Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This gap in reliable fault detection necessitates more advanced methodologies to improve system stability and safety. Here, a novel approach, using learning spiking neural P systems combined with a normalized least mean squares (NLMS) algorithm to enhance faulty line detection in small-current, grounded systems, is proposed. The proposed method analyzes the features of current and voltage signals, as well as active and reactive power, by separately considering their transient and steady-state components. To improve fault detection accuracy, we quantified the likelihood of a fault occurrence based on feature changes and expanded the feature space to higher dimensions using an ascending dimension structure. An adaptive learning mechanism was introduced to optimize the convergence and precision of the detection model. Simulation scheduling datasets and real-world data were used to validate the effectiveness of the proposed approach, demonstrating significant improvements over traditional methods. These findings provide a robust framework for faulty-line detection in small-current, grounded systems, contributing to enhanced reliability and safety in power system operations. This approach has the potential to be widely applied in power system protection and maintenance, advancing the broader field of intelligent fault diagnosis.

The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection.

The coupled effects of the contact wire (CW) working surface profile and ambient humidity on the contact strip (CS) wear were investigated using a high-speed current-carrying friction and wear test rig. The experimental results revealed that the CW’s working surface profile determined the CS wear law of different friction pairs under high ambient humidity. Under low ambient humidity, when the current was less than or equal to 200 A, the ambient humidity determined the CS wear law; after the current exceeded 200 A, the change in the CW working surface profile became the decisive factor. Interestingly, when the current surpassed 300 A, the CW working surface profile significantly influenced the CS wear law.

In artificial breeding of forest musk deer (Moschus berezovskii), common diseases such as abscess, enteritis, pneumonia, and parasitic infections exhibit persistently high morbidity rates. The early symptoms of certain diseases are often insidious and difficult to discern. Conventional manual inspection routines not only fail to achieve accurate diagnosis but also frequently disturb the animals, induce stress responses, and consequently delay optimal treatment windows. To address this practical challenge, this study employs an improved BRW-GPLinker joint entity-relationship extraction approach to perform integrated extraction and structural organization of disease entities, symptom manifestations, etiological associations, and preventive and therapeutic measures from farming literature and clinical records, thereby constructing a disease knowledge graph for forest musk deer. Through the introduction of a Boundary-Aware Module for refined entity boundary detection, a Relative Distance Bias Module to mitigate pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy loss function to enhance recall for infrequent relations, the proposed model achieves an F1 score of 0.887 on a self-constructed dataset and demonstrates favorable generalization capability on medical-domain datasets. By transforming fragmented clinical logs and manuals into structured medical associations, this knowledge graph facilitates rapid retrieval of forest musk deer disease information, thereby enhancing veterinary decision-making efficiency and assisting forest musk deer health management.

This paper investigates the underlying cause of inner rail corrugation on large-radius curved tracks in metro systems. A dynamic model of the vehicle–track system (VTS) was developed to analyze the creep characteristics between the guiding wheelset and the rails when the vehicle negotiates large-radius curves under coasting, traction, and braking conditions. A finite element-based complex eigenvalue analysis was conducted to evaluate the stability of the wheel–rail frictional system. The results reveal that under coasting conditions, the wheel–rail creep forces on large-radius curves remain unsaturated, substantially reducing the likelihood of corrugation formation. In contrast, during braking, the creep force may approach saturation on the guiding inner wheel, increasing the possibility of wheel–rail sliding. This braking-induced sliding can trigger friction-induced self-excited vibrations at the wheel–rail interface, leading to the development of inner rail corrugation on large-radius curves.

The expansion of captive breeding of the forest musk deer (Moschus berezovskii)—a first-class protected species in China with significant economic and medicinal value—has led to escalating disease threats, underscoring the need for intelligent disease management based on knowledge graphs. However, the unstructured nature of veterinary texts, characterized by nested entities, ambiguous boundaries, and sparse relations, poses substantial challenges to accurate joint entity-relation extraction. To address these issues, this study proposes BRW-GPLinker, an enhanced joint extraction model built upon the GPLinker framework. This model integrates a Boundary-Aware Module (BAM) for precise entity boundary detection, a Relative Distance Bias Module (RDBM) to minimize pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy (WSMCE) loss function to improve recall for infrequent relations. Experimental results on the constructed MS-Data dataset demonstrate that BRW-GPLinker achieves an F1 score of 0.887, outperforming the baseline GPLinker by 2.0 percentage points. It also exhibits strong generalization, with an F1 score of 0.590 on the general-domain CMeIE-V2 dataset. These findings confirm that the proposed model provides reliable support for constructing disease knowledge graphs in forest musk deer farming.