Ultra-Weak Polarization-Strain Coupling Effect Boosts Capacitive Energy Storage.

In pulse power systems, multilayer ceramic capacitors (MLCCs) encounter significant challenges due to the heightened loading electric field (E), which can lead to fatigue damage and ultrasonic concussion caused by electrostrictive strain. To address these issues, an innovative strategy focused on achieving an ultra-weak polarization-strain coupling effect is proposed, which effectively reduces strain in MLCCs. Remarkably, an ultra-low electrostrictive coefficient (Q33) of 0.012 m4 C-2 is achieved in the composition 0.55(Bi0.5Na0.5)TiO3-0.45Pb(Mg1/3Nb2/3)O3, resulting in a significantly reduced strain of 0.118% at 330 kV cm-1. At the atomic scale, the local structural heterogeneity leads to an expanded and loose lattice structure, providing ample space for large ionic displacement polarization instead of lattice stretching when subjected to the applied E. This unique behavior not only promotes energy storage performance (ESP) but also accounts for the observed ultra-low Q33 and strain. Consequently, the MLCC device exhibits an impressive energy storage density of 14.6 J cm-3 and an ultrahigh efficiency of 93% at 720 kV cm-1. Furthermore, the superior ESP of the MLCC demonstrates excellent fatigue resistance and temperature stability, making it a promising solution for practical applications. Overall, this pivotal strategy offers a cost-effective solution for state-of-the-art MLCCs with ultra-low strain-vibration in pulse power systems.

[The effect of narrow band imaging in CO2 laser therapy in early-stage glottic cancer].

Objective:To explore efficacy of narrow band imaging（NBI） technique in CO2laser therapy in Early-Stage Glottic cancer. Methods:The clinical data of patients with Early-Stage Glottic cancer who underwent CO2laser vocal cord resection from June 2011 to August 2022 were retrospectively analyzed. Among these, 27 patients who underwent surgery assisted by NBI were assigned to the observation group, while 25 patients who underwent conventional CO2 laser microsurgery with a suspension laryngoscope were assigned to the control group. The differences between the two groups were analyzed in terms of intraoperative frozen pathology results, postoperative recurrence rates, 5-year cumulative disease-free survival rates, complications, and voice recovery. Results:All 52 patients were operated successfully. Temporary tracheostomy and serious complications did not occur during the operation. The postoperative patient's pronunciation was satisfactory. One patient experienced vocal cord adhesion, but there were no severe complications such as breathing difficulties or bleeding, with an overall complication rate of 1.92%. Postoperative follow-up was 1-5 years. The 5 years recurrence free survival in the general group was 77.90％, and the 5 years recurrence free survival in the NBI group was 100％, the difference was statistically significant（P<0.05）. NBI endoscopy is safer and more accurate than the general group in determining the safe margin of tumor mucosal resection（P<0.05）. Among the patients who accepted the voice analysis, the difference was no statistically significant（P>0.05）. Conclusion:Compared with conventional CO2laser surgery under microscope, NBI guided laser resection of Early-Stage Glottic cancer is more accurate. NBI guided laser resection could improve 5 years recurrence free survival rate. In a word, narrow-band imaging endoscopy can has very high value in clinical application.

Unsupervised Multivariate Feature-Based Adaptive Clustering Analysis of Epileptic EEG Signals.

Supervised classification algorithms for processing epileptic EEG signals rely heavily on the label information of the data, and existing supervised methods cannot effectively solve the problem of analyzing unlabeled epileptic EEG signals. In the traditional unsupervised clustering algorithm, the number of clusters and the global parameters must be predetermined, and the algorithm's analytical results are combined with a huge number of subjective errors, which affects the detection accuracy. For this reason, this paper proposes an unsupervised multivariate feature adaptive clustering analysis algorithm based on epileptic EEG signals. First, CEEMDAN and CWT are introduced into the epileptic EEG signal after preprocessing for joint denoising to further improve the signal quality. Then, the multivariate feature set of the signal is extracted and constructed, which includes nonlinear, time, frequency, and time-frequency characteristics. To reveal the hidden structures and correlations in the high-dimensional feature data, t-SNE dimensionality reduction is introduced. Finally, the DBSCAN clustering algorithm is optimized using the SSA algorithm to achieve adaptive selection of cluster number and global parameters.It not only enhances the clustering performance and reliability of the clustering results, but also avoids subjective errors in the analysis results. It provides a pre-theoretical foundation for the successful development of future seizure prediction devices and has good application prospects in clinical diagnosis and daily monitoring of patients.

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition.

Emotion recognition using the electroencephalogram (EEG) has garnered significant attention within the realm of human-computer interaction due to the wealth of genuine emotional data stored in EEG signals. However, traditional emotion recognition methods are deficient in mining the connection between multi-domain features and fitting their advantages. In this paper, we propose a novel capsule Transformer network based on a multi-domain feature for EEG-based emotion recognition, referred to as MES-CTNet. The model's core consists of a multichannel capsule neural network(CapsNet) embedded with ECA (Efficient Channel Attention) and SE (Squeeze and Excitation) blocks and a Transformer-based temporal coding layer. Firstly, a multi-domain feature map is constructed by combining the space-frequency-time characteristics of the multi-domain features as inputs to the model. Then, the local emotion features are extracted from the multi-domain feature maps by the improved CapsNet. Finally, the Transformer-based temporal coding layer is utilized to globally perceive the emotion feature information of the continuous time slices to obtain a final emotion state. The paper fully experimented on two standard datasets with different emotion labels, the DEAP and SEED datasets. On the DEAP dataset, MES-CTNet achieved an average accuracy of 98.31% in the valence dimension and 98.28% in the arousal dimension; it achieved 94.91% for the cross-session task on the SEED dataset, demonstrating superior performance compared to traditional EEG emotion recognition methods. The MES-CTNet method, utilizing a multi-domain feature map as proposed herein, offers a broader observation perspective for EEG-based emotion recognition. It significantly enhances the classification recognition rate, thereby holding considerable theoretical and practical value in the EEG emotion recognition domain.

