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The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In addition to weather factors such as low visibility and crosswinds, human factors also account for a large part of the risk. From the accidents studied in the stochastic report of the Flight Safety Foundation, nearly 70% of the accidents occurred during the approach and landing phases, which were caused by improper control of aircraft energy. Since the information of the flight data recorder (FDR) is regarded as the airline's confidential information, this study calculates the aircraft's energy-related metrics and investigates the influence of non-weather-related factors on unstable approaches through a publicly available source, automatic dependent surveillance-broadcast (ADS-B) flight data. To evaluate the influence of weather- and non-weather-related factors, the outliers of each group classified by weather labels are detected and eliminated from the analysis by applying hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which is utilized for detecting abnormal flights that are spatial anomalies. The deep learning method was adopted to detect and predict unstable arrival flights landing at Taipei Songshan Airport. The accuracy of the prediction for the normalized total energy and trajectory deviation of all flights is 85.15% and 82.11%, respectively. The results show that in different kinds of weather conditions, or not considering the weather, the models have similar good performance. The input features were analyzed after the model was obtained, and the flights detected as abnormal are discussed. [ABSTRACT FROM AUTHOR]

Biomolecular condensates (BCs) are membraneless hubs enriched in proteins and nucleic acids that have become important players in many cellular functions. Uncovering the sequence determinants of proteins for phase separation is important in understanding the biophysical and biochemical properties of BCs. Despite significant discoveries in the last decade, the role of cysteine residues in BC formation and dissolution has remained unknown. Here, to determine the involvement of disulfide crosslinks and their redox sensitivity in BCs, we designed a 'stickers and spacers' model of phase-separating peptides interspersed with cysteines. Through biophysical investigations, we learned that cysteines promote liquid-liquid phase separation in oxidizing conditions and perpetuate liquid condensates through disulfide crosslinks, which can be reversibly tuned with redox chemistry. By varying the composition of cysteines, subtle but distinct changes in the viscoelastic behavior of the condensates were observed. Empirically, we conclude that cysteines are neither stickers nor spacers but function as covalent nodes to lower the effective concentrations for sticker interactions and inhibit system-spanning percolation networks. Together, we unmask the role of cysteines in protein phase behavior and the potential to develop tunable, redox-sensitive viscoelastic materials.