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301 results for “Risk management”
Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)
Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaura
Clinical evidence for high-risk CE-marked medical devices for glucose management: a systematic review and meta-analysis
<p><strong><span>Aims: </span></strong><span>High-risk medical devices are increasingly used in diabetes management, but<span><span> there are no specific European recommendations on how they should be evaluated. Within</span></span> the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we conducted a systematic review and meta-analysis evaluating CE-marked high-risk devices for glucose management. </span></p> <p><strong><span>Materials and Methods: </span></strong><span>We<span> identified interventional and observational studies evaluating the </span>efficacy and safety of 8 automated insulin delivery (AID) systems, 2 implantable insulin pumps, and 3 implantable continuous glucose monitoring (CGM) devices.<span> </span></span><span>We meta-analysed randomized controlled trials (RCTs) comparing AID systems with other treatments.</span></p> <p><strong><span>Results:</span></strong><span> 99 studies published from 2009–2022 were included, comprising 83 on AID systems, 6 on insulin pumps, and 10 on CGM; 43% reported industry funding;30% were pre-market; 45% had a comparator group. 33% were RCTs, 25% non-randomized trials, and 41% observational studies. Median sample size was 52 (interquartile range 25–111), age 37.8 years (17–45.5), and study duration 13 weeks (4.5–26). AID systems lowered HbA1c by 0.3 percentage points (absolute mean difference [MD]=-0.3; 9 RCTs; I<sup>2</sup>=85%) and increased time in target range for sensor glucose level by 10.5 percentage points (MD=10.5; 14 RCTs; I<sup>2</sup>=89%). 69% of studies reported on at least one safety outcome.</span></p> <p><strong><span>Conclusions:</span></strong><span> High-risk devices for glucose monitoring or insulin dosing, in particular AID systems, improve glucose control safely but evidence on diabetes-related end organ damage is lacking due to short study durations. Methodological heterogeneity highlights the need for d<span><span>eveloping standards for future pre- and post-market investigations of diabetes-specific high-risk medical devices.</span></span></span></p>
Rapid Landslide Risk Zoning toward Multi-Slope Units of the Neikuihui Tribe for Preliminary Disaster Management repository
<p> Taiwan features steep terrain and a fragile geology environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to the Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan evolves landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of Risk, which comprises Hazard, Exposure, and Vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.</p>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Respondents' perspectives on the impact of digital data-based health services on disaster risk management in Indonesia.
<p>This data contains respondents' perspectives on the impact of digital data-based health services on disaster risk management. Digital health services are the implementation of digital, information, and communication technologies in the context of health services. Digital health services include: mHealth, Health Information Technology, Wearable Devices, Telehealth and Telemedicine, and Personalized Medicine. </p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) would be advisable.</p>
Farmer adaptive behavior and risk management in EU agriculture
<p>Risk and risk management are essential elements of agriculture and affect the wellbeing of farm households. Farmers react to production, market and institutional risks and challenges by taking measures on or off the farm. Such risk management measures are often costly and have implications for up- and downstream industries as well as the environment. The risk exposure of European farms is increasing. For example, climate change will increase the frequency and magnitude of extreme weather events like droughts, heatwaves and heavy rainfalls that potentially have detrimental effects on agricultural production. Thus, the adaptive capacity and risk management options in European agriculture need to be improved. Policy shall support this process. Policies are needed to support a diversity of risk management solutions and not only focus on a few solutions. Strategies to cope with risk often go beyond the level of the individual farm. Cooperation, learning and sharing of risks play a vital role in European agriculture and shall be strengthened. Thus, coordinated policies targeting beyond the individual farm and considering all the stakeholders involved in the risk management strategies are needed to ensure their effective implementation. Moreover, policies need to facilitate to take full advantage of the rapid technological progress and improved data availability (e.g. based on satellite imagery) to develop a wider set of risk management strategies.</p>
Improved risk management towards more resilient EU faming systems
<p>EU farming systems are facing increasing economic, social, environmental and institutional challenges. Finding the opportunities to improve risk management contributes to enhancing the farming systems’ resilience. Based on the participation of a wide variety of stakeholders across European agricultural sectors, four main avenues to improve risk management are proposed: 1) Useful, accessible and well-structured information; 2) Professional, adapted and widespread training and advice and boosted knowledge transfer; 3) Developing and spreading new forms of cooperation among farming system actors; and 4) New/ improved products and services adapted to current and future needs of the farming systems. Not only farmers, farmers’ households and associations but also value chain actors, financial institutions, NGOs and public administration are encouraged to be part of the opportunities to improve risk management towards resilient farming systems.</p>
Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management
<p>The datasets and accompanying R script included in this upload are provided to complement the manuscript titled <em>"Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management."</em> These resources are intended to facilitate the replication and verification of the analyses presented in the paper.</p> <p> </p> <p> </p>
Figure 3 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
Figure 3. The proportion of risk statuses of fish families with four or more species assessed at the extent of Florida. Panel (A) shows the proportion of species with high, moderate, low, multiple, and undetermined risk statuses of assessed species (total number of species evaluated in a family). Panel (B) shows the ratio of assessed to unassessed species in a given family (total species in a family). Total species in a family were obtained from FishBase (Froese and Pauly 2023).
