The aim of this research is to determine the effects of a ten-week modern and recreational dance exercise program and trunk and leg muscle strengthening exercises on the coordination and explosive power of student-age female dancers. The total number of participants was 54, of which 27 made up the experimental group who participated in an experimental exercise program and 27 the control group. The experimental group performed Hip Hop and Dancehall dances and trunk and leg muscle strengthening exercises 3 times a week for 90 min each. The control group had no additional forms of exercise other than regular daily activities. The coordination of the participants was evaluated on the basis of six tests (Side Steps, 20 Steps forward Twirling a Baton, Skipping the Horizontal Jump Rope, Turning in 6 squares, Hand-Foot Drumming and Agility test with a Baton) and two tests for determining explosive power parameters (the squat jump and countermovement jump). Results showed statistical significance between the groups in 5 variables of coordination at the multivariate and univariate level (p<.05, p<.01), and in both variables of explosive power at the univariate level (p<.05). A large and intermediate effect size of the experimental program was determined for 5 variables of coordination, and intermediate effect size for both variables of explosive power. The results of this study showed that a ten-week exercise program for recreational and modern dance and exercises for strengthening the muscles of the torso and legs have a positive effect on the changes in the parameters of coordination and explosive power in student-age female dancers.

Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented—two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast.

Dentoalveolar trauma is considered an emergency condition and is challenging for every dentist. As primary and permanent teeth may suffer repercussions from an injury, a therapist must be mindful of which situations the use of splinting methods is required. In dentistry, a splint is a rigid or flexible device with the function of supporting, protecting, and immobilizing teeth that have been weakened (endodon-tically, periodontally), traumatically injured, replanted, or fractured. Generally, splinting is not recommended for primary teeth injuries such as luxation and avulsion. In permanent dentition, splint appliances are indicated for periodontal injuries, such as subluxation, luxation and avulsion, and hard tissue injuries such as class IV root fractures. Nowadays, there are many appliances that may be used for immobilization of traumatized teeth. Since this issue may sometimes be confusing for dental practitioners, this chapter deals with splint classification (rigid and flexible), the basic characteristics of splints, the indications, and methods of application.

Model extrapolation to unseen flow is one of the biggest challenges facing data-driven turbulence modeling, especially for models with high dimensional inputs that involve many flow features. In this study we review previous efforts on data-driven Reynolds-Averaged Naiver Stokes (RANS) turbulence modeling and model extrapolation, with main focus on the popular methods being used in the field of transfer learning. Several potential metrics to measure the dissimilarity between training flows and testing flows are examined. Different Machine Learning (ML) models are compared to understand how the capacity or complexity of the model affects its behavior in the face of dataset shift. Data preprocessing schemes which are robust to covariate shift, like normalization, transformation, and importance re-weighted likelihood, are studied to understand whether it is possible to find projections of the data that attenuate the differences in the training and test distributions while preserving predictability. Three metrics are proposed to assess the dissimilarity between training/testing dataset. To attenuate the dissimilarity, a distribution matching framework is used to align the statistics of the distributions. These modifications also allow the regression tasks to have better accuracy in forecasting under-represented extreme values of the target variable. These findings are useful for future ML based turbulence models to evaluate their model predictability and provide guidance to systematically generate diversified high-fidelity simulation database.