High‐resolution fluid dispensing techniques play a critical role in modern digital microfluidics, micro‐biosensing, and advanced fabrication. Though most of existing dispensers can achieve precise and high‐throughput fluid dispensing, they suffer from some inherent problems, such as specially fabricated dispensing micronozzles/microtips, large operating systems, low volume tunability, and poor performance for low surface tension liquids and liquids containing solid/liquid additives. Herein, the authors propose a facile, low‐frequency micro dispensing technique based on the Rayleigh–Plateau instability of singular liquid jets, which are stimulated by the air cavity collapse arising in the impact of microliter drops on non‐wetting surfaces. This novel dispensing strategy is capable to produce single microdrops of low‐viscosity liquids with a tunable volume from picoliters to nanoliters, and the operational surface tension range covers most laboratory solvents. The dispensing function is implemented without using small‐dimension nozzles/tips and enables handling diverse complex liquids. Moreover, the rather simple operating platform allows the integration of the whole dispensing function into a handy portable device with a low cost. Employing this microdispensing technique, the authors have controlled microchemical reactions, handled liquid samples in biological analysis, and fabricated smart materials and devices. The authors envision that this rational microdrop generator would find applications in various research areas.

Most sleep-staging research has focused on developing and optimizing algorithms for sleep scoring, but little attention has been paid to the effect of different electroencephalogram (EEG) derivations on sleep staging. To explore the possible effects of EEG derivations, an automatic computer method was established and confirmed by agreement analysis between the computer and two independent raters, and four fronto-parietal bipolar leads were compared for sleep scoring in rats. The results demonstrated that different bipolar electrodes have significantly different sleep-staging accuracies, and that the optimal frontal electrode for sleep scoring is located at the anterior midline.

For the electroencephalogram (EEG), topographic differences in the long-range temporal correlations would imply that these signals might be affected by specific mechanisms related to the generation of a given neuronal process. So the properties of the generators of various EEG oscillations might be investigated by their spatial differences of the long-range temporal correlations. In the present study, these correlations were characterized with respect to their topography during different vigilance states by detrended fluctuation analysis (DFA). The results indicated that (1) most of the scaling exponents acquired from different EEG derivations for various oscillations were significantly different in each vigilance state; these differences might be resulted from the different quantities and different locations of sleep stage-dependent generators of various neuronal processes; (2) there might be multiple generators of delta and theta over the brain and many of them were sleep stage-dependent; (3) the best site of the frontal electrode in a fronto-parietal bipolar electrode for sleep staging might be above the anterior midline cortex. We suggest that DFA analysis can be used to explore the properties of the generators of a given neuronal oscillation, and the localizations of these generators if more electrodes are involved.

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The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT.