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Related Concept Videos

Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the progression...

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Related Experiment Video

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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A physics-informed attention model for integrated driving risk assessment.

Tianle Lu1, Gaoyuan Kuang2, Dongyang Xu2

  • 1School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China; Beijing Lixiang Automobile Co., Ltd, China.

Accident; Analysis and Prevention
|October 10, 2025
PubMed
Summary

This study introduces a physics-informed integrated risk assessment model (PIRAM) for autonomous vehicles (AVs). PIRAM improves driving risk prediction accuracy and stability, enabling earlier warnings for enhanced safety.

Keywords:
Autonomous vehicleDriving risk quantificationIntelligent occupant protectionPhysics-informed neural network

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Area of Science:

  • Autonomous Driving Systems
  • Road Safety
  • Artificial Intelligence

Background:

  • Quantifying driving risk is crucial for autonomous vehicle (AV) safety.
  • Accurate risk assessment aids in accident prevention and occupant protection.
  • Existing methods may lack the stability and accuracy needed for real-world AV applications.

Purpose of the Study:

  • To develop a novel physics-informed integrated risk assessment model (PIRAM).
  • To fuse collision probability and severity predictions into a unified integrated driving risk (IDR) metric.
  • To enhance the safety performance and reliability of autonomous vehicles.

Main Methods:

  • Constructed an integrated driving risk prediction dataset (IDRPD) using driving simulator experiments in CARLA.
  • Developed a neural network model incorporating data-driven methods and physics-based constraints.
  • Integrated an attention mechanism for spatiotemporal dependencies and a dynamic bicycle model for physical guidance.

Main Results:

  • PIRAM demonstrated superior performance over baseline models on the IDRPD.
  • Achieved significant improvements in prediction accuracy for collision probability (7.9%) and severity (3.2%).
  • Enhanced prediction stability (10.3% and 5.9%) and provided earlier risk warnings (average 0.5s).

Conclusions:

  • PIRAM offers a reliable quantitative basis for occupant protection in AVs.
  • The model's physics-informed approach significantly improves prediction accuracy and stability.
  • PIRAM holds substantial potential for advancing safety in autonomous driving applications.