Figure 1 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
Figure 1. The proportion of risk statuses of fish families with four or more species assessed at the extent of the conterminous U.S. Panel (A) shows the proportion of species with high, moderate, low, multiple, and undetermined risk statuses of assessed species (total number of species evaluated in a family). Panel (B) shows the ratio of assessed to unassessed species in a given family (total species in a family). Total species in a family were obtained from FishBase (Froese and Pauly 2023).
Figure 1 in Risk screening and management of alien terrestrial planarians in The Netherlands
Figure 1. Photographs of the alien terrestrial planarian species found indoors and outdoors in The Netherlands: Anisorhynchodemus sp., found in greenhouses in Rotterdam, Amsterdam and Arnhem (A, Photo by Roy Kleukers); Bipalium kewense found in greenhouses Amsterdam, Utrecht and Leiden (B, Photo by Pierre Gros); Parakontikia ventrolineata found in a garden in Amsterdam Noord (C, Photo by Roy Kleukers); Caenoplana coerulea found in a greenhouse in Nijmegen (D, Photo by Roy Kleukers); Caenoplana variegata found in gardens in Castricum, Bleiswijk, Hillegersberg, Zwijndrecht, Zaandam and Heemstede (E, Photo by Roy Kleukers); Caenoplana cf. micholitzi found in a greenhouse in Arnhem (F, Photo by Roy Kleukers); Dolichoplana sp. found in greenhouses in Amsterdam and Arnhem, (G, Photo by Roy Kleukers); Marionfyfea adventor found in gardens in Goes, Schiedam and Beek-Ubbergen (H, Photo by Jochem Kuhnen) and Obama cf. nungara found in a garden center in Gilzen (I, Photo by Pierre Gros).
Figure 3 in Risk screening and management of alien terrestrial planarians in The Netherlands
Figure 3. Distribution of alien terrestrial planarian species in The Netherlands (A sp = Anisorynchodemus sp; BK = Bipalium kewense; CC = Caenoplana coerulea; CM = Caenoplana cf. micholitzi; CV = Caenoplana variegata; D sp = Dolichoplana sp. MA = Marionfyfea adventor; ON = Obama cf. nungara; PV = Parakontikia ventrolineata). For details see Supplementary material Table S24.
Figure 2 in America's Most Wanted Fishes: cataloging risk assessments to prioritize invasive species for management action
Figure 2. The proportion of risk statuses of fish families with four or more species assessed at the extent of the Great Lakes region. Panel (A) shows the proportion of species with high, moderate, low, multiple, and undetermined risk statuses of assessed species (total number of species evaluated in a family). Panel (B) shows the ratio of assessed to unassessed species in a given family (total species in a family). Total species in a family were obtained from FishBase (Froese and Pauly 2023).
Figure 1 in Developing biosecurity plans for non-native species in marine dependent areas: the role of legislation, risk management and stakeholder engagement
Figure 1. Five-stage approach for risk assessment management of NNS in Shetland, adapted from the ecosystem-based risk management framework (Cormier et al. 2013).
Figure 2 in Risk screening and management of alien terrestrial planarians in The Netherlands
Figure 2. Cumulative number of introduced species based on the years of first records of alien terrestrial planarians in the United Kingdom, France, and The Netherlands.
