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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

193
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
193
Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.0K
The Ideal Transformer01:26

The Ideal Transformer

491
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
491
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Reducing Line Loss01:18

Reducing Line Loss

186
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
186

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Related Experiment Video

Updated: Aug 20, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

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A transformer fine-tuning strategy for text dialect identification.

Mohammad Ali Humayun1, Hayati Yassin1, Junaid Shuja2

  • 1Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, Brunei Darussalam.

Neural Computing & Applications
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new fine-tuning strategy for AI models to identify patient social origins from text, improving online medical consultations. This method enhances Arabic dialect identification accuracy, boosting healthcare communication efficiency.

Keywords:
Arabic languageAuthor profilingDialect identificationText classification

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Healthcare Informatics

Background:

  • Online medical consultations enhance primary care efficiency.
  • Current systems connect patients to consultants based on questions.
  • Linguistic variations in patient queries necessitate improved referral systems.

Purpose of the Study:

  • To propose a novel fine-tuning strategy for pre-trained transformers.
  • To identify the social origin of text authors for better patient-doctor matching.
  • To improve the efficiency of online medical consultation referral systems.

Main Methods:

  • Developed a novel fine-tuning strategy for pre-trained transformer models.
  • Integrated the proposed strategy with an existing adapter model.
  • Evaluated performance on the Nuanced Arabic Dialect Identification (NADI) dataset.

Main Results:

  • Achieved an overall accuracy of 53.96% for Arabic dialect identification.
  • Exceeded the previous best accuracy by 0.54% on the NADI dataset.
  • Demonstrated the utility of custom fine-tuning for transformer models.

Conclusions:

  • Custom fine-tuning strategies are effective for pre-trained transformer models.
  • Social origin identification can enhance online medical consultation referral systems.
  • The proposed method shows promise for improving cross-cultural communication in healthcare.