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115 results for “fogging”
Analyzing coastal fog effects on carbon and water fluxes in a California agricultural system using approaches in biometeorology, remote sensing, and plant physiology
Open the record for dataset details and reuse information.
Data from: Fog controls biological cycling of soil phosphorus in the Coastal Cordillera of the Atacama Desert
Open the record for dataset details and reuse information.
Summer marine fog distribution in the Chukchi–Beaufort Seas
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Data support to Characteristics of persistent heavy fog events retrieved by microwave radiometer profiler during 2010–2013 in northern China
<p>The dataset include the observation data from microwave radiometer, tethered balloon and the surface.</p>
Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
<p>The quality of human life increases with increased usage of the Internet of Things (<i>IoT</i>). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012.</p> <p>Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide promising solutions to deal with compute-intensive applications. The scheduling challenges faced by the Fog service provider are to fulfill user requests with the maximum resource utilization of Fog devices while reducing the monetary cost of Cloud resource usage. In this research, a resource-aware scheduler has been proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications, and minimum bandwidth usage.</p> <p>The proposed scheduler Resource Aware Cost-Efficient Scheduler (RACE) consists of two algorithms: ModuleScheduler: which categorizes the incoming application modules according to their computation and bandwidth requirements. The ModuleScheduler also creates a prioritized list considering the categories of application modules. Compare Module: Finds the suitable Fog device for modules execution in the prioritized list. The proposed scheduler (RACE) schedule the modules at the Fog layer based on their computation requirement and bandwidth requirement until the devices at the Fog layer have enough CPU capacity to accommodate the modules.</p>
FIGURES 1A–K. A–J. Penapis larraini Packer, n in Penapis larraini Packer, a new species of rophitine bee (Hymenoptera: Halictidae) from a fog oasis in Northern Chile
FIGURES 1A–K. A–J. Penapis larraini Packer, n. sp. A–H male, I–J female. K. Penapis toroi female. A. head lateral view. B. S4 ventral view. C. apex of metasoma ventral view. D. S5 ventral view. E. S5 lateral view. F. S6 ventral view. G. S8 ventral view. H. Genital capsule ventral view. I. Head lateral view. J. T1–T3 dorsal view. K. T1–T3 dorsal view. Note that the individual shown in J has the most sparse but large punctures of all the specimens available. Scale line = 1mm.
Groping in the fog: Soaring migrants exhibit wider scatter in flight directions and respond differently to wind under low visibility conditions
<p>Atmospheric conditions are known to affect flight propensity, behaviour during flight, and migration route in birds. Yet, the effects of fog have only rarely been studied although they could disrupt orientation and hamper flight. Fog could limit the visibility of migrating birds such that they might not be able to detect landmarks that guide them during their journey. Soaring migrants modulate their flight speed and direction in relation to the wind vector to optimise the cost of transport. Consequently, landmark-based orientation, as well as adjustments of flight speed and direction in relation to wind conditions, could be jeopardised when flying in fog. Using a radar system operated in a migration bottleneck (Strait of Messina, Italy), we studied the behaviour of soaring birds under variable wind and fog conditions over two consecutive springs (2016 and 2017), discovering that migrating birds exhibited a wider scatter of flight directions and responded differently to wind under fog conditions. Birds flying through fog deviated more from the mean migration direction and increased their speed with increasing crosswinds. In addition, airspeed and groundspeed increased in the direction of the crosswind, causing the individuals to drift laterally. Our findings represent the first quantitative empirical evidence of flight behaviour changes when birds migrate through fog and explain why low visibility conditions could risk their migration journey.</p>
Data from: Species boundaries in the messy middle – testing the hypothesis of micro-endemism in a recently diverged lineage of coastal fog desert lichen fungi