Dataset: Global X NASDAQ 100 Risk Managed Income ETF (QRMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 1. A in The effectiveness of field pest management and culling at harvest for risk mitigation of two fruit flies affecting citrus in China
Fig. 1. A logic chart illustrating work flow and calculating efficacies of pest management and culling at harvest (systems approach efficacy = the efficacy of the 2 measures together).
A dataset of community perspectives on living conditions and disaster risk management in informal settlements: A case study in KwaZulu-Natal Province, South Africa
<p>This article describes a dataset of community perspectives on living conditions and disaster risk management in Khan Road, a non-serviced informal settlement, located in Pietermaritzburg, the capital of KwaZulu-Natal province in South Africa. The data were collected by local community researchers via a structured questionnaire of 159 participants conducted between August and September 2022, using mobile phones via KoboToolbox. The dataset was analysed using exploratory data analysis (EDA) techniques. This household survey is part of a research project aiming to develop an evidence base of opportunities, risks and vulnerabilities related to housing construction and resource management in incremental upgrading of informal settlements in South Africa. This dataset can be used by local practitioners and policymakers involved in decision-making for informal settlement upgrading and help them prioritise resources and upgrading interventions based on what informal dwellers need. Furthermore, this cleaned dataset could support the analysis of further South African data guiding the development of digital platforms as a real-time resource management tool or guide the enhancement of existing theoretical frameworks in the field of participatory design and co-production used by academic scholars. </p>
Data and Software for "Probabilistic Trade-offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks"
<p><span>Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"</span></p> <p><span>This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.</span></p> <p><strong><span>Contents</span></strong></p> <ol> <li><strong><span>Data: Model Inputs</span></strong><span><br>This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.<br>The key files are:</span></li> <ul> <li><span>Input_HydrologicalData</span></li> <li><span>Input_SystemData</span></li> </ul> <li><strong><span>Results: Model Output Analysis</span></strong><span><br>This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).<br>Key files include:</span></li> <ul> <li><strong><span>SDDP Model Outputs</span></strong><span> (MATLAB format): </span></li> <ul> <li><span>EnergyPriority_Baseline.mat</span></li> <li><span>EnergyPriority_2070.mat</span></li> <li><span>EnergyPriority_2100.mat</span></li> <li><span>AgriculturePriority_Baseline.mat</span></li> <li><span>AgriculturePriority_2070.mat</span></li> <li><span>AgriculturePriority_2100.mat</span></li> </ul> <li><strong><span>Extracted Model Results</span></strong><span> (Excel format): </span></li> <ul> <li><span>Organized for each policy and climate scenario to facilitate analysis.</span></li> </ul> </ul> <li><strong><span>Software: Data Analysis and Visualization</span></strong><span><br>Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.<br>Scripts provided:</span></li> <ul> <li><span>CDF_outflow.py</span><span>: Analyzes cumulative distribution functions for river discharge.</span></li> <li><span>CDF_sectors.py</span><span>: Examines sectoral water use distributions.</span></li> <li><span>PCP_SI.py</span><span>: Generates Parallel Coordinate Plots for trade-offs analysis.</span></li> </ul> <li><strong><span>Instructions: README File</span></strong><span><br>A comprehensive README file explains:</span></li> <ul> <li><span>Details of model input data.</span></li> <li><span>Instructions to interpret the SDDP model outputs.</span></li> </ul> </ol> <p><strong><span>Instructions:</span></strong><span><br></span><span>The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.</span></p>
Leadership Styles in International Conflict Management. Action by the European Union Against Radicalization, Terrorism and Violent Extremism – Risks and Threats
<p>The article focuses on the antecedents of the emergence and management of international conflicts related to radicalization and terrorism in a European context and the interaction between leader and team. The objective of the desk research is to examine the correlation between the leadership style and the team, the leadership competencies, and to develop strategies for conflict resolution and management. It is achieved by analysing the European approach to the prevention of radicalization, terrorism and violent extremism, and exploring their positive and negative effects on a specific individual or group. The focus is on the role of leadership styles and competences at different hierarchical levels in order to achieve results and find solutions to problems. This paper presents the desk research conducted on countering radicalization and extremism.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.