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95 results for “Logistics”
Impact of a Multimodal Lifestyle Intervention on Dementia Risk Factors and Attitude Related to Dementia Risk: A Logistical Pilot Study
ClinicalTrials.gov study NCT07146412. IPD Sharing: YES. Countries: 1. Publications: 9.
ETEC Logistics Trial (TREK)
ClinicalTrials.gov study NCT00516659. IPD Sharing: Not stated. Countries: 3. Publications: 1.
Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
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Data from: Logistic regression analysis of factors influencing the effectiveness of intensive sound masking therapy in patients with tinnitus
Objectives: To investigate factors influencing the effectiveness intensive sound masking therapy on tinnitus using Logistic Regression Analysis. Design: The study used a retrospective cross-section analysis. Participants: 102 patients with tinnitus were recruited at the Sun Yat-sen Memorial Hospital of Sun Yat-sen University, China. Intervention: Intensive sound masking therapy was used as an intervention approach for patients with tinnitus. Primary and secondary outcome measures: participants underwent audiological investigations and tinnitus pitch and loudness matching measurements, followed by intensive sound masking therapy. The Tinnitus Handicap Inventory (THI) was used as the outcome measure pre- and post-treatment. Multivariate logistic regression was performed to investigate the association of demographic and audiological factors with effective therapy. Results: According to the THI score changes pre-and post-sound masking intervention, fifty-one participants were categorised into an effective group, the remaining 51 participants were placed in a non-effective group. Those in the effective group were significantly younger than those in the non-effective group (p=0.012). Significantly more participants had flat audiogram configurations in the effective group (p=0.04). Multivariable logistic regression analysis showed that age (OR=0.96, 95% CI: 0.93, 0.99, p=0.007), audiometric configuration (p=0.027) and THI score pre-treatment (OR=1.04, 95% CI: 1.02, 1.07, p<0.001) were significantly associated with therapeutic effectiveness. Further analysis showed that patients with flat audiometric configurations were 5.45 times more likely to respond to intervention than those with high-frequency steeply sloping audiograms (OR=5.45, 95% CI: 1.67, 17.86, p=0.005). Conclusion: Audiometric configuration, age and THI scores appear to be predictive for the effectiveness of sound masking treatment. Gender, tinnitus characteristics and hearing threshold measures seem not to be related to treatment effectiveness. Further randomized control study is needed to provide further evidence of the effectiveness of prognostic factors in tinnitus interventions.
Fig. 2 in Aquatic Beetles in the Ravenna Training and Logistics Site of Northeastern Ohio
Fig. 2. Species – area plot for RTLS site. Numbers next to points refer to the watershed
Fig. 1 in Aquatic Beetles in the Ravenna Training and Logistics Site of Northeastern Ohio
Fig. 1. Map of RTLS showing facility boundary, major roads, the four named creeks,
Deep Learning-Enhanced GNSS Signal Integrity for Critical Supply Chain Logistics (Data availability statement)
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Repurposing Domperidone in Secondary Progressive MS - A Simon 2-Stage Phase 2 Futility Trial - Table e1: Results of the binary logistic regression model
<p><b>Objective:</b> To assess whether treatment with the generic drug domperidone can reduce the progression of disability in secondary progressive multiple sclerosis (SPMS), we conducted a phase 2 futility trial following the Simon two-stage design.</p> <p><b>Methods:</b> We enrolled patients in an open-label, Simon two-stage, single-center, phase 2, single-arm futility trial at the Calgary MS Clinic if they met the following criteria: age 18 to 60 years, SPMS, screening EDSS score of 4.0 to 6.5 and screening T25FW of 9 seconds or more. Patients received domperidone 10mg QID for one year. The primary outcome was worsening of disability, defined as worsening of the T25FW performance by 20% or more at 12 months compared to at baseline. This trial is registered with ClinicalTrials.gov, number NCT02308137.</p> <p><b>Results:</b> Between February 13<sup>th</sup>, 2015 and January 3<sup>rd</sup>, 2020, 110 patients were screened, 81 received treatment, 64 completed follow-up, of whom 62 were analysed. The study did not meet its primary endpoint: 22 of 62 (35%) patients experienced significant worsening of disability, which is close to the expected proportion of 40%, and above the pre-defined futility threshold. Patients with higher prolactin levels during the study had a significantly lower risk of disability progression, which may warrant further investigation. Domperidone treatment was reasonably well tolerated, but adverse events occurred in 84% and serious adverse events in 15% of patients.</p> <p><b>Conclusions:</b> Domperidone treatment could not reject futility in reducing disability progression in SPMS. The Simon two-stage trial model may be a useful model for phase 2 studies in progressive MS.</p> <p><b>Classification of Evidence:</b> This study provides Class III evidence that in individuals with secondary progressive multiple sclerosis participating in a futility trial, domperidone treatment could not reject futility in reducing disability progression at 12 months.</p>
IMPLEMENTING MODERN INFORMATION TECHNOLOGIES IN LOGISTICS.
