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Related Concept Videos

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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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Yield Criteria for Ductile Materials under Plane Stress01:25

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In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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A Minimum Cost Consensus-Based Failure Mode and Effect Analysis Framework Considering Experts' Limited Compromise and

Hengjie Zhang, Shenghua Liu, Yucheng Dong

    IEEE Transactions on Cybernetics
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    This study introduces a minimum cost consensus-based Failure Mode and Effect Analysis (FMEA) framework. It incorporates expert compromise and tolerance behaviors to achieve effective risk assessment consensus, even when initial solutions are unachievable.

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    Area of Science:

    • Risk Management
    • Decision Analysis
    • Engineering Management

    Background:

    • Traditional Failure Mode and Effect Analysis (FMEA) may not fully capture expert behaviors in consensus-building.
    • Limited expert compromise and tolerance can hinder achieving a collective risk assessment solution.

    Purpose of the Study:

    • To propose a Minimum Cost Consensus-based Failure Mode and Effect Analysis (MCC-FMEA) framework.
    • To integrate expert limited compromise and tolerance behaviors into the FMEA consensus process.
    • To develop models ensuring a consensual collective solution for FMEA problems.

    Main Methods:

    • Development of an MCC-FMEA framework incorporating limited compromise behaviors.
    • Integration of expert tolerance behaviors into the MCC-FMEA framework.
    • Design of a minimum compromise adjustment consensus model and a maximum consensus model.
    • Application of an interactive MCC-FMEA framework for collective solution generation.

    Main Results:

    • Theoretical analysis identified scenarios where the proposed framework might lack an initial solution.
    • Developed consensus models address potential solution gaps by adjusting compromises or maximizing consensus.
    • The framework successfully reached a consensual collective solution in a case study.

    Conclusions:

    • The proposed MCC-FMEA framework effectively handles expert behaviors for consensus-reaching.
    • The developed models provide robust solutions even when initial FMEA consensus is challenging.
    • The framework's effectiveness is validated through a COVID-19 risk assessment case study and comparative analysis.