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870 results for “Model development”
Fig. 4. A in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 4. A model representing sugarcane dynamics as currently implemented in the simulation tool for the field application of a SIT/IS strategy against Eldana saccharina in sugarcane.
Fig. 3 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 3. The Eldana saccharina module developed in this study. The module describes the dynamics of all E. saccharina life stages under the influence of the SIT/IS technique. Other control measures may also be included, and are currently under investigation.
Fig. 6 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 6. The graphical user interface designed for the SIT/IS simulation tool for the field application of a SIT/IS strategy against Eldana saccharina in sugarcane. The initial infestation, e/100s, is the number of borers per 100 stalks of sugarcane.
Fig. 2 in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 2. The pest species subsystem which may include all the important pest species in South African sugarcane.The total damage caused by the various pest species may be estimated by such a system. Currently, only the Eldana saccharina module has been developed.
Fig. 9. A in Simulation modelling as a decision support in developing a sterile insect-inherited sterility release strategy for Eldana saccharina (Lepidoptera: Pyralidae)
Fig. 9. A spatial overview of the Eldana saccharina larval infestation at the end of a 24 mo simulation of a SIT/IS program at the pilot site near the Eston area of KwaZulu-Natal, South Africa. The colors indicate infestation levels measured in e/100 stalks, i.e., number of borers per 100 stalks of sugarcane. The fields colored in dark blue in the top right corner are aged 0, 1 and 2 mo, and they were harvested just before the end of the simulation; therefore the infestation levels are still low and this is before the commencement of releases of irradiated adult moths.
PREPCLIM software - additional material for publication in "Geoscientific Model Development" 2024
<p>Data set illustrating the software developed in the PREPCLIM project and used in the proposed paper:</p> <p>A Modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen<br>fertilization under climate change – PREPCLIM-v1</p> <p>https://doi.org/10.5194/gmd-2024-105<br>Preprint. Discussion started: 11 July 2024<br>c Author(s) 2024. CC BY 4.0 License.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure7. Model 3D of women body.
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 7 .</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 8. Model 3D of man body
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 8 .</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
Business Model Development_Video 1
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Business Model Development_Video 3
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Business Model Development_Video 2
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Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
<p>This dataset comes from the following paper:</p> <p>Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>
Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)
<p>Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH<sub>4</sub>) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH<sub>4</sub> modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000-2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and seagrasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km<sup>2</sup> (Mkm<sup>2</sup>), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm<sup>2</sup>, seasonally inundated wetlands at 4.0 Mkm<sup>2</sup> (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm<sup>2</sup> (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen’s kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH<sub>4</sub> fluxes for global-scale land surface modeling. </p> <p> </p> <p>Update: Oct.08.2021</p> <p>Documentation for WAD2M Version 2.0 can be found at <a href="https://drive.google.com/file/d/1adoAnuqu6uBWnTYKI8u_S4OAQSdgOtd6/view?usp=sharing">WAD2M_V2_update</a></p>
Dataset for "Radiation environment at the surface and subsurface of the Moon: Model development and validation" publication in Journal of Geophysical Research: Planets
<p>data set used for plots in manuscript "Radiation environment at the surface and subsurface of the Moon: Model development and validation" submitted to GRL</p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation
<p>Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia.</p> <p>IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of:</p> <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> <p>Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as:</p> <p><code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code></p>
Simulated Well Production Data using a Transient Well Model and a Developed Simulator
<p>This is a simulated dataset of transient well production data. This dataset was used in my Masters thesis at King Abullah University of Science and Technology (KAUST), and it is shared for academic use and research work.</p> <p>The dataset has 100 wells simulated at time steps of 0.2 hours for an entire year. This gives 43,800 observations per well, and grand total of 4,380,000 observations in the entire dataset. The resulting production data is then perturbed with systemic and random gauge errors to better simulate real-world gauge readings.</p> <p>The simulator code used to generate this dataset can be found at: <a href="https://github.com/ykh-1992/TransientNodalAnalysis.jl">https://github.com/ykh-1992/TransientNodalAnalysis.jl</a></p> <p>The data consists of three files:<br> - "wells.csv": This file details the input parameters for each simulated well.<br> - "data.zip": This file houses an 850 MB "data.csv" that includes the simulated well production data.<br> - "auxiliary.csv": This file includes information related to the simulation run.</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.