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1601
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1602
Influencing factors of APCA.
Published 2024“…When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile.…”
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1603
Temporal trends in APCA.
Published 2024“…When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile.…”
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1604
Tornado diagram of one-way sensitivity analysis.
Published 2025“…With Zvandiri, ART adherence improves, decreasing annual probability of virological failure or death. …”
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1605
Zvandiri Markov Model Parameters.
Published 2025“…With Zvandiri, ART adherence improves, decreasing annual probability of virological failure or death. …”
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1606
Four‐state Markov model parameter inputs.
Published 2025“…With Zvandiri, ART adherence improves, decreasing annual probability of virological failure or death. …”
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1607
Traditional Markov transfer probability matrices.
Published 2024“…When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile.…”
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1608
Spatial Markov transfer probability matrix.
Published 2024“…When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile.…”
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1609
Three-dimensional kernel density of APCA.
Published 2024“…When the spatial lag type is high-level advantage, the probability of stabilization of the low-level advantage decreases from 68.42% to 0.00%. 3) Nonfarm payrolls have the largest dampening effect at the 40% quantile.…”
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1610
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1611
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1612
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1613
Navigation error analysis.
Published 2025“…The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. …”
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1614
Summary of related works.
Published 2025“…The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. …”
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1615
Research methodology flow diagram.
Published 2025“…The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. …”
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1616
Positioning error analysis.
Published 2025“…The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. …”
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1617
Error-Bar graph.
Published 2025“…The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. …”
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1618
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1619
SHAP dependence plots with interaction coloring.
Published 2025“…<div><p>Objective</p><p>As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. …”
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1620
Screening process diagram.
Published 2025“…<div><p>Objective</p><p>As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. …”