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Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.

Neuromuscular fatigue can be characterized by an exercise-induced reduction in force-generating capacity involving both neural and muscular mechanisms. Previous research has suggested that the functional organization of the neuromuscular system differs between endurance-trained individuals and sedentary or recreationally active individuals. This difference may lead to distinct neuromuscular responses to fatigue. The aim of this study was to compare neuromuscular fatigue responses between endurance-trained (ET) and recreationally active (RA) males during a sustained submaximal isometric knee extension contraction. Eleven ET and 11 RA participants performed maximal voluntary isometric contractions (MVIC) of the knee extensors (KE), followed by a trapezoidal contraction (ascending to 60% MVIC) and an isometric fatiguing task at 30% MVIC sustained to task failure. An additional MVIC was completed immediately post-fatigue task. High-density surface electromyography (HDsEMG) was simultaneously recorded from the vastus lateralis, and HDsEMG root mean square (RMS), median frequency (MDF) and muscle fibre conduction velocity (MFCV) were estimated. The MFCV-torque relationship during the ascending phase of the trapezoidal contraction (up to 60% MVIC) was assessed using the mixed linear model. Baseline MVIC of the KE did not differ between groups. The ET group showed a significantly lower rate of increase in MFCV (  < 0.001) during the ascending phase of the contraction and lower absolute MFCV at 60% MVIC (  < 0.001) compared to the RA group. During the sustained fatiguing task, both groups reached task failure at similar times with comparable MVIC reductions (∼25%). However, the RA group exhibited significant declines in both MDF (  < 0.001) and MFCV (  = 0.04), whereas these parameters remained unchanged in the ET group. While ET and RA individuals exhibited similar levels of fatigue, the underlying neuromuscular mechanisms may differ. The ET group showed a lower rate of increase in MFCV with increasing voluntary force and unchanged MFCV and MDF during fatiguing contractions, whereas the RA group exhibited fatigue-induced decreases in both MFCV and MDF. These findings suggest that endurance training is associated with altered recruitment and/or muscle membrane properties, likely linked to differences in muscle fibre characteristics.

The rapid advancement of soft robotic technology emphasizes the growing importance of tactile perception. Soft grippers, equipped with tactile sensing, can gather interactive information crucial for safe human–robot interaction, wearable devices, and dexterous manipulation. However, most soft grippers with tactile sensing abilities have limited modes of tactile perception, restricting their dexterity and safety. In addition, existing tactile systems are often complicated, leading to unstable perception signals. Inspired by various organisms, a novel multimodal tactile‐sensing soft robotic finger is proposed. This finger, based on a modified fin ray structure, integrates a distributed fiber optic sensing system as part of its tactile sensory neural system. It replicates human finger capabilities, discerning contact forces as low as 0.01 N with exceptional sensitivity (106.96 mN nm−1). Through training neural networks models, the finger achieves an accuracy exceeding 96% in recognizing roughness, material stiffness, and finger pad position. Assembled into two‐finger parallel gripper, it demonstrates precise manipulation capabilities for fragile items like strawberries and potato chips. Moreover, through synergistic interplay of multimodal tactile sensing, this finger can successfully grasp an underwater transparent sphere, mitigating limitations of visual perception. The developed soft finger holds promise in various scenarios including hazardous environment detection and specialized grasping tasks. Soft grippers with tactile sensing gather vital interactive data for human–robot interaction safety. Inspired by diverse biological mechanisms, a multimodal tactile sensing soft robotic finger is reported. This finger can offer precise force detection, surface roughness perception, contact position sensing, and material stiffness discrimination. It holds promise for applications in space explorationand dexterous grasping.

