If brief and easy to use self report screening tools are available to identify frail elderly, this may avoid costs and unnecessary assessment of healthy people. This study investigates the predictive validity of three self-report instruments for identifying community-dwelling frail elderly.
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The decarbonisation of the aviation industry requires strict regulation to align with the EU Green Deal, which aims to make the EU the world’s first climate-neutral region by 2050. EU regulations continuously evolve and impact the key performance indicators (KPIs) used to measure progress towards this ambitious objective. Supported by the Marie Skłodowska-Curie Actions (MSCA) programme, the AZERO project assesses airline reduction commitments to achieve net-zero carbon by 2050. It uses an interdisciplinary approach to map greenhouse gas (GHG) KPIs, evaluate actions taken, and simulate traffic scenarios to estimate feasibility using the System Dynamics method for the timeframes of 2030, 2040, and 2050. This advanced simulation method uses real airline emission data and environmental, social and governance (ESG) report commitments.
MULTIFILE
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of 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 metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
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