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24,623 results for “Figure 6”
Figures 1–6. Telicota paceka mesoptis Lower, 1911 in The Nomenclature and Type Status of Telicota paceka mesoptis Lower, 1911 (Lepidoptera: Hesperiidae)
Figures 1–6. Telicota paceka mesoptis Lower, 1911 type material in the South Australian Museum, Adelaide (SAMA): (1–3) lectotype male, showing dorsal, ventral and labels; (4–6) paralectotype female, showing dorsal, ventral and labels. Scale bar = 10 mm.
Figures 6–11 in A Key to the Australian Genera of Eumeninae (Hymenoptera: Vespidae)
Figures 6–11. Lateral views of metasoma: (6) Acarodynerus dietrichianus (de Saussure); (7) Pararrhynchium sp. Dorsal views of male metasomal segments II–VII: (8) Parifodynerus alariformis (de Saussure); (9) Pseudalastor sp. Dorsal views of metasoma: (10) Stenodyneriellus bicoloratus (de Saussure); (11) Eudiscoelius jacinthae (Gribodo). Scale = 1.0 mm.
Figure 6 in Egyptian Funerary Boat Model in the Australian Museum: Dating and Analysis
Figure 6. Figures on board, showing separately attached arms, belt around the waist (figure on the right), dark hair
Figure 6 in A New Species of Caprella (Crustacea: Amphipoda: Caprellidae) from Gippsland Lakes, Australia, with a Redescription of Caprella acanthogaster Mayer, 1890 from Northern Japan
Figure 6. Caprella tamboensis sp. nov. from Tambo Bay, Lake King, Gippsland Lakes, Victoria, Australia. Holotype, male, AM P.102618, 21.93 mm. Bar indicates 1.0 mm, except for 0.1 mm to AB.
Figures 5–6 in A New Species of Teratomyza, the First Fern Fly from New Guinea (Diptera, Teratomyzidae)
Figures 5–6. Teratomyza ismayi sp. nov. (5) proboscis and ventral edge of gena of male, anterolateral view; (6) part
Figure 6 in New Eastern Australian Species in the Wolf Spider Genus Artoriopsis (Araneae, Lycosidae, Artoriinae)
Figure 6. Habitat where the holotype of Artoriopsis murphyi sp. nov. was found on Deal Island, Tasmania.
Figures 5–6 in New and Poorly Known Species of Crane Flies (Diptera: Limoniidae) from New South Wales, Australia
Figures 5–6. Molophilus (Molophilus) flavocingulatus, male. (5) head colouration, dorsal; (6) hypopygium, ventral view.
Figure 6 in Revision of the Indo-West Pacific Coral Reef Mantis Shrimp Genus, Gonodactylopsis Manning, 1969 (Crustacea: Stomatopoda: Gonodactylidae)
Figure 6. Gonodactylopsis lata sp. nov.: A–J, holotype female, TL 22 mm, Fiji, VGS 82-9, USNM 307139; K–L, paratype male, TL 17 mm, Fiji, MUSORSTOM 10 CP1364, MNHN. (A) anterior cephalothorax, dorsal view; (B) right eye, lateral view; (C) ocular scales; (D) rostral plate, lateral view; (E) right antenna, lateral view; (F) right raptorial claw, lateral view; (G) thoracic somites 6–8, right lateral view; (H) abdominal somites 5 and 6, telson and right uropod; (I) abdominal somites 5, 6 and telson, lateral view; (J) right uropod, ventral view; (K) telson, dorsal view; (L) right pleopod 1 endopod, anterior view. Scale: A–K = 2.0 mm; L = 1.0 mm.
Figure 6 in Tyrannosaurs from the Late Cretaceous of western Canada
Figure 6 Daspletosaunts torosus, reconstruction of the skull in lateral aspect, based on NMC 8506. The foramen in the quadratojugal is taken from NMC 11315, where the element is better preserved.
