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384 results for “risk model”
Enhancing Credit Risk Assessment in Digital Finance through a Hybrid Deep Learning Model Integrated with Blockchain on the Edge of Things F
<p><span>This work proposes a credit risk assessment model using deep learning models such as self-attention generative adversarial networks (SA-GAN) and deep multi-layer perceptron (DMLP). Blockchain is used to improve the security aspects of the model by employing Brakerski-Gentry-Vaikuntanathan (BKV) encryption technique. Further, the proposed system is implemented in Edge-of-things network and communications are enabled via LoRaWAN server.</span></p>
Osteolitic vs Osteoblastic metastatic lesion: Computational modeling of fracture risk in the human vertebra after screws fixation procedure
<p>Metastatic lesions compromise the mechanical integrity of vertebrae, increasing the fracture risk. Screwfixation is usually performed to guarantee spinal stability and prevent dramatic fracture events. Accordingly, predicting the overall mechanical response in such conditions is critical to planning and optimizing the surgical treatment. This work proposes an image-basedfinite element computational approach describing the mechanical behavior of a patient-specific instrumented metastatic vertebra by assessing the effect of lesion size, location, type and shape on the fracture load and fracture patterns under physiological loading conditions. A specific constitutive model for the metastasis is integrated to account for the effect of the diseased tissue on the bone material properties. Computational results demonstrate that size, location, and type of metastasis significantly affect the overall vertebral mechanical response, and suggest better account these parameters in estimating the fracture risk. Combining multiple osteolytic lesions to account for irregular shape of the overall metastatic tissue has a not significant effect on fracture load of vertebra macroscopically. In addition, the combination of loading mode and metastasis type is shown for the first time as a critical modeling parameter in determining the fracture risk. The proposed computational approach moves towards defining a clinically integrated tool to improve the management of metastatic vertebrae and quantitatively evaluate fracture risk.</p>
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
<p>The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.</p>
Risk-of-bias v.2 assessment with large language models
<p>See https://bitbucket.org/aimedtech/fewshot_rob for more information.</p>
Dataset: Risk Transfer Model for Flood Risk Evolution in a Multi-reservoir System
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p> </p> <p>The files consist of:</p> <p> </p> <p>Data:</p> <ul> <li>Figure 11;</li> <li>Figure 12;</li> <li>Figure S1;</li> <li>Figure S4</li> <li>Relative prediction error</li> <li>Reservoir information</li> <li>Streamflow</li> </ul> <p>Model code:</p> <ul> <li>Calculation of entropy</li> <li>Forecasting error simulation model</li> <li>LHS</li> <li>Analytic code of transfer model</li> </ul>
GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks
<p>These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology. </p> <p>The following data sources were used in this modeling effort:</p> <p><strong>National Hydrography Dataset (NHD): United States Geological Survey (USGS)</strong></p> <p>Upstate NY Lakes, ponds, streams, rivers, springs, and wells</p> <p><strong>Critical Environmental Areas in New York State: New York State Department of Environmental Conservation </strong></p> <p>Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)</p> <p><strong>National Land Cover Dataset (NLCD): United States Geological Survey (USGS)</strong></p> <p>National Land Cover Database classification schemes based primarily on Landsat data (2011)</p> <p><strong>Elevation Data: United States Geological Survey (USGS)</strong></p> <p>Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000. </p> <p><strong>Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database</strong></p> <p>Rural and urban highways for New York</p> <p> </p> <p><strong>Other references</strong></p> <p>Bureau, U.S. Census., American community survey 5-year estimates. 2017.</p> <p>EPA, Toxics Resource Inventory. 2019</p> <p> </p> <p>.</p>
Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling
<p>Description of Research Data and Code<br>This repository contains the data and code associated with the research paper: Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling, currently under review at Water Resources Research.</p> <p>Overview<br>To investigate the risk interdependencies arising from hydraulic interactions in cascading reservoir systems, we developed a risk propagation model using Bayesian networks (see file: Risk_propagation_model). Building on this model, we employed EMODPS to create a robust operational model for the reservoirs (see file: Robust_operation_model). Our goal was to minimize the joint risks of insufficient hydropower output and ecological water shortages while formulating robust operating policies to mitigate system performance degradation in the face of uncertain future runoffs (generated from our runoff simulations, see file: runoff simulation).