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29 results for “Construction sites”
Dataset for the paper "A framework for robotic excavation and dry stone construction using on-site materials"
<p>Stone data from the <i>Science Robotics</i> paper "A framework for robotic excavation and dry stone construction using on-site materials" containing:</p><ul><li>Mesh files of 1,100 stones (quarried boulders, erratics, and concrete debris) that were digitized by the autonomous excavator HEAP<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/1100%20Unprocessed%20Stone%20Meshes.zip/content">1100 Unprocessed Stone Meshes.zip: </a>Raw mesh files directly from the poisson reconstruction of accumulated LiDAR points, containing some artifacts and floating geometries</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/1100%20Closed%20Stone%20Meshes.zip/content">1100 Closed Stone Meshes.zip: </a>Clean, closed, downsampled meshes</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/Stone_Shape_Properties.csv/content">Stone_Shape_Properties.csv: </a>Properties file with a list of the stone IDs (IDs in the 1xxx and 3xxx range typically correspond to concrete elements) and select shape properties</li></ul></li><li>A dataset of candidate placements from automatically generated stone walls. The candidate placement data zip files contain:<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/Candidate_Placement_Data-npy.zip/content">Candidate_Placement_Data-npy.zip: </a>SDF (.npy) representation of each candidate, with three channels of 32x32x32 for distances to the stone, the already-placed stones, and the target wall</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/Candidate_Placement_Data-pcd.zip/content">Candidate_Placement_Data-pcd.zip: </a>Point cloud (.pcd) representations of each candidate, with separate files for the placed stone, target wall (search volume), and already-placed stones (where they exist)</li></ul></li><li><a href="https://zenodo.org/api/records/10038881/draft/files/sdf_classifier.zip/content">sdf_classifier.zip: </a>Python examples:<ul><li>Rendering the three channel SDF data to mesh geometry using marching cubes and libigl</li><li>Candidate SDF classification using the pretrained model</li></ul></li><li>Candidate attributes and labels<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/candidate_attributes_labels.csv/content">candidate_attributes_labels.csv: </a>CSV file containing a list of UUID's corresponding to each candidate placement in the dataset. For each candidate, additional information is included about the dimensions and location of the solution, together with the (subjectively) hand-labelled binary value for placement viability. </li></ul></li><li><a href="https://zenodo.org/api/records/10038881/draft/files/README.md/content">README.md: </a>An additional readme with some details on the attributes file</li></ul><p>If you use this data in your research, please cite the <a href="https://www.science.org/doi/10.1126/scirobotics.abp9758">journal article</a>.</p><p> </p>
Person Detection on Construction Sites
<p>All information can be found here:</p> <p><a href="https://github.com/aidresden/person_detection_construction_sites" target="_blank" rel="noopener">https://github.com/aidresden/person_detection_construction_sites</a></p> <p>The development of this data set was funded by the Federal Ministry of Labour and Social Affairs (BMAS) and the Federal Institute for Occupational Safety and Health (BAuA) under the administrative agreement ‘Artificial Intelligence in a Safe and Healthy Working Environment’.</p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</p>
Data from "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction"
<p>Sediment data from Steart Marshes described in Mossman et al. "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction".</p> <p>Data are provided as a .xlsx file (Data package.xlsx) with four tabs. Tab 1 has column heading descriptions. Tab 2 has total carbon samples. Tab 3 has total organic carbon samples. Tab 4 has bulk density samples. Each tab is also provided as a seperate csv file.</p>
Рис. 5. Морские двустворчатые моллюски иЗ раскопов памЯтника Константиновка-1: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – раскоп 3, постройка № 40, пласт 6, квадрат Ж.3-10, длина раковины 49.3 мм; C, D – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 40.8 мм; G, H – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 41.6 мм; I–K – раскоп 1, пласт 1, квадрат Б2, длина фрагмента 35.8 мм; E, F – Crenomytilus grayanus (Dunker, 1853), подъемный материал, длина фрагмента 100.7 мм. Fig. 5. Marine bivalves from the Konstantinovka-1 site excavations: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – excavation 3, construction N 40, layer 6, square Ж.3-10, shell length 49.3 mm; C, D – excavation 3, construction N 40, square Ж.3-10, fragment length 40.8 mm; G, H – excavation 3, construction N 40, square Ж.3-10, fragment length 41.6 mm; I–K – excavation 1, formation 1, square B2, fragment length 35.8 mm); E, F – Crenomytilus grayanus (Dunker, 1853), lifting material, fragment length 100.7 mm. in Mollusks from the archaeological site Konstantinovka-1 in Primorye (Russian Far East)
Рис. 5. Морские двустворчатые моллюски иЗ раскопов памЯтника Константиновка-1: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – раскоп 3, постройка № 40, пласт 6, квадрат Ж.3-10, длина раковины 49.3 мм; C, D – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 40.8 мм; G, H – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 41.6 мм; I–K – раскоп 1, пласт 1, квадрат Б2, длина фрагмента 35.8 мм; E, F – Crenomytilus grayanus (Dunker, 1853), подъемный материал, длина фрагмента 100.7 мм. Fig. 5. Marine bivalves from the Konstantinovka-1 site excavations: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – excavation 3, construction N 40, layer 6, square Ж.3-10, shell length 49.3 mm; C, D – excavation 3, construction N 40, square Ж.3-10, fragment length 40.8 mm; G, H – excavation 3, construction N 40, square Ж.3-10, fragment length 41.6 mm; I–K – excavation 1, formation 1, square B2, fragment length 35.8 mm); E, F – Crenomytilus grayanus (Dunker, 1853), lifting material, fragment length 100.7 mm.