<p><span><span><span><span><span><span><span><span><span><span><span>Species delimitation among closely related species is challenging because traditional phenotype-based approaches, e.g., morphology, ecological, or chemical characteristics, often produce conflicting results. With the advent of high-throughput sequencing, it has become increasingly cost-effective to acquire genome-scale data which can resolve previously ambiguous species boundaries. As the availability of genome-scale data has increased, numerous species delimitation analyses, such as BPP and SNAPP+Bayes factor delimitation (BFD*), have been developed to delimit species boundaries. However, even empirical molecular species delimitation approaches can be biased by confounding evolutionary factors, e.g., hybridization/introgression and incomplete lineage sorting, and computational limitations. Here we investigate species <span><span>boundaries and the potential for micro-endemism in a lineage of lichen-forming fungi, <i>Niebla </i>Rundel & Bowler in the family Ramalinaceae. The species delimitation models tend to support more specious groupings, but were unable to infer robust, consistent species delimitations. </span></span>The results of our study highlight the problem of delimiting species, particularly in groups such as <i>Niebla</i>, with complex, recent phylogeographic histories.</span></span></span></span></span></span></span></span></span></span></span></p>
On following pages: 48. Mutable Shrew (Sorex mutabilis); 49. Verapaz Shrew (Sorex veraepacis); 50. Rock Shrew (Sorex Mexican Long-tailed Shrew (Sorex oreopolus); 55. Orizaba Long-tailed Shrew (Sorex orizabae); 56. Chestnut-bellied Shrew (Sorex rohweri): 60. South-eastern Shrew (Sorex longirostris); 61. Masked Shrew (Sorex cinereus); 62. Maryland Shrew Mountain Shrew (Sorex milleri): 66. Preble's Shrew (Sorex preblei); 67. Prairie Shrew (Sorex hayden); 68. Pribilof Island Saint Lawrence Island Shrew (Sorex jackson); 72. Kamchatka Shrew (Sorex camtschaticus); 73. Paramushir Shrew (Sorex (Sorex ornatus); 77. American Water Shrew (Sorex palustris); 78. Western Water Shrew (Sorex navigaton; 79. Glacier Bay Baird's Shrew (Sorex bairdi); 83. Pacific Shrew (Sorex pacificus); 84. Fog Shrew (Sorex sonomae); 85. New Mexico Shrew dispan; 51. Smoky Shrew (Sorex fumeus); 52. Inyo Shrew (Sorex tenellus); 53. Dwarf Shrew (Sorex nanus); 54. (Sorex ventralis); 57. Veracruz Shrew (Sorex veraecrucis); 58. Ixtlan Shrew (Sorex ixtlanensis); 59. Olympic Shrew (Sorex fontinalis); 63. Mount Lyell Shrew (Sorex lyell); 64. Zacatecas Shrew (Sorex emarginatus), 65. Carmen Shrew (Sorex pribilofensis); 69. Barren Ground Shrew (Sorex ugyunak); 70. Portenko's Shrew (Sorex portenkol); 71. leucogasten; 74. American Pygmy Shrew (Sorex hoyi); 75. Vagrant Shrew (Sorex vagrans); 76. Ornate Shrew Water Shrew (Sorex alaskanus); 80. Eastern Water Shrew (Sorex albibarbis); 81. Marsh Shrew (Sorex bendiril); 82. (Sorex neomexicanus); 86. Montane Shrew (Sorex monticolus). in Soricidae
On following pages: 48. Mutable Shrew (Sorex mutabilis); 49. Verapaz Shrew (Sorex veraepacis); 50. Rock Shrew (Sorex Mexican Long-tailed Shrew (Sorex oreopolus); 55. Orizaba Long-tailed Shrew (Sorex orizabae); 56. Chestnut-bellied Shrew (Sorex rohweri): 60. South-eastern Shrew (Sorex longirostris); 61. Masked Shrew (Sorex cinereus); 62. Maryland Shrew Mountain Shrew (Sorex milleri): 66. Preble's Shrew (Sorex preblei); 67. Prairie Shrew (Sorex hayden); 68. Pribilof Island Saint Lawrence Island Shrew (Sorex jackson); 72. Kamchatka Shrew (Sorex camtschaticus); 73. Paramushir Shrew (Sorex (Sorex ornatus); 77. American Water Shrew (Sorex palustris); 78. Western Water Shrew (Sorex navigaton; 79. Glacier Bay Baird's Shrew (Sorex bairdi); 83. Pacific Shrew (Sorex pacificus); 84. Fog Shrew (Sorex sonomae); 85. New Mexico Shrew dispan; 51. Smoky Shrew (Sorex fumeus); 52. Inyo Shrew (Sorex tenellus); 53. Dwarf Shrew (Sorex nanus); 54. (Sorex ventralis); 57. Veracruz Shrew (Sorex veraecrucis); 58. Ixtlan Shrew (Sorex ixtlanensis); 59. Olympic Shrew (Sorex fontinalis); 63. Mount Lyell Shrew (Sorex lyell); 64. Zacatecas Shrew (Sorex emarginatus), 65. Carmen Shrew (Sorex pribilofensis); 69. Barren Ground Shrew (Sorex ugyunak); 70. Portenko's Shrew (Sorex portenkol); 71. leucogasten; 74. American Pygmy Shrew (Sorex hoyi); 75. Vagrant Shrew (Sorex vagrans); 76. Ornate Shrew Water Shrew (Sorex alaskanus); 80. Eastern Water Shrew (Sorex albibarbis); 81. Marsh Shrew (Sorex bendiril); 82. (Sorex neomexicanus); 86. Montane Shrew (Sorex monticolus).
FIGURE 6 in The Little Fog Dragon-a new genus of Mountain Lichen Katydid (Orthoptera: Tettigoniidae: Phaneropterinae: Dysoniini) from the Serra do Sol, Roraima, Brazil
FIGURE 6. Habitat of Nebulodraculus marioi sp. nov.: A–B: Primary Mountain Rainforest in Serra do Sol, Roraima, Brazil.; C–D: Detail of the extensive presence of bryophytes, lichens and epiphytic plants (emphasis on Araceae and Bromeliaceae) in the trunks and branches of the trees (Photos: Mario Cohn-Haft).