</p> <p>Additionally, we analyzed the relationship between overall risk and risk at individual reservoir sites using a scenario discovery algorithm to pinpoint scenarios that reveal vulnerabilities (see file: python_project_scenariodiscovery).</p> <p>Acknowledgments<br>This project builds upon the code developed by Giuliani et al. (2016) M3O-Multi-Objective-Optimal-Operations (https://mxgiuliani00.github.io/M3O-Multi-Objective-Optimal-Operations/), Hadka and Reed (2013) BORG MOEA (http://borgmoea.org/), and Kevin Patrick Murphy et al. (2007) Bayesian Network Toolbox (https://www.ipcc.ch/report/ar6/wg1/#InteractiveAtlas). We are grateful to the original authors for their contributions.</p> <p>While we have made modifications and extensions to the original code, we have not altered its license. Users should refer to the original repositories for more details and ensure compliance with the terms of the original authors' licenses.</p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
SAMC Model Inputs from: Predicting dispersal and conflict risk for wolf recolonization in Colorado
<p>The colonization of suitable yet unoccupied habitat due to natural dispersal or human introduction can benefit recovery of threatened species. Predicting habitat suitability and conflict potential of colonization areas can facilitate conservation planning.</p> <p>Planning for reintroduction of gray wolves (Canis lupus) to the U.S. state of Colorado is underway. Assessing which occupancy sites minimize the likelihood of human-wolf conflict during dispersal events and seasonal movements is critical to the success of this initiative.</p> <p>We used a spatial absorbing Markov chain (SAMC) framework, which extends random walk theory and probabilistically accounts for both movement behavior and mortality risk, to compare the viability of potential occupancy sites (public lands >500 km2 to minimally meet wolf pack range area). The SAMC framework produced spatially explicit predictions of wolf dispersal, philopatry, and conflict risk ahead of recolonization prior to reintroduction efforts. Our SAMC model included: 1) movement resistance based on terrain, roads, and housing density; 2) mortality risk and potential conflict (absorption) based on livestock presence, social tolerance, land ownership, and state boundaries; and 3) site fidelity based on habitat quality. Using this model, we compared 21 public land units by deriving predictions of: A) relative survival time outside each site, B) intensity of use and retention time within each site, and C) the probability of use on adjacent public lands. We also predicted and mapped potential conflict hotspots associated with each site.</p> <p>Among the units assessed, a complex of USFS Wilderness areas near Aspen, chiefly the Hunter-Fryingpan and Collegiate Peaks Wilderness areas, had the best overall rankings when comparing predictions of each metric. The area balances high-quality, well-connected habitat with relatively low livestock density and high social tolerance. </p> <p>Synthesis and applications: Our findings highlight the utility of the SAMC framework for assessing colonization areas and the capacity to identify locations for effective proactive management, especially of conflict-prone species. The flexibility of the SAMC framework enables predicting likely areas of philopatry and human-wildlife conflict using spatially-explicit metrics which can improve the success of conservation translocations and management of species with changing geographic extents.</p>
Prediction Models for Risk Score and Prognosis of Intraoperatively Acquired Pressure Injury in Surgical Patients
ClinicalTrials.gov study NCT06166641. IPD Sharing: UNDECIDED. Countries: 1. Publications: 41.
Validation of a Predictive Model to Estimate the Risk of Conversion to Clinically Significant Macular Edema and/or Vision Loss in Mild Nonproliferative Diabetic Retinopathy in Diabetes Type 2
ClinicalTrials.gov study NCT00763802. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Neuropsychobiological Correlates of Sex-steroid Hormone Manipulation in Healthy Women: a Risk Model for Depression
ClinicalTrials.gov study NCT02661789. IPD Sharing: YES. Countries: 1. Publications: 2.
Detection of Endometrial Cancer Through Risk Modelling
ClinicalTrials.gov study NCT06268626. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Validation of DM and Pre-DM Risk Prediction Model
ClinicalTrials.gov study NCT04881383. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prediction Model of Cardiac Risk for Dental Extraction in Elderly Patients With Cardiovascular Diseases
ClinicalTrials.gov study NCT03211312. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Using a Real-Time Risk Prediction Model to Predict Pediatric Venous Thromboembolism (VTE) Events
ClinicalTrials.gov study NCT04574895. IPD Sharing: NO. Countries: 1. Publications: 3.
Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients
ClinicalTrials.gov study NCT06423547. IPD Sharing: NO. Countries: 1. Publications: 2.
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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.