Structural variants (SVs) in microbial genomes play a critical role in phenotypic changes, environmental adaptation, and species evolution, with deletion variations particularly closely linked to phenotypic traits. Therefore, accurate and comprehensive identification of deletion variations is essential. Although long-read sequencing technology can detect more SVs, its high error rate introduces substantial noise, leading to high false-positive and low recall rates in existing SV detection algorithms. This paper presents an SV detection method based on graph convolutional networks (GCNs). The model first represents node features through a heterogeneous graph, leveraging the GCN to precisely identify variant regions. Additionally, a knowledge-augmented activation layer (KANLayer) with a learnable activation function is introduced to reduce noise around variant regions, thereby improving model precision and reducing false positives. A clustering algorithm then aggregates multiple overlapping regions near the variant center into a single accurate SV interval, further enhancing recall. Validation on both simulated and real datasets demonstrates that our method achieves superior F1 scores compared to benchmark methods (cuteSV, Sniffles, Svim, and Pbsv), highlighting its advantage and robustness in SV detection and offering an innovative solution for microbial genome structural variation research.

The electrocardiogram (ECG) serves as a crucial tool for myocardial infarction (MI) localization, and deep learning methods have proven effective in assisting physicians with MI localization. Traditional MI localization methods are purely data-driven, and the quality of the data significantly affects the model's performance, particularly in the localization of rare MI. We propose a knowledge-driven graph representation learning (KD-GRL) framework which is designed to guide deep learning models in identifying key features for MI localization using prior knowledge. The MI localization knowledge graph (KG) is constructed by integrating medical knowledge about MI localization, including ECG leads and morphological manifestations, the correlations between MI localization labels, diagnostic rules, and patient demographic information. KG effectively represents the relationships among various entities, which include ECG signal entities, morphological feature entities, and demographic feature entities. The embeddings of these entities are obtained using parallel patient multi-feature extractors. Additionally, a KG aggregation method based on edge relation projection (ERP) is proposed to aggregate the relational information in the MI localization KG. Ultimately, the MI localization task is transformed into a link prediction task between patient entity and localization label entities within the KG. We conduct experiments on two public datasets, PTB and PTBXL, achieving F1-scores of 48.90% and 46.06%, respectively, both surpassing the comparison methods. Additionally, due to the incorporation of diagnostic knowledge, our method outperforms the comparison methods in localizing rare MIs.

To quickly and accurately detect the series arc fault (SAF) in three-phase motor with frequency converter load (TMFCL) circuit, a SAF identification model based on convolutional neural network was proposed. The point-by-point isometric mapping was presented to construct input matrix. The lightweight design of the model was realized, respectively, by using bottleneck building block and depthwise separable convolution. A roofline model was used to analyze the complexity and theoretical runtime of the convolution operators. According to the runtime of the operators, the optimal lightweight SAF identification model was determined and labeled as SAFNet. A SAF on-line detection device was designed by deploying SAFNet to an embedded device. And its performance was evaluated by on-line tests. When the sampling frequency is 2.5 kHz, the accuracy is higher than 99.44%, and the runtime is less than 26.48 ms. It can be used to develop arc fault circuit interrupter for the TMFCL circuit.

Sliding friction pair composed of contact wire and pantograph slide is the key component of train current collection system. Contact temperature is one of the important factors affecting the current-carrying and wear performance of the friction pair. In this article, sliding electrical contact experiments under different experimental conditions were conducted. Based on the measured contour curve of the slide surface, two fractal parameters of the slide surface were calculated by using the structure function method. And the effect of contact current, contact pressure, and sliding speed on the fractal parameters was discussed. A temperature field simulation model of sliding electrical contact considering two rough contact surfaces was established with COMSOL Multiphysics software. The effect of surface roughness and fractal parameters on contact temperature was analyzed by simulation. When other simulation parameters keep constant, the contact temperature first drops and then rises as the average roughness height of the contact surface increases. The average roughness slope of the contact surface has little effect on the contact temperature under high speed and strong current conditions. The contact temperature continuously decreases with the increase of fractal dimension or the decrease of fractal roughness. The conclusions can be used as a basis for further research on the contact temperature characteristics of the friction pair to improve its electrical contact performance.