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887 results for “tunnels”
Figure 5 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 5. Acila cf. Aci. empirensis Howe, left valve. Tsm Caldecott Tunnel fauna. Hypotype from UCMP locality IP13003, UCMP 410923.
Figure 25 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 25. Indeterminate Naticidae, partly crushed, from the Tsm Caldecott Tunnel fauna, adaperical view (left); aperical view (right). Hypotype from UCMP locality IP13005, UCMP 218737.
Figure 16 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 16. Cyclocardia sp. Indeterminate from the Tsm Caldecott Tunnel fauna, right valve(?). Hypotype from UCMP locality IP13001, UCMP 410563.
Figure 10 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 10. Valves of Anadara cf. An. osmonti from the Tsm Caldecott Tunnel fauna. Hypotype from UCMP locality IP13003, UCMP 412699.
Figure 22 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 22. Aperical view of indeterminate Bruclarkia from the Tsm Caldecott Tunnel fauna, UCMP locality IP13003, hypotype UCMP 218695.
Figure 14 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 14. aff. Lucinoma? n. sp. from the Tsm Caldecott Tunnel fauna, left valve. Hypotype from UCMP locality IP13005, UCMP 218741.
Figure 21 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 21. Veneridae, indeterminate from the Tsm Caldecott Tunnel fauna, right valve (left), left valve (right). Hypotype from UCMP locality IP13004, UCMP 410418.
Figure 8 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 8. Yoldia cf. Y. supramonterensis from the Tsm Caldecott Tunnel fauna, left valve. Hypotype from UCMP locality IP13003, UCMP 218714.
Figure 3 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 3. Cheilostomata? bryozoan. Tsm Caldecott Tunnel fauna. Hypotype from UCMP locality IP13003, UCMP 412845.
Figure 20 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 20. Spisula aff. Spi. eugenensis from the Tsm Caldecott Tunnel fauna, right valve(?). Hypotype from UCMP locality IP13004, UCMP 410439.
Figure 18 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 18. Vesicomyidae indeterminate from the Tsm Caldecott Tunnel fauna, right valve. Hypotype from UCMP locality IP16027, UCMP 123690.
Figure 15. aff. Tehamatea n in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 15. aff. Tehamatea n. sp., from the Tsm Caldecott Tunnel fauna, right valve. Hypotype from UCMP locality IP13001, UCMP 410562.
Figure 2 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 2. Geologic cross section along the alignment of the Fourth Bore looking north. The solid red lines indicate inactive faults along the contacts between different rock units. The dashed red lines indicate the approximate position of contacts or gradational contacts. Based on Figure 3.1, Caldecott Improvement Project: Geotechnical Baseline Report, prepared by Jacobs Associates, June 2009, from http://www.ucmp.berkeley.edu/exhibits/caltrans/fourthbore2.php. Sobrante Formation of Caltrans is unnamed glauconitic mudstone (Tsm) in this report.
Figure 6 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 6. Yoldia cf. Y. submontereyensis from the Tsm Caldecott Tunnel fauna, left valve. Hypotype from UCMP locality IP13008, UCMP 218804.
Water migration through enzyme tunnels is sensitive to the choice of explicit water model (DhaA)
<p>This repository contains data for the haloalkane dehalogenase DhaA. Data underpinning analyses of alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6) are available from the related repository: <a href="https://doi.org/10.5281/zenodo.11545455">https://doi.org/10.5281/zenodo.11545455</a></p> <p> </p> <p><strong>Content:</strong></p> <p><strong>tt_conda.yml -> conda environment used for the calculations. </strong><br> Usage :<br> conda env create -f tt_conda.yml<br> conda activate tt_conda.yml </p> <p><strong>01_MD_simulations.tar.gz -> the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs).</strong></p> <p>├── 01_inputs<br>│ ├── opc<br>│ │ ├── TCG_d1.0_o1.1<br>│ │ ├── TCG_d1.4_o1.2<br>│ │ ├── TCG_d1.8_o1.4<br>│ │ ├── TCG_d2.5_o2.1<br>│ │ └── TCG_d3.0_o2.5<br>│ ├── scripts<br>│ ├── tip3p<br>│ │ ├── TCG_d1.0_o1.1<br>│ │ ├── TCG_d1.4_o1.2<br>│ │ ├── TCG_d1.8_o1.4<br>│ │ ├── TCG_d2.5_o2.1<br>│ │ └── TCG_d3.0_o2.5<br>│ └── tip4pew<br>│ ├── TCG_d1.0_o1.1<br>│ ├── TCG_d1.4_o1.2<br>│ ├── TCG_d1.8_o1.4<br>│ ├── TCG_d2.5_o2.1<br>│ └── TCG_d3.0_o2.5<br>├── 02_outputs<br>│ ├── opc<br>│ │ ├── TCG_d1.0_o1.1<br>│ │ ├── TCG_d1.4_o1.2<br>│ │ ├── TCG_d1.8_o1.4<br>│ │ ├── TCG_d2.5_o2.1<br>│ │ └── TCG_d3.0_o2.5<br>│ ├── tip3p<br>│ │ ├── TCG_d1.0_o1.1<br>│ │ ├── TCG_d1.4_o1.2<br>│ │ ├── TCG_d1.8_o1.4<br>│ │ ├── TCG_d2.5_o2.1<br>│ │ └── TCG_d3.0_o2.5<br>│ └── tip4pew<br>│ ├── TCG_d1.0_o1.1<br>│ ├── TCG_d1.4_o1.2<br>│ ├── TCG_d1.8_o1.4<br>│ ├── TCG_d2.5_o2.1<br>│ └── TCG_d3.0_o2.5<br>└── 03_analysis<br><br> <br><strong>02_caver.tar.gz -> results of CAVER calculations, organized by models and TCGs.</strong></p> <p>├── config_files<br>├── opc<br>│ ├── TCG_d1.0_o1.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.4_o1.2<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.8_o1.4<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d2.5_o2.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ └── TCG_d3.0_o2.5<br>│ ├── 1<br>│ ├── 2<br>│ ├── 3<br>│ ├── 4<br>│ └── 5<br>├── tip3p<br>│ ├── TCG_d1.0_o1.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.4_o1.2<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.8_o1.4<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d2.5_o2.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ └── TCG_d3.0_o2.5<br>│ ├── 1<br>│ ├── 2<br>│ ├── 3<br>│ ├── 4<br>│ └── 5<br>└── tip4pew<br> ├── TCG_d1.0_o1.1<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> ├── TCG_d1.4_o1.2<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> ├── TCG_d1.8_o1.4<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> ├── TCG_d2.5_o2.1<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> └── TCG_d3.0_o2.5<br> ├── 1<br> ├── 2<br> ├── 3<br> ├── 4<br> └── 5</p> <p><strong>03_aquaduct.tar.gz -> results of AQUA-DUCT calculations, organized by models and TCGs. </strong></p> <p>├── opc<br>│ ├── TCG_d1.0_o1.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.4_o1.2<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.8_o1.4<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d2.5_o2.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ └── TCG_d3.0_o2.5<br>│ ├── 1<br>│ ├── 2<br>│ ├── 3<br>│ ├── 4<br>│ └── 5<br>├── tip3p<br>│ ├── TCG_d1.0_o1.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.4_o1.2<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d1.8_o1.4<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── TCG_d2.5_o2.1<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ └── TCG_d3.0_o2.5<br>│ ├── 1<br>│ ├── 2<br>│ ├── 3<br>│ ├── 4<br>│ └── 5<br>└── tip4pew<br> ├── TCG_d1.0_o1.1<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> ├── TCG_d1.4_o1.2<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> ├── TCG_d1.8_o1.4<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> ├── TCG_d2.5_o2.1<br> │ ├── 1<br> │ ├── 2<br> │ ├── 3<br> │ ├── 4<br> │ └── 5<br> └── TCG_d3.0_o2.5<br> ├── 1<br> ├── 2<br> ├── 3<br> ├── 4<br> └── 5</p> <p> </p> <p><strong>04_transport_tools.tar.gz -> the results of TransportTools and analysis done from TransportTools results.</strong></p> <p>├── bottleneck_analyses<br>│ ├── data<br>│ │ └── super_clusters<br>│ ├── statistics<br>│ │ └── comparative_analysis<br>│ └── visualization<br>│ ├── comparative_analysis<br>│ └── sources<br>├── overall_results<br>│ ├── data<br>│ │ ├── exact_matching_analysis<br>│ │ └── super_clusters<br>│ ├── statistics<br>│ │ └── comparative_analysis<br>│ └── visualization<br>│ ├── comparative_analysis<br>│ └── sources<br>└── scripts<br> ├── bottleneck_residues<br> ├── presence_of_tunnels<br> └── water_transport_analysis<br> └── output</p> <p><br><strong>05_hbonds.tar.gz -> contains raw results of hydrogen bond analysis of transported waters for P1 tunnel of DhaA </strong></p> <p> </p> <p><strong>06_control_NVE_calculations.tar.gz -> data obtained from NVE simulations of DhaA TCG o1.4d1.8 and files necessary to reproduce</strong></p> <p>├── 01_MD_simulations<br>│ ├── 01_inputs<br>│ │ ├── configs<br>│ │ │ ├── NVE_equil.in<br>│ │ │ ├── NVE_prod.in<br>│ │ │ ├── NVT_cool.in<br>│ │ │ └── NVT_equil.in<br>│ │ ├── opc<br>│ │ ├── tip3p<br>│ │ └── tip4pew<br>│ └── 02_outputs<br>│ ├── opc<br>│ ├── tip3p<br>│ └── tip4pew<br>├── 02_caver<br>│ ├── configs<br>│ │ ├── calculate_tunnels.txt<br>│ │ └── clustering.txt<br>│ ├── opc<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── tip3p<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ └── tip4pew<br>│ ├── 1<br>│ ├── 2<br>│ ├── 3<br>│ ├── 4<br>│ └── 5<br>├── 03_aquaduct<br>│ ├── opc<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ ├── tip3p<br>│ │ ├── 1<br>│ │ ├── 2<br>│ │ ├── 3<br>│ │ ├── 4<br>│ │ └── 5<br>│ └── tip4pew<br>│ ├── 1<br>│ ├── 2<br>│ ├── 3<br>│ ├── 4<br>│ └── 5<br>└── 04_transport_tools<br> ├── data<br> ├── statistics<br> ├── transport_tools.log<br> ├── tt_config.in<br> └── visualization</p> <p> </p>
