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295 results for “Approximation”
Distribution. DR Congo S to N Namibia and N Botswana, and S through Zimbabwe, S Mozambique (approximately S of Zambezi River), NE South Africa (including KwaZulu-Natal), and Swaziland; an isolated population is found in S Gabon. No studies have attempted to distinguish the exact geographical range of R. arundinum from that of the Zambian Reedbuck (R. occidentalis) where the two populations meet. in Bovidae
Distribution. DR Congo S to N Namibia and N Botswana, and S through Zimbabwe, S Mozambique (approximately S of Zambezi River), NE South Africa (including KwaZulu-Natal), and Swaziland; an isolated population is found in S Gabon. No studies have attempted to distinguish the exact geographical range of R. arundinum from that of the Zambian Reedbuck (R. occidentalis) where the two populations meet.
Approximation
<p>T. Approximation </p>
Rectus Muscle Re-approximation at Cesarean Delivery
ClinicalTrials.gov study NCT03296969. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Nano-hydroxyapatite and Ozone - Effect on Approximal Initial Caries
ClinicalTrials.gov study NCT04147091. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Purpose of This Registry Study is to Learn More About Metabolic Health: Approximately 15,000 Participants Who Are Overweight or Have Obesity With or Without Metabolic Diseases Are Expected to Part
ClinicalTrials.gov study NCT06354101. IPD Sharing: NO. Countries: 0. Publications: 0.
Instruction to Compress Chest Approximately 6-7cm for Healthcare Provider in Hospital
ClinicalTrials.gov study NCT01936402. IPD Sharing: Not stated. Countries: 0. Publications: 0.
approximately 5 in Taxonomy of Rhinolophus yunanensis Dobson, 1872 (Chiroptera: Rhinolophidae) with a description of a new species from Thailand
approximately 5 mm
Approximation-guided Fairness Testing through Discriminatory Space Analysis
<h3>Description:</h3> <p>This dataset contains the experimental results from the paper titled "Approximation-guided Fairness Testing through Discriminatory Space Analysis".</p> <p>In this paper, we conducted 24 fairness testing tasks using 5 different fairness testing algorithms: AFT (our proposed algorithm), VBT-X, VBT, THEMIS, and ExpGA. Each execution was repeated 30 times, with a runtime of 1 hour.</p> <h3>Files Included:</h3> <ol> <li><strong>log.txt</strong>: This file contains the logs of each execution. Each log is labeled with an identifier, such as "'aft-LogReg-Adult-sex-0", which represents the 1st execution of the fairness testing task on (LogReg, Adult, sex) using AFT.</li> <li><strong>discriminatory_instances.zip</strong>: This archive includes all the IDIs (individual discriminatory instances) identified during the experiments.</li> <li><strong>test_cases_1.zip, test_cases_2.zip, and test_cases_3.zip</strong>: These archives includes all the test cases generated during the experiments. Find them at <a href="https://doi.org/10.5281/zenodo.11650346" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11650346</a> and <a href="https://doi.org/10.5281/zenodo.11650369" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11650369</a>.</li> <li> <p><strong>res.txt</strong>: This file contains the averaged results of the 30 repetitions. It includes four metrics: #IDIs (the number of indentified IDIs), #Tests (the number of generated test cases), Succ (the success ratio of test case), and DivIDIs (the diversity of IDIs).</p> </li> </ol>
Table data for Optimum Interval Approximation
<p>Table data for Optimum Interval Approximation, read the README.md file on GitHub for further information.</p>
Asymptotic approximations for Bloch waves and topological mode steering in a planar array of Neumann scatterers
<p>We study the canonical problem of wave scattering by periodic arrays, either of infinite or finite extent, of Neumann scatterers in the plane; the characteristic lengthscale of the scatterers is considered small relative to the lattice period. We utilise the method of matched asymptotic expansions, together with Fourier series representations, to create an efficient and accurate numerical approach for finding the dispersion curves associated with Floquet–Bloch waves through an infinite array of scatterers. The approach lends itself to direct scattering problems for finite arrays and we illustrate the flexibility of these asymptotic representations on topical examples from topological wave physics.</p>
Dataset for 'Comparison of approximate methods for modelling coherence scanning interferometry'
Open the record for dataset details and reuse information.
