Hai Li

Jan 24, 2025 | sportsmedicine-open.springeropen.com | Hai Li |Xin Wei |Steve Chen |Haiquan Qin |Hongke Jiang |Majed M. Alhumaid | +3 more

While the effects of sleep deprivation on cognitive function are well-documented, its impact on high-intensity endurance performance and underlying neural mechanisms remains underexplored, especially in the context of search and rescue operations where both physical and mental performance are essential. This study examines the neurophysiological basis of sleep deprivation on high-intensity endurance using electroencephalography (EEG). In this crossover study, twenty firefighters were subjected to both sleep deprivation (SD) and normal sleep conditions, with each participant performing endurance treadmill exercise the following morning after each condition. EEG signals were recorded before and after high-intensity endurance exercise, and power spectrum analysis and functional connectivity analysis were performed on sleep related frequency bands rhythm: δ (0.5–4 Hz) and θ (4–8 Hz). The EEG power spectral and functional connectivity were measured by repeated measure analysis of variance. The SD condition had an average sleep duration of 3.78 ± 0.69 h, while the duration for normal sleep was 7.63 ± 0.52 h. After high-intensity endurance exercise, the SD condition had a higher maximum heart rate (p < 0.05) and shorter exercise time (p < 0.05) than normal sleep. Compared with before exercise, the δ band in the left parietal lobe P7 channel increased significantly (p < 0.01), and the θ band in the central Cz channel and the left and right parietal lobe P7 and P8 channel increased significantly (p < 0.01 & p < 0 0.05) in SD and normal sleep conditions after exercise. After exercise, compared with normal sleep, the δ band power in occipital O1 and Oz channels and parietal P7 and TP7 channels in SD significantly decreased (p < 0.05 & p < 0.01); the power of the θ band decreased significantly in the occipital O1 channel, central CZ channel and the left and right parietal P7 and P8 channel (p < 0.05 & p < 0.01). Whole connectivity showed a significant increase (p = 0.001) in the δ band for the SD condition at post-exhaustion. Local connectivity analysis identified a localized network in the δ band with reduced (p < 0.001) post-exhaustion in the SD condition displaying inter-hemispheric differences in certain connections (FP1-CP4, T7-C4, T7-TP8, and O1-FT8) and intra-hemispheric (C3-CPz and Pz-P4) variations. Sleep deprivation significantly reduced maximum endurance performance, indicating decreased neural activity in the central and parietal brain regions. Alterations in δ and θ frequency band power, along with disrupted connectivity, may highlight the neurophysiological basis underlying this decline. • Sleep deprivation significantly impairs high-intensity endurance performance, demonstrated by reduced time to exhaustion and increased maximum heart rate compared to normal sleep conditions. • Sleep-deprived states result in a marked decrease in δ and θ band activity within the central and parietal brain regions, which are essential for motor control and endurance capacity. • Sleep deprivation diminishes the efficiency of information transmission within the δ band network, suggesting disrupted connectivity that may hinder cognitive and motor processes critical to sustaining performance.

Dec 16, 2024 | mdpi.com | Hai Li

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May 6, 2024 | link.aps.org | Haotian Cao |Beijing Normal |Hai Li |Zihao Mi

Abstract We present the complete spectrum for the Bjorken x weighted energy-energy correlation in the deep inelastic scattering (DIS) process, from the target fragmentation region to the current fragmentation region, in the Breit frame. The corresponding collinear and transverse momentum-dependent logarithms are resummed to all orders with the accuracy of NLL and N3LL, respectively. The results in the full region are matched with an O(αs2) fixed-order calculation.

Dec 23, 2023 | mdpi.com | Feng Tian |Jian Zou |Hai Li |Liping Han

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Dec 7, 2023 | nature.com | Bokyung Kim |Hai Li

A 3D stackable computing-in-memory array that is based on resistive random-access memory could accelerate the implementation of machine learning algorithms. The development of artificial intelligence (AI) models has led to remarkable results in complex tasks such as recognizing speech, categorizing images and detecting objects1. To achieve this, AI models have evolved from having thousands of parameters2 to billions of parameters.