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261
Edge device performance benchmarking.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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262
Typical error cases.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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263
Computational efficiency comparison.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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264
Backbone comparison in crack detection.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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265
Statistical analysis table for ablation tests.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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266
Summary of false positives and false negatives.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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267
PR parameter comparison chart.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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268
RAK-Conv.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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269
Structure of YOLOv8n.
Published 2025“…Additionally, the model achieves a 6.55% reduction in size and a 0.03% decrease in computational complexity. These results highlight the practical applicability and efficiency of the proposed approach for automatic crack detection in building structures, emphasizing the novel integration of feature fusion and attention mechanisms to address challenges in real-time and high-accuracy detection of micro-cracks in complex environments.…”
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270
Underlying data for the main figures.
Published 2024“…Loss of RBM-26 function causes a dramatic overexpression of <i>mals-1</i> mRNA and MALS-1 protein. …”
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271
Underlying data for the supplemental figures.
Published 2024“…Loss of RBM-26 function causes a dramatic overexpression of <i>mals-1</i> mRNA and MALS-1 protein. …”
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272
Room-Temperature Self-Healable Glassy Semicrystalline Polymers via Ionic Aggregations
Published 2024“…Semicrystalline polymers constitute the largest fraction of industrial and engineering plastics but are difficult to automatically self-heal in their glassy state due to the frozen molecular chains. …”
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273
Room-Temperature Self-Healable Glassy Semicrystalline Polymers via Ionic Aggregations
Published 2024“…Semicrystalline polymers constitute the largest fraction of industrial and engineering plastics but are difficult to automatically self-heal in their glassy state due to the frozen molecular chains. …”
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274
Room-Temperature Self-Healable Glassy Semicrystalline Polymers via Ionic Aggregations
Published 2024“…Semicrystalline polymers constitute the largest fraction of industrial and engineering plastics but are difficult to automatically self-heal in their glassy state due to the frozen molecular chains. …”
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275
Room-Temperature Self-Healable Glassy Semicrystalline Polymers via Ionic Aggregations
Published 2024“…Semicrystalline polymers constitute the largest fraction of industrial and engineering plastics but are difficult to automatically self-heal in their glassy state due to the frozen molecular chains. …”
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276
R project including metadata update.
Published 2025“…Without such amendments, the accumulation of contradictory species assignments within BINs will continue to rise and the reliability of specimen identification by BOLD will decrease.</p></div>…”
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277
Classes of errors and gaps in BOLD metadata.
Published 2025“…Without such amendments, the accumulation of contradictory species assignments within BINs will continue to rise and the reliability of specimen identification by BOLD will decrease.</p></div>…”
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