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Design Consideration01:22

Design Consideration

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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

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Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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Operational resilience: concepts, design and analysis.

Alexander A Ganin1,2, Emanuele Massaro1,3, Alexander Gutfraind4

  • 1U.S. Army Corps of Engineers - Engineer Research and Development Center, Environmental Laboratory, Concord, MA, 01742, USA.

Scientific Reports
|January 20, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces quantitative measures for engineering resilience in complex systems. Achieving desired resilience and robustness is possible by adjusting design parameters like redundancy and recovery time.

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Area of Science:

  • Engineering
  • Systems Science
  • Network Science

Background:

  • Modern infrastructures face increasing threats from natural disasters, epidemics, and cyber-attacks.
  • Engineering resilience is crucial for societal function and recovery from disruptions.
  • Existing resilience frameworks lack quantitative measures applicable across diverse domains.

Purpose of the Study:

  • To propose and validate quantitative measures for engineering resilience based on the National Academy of Sciences definition.
  • To develop an approach applicable to physical, information, and social systems.
  • To evaluate critical functionality as a performance function over time.

Main Methods:

  • Formulation of quantitative resilience measures.
  • Application to multi-level directed acyclic graphs and interdependent coupled networks.
  • Case studies using synthetic data and the Linux operating system.

Main Results:

  • Demonstrated quantitative assessment of integrated system resilience and robustness.
  • Identified trade-offs between design parameters (redundancy, recovery time, backup supply) and resilience levels.
  • Confirmed nonlinear relationships between network parameters and resilience.

Conclusions:

  • The proposed quantitative approach effectively measures and enhances engineering resilience in complex systems.
  • Achieving target resilience and robustness is feasible through strategic design parameter adjustments.
  • The methodology provides valuable insights for analysts and designers of complex systems and networks.