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95 results for “Logistics”
Data from: Delineating important killer whale foraging areas using a spatiotemporal logistic model
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Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
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Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.
Measures of dynamism and urgency in logistics - dataset & results
<p>Dataset and results of <em>Measures of dynamism and urgency in logistics</em>. Rinde R.S. van Lon, Eliseo Ferrante, Ali E. Turgut, Tom Wenseleers, Greet Vanden Berghe, and, Tom Holvoet. European Journal of Operational Research. ISSN 0377-2217. http://dx.doi.org/10.1016/j.ejor.2016.03.021.</p>
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <blockquote> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> </blockquote> <p>At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code</p> <p>Note that unpacked, all files are about 5 GB in total.</p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <ul> <li>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</li> <li>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</li> </ul>
Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics - datasets and results
<p>This directory contains the data and results that we used and obtained during the realization of the following paper:</p> <blockquote> <p>Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics. Rinde R.S. van Lon, Juergen Branke, and Tom Holvoet. Genetic Programming and Evolvable Machines (2017).</p> </blockquote> <p>The code that has been used can be found on GitHub (https://github.com/rinde/vanLon17-GPEM-code), an archive is available at https://doi.org/10.5281/zenodo.260130.</p> <p>This repository contains the following files:</p> <ul> <li>evo.zip (expands to 21.2 GB) A zip file containing all results of the training experiment and tuning experiment.</li> <li>JAAMAS.zip (expands to 227.6 MB) A zip file containing the main results of the JAAMAS experiment, also available at https://doi.org/10.5281/zenodo.209760</li> <li>realtime.zip (expands to 913.5 MB) A zip file containing the main results</li> <li>dataset10k.zip (expands to 4.9 GB) Contains the scenarios used for the training and tuning experiments.</li> <li>overview.zip (expands to 42 MB) Contains the following files: <ul> <li>overview/experiment-overview.csv - table containing basic overview of the 40 main evolutionary runs that were performed for this paper.</li> <li>overview/heuristics/ - folder with a tree visualization for every evolved heuristic.</li> <li>overview/makefile - executes the analysis scripts that were used in the paper (requires the R programming language, https://www.r-project.org/). All tables and figures will be placed in results/generated/.</li> <li>readme.txt - this file.</li> <li>overview/results/ - contains the scripts</li> <li>overview/results/data/ - empty directory in which evo.zip, JAAMAS.zip, and realtime.zip have to be unpacked for the analysis scripts to work.</li> </ul> </li> </ul>
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results v1.1.0
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> <p>This is version v1.1.0. At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</p> <p>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</p>
Exploring Factors Promoting Dependency Updates with Survival Time Analysis and Logistic Regression
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FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots (video results)
<p>Video result of submitted paper "FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots" in ICARSC confrence</p>
Pharmaceutical Logistics Supply Chain: Challenges and Possible Solutions
<p>Explore the complexities of pharmaceutical supply chain management in this comprehensive article. Discover the challenges faced, such as regulatory compliance and counterfeit drugs, and delve into innovative solutions like blockchain technology and smart packaging. Learn how these advancements enhance efficiency, ensure product safety, and improve patient outcomes in the global pharmaceutical industry</p>
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
Simulation results of two agent-based models of logistics systems
<p>The data set contains the simulation results of the two different agent-based models of logistics systems: <br> 1. A medical treatment facility (MTF) model, consisting of agents representing wounded soldiers and fixed sites representing medical facilities.<br> 2. A ship fueling (SF) simulation, consisting of agents representing fuel transport ships and fixed sites representing fuel-using bases.</p> <p>Folders in folder "MTF" are related to the MTF model.</p> <p>Files in folder "casualty_rateX" are related to the case with casualty rate of X new casualties per time step, where X = 30, 50, 70, . . . , 330.</p> <p>Each file "RunN_casualtyX_fullness.txt" has the "fullness" (defined as the ratio of the number of patients at a site to the total patient capacity of that site) of each site for each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Each file "RunN_casualtyX_dow.txt" has the total number of Dead Of Wounds that occur in all sites in each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Folders in folder "SF" are related to the SF model.</p> <p>Files in folder "siteMaxX" are related to the case with an initial (and maximum) site fuel value of X units, where X = 25, 50, 75, . . . , 200.</p> <p>Each file "assetTowedFuelHistory_siteMaxX_N.txt" has the number of towed fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "assetUseFuelHistory_siteMaxX_N.txt" has the number of onboard fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "siteHistory_siteMaxX_N.txt" has the number of fuel units at each site for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p>
Understanding and Modeling Middle-Mile Logistics Automation
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ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
Earthquake Logistics in Indonesia: Twitter (X) Data
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Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Development and Validation of a Logistic Regression Algorithm to Predict the Risk of Obstetric Anal Sphincter Injury.
ClinicalTrials.gov study NCT05218837. IPD Sharing: NO. Countries: 1. Publications: 4.
Logistic Regression and Elastic Net Regularization for the Diagnosis of Fibromyalgia
ClinicalTrials.gov study NCT04088747. IPD Sharing: NO. Countries: 1. Publications: 12.
Predicting Postoperative Pulmonary Infection in Elderly Patients Undergoing Major Surgery: a Study Based on Logistic Regression and Machine Learning Models
ClinicalTrials.gov study NCT06491459. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reducing Delay in Vaccination of Children: Logistic Barriers
ClinicalTrials.gov study NCT03516682. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.