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In this paper, the finite-time synchronization issue is addressed for discontinuous fuzzy neural networks with mixed time-varying delays and impulsive disturbances. The dynamic impulsive gain about the impulsive moment is proposed as an alternative to the previous static impulsive gain. The time-varying impulsive gain can combine the synchronizing and desynchronizing impulses to directly observe their effects on the system, avoiding the classification discussion. In order to study the finite-time synchronization of the drive–response system, we obtain an equivalent system from the original fuzzy neural networks based on the concepts of Filippov solutions and differential inclusion theory. Furthermore, by constructing new Lyapunov functions that combine the 1-norm and the comparative system method, some algebraic sufficient conditions for the synchronization of the drive system and the response system in finite time are established, which are based on two types of novel non-chattering controllers. In addition, the parameters of the system itself can simply demonstrate the sufficient conditions that have been obtained her e, avoiding the time-consuming calculation of matrix inequalities. Moreover, the settling time estimation scheme have been established. Eventually, numerical examples are provided to demonstrate the effectiveness of the control scheme.

BackgroundEfforts to resection of glioma lesions located in brain-eloquent areas must balance the extent of resection (EOR) and functional preservation. Currently, intraoperative direct electrical stimulation (DES) is the gold standard for achieving the maximum EOR while preserving as much functionality as possible. However, intraoperative DES inevitably involves risks of infection and epilepsy. The aim of this study was to verify the reliability of individual-target transcranial magnetic stimulation (IT-TMS) in preoperative mapping relative to DES and evaluate its effectiveness based on postsurgical outcomes.MethodsSixteen language-eloquent glioma patients were enrolled. Nine of them underwent preoperative nTMS mapping (n=9, nTMS group), and the other seven were assigned to the non-nTMS group and did not undergo preoperative nTMS mapping (n=7). Before surgery, online IT-TMS was performed during a language task in the nTMS group. Sites in the cortex at which this task was disturbed in three consecutive trials were recorded and regarded as positive and designated nTMS hotspots (HSnTMS). Both groups then underwent awake surgery and intraoperative DES mapping. DES hotspots (HSDES) were also determined in a manner analogous to HSnTMS. The spatial distribution of HSnTMS and HSDES in the nTMS group was recorded, registered in a single brain template, and compared. The center of gravity (CoG) of HSnTMS (HSnTMS-CoG)-based and HSDES-CoG-based diffusion tensor imaging-fiber tracking (DTI-FT) was performed. The electromagnetic simulation was conducted, and the values were then compared between the nTMS and DES groups, as were the Western Aphasia Battery (WAB) scale and fiber-tracking values.ResultsHSnTMS and HSDES showed similar distributions (mean distance 6.32 ± 2.6 mm, distance range 2.2-9.3 mm, 95% CI 3.9-8.7 mm). A higher fractional anisotropy (FA) value in nTMS mapping (P=0.0373) and an analogous fiber tract length (P=0.2290) were observed. A similar distribution of the electric field within the brain tissues induced by nTMS and DES was noted. Compared with the non-nTMS group, the integration of nTMS led to a significant improvement in language performance (WAB scores averaging 78.4 in the nTMS group compared with 59.5 in the non-nTMS group, P=0.0321 < 0.05) as well as in brain-structure preservation (FA value, P=0.0156; tract length, P=0.0166).ConclusionPreoperative IT-TMS provides data equally crucial to DES and thus facilitates precise brain mapping and the preservation of linguistic function.

GaN high-electron-mobility transistor (HEMT) terahertz (THz) detectors have been widely studied and applied in the past few decades. However, there are few reports about the influence of GaN/AlGaN heterostructure material properties on the detection model at present. In this paper, a response voltage model for a GaN HEMT THz detector that considers the carrier scattering in a GaN/AlGaN heterostructure is proposed. The phonon scattering, dislocation scattering, and interface roughness scattering mechanisms are taken into account in the classic THz response voltage model; furthermore, the influence of various material parameters on the response voltage is studied. In a low-temperature region, acoustic scattering plays an important role, and the response voltage drops with an increase in temperature. In a high temperature range, optical phonon scattering is the main scattering mechanism, and the detector operates in a non-resonant detection mode. With an increase in carrier surface density, the response voltage decreases and then increases due to piezoelectric scattering and optical phonon scattering. For dislocation and interface roughness scattering, the response voltage is inversely proportional to the dislocation density and root mean square roughness (RMS) but is positively related to lateral correlation length. Finally, a comparison between our model and the reported models shows that our proposed model is more accurate.

Suicidal ideation increases precipitously in patients with depression, contributing to the risk of suicidal attempts. Despite the recent advancement in transcranial magnetic stimulation, its effectiveness in depression disorder and its wide acceptance, the network mechanisms of the clinical response to suicidal ideation in major depressive disorder remain unclear. Independent component analysis for neuroimaging data allows the identification of functional network connectivity which may help to explore the neural basis of suicidal ideation in major depressive disorder. Resting-state functional magnetic resonance imaging data and clinical scales were collected from 30 participants (15 major depressive patients with suicidal ideation and 15 healthy subjects). Individual target-transcranial magnetic stimulation (IT-TMS) was then used to decrease the subgenual anterior cingulate cortex activity through the left dorsolateral prefrontal cortex. Thirty days post IT-TMS therapy, seven of 15 patients (46.67%) met suicidal remission criteria, and 12 patients (80.00%) met depression remission criteria. We found that IT-TMS could restore the abnormal functional network connectivity between default mode network and precuneus network, left executive control network and sensory-motor network. Furthermore, the changes in functional network connectivity between the default mode network and precuneus network were associated with suicidal ideation, and depressive symptoms were related to connectivity between left executive control network and sensory-motor network. These findings illustrate that IT-TMS is an effective protocol for the accurate restoration of impaired brain networks, which is consistent with clinical symptoms.

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process.