With the widespread application of high-speed and heavy-load railways, the real-time detection of track surface defects has become increasingly crucial. To address the challenges in rail defect detection, this study proposes the PerMSCA-YOLO model, which aims to overcome the limitations of traditional object detection models in multi-scale, small target, and complex background scenarios. By incorporating the lightweight FasterNet backbone network, a multi-scale convolutional attention module, and perceptual loss, the proposed model significantly enhances the detection accuracy and robustness of track defects. Experimental results show that PerMSCA-YOLO achieves an mAP@0.5 of 0.856, an F1-score of 0.79, and an inference frame rate of 142 FPS, demonstrating superior detection accuracy and real-time performance compared to other mainstream models like YOLOv8n. Furthermore, the model exhibits strong adaptability and efficiency when dealing with complex track defects, such as microcracks and corrosion patches, indicating its broad practical application potential. The innovative contribution of this research lies in its effective strategy for improving detection accuracy and real-time performance through multi-scale feature fusion and deep semantic alignment mechanisms, providing a solution that balances both precision and efficiency for defect detection in complex track environments, with substantial engineering application potential.

Timely prediction of memory failures is crucial for the stable operation of data centers. However, existing methods often rely on a single classifier, which can lead to inaccurate or unstable predictions. To address this, we propose a new ensemble model for predicting CE-driven memory failures, where failures occur due to a surge of correctable errors (CEs) in memory, causing server downtime. Our model combines several strong-performing classifiers, such as Random Forest, LightGBM, and XGBoost, and assigns different weights to each based on its performance. By optimizing the decision-making process, the model improves prediction accuracy. We validate the model using in-memory data from Alibaba’s data center, and the results show an accuracy of over 84%, outperforming existing single and dual-classifier models, further confirming its excellent predictive performance.

The utilization of downhole optical cables has significantly enhanced the efficiency and reliability of oilfield production operations; however, the challenging high-temperature and high-pressure conditions prevalent in oil-gas fields markedly reduce the service lifespan of these optical cables. This limitation severely impedes their application and further development in subterranean environments. In this study, a qualitative analysis was conducted on the structural materials utilized in two types of optical cables to identify these materials and assess the high-temperature tolerance and aging resistance properties of the optical fibers incorporated within. It was discovered that hydrogen infiltration into the subterranean optical cables predominantly accounts for their operational failure. To address this issue, an optical loss testing platform was established, facilitating the execution of a high-temperature and high-pressure hydrogen permeation aging experiment on the optical fibers, allowing for the evaluation of the hydrogen resistance capabilities of the two types of optical fibers. The findings from this study provide a theoretical foundation and methodological guidance for the optimization of optical fibers, aiming to enhance their durability and functional performance in adverse environmental conditions encountered in oil-gas field applications.

With the increasingly busy transportation of cargo at container terminals (CTs), the requirements for terminal throughput and operational efficiency are constantly increasing. The operational efficiency and cost of CTs are closely related to the seamless docking of terminal facilities, especially the joint operation between berths and quay cranes (QCs). Therefore, a joint allocation problem of berths and QCs (BACASP) is presented in this paper and formalized as a mathematical model to minimize terminal operation costs and shipowner dissatisfaction. Given that BACASP is an NP-hard problem, an improved multi-objective cuckoo search (IMOCS) algorithm is proposed to solve this problem, in which an elite-guided tangent flight strategy is presented to speed up the convergence for making up the lack of random search direction of the traditional cuckoo search algorithm; and an information-enhanced abandonment strategy is put forward to increase the possibility of escaping from local optima. Numerical experimental results show the effectiveness of the proposed algorithm.

Extracting entities and relations, as a crucial part of many tasks in natural language processing, transforms the unstructured text information into structured information and provides corresponding data support for knowledge graph (KG) and knowledge vault (KV) construction. Nevertheless, the mainstream relation-extraction methods, the pipeline method and the joint method, ignore the dependency between the subject entity and the object entity. This work introduces a pre-trained BERT model and a dilated gated convolutional neural network (DGCNN) as an encoder to distinguish the long-range semantics representation from the input sequence. In addition, we propose a cross-attention neural network as a decoder to learn the importance of each subject word for each word of the input sequence. Experiments were undertaken with two extensive datasets, the New York Times Corpus (NYT) and WebNLG Corpus, and showed that our model performs significantly better than the CasRel model, outperforming the baseline by 1.9% and 0.7% absolute gain in terms of F1-score.