University of Niš: Facta Universitatis (E-Journals) / Универзитет у Нишу
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MUZIKA NA PORTALU SRPSKOG JAVNOG MEDIJSKOG SERVISA (RTS)
No full textThe aim of this paper is to explore how the portal of the Radio Television of Serbia (RTS) reports on music (musical works, musicians, and music events). Through a quantitative-qualitative content analysis, journalistic articles published in the Magazine/Music section of the RTS portal (https://www.rts.rs/lat/magazin/muzika.html) during the first half of 2023 were analyzed. The research questions were: which type of music is most represented, which articles are the predominant ones (on national or world music), how prevalent is music criticism compared to other journalistic genres. A specially created coding sheet was used in the analysis and a statistical method was applied to analyze the results. The research results show that the majority of articles published during the observed period focused on national music, with the most represented music genre being popular music. The main topic of these articles was most often music events, and the context in which music was discussed was positive or neutral. News and reports dominated the journalistic genres, while music criticism was lacking indicating a superficial approach to music coverage on the Serbian Public Media Service portal.Cilj rada je istražiti na koji način portal Radio-televizije Srbije (RTS) izveštava o muzici (muzičkim delima, muzičarima i muzičkim događajima). Primenom kvantitativno-kvalitativne analize sadržaja analizirani su novinarski tekstovi objavljeni na portalu RTS-a, u rubrici „Magazin/Muzika” (https://www.rts.rs/lat/magazin/muzika.html) u prvoj polovini 2023. godine. Istraživačka pitanja bila su: koja vrsta muzike je najzastupljenija, koji tekstovi dominiraju (o nacionalnoj ili svetskoj muzici), koliko je zastupljena novinarska muzička kritika, a koliko ostali novinarski žanrovi. Tokom analize korišćen je posebno kreirani kodni list, a u obradi rezultata korišćen je statistički metod. Rezultati istraživanja pokazuju da je najveći broj novinarskih tekstova tokom posmatranog perioda objavljen o nacionalnoj muzici, a najzastupljeniji muzički žanr bila je zabavna muzika. Tema novinarskih tekstova najčešće su bili muzički događaji, a kontekst u kome se o muzici piše je pozitivan ili neutralan. Od novinarskih žanrova dominiraju vest i izveštaj, a muzičke kritike nema, što svedoči o površnom izveštavanju portala Javnog medijskog servisa o muzici
SPECIFIČNOSTI NASTAVE FILOZOFIJE U SREDNJIM ŠKOLAMA I GIMNAZIJAMA, ANALIZA REZULTATA ANKETE I OKRUGLOG STOLA
No full textThis paper discusses the specifics of philosophy teaching in secondary schools and gymnasiums in the municipality of Niš (Serbia). In this paper, we focus on the analysis of data collected as part of an internal project conducted by the Department of Philosophy, Faculty of Philosophy, University of Niš. We examine the results obtained from two segments of the research. In the first part of the paper, we present and interpret the results of a survey completed by philosophy teachers in secondary schools and gymnasiums in the municipality of Niš, while in the second part, we set forth and analyze their responses provided during the round table discussion. Then, based on the stated positions and observations, we gain a comprehensive insight and draw conclusions.Ovaj rad razmatra specifičnosti nastave filozofije u srednjim školama i gimnazijama u opštini Niš (Srbija). U ovom radu fokusiramo se na analizu podataka prikupljenih u okviru internog projekta Departmana za filozofiju, Filozofskog fakulteta, Univerziteta u Nišu. Ispitaćemo rezultate dobijene iz dva segmenta istraživanja. U prvom delu rada, predstavićemo i interpretirati rezultate ankete koju su popunili nastavnici filozofije u srednjim školama i gimnazijama u opštini Niš, dok ćemo u drugom delu izložiti i analizirati njihove odgovore date tokom diskusije za okruglim stolom. Zatim, na osnovu iznetih stavova i zapažanja, dobićemo sveobuhvatan uvid i izvesti zaključke.
