Search Results (6)

As a forward-looking development in air traffic control (ATC), remote towers rely on virtualized information presentation, which may exacerbate visual fatigue among controllers and compromise operational safety. This study proposes a visual fatigue recognition model based on multimodal physiological signals. A 60-min simulated remote tower task was conducted with 36 participants, during which eye-tracking (ET), electroencephalography (EEG), electrocardiography (ECG), and electrodermal activity (EDA) signals were collected. Subjective fatigue questionnaires and objective ophthalmic measurements were also recorded before and after the task. Statistically significant features were identified through paired t-tests, and fatigue labels were constructed by combining subjective and objective indicators. LightGBM was then employed to rank feature importance by integrating split frequency and information gain into a composite score. The top 12 features were selected and used to train a multilayer perceptron (MLP) for classification. The model achieved an average balanced accuracy of 0.92 and an F1 score of 0.90 under 12-fold cross-validation, demonstrating excellent predictive performance. The high-ranking features spanned four modalities, revealing typical physiological patterns of visual fatigue across ocular behavior, cortical activity, autonomic regulation, and arousal level. These findings validate the effectiveness of multimodal fusion in modeling visual fatigue and provide theoretical and technical support for human factor monitoring and risk mitigation in remote tower environments. Full article

In recent years, with the increasing complexity of air traffic management and the rapid development of automation technology, efficiently and accurately extracting key information from large volumes of air traffic control (ATC) instructions has become essential for ensuring flight safety and improving the efficiency of air traffic control. However, this task is challenging due to the specialized terminology involved and the high real-time requirements for data collection and processing. While existing keyword extraction methods have made some progress, most of them still perform unsatisfactorily on ATC instruction data due to issues such as data irregularities and the lack of domain-specific knowledge. To address these challenges, this paper proposes a Roberta-Attention-BiLSTM-CRF model for keyword extraction from ATC instructions. The RABC model introduces an attention mechanism specifically designed to extract keywords from multi-segment ATC instruction texts. Moreover, the BiLSTM component enhances the model’s ability to capture detailed semantic information within individual sentences during the keyword extraction process. Finally, by integrating a Conditional Random Field (CRF), the model can predict and output multiple keywords in the correct sequence. Experimental results on an ATC instruction dataset demonstrate that the RABC model achieves an accuracy of 89.5% in keyword extraction and a sequence match accuracy of 91.3%, outperforming other models across multiple evaluation metrics. These results validate the effectiveness of the proposed model in extracting keywords from ATC instruction data and demonstrate its potential for advancing automation in air traffic control. Full article

This study addresses the challenges of complex noise and short speech in civil aviation air-ground communication scenarios and proposes a novel speaker identification model, Chrono-ECAPA-TDNN (CET). The aim of the study is to enhance the accuracy and robustness of speaker identification in these environments. The CET model incorporates three key components: the Chrono Block module, the speaker embedding extraction module, and the optimized loss function module. The Chrono Block module utilizes parallel branching architecture, Bi-LSTM, and multi-head attention mechanisms to effectively extract both global and local features, addressing the challenge of short speech. The speaker embedding extraction module aggregates features from the Chrono Block and employs self-attention statistical pooling to generate robust speaker embeddings. The loss function module introduces the Sub-center AAM-Softmax loss, which improves feature compactness and class separation. To further improve robustness, data augmentation techniques such as speed perturbation, spectral masking, and random noise suppression are applied. Pretraining on the VoxCeleb2 dataset and testing on the air-ground communication dataset, the CET model achieves 9.81% EER and 88.62% accuracy, outperforming the baseline ECAPA-TDNN model by 1.53% in EER and 2.19% in accuracy. The model also demonstrates strong performance on four cross-domain datasets, highlighting its broad potential for real-time applications. Full article

Phytoplankton diversity and community characteristics are closely associated with aquatic environmental factors. Understanding these dynamics can provide insights into the ecological health of water bodies. We investigate the spatial and temporal characteristics of phytoplankton communities in 27 drinking water source reservoirs in Shenzhen, China. As a method, we collected samples during the dry season in 2021 and the wet season in 2022, analyzed the alpha and beta diversities of phytoplankton communities, and correlated these with the environmental factors. The results reveal that Cyanobacteria dominate the phytoplankton communities in the Shenzhen reservoirs. Phytoplankton diversity is greater during the dry season. The algal composition varies spatially, and the phytoplankton diversity tends to decrease with increasing eutrophication. A co-occurrence network analysis indicates denser and stronger correlations among phytoplankton nodes during the wet season than dry season. Reservoirs with moderate eutrophication levels exhibit denser nodes and stronger correlations compared to those with low or high eutrophication levels. The chemical oxygen demand, water temperature, pH, and total nitrogen are identified as key influencers of the phytoplankton community structure. Our results contribute to the enhanced understanding of the spatial and temporal dynamics of phytoplankton communities in reservoirs in South China and provides insights into the management and conservation of these drinking water reservoirs. Full article

Maternal folate has been shown to relate to the risk of gestational diabetes mellitus (GDM). However, the existing studies have yielded inconsistent conclusions. The purpose of this study was to systematically review the association between maternal folate status and the risk of GDM. Observational studies up to 31 October 2022 were included. Study characteristics, the means and standard deviations (SDs) of folate levels (serum/red blood cell (RBC)), the odds ratios (ORs) with 95% confidence intervals (CIs) and the time for folate measurement were extracted. Compared with the non-GDM group, serum and RBC folate levels in women with GDM were significantly higher. Our subgroup analysis demonstrated that serum folate levels in the GDM group were significantly higher than in the non-GDM group only in the second trimester. RBC folate levels in the GDM group were significantly higher than in the non-GDM group in the first and second trimesters. Taking serum/RBC folate levels as continuous variables, the adjusted odds ratios of GDM risk showed that increased serum folate concentration rather than RBC folate elevated the risk of GDM. In the descriptive analysis, five studies reported high serum folate levels increased GDM risk, whereas the other five showed no association between serum folate levels and GDM risk. Moreover, the rest three studies pointed out high RBC folate levels increased GDM risk. Altogether we found that the risk of GDM is associated with high serum/plasma and RBC folate levels. Future studies should determine the recommended folic acid cutoff balancing the risk for GDM and fetal malformations. Full article

A Mach-Zehnder interferometer is employed to detect localized corrosion at the metal/solution interface in the potentiodynamic sweep of the iron electrode in solutions. During the electrochemical reactions, local variations of the electrolyte’s refractive index, which correlate with the concentration of dissolved species, change the optical path length (OPL) of the object beam when the beam passes through the electrolyte. The distribution of the OPL difference was obtained to present the concentration change of the metal ions visually, which enable direct evidence of corrosion processes. The OPL difference distribution shows localized and general corrosion during the anodic dissolution of the iron electrode in solutions with and without chloride ions, respectively. This method provides an approach for dynamic detection of localized corrosion at the metal/solution interface. Full article