Comparative exterior measures of pramenka sheep raised in three localities in Bosnia and Herzegovina

The aim of the research was to measure the basic external characteristics of Pramenka sheep (ridge height, from the ground to the highest ridge point, lower back height, from the ground to the highest lower back point, hull lenght, chest width, chest depth, chest circumference, hull circumference, shin circumference, body weight) with the aim of comparing the measured values in order to asses the impact of breeding areas on them. Domestic kind Pramenka (Kupres strain, Vlašić strain) were used in this research. There were 36 sheeps in the experimental group, and the same number in the control group. Experimets were performed on long-term  purebred herds of Pramenka on three private farms in the Una-Sana Canton, 2 municipilities of Cazin, 1 municipality of Bihać, as well as on one private farm in Central Bosnia (area of the municipality of Travnik), and one in the municipality of Kupres (Livno Canton). Based on the presented average values of the external properties of Pramenka sheep and their variations for all examined localities, we can conclude the following: that the sheep are longer in relation to their height and that the Pramenka is of medium physical development, that the differences in body measures in the examined areas are greatly influenced by the origin of certain breeds of Pramenka sheep (Kupres strain, Vlašić strain), as well as the quality of pastures and unequal access to food. By comparing our results with the results of other authors who examined the exterior of other strains of Pramenka (from region in Croatia: Rab, Lika, Pag, Istria) in our wider enviroment concluded that Vlašić Pramenka is the largest strain of Pramenka in this area

Buckwheat in the nutrition of cock laying as a factor of egg quality

The subject of this paper is the research of the influence of different ratios of buckwheat, in concentrated feed, on the qualitative values of laying hen eggs. The study was conducted in four groups of laying hens: one control and three experimental, which were formed with respect to different proportions of buckwheat in meals. Within the first group of laying hens, a concentrated feed mixture with 10% relative share of buckwheat was used, within the second group with 20% relative share of buckwheat and within the third group with 30% relative share of buckwheat, while in the control group standard concentrated food was used. Based on the conducted research, it can be concluded that buckwheat in the meal of laying hens has positive effects on quality, physical properties, sensory properties and frequency of carrying. It was shown that there are statistically significant differences in mean values ​​for the following variables: protein content in egg white, protein content in egg yolk (%), fat content in egg yolk (%), where the highest value was recorded in the third group of laying hens. Also, statistically significant differences were found in terms of shell weight (g), shell thickness (mm), yolk diameter (mm) and egg white pH. The highest average frequency of egg laying was found in the first group and the lowest average frequency of egg laying was in the control group. The general conclusion is that buckwheat can be used in poultry feed, because it has a much greater positive than negative effect on the production and quality characteristics of eggs for consumption

Synthesis, characterization and bioactivity of selected metal complexes with imine ligands

The chemistry of complex compounds containing imine ligands is attracting significant attention from researchers today. In this work, complexes of selected transition metals (Cu, Co, Ni and Fe) with imines based on ninhydrin and amino acids methionine and cysteine were synthesized. FTIR and UV/VIS spectroscopy were used for structural characterization. Antioxidant activity of the complex was analyzed by the FRAP method. The synthesized compounds showed a significant reducing ability, ranging from 221.94 to 756.30 µmol/L. In vitro antimicrobial activity was tested on strains from the ATCC collection. Inhibitory activity against the tested microorganisms was recorded, and the zones of inhibition ranged from 10-24 mm. Preliminary research shows that these compounds have biological potency, but more detailed in vitro and in vivo studies are required for their use.</p

An assessment of regulation, education practices and socio-economic perceptions of non-native aquatic species in the Balkans