Swansea University Wind Tunnel Gust Generator
<p>Initial experimental test of the gust generator at the Swansea University wind tunnel. As a preliminary study smoke test has been used to prove the concept. Experimental data collected from a cross-hot wire sensor indicate that the system is reliably capable of creating single and continuous gusts.</p> <p>More information:</p> <p>[1] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. Journal of Aircraft, https://doi.org/10.2514/1.C036772.</p> <p>[2] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. AIAA 2022-2485. <em>AIAA SCITECH 2022 Forum</em>. January 2022.</p>
Finding shortcuts through collective tunnel excavation in a subterranean termite
<p>Facilitating efficient resource transfer requires building an optimized transportation network that balances cost minimization with benefit maximization. For animals that forage for food located remotely, optimizing their transportation networks is critically related to survival. This process often involves finding and using the shortest route to save time and energy. Subterranean termites forage for wood resources by excavating underground foraging networks for search and transport. Because termites have no prior knowledge of food location during the food searching phase, establishment of a short tunnel between the nest and feeding site is difficult at the beginning of foraging. Thus, finding a short route should logically follow initial food discovery. However, it remains elusive as to how subterranean termites find the shortest route for food transportation. We simulated different scenarios using <em>Coptotermes formosanus</em> by providing different shapes and distances of pre-formed tunnels (straight, detour, and detour + twisting arenas) to food, where food items were located at a fixed distance from the arena entrance. Termites in the straight arena continuously used the pre-formed tunnel, showing negligible branching efforts. However, termites in the detour and detour + twisting arenas followed the pre-formed tunnel only for the initial few hours before excavating many branching tunnels. This branching activity ultimately resulted in termites finding shorter commuting routes than the pre-formed tunnels. In addition, the shortest established routes were widened over time. This study demonstrated that <em>C. formosanus</em> could actively alter tunnel networks to minimize the cost in food transportation by using short and wide tunnels. </p>
Effects of hydrogen jet fires on the erosion of tunnel road materials and lining materials
<p>This HSE test programme investigated erosive effects of an ignited high pressure hydrogen jet impinging onto concrete and tarmac structural materials. The chosen test conditions mimicked the scenario where a high-pressure release (700bar) occurs from a fuel cell hydrogen (FCH) car as a result of activation of the thermal pressure relief device (TPRD) on the fuel tank. Two nozzle sizes were used for the releases; the first had a diameter of 2.1mm (mimicking existing TPRD) and the second had a diameter of 0.57mm (mimicking a proposed alternative TPRD diameter. The reduced diameter is suggested as a strategy to reduce release hazard safety distances).</p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
The influence of twin tunnel excavation on single and group pile loading by physical modeling
<p>This study uses small-scale physical models to assess the interaction between piles and twin tunnels, considering varying tunnel distances and surface loads. The excavation process of the tunnels is simulated by gradually releasing air pressure from rubber tubes while hydraulic jacks apply loads to aluminum piles. Surface linear variable differential transformers (LVDTs) and strain gauges are utilized for measuring pile responses. Data related to bending moment and axial forces extracted from tests are also provided.</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.