Design and cloning of a pair of split-GFP constructs to examine one of the split-sites for SCN1A
<p>Design and cloning of split-GFP constructs to investigate a potential split-sites for SCN1A fragments complementation.</p>
Matching Construction Workers across Views for Automated 3D Vision Tracking On-Site
<p>Computer vision based tracking of construction resources is one of the several options available for obtaining trajectories useful in safety and productivity applications. This type of tracking requires that targets are accurately matched across multiple camera views to obtain a 3D trajectory out of two or more 2D trajectories. This matching is straightforward when it involves easily distinguishable targets in uncluttered scenes. This can be challenging in industrial scenes such as construction sites due to congestion, occlusions and workers in greatly similar high visibility apparel. This paper proposes a novel vision based method that addresses all these issues. It uses as input the output of a 2D vision based tracking method and searches for potential matches in three sequential steps. It terminates only when a positive match is found. The first step returns the strongest candidate by correlating a segment of workers’ past 2D trajectories. The second employs geometric restrictions, whilst the third correlates colour intensity values. The proposed method featured a promising performance of 97% precision, 98% recall and 95% accuracy.</p>
Dataset related to paper "Preliminary Analysis of Non-destructive Test Methods to Evaluate the Self-healing Efficiency on the Construction Site"
<p>Dataset of the study reported in the conference paper ""Preliminary Analysis of Non-destructive Test Methods to Evaluate the Self-healing Efficiency on the Construction Site" presented at the SynerCrete'23 Conference (14-16 June 2023, Milos Island, Greece).</p>
Construction site
Construction site, (Belgium). Made of 500 pictures with Zephyr3D Lite from 3DFlow. For more updates, please consider to follow me on Twitter at @GeoffreyMarchal. (https://twitter.com/GeoffreyMarchal) Source: Objaverse 1.0 / Sketchfab
Figure 12 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 12. Logistic regression showing the effect of the folding strength of the leaves on the presence (1) or absence (0) of Aysha piassaguera nests. N0 = 36; N1 = 18.
Figure 11 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 11. Logistic regression showing the effect of leaf thickness on the presence (1) or absence (0) of Aysha piassaguera nests. N0 = 36; N1 = 18.
Figure 3 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 3. Device used to measure the folding strength of leaves. (a) Wooden support. (b) U-shaped frame device. (c) Sewing thread. (d) Pulley. (e) Plastic bag containing flour. (f) Test leaf. (g) Weight to hold the leaf. (h) Hooks to hold the bag and tip of the leaf. (i) Table.
Figure 2 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 2. Graphic scheme of the plots delimited in the field and the subdivision made for the correct registration of the nests of Aysha paissaguera.
Figure 4 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 4. Average proportion of branches with Aysha piassaguera nests at each vegetation height. Error bars represent ± 1 standard error. NBase = 1828; NCrown = 817.
Figure 1 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 1. Aysha piassaguera nest on a Crocosmia crocosmiiflora (Iridaceae) leaf, with a distinct pyramidlike format.
Figure 13 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 13. Logistic regression showing the effect of the leaf mass per area (LMA) of the leaves on the presence (1) or absence (0) of Aysha piassaguera nests. N0 = 36; N1 = 18.
Figure 9 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 9. Frequency of available branches and branches with Aysha piassaguera nests in Iridaceae and Gramineae. Nbranches with nests = 141; Navailable branches = 857.
Figure 8 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 8. Frequency of available branches and branches with Aysha piassaguera nests in monocotyledons and eudicotyledons. Nbranches with nests = 153; Navailable branches = 1828.
Figure 7 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 7. Average proportions of branches with Aysha piassaguera nests in each family of monocotyledons at the lower vegetation of the forest edge. Error bars represent ± 1 standard error. NIridaceae = 502; NGramineae = 355.
Figure 6 in Leaf and site selection for nest construction and oviposition in Aysha piassaguera Brescovit, 1992 (Araneae: Anyphaenidae)
Figure 6. Average proportions of branches with Aysha piassaguera nests in each group of plants at the base (lower vegetation) of the forest edge. Error bars represent ± 1 standard error. NMonocotyledons = 857; NEudicoctyledons = 971.
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