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393 results for “Cuckoo”
Cuckoo male bumblebees perform slower and longer flower visits than free-living male and worker bumblebees
<p>These .txt files include the dataset (tab-separated) and the annotated R-scripts (R-scripts_R1 is the final version) used in the analyses reported in the preprint "Cuckoo male bumblebees perform slower and longer flower visits than free-living male and worker bumblebees".</p> <p>The preprint is available on Zenodo (<a href="https://doi.org/10.5281/zenodo.4489066">https://doi.org/10.5281/zenodo.4489066</a>) and has been recommended by PCI Zoology (<a href="http://zool.peercommunityin.org/articles/rec?id=44">https://zool.peercommunityin.org/articles/rec?id=44</a>). The article has then been published in the Belgian Journal of Zoology (2021, 151:193:203, <a href="https://belgianjournalofzoology.eu/index.php/BJZ/article/view/93">https://doi.org/10.26496/bjz.2021.93</a>)</p> <p> </p>
Figs 10–18 in Three new species and new records of cuckoo bees of the genus Epeolus in Turkey (Hymenoptera: Apidae: Nomadinae)
Figs 10–18. Epeolus warnckei sp. nov. 10 – holotype, ♀, dorsal view; 11 – allotype,, dorsal view. 12–17 – holotype: 12 – head, frontal view; 13 – labrum; 14 – mesopleuron, lateral view; 15 – mesosoma; 16 – terga; 17 – sterna. 18 – allotype, pygidial plate.
Figure 2 in Host-parasite relationships and life cycles of cuckoo wasps in agro-ecosystems in Argentina (Hymenoptera: Chrysididae: Chrysidini)
Figure 2. Emergence patterns of (a) Chrysis boutheryi (Brèthes) (squares; n = 20) and (b) C. saltana Bohart (triangles, n = 19) adults reared from trap-nests in Toay, La Pampa Province.
Figure 1 in Host-parasite relationships and life cycles of cuckoo wasps in agro-ecosystems in Argentina (Hymenoptera: Chrysididae: Chrysidini)
Figure 1. (a) Study sites: Toay (inverted triangle), Hortensia (square), Pila (triangle) and Colonia Elía (hexagon), situated in the Pampean region. The area encircled by thick line indicates the location of the Río de la Plata grasslands. Subdivisions are limited by dotted lines and identified by capital letters. A: Rolling Pampa; B: Inland Pampa; C: Southern Pampa; D: Flooding Pampa; E: Mesopotamic Pampa; F: Campos (modified from Medan et al.2011). (b–c) Trap-nests located in one tree and on fence posts.
Fig. 9 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 9. Austrosphecodes tartarus sp. nov., holotype, ♀ (DZUP 568519). A. Head. B. Mesosoma. C. Propodeum and metasoma. Scale bar = 1 mm.
Fig. 8 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 8. Austrosphecodes orcus sp. nov. A–C. Paratype (DZUP 568527). D–F. Holotype, ♂ (DZUP 568538). A. Female head. B. Female mesosoma. C. Female propodeum and metasoma. D. Male head. E. Male mesosoma. F. Male propodeum and metasoma. G. Male metasomal sterna. Scale bar = 1 mm.
Fig. 7 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 7. Austrosphecodes lucifer sp. nov. A –C. Holotype, ♀ (DZUP 568517). D–F. Paratype, ♂. A. Female head. B. Female mesosoma. C. Female propodeum and metasoma. D. Male head. E. Male mesosoma. F. Male metasoma. Scale bar = 1 mm.
Fig. 10. Geographic records map. A in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 10. Geographic records map. A. Austrosphecodes asmodeus sp. nov., A. balrog sp. nov., A. brasiliensis (Schrottky, 1910). B. A. cerberus sp. nov., A gorgon sp. nov., A. inornatus (Schrottky, 1902). C. A. jurupari sp. nov., A. krampus sp. nov., A. lucifer sp. nov. D. A. minarum (Schrottky, 1910), A. orcus sp. nov., A. tartarus sp. nov.
