AUTONOMY OF UNIVERSITY: BURDEN OR FUNDAMENT?

In this paper the perspectives will be outlined from which the function and position of university could be altered, consolidated and improved, in the light of recent and current attempts of destabilisation of university. External causes of course are not the only culprit for gradually reduced role that university has in Croatian scientific, cultural and social scene. Students‟ struggle that started in spring 2009, caused a lot of commotion in public and showed injustices and inconsistencies in higher education system. The fight for free education was then articulated for the first time. Main thesis of this paper is that if the “burden of autonomy” is skillfully handled and properly situated, the “fundament of the university” can be strengthened. Improvement of the university as a whole can only be achieved through the permanent dialogue and cooperation between its components (faculties) and by strengthening relation faculty-university. Main question is how to change frames and relationships within the university itself in cooperation with institutions responsible for participation in the processes of creating science and social politics, without directly imperiling the autonomy of university

Primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva

Neural networks have been in use since the 1950s and are increasingly prevalent in various domains of human activity, including military applications. Notably, GoogLeNet and convolutional neural networks, when appropriately trained, are instrumental in identifying and detecting individual objects within a complex set. In military scenarios, neural networks play a crucial role in the fire support process, especially when receiving target descriptions from forward observers. These networks are trained on image datasets to recognize specific features of individual elements or military objects, such as vehicles. As a result of this training, when presented with a new image, the network can accurately determine the type of vehicle, expediting the targeting process and enhancing the ability to provide a suitable response. This paper describes the application of neural networks for detecting and classifying artillery targets. It presents a specific problem and proposes a scientific solution, including explaining the methodology used and the results obtained.Neuronske mreže u uporabi su od pedesetih godina prošlog stoljeća i sve su zastupljenije u različitim područjima ljudske aktivnosti, uključujući vojne primjene. Posebno, GoogleNet i konvolucijske mreže, kada su odgovarajuće utrenirane, ključne su u prepoznavanju i otkrivanju pojedinih objekata unutar složenog skupa. U vojnim scenarijima neuronske mreže imaju ključnu ulogu u postupku pružanja potpore vatrom, posebno kada primaju opise ciljeva od prednjih topničkih motritelja. Ove mreže trenirane su na slikovnim skupovima podataka kako bi prepoznale specifičnosti pojedinih elemenata ili vojnih objekata, kao što su vozila. Kao rezultat treniranja, kada se prikaže nova slika, mreža može točno odrediti tip vozila, ubrzati postupak ciljanja i poboljšati sposobnost pružanja prikladnog odgovora. U radu se opisuje primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva. Rad predstavlja poseban problem i predlaže rješenje primjenom znanstvenog pristupa, uključujući objašnjenje korištene metodologije i dobivene rezultate

IMPROVING ENERGY EFFICIENCY IN BUILDINGS USING MICROGRIDS

Modern society has a growing need for the electricity. To protect the environment, future energy demand must be met with more environmentally friendly technologies, such as renewable energy sources. Because of its vast availability, solar radiation has been used for decades to generate electricity through photovoltaic systems (PV) for residential, educational, and commercial buildings. However, the growth of distributed generation (and renewable energy sources) across power systems in industrialized countries has created new challenges. Random renewable generation causes an imbalance between electricity production and consumption, so smart grids and microgrids may be solutions. In this article, we investigate improving the energy efficiency in the Faculty of Electrical Engineering building in Osijek by using a microgrid. To do so, we compared the total electricity consumption of the building and the production of a 10 kWp photovoltaic power plant on that building. The improvement in energy efficiency of the building produced a maximum savings of up to 10% of the building’s total electricity consumption

Primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva

Neural networks have been in use since the 1950s and are increasingly prevalent in various domains of human activity, including military applications. Notably, GoogLeNet and convolutional neural networks, when appropriately trained, are instrumental in identifying and detecting individual objects within a complex set. In military scenarios, neural networks play a crucial role in the fire support process, especially when receiving target descriptions from forward observers. These networks are trained on image datasets to recognize specific features of individual elements or military objects, such as vehicles. As a result of this training, when presented with a new image, the network can accurately determine the type of vehicle, expediting the targeting process and enhancing the ability to provide a suitable response. This paper describes the application of neural networks for detecting and classifying artillery targets. It presents a specific problem and proposes a scientific solution, including explaining the methodology used and the results obtained.Neuronske mreže u uporabi su od pedesetih godina prošlog stoljeća i sve su zastupljenije u različitim područjima ljudske aktivnosti, uključujući vojne primjene. Posebno, GoogleNet i konvolucijske mreže, kada su odgovarajuće utrenirane, ključne su u prepoznavanju i otkrivanju pojedinih objekata unutar složenog skupa. U vojnim scenarijima neuronske mreže imaju ključnu ulogu u postupku pružanja potpore vatrom, posebno kada primaju opise ciljeva od prednjih topničkih motritelja. Ove mreže trenirane su na slikovnim skupovima podataka kako bi prepoznale specifičnosti pojedinih elemenata ili vojnih objekata, kao što su vozila. Kao rezultat treniranja, kada se prikaže nova slika, mreža može točno odrediti tip vozila, ubrzati postupak ciljanja i poboljšati sposobnost pružanja prikladnog odgovora. U radu se opisuje primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva. Rad predstavlja poseban problem i predlaže rješenje primjenom znanstvenog pristupa, uključujući objašnjenje korištene metodologije i dobivene rezultate

