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Modern unmanned air vehicles (UAVs) are usually equipped with rotors connected to electric motors that enable them to hover and fly in all directions. The purpose of the paper is to design optimal composite rotor blades for such small UAVs and investigate their aerodynamic performances both computationally and experimentally.

Artificial intelligence method (genetic algorithm) is used to optimize the blade airfoil described by six input parameters. Furthermore, different computational methods, e.g. vortex methods and computational fluid dynamics, blade element momentum theory and finite element method, are used to predict the aerodynamic performances of the optimized airfoil and complete rotor as well the structural behaviour of the blade, respectively. Finally, composite blade is manufactured and the rotor performance is also determined experimentally by thrust and torque measurements.

Complete process of blade design (including geometry definition and optimization, estimation of aerodynamic performances, structural analysis and blade manufacturing) is conducted and explained in detail. The correspondence between computed and measured thrust and torque curves of the optimal rotor is satisfactory (differences mostly remain below 15%), which validates and justifies the used design approach formulated specifically for low-cost, small-scale propeller blades. Furthermore, the proposed techniques can easily be applied to any kind of rotating lifting surfaces including helicopter or wind turbine blades.

Blade design methodology is simplified, shortened and made more flexible thus enabling the fast and economic production of propeller blades optimized for specific working conditions.

Student recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial. Traditionally, students voice their opinions through official surveys organized by the universities. In addition to that, nowadays, social media and review websites such as “Rate my professors” are rich sources of opinions that should not be ignored. Automated mining of students’ opinions can be realized via aspect-based sentiment analysis (ABSA). ABSA s is a sub-discipline of natural language processing (NLP) that focusses on the identification of sentiments (negative, neutral, positive) and aspects (sentiment targets) in a sentence. The purpose of this paper is to introduce a system for ABSA of free text reviews expressed in student opinion surveys in the Serbian language. Sentiment analysis was carried out at the finest level of text granularity – the level of sentence segment (phrase and clause).

The presented system relies on NLP techniques, machine learning models, rules and dictionaries. The corpora collected and annotated for system development and evaluation comprise students’ reviews of teaching staff at the Faculty of Technical Sciences, University of Novi Sad, Serbia, and a corpus of publicly available reviews from the Serbian equivalent of the “Rate my professors” website.

The research results indicate that positive sentiment can successfully be identified with the F-measure of 0.83, while negative sentiment can be detected with the F-measure of 0.94. While the F-measure for the aspect’s range is between 0.49 and 0.89, depending on their frequency in the corpus. Furthermore, the authors have concluded that the quality of ABSA depends on the source of the reviews (official students’ surveys vs review websites).

The system for ABSA presented in this paper could improve the quality of service provided by the Serbian higher education institutions through a more effective search and summary of students’ opinions. For example, a particular educational institution could very easily find out which aspects of their service the students are not satisfied with and to which aspects of their service more attention should be directed.

To the best of the authors’ knowledge, this is the first study of ABSA carried out at the level of sentence segment for the Serbian language. The methodology and findings presented in this paper provide a much-needed bases for further work on sentiment analysis for the Serbian language that is well under-resourced and under-researched in this area.

The aim of this paper is to develop a system for automatic extraction of metadata from scientific papers in PDF format for the information system for monitoring the scientific research activity of the University of Novi Sad (CRIS UNS).

The system is based on machine learning and performs automatic extraction and classification of metadata in eight pre‐defined categories. The extraction task is realised as a classification process. For the purpose of classification each row of text is represented with a vector that comprises different features: formatting, position, characteristics related to the words, etc. Experiments were performed with standard classification models. Both a single classifier with all eight categories and eight individual classifiers were tested. Classifiers were evaluated using the five‐fold cross validation, on a manually annotated corpus comprising 100 scientific papers in PDF format, collected from various conferences, journals and authors' personal web pages.

Based on the performances obtained on classification experiments, eight separate support vector machines (SVM) models (each of which recognises its corresponding category) were chosen. All eight models were established to have a good performance. The F‐measure was over 85 per cent for almost all of the classifiers and over 90 per cent for most of them.

Automatically extracted metadata cannot be directly entered into CRIS UNS but requires control of the curators.

