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Network Function of a Circuit01:25

Network Function of a Circuit

1.1K
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
1.1K
Block Diagram Reduction01:22

Block Diagram Reduction

734
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
734
Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

1.2K
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
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Net Change Theorem01:22

Net Change Theorem

223
The Net Change Theorem is a fundamental principle in calculus that establishes a direct relationship between a function’s rate of change and its accumulated change over an interval. Mathematically, it states that the definite integral of a function's derivative over a given interval [a,b] yields the net change in the original function:This theorem has significant applications in various real-world scenarios, including physics, economics, and engineering. A particularly useful application...
223
Norton's Theorem01:14

Norton's Theorem

2.1K
Norton's theorem is a fundamental principle stating that a linear two-terminal circuit can be substituted with an equivalent circuit, which comprises a current source (ⅠN) in parallel with a resistor (RN). Here, ⅠN represents the short-circuit current flowing through the terminals, and RN stands for the input or equivalent resistance at the terminals when all independent sources are deactivated. This implies that the circuit illustrated in Figure (a) can be exchanged with the one...
2.1K
Castigliano's Theorem: Problem Solving01:14

Castigliano's Theorem: Problem Solving

1.6K
The deflection of a simply supported beam that carries a central point load can be analyzed using structural mechanics principles, particularly by applying Castigliano's theorem. This theorem relates the displacement at the load application point to the partial derivatives of the strain energy in the structure. The simply supported beam with a point load at its center has symmetric reaction forces at the supports, each bearing half of the load. The bending moment at any point along the beam is...
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Related Experiment Video

Updated: May 5, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

11.5K

Computing preimages of Boolean networks.

Johannes Klotz, Martin Bossert, Steffen Schober

    BMC Bioinformatics
    |November 26, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new algorithm for Boolean networks, efficiently finding network states from outputs. The probabilistic method offers a fast solution for complex biological regulatory networks.

    Related Experiment Videos

    Last Updated: May 5, 2026

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    11.5K

    Area of Science:

    • Computational Biology
    • Network Science
    • Systems Biology

    Background:

    • Boolean networks are widely used to model gene regulatory networks and other biological systems.
    • Identifying the input states (preimages) that lead to a specific output state is a fundamental problem in network analysis.

    Purpose of the Study:

    • To develop an efficient algorithm for finding preimages in feed-forward Boolean networks.
    • To provide a probabilistic method that runs in linear time relative to network size.

    Main Methods:

    • The study employs the sum-product algorithm, a probabilistic inference technique.
    • The algorithm is designed for feed-forward Boolean networks, a specific network topology.

    Main Results:

    • The proposed algorithm successfully finds elements in the preimage of Boolean networks given a specific output.
    • The method demonstrates linear time complexity with respect to the number of nodes.
    • Validation on random networks and the Escherichia coli regulatory network shows high success rates.

    Conclusions:

    • The developed sum-product based algorithm is an efficient and effective tool for preimage identification in Boolean networks.
    • This method has practical applications in analyzing biological regulatory networks, such as those in E. coli.