Search

In this paper we propose a machine learning-based approach to predict a multitude of insurance claim categories related to canine diseases. We introduce several machine learning approaches that are evaluated on a pet insurance dataset consisting of 785,565 dogs from the US and Canada whose insurance claims have been recorded over 17 years. 270,203 dogs with a long insurance tenure were used to train a model while the inference is applicable to all dogs in the dataset. Through this analysis we demonstrate that with this richness of data, supported by the right feature engineering, and machine learning approaches, 45 disease categories can be predicted with high accuracy. [ABSTRACT FROM AUTHOR]

"Short-term load forecasting (STLF)" is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario's electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approach as an additional decision-making tool to improve their real-time decisions. [ABSTRACT FROM AUTHOR]

• Column height is the parameter with more impact on longitudinal displacements. • Deck skew is the parameter with lesser influence on the seismic response. • Minimum required support lengths by actual codes are found to be very conservative. • The period is a more accurate parameter to predict longitudinal displacements. • An equation is proposed to estimate the required support length from the period. Loss of bridge deck supports is one of the major failures observed during past earthquakes. Deck collapse due to loss of support has been observed mainly on simply supported bridge structures with insufficient support length at expansion bearings. To accommodate the large displacements induced by seismic movements and to avoid bridge collapse, modern seismic standards and regulations require a minimum support length at expansion deck bearings. This paper presents the state of knowledge on the empirical support length equation and the main geometric parameters influencing the required support length at deck expansion bearings. It also presents the results of the parametric study performed on typical existing single and multi-span simply supported bridges subjected to seismic loads typical of Eastern Canada. The results allowed to identify and describe the key geometric parameters influencing the nonlinear responses of existing bridges, evaluate their relative influence in terms of displacements and ductility associated with the loss of deck supports. It also compares the current empirical equation with predicted displacements and proposes an equation more suitable for predicting expected displacements (lower than code values) in an evaluation framework for judging the seismic vulnerability of existing such as the bridge models considered in this study. [ABSTRACT FROM AUTHOR]