Fig. 5 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 5. Austrosphecodes jurupari sp. nov., holotype ♂ (RPSP). A. Head and mesosoma, blue arrow: strigate vertex, red arrow: carinate anterior margin of mesoscutum. B. Propodeum and metasoma. C. Metasomal sterna. Scale bar = 1 mm.
Fig. 4 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 4. Austrosphecodes gorgon sp. nov. A–C. Holotype, ♀ (DZUP 568520). D–F. Paratype, ♂ (DZUP 568521). A. Female head. B. Female mesosoma. C. Female propodeum and metasoma. D. Male head. E. Male mesosoma. F. Male propodeum and metasoma. Scale bar = 1 mm.
Fig. 6 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 6. Austrosphecodes krampus sp. nov., holotype, ♀ (DZUP 568515). A. Head. B. Mesosoma. C. Propodeum and metasoma, red arrow: punctate terga. Scale bar = 1 mm.
Fig. 2 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 2. Austrosphecodes balrog sp. nov., holotype ♀ (DZUP 568513). A. Head. B. Mesosoma, red arrow: crowded punctures on anterior margin of mesoscutum. C. Propodeum and metasoma, red arrow: metapostnotum triangular. Scale bar = 1 mm.
Fig. 1 in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 1. Austrosphecodes asmodeus sp. nov. A –C. Holotype, ♀ (DZUP 411614). D–F. Paratype, ♂ (DZUP 572413). A. Female head. B. Female mesosoma, red arrow: punctate anterior margin of mesoscutum. C. Female propodeum and metasoma, blue arrow: metapostnotum trapezoidal, red arrow: puncticulate terga. D. Male head. E. Male mesosoma. F. Male metasoma. Scale bar = 1 mm.
Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Based on that coefficient, different bands have to be extracted. The bands are alpha, beta,<br> theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band<br> diagrams.</p>
Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground<br> electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is<br> 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels.<br> Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG<br> signal is used to recognize the different level of emotions.</p>
Figure 2. Emotion recognition using EEG-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>This section describes that collection of EEG signals for different emotion recognition<br> experiments. The electroencephalography signals of 32 participants were recorded during one<br> minute videos. Based on that participants are rated in terms of valence and arousal, like/dislike,<br> familiarities and dominance. Emotion related ratings are given based on the online self assessment<br> which is 120 one minute extracted music videos, which are rated by 14-16 volunteers based on<br> arousal and valence. The EEG signals were recorded using 64 electrodes, first 62 electrodes are<br> active electrode, one for reference and remaining one is ground electrode. All the electrodes are<br> placed on the scalp which is made up of the Ag/Ag-Cl. Figure 2 shown in below which is represent<br> the basic EEG signal recording methods and emotion analysis process.</p>
Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>EEG data have collected from<br> desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta,<br> Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha<br> band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal<br> Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta<br> band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these<br> bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.</p>
Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>For all the 32 participants the EEG signal has to be sampled and process their emotions. The<br> emotions are depends on the music and video clips. In this paper the video clips are changed from<br> one person to other person. Here hybrid feed forward neural networks with radial basis function;<br> probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast<br> and insensitive neural network which provides the optimized classification result. Compare to the<br> multi layer perception neural network it provide accurate result. It classifies the emotion into two<br> different groups like arousal and valence. Figure 8 shows that the model implemented result of<br> emotion classification and person identification.</p>
Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>In Probabilistic Neural Network the operations are organized into a multilayer feed forward<br> neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN<br> use the Euclidean distance measure the difference between one neuron to other neurons. The actual<br> target values are stored in the hidden neuron and the optimized weighted values are fed into the<br> same category hidden neuron. Then finally the output layer compared the weighted votes of each<br> target values and the target votes are used to predict the emotions.</p>
Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Figure10 has shown in sensitivity and specificity of different neural network which is used<br> to explain how exactly the emotions are classified into groups and accuracy value shown in above<br> table 3.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.