Approximation-guided Fairness Testing through Discriminatory Space Analysis -- test cases of AFT
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Approximation-guided Fairness Testing through Discriminatory Space Analysis -- test cases of VBT-X, VBT, THEMIS, and ExpGA
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Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant
<p>Sturla F, Caimi A, Romarowski RM, Nano G, Glauber M, Redaelli A, Votta E, Marrocco-Trischitta MM. Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant. J Endovasc Ther. 2022 May 19:15266028221095403. doi: 10.1177/15266028221095403. Epub ahead of print. PMID: 35588222.</p> <p>Abstract</p> <p><strong>Purpose: </strong>Displacement forces (<strong><em>DF</em></strong>s) identify hostile landing zones for stent graft deployment in thoracic endovascular aortic repair (TEVAR). However, their use in TEVAR planning is hampered by the need for time-expensive computational fluid dynamics (CFD). We propose a novel fast-approximate computation of <strong><em>DF</em></strong>s merely exploiting aortic arch anatomy, as derived from the computed tomography (CT) and a measure of central aortic pressure.</p> <p><strong>Materials and methods: </strong>We tested the fast-approximate approach against CFD gold-standard in 34 subjects with the "bovine" aortic arch variant. For each dataset, a 3-dimensional (3D) model of the aortic arch lumen was reconstructed from computed tomography angiography and CFD then employed to compute <strong><em>DF</em></strong>s within the aortic proximal landing zones. To quantify fast-approximate <strong><em>DF</em></strong>s, the wall shear stress contribution to the <strong><em>DF</em></strong> was neglected and blood pressure space-distribution was averaged on the entire aortic wall to reliably approximate the patient-specific central blood pressure. Also, <strong><em>DF</em></strong> values were normalized on the corresponding proximal landing zone area to obtain the equivalent surface traction (<strong><em>EST</em></strong>).</p> <p><strong>Results: </strong>Fast-approximate approach consistently reflected (r<sup>2</sup>=0.99, p<0.0001) the <strong><em>DF</em></strong> pattern obtained by CFD, with a -1.1% and 0.7° bias in <strong><em>DF</em></strong>s magnitude and orientation, respectively. The normalized <strong><em>EST</em></strong> progressively increased (p<0.0001) from zone 0 to zone 3 regardless of the type of arch, with proximal landing zone 3 showing significantly greater forces than zone 2 (p<0.0001). Upon DF normalization to the corresponding aortic surface, fast-approximate <strong><em>EST</em></strong> was decoupled in blood pressure and a dimensionless shape vector (S) reflecting aortic arch morphology. <strong><em>S</em></strong> showed a zone-specific pattern of orientation and proved a valid biomechanical blueprint of <strong><em>DF</em></strong> impact on the thoracic aortic wall.</p> <p><strong>Conclusion: </strong>Requiring only a few seconds and quantifying clinically relevant biomechanical parameters of proximal landing zones for arch TEVAR, our method suits the real preoperative decision-making process. It paves the way toward analyzing large population of patients and hence to define threshold values for a future patient-specific preoperative TEVAR planning.</p>
A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem
<p>The dataset for our paper 'A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem'. It consists of 5 real KGs (<code>Mondial</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, <code>DBpedia</code>) and 5 synthetic KGs (<code>LUBM-10U</code>, <code>LUBM-50U</code>, <code>LUBM-250U</code>, <code>LUBM-2U</code>, <code>DBP-50K</code>). Each KG is compressed in one file, which including (for example, in <code>LUBM-2U</code>):</p> <ul> <li> <p><code>lubm_2u_nodes.sql</code>: the id, the name and the weight of a node,</p> </li> <li> <p><code>lubm_2u_edges.sql</code>: the ids of two nodes an edge connects,</p> </li> <li> <p><code>lubm_2u_queries.sql</code>: a query consists of some keywords,</p> </li> <li> <p><code>lubm_2u_keymap.sql</code>: a keyword maps to a set of nodes,</p> </li> <li> <p><code>lubm_2u_nodevec.sql</code>: the vector of a node, used to compute quadratic function qw,</p> </li> <li> <p><code>lubm_2u_hub_hop.sql</code>: the hub labeling index to compute in Section 4.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_1.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_5.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.5,</p> </li> <li> <p><code>lubm_2u_hub_mix_9.sql</code>: the hub labeling index to compute in Section 4.1 where α=0.9.</p> </li> </ul> <p>You can dump the data into MySQL database. For example,</p> <pre><code>create database lubm_2u; use lubm_2u; source lubm_2u_nodes.sql; …</code></pre> <p>Unfortunately, due to the limit of space, for large KGs (<code>DBpedia</code> and <code>LUBM-250U</code>), we don't directly provide the data of hub labeling, i.e., these two compressed files only contains the first 5 sql files. You should generate hub labeling by yourself where the process is detailed in <a href="https://github.com/nju-websoft/QGSTP-HB/blob/main/README.md">README</a> of our project.</p> <p> </p>
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Allen Brain Atlas
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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.