Odontogenic cysts are a group of common pathological lesions of the jaw. Typically, they can be found randomly on X-rays as round benign lesions. However, some of them can behave aggressively with a tendency toward malig-nancy. Among odontogenic cysts with benign pathology, up to 60% of all jaw cysts are radicular cysts, which originate from root canal infection. Pathogenesis involves the interaction between osteoblasts, osteocytes, and osteoclasts as well as the expression of RANK-RANKL/OPG signaling system. Furthermore, collagenases (e.g., MMPs) are expressed in epithelial lining of the cyst. Among odontogenic cysts with potentially aggressive behavior, odontogenic keratocysts (OKCs) have a high rate of recurrence and very debatable treatment options; they can be associated with Gorlin syndrome. Keratocysts have developmental origin and show variability in their gene expression profiles. Their etiology is closely related to genetic factors, especially mutations in different members of Shh signaling pathway, including PTCH gene. cells proliferate, the epithelial nest is formed. When the epithelial nest reaches the size of 1 cm, the center becomes necrotic leading to the formation of future cystic cavity, which becomes lined with the epithelium. For unknown reasons, this epithelium starts secreting fluid, which is called cystic fluid. These steps lead to the formation of radicular cyst, a round cavity filled with fluid and lined with the epithelium and fibrous connective tissue. This description of the radicular cyst development is the prevailing theory.

The application of information technology in all areas represents a significant facilitation of all business processes and activities. A competitive business system is hardly imaginable without adequate information technology. Therefore, this paper evaluates the conditions for the implementation of barcode technology in a warehouse system of a company for the manufacture of brown paper. SWOT (Strengths, Weaknesses, Opportunities, Threats) matrix was formed with a total of 27 elements based on which the benefits of the implementation of barcode technology in the warehouse system need to be analysed. For this purpose, a new fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method has been developed to evaluate all elements in SWOT matrix. In addition, a part of the new developed approach includes new fuzzy scales for criterion assessment that are adapted to the methodology required by the fuzzy PIPRECIA method. To determine the consistency of the method, Spearman and Pearson correlation coefficients are applied. The results obtained in this study show that weaknesses are most noticeable in the current system. By implementing barcode technology, it is possible to create opportunities defined in SWOT matrix, which, in a very efficient way, allow elimination of the current weaknesses of the system.

Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina KeYWORdS: carotid artery stenosis, peak-systolic velocity, multiple risk factors. citAtiON: Cardiol Croat. 2018;13(11-12):443. | https://doi.org/10.15836/ccar2018.443 *AddReSS fOR cORReSpONdeNce: Amina Bičo, Clinic of Heart and Blood Vessel Diseases, Clinical Center University of Sarajevo, Bolnička 25, 71000 Sarajevo, Bosnia and Herzegovina. / Phone: +387-61-348-688 / Email: amina.osmanagic@yahoo.com ORcid: Amina Bičo, https://orcid.org/0000-0001-6937-1772 • Alden Begić, https://orcid.org/0000-0002-5374-0892 Dževdet Radončić, https://orcid.org/0000-0001-5917-2956 • Mirza Dilić, https://orcid.org/0000-0002-7309-1455

Studying the behavior of social insect using computer vision algorithms is an interesting topic for both biological and signal processing communities. One of the most interesting aspects in the field is tracking of honeybees. Regarding computer vision method, honeybees’ behavior has been mostly monitored inside and at the entrance of the hive. In this research we are proposing the method for automatic monitoring of honeybees’ activity outside of the hive. Experiments showed that the activity of honeybees outside the hive can estimated using an ultra-high definition video captured with UAV from distance of 10 meters. Specific spots where honeybees are gathered can be detected using heat maps which represent the density of their occurrence in the observed time interval.

This paper investigates the importance of different parameters of split-brain autoencoder to performance of learned image representations for remote sensing scene classification. We investigate the usage of LAB color space as well as color space created using PCA applied to RGB pixel values. We show that these two spaces give almost equal results, with slight favor towards the LAB color space. We also investigate choices of different quantization methods of color targets and number of quantization bins. We have found that using k-means clustering for quantization works slightly better than using uniform quantization. We also show that even when using really small number of bins it is possible to get only slightly worse results.