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AI and DSS in Humanitarian Logistics
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EVALUATION OF USAGE CHARACTERISTICS OF DIFFERENT TRANSPORTS IN TRANSPORT LOGISTICS SYSTEM
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Association between beliefs about mediations and adherence to medications: a stepwise binary logistic regression model
<p>Dataset of research article.</p>
Data from: Using local ecological knowledge to build mutualistic networks in hyper-diverse and logistically challenging ecosystems
<p>1. Collecting interaction data to build frugivory or seed dispersal networks is logistically challenging in ecosystems that have very high plant and animal diversity and/or where fieldwork is difficult or dangerous. Consequently, the majority of available networks are from ecosystems with low species diversity or they represent a sub-set of the community. </p> <p>2. Here, we propose an approach using local ecological knowledge (LEK) of indigenous communities to build interaction databases and weighted networks that would otherwise be difficult to achieve with direct observations. Indigenous communities live and work in many hyper-diverse ecosystems and the people within these communities often have detailed knowledge of ecological processes. </p> <p>3. Working in a Sundaland biodiversity hotspot – Royal Belum State Park, Peninsular Malaysia – we used field data, visually-oriented interviews with indigenous people (Orang Asli, in the Jahai and Temiar ethnic subgroups), and published records to collate interactions, and their frequency of occurrence of animal fruit consumption and seed dispersal. </p> <p>4. We documented 2060 fruit consumption and 1330 seed dispersal interactions among 164 plant species and 34 animal taxa, the latter representing groups of closely related species or individual species. The majority of the interactions (97%) were identified by the LEK interviews, with the additional methods (field data and published records) used to support and marginally expand the interview data. The metrics for the networks we built reflect those of networks structured by biological mechanisms, supporting the validity of our novel method. </p> <p>5. Local ecological knowledge is highly relevant for building detailed databases for mutualistic interactions in hyper-diverse and/or challenging ecosystems. Such ecosystems are among the most vulnerable on earth, harbouring ecological interactions that are often poorly documented at a community-level. We show how LEK can broaden our knowledge of such sensitive ecosystems, but our approach is useful for any ecosystem where people retain rich local ecological knowledge.</p>
Data and code for "Logistic and preference bias in participatory science butterfly observations"
<p>This repository contains all code needed to replicate the analyses performed in "Identification ease, wing pattern diversity, and family explain taxonomic bias in butterfly observations" by Goldstein, Stoudt, Lewthwait, Shirey, Mendoza, and Guzman.</p> <p> </p> <p>Components</p> <p>The main code to be executed is contained in the "code" folder and should be run in numeric order (00 through 03) with two helper scripts.</p> <p>All but one of the data inputs can be found in the "data" folder. The iNaturalist data is required but not published within this directory. It may be obtained from GBIF at <a href="https://doi.org/10.15468/dl.rhmxtn">this link</a> and should be saved into the "data" folder.</p> <p>Note that, at the request of eButterfly, we have removed location information for eButterfly observations of sensitive species in this repository. Please contact eButterfly directly for the uncensored data.</p> <p> </p> <p>Other notes</p> <p>The code in the "01" file takes a long time to run (all of the GAMs for each species). You can skip ahead to "02" to run meta-analysis code on the species-level results, which we provide in "output" as "estimated_indices_fromGAMs.csv".</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
Container Logistics Object-centric Event Log
<p><strong>General Description</strong></p> <p>Our company sells goods overseas. After receiving an order, the shipment of goods is scheduled. According to this schedule, the goods are picked up from the local production site and brought to a terminal where a logistics service provider receives and ships them.</p> <p>This is an artificial event log according to the <a href="https://www.ocel-standard.org/">OCEL 2.0 Standard</a> simulated using CPN-Tools. Both the CPN and the SQLite can be downloaded. </p> <p><strong>Process Overview</strong></p> <p>From a <strong>customer order</strong> perspective, the process begins when the order is registered at our company <em>(register customer order)</em>. After registration, a <strong>transport document</strong> is created in which details of the further process are recorded <em>(create transport document)</em>.</p> <p>Using this information, the logistics service provider is contacted to coordinate the transport of the ordered goods to the seaport. Twice a week, that provider sends a <strong>vehicle</strong> to a terminal, with a limited capacity for containers of ordered goods to be transported from the terminal to a seaport. For our company, available capacties vary from vehicle to vehicle, as we are not the only company booking spots. Once the logistics service provider receives our transport documents, they book capacities according to availability and container prioritizations in the upcoming weeks <em>(book vehicles)</em>. Once the dates for transporting the goods to the terminal are set, our company contacts a <strong>container</strong> depot to reserve the required containers <em>(order empty containers)</em>.