The burnout syndrome is a response to a long term chronic emotional and interpersonal stressors that are related to workplace. It emerges as the consequence of non-harmonized relations between employees on one, and working environment on the other side. It is defined as chronic work stress that includes three dimensions: the sense of the emotional exhaustion, the negative approach to providing services (depersonalization) and the sense of reduced personal accomplishment. It occurs most often in persons who work in direct contact with other people. Medicine is one of the professions at the greatest risk of suffering from burnout syndrome. The results of the studies conducted in the neighbouring countries, in Europe and in the world showed a big prevalence of burnout syndrome among medical workers, especially in physicians. The acquired results indicate that there is a need to undertake measures for prevention of the burnout syndrome.

The scientific investigation of aquatic ecosystems in Bosnia and Herzegovina has become increasingly intense in recent years. Due to a deficit in studies regarding parasitology and biological control of diseases, two important fish ectoparasites (Chilodonella cyprini and Ichthyophthirius multifiliis) were investigated in 22 fish species (400 individuals) during 2017 from the middle flow of the Sava River. The prevalence of infection and infection intensity were analyzed and signs of ichthyophthiriasis were also documented. The study gives recommendations for the development of aquatic ecosystem management.

The main aim of the study is to develop a real-time epilepsy prediction approach by using the ensemble machine learning techniques that might predict offline seizure paradigms. The proposed seizure prediction algorithm is patient-specific since generalization showed no satisfactory results in our previous studies. The algorithm is tested on CHB-MIT database comprised of EEG data from pediatric epileptic patients. Based on relations to number of seizures and number of files, gender and age, three patients have been chosen for this study. The special majority voting algorithm is proposed and used for raising an alarm of upcoming seizure. EEG signals are denoised using MSPCA (Multiscale PCA), the features were extracted by WPD (wavelet packet decomposition), and EEG signals were classified using Rotation Forest. The significance of the study lies in the fact that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications for pediatric patients.

This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal solution was still guaranteed since dynamic obstacles can only increase the cost. However, significant variations in the search efficiency are observed depending whether dynamic obstacles are present or not. This paper introduces machine learning (ML) based heuristic that takes into account dynamic obstacles, thus adding to the performance consistency for achieving real-time implementation.