USPOREDBA NELINEARNIH MAKROMODELA ZIDANIH ISPUNA

Reinforced-concrete frames with masonry infill are composite systems, common in low and medium-high buildings, whose behavior under earthquake loads is difficult to predict. Because of that, most national codes ignore the contribution of infill to the structural response. In this paper, we determined the seismic behavior of infilled frames by using a numerical non-liner model (with various geometries and various masonry and r/c frame properties), using samples taken from the originally collected experimental data base on infilled frames “EDIF”. We then compared these experimental results to numerical models designed using recommendations from FEMA 356 (nonlinear model), Eurocode 8 (linear model), and other published methods (Stafford-Smith and Carter, Mainstone, Žarnić, Paulay and Priestley) in order to assess the most suitable, rational macro model for evaluating the behaviour of masonry infill in a reinforced-concrete frame during earthquake excitation.Armiranobetonski okviri sa zidanom ispunom predstavljaju kompozitne konstrukcije, dominante u području niskih i srednje visokih građevina, čije je ponašanje pri djelovanju potresa teško predvidjeti. Zbog toga se doprinos zidane ispune odgovoru konstrukcije općenito zanemaruje u većini nacionalnih propisa. Ponašanje uokvirenog ziđa pri djelovanju potresa primjenom numeričkog nelinearnog modela dokazano je uporabom eksperimentalnih uzoraka iz EDIF eksperimentalne baze i usporedbom prema metodama projektiranja uokvirenog ziđa u FEMA 356 (nelinearni model), Eurokodu 8 (linearni model), te drugim metodama (Stafford-Smith i Carter, Mainstone, Žarnić, Paulay and Priestley). Ova usporedna studija provedena je radi dobivanja najprikladnijeg i najracionalnijeg modela zidane ispune u armiranobetonskim okvirima pri djelovanju potresa

Demand Side Management inside a Smart House

The upgraded traditional grid, also known as the smart grid, that incorporates information and communications technologies will change not only electricity production but also consumption. In combination with Photovoltaics (PV) and electrical storage, demand side management (DSM) is a promising solution for net-zero energy building (NZEB). NZEB will be able to produce energy for its own needs and also feed a surplus back to the grid. In scientific papers, it has already been proven that the use of electrical energy storage can improve the power quality and store variable production of renewable energy. Smart meters are a step forward because they enable a two-way communication between a customer and a utility. In this way, it will be possible to monitor consumption and electricity prices on the market in real time. Furthermore, this will enable the consumer to turn off devices that are large loads, or let the DSM system known as load management do its job such to reduce energy consumption in a given period. DSM will automatically switch off a big load in a manner that does not disturb user comfort. Smart appliances at the end-user level such as the Internet protocol (IP) addressable appliance controlled by external signals from the utility or end-user will enable load shifting to off-peak periods. Solar radiation is prevalent everywhere and can be used to generate electricity at the point of consumption, thereby reducing the losses in transmission. Only one hour of solar radiation is sufficient to cover the annual consumption; this shows that the future of low-carbon energy production lies in the use of solar radiation

VORHERSAGE DER EIGENSCHAFTEN VON RECYCLINGBETON

Upotreba recikliranih materijala kao zamjene prirodnom agregatu u betonu značajna je iz ekološki prihvatljivog aspekta njegove ponovne upotrebe. Veza između komponenti betona i njegovih svojstava ne može biti prikazana samo na osnovi matematičkih formula. Prema tome, primijenjene su umjetne neuronske mreže i regresijska analiza eksperimentalnih rezultata postignutih planiranjem eksperimenta. Dokazano je da obje primijenjene tehnike omogućuju visoku pouzdanost za modeliranje svojstava betona na osnovi njegovih komponenti.Use of recycled materials as replacement for natural aggregate in concrete is important considering the environmentally beneficial aspect of its re-use. Relations between concrete components and concrete properties cannot be presented based on mathematical formulas only. Consequently, artificial neural networks and regression techniques were applied to analyse experimental results obtained according to previous experimental design. It was established that both techniques enable highly reliable modelling of concrete properties based on its components.Die Anwendung von recyceltem Material als Ersatz natürlicher Gesteinskörnung für Beton ist aufgrund des ökologisch akzeptablen Aspekts der Wiederverwertung bedeutend. Der Zusammenhang zwischen den Betonkomponenten und seinen Eigenschaften kann nicht ausschließlich mittels mathematischer Formeln dargestellt werden. Daher wurden künstliche neuronale Netze und eine Regressionsanalyse experimenteller Resultate im Bezug zur Planung des Experiments angewandt. Es wurde bewiesen, dass beide eingesetzten Techniken eine hohe Zuverlässigkeit bei der Modellierung von Betoneigenschaften aufgrund seiner Komponenten aufweisen

Management Intensity and Forest Successional Stages as Significant Determinants of Small Mammal Communities in a Lowland Floodplain Forest

The conversion of forests from complex natural ecosystems to simplified commercial woodlands is one of the major causes of biodiversity loss. To maintain biodiversity, we need to understand how current management practices influence forest ecosystems. We studied the effects of forest successional stage and management intensity on the abundance, species richness, and assemblage composition of small mammals. Our results show that management intensity significantly contributes to reducing the number of species after clearcutting. We revealed that intensively managed clearings can make the dispersal or foraging activity of small mammals diffcult and hence negatively influence their abundance and species richness. The significantly higher species richness of small mammal species was recorded within more extensively rather than intensively managed clearings. In contrast, we did not observe significant changes in species richness and abundance after intensive management in old-growth forests. Species Clethrionomys glareolus and Apodemus flavicollis reached the greatest abundance in old-growth forest patches. On the other hand, Microtus arvalis and Microtus subterraneus were species mainly associated with the successionally youngest forest stands. Our analysis suggests that intensive management interventions (i.e., vegetation destruction by pesticides and wood debris removal by soil milling) in clearings produce unhostile environments for majority of the small mammal species.O