Practical Application of the Probabilistic‐Statistical Model of the Suspension Separation in Hydrocyclones

An effective practical approach that allows not only a significant reduction in the scope of practical experiments in the course of studying suspension separation processes in hydrocyclones, but also makes it possible to assess the intensity of random components of the processes and define the interrelation between such components and hydrodynamics of flows in a hydrocyclone is presented. Within the frames of the developed probabilistic‐statistical model of suspension separation in hydrocyclones on the basis of statistical self‐similarity properties, a relationship was found between determined and random components of the processes. This allowed transitioning from three‐parameter probability density functions for suspension particles in hydrocyclones to two‐parameter functions; thus significantly improving the efficiency of practical application of the developed model.     

Decision Making Effectiveness in Wildfire Incident Management Teams

During large scale wildfires, suppression activities are carried out under the direction of an Incident Management Team (IMT). The aim of the research was to increase understanding of decision processes potentially related to IMT effectiveness. An IMT comprises four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to study IMT processes: computer simulation experiments; analyses of wildfire reports; interviews with IMT members; and cognitive ethnographic studies of IMTs. Three processes were important determinants of IMT effectiveness: information management and cognitive overload; matching component function goals to overall goals; and team metacognition to detect and counter task‐disruptive developments. These processes appear to be complex multi‐person analogues of individual Incident Command processes identified previously. The findings have implications for issues such as: creating IMTs; training IMTs; managing IMTs; and providing decision support to IMTs.     

3D Hair sketching for real-time dynamic & key frame animations

Physically based simulation of human hair is a well studied and well known problem. But the “pure” physically based representation of hair (and other animation elements) is not the only concern of the animators, who want to “control” the creation and animation phases of the content. This paper describes a sketch-based tool, with which a user can both create hair models with different styling parameters and produce animations of these created hair models using physically and key frame-based techniques. The model creation and animation production tasks are all performed with direct manipulation techniques in real-time.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.