Figure 6: Simulation computation, at successive steps: iterations 0, 152, 250, 370, 601, 1601. 29
<p>On the figure 6, we present a complete simulation with several centers<br> and several queens [13]. On each center, a queen is emitting several colored<br> pheromons and are able to attract some multi-colored material according to<br> their initial location. Simulation outputs at different iteration times are presented,<br> from RePast implementation and OpenMap GIS visualization.</p>
Figure 6: Erythema values before and after 10 minutes from application 57 -STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES
<p>The values of the erythema are relatively low, which indicates that the<br> product is compatible with the skin and has no sensitizing potential (¯gure6).<br> Having a complex composition of ingredients with varying degrees of un-<br> saturation, with the major organic structure, the product is subjected to de-<br> structive processes at the impact of UV radiation and atmospheric oxygen.<br> As a result this organic substances undergo structural changes, embodied in<br> the initial color change, division of chain, forming oxidizing action groups and<br> oxygen free radicals that continue the destructive process. Because of these<br> features, the product was subjected to accelerated tests of thermal and photo<br> destruction. As a result of these tests, the product's characteristics have not<br> changed signi¯cantly.</p>
Figure 6. Change the frequency at Hertz scale to Mel Scale.-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Following computing the spectrum power and applying the above equation on frequency<br> axis, some mediating filters equal to identical overlapping are applied on the scaled spectrum and<br> each filters energy is computed as particularity. This is because conception of a particular frequency<br> by the auditory system is affected by a critical band of frequencies surrounding it. Number of filters<br> is usually between 20 and 30. Logarithmic non linear change operations on obtained particulates for<br> adjusting amplified of particularities and their important though coordinating them with the<br> structure of the auditory system following computing energy of each filter is done as follows: in the<br> following equation F1 is filter in I th, and e (i) is logarithm of energy at ith band.</p>
Figure 6. The mental spaces set up by the sentence Mary thinks that John smokes.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>The proper nouns Mary and John setup a base space (B). By the help of background<br> knowledge and activated frames we know that they are names of female and male humans. Not<br> having access to the previous discourse, we also consider their existence presupposed. The SVSB<br> Mary thinks that sets up a belief space (L) relative to space B (Figure 6). The identity connector<br> maintains the referential link between elements a and a ׳ both referring to the same person.</p>
Figure 6-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>At a ¯rst sight of the graphics, it is obvious that both in discrete and continuous case, the phenomena is not linear; there are relatively linear cases<br> { ¯g. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 6. Two point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
<p>Two point Crossover<br> For the two point crossover we get two parent HMMs and choose at random two cutting points for<br> the weights that have a sum of 1.0 and swap the contents between the crossing points. This is<br> illustrated in the example shown in Figure 6.</p>
Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>For example comparing the<br> chromosome given in Figure 6 with the first chromosome in the evaluation partition, the<br> difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference<br> between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between<br> the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation<br> High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of<br> compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the<br> compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now<br> we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434.<br> The highest is the fitness value, the better is the performance of the chromosome.</p>
Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The complete agents “environment” is shown in figure 6. Each agent receives the necessary<br> environmental or plant condition measurements. While each agent keeps its autonomy and has its<br> own “personal” goals, it interacts with the other agents through some specific communication<br> language, in the “Agent discussion area”, where the overall goal is taken into account. This<br> interaction takes place under the presence of information from the existing models and the target<br> goals. At that point, each agent has decided on its best strategy.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 6. Overview of Brain-Inspired Architecture for Machine Perception
<p>Figure 6 gives an overview about the developed architecture for human-like machine perception which bases on insights about the working mechanisms of the human perceptual system. The central element of the model is the so-called “neuro-symbolic network”, which processes data coming from different sensor sources and additionally considers information coming from<br> “higher-level” sources referred to as memory, knowledge, and focus of attention . Within the neuro-symbolic network, so called “neuro-symbolic information processing” takes place based on information exchange of “neuro-symbols”. The focus in this article will be on the description of the<br> functioning of neuro-symbols and the neuro-symbolic network. Details about the other modules and functional aspects of the model can amongst others be found in.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 6, One image in sight of artificial creature.
<p>Figure 6 shows an image in sight of the creature, where the food in this image is the dark part of the vision. If the food object places in the vision edge of the artificial creature (2 meters), two squares in the image in sight of the creature becomes black and by getting the artificial creature closer to the food, more squares of image in sight become block and if all parts of the image in sight of the creature become black, the artificial creature has been successful in finding the food object. In Figure 6 each part of the image divided to 3 subparts because 3 neurons per part of image in the input layer of the artificial creature network (vision) have been considered. So vision of the artificial creature composed of 60 neurons due to:</p> <p>For each black part of image in sight of the artificial creature, three signals as the input signals is applied to the three respective neurons.</p>
Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be<br> used to anticipate the position of the obstacles and make better decisions in order to reach the<br> desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that<br> is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the<br> value of the potential at all other points is positive.</p>
ScienceDex guides
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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.