As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
Background: The concept of Functional Independence (FI), defined as ‘functioning physically safe and independent from other persons, within one’s context”, plays an important role in maintaining the functional ability to enable well-being in older age. FI is a dynamic and complex concept covering four clinical outcomes: physical capacity, empowerment, coping flexibility, and health literacy. As the level of FI differs widely between older adults, healthcare professionals must gain insight into how to best support older people in maintaining their level of FI in a personalized manner. Insight into subgroups of FI could be a first step in providing personalized support This study aims to identify clinically relevant, distinct subgroups of FI in Dutch community-dwelling older people and subsequently describe them according to individual characteristics. Results: One hundred fifty-three community-dwelling older persons were included for participation. Cluster analysis identified four distinctive clusters: (1) Performers – Well-informed; this subgroup is physically strong, well-informed and educated, independent, non-falling, with limited reflective coping style. (2) Performers – Achievers: physically strong people with a limited coping style and health literacy level. (3) The reliant- Good Coper representing physically somewhat limited people with sufficient coping styles who receive professional help. (4) The reliant – Receivers: physically limited people with insufficient coping styles who receive professional help. These subgroups showed significant differences in demographic characteristics and clinical FI outcomes. Conclusions: Community-dwelling older persons can be allocated to four distinct and clinically relevant subgroups based on their level of FI. This subgrouping provides insight into the complex holistic concept of FI by pointing out for each subgroup which FI domain is affected. This way, it helps to better target interventions to prevent the decline of FI in the community-dwelling older population.
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
