Perineural adhesions resulting from surgery, trauma, or chronic inflammation can impair nerve mobility, promote fibrosis, and contribute to neuropathic pain and autonomic dysfunction. Effective minimally invasive therapeutic strategies for established neural adhesions remain limited. This study aimed to establish reproducible rat models of nerve adhesion and neuropathic pain and to evaluate the therapeutic efficacy of acupotomy in alleviating adhesion-related pathological and inflammatory changes. Sciatic nerve chronic constriction injury (SCCI) and vagus nerve adhesion (V-CCI) models were established in rats. Acupotomy treatment was applied to designated groups following adhesion formation. In the S-CCI model, mechanical allodynia was evaluated using the von Frey filament test. Histological analyses, including Masson's trichrome staining, were performed to assess collagen deposition, inflammation, and adhesion severity. In the V-CCI model, systemic inflammation was assessed by measuring plasma levels of pro-inflammatory cytokines (TNF-alpha, IL-6, and IL-1 beta), along with similar histological evaluations. Acupotomy significantly attenuated mechanical allodynia in S-CCI animals, as evidenced by recovery of paw withdrawal threshold ratios following chronic sciatic nerve constriction. Histological analyses demonstrated reduced collagen deposition, fibrosis, inflammatory cell infiltration, and adhesion severity in both S-CCI and V-CCI models following acupotomy treatment. In the V-CCI model, acupotomy significantly decreased circulating IL-1 beta, IL-6, and TNF-alpha levels compared with untreated animals, suggesting attenuation of systemic inflammatory responses. Acupotomy effectively alleviated perineural fibrosis, inflammatory changes, and neuropathic pain-related mechanical allodynia while modulating systemic inflammation in experimental neural adhesion models. These findings support acupotomy as a promising minimally invasive therapeutic strategy for the management of adhesion-related neural disorders.
The advanced brain–computer interface (BCI) uses electroencephalography (EEG) to decode motor imagery (MI) tasks with applications in stroke rehabilitation. Convolutional neural networks (CNNs) have enhanced this technology using end-to-end algorithms in recent years. However, the MI-BCI paradigms display subject (humans) variability (SV) owing to the variable EEG characteristics. This paper proposes a contrastive attention network (CANet) to address SV and limited training data issues for subject-dependent and subject-independent (SI) evaluations. Specifically, CANet leverages the invariant characteristics of pooling layers to modify the spatial attention module by adding intraclass invariance (ICI) to interclass discrimination (ICD). Moreover, the channel attention module is modified by leveraging the equivariant characteristics of convolutional layers to add ICD to ICI. These modifications were considered because of the EEG’s intensive SV characteristics. The novel hetero-epitaxial attention provides robust refined features for SI evaluation. These mechanisms embed contrastive characteristics to mitigate SV along multiple dimensions using limited training data. The effectiveness of CANet was evaluated using two public MI-BCI datasets through quantitative performance comparisons and qualitative visualization of learned feature representations. The results demonstrate competitive performance and improved robustness under reduced training-data conditions, while t-SNE visualization indicates complementary contributions of the proposed spatial and channel attention branches to feature compactness and class separation.
Reliable fault detection and health monitoring of industrial robots are important for smart manufacturing in Industry 4.0. However, robotic platforms differ in their mechanical design, sensing characteristics, and operating conditions. These differences make it difficult for data-driven models to generalize to unseen robots and working conditions. To address this challenge, this paper proposes a cross-robot framework for open-set fault detection using only normal data from the target robot. The framework uses a condition-normalized residual adaptation mechanism in the latent space. First, a structured representation is learned from the source robot. The learned representation is then adapted to the target robot using only normal samples. Compactness-based constraints and statistical modeling of normal behavior are used to detect unknown faults as deviations from the normal representation. The framework is evaluated on two industrial robots under variations in load, speed, and motion. Two evaluation strategies are also considered: sequential normal-only adaptation to welding motion and direct open-set detection without further adaptation. The results show consistent fault detection across different robots and operating conditions. A comparative benchmarking analysis further demonstrates the effectiveness of the proposed approach and its ability to maintain reliable fault detection performance under different loading and motion conditions. This demonstrates the potential of the framework for practical industrial health monitoring.
Two-dimensional (2D) semiconductors enable atomically thin channels and attractive electrostatics, but practical scaling increasingly hinges on gate-dielectric integration rather than channel performance. A key challenge is forming high-quality dielectrics on chemically inert, dangling-bond-free 2D surfaces while pushing equivalent oxide thickness to the sub-nanometer regime without excessive leakage, traps, or electrical breakdown. This review addresses the materials and process physics that govern dielectric formation in 2D devices, with an emphasis on atomic layer deposition nucleation, surface pretreatment and functionalization, and the use of seed and buffer layers for conformal high-κ oxides. The roles of layered insulators, such as hexagonal boron nitride, are discussed in terms of interface quality, electrostatic scaling limits, and transport limitations. The impact of dielectrics and processing on leakage mechanisms, defect generation, device-to-device variability, and reliability metrics, including time-dependent dielectric breakdown, bias-temperature instability, hysteresis, and threshold-voltage drift, is examined. Finally, we highlight van der Waals dry integration and dielectric transfer approaches that reduce process-induced damage and support wafer-scale uniformity, as well as opportunities for mixed-dimensional and 3D stacked architectures across logic, memory, and emerging functional systems.
We investigated whether the relationship between partisan YouTube channel use and conspiracy mentality depends on like-minded discussion and interest in politics. Using a two-wave panel survey conducted around South Korea's 2024 general election (Wave 1: N = 1,550; Wave 2: N = 908) and lagged regression models controlling for prior conspiracy mentality, partisan YouTube channel use showed no direct association with conspiracy mentality. The association between partisan channel use and conspiracy mentality strengthened as like-minded discussion increased, and this pattern weakened as political interest increased. The three-way pattern emerged for liberal partisan channels but not for conservative partisan channels, indicating ideological asymmetry in the conditional association. Overall, the findings suggest that partisan YouTube channel use relates to conspiracy mentality mainly when users frequently engage in like-minded discussions and have lower interest in politics. The results also highlight that interaction and discussion settings are central on the YouTube platform for understanding when partisan channel use is associated with conspiracy mentality.