PRIMENA PRINCIPA BONA FIDES NA UPOTREBU VEŠTAČKE INTELIGENCIJE U STUDENTSKIM RADOVIMA
No full textThe principle of good faith and fair dealing, originating in Roman law and continually evolving through various legal traditions, serves as a cornerstone of contemporary legal and ethical standards. In the context of academic writing, this principle demands that students act with integrity when utilizing external sources, including those generated by artificial intelligence. Respect for good faith and fair dealing ensures the preservation of academic values, fosters trust within the academic community, and upholds the dignity of scholarly work. The use of artificial intelligence (AI) in academic writing has raised significant ethical and legal questions, particularly regarding the proper attribution of sources and the integrity of scholarly work. As AI tools become more sophisticated, understanding the application of traditional legal principles, such as good faith and fair dealing, is essential to ensuring academic integrity. To better understand this principle, we will explore its roots, the motivation behind its establishment, and trace its development alongside the evolution of social and economic conditions. Načelo savesnosti poštenja ima koren u rimskom pravu, ali se stalno razvija kroz različite pravne tradicije i predstavlja osnov savremenih pravnih i etičkih principa. U kontekstu pisanja akademskih radova, ovaj princip podrazumeva da studenti postupaju sa integritetom kada koriste spoljne izvore, uključujući i one koje generiše veštačka inteligencija (VI). Poštovanje načela savesnosti i poštenja osigurava očuvanje akademskih vrednosti, podstiče poverenje unutar akademske zajednice i podržava dostojanstvo naučnog rada. Upotreba veštače intelignecije u akademskim radovima pokrenula je značajna etička i pravna pitanja, posebno u vezi sa pravilnim navođenjem izvora i integritetom naučnog rada. Kako alati VI postaju sofisticiraniji, pravilno razumevanje i primena tradicionalnih pravnih principa, kao što je načelo savesnosti i poštenja, neophodni su za obezbeđivanje akademskog integriteta. Da bismo bolje razumeli ovaj princip, istražićemo njegove korene, motivaciju za njegovu primenu i pratiti njegov razvoj uporedo sa evolucijom društenvih i ekonomskih uslova.
USPEŠNOST AI MATH SOLVER ALATA U REŠAVANJU NESTANDARDNIH ZADATAKA SA MATEMATIČKIH TAKMIČENJA
No full textArtificial intelligence is increasingly transforming how students learn, including their approach to mathematics and problem-solving, by offering additional support and assistance—a trend that continues to attract research interest. One line of research focuses on helping students prepare for math competitions by solving more complex mathematical problems. In addition to regular national math competitions, which allow students to progress to international mathematical Olympiads, there are also competitions aimed at popularizing mathematics and developing logical thinking in students. One such competition is the international Kangaroo competition. In this paper, we examine the performance of the AI Math Solver, available on the Interactive Mathematics platform, in solving tasks from the 2024 Kangaroo competition. The selected tasks targeted three student categories: 3rd and 4th grade elementary, 7th and 8th grade elementary, and 3rd and 4th grade high school students. The problems were uploaded as images (screenshots) in both Serbian and English, since visual elements frequently appear in the problem formulations and answer choices in the Kangaroo competition. The results are presented in two sections: a qualitative analysis of selected problems that illustrate common patterns and errors, and a quantitative analysis that summarizes the tool’s overall performance. Out of a total of 84 tasks, in both Serbian and English, the solver correctly answered 24, corresponding to a success rate of just under 30% in both languages. Furthermore, some tasks solved in Serbian were not solved in English, and vice versa. Additionally, differences were observed in the distribution of correct answers across tasks of varying difficulty levels.Veštačka inteligencija sve više menja način na koji se učenici obrazuju, pa tako i pristup učenju matematike i rešavanju zadataka, pružajući dodatnu podršku i pomoć, što predstavlja sve češćipredmet naučnih istraživanja. Jedan pravac istraživanja fokusira se na pomoć učenicima koji žele da učestvuju na matematičkim takmičenjima u rešavanju složenijih matematičkih problema. Pored redovnih nacionalnih matematičkih takmičenja, koja učenicima