Scots pine (Pinus sylvestris L.) is one of the most important tree species in European forests. This study aims to determine whether there is inter-provenance variability in researched morphological traits in two international provenance tests of Scots pine in Bosnia and Herzegovina. We measured height, root collar diameter, and latest shoot length and counted branches on the latest branch whorl of Scots pine plants in two provenance tests. The provenance tests are located in Kupres and Žepce, in different climatic, edaphic, and orographic conditions. Kupres and Žepce contain 15 and 14 provenances, respectively, eleven of which are mutual to both sites. Descriptive statistics and analysis of variance showed differences among provenances in all investigated morphological traits. These differences were attributable to provenance test, provenance, and interaction between provenance test and provenance. The average values were higher in Žepce for all provenances and all studied traits. The Austria A1, Austria A2, Austria A3, and Poland P1 provenances showed the best growth in both tests, while the Italy I1 provenance showed good growth in Žepce but not in Kupres.

A brain anomaly localization algorithm in an unsupervised machine learning (ML) framework is presented for electromagnetic brain imaging. The method is based on expected value estimation and takes the advantage of the highly symmetrical human brain. The algorithm processes signals collected from pairs of antennas that are positioned symmetrically around the head, discretizes the imaging domain into pixels, and computes the statistical fields between the antennas on the left and right sides of the head. Then, it concatenates their intensities along the axis normal to the imaging domain to compute the expected value for every pixel. The computed expected values are merged into a matrix containing expected values for all pixels. Pixels with higher intensity show the likelihood of an anomaly being present at that location. The assumption on brain symmetry from the electromagnetic perspective was tested on healthy volunteers using a 14-element array system with a working frequency band of 0.5 - 2.0 GHz. The obtained average similarity is 92% and it confirms the validity of the assumption. The same system is used to test the algorithm on different scenarios in simulations and experiments using realistic 3D head phantoms designed based on MRIs of real patients. The imaging results demonstrate the capability of the proposed algorithm to localize bleeding and estimate its size with less than 10% error in less than a minute, which makes it suitable for real-time use in emergency stroke scenarios.