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: Work-related musculoskeletal disorders (WMSDs) are a key topic in occupational health. In the primary prevention of these disorders, interventions to minimize exposure to work-related physical risk factors are widely advocated. Besides interventions aimed at the work organisation and the workplace, interventions are also aimed at the behaviour of workers, the so-called individual working practice (IWP). At the moment, no conceptual framework for interventions for IWP exists. This study is a first step towards such a framework.METHODS: A scoping review was carried out starting with a systematic search in Ovid Medline, Ovid Embase, Ovid APA PsycInfo, and Web of Science. Intervention studies aimed at reducing exposure to physical ergonomic risk factors involving the worker were included. The content of these interventions for IWP was extracted and coded in order to arrive at distinguishing and overarching categories of these interventions for IWP.RESULTS: More than 12.000 papers were found and 110 intervention studies were included, describing 810 topics for IWP. Eventually eight overarching categories of interventions for IWP were distinguished: (1) Workplace adjustment, (2) Variation, (3) Exercising, (4) Use of aids, (5) Professional skills, (6) Professional manners, (7) Task content & task organisation and (8) Motoric skills.CONCLUSION: Eight categories of interventions for IWP are described in the literature. These categories are a starting point for developing and evaluating effective interventions performed by workers to prevent WMSDs. In order to reach consensus on these categories, an international expert consultation is a necessary next step.KEYWORDS: Work related risk factors, Occupational training, Ergonomic interventions, Musculoskeletal diseases, Prevention and control
The 3D Additivist Cookbook, devised and edited by Morehshin Allahyari & Daniel Rourke, is a free compendium of imaginative, provocative works from over 100 world-leading artists, activists and theorists. The 3D Additivist Cookbook contains .obj and .stl files for the 3D printer, as well as critical and fictional texts, templates, recipes, (im)practical designs and methodologies for living in this most contradictory of times.
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