omogućavaju napredovanje do međunarodnih matematičkih olimpijada, postoje takmičenja usmerena na popularizaciju matematike i razvoj logičkog mišljenja kod učenika. Jedno od takvih jeste međunarodno takmičenje Kengur bez granica. U ovom radu ispitujemo uspešnost alata AI Math Solver, dostupnog na platformi Interactive Mathematics, u rešavanju zadataka sa takmičenja Kengur bez granica iz 2024. godine. Istraživanje obuhvata zadatke namenjene za tri uzrasne grupe: 3. i 4. razreda osnovne škole, 7. i 8. razreda osnovne škole i 3. i 4. razreda srednje škole. Zadaci su postavljani u vidu slika (screenshot-ova), na srpskom i engleskom jeziku, zbog česte prisutnosti vizuelnih elemenata u formulacijama i ponuđenim odgovorima. Rezultati su predstavljeni kroz dve celine: kvalitativnu analizu odabranih zadataka i kvantitativnu analizu dobijenih rezultata. Od ukupno 84 zadatka, kako na srpskom tako i na engleskom jeziku, tačno su rešena 24 zadatka, što je nešto manje od 30% uspešnosti u oba slučaja. Dalje, neki zadaci rešeni na srpskom nisu rešeni na engleskom jeziku, i obrnuto. Pored toga, uočene su razlike u raspodeli tačnih odgovora među zadacima različitih nivoa težine
MATRIX TRANSFORMS BETWEEN SPEED-MADDOX SPACES OVER ULTRAMETRIC FIELD
Full text linkLet \K be a complete, non-trivially valued, ultrametric (or non-archimedean) field, and be a sequence in \K with the property , i.e., the speed of convergence. In this paper, we study the speed-Maddox spaces over\K, defined by the parameter , and investigate their structure for paranormally -zero-convergent, paranormally -convergent, and paranormally -bounded sequences. Earlier, in classical cases, the matrix transforms between different Maddox spaces have been widely investigated. In the present paper necessary and sufficient conditions are found for a matrix over \K to transform all paranormally -zero-convergent or all paranormally -convergent sequences into the spaces of all paranormally -zero-convergent, all paranormally -convergent or all paranormally -bounded sequences, where is another speed of convergence in \K. As an application of main results, one example where is the Srinivasan summation method is presented
GENERALIZED -VECTOR FIELDS IN K\"AHLER SPACES
No full textIn this work, the fundamental equations of generalized -vector fields in both classical and hyperbolic K\"ahler spaces are derived and expressed as systems of linear partial differential equations in the Cauchy-type covariant derivative. Additionally, the specific form of the Ricci tensor for these spaces is determined
CONVOLUTIONAL NEURAL NETWORKS FOR SEMANTIC SEGMENTATION IN 3D RECONSTRUCTION PROCESS OF THE PULMONARY ACINUS
Full text linkThe pulmonary acinus, encompassing respiratory bronchioles, alveolar ducts, alveoli, alveolar sacs, and their associated blood vessels, serves as the main unit for gas exchange in the lung. To accurately characterize fluid micromechanics within this complex 3D acinar structure and the corresponding airflow, 3D reconstruction of the lung parenchyma using computational fluid dynamics is crucial. Essential to this process is the precise segmentation of stacked 2D images, forming the basis for a reliable 3D model. Convolutional neural networks (CNNs) have shown remarkable success in tasks such as image classification, semantic segmentation, and object detection, and are particularly effective in medical image segmentation. This study explores the efficacy of several advanced deep convolutional architectures and their variants for segmenting the pulmonary acinus lung field. The models were assessed using rat lung images acquired through synchrotron radiation-based X-ray tomographic microscopy (SRXTM). Balancing accuracy and computational efficiency, our results demonstrate that SegNet (trained from scratch) and pretrained PSPNet are the most suitable for this type of imagery, achieving mean Intersection over Union (mIoU) values of 0.85 and 0.9, respectively. Ultimately, these segmented images facilitated the complete 3D reconstruction of the pulmonary acinus
SECTIONAL CURVATURE OF STATISTICAL STRUCTURES ON TANGENT BUNDLES
Full text linkWe study the relationship between the statistical sectional curvature of statistical manifolds and the prolongation of these structures to their tangent bundles. We establish a necessary and sufficient condition for the constancy of the sectional Kh-curvature of the tangent bundle TM and the sectional K-curvature of the base manifold M. Furthermore, we compute the statistical sectional curvature of several well-known statistical manifolds