Neuromuscular Blocking Drugs in Man

Smith, S. E.

doi:10.1007/978-3-642-45476-9_9

Cited by 110 publications

(2 citation statements)

References 303 publications

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“…All cited bib entries are printed at the end of this article: [3], [4], [5], [6], [7], [8], [9], [10], [11], [12] and [13].…”

Section: Details On Reference Citationsmentioning

confidence: 99%

Comparing Ground-Based Lightning Detection Networks Near Wildfire Points-of-Origin

Hatchett,

Nauslar,

Brown

2024

Preprint

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Lightning detection and attribution to wildfire ignitions is a critical component of fire management worldwide to both reduce hazards of wildfire to exposed values-at-risk and to enhance the potential for wildland fire to provide resource benefits in fire-adapted ecosystems.We compared two operational ground-based lightning detection networks used by fire managers to identify cloud-to-ground strokes within operationally-relevant distances (1.6 km) of the origins of 4,408 western United States lightning-ignited wildfires spanning May-September 2020. Applying two sets of constraints--varying holdover time and applying a quality control measure--we found strokes were co-detected near 55--65% of fires, increasing to 65--79% for detection by at least one network, with neither network detecting lightning near 1,024--1,666 fires.Because each network detected strokes near 136--376 unique fires, use of both networks is suggested to increase the probability of identifying potential fire starts. Given the number of fires with network-unique detections and no detections be either network, improvements in lightning detection networks are recommended given increasing fire hazard.

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“…All cited bib entries are printed at the end of this article: [3], [4], [5], [6], [7], [8], [9], [10], [11], [12] and [13].…”

Section: Details On Reference Citationsmentioning

confidence: 99%

Comparing Ground-Based Lightning Detection Networks Near Wildfire Points-of-Origin

Hatchett,

Nauslar,

Brown

2024

Preprint

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“…Neuro-Cognitive Collaborative Filtering (NCCF) generates personalized learning paths based on neuro-collaborative filtering [6],cognitive level diagnosis [7], and learner goals. We correlate the neuro-collaborative filtering model with the cognitive level diagnosis model, and input the output of the neuro-collaborative filtering model as one of the input features along with the learner's historical behavioral data into the cognitive level diagnosis model.…”

Section: Neuro-cognitive Collaborative Filterinmentioning

confidence: 99%

Personalized Learning Path Generation Based On Neuro-cognitive Collaborative Filtering

Qu,

Wang,

Zhang

et al. 2024

Preprint

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With the rapid development of information technology in education, teaching resources have increased dramatically and the teaching environment has improved. However, resource overload and learning disorientation are challenges faced by learners. This study proposes a personalized learning path generation model based on neurocognitive collaborative filtering technology. By analyzing learners' personalized static parameters and dynamic learning behavior data, explicit and implicit weak knowledge points are identified, and optimized learning paths are generated. The empirical results show that the method significantly improves learners' learning outcomes and satisfaction over traditional methods. This study promotes the development of personalized learning design and effective learning support systems with important implications for educational practice.

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Muscle Relaxants

Hoover¹

1988

Drugs, Athletes, and Physical Performance

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Neuromuscular Blocking Drugs in Man

Cited by 110 publications

References 303 publications

Comparing Ground-Based Lightning Detection Networks Near Wildfire Points-of-Origin

Comparing Ground-Based Lightning Detection Networks Near Wildfire Points-of-Origin

Personalized Learning Path Generation Based On Neuro-cognitive Collaborative Filtering

Muscle Relaxants

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