As an essential subsystem of electrified railway operation and maintenance, intelligent detection of catenary support components still faces several critical challenges: (1) the number of abnormal (negative) samples for components is severely limited; (2) component anomalies are highly diverse and exhibit heterogeneous visual characteristics; and (3) existing models generally show unsatisfactory detection performance when confronted with previously unseen anomaly types. To address these issues, this paper proposes a novel few-shot anomaly detection model for catenary components, termed BCLIP-ADer, built upon a Bayesian prompt contrastive vision-language pretraining framework. Specifically, a Bayesian prompt flow module (PFM) is designed to regularize the text prompt space via the jointly learned image-specific feature distribution (ISFD) and image-agnostic feature distribution (IAFD), thereby mitigating the degradation in detection performance on unseen component anomalies. Monte Carlo sampling over these learned distributions is further employed to generate diverse text prompts, leading to more comprehensive coverage of the prompt space. In addition, a cross-modal feature refinement module (CFRM) is designed to more effectively align dynamic text embeddings with fine-grained image features, thus enhancing anomaly detection at the component level. Finally, extensive experiments conducted on a UAV-based catenary dataset (CSCUD) demonstrate the effectiveness and superiority of the proposed approach. Specifically, the proposed method achieves I-AUROC/I-AP/I-F1_max scores of 94.2/93.2/93.1 under few-shot conditions.

Constructed wetlands (CWs) have been proven to effectively immobilize plastic particles. However, little is known about the differences in the impact of varying sized plastic particles on nitrous oxide (N₂O) release, as well as the intervention mechanisms in CWs. Here, we built a lab-scale wetland model and introduced plastic particles of macro-, micro-, and nano-size at 100 μg/L for 370 days. The results showed that plastic particles of all sizes reduced N₂O release in CWs, with the degrees being the strongest for the Nano group, followed by Micro and Macro groups. Meanwhile, ¹⁵N- and ¹⁸O-tracing experiment revealed that the ammoxidation process contributed the most N₂O production, followed by denitrification. While for every N₂O-releasing process, the contributing proportion of N₂O in nitrification-coupled denitrification were most significantly cut down under exposing to macro-sized plastics and had an obvious increase in nitrifier denitrification in all groups, respectively. Finally, we revealed the three mechanism pathways of N₂O release reduction with macro-, micro-, and nano-sized plastics by impacting carbon assimilation (RubisCO activity), ammonia oxidation (gene amo abundance and HAO activity), and N-ion transmembrane and reductase activities, respectively. Our findings thus provided novel insights into the potential effects of plastic particles in CWs as an eco-technology.

The online identification of rock damage states is crucial for safety monitoring in geotechnical and mining engineering. By analyzing spatiotemporal evolution patterns of infrared radiation in various rock damage states, we established the first infrared temperature field dataset for rock damage state identification. We then constructed a deep convolutional neural network, RESD-CNN, and performed its training and optimization. Results showed that infrared radiation patterns of different rock samples exhibit similarities. RESD-CNN achieved outstanding performance in identifying rock damage states with metrics of ACC 99.04%, Precision 99.39%, Recall 99.52%, and F1-score 99.46% on the validation set. Generalization tests on datasets of different rock types revealed that RESD-CNN significantly outperformed traditional classification methods, demonstrating the feasibility of infrared radiation technology for intelligent coal rock damage identification. This research provides a crucial foundation for developing online identification and early warning systems for rock damage evolution in engineering.

Superior thermal quenching and degradation of phosphors are required for long lifetime lighting devices, such as light-emitting diodes, which can be realized through composition modification. Here, Al-N bonds in AlON: Eu²⁺ phosphors are substituted by higher bond order of Si-C. Photoluminescence (PL) results show thermal quenching (at 150 °C) and thermal degradation (after 600 °C treatment in air) are improved by 5% and 8% with a small decrease of PL intensity in 5% SiC doped AlON: Eu²⁺ phosphor. To explain these observations, first-principles computational study was performed to understand the Si and C configuration in AlON:Eu²⁺. The calculations reveal that Si and C elements are not randomly distributed in AlON lattice. It was found that Si prefers occupying tetrahedral sites (Td-Si) and the insertion of C in Td-Si is always energetically favorable, which results in the formation of SiC₄ and SiNC₃ clusters. Thus, the Al-N substitution by Si-C induces a stronger local structure, which accounts for the emission redshift and better thermal stability.