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Related Concept Videos

Lung Capacity01:47

Lung Capacity

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The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures.
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Strategies of Self-Presentation I: Strategic Self-Presentation01:12

Strategies of Self-Presentation I: Strategic Self-Presentation

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Strategic self-presentation refers to individuals' intentional efforts to influence how others perceive them. This process is employed in various social and professional settings, such as job interviews, dating, politics, and legal contexts, where individuals seek to shape impressions to gain social or material advantages. While people generally present themselves in ways that align with their authentic characteristics, external factors, such as cognitive load, can hinder their ability to...
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Respiratory Capacities01:24

Respiratory Capacities

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Respiratory capacities are crucial indicators of lung function, representing the maximum amount of air an individual's respiratory system can handle during various breathing phases.
One key metric is the Inspiratory Capacity (IC), which represents the maximum amount of air that can be inhaled with full effort. IC is calculated by summing the tidal volume and inspiratory reserve volume, typically ranging from 2.4 to 3.6 liters.
The Functional Residual Capacity (FRC) represents the air in the...
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Respiratory Volumes and Capacities01:22

Respiratory Volumes and Capacities

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The respiratory system is responsible for the intake of oxygen and the expulsion of carbon dioxide from the body. Respiratory volumes describe the volume of air in the lungs at different phases of the respiratory cycle. Tidal volume is the air breathed in and out during normal, quiet breathing. Inspiratory reserve volume is the air that can be forcefully inspired beyond the tidal volume. In contrast, expiratory reserve volume refers to the air that can be expelled from the lungs after a normal...
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Buffers: Buffer Capacity01:09

Buffers: Buffer Capacity

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Buffer capacity is the quantitative measure of a buffer to resist the change in pH. As shown in the following equation, the buffer capacity, denoted by 'beta', is expressed as the number of moles of acid or base needed to change the pH of a one-liter buffer solution by 1 unit. Here, Ca and Cb indicate the number of moles of acid and base, respectively. Note that dpH represents the change in pH.
In the graph, pH is plotted as a function of the number of moles of base (Cb) added to a weak...
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Heat Capacity: Problem-Solving01:17

Heat Capacity: Problem-Solving

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The heat capacity of a gas is the amount of heat energy required to raise the temperature of a unit mass of gas by one degree Celsius. It is an important thermodynamic property of gases, and its determination is essential in many industrial and scientific applications. Here are the steps to solve problems related to the heat capacities of gases:
Determine the type of gas: The heat capacity of a gas depends on its molecular structure and the degree of freedom of its molecules. Different types of...
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Related Experiment Video

Updated: Jan 26, 2026

The Modified Temptation Resistance Task: A Paradigm to Elicit Children's Strategic Lie-telling
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Pattern-based strategic surgical capacity allocation.

Miao Bai1, Kalyan S Pasupathy1, Mustafa Y Sir1

  • 1Department of Health Sciences Research, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, United States.

Journal of Biomedical Informatics
|April 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a mathematical model for optimizing operating room (OR) capacity allocation among surgeons. The approach improves OR utilization and reduces daily OR needs, supporting efficient surgical scheduling.

Keywords:
Data-drivenMathematical optimizationPattern-basedSurgery capacity allocation

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Surgical Workflow Optimization

Background:

  • Efficient operating room (OR) capacity allocation is vital for surgical workflow and staff coordination.
  • Current OR allocation challenges include achieving schedule cyclicity, accommodating surgeon preferences, and managing shared ORs.

Purpose of the Study:

  • To develop a mathematical optimization model for coordinating OR capacity allocation among surgeons.
  • To improve the utilization of surgical capacity and enhance predictability in surgical scheduling.

Main Methods:

  • Proposed a mathematical optimization model incorporating capacity allocation patterns for cyclicity and surgeon preferences.
  • Developed a data-driven approach for coordinating shared OR usage based on historical data.
  • Applied the methodology to a case study at Mayo Clinic's surgical division.

Main Results:

  • The proposed approach demonstrated substantial potential in reducing the maximum daily OR allocation.
  • Achieved significant improvements in OR utilization with minimal overtime.
  • The model provided solutions in under 0.5 seconds, indicating practical applicability.

Conclusions:

  • The developed methodology offers an effective decision support tool for optimizing OR capacity allocation.
  • The approach balances schedule cyclicity, surgeon preferences, and OR sharing for improved efficiency.
  • This optimization can lead to significant resource savings in surgical divisions.