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3,145 results for “Well Being”
Distribution. NE Egypt (coastal region of Sinai), S Israel (Negev Desert), and Palestine. Descriptive notes. Head-body 130-170 mm, tail 120-180 mm, ear 17-22 mm, hindfoot 30-41 mm; weight 125-275 g. A medium-sized jird, Buxton's Jird has tail of about same length as head-body length and partially hairy soles of hindfeet. Bicolored tail ends with well-developed pencil of black hairs. Dorsal pelage is reddish sandy, diffusely speckled with black hairs, and ventral is white. Enlarged tympanic bullae project over back of skull and represent c.35-37% ofskull length. Karyotype 2n = 46. in Muridae
Distribution. NE Egypt (coastal region of Sinai), S Israel (Negev Desert), and Palestine. Descriptive notes. Head-body 130-170 mm, tail 120-180 mm, ear 17-22 mm, hindfoot 30-41 mm; weight 125-275 g. A medium-sized jird, Buxton's Jird has tail of about same length as head-body length and partially hairy soles of hindfeet. Bicolored tail ends with well-developed pencil of black hairs. Dorsal pelage is reddish sandy, diffusely speckled with black hairs, and ventral is white. Enlarged tympanic bullae project over back of skull and represent c.35-37% ofskull length. Karyotype 2n = 46.
Leadership, technology and well-being in childcare
<p>Data on leadership, technostress, well-being and performance among n= 339 Dutch childcare workers</p>
Fig. 5 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 5. Visualization of a feature with high importance and a feature with low importance from a configuration B. The importance of these features for the identification accuracy was determined using permutation tests (see the methods). For each taxon or group (rows) several randomly selected specimens (columns) are shown. For the two selected Global Average Pooling layer features, the corresponding features of the preceding (Max Pooling) layer are visualized as those show specific image parts that had higher activations.Yellow represents the maximal activation strength; dark blue represents the minimal activation strength.
Fig. 2 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 2. Schematic representation of the networks used. On top are the convolutional layers of VGG16, grouped into five blocks. Output of each Max Pooling layer is fed into Global Average Pooling layer. Numbers near each block name indicate number of features in the Global Average Pooling layer. Height of layers roughly corresponds to resolution (except for Global Average Pooling layer), while width roughly corresponds to the number of feature maps or features produced.Then, in approach A, outputs of five blocks are concatenated and passed to the linear classifier. In approach B, output of only one block (block 3 in the final configuration) is passed to the linear classifier. In approach C CNN outputs are as in approach A, but instead connected to a DNN with two layers of 320 fully connected (FC) neurons followed by a prediction layer (PL), with number of neurons equal to number of species classified. Finally, approach D features CNN as in approach B which is connected to DNN as in approach C.
Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.
Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.
Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.
Well of Iwaojou castle
岩尾城跡主郭部付近にある井戸。 Well of Iwaojou castle . Source: Objaverse 1.0 / Sketchfab
MHC20 F1404 Watering Hole Wicker Well
Source: Objaverse 1.0 / Sketchfab
Well
This was made as a challenge to keep the poly count under 200. Source: Objaverse 1.0 / Sketchfab
Well
Well model i did in blender. Influenced by Grant Abbitt! Source: Objaverse 1.0 / Sketchfab
well teoay be'r
3d Old Water Well traditional UAE mdel Source: Objaverse 1.0 / Sketchfab
Wells Fargo & Co. Diorama Team 2
Diorama for skills competition 2021 Source: Objaverse 1.0 / Sketchfab
Well
Source: Objaverse 1.0 / Sketchfab
Low poly town well
created in blender for learning and practice Source: Objaverse 1.0 / Sketchfab
Well
A well with a special inside if the textures load XD Source: Objaverse 1.0 / Sketchfab
Dataset download from Web of science for paper entitled "An Overview of the Future Tourism Wellness as an Alternative Solution to Enhance Halal Tourism Industry in Indonesia"
Open the record for dataset details and reuse information.