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method.

High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbalance between HFOs and false HFOs, it is difficult to obtain satisfactory detection performance by the existing methods. To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors. First, the adjustable threshold of Hilbert envelopes is proposed to isolate the events of interest (EoIs) from background activities. Then, the SDAE network is utilized to automatically extract features of EoIs in the time-frequency domain. Finally, the AdaBoost-based support vector machine ensemble classifier with sample weight adjusting factors is devised to separate HFOs from EoIs by using the extracted features. These adjusting factors are used to solve the class imbalance problem by adjusting sample weights when learning the base classifiers. Our HFO detection method is evaluated by using clinical iEEG data recorded from 20 patients with medically refractory epilepsy. The experimental results show that our detection method outperforms some existing methods in terms of sensitivity and false discovery rate. In addition, the HFOs detected by our method are effective for localizing seizure onset zones.

A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones.

This paper presents a new unsupervised detector for automatically detecting high-frequency oscillations (HFOs) using intracranial electroencephalogram (iEEG) signals. This detector does not presuppose a specific number of clusters and has a good performance. First, the HFO candidates are detected by an initial detection method which distinguishes HFOs from background activities. Then, as significant features, fuzzy entropy, short-time energy, power ratio, and spectral centroid of the HFO candidates are investigated and constructed as a feature vector. Finally, the feature vector is used as the input of the fuzzy- -means-quantization-error-modeling-based expectation-maximization-Gaussian mixture model clustering algorithm. This algorithm has the advantages of detecting HFOs and avoiding false detection caused by artifacts. The concentrations of detected HFOs are used to localize epileptic seizure onset zones in epileptic iEEG signal analysis. A comparison shows that our detector provides better localization performance in terms of sensitivity and specificity than five existing detectors.

Fast, Accurate Localization of Epileptic Seizure Onset Zones Based on Detection of High-Frequency Oscillations Using Improved Wavelet Transform and Matching Pursuit Methods.

This letter describes the improvement of two methods of detecting high-frequency oscillations (HFOs) and their use to localize epileptic seizure onset zones (SOZs). The wavelet transform (WT) method was improved by combining the complex Morlet WT with Shannon entropy to enhance the temporal-frequency resolution during HFO detection. And the matching pursuit (MP) method was improved by combining it with an adaptive genetic algorithm to improve the speed and accuracy of the calculations for HFO detection. The HFOs detected by these two methods were used to localize SOZs in five patients. A comparison shows that the improved WT method provides high specificity and quick localization and that the improved MP method provides high sensitivity.

[Expression and significance of integrin beta1 and integrin-linked kinase in laryngeal carcinoma].

OBJECTIVE: To study the expressions and clinical significance of integrin beta1 and integrin-linked kinase(ILK) in laryngeal carcinoma (LSCC). METHOD: The mRNA and protein levels of integrinbeta1 and integrin-linked kinase in 43 specimens of laryngeal carcinoma, its surrounding tissue and 18 specimens of vocal cord polyp were detected by reverse transcription-polymerase chain reaction (RT-PCR) and immunohistochemistry SABC method. RESULT: The mRNA levels of integrin beta1 and ILK protein were significantly higher in laryngeal carcinoma than that in its surrounding tissue and vocal cord polyp (P < 0.05). There was significant correlation between the levels of integrin beta1 expression and the status of cervical lymph node, either between the level of ILK expression and cervical lymph node status (P < 0.05). There was significant correlation between the levels of integrin beta1 expression and T-category, either between the level of ILK expression and T-category (P < 0.05). But pathological grades was not significantly related with the level of integrin beta1 or ILK expression (P > 0.05). CONCLUSION: The expressions of integrin beta1 and ILK are increased in LSCC. Integrin beta1 and ILK may contribute significantly to invasion and metastasis of LSCC.

[The expression and significance of integrin beta1 and focal adhesion kinase and its clinical value in laryngeal carcinoma].

OBJECTIVE: To study the expressions and clinical significance of integrin beta1 and focal adhesion kinase (FAK) in laryngeal carcinoma (LSCC). METHOD: The mRNA and protein levels of integrin beta1 and focal adhesion kinase and the surrounding tissue of laryngeal carcinoma in 48 specimens and 20 specimens of vocal cord polyp were detected by reverse transcription-polymerase chain reaction (RT-PCR) and immunohistochemistry SABC method. RESULT: The mRNA levels and positive rates of integrin beta1 and FAK protein were significantly higher in laryngeal carcinoma than that in the surrounding tissue and vocal cord polyp (P < 0.05). The expression levels of integrin beta1 and FAK were significantly higher in the group with cervical lymph node metastasis than those without (P < 0.05), and they were significantly higher in the tissue of stage of T3 and T4 than those of T1 and T2 (P < 0.05). But pathological grades was not significantly related with the expression levels of integrin beta1 or FAK (P > 0.05). CONCLUSION: The expression levels of integrin beta1 and FAK were increased in LSCC, and they may contribute significantly to invasion and metastasis of LSCC.