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Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Abstract In the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety.

Multi-channel neural electrodes as a crucial means are of great significance for information exchange between the brain and computers. Herein, we present a 16-channel Si-based active neural probe system that achieves a monolithic integration between the electrodes and circuits in a single probe, making it a standalone integrated electrophysiology recording system. The ASIC prepared on a base (2×2mm2) is a 16-channel analog frontend (AFE) for neural recording, and each channel has a low-noise amplifier (LNA), a bandpass filter (BPF), a buffer and a current bias circuit. The 258 neural signal recording electrodes (22×24μm2) are densely packed on a 50 μm thick, 100 μm wide, and 3 mm long shank. The ASIC of neural probe, internal interconnecting wires are all implemented in commercial SMIC 0.18 μm CMOS technology. The neural probe system achieves a 3.6 μVrms input-referred noise (IRN) in a bandwidth of 1.1Hz-10 kHz, 70.8 μW power consumption, 0.0785 mm2 area per channel, as well as an AFE gain of 58.1 dB Furthermore, the impedances of the Au electrodes can be obtained as 0.5–2.1 MΩ at a frequency of 1 kHz. The functionality of a 16-channel silicon-based neural probe is validated in an in-vivo experiment on lab rats.

Marine biological fouling is a widespread phenomenon encountered by various oceanic ships and naval vessels, resulting in enormous economic losses. Herein, novel 4,5-dichloro-2-octyl-isothiazolone@sodium alginate/chitosan microcapsules (DCOIT@ALG/CS) were prepared through composite gel method using DCOIT as core materials, ALG and CS as shells, and CaCl2 as the cross-linking agent. The formed microcapsules (MCs) with Ag nanoparticles (AgNPs) were then filled in UV-curable polysiloxane (UV-PDMS), followed by UV irradiation to yield UV-PDMS/microcapsules/AgNPs (UV-PDMS/MCs/Ag) composite coatings. The constructed micro–nano dual-scale surface using the MCs and AgNPs improved the antifouling and antibacterial properties of UV-PDMS/MCs/Ag coatings. The as-obtained UV-PDMS/MCs/Ag coatings exhibited a static contact angle of about 160°, shear strength of 2.24 MPa, tensile strength of 3.32 MPa and elongation at break of 212%. The synergistic bacteriostatic effects of DCOIT and AgNPs in UV-PDMS/MCs/Ag coatings resulted in a bactericidal rate of 200 μg ml−1 towards Escherichia coli and Staphylococcus aureus with saturation at 100% within 10 min. In sum, the proposed composite coatings look promising for future marine transportation, pipeline networks and undersea facilities.

The hemispherical shell resonator (HSR) is the core element of the hemispherical resonator gyroscope (HRG), and its frequency mismatch is a key property influencing gyroscope accuracy. Investigating the mechanism of frequency mismatch is vital for improving the quality of HSRs and performance of HRGs. Midsurface radius imperfections and thickness imperfections are two principal causes of frequency mismatch, but their combined effects have rarely been discussed. This paper develops a model to comprehensively analyze the frequency mismatch of HSRs with both radius and thickness imperfections. The model derives a quantitative relation between the frequency mismatch and the two imperfections, and provides principles for evaluating dominant imperfections. To validate the model, we conduct experiments on a batch of 12 HSRs with random geometric imperfections. We apply the model in calculating the theoretical frequency mismatches of these HSRs, and the results agree well with the experimental data. The experiment also confirms that for macro-HSRs, thickness imperfections have a larger impact than radius imperfections, and the frequency mismatch is approximately linear to the fourth thickness harmonic. Our research can be a useful reference for the design and fabrication of HSRs and may open new possibilities for high-precision manufacture of HRGs. [ABSTRACT FROM AUTHOR]

Transactive memory system (TMS), as the powerful collective cognition, is well-documented to support team performance. Given today’s boundaryless era with great uncertainty, managing boundaries has become challenging but critical for teams. However, how TMS would influence teams to engage with external environments through boundary management remains underexplored. Adopting an open-system perspective, we explore how TMS affects team boundary management and further contributes to team performance in uncertain environments. Empirical results from 337 respondents embedded in 56 teams revealed that TMS was positively related to all three types of team boundary management. Among them, boundary spanning and reinforcement was positively associated with team performance, while boundary reinforcement mediated the relationship between TMS and team performance. Lastly, environmental uncertainty was found to play an important moderating role in the TMS - team performance relationship. Accordingly, this research advances the knowledge of how teams would manage while being influenced by their external environments.

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.

Abstract Background Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. Methods We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. Results We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. Conclusion The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.