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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.

Zhaobin Kuang1, Yujia Bao2, James Thomson3

  • 1The University of Wisconsin, Madison, WI, USA. zkuang@wisc.edu.

Methods in Molecular Biology (Clifton, N.J.)
|December 15, 2018
PubMed
Summary
This summary is machine-generated.

We developed a new computational model for drug repurposing using electronic health records. This method analyzes drug prescriptions and patient measurements to find new uses for existing medications.

Keywords:
Computational drug repurposingElectronic health recordsLongitudinal dataSelf-controlled case seriesSilico repurposing

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Area of Science:

  • Computational biology
  • Pharmacology
  • Health informatics

Background:

  • Electronic health records (EHRs) contain longitudinal data on patient prescriptions and physical measurements.
  • Identifying new therapeutic uses for existing drugs (drug repurposing) is a cost-effective strategy in pharmaceutical research.

Purpose of the Study:

  • To introduce a novel baseline regularization model for computational drug repurposing.
  • To leverage statistical relationships within EHR data for identifying potential drug repurposing opportunities.

Main Methods:

  • The model analyzes temporal correlations between drug prescriptions and changes in numeric physical measurements recorded in EHRs.
  • Statistical relationships are identified to link specific drug occurrences with increases or decreases in physiological parameters.

Main Results:

  • The baseline regularization model establishes a framework for drug repurposing using real-world EHR data.
  • The approach facilitates the discovery of potential new indications for existing drugs based on observed patient data patterns.

Conclusions:

  • Baseline regularization offers a data-driven approach to uncover novel drug repurposing candidates.
  • This methodology holds promise for advancing precision medicine and optimizing drug utilization.