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method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
complement based » complement past (Expand Search), complement cascade (Expand Search), complement system (Expand Search)
coding algorithm » cosine algorithm (Expand Search), modeling algorithm (Expand Search), finding algorithm (Expand Search)
level coding » level according (Expand Search), level modeling (Expand Search), level using (Expand Search)
element » elements (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
complement based » complement past (Expand Search), complement cascade (Expand Search), complement system (Expand Search)
coding algorithm » cosine algorithm (Expand Search), modeling algorithm (Expand Search), finding algorithm (Expand Search)
level coding » level according (Expand Search), level modeling (Expand Search), level using (Expand Search)
element » elements (Expand Search)
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261
YOLOv8 model architecture diagram.
Published 2025“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”
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262
FLMP-YOLOv8 architecture diagram.
Published 2025“…To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. …”
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263
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264
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265
Code and data for evaluating oil spill amount from text-form incident information
Published 2025“…<h2>Dataset and Code Description</h2><p dir="ltr">This repository includes the code and data for evaluating oil spill amounts from incident textual information. …”
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266
Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence"
Published 2025“…<p dir="ltr">This thesis bridges the gap between field observations and the empirical understanding of ecological systems by applying AI models at different spatial scales and hierarchical levels to study ecological complexity. The detailed implementation, source code and demo dataset are included in dedicated folders for each chapter.…”
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267
Image 5_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg
Published 2025“…</p>Conclusions<p>We have established RIs for six trace elements for children, and the methods we use provide reference for laboratories around the world.…”
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268
Image 3_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg
Published 2025“…</p>Conclusions<p>We have established RIs for six trace elements for children, and the methods we use provide reference for laboratories around the world.…”
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269
Image 2_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg
Published 2025“…</p>Conclusions<p>We have established RIs for six trace elements for children, and the methods we use provide reference for laboratories around the world.…”
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270
Image 4_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg
Published 2025“…</p>Conclusions<p>We have established RIs for six trace elements for children, and the methods we use provide reference for laboratories around the world.…”
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271
Table 1_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.docx
Published 2025“…</p>Conclusions<p>We have established RIs for six trace elements for children, and the methods we use provide reference for laboratories around the world.…”
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272
Image 6_Age- and sex-specific reference intervals for trace elements in infants and children: a multi-center study in Lincang, China.jpg
Published 2025“…</p>Conclusions<p>We have established RIs for six trace elements for children, and the methods we use provide reference for laboratories around the world.…”
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273
Measurement parameters of five BF.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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274
Parameters required in DEM simulation.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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275
BP neural network topology structure.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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276
Raw materials obtained from BFs.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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277
Schematic diagram of the JKR bonding model.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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278
Particle size distribution of arrow BF.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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279
Particle size distribution of palm BF.
Published 2024“…To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. …”
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280
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
Published 2025“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”