The first records of Trithemis annulata (Palisot de Beauvois, 1807) (Odonata: Libellulidae) in Croatia

In August 2022 the first individuals of the dragonfly species Violet dropwing, Trithemis annulata (Palisot de Beauvois, 1807), were observed in Croatia, at three localities in southern Dalmatia. Two males were observed at the Peračko Blato lake, while both males and females were recorded at two localities at the Baćinska Lakes. At the Baćinska Lakes, more than 10 individuals were observed indicating a possible established population. The nearest known reproducing population is located about 160 km to the south, in Montenegro. Due to the species expansion in Europe, and recent records as north as Slovenia, additional records and established populations are to be expected in Croatia. As the species is now known from Croatia, we propose a vernacular name for this species, “ljubičasta skitnica” meaning purple tramp, referring to its coloration, wandering behavior and dispersal potential

The butterfly (Lepidoptera: Papilionoidea) diversity of the Barać Caves Significant Landscape (Kordun, Croatia)

We studied the butterfly fauna of the protected area Barać Caves Significant Landscape, which is located in the southern part of the Kordun region. The surveys,carried out during 2020 and 2021, recorded a total of 79 butterfly species. The species recorded in the area outnumber those far recorded in northern Kordun (74) and Plitvice Lakes NP (71), but this is probably due to the lack of systematic surveys of those two areas. The comparison of habitat and biogeographical affiliation between these three areas revealed a similar number of species per habitat and affiliation type. During this survey, several interesting or rare species were recorded like Lycaena hippothoe, L. dispar, Phengaris arion, Melitaea aurelia, M. britomartis, Euphydryas aurinia, Apatura ilia, A. iris, and Boloria selene and their records are discussed. The results of the present study greatly contribute to the knowledge of the butterfly fauna of the Barać Caves Significant Landscape, and they can be used as a basis for the future conservation of butterfly species of the Kordun and Lika region

The current distribution and status of the Hermann’s tortoise, Testudo hermanni boettgeri (Reptilia, Testudines, Testudinidae) in Croatia

Hermann’s tortoise (Testudo hermanni) is listed as “Near threatened” in the IUCN Red list of endangered species. The importance of protecting the Hermann’s tortoise populations and its habitats have led to the inclusion of the species within CITES Convention (Annex II), Annex A of EU Wildlife Trade Regulation, Annex II of the Bern Convention and Annexes II and IV of the EU Habitats Directive. To assess the distribution and status of the eastern Hermann’s tortoise (Testudo hermanni boettgeri) in Croatia, historical and recent records were gathered and analyzed. The species was recorded in all three biogeographical regions in the country, but it’s native to the Mediterranean and a small part of the Alpine region. With the increase of recent surveys and the use of citizen science platforms, the known range of the species in Croatia was increased by 35.8% and is now encompassing 123 10 × 10 km EEA reference grid cells. Most records (66%) originate from lower elevations (up to 199 m), and the highest was recorded at 570 m. Sparse forests are the most preferred habitats, followed by semi-open habitats, such as grasslands and shrubs. The most serious threat to the species is natural succession due to the increased abandonment of traditional farming and grazing. Other threats include touristic infrastructure and urban development, transportation, illegal collecting, and invasive species. The Area of Occupancy calculated using 2 × 2 km grids resulted in an AOO of 1,372.00 km2, while Extent of Occurrence (EOO) is calculated to be 18,145.07 km2. The current network of National protected areas includes 14% of the species’ AOO while the designated Natura 2000 areas include 29.30% of its AOO. We propose to designate an additional 10 Natura 2000 areas to help with the long-term protection of the species

Identification of muons with the CMS detector in proton-proton collisions at s=13\sqrt{s}=13 TeV using machine-learning methods

