Blockchain technology has revolutionized the management of Unmanned Aerial Vehicle (UAV) networks by enhancing security, enabling decentralized control, and improving operational efficiency. This study assesses the efficiency of private blockchain architectures in UAV networks, specifically examining important performance metrics such as throughput, latency, scalability, and packet size. Furthermore, we evaluate the effectiveness of UAV networks when integrating private blockchain technologies, focusing particularly on key performance indicators such as area, altitude, and data rate. The scope of our work includes extensive simulations that employ a private blockchain to assess its impact on UAV operations. In the blockchain network, throughput decreased as the number of UAVs and transactions increased, while delay remained constant up to a certain point. In contrast, the UAV network saw improved throughput but increased delay with more UAVs and transactions. Changes in area and altitude had little impact on the blockchain network but increased delays in the UAV network. Higher data rates enhanced the UAV network by reducing latency and improving throughput, though this effect was less pronounced in the blockchain network. The aforementioned results highlight the potential and limitations of private blockchains in enhancing the durability and efficiency of UAV networks.

Artificial intelligence (AI) has demonstrated success across various industries; however, its adoption in aviation remains limited due to concerns regarding the interpretability of AI models, which often function as black box systems with opaque decision-making processes. Given the safety-critical nature of aviation, the lack of transparency in AI-generated predictions poses significant challenges for industry stakeholders. This study investigates the classification performance of multiple supervised machine learning models and employs SHapley Additive exPlanations (SHAPs) to provide global model explanations, identifying key features that influence decision boundaries. To address the issue of class imbalance in the Australian Transport Safety Bureau (ATSB) dataset, a Variational Autoencoder (VAE) is also employed for data augmentation. A comparative evaluation of four machine learning algorithms is conducted for a three-class classification task:—Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and a deep neural network (DNN) comprising five hidden layers. The results demonstrate a competitive performance across accuracy, precision, recall, and F1-score metrics, highlighting the effectiveness of explainable AI techniques in enhancing model transparency and fostering trust in AI-driven aviation safety applications.

We aimed to explore TREM-1 activation in pregnant women who has preeclampsia through the measurement of its soluble form sTREM. A prospective cohort study was conducted. Participants were recruited...

The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive.

This study presents a comparative analysis of four topic modeling techniques —Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), Probabilistic Latent Semantic Analysis (pLSA), and Non-negative Matrix Factorization (NMF)—applied to aviation safety reports from the ATSB dataset spanning 2013–2023. The evaluation focuses on coherence, interpretability, generalization, computational efficiency, and scalability. The results indicate that NMF achieves the highest coherence score (0.7987), demonstrating its effectiveness in extracting well-defined topics from structured narratives. pLSA performs competitively (coherence: 0.7634) but lacks the scalability of NMF. LDA and BERTopic, while effective in generalization (perplexity: −6.471 and −4.638, respectively), struggle with coherence due to their probabilistic nature and reliance on contextual embeddings. A preliminary expert review by two aviation safety specialists found that topics generated by the NMF model were interpretable and aligned well with domain knowledge, reinforcing its potential suitability for such aviation safety analysis. Future research should explore new hybrid modeling approaches and real-time applications to enhance aviation safety analysis further. The study contributes to advancing automated safety monitoring in the aviation industry by refining the most appropriate topic modeling techniques.

Aviation safety analysis increasingly relies on extracting actionable insights from narrative incident reports to support risk identification and improve operational safety. Topic modeling techniques such as Probabilistic Latent Semantic Analysis (pLSA) and BERTopic offer automated methods to uncover latent themes in unstructured safety narratives. This study evaluates the effectiveness of each model in generating coherent, interpretable, and semantically meaningful topics for aviation safety practitioners and researchers. We assess model performance using both quantitative metrics (topic coherence scores) and qualitative evaluations of topic relevance. The findings show that while pLSA provides a solid probabilistic framework, BERTopic leveraging transformer-based embeddings and HDBSCAN clustering produces more nuanced, context-aware topic groupings, albeit with increased computational demands and tuning complexity. These results highlight the respective strengths and trade-offs of traditional versus modern topic modeling approaches in aviation safety analysis. This work advances the application of natural language processing (NLP) in aviation by demonstrating how topic modeling can support risk assessment, inform policy, and enhance safety outcomes.