Scenario Generation
Scenario Generation - Related Videos
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
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Cited by 1 •
2016
There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.
01:26
Pharmaceutical Poisoning: Potential Scenarios
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2026
Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
06:59
A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings
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Cited by 5 •
2016
This study presents a novel device that offers an easy route to quickly provide medications and fluids in patients with limited or difficult IV access. This small-diameter device is placed in the distal third of the rectum, allowing for ongoing medication and fluids administration.
07:13
Using Topographic Maps to Generate Topographic Profiles
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2023
Source: Laboratory of Alan Lester - University of Colorado Boulder Topographic maps are "plan-view" representations of Earth's three-dimensional surface. They are a standard type of map-view that provides an overhead, or aerial, perspective. Among the defining features of a topographic map are the contour lines that indicate locations of constant elevation. The elevation interval between the contour lines is dependent on the level of detail provided by the map and the kind of topography present.
06:02
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
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Cited by 1 •
2020
This protocol delineates the technical setting of a developed mixed reality application that is used for immersive analytics. Based on this, measures are presented, which were used in a study to gain insights into usability aspects of the developed technical solution.
FAQs
They establish the boundaries and internal logic of each case. Assumptions describe conditions accepted for analysis, variable inputs represent factors that may change, and constraints limit which combinations remain feasible. Keeping these elements explicit helps engineers trace why scenarios differ and determine whether a result reflects system behavior or an unrealistic premise.
These approaches create alternatives from different sources of evidence. Simulation applies a model to selected operating conditions, historical data derives cases from previously observed conditions, and probabilistic methods represent uncertainty through varied likelihood-based inputs. Engineers may compare or combine these approaches, depending on whether they need modeled possibilities, experience-based cases, or broader uncertainty coverage.
A single forecast can conceal how a design responds when conditions change. By examining multiple plausible cases, engineers can observe performance across varied inputs, expose potential failure modes, and distinguish strategies that remain effective from those that depend on narrow assumptions. This supports robustness, meaning acceptable performance across the range of conditions represented.
A practical workflow begins by specifying the engineering question, relevant assumptions, variable inputs, and system constraints. Engineers then select an appropriate source or method, such as simulation, historical data, or probabilistic generation, and produce alternative operating conditions. Finally, they evaluate system or design performance across those cases and use the results to compare options or plan for risk.
The results can show how competing designs or decisions perform under different operating conditions. They may reveal sensitivity to particular inputs, expose failure modes, and indicate whether a system remains robust when conditions depart from a central expectation. Engineers can use these comparisons to support risk planning and make decisions without relying only on controlled experiments or one forecast.
Its applications span infrastructure, energy systems, manufacturing, transportation, and autonomous technologies. In each setting, engineers can examine alternative conditions that affect system operation, safety, or decision quality. The approach is especially useful when real-world conditions vary and controlled experiments cannot represent every relevant case, helping teams assess resilience before selecting or deploying a design.