U ovom diplomskom radu napravljena je identifikacija objekata dobivenih simulacijom Drell-Yan procesa. Rekonstruirani objekti podijeljeni su u dvije klase: u klasu signalnih i u klasu pozadinskih objekata koji su zatim spremljeni u skup podataka. Signalni objekti su mioni najvjerojatnije nastali u primarnom verteksu, dok su pozadina objekti pogrešno rekonstruirani kao mioni. Analize podataka na CERN-u nerijetko koriste takozvane cut based ID metode za identifikaciju objekata, stoga ovaj rad razmatra mogućnost zamjene tog pristupa pristupom strojnog učenja. Strojno učenje uči model razlikovati objekte treniranjem na već klasificiranim podacima da bi bio sposoban identificirati signal u eksperimentalnim podacima prikupljenim radom CMS detektora. Želeći imitirati ovaj proces, kreirani skup podataka dijeli se na skup podatka za treniranje i na skup za testiranje. Glavnu prepreku u ostvarenju cilja predstavlja neuravnoteženost klasa jer je broj pozadinskih objekata uvelike, približno 14 puta, manji od signalnih. Obrađena su dva pristupa problemu neuravnoteženosti podataka. Prvi pristup je težinsko treniranje koje mijenja težine funkciji pogreške. Drugi pristup je balansiranje klasa dodavanjem ili izbacivanjem objekata skupa podataka za treniranje.In this thesis, the identification of objects obtained by simulation of the Drell-Yan process is made. The reconstructed objects were divided into two classes: the signal object class and the background object class, which were then stored in a data set. Signal objects are muons most likely formed in the primary vertex, while background objects are inaccurately reconstructed as muons. Data analysis at CERN often use so-called cut-based ID methods to identify objects, therefore this paper considers the possibility of replacing this approach with a machine learning strategy. Machine learning teaches the model to distinguish objects by training on already classified data to be able to identify the signal in the experimental data collected by the operation of the CMS detector. Wanting to mimic this process, the created data set is divided into a training data set and a test data set. In achieving the goal, the main impediment were the imbalance of classes which means the number of background objects is much, approximately 14 times, less than the signal. Two approaches to the problem of data imbalance are discussed. The first approach is weight balancing which changes the weights of the error function. The second approach is to balance classes by adding or removing objects from a training data set

Intrabody communication using pulse modulation techniques

Prijenos signala ljudskim tijelom (eng. Intrabody communication, IBC) je relativno mlada tehnologija koja koristi tijelo kao medij za prijenos informacija. Začetnik ove vrste prijenosa signala je T. G. Zimmerman, koji je 1996. godine konstruirao prvi uređaj za prijenos signala ljudskim tijelom. IBC sustav sastoji se od odašiljača i prijamnika koji su sa tijelom spojeni preko signalne i referentne elektrode. Signalna elektroda služi za prijenos signala po ljudskom tijelu dok referentna služi da bi se zatvorio povratni put signala. Obje elektrode su postavljene na tijelo te galvanski odvojene od tijela. Sustav pokazan u ovome radu sastoji se od odašiljača, prijamnika te bežičnog modula za vezu prema računalu. Odašiljač šalje Manchester kodirani signal direktno u tijelo gdje se zbog visokopropusne karakteristike ljudskog tijela rastući brid Manchester kodiranog signala pretvara u pozitivni impuls, a padajući brid Manchester kodiranog signala u negativni impuls. Zadaća prijamnika je filtrirati primljene impulse, pojačati ih te rekonstruirati u poslani Manchester kodirani signal. Obrađeni primljeni signali se zatim šalju na računalo putem bežičnog modula NRF24L01Intrabody communication (IBC) is relatively new method which uses the human body as a signal transmission medium. The founder of this method is T.G. Zimmerman who constructed the first intrabody device in 1996. The parts of IBC system are transmitter and receiver, which are connected to the human body via signal and referent electrodes. The signal electrode is used to transfer signal through the body and the referent electrode is used to close signal return path. Both electrodes are placed on body and galvanically isolated from the human body. The IBC system developed in this master thesis consists of transmitter, receiver and wireless module for connection with a PC. Transmitter sends Manchester coded signals directly into the body, where the rising edge of a Manchester signal is transformed into the positive impulse while the falling edge is transformed into the negative impulse, due to the high-pass characteristic of the human body. Receiver must filter incoming impulses then amplify them before they can be reconstructed back in Manchester coded signal. Reconstructed signal is then sent to PC via NRF24L01 wireless module