Alongside climate change, the introduction of non-native species (NNS) is widely recognized as one of the main threats to aquatic biodiversity and human wellbeing. Non-native species and biodiversity are generally low priority issues on the political agendas of many countries, particularly in European countries outside the European Union (EU). The objectives and tasks of this study were to address the policy regulation, education level, education practices, and socioeconomic perceptions of NNS in the Balkans. A questionnaire-based survey was conducted in Albania, Bosnia and Herzegovina, Montenegro, North Macedonia and Turkey (Balkan EU candidate and potential candidate members), in Croatia and Greece (Balkan EU Member States) and Italy (non-Balkan EU Member State). The EU Alien Regulation (1143/2014) concerning NNS is implemented in EU Member States and Montenegro, whereas Albania, Bosnia and Herzegovina and Turkey have not reported specific policy regulations for NNS. Permanent monitoring programmes specifically designed for NNS have not yet been established in the EU Member States. Most countries tackle the issue of NNS through educational activities as part of specific projects. Education level is indicative of the implementation of NNS policy regulation, and efforts are needed for the proper development of relative study programmes. Public awareness and educational preparedness concerning NNS in the Balkans were identified as poor. Strong programmes for management and education should be developed to increase public awareness to prevent further biodiversity losses in the Balkan region

Control of an unmaned aerial vehicle using a camera

V diplomski nalogi se lotimo naloge vodenja kvadrokopterja skozi poligon obročev, ki imajo na spodnji strani nameščeno ArUco značko. Prvi korak je modeliranje gibanja letanika s prenosnimi funkcijami, posamezno za vsako prostorno stopnjo premika. S pomočjo Strejčeve metode identificiramo prenosne funkcije in prilagodimo parametre tako, da se ujemajo s posnetim signalom odziva realnega sistmea pri vzbujanju s stopničastim vhodnim signalom. Na osnovi dokončanih modelov izberemo vrsto regulatorja ter ga načrtamo, spet, za vsak premik posamezno. V nadaljevanju analiziramo stabilnost zaprtozančnega sistema s pomočjo Nyqustiovega in Bodejevega diagrama in prilagodimo konstante regulatorja tako, da je model robustno stabilen. Ko imamo vse parametre regulatorja, regulator implementiramo v programsko kodo in poskus izvedemo na letalniku. V teku poskusa snemamo izhodne signale, da jih lahko primerjamo s simuliranimi.In the thesis we address the problem of controlling a quad-copter trough a polygon of rings witch have an ArUco marker on the bottom. The first step is to model drone’s movements with transfer functions for each degree of freedom individually. With the use of Strejc method we identify transfer functions and adjust parameters in such a way that the signals of the models match recorded output of a real system when the input is a step function signal. On the basis of finished models we pick a controller and construct it, once again, for each degree of freedom individually. In continuation, we analyze stability with Nyquist and Bode diagrams and adjust controller constants so the model is robustly stable. Once we have all controller parameters, the controller is implemented in the code and the experiment is done on the real drone. During the experiment, we record all output signals so we can compare them to simulated ones

Izboljšanje zaznavanja objektov v naprednih sistemih za pomoč voznikom z uporabo sintetičnih vremenskih razmer