</p> <p>When a container’s vehicle departure approaches, the goods are prepared, packed and shipped to the terminal. For this purpose, a <strong>truck</strong> is sent to the container depot <em>(pick up empty container)</em>. Meanwhile, the ordered goods to be shipped are packed into <strong>handling units</strong> at the production site. After loading the handling units <em>(load truck)</em>, the truck drives the full container to the terminal <em>(drive to terminal)</em>.</p> <p>At the terminal, the container is picked up by a free <strong>forklift</strong> and weighed <em>(weigh)</em>. Unless the vehicle departure is imminent, the container is placed in the storage location at the terminal <em>(place in stock)</em>. Finally, it is moved to the vehicle <em>(bring to loading bay, load to vehicle)</em> which departs at a fixed time <em>(depart)</em>.</p> <p>Despite careful planning, containers sometimes miss a vehicle’s departure. In this case, the container is rescheduled to the next possible vehicle <em>(reschedule container)</em> and kept near the loading ramp until then.</p> <p>Further information can be found at: <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/">https://www.ocel-standard.org/beta/event-logs/simulations/logistics/</a></p> <p><strong>General Properties </strong></p> <p>An overview of log properties is given below.</p> <table> <thead> <tr> <th>Property</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Event Types</td> <td>14</td> </tr> <tr> <td>Object Types</td> <td>7</td> </tr> <tr> <td>Events</td> <td>35761</td> </tr> <tr> <td>Objects</td> <td>14013</td> </tr> </tbody> </table> <p><strong>Control-Flow Behavior </strong></p> <p>The behavior of the log is described by a <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/full-ocpn.svg">respective object-centric Petri net</a>. Also, individual object types exhibit behavior that can be described by simpler Petri nets. See below.</p> <table> <tbody> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Container-ocpn.svg">Container</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Transport%20Document-ocpn.svg">Transport Documents</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Customer%20Order-ocpn.svg">Customer Order</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Truck-ocpn.svg">Truck</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Forklift-ocpn.svg">Forklift</a></td> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Vehicle-ocpn.svg">Vehicle</a></td> </tr> <tr> <td><a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/images/Handling%20Unit-ocpn.svg">Handling Unit</a></td> <td> </td> </tr> </tbody> </table> <p> </p> <p><strong>Object Relationships </strong></p> <p> </p> <p>During the process, object-to-object relations can emerge at activity occurrences as follows.</p> <table> <thead> <tr> <th>Activity</th> <th>Source Object Type</th> <th>Target Object Type</th> <th>Qualifier</th> </tr> </thead> <tbody> <tr> <td>Create Transport<br> Document</td> <td>Customer Order</td> <td>Transport Document</td> <td>TD for CO</td> </tr> <tr> <td>Book Vehicle</td> <td>Transport Document</td> <td>Vehicle</td> <td>Regular VH for TD</td> </tr> <tr> <td>Book Vehicle</td> <td>Transport Document</td> <td>Vehicle</td> <td>High-Prio VH for TD</td> </tr> <tr> <td>Order Empty<br> Containers</td> <td>Transport Document</td> <td>Container</td> <td>CR for TD</td> </tr> <tr> <td>Pick Empty<br> Container</td> <td>Truck</td> <td>Container</td> <td>TR loads CR</td> </tr> <tr> <td>Load Truck</td> <td>Container</td> <td>Handling Unit</td> <td>CR contains HU</td> </tr> <tr> <td>Reschedule<br> Container</td> <td>Transport Document</td> <td>Vehicle</td> <td>Substitute VH for TD</td> </tr> </tbody> </table> <p><strong>Simulation Model </strong></p> <p>The CPN used to create this event log can also be downloaded.To obtain simulated data, extract the linked ZIP file and play out the CPN therein, e.g., by using <a href="https://cpntools.org/">CPN Tools</a>.</p> <p>The play-out produces CSV files according to the schema of OCEL2.0. <a href="https://www.ocel-standard.org/beta/event-logs/simulations/logistics/data/csv_to_sql.ipynb">This Python notebook</a> can be used to convert these files to an SQLite dump.</p> <p>For a technical documentation of the simulation model, please open the attached CPN with CPN Tools and see the annotations therein.</p> <p><strong>Acknowledgements</strong></p> <p>Funded under the Excellence Strategy of the Federal Government and the Länder<em>. </em>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p>
App-Assisted Day Reconstruction to Reduce Logistic Toxicity in Cancer
ClinicalTrials.gov study NCT05502302. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable City Logistics Strategies
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Repurposing Domperidone in Secondary Progressive MS - A Simon 2-Stage Phase 2 Futility Trial - Table e1: Results of the binary logistic regression model
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Data from: Logistic regression analysis of factors influencing the effectiveness of intensive sound masking therapy in patients with tinnitus
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ScienceDex guides
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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.