Intrabody communication using pulse modulation techniques

Prijenos signala ljudskim tijelom (eng. Intrabody communication, IBC) je relativno mlada tehnologija koja koristi tijelo kao medij za prijenos informacija. Začetnik ove vrste prijenosa signala je T. G. Zimmerman, koji je 1996. godine konstruirao prvi uređaj za prijenos signala ljudskim tijelom. IBC sustav sastoji se od odašiljača i prijamnika koji su sa tijelom spojeni preko signalne i referentne elektrode. Signalna elektroda služi za prijenos signala po ljudskom tijelu dok referentna služi da bi se zatvorio povratni put signala. Obje elektrode su postavljene na tijelo te galvanski odvojene od tijela. Sustav pokazan u ovome radu sastoji se od odašiljača, prijamnika te bežičnog modula za vezu prema računalu. Odašiljač šalje Manchester kodirani signal direktno u tijelo gdje se zbog visokopropusne karakteristike ljudskog tijela rastući brid Manchester kodiranog signala pretvara u pozitivni impuls, a padajući brid Manchester kodiranog signala u negativni impuls. Zadaća prijamnika je filtrirati primljene impulse, pojačati ih te rekonstruirati u poslani Manchester kodirani signal. Obrađeni primljeni signali se zatim šalju na računalo putem bežičnog modula NRF24L01Intrabody communication (IBC) is relatively new method which uses the human body as a signal transmission medium. The founder of this method is T.G. Zimmerman who constructed the first intrabody device in 1996. The parts of IBC system are transmitter and receiver, which are connected to the human body via signal and referent electrodes. The signal electrode is used to transfer signal through the body and the referent electrode is used to close signal return path. Both electrodes are placed on body and galvanically isolated from the human body. The IBC system developed in this master thesis consists of transmitter, receiver and wireless module for connection with a PC. Transmitter sends Manchester coded signals directly into the body, where the rising edge of a Manchester signal is transformed into the positive impulse while the falling edge is transformed into the negative impulse, due to the high-pass characteristic of the human body. Receiver must filter incoming impulses then amplify them before they can be reconstructed back in Manchester coded signal. Reconstructed signal is then sent to PC via NRF24L01 wireless module

Measurments of gamma-ray absorption coefficient

U ovom radu je izmjeren koeficijent apsorpcije gama zračenja poznat još i kao linearni koeficijent atenuacije. Gama zračenje predstavlja elektromagnetsko zračenje visoke energije. Emitira ga nestabilna jezgra kada prelazi iz stanja više energije na nižu energijsku razinu što nam je poznato i kao gama raspad. Medudjelovanje gama zračenja s materijom i sama detekcija gama zračenja odvija se putem nekoliko efekata. To su fotoelektrični efekt, Comptonovo raspršenje i produkcija parova. Eksperimentalnim mjerenjem dobili smo eksponencijalnu ovisnost broja detektiranih gama zraka o debljini olovnog apsorbera. Statističkom metodom najvjerojatnijeg procjenitelja (eng. maximum likelihood estimator) želimo dobiti najvjerojatniju vrijednost parametra µ koji predstavlja koeficijent apsorpcije gama zraka. Atenuacijski koeficijent nam je od velikog značaja u medicini gdje se primjenjuje metoda zračenja u tretmanu liječenja oboljelih od raka, stoga je od iznimne važnosti dobro poznavati metode zaštite od zračenja kao i izradu adekvatne opreme koja će gotovo potpuno zaustaviti neželjene efekte na biološko tkivo

Identification of muons with the CMS detector in proton-proton collisions at s=13\sqrt{s}=13 TeV using machine-learning methods

U ovom diplomskom radu napravljena je identifikacija objekata dobivenih simulacijom Drell-Yan procesa. Rekonstruirani objekti podijeljeni su u dvije klase: u klasu signalnih i u klasu pozadinskih objekata koji su zatim spremljeni u skup podataka. Signalni objekti su mioni najvjerojatnije nastali u primarnom verteksu, dok su pozadina objekti pogrešno rekonstruirani kao mioni. Analize podataka na CERN-u nerijetko koriste takozvane cut based ID metode za identifikaciju objekata, stoga ovaj rad razmatra mogućnost zamjene tog pristupa pristupom strojnog učenja. Strojno učenje uči model razlikovati objekte treniranjem na već klasificiranim podacima da bi bio sposoban identificirati signal u eksperimentalnim podacima prikupljenim radom CMS detektora. Želeći imitirati