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Dataset results
29 results for “parameter identification”
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Fig. 7 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 7. Comparison of 3 characteristic forms of damage: (a) hole and feces coming from inside the pad, useful to distinguish pads with Cactoblastis cactorum; (b) typical damage observed in plants that were attacked by C. cactorum; (c) circular black spot fungal damage; (d) map black spot fungal damage.
Fig. 6 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 6. Proportion of Cactoblastis cactorum infected cladodes parasitized by Apanteles opuntiarum and proportion per cladode of C. cactorum larvae parasitized by A. opuntiarum throughout the yr for sites from Santiago del Estero, Córdoba, and Tucumán provinces. The average and standard deviation of the number of pupae of A. opuntiarum per C. cactorum larvae also is shown.
Fig. 2 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 2. Description of spots and setae from larval I to VI, shown in the pro- and meso-thoracic segment, and the seventh and anal abdominal segments: D1–2: dorsal setae; SD1–2: subdorsal setae; XD1–2: prothoracic setae; L1–3: lateral setae; SV1–2: subventral setae; PP1: posterior setae; spot "k" in prothorax, "h" in mesothorax, "a" and "c" in the seventh abdominal segment, and anal shield in the tenth and last abdominal segment.
Fig. 5 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 5. Number of larvae of Cactoblastis cactorum per mo from all sites of Tucumán and the proportion of those that were parasitized by Apanteles.
Fig. 1 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 1. (a) An eggstick oviposited on a pad of Opuntia ficus indica; (b) Anterior part of the larva exhibiting the cephalic capsule and prothorax starting to sclerotize; (c) Larva II has a dark shield on the prothorax and small macula at the base of each setae in the abdomen; (d) Larva III with bigger maculae with alternating color intensity on successive segments; (e) Larva IV with a white line between the head capsule and prothorax shield; (f) Larva V characterized by almost continuous black rings on the abdomen on an orange-brownish back- ground; (g) Typical bright orange larval VI with the prothorax shield fractured in 2 and apparently continuous black rings; (h) pupa within silk cocoon and naked pupa; Cactoblastis cactorum females (lef) and males (right); females have longer palps (i) than males (j). Both genders are characterized by a transverse line in the distal part of the wings (k, l).
Fig. 3 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 3. Proportion of individuals of different developmental stages of Cactoblastis cactorum in Tucumán throughout the year. Inside the bars: E = eggsticks, L = larvae, P = pupae.
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
Identification and Regionalization of Streamflow Routing Parameters for the HLM Hydrological Model in Iowa
<p>The tables contained the metrics and the peak flows estimated using HLM under three different parameterizations. </p>
Fig. 4 in A study of Cactoblastis cactorum (Lepidoptera: Pyralidae) in its native range: further insights into life cycle, larval identification, developmental parameters, natural enemies, and damage to the host plant Opuntia ficus-indica (Caryophyllales: Cactaceae)
Fig. 4. Number of eggs across months for all sampling sites from Tucumán.
High strain rate micro-compression for crystal plasticity constitutive law parameters identification
<p>Experimental data and numerical model attached to the article "High strain rate micro-compression for crystal plasticity constitutive law parameters identification" (DOI to be generated soon)</p>
Diagnosis of lithium-ion batteries degradation with P2D model parameters identification: a case study on low temperature charging
<p>Dataset and code of the equilibrium model. They refer to publication <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.fub.2024.100006" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.fub.2024.100006</span></span></a></p>
Unambiguous Identification of Crystal Plasticity Parameters from Spherical Indentation
<p>The main results are in folders Population1 and Population2 (that correspondes to initial Popolation 1 and 2, respectively). Each of them contains 3 folders with the results for the investigated cases). The other two folders contain the sensitivity analysis and analysis of the influence of friction. In order to run the simulations one has to first have Wolfram Mathematica with the AceFEM package installed. Running each optimization is possible using the EA.nb file. Running the sensitivity analysis is possible using the Sensitivity.nb file. In order to run the friction analysis one has to run virgin.nb file for each firction coefficient value. Analysis of the results is possible using the Podsumowanie.nb file in each folder. There are several codes used to conduct the simulations. In case of using the contact code one should cite Kucharski, S., S. Stupkiewicz, and H. Petryk. "Surface pile-up patterns in indentation testing of Cu single crystals." Experimental Mechanics 54 (2014): 957-969. In case of using the hanging nodes formulation one should cite: Frydrych, Karol. "Crystal plasticity finite element simulations of the indentation test." Comput. Methods Mater. Sci 19 (2019): 41-49. In case of using the crystal plasticity code one should cite: Kucharski, S., S. Stupkiewicz, and H. Petryk. "Surface pile-up patterns in indentation testing of Cu single crystals." Experimental Mechanics 54 (2014): 957-969. AND Lewandowski, M. J., and S. Stupkiewicz. "Size effects in wedge indentation predicted by a gradient-enhanced crystal-plasticity model." International Journal of Plasticity 109 (2018): 54-78. AND Frydrych, Karol, and Katarzyna Kowalczyk-Gajewska. "Grain refinement in the equal channel angular pressing process: simulations using the crystal plasticity finite element method." Modelling and Simulation in Materials Science and Engineering 26.6 (2018): 065015.</p>
Deep Learning for Microfluidic Assisted Caenorhabditis elegans Multi-parameter Identification Using YOLOv7
<p>The effectiveness of deep learning model relies on a large number of labeled image datasets. We have collected a dataset of 3373 worm images from microfluidic devices in various studies as datasets. Then, the datasets were labeled for training methods. The acquired videos were continuously intercepted at 10-frame intervals to capture cropped worm images. Totally 3931 images were extract. Worms in each image were manually annotated using LabelImg. This VOC-format annotation tool generated Extensible Markup Language (XML) files for the model. There were 2426 labels for the WT category and 1505 for the GFP category.</p>
Identification of Predictive Factors of Postoperative Pain After Oral Surgery From Phenotypic Parameters Easily Accessible Preoperatively (PHEDOPO)
ClinicalTrials.gov study NCT05405088. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predictive Parameters for Difficult Tracheal Intubation Identification in Thyroid Surgery
ClinicalTrials.gov study NCT03578601. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Joint identification of groundwater contamination source and heterogeneous hydrogeological parameters in LNAPL contaminated site based on deep convolutional encoder-decoder neural networks
Open the record for dataset details and reuse information.
Identification of Hypospadias Parameters Using Digital Photography and Artificial Intelligence
ClinicalTrials.gov study NCT05569863. IPD Sharing: NO. Countries: 0. Publications: 3.
On the Identification of Power-Law Creep Parameters from Conical Indentation
Open the record for dataset details and reuse information.
Gene profiling-based phenotyping for identification of cellular parameters that contribute to fitness, stress-tolerance and virulence of Listeria monocytogenes variants
GEO Series GSE114672. Listeria monocytogenes; Listeria monocytogenes LO28. 10 samples. Type: Expression profiling by array.
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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.