Jove
Visualize
Contact Us

Related Concept Videos

Mass Analyzers: Overview01:13

Mass Analyzers: Overview

The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
Atomic Absorption Spectroscopy: Overview01:27

Atomic Absorption Spectroscopy: Overview

Atomic absorption spectroscopy (AAS) is a technique used to analyze elements by measuring electromagnetic radiation (EMR) absorbed by atoms, which causes them to transition to a higher-energy orbit. The most crucial step in AAS is atomization, where the analyte is converted into gas-phase atoms, typically through a flame or furnace. Some of these atoms become thermally excited in the flame, while most remain in the ground state.
When irradiated by EMR of a particular wavelength, these...
Atomic Absorption Spectroscopy: Interference01:25

Atomic Absorption Spectroscopy: Interference

Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
Spectral interference occurs when signals from other elements or molecules overlap with the analyte signal, falsely elevating or masking the analyte's absorbance. This interference can be corrected using Zeeman,...
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Artificial intelligence applications in surgical education and training: a systematic review.

Frontiers in artificial intelligence·2026
Same journal

AI product liability under EU and Canadian laws.

Frontiers in artificial intelligence·2026
Same journal

Statistical limits and conditional complexity in real-world reinforcement learning: a tutorial survey.

Frontiers in artificial intelligence·2026
Same journal

Editorial: Advancing human wellbeing: environment-focused AI technologies.

Frontiers in artificial intelligence·2026
Same journal

Enhancing financial data collection and reporting in small businesses through IoT integration: an exploration of IFRS standard.

Frontiers in artificial intelligence·2026
Same journal

Automatic speech recognition for Telugu: a comparative analysis of Wav2Vec 2.0 model variants and hyperparameter tuning.

Frontiers in artificial intelligence·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 27, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

TEGAA: transformer-enhanced graph aspect analyzer with semantic contrastive learning for implicit aspect detection.

Piyush Kumar Soni1, Radhakrishna Rambola1

  • 1SVKM'S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, India.

Frontiers in Artificial Intelligence
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the Transformer-Enhanced Graph Aspect Analyzer (TEGAA) to improve implicit aspect detection by addressing ambiguity and data challenges. TEGAA significantly enhances performance on benchmark datasets, offering a robust solution for analyzing user reviews.

Keywords:
attention sinkcontext-aware graph attentioncontrastive learningimplicit aspect detectionpyramid poolingtransformer

More Related Videos

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Published on: July 30, 2020

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
08:12

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

Related Experiment Videos

Last Updated: Jun 27, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Published on: July 30, 2020

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
08:12

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Implicit aspect detection models struggle with aspect ambiguity, data imbalance, noisy text, and aspect drift.
  • Existing methods face challenges in accurately identifying unstated aspect categories in user-generated content.

Purpose of the Study:

  • To propose the Transformer-Enhanced Graph Aspect Analyzer (TEGAA), a unified framework to overcome limitations in implicit aspect detection.
  • To enhance the accuracy and robustness of identifying implicit aspects in unstructured text.

Main Methods:

  • Developed a Dynamic Expert Transformer (DET) with Dynamic Adaptive Expert Engine (DAEE) to handle syntactic complexity and contextual noise.
  • Implemented Semantic Contrastive Learning (SCL) to address data imbalance and sparse implicit cues.
  • Utilized a Graph-Enhanced Hierarchical Aspect Detector (GE-HAD) with Attention Sinks and Pyramid Pooling for ambiguity and drift resolution.

Main Results:

  • TEGAA achieved F1-scores above 0.88, precision above 0.89, recall above 0.87, accuracy exceeding 89%, and AUC above 0.89 on benchmark datasets.
  • Demonstrated superior performance over state-of-the-art methods in implicit aspect detection.
  • Showcased effectiveness in resolving implicit aspect ambiguity and handling noisy, imbalanced data.

Conclusions:

  • TEGAA offers a robust and effective framework for implicit aspect detection, outperforming existing approaches.
  • The integrated approach of dynamic expert routing, semantic refinement, and hierarchical graph reasoning addresses key challenges in the field.
  • TEGAA provides a stable and adaptive solution for aspect inference across diverse linguistic scopes and domains.