How well do we know the seasonal cycle in ocean bottom pressure?
<p>Sa_harmonics.mat: Annual harmonics (amplitude and phase) calculated from ECCOv4r5, JPL GRACE, and GSFC GRACE data.</p> <p>Ssa_harmonics.mat: Semiannual harmonics (amplitude and phase) calculated from ECCOv4r5, JPL GRACE, and GSFC GRACE data.</p> <p>Sta_harmonics.mat: Terannual harmonics (amplitude and phase) calculated from ECCOv4r5, JPL GRACE, and GSFC GRACE data.</p> <p>GAL_LA_Sa_std.mat: Standard deviation of annual harmonics for gravitational attraction and loading effects.</p> <p>GAL_La_Ssa_std.mat: Standard deviation of semiannual harmonics for gravitational attraction and loading effects.</p> <p>g0_getOBP.m: Script to read GSFC GRACE data and remove earthquake signals.</p> <p>g1_fitcycleOBP.m: Script to calculate harmonics from bottom pressure data.</p> <p>get_EQphase.m: Function that yields the phases for the equilibrium tide of the major tidal constituents from HW95 catalogue</p> <p> </p> <p> </p>
Datasets from: "Deep learning for detecting and characterizing oil and gas well pads in satellite imagery"
<p>This repository contains the following: </p> <p>- <code>training</code> folder: Datasets used to train well pad and storage tank models. Note that <code>annotations_image</code> for each example are with respect to a image downloaded from the Google Earth satellite basemap at zoom level 1600 and EPSG:3857, with sizes 640x640 px and 512x512 px for well pads and storage tanks respectively (see also the <code>image_extent </code>column). <code>annotations_latlon</code> also provides the annotations in coordinate space. We also note that we are unable to redistribute the satellite imagery used to train the models in this study due to data licensing. Samples of satellite images may be made available upon request to the corresponding author.</p> <p>- <code>deployment</code> folder: Deployment detections for well pads and storage tanks across the entire Permian and Denver basins. Datasets contain confidence scores (<code>bbox_score</code>) and coordinate locations (<code>geometry</code>) for each detection, as well as a well pad identifier (<code>wp_id</code>) that indicates which well pad a storage tank belongs to.</p>
On the layered structure and Seasonal Variation of the Water Exchanges in the Sulawesi Sea Areas as well as the associated dynamics
<p><span>The layered structure and seasonal variation of the water exchanges in the Sulawesi Sea areas, as well as the corresponding dynamics, are investigated using HYCOM data. A net inward water mass transport is identified in the upper and intermediate layers in the Sulawesi Sea and it is compensated by the net outward transport in the deep layer. As an important source of this net inward transport, </span><span>the</span><span> southward flow through </span><span>the </span><span>Sibutu Passage (denoted hereafter as Sibutu Flow) reaches 5.06Sv in winter, accounting for ~43% of the net inflow. However, it </span><span>decreases substantially</span><span> to 1.79Sv in summer</span><span>,</span><span> accounting for only 12% of the inflow. Further analysis reveals that </span><span>this</span><span> Sibutu Flow modulates the seasonal variation of the corresponding outward flow at the Makassar Strait, the major exit of the water mass in the Sulawesi Sea. In winter, the enhancement of the Sibutu Flow associated with the greater intrusion of Kuroshio water into the Luzon Strait</span> <span>produces a stronger eastward pressure gradient force, suppressing the surface intrusion of the Mindanao Current. Conversely, this pressure gradient force weakens and shifts westward in summer, permitting a larger water intrusion from the eastern section. Given that the outward Makassar Flow</span><span> is the main passage of </span><span>the Indonesian Throughflow (ITF), our study highlights the essential role of the Sibutu Flow in modulating seasonal variabilities of the ITF via the Makassar Strait and, thus, the transfer of water masses and heat content between the Pacific and Indian Oceans.</span></p>
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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.