Purpose: With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. Design/methodology/approach: This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. Findings: As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. Social implications: The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. Originality/value: A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

U ovom radu, prikazujemo ažurirane podatke o distribuciji kompleksa vrsta slepića (Anguis fragilis complex) u Srbiji. Podaci se sastoje iz nalaza objavljenih u literaturi ili na Internetu, zajedno sa prethodno neobjavljenim distribucionim podacima sakupljenim na terenu. Od dve vrste kompleksa prisutne u Srbiji, Anguis fragilis je rasprostranjen u zapadnim i južnim krajevima naše zemlje dok je Anguis colchica prisutan u severoistočnim i istočnim krajevima. U najsevernijim delovima naše zemlje konstatovano je odsustvo obe vrste, zbog nedostatka odgovarajućeg staništa. Nalazi su brojniji u planinsko-kotlinskom regionu i ređi i raštrkaniji u peripanonskom i panonskom regionu, što se delimično može objasniti nesistematskim uzorkovanjem. Lokacija i širina kontaktne zone dve vrste slepića u Srbiji je još uvek nedovoljno poznata. Pretpostavlja se da se pruža duž doline Velike Morave ka jugu a zatim prati granicu između Rodopskog i Karpatsko-Balkanskog planinskog masiva ka jugo-istoku, ali postoje indicije da se hibridna zona nalazi i zapadnije. Postoji izvesno neslaganje, u literaturi, između kontaktne zone predložene na osnovu morfoloških analiza i one predložene na osnovu molekularnih analiza. Ovo ukazuje na potrebu za opsežnijim uzorkovanjem i daljim analizama. Konačno, pošto razdvajanje dve kriptičke vrste slepića do sada nije prepoznato u našoj nomenklaturi, u ovom radu predlažemo zvanične srpske nazive za navedene vrste. Vrsta Anguis fragilis će zadržati standardno srpsko ime "Slepić", dok za vrstu Anguis colchica predlažemo ime "Istočni slepić".

Designing electrical circuits and devices is usually based on expertise in electronics and the thorough use of numerical software tools. This procedure can be time-consuming, and the designer has only one solution. This paper introduces a new approach focused on new concept design and optimization of specific circuits using symbolic expressions. The primary amplifier circuit, realized by a deep learning module, changes the value to reduce power dissipation. The control signal of the deep learning module is output from an amplifier that depends on the statistics of the input signal value.

The smart visualization of medical images (SVMI) model is based on multi-detector computed tomography (MDCT) data sets and can provide a clearer view of changes in the brain, such as tumors (expansive changes), bleeding, and ischemia on native imaging (i.e., a non-contrast MDCT scan). The new SVMI method provides a more precise representation of the brain image by hiding pixels that are not carrying information and rescaling and coloring the range of pixels essential for detecting and visualizing the disease. In addition, SVMI can be used to avoid the additional exposure of patients to ionizing radiation, which can lead to the occurrence of allergic reactions due to the contrast media administration. Results of the SVMI model were compared with the final diagnosis of the disease after additional diagnostics and confirmation by neuroradiologists, who are highly trained physicians with many years of experience. The application of the realized and presented SVMI model can optimize the engagement of material, medical, and human resources and has the potential for general application in medical training, education, and clinical research.

International scientific conference of IT and Business Related Research - Synthesis 2015, was held on April 16, 2015 at Singidunum University in Belgrade