The proposed system for automatic metadata extraction using support vector machines model was integrated into the software system, CRIS UNS. Metadata extraction has been tested on the publications of researchers from the Department of Mathematics and Informatics of the Faculty of Sciences in Novi Sad. Analysis of extracted metadata from these publications showed that the performance of the system for the previously unseen data is in accordance with that obtained by the cross‐validation from eight separate SVM classifiers. This system will help in the process of synchronising metadata from CRIS UNS with other institutional repositories.

The paper documents a fully automated system for metadata extraction from scientific papers that was developed. The system is based on the SVM classifier and open source tools, and is capable of extracting eight types of metadata from scientific articles of any format that can be converted to PDF. Although developed as part of CRIS UNS, the proposed system can be integrated into other CRIS systems, as well as institutional repositories and library management systems.

The purpose of this paper is to present the results of interaction occurring during the exposition of some specific carbon textile materials obtained in laboratory conditions to beams of various laser types.

Carbon fabric materials – fiber, felt and cloth – obtained from different precursor materials and prepared at various process conditions (oxidized, partially carbonized, carbonized, graphitized), were exposed to pulses of various lasers (Nd3+: YAG, alexandrite, ruby).

Depending on the laser power, plasma and destructive phenomena occurred. In the case of an interaction between a Nd3+: YAG laser beam and specimens of thickness in millimeter range, the authors have estimated the threshold of the energy density for drilling and discussed the possible models of the interaction.

The results have implications in the estimations of quality as well as in the improvement of material processing, giving some new light to the changes of mechanical and optical constants of the material, as well as to the changes of carbon groups of the material, which would be useful for different types of modeling. Future research will be in the interaction of laser beams with various textile materials, where the investigation would cover the microstructure changes and the implications on cloth cutting and welding, concerning the damages as well as relief structures, specially renew for fs laser regimes.

The area of laser applications in the textile industry is supported by scientific and applicative exploration. However, fewer results are concerned with deep introspection into the microstructure of the damages considering the laser interaction with carbon fiber and other carbon-based textiles.

The study deals with key questions of Serbia’s economic development, including the regularities of transforming self-managed socialism to a standard capitalist system. It is based on the concept of endogenous growth and the general theory of market transition. In the empirical part of investigation, the main directions of economic development and transition in Serbia are analysed. Crucial issues of economic policy are also considered with a particular emphasis on the latest phase of transition. Concerning the problems of economic efficiency, an attempt is made to quantify the various types of technical progress and determine their contribution to its overall rate. The macroeconomic role of external factors is quantitatively shown through a globalisation effect related to inflows of FDI. The author believes that the Serbian economy, despite all its problems and difficulties, in principle has the potential necessary for finding adequate answers to the challenges of ‘neo-transition’. Of these challenges, he regards as most serious Serbia’s ability to comply with the standards of accession to the European Union, which, among others, requires closer regional cooperation.

This paper aims to describe the structure of an aligned Serbian-German literary corpus (SrpNemKor) contained in a digital library Bibliša. The goal of the research was to create a benchmark Serbian-German annotated corpus searchable with various query expansions.

The presented research is particularly focused on the enhancement of bilingual search queries in a full-text search of aligned SrpNemKor collection. The enhancement is based on using existing lexical resources such as Serbian morphological electronic dictionaries and the bilingual lexical database Termi.

For the purpose of this research, the lexical database Termi is enriched with a bilingual list of German-Serbian translated pairs of lexical units. The list of correct translation pairs was extracted from SrpNemKor, evaluated and integrated into Termi. Also, Serbian morphological e-dictionaries are updated with new entries extracted from the Serbian part of the corpus.

A bilingual search of SrpNemKor in Bibliša is available within the user-friendly platform. The enriched database Termi enables semantic enhancement and refinement of user’s search query based on synonyms both in Serbian and German at a very high level. Serbian morphological e-dictionaries facilitate the morphological expansion of search queries in Serbian, thereby enabling the analysis of concepts and concept structures by identifying terms assigned to the concept, and by establishing relations between terms in Serbian and German which makes Bibliša a valuable Web tool that can support research and analysis of SrpNemKor.