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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]

Based on the International Classification of Functioning, Disability, and Health (ICF) and the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), The Functioning Disability Evaluation Scale-Adult version (FUNDES-Adult) began development in 2011. The FUNDES-Adult was designed to assess the difficulty level of an individual's activities and participation in daily life. There is a lack of research regarding the profile of activity and participation for the general adult population. The purposes of this study were to establish activity and participation norms for the general adult population in Taiwan and to describe, discuss, and compare the activity and participation profile with other population., Method: A population-based survey was administered in 2013 using a computer-assisted telephone interviewing system (CATI system). Using probability proportional to size (PPS) sampling and systematic sampling with random digit dialing (RDD), 1500 adults from Taiwan's general population were selected to participate in the survey. The FUNDES-Adult with six domains and two dimensions (performance and capability) was used to obtain data on activities and participation levels. A higher domain score indicated higher participation restriction., Results: Approximately 50% of the respondents were male, and the average age of the respondents was 45.23 years. There were no significant differences in the demographic features between the sample and the population. Among the six domains, the self-care domain score was the lowest (least restriction) and the participation domain score was the highest (most restriction). Approximately 90% of the sample scored were less than 15, and only 0.1% scored more than 80. This is the first cross-national population-based survey to assess norms of activity and participation relevant to the general population of Taiwan. As such, the results of this survey can be used as a reference for comparing the activity and participation (AP) functioning of other countries and subgroups., Competing Interests: The authors declare no conflict of interest. The funding agencies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.