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Related Concept Videos

Network Covalent Solids02:18

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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IP3/DAG Signaling Pathway

Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and produces two-second...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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Porosity and Absorption of Aggregate

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Related Experiment Videos

A degradation-aware and boundary-constrained network for coal-rock interface recognition.

Yunfen Qiao1,2, Shujing Su3, Weijie Qiao4

  • 1State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, School of Instrument and Electronics, North University of China, Taiyuan, 030051, China.

Scientific Reports
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces CRFormer, a novel network for coal-rock interface recognition in challenging mining conditions. It improves accuracy and efficiency for safer, greener coal extraction.

Keywords:
Boundary lossCoal-rock interface recognitionDegradation-aware moduleUnderground coal mine

Related Experiment Videos

Area of Science:

  • Geological Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Coal-rock interface recognition is vital for intelligent and green coal mining, impacting safety and resource use.
  • Underground coal mine images suffer from low contrast, blur, and weak textures due to poor illumination and dust.
  • Current methods fail to balance recognition accuracy with computational efficiency.

Purpose of the Study:

  • To develop an efficient and accurate coal-rock interface recognition network for challenging underground mining environments.
  • To address limitations of existing methods in handling degraded image quality and achieving practical deployment.

Main Methods:

  • Proposed CRFormer network with a degradation-aware feature enhancement module to compensate for shallow feature degradation.
  • Implemented a stage-wise hybrid attention mechanism for efficient global context modeling.
  • Introduced a boundary-constrained auxiliary loss to improve interface continuity and localization accuracy.

Main Results:

  • Achieved high performance on a self-built dataset with 98.97% pixel accuracy (PA) and 97.92% mean Intersection over Union (mIoU).
  • CRFormer demonstrated a superior balance between recognition accuracy and inference speed compared to existing models.
  • The degradation-aware module effectively enhanced texture and structure representation at the coal-rock interface.

Conclusions:

  • CRFormer offers a robust solution for coal-rock interface recognition in adverse mining conditions.
  • The network's efficiency and accuracy support practical on-site deployment for intelligent coal mining.
  • The proposed methods contribute to advancing safety and resource utilization in underground coal mines.