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Longitudinal Research02:20

Longitudinal Research

13.3K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Transfer Function in Control Systems01:21

Transfer Function in Control Systems

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The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
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Second Order systems II01:18

Second Order systems II

408
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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Related Experiment Video

Updated: Feb 3, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Exploring Risks Transferred from Cloud-Based Information Systems: A Quantitative and Longitudinal Model.

Wafa Bouaynaya1, Hongbo Lyu2, Zuopeng Justin Zhang3

  • 1Polytech Nantes, University of Nantes, 44200 Nantes, France. wafa.bouaynaya@univ-nantes.fr.

Sensors (Basel, Switzerland)
|October 19, 2018
PubMed
Summary

This study introduces a quantitative model to track risks in cloud-based information systems. It helps differentiate mitigated from unmitigated risks, aiding companies in assessing cloud computing

Keywords:
IS riskcloud computinglongitudinal studymathematical modelingorganizational transformation

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Area of Science:

  • Information Systems Security
  • Risk Management
  • Cloud Computing

Background:

  • The increasing adoption of Internet of Things (IoT) and Cyber-Physical Systems (CPS) has amplified the importance of cloud-based systems.
  • Current methodologies lack formal approaches for modeling risks transferred within cloud-based information systems.
  • Understanding risk dynamics is crucial for the successful implementation and sustainability of cloud environments.

Purpose of the Study:

  • To explore formal methods for quantifying risks in cloud-based information systems.
  • To evaluate the variation of these risks throughout the implementation lifecycle.
  • To propose a redefined risk estimation method distinguishing between mitigated and unmitigated risks.

Main Methods:

  • Development of a quantitative and longitudinal risk model.
  • Analysis spans from project inception to completion of cloud-based information systems.
  • Introduction of a novel risk estimation approach for improved accuracy.

Main Results:

  • Quantification of risk variations in cloud-based information systems over time.
  • A clear distinction between mitigated and unmitigated risks is established.
  • The model provides insights into the longitudinal behavior of system risks.

Conclusions:

  • Formal methods can effectively model and quantify risks in cloud environments.
  • The proposed risk estimation method enhances understanding of risk mitigation effectiveness.
  • Findings assist practitioners in evaluating cloud computing's impact on company sustainability and competitive advantage.