The definitions of physical abuse are mentioned in this article. The most common types and forms of physical abuse are also described. Risk factors for abuse depend on the characteristics of the child, characteristics of the parents, as well as the cultural characteristics of the environment and these are mentioned and described. Further on in the text we have described the neurological, cognitive, emotional and social consequences of physical abuse and have taken into consideration different forms of treatment and possibilities of preventing physical abuse.

The paper presents implementation and performance of an irregular sea wavemaker system. The system is designed for the purpose of testing sea waves impact on coastal facilities. It is suitable for long-crested two-dimensional irregular sea waves generation. The control algorithm is comprised of the offline calculation of the control waveform and the real-time governing of the wave paddle. Offline algorithm is implemented on a personal computer, whereas paddle control is realized on a programmable automation controller. The paper describes the generation procedure of a stochastic waveform according to criteria set in advance, as well as the control algorithm for producing the desired waveform. Functionality of the system is validated by experiments.

For medical volume visualization, one of the most important tasks is to reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the coronary arteries, without obscuring them with less significant parts. These volume datasets contain different materials which are difficult to extract and visualize with 1D transfer functions based solely on the attenuation coefficient. Multi-dimensional transfer functions allow a much more precise classification of data which makes it easier to separate different surfaces from each other. Unfortunately, setting up multi-dimensional transfer functions can become a fairly complex task, generally accomplished by trial and error. This paper explains neural networks, and then presents an efficient way to speed up visualization process by semi-automatic transfer function generation. We describe how to use neural networks to detect distinctive features shown in the 2D histogram of the volume data and how to use this information for data classification.