Ensuring reliable object detection under adverse weather conditions, such as rain and snow, remains a significant challenge for visual sensing in the automotive world. These conditions can severely impact the performance of sensors, leading to reduced accuracy in object detection and an increased risk of accidents. This thesis focuses on enhancing object detection capabilities in ADAS/ADS by incorporating synthetic weather condition images into the training process in order to improve object detection in those conditions. Utilizing generative models we simulate the effects of adversarial weather on driving images in both city and highway environments. The primary objective is to create a comprehensive dataset of synthetic weather images, which, when integrated into the training pipeline for object detection, can improve the robustness of object detection models under adverse weather conditions. Our findings demonstrate that integrating synthetic weather images into the training process significantly enhances the performance of object detection models, making them more resilient to adverse weather conditions. This research not only addresses a critical safety concern in the automotive industry but also paves the way for future advancements in the field of autonomous driving.Zagotavljanje zanesljivega zaznavanja objektov v neugodnih vremenskih razmerah, kot sta dež in sneg, ostaja velik izziv za vizualne senzorje v avtomobilskem svetu. Te razmere lahko resno vplivajo na delovanje senzorjev, kar vodi do zmanjšane natančnosti zaznavanja objektov in povečanega tveganja nesreč. Ta magistrska naloga se osredotoča na izboljšanje zmogljivosti zaznavanja objektov v naprednih sistemih za pomoč voznikom (ADAS) in sistemih za avtonomno vožnjo (ADS) z vključevanjem slik sintetičnih vremenskih razmer v proces učenja, da bi izboljšali zaznavanje objektov v teh pogojih. S pomočjo generativnih modelov simuliramo učinke neugodnih vremenskih razmer na slike vožnje tako v mestnem kot avtocestnem okolju. Glavni cilj je ustvariti obsežen nabor sintetičnih vremenskih slik, ki lahko, ko so vključene v učni proces zaznavanja objektov, izboljšajo robustnost modelov zaznavanja objektov v neugodnih vremenskih razmerah. Pregledali smo prejšnje raziskave na področju generativnih modelov za slike in njihovih aplikacij pri simulaciji različnih vremenskih razmer. Obravnavali smo metode, kot so generativni nasprotniki modeli, ter izpostavili potrebo po bolj robustnih in natančnih metodah za simulacijo vremenskih razmer v mestnem in avtocestnem okolju. Poleg pregleda generativnih modelov smo pregledali tudi evalvacijske mere za kakovost generiranih slik ter prišli do zaključka, da trenutno ne obstaja ena mera, ki nam pove, kako dobra je sintetična slika. Pregled literature obsega tudi pregled metod zaznavanja objektov pri ADAS/ADS ter težave vizualnih senzorjev pri neugodnih vremenskih razmerah. V tretjem poglavju opisujemo uporabljene metode, ki vključujejo generativne modele, zlasti GAN arhitekturo, za simulacijo učinkov dežja in snega na slike vožnje. Podrobno smo predstavili ozadje ter kriterijske funkcije modela QS-Attn, ki omogoča natančno ustvarjanje vremenskih učinkov z uporabo pozornostnih mehanizmov za izbiro najpomembnejših značilnosti slik. Za treniranje QS-Attn modela potrebujemo podatke, in ker jih na trgu ne dobimo na enostaven način, smo posneli svoje podatke vožnje. Razvili smo lastni nabor podatkov, ki se imenuje pozicijsko-uparen nabor, ki vključuje slike vožnje v različnih vremenskih razmerah, posnete na enakih lokacijah v suhem vremenu, dežju in snegu. Ta nabor podatkov omogoča modelom, da se lažje naučijo razlik med različnimi vremenskimi pogoji, ter nam olajša evalvacijo. Peto poglavje opisuje rezultate QS-Attn modela. Vidimo, da z našim naborom podatkov, ko je model natreniran, dobimo vizualno dobre rezultate. Kakovost vseh slik izmerimo z v literaturi popularnimi merami, kot so FID in KID, obe z različnimi verzijami luščenja značilk. Rezultate tudi kvalitativno primerjamo z dejanskimi slikami dežja in snega. Model QS-Attn testiramo tudi na dodatnem naboru podatkov, ki ni del originalnega, ter na slikah popolnoma izven razpodelitve. V šestem poglavju pokažemo, kako model za zaznavanje vozil, treniran samo na slikah zajetih v suhem vremenu, deluje v dežju in snegu. Rezultate prikažemo kvantitativno in kvalitativno. Ugotovitve so, da model, treniran samo na suhih slikah, ne zadostuje izzivom, kot so dež in sneg, in ima resne probleme pri zaznavanju objektov. Takšni modeli pogosto ne zaznajo vozil, ki so ravno pred kamero, zaradi okoliščin, kot so vklopljene luči ali delna pokritost s snegom. V sedmem poglavju preverimo, kaj se dogaja z modeli za zaznavanje objektov, če v nabor podatkov za trening uporabimo prave slike, sintetične slike in kombinacijo pravih in sintetičnih slik dežja ter snega. Izkazalo se je, da lahko s tem znatno izboljšamo zaznavanje v obeh vremenskih razmerah. Modeli imajo več pozitivnih zaznavanj in so splošno bolj robustni. Naše ugotovitve kažejo, da vključitev sintetičnih vremenskih slik v učni proces znatno izboljša zmogljivost modelov zaznavanja objektov, zaradi česar so bolj odporni na neugodne vremenske razmere. Ta raziskava ne samo da naslavlja kritično varnostno vprašanje v avtomobilski industriji, ampak tudi odpira pot za prihodnje napredke na področju avtonomne vožnje