FIGURE 4 in The Little Fog Dragon-a new genus of Mountain Lichen Katydid (Orthoptera: Tettigoniidae: Phaneropterinae: Dysoniini) from the Serra do Sol, Roraima, Brazil
FIGURE 4. Nebulodraculus marioi sp. nov., left tegmina of male in dorsal view. Abbreviations: AP: Posterior anal vein; AA: anterior anal vein; CuA: anterior cubital vein; CuP: posterior cubital vein; MA: anterior median vein; MP: posterior median vein; R: radial vein; Sc: subcostal vein.
FIGURE 3 in The Little Fog Dragon-a new genus of Mountain Lichen Katydid (Orthoptera: Tettigoniidae: Phaneropterinae: Dysoniini) from the Serra do Sol, Roraima, Brazil
FIGURE 3. Nebulodraculus marioi sp. nov., holotype male. A: habitus, dorsal view; B: head, frontal view; C: head and pronotum, dorsal view; D: head and pronotum, lateral view; E: Thoracic sternites, ventral view; F: foreleg, lateral view; G: midleg, lateral view; H: hindleg, lateral view; I–J: Terminalia in ventral and dorsal view respectively; K: Apex of cerci, dorsal view. Abbreviations: Mes: mesobasisternum; Met: metabasisternum; Cer: cerci; Sty: styli; Pl: subgenital plate.
Fog collection by cylindrical and conical fibers
<p>Chapter 5 : Droplets on a conical fiber. </p> <p>Two experimental videos show water collection on cylindrical and conical fibers in a fog flow. The fog enters from the bottom of the video, and the fibers are observed from beneath. Both fibers are 3 cm long. The cylindrical fiber has a radius of 1.58 mm, while the conical fiber has a half-angle of 6°. <br>The video is accelerated 5 times.</p>
data of " Arctic sea fog observation along trans-Arctic shipping routes"
<p>2025-03-08 Erratum for the 【README.txt】 file : The ASOS station data in the 【ASOS Arctic sites.zip】 file covers the period from 1971 to 2023. However, in the article 'Observed Climatology and Formation Mechanisms of Sea Fog Along the Trans-Arctic Shipping Routes', the study timeframe was selected as 1979–2023 to account for data quality considerations and to facilitate cross-dataset comparisons.</p>
Dataset from: Resolving marine–freshwater transitions by diatoms through a fog of gene tree discordance
<p>This repository contains the datasets, code, and results for:</p> <p>Roberts et al. Resolving marine-freshwater transitions by diatoms through a fog of gene tree discordance.</p> <p>CONTACT INFORMATION:</p> <p>Andrew J. Alverson<br> University of Arkansas<br> aja [at] uark [dot] edu</p> <p>Wade R. Roberts<br> University of Arkansas<br> wader [at] uark [dot] edu</p> <p>TAXON LABEL INFORMATION:</p> <p>Genomes have strain ID before genus_species (e.g., CCMP332_Cyclotella_cryptica)<br> Transcriptomes have genus_species before strain ID (e.g., Cyclotella_nana_AJA048-54)</p> <p><br> DATA AND DIRECTORY OVERVIEW:</p> <p>Voucher images of diatom strains collected:</p> <ul> <li>voucher-images.tgz</li> </ul> <p>OrthoFinder output:</p> <ul> <li>orthofinder.zip</li> </ul> <p>Predicted proteomes and coding sequences for each strain:</p> <ul> <li>proteomes.zip</li> <li>coding-sequences.zip</li> </ul> <p>Workflow for phylogenomic dataset assembly and species tree analyses:</p> <ul> <li>phylogenomic-workflow.html</li> <li>phylogenomic-workflow.md</li> <li>phylogenomic-workflow.pdf</li> </ul> <p>Fasta files, alignments, and gene trees for each ortholog:</p> <ul> <li>datasets.zip</li> </ul> <p>Concatenated alignments, partition models, and estimated species trees:</p> <ul> <li>species-trees.zip</li> <li>species-trees.nexus</li> </ul> <p>Analyses results and code to plot figures:</p> <ul> <li>analyses-and-plots.zip</li> </ul> <p>Additional scripts and miscellaneous files:</p> <ul> <li>scripts-and-misc.zip</li> </ul> <p>Supplemental Materials for publication:</p> <ul> <li>supplemental-materials.zip</li> </ul> <p> </p>
Wyniki dla Green Time-Critical Fog Communication and Computing
<p>This resource contains the results for Green Time-Critical Fog Communication and Computing</p>
Rysunki dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains the figures for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Wyniki symulacji dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains simulation results for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Data for 'Interannual Variability of Summertime Sea Fog over North Pacific'
<p>[ISFFandIAPO.m] The code for calculating the ISFF and IAPO.</p> <p>[sea_fog_data_set.mat] Sea fog frequency data for the northern hemisphere that have been processed.</p> <p>More detail please sees the paper 'Interannual Variability of Summertime Sea Fog over North Pacific'</p>
Brain Fog and Life Quality in Menopause
ClinicalTrials.gov study NCT06978218. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
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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.