This study introduces a novel electrocardiogram (ECG) arrhythmia classification model, PathFusion-Net, which integrates Rough Path Theory with deep learning technologies. The model combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Path Signatures, and Path Development to extract spatial morphological features from ECG images and multi-order temporal representations from ECG signals. By adopting an inter-patient split paradigm, our approach more closely reflects real-world clinical diagnostic settings compared to intra-patient methods. The model demonstrates state-of-the-art overall classification performance on both the MIT-BIH Arrhythmia Database and a private clinical dataset, achieving 94.7% and 95.1% accuracy, respectively, under the AAMI four-class standard with an inter-patient split paradigm. On the MIT-BIH dataset, the proposed method attains competitive precision and recall across multiple arrhythmia types, including 95.2% /87.9% for ventricular ectopic beats (V) and 75.7% /92.3% for supraventricular ectopic beats (S), indicating balanced performance across clinically diverse categories. This research highlights the potential of Rough Path Theory in time-series analysis and offers a novel deep learning framework for automated early detection and monitoring of ECG arrhythmias. The code used in this study is available at: https://github.com/Rand2AI/PathFusion-Net.

Labeling large amounts of medical data is travailing, leading to the blooming of few-shot medical image segmentation, which aims to segment the foreground of a query image given a labeled support set. Almost all current models adopt the cosine distance to measure the similarity between prototypes and query features. However, the limitation of the cosine distance is exacerbated by intra-class differences and inter-class imbalances in medical image scenarios, where angle-only evaluation can induce misclassification to under- and over-segmentation. Motivated by this, we propose a High-Confidence Prior Mask-guided Network (HCPMNet), comprising a High-Confidence Mask Generator (HCPMG), a Target Region Mining (TRM) module, and a Prototype-Oriented Expansion Match (POEM) module. Our HCPMNet offers key advantages: 1) HCPMG is the first to combinatively evaluate angle and magnitude similarity, generating high-confidence priori masks that accurately and completely localize target regions. 2) TRM mines and aggregates target class information under the guidance of priori masks. 3) POEM, based on both similarity metrics, correctly matches prototypes with query features. Extensive experiments on three general medical datasets show that our HCPMNet achieves a new SoTA with great superiority.

With the recent advancement of science and technology, the modern industry-oriented competency requirements in mechanical engineering demand higher education for essential computer based knowledge. Production processes in modern factories these days, for example, mostly are controlled and operated by computers but current mechanical engineering curriculum doesn't provide such knowledge. In this paper, we proposed an interdisciplinary curriculum that is designed for undergraduate mechanical engineering major students so they may meet the competency requirements for the industry. An ideal industrial production process management method, where computer networking and cloud computing concepts are considered in the design, was proposed to illustrate such needs. The proposed method is designed to solve the problems that the modern production industries are experiencing. The proposed curriculum is designed to provide such computer knowledge to the mechanical engineering major students, which attempts to satisfy the modern industry-oriented competency requirements.