This study aimed to investigate the effect of training background on neuromuscular responses to submaximal isometric fatiguing contraction. Eleven strength‐trained (ST) and 11 endurance‐trained (ET) males performed maximal voluntary isometric knee extension contractions (MVIC) and a torque steadiness task at 20% MVIC before and immediately after a submaximal isometric contraction at 30% MVIC performed until task failure. High‐density surface EMG was recorded from the vastus lateralis muscle. The EMG root mean square (RMS) amplitude, median frequency (MDF) and muscle fibre conduction velocity (MFCV) were estimated. There was no significant difference in time‐to‐failure (88.2 ± 34.3 vs. 157.6 ± 126.1 s) or post‐fatigue MVIC reduction (19.4% vs. 22.5%) between the ST and ET groups. However, post‐fatigue EMG MDF decreased by 10.8% in ST (p < 0.05), but remained unchanged in ET. Furthermore, only the ET group showed a significant decrease in torque steadiness (p < 0.05) and an increase in physiological tremor (8–12 Hz; p < 0.01) during the post‐fatigue steadiness task. Throughout the fatiguing task, the mixed effects model revealed decreases in MFCV (p < 0.01) and MDF% (p < 0.001) in ST, with no differences observed in the ET group. EMG RMS amplitude increased similarly in both groups during the fatiguing contraction (p < 0.001). Fatiguing exercise induced comparable reductions in MVIC in ST and ET males. However, fatigue differentially affected the neuromuscular strategies adopted by each group when generating maximal strength and maintaining submaximal torque output, possibly reflecting differences in muscle fibre distribution between the two groups. Highlights Fatigue‐induced reductions in maximal knee extensor strength were similar between endurance‐ and strength‐trained males. Endurance‐ and strength‐trained males may adopt distinct neuromuscular strategies during sustained submaximal isometric contractions. Strength‐trained males demonstrated a better ability maintain force steadiness following the isometric fatiguing contraction task.

Geological disasters refer to adverse geological phenomena that occur under the influence of natural or human factors and cause damage to human life and property. Establishing prevention and control zones based on geological disaster risk assessment results in land planning and management is crucial for ensuring safe regional development. In recent years, there has been an increase in extreme rainfall events, so it is necessary to conduct effective geological hazard and risk assessments for different extreme rainfall conditions. Based on the first national geological disaster risk survey results, this paper uses the analytic hierarchy process (AHP) combined with the information method (IM) to construct four extreme rainfall conditions, namely, 10-year, 20-year, 50-year, and 100-year return periods. The susceptibility, hazard, vulnerability, and risk of geological disasters in the Laoshan District in eastern China are evaluated, and prevention and control zones are established based on the evaluation results. The results show that: (1) There are 121 collapse geological disasters in Laoshan District, generally at a low susceptibility level. (2) A positive correlation exists between extreme rainfall and hazards/risks. With the rainfall condition changing from a 10-year return period to a 100-year return period, the proportion of high-hazard zones increased from 20% to 41%, and high-risk zones increased from 31% to 51%, respectively. The Receiver operating characteristic (ROC) proved that the assessment accuracy was acceptable. (3) Key, sub-key, and general prevention zones have been established, and corresponding prevention and control suggestions have been proposed, providing a reference for geological disaster prevention and early warning in other regions.

An imbalance between excitation (E) and inhibition (I) in the brain has been identified as a key pathophysiology of epilepsy over the years. The hippocampus and amygdala in the limbic system play a crucial role in the initiation and conduction of epileptic seizures and are often referred to as the transfer station and amplifier of seizure activities. Existing animal and imaging studies reveal that the hippocampus and amygdala, which are significant parts of the vagal afferent network, can be modulated in order to generate an antiepileptic effect. Using stereo-electroencephalography (SEEG) data, we examined the E/I imbalance in the hippocampus and amygdala of ten drug-resistant epilepsy children treated with acute vagus nerve stimulation (VNS) by estimating the 1/f power slope of hippocampal and amygdala signals in the range of 1–80 Hz. While the change in the 1/f power slope from VNS-BASE varied between different stimulation amplitudes and brain regions, it was more prominent in the hippocampal region. In the hippocampal region, we found a flatter 1/f power slope during VNS-ON in patients with good responsiveness to VNS under the optimal stimulation amplitude, indicating that the E/I imbalance in the region was improved. There was no obvious change in 1/f power slope for VNS poor responders. For VNS non-responders, the 1/f power slope slightly increased when the stimulation was applied. Overall, this study implies that the regulation of E/I imbalance in the epileptic brain, especially in the hippocampal region, may be an acute intracranial effect of VNS.

The growing interest in the flexibility and operational capabilities of soft manipulators in confined spaces emphasizes the need for precise modeling and accurate motion control. Conventional control methods encounter difficulties in modeling and involve intricate computations. This work introduces a novel deep reinforcement learning (DRL) control algorithm based on neural network modeling. Using the Whale Optimization Algorithm, an approximate dynamic model for the soft manipulator is established. The twin delayed deterministic policy gradient is employed for DRL control. Domain randomization is applied during pretraining in a simulated environment. The algorithm addresses issues related to dependency on measurement data quality and redundant mappings, outperforming other methods by 8–15 mm in control accuracy. The trained DRL controller achieves precise trajectory tracking within the soft manipulator's task space, enabling successful grasping tasks in various complex environments, including pipelines and other narrow spaces. Experimental results confirm the autonomy of our controller in performing these tasks without human intervention. This work proposes a modified twin delayed deterministic policy gradient algorithm in combination with long short‐term memory neural networks to control a soft manipulator. Multiscenario experiments are carried out to validate its effectiveness, such as pipeline operation and delicate object grasping.

The combination of deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS) is expected to provide additional insights into the pathophysiology of some brain diseases. However, when using TMS in patients with DBS implants, the induced voltage between DBS electrodes presents the greatest risk of brain damage. This paper describes the characteristics of the induced DBS electrode voltage due to TMS. We first examined the TMS stimulus signal and the DBS output impedance characteristics, and then experimentally investigated the induced DBS electrode voltage for various DBS and TMS conditions. The results show that many factors impact the induced electrode voltage. The induced electrode voltage with DBS device working in the unipolar mode is greater than that with DBS device working in the bipolar mode. No matter DBS device is turned on or turned off, the induced electrode voltage is almost the same, but it can provide a significant addition to the original stimulus waveform. There are no significant differences in the induced DBS electrode voltage when the DBS system is working at different stimulus intensities. Lowering the TMS stimulus intensity could effectively reduce the induced DBS electrode voltage. The induced electrode voltage is also strongly related to the position of the TMS coil relative to the DBS lead. This study provides further information about the characteristics of the induced DBS electrode voltage in TMS applications and a reference for the combined use of DBS and TMS.

To study the thermal and mass distribution in proton exchange membrane electrolytic cells (PEMEC), a two-phase numerical model of a single-channel electrolytic cell was established. The polarization performance and gas-heat distribution in a single-channel electrolytic cell under five different flow channel cross-sections (rectangle, trapezoid, inverted trapezoid, triangle, and circle) were studied. The findings indicate that the inverted trapezoidal flow channel’s gas buildup effect is rather severe in both the catalytic and diffusion layers. In the trapezoidal flow channel, the temperature of the proton exchange membrane region is lower, and the electrochemical performance of the rectangular flow channel electrolytic cell is the best, with an improvement rate of up to 11.47%. The electrochemical performance of the triangular and circular flow channels is the worst.

Astronaut＇s body mass is an essential factor of health monitoring in space. The latest mass measurement device for the Interna- tional Space Station （ISS） has employed a linear acceleration method. The principle of this method is that the device generates a constant pulling force, and the astronaut is accelerated on a parallelogram motion guide which rotates at a large radius to achieve a nearly linear trajectory. The acceleration is calculated by regression analysis of the displacement versus time trajec- tory and the body mass is calculated by using the formula m=F/a. However, in actual flight, the device is instable that the de- viation between runs could be 6-7 kg. This paper considers the body non-rigidity as the major cause of error and instability and analyzes the effects of body non-rigidity from different aspects. Body non-rigidity makes the acceleration of the center of mass （C.M.） oscillate and fall behind the point where force is applied. Actual acceleration curves showed that the overall effect of body non-rigidity is an oscillation at about 7 Hz and a deviation of about 25%. To enhance body rigidity, better body re- straints were introduced and a prototype based on linear acceleration method was built. Measurement experiment was carried out on ground on an air table. Three human subjects weighing 60-70 kg were measured. The average variance was 0.04 kg and the average measurement error was 0.4%. This study will provide reference for future development of China＇s own mass measurement device.