGURE 4 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 4. Graphic correlation between the La Guàrdia d'Ares section and the composite standard (third round). The numbers represent the taxa listed in the range chart (Figures 5 and 6), error boxes indicate the sampling intervals.
FIGURE 6 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 6. Conodont range chart based on the Spanish Central Pyrenean sections (continued). Precise CSU values are given in the Appendix.
FIGURE 3 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 3. Graphic correlation between the Villech section and the composite standard for the Spanish Central Pyrenees (third round). The numbers represent the taxa listed in the range chart (Figures 5 and 6), the error boxes indicate the sampling intervals. (sem./lat.: semialternans/latifossatus).
FIGURE 5 in Graphic correlation of the upper Eifelian to lower Frasnian (Middle-Upper Devonian) conodont sequences in the Spanish Central Pyrenees and comparison with composite standards from other areas
FIGURE 5. Conodont range chart based on the Spanish Central Pyrenean sections. Precise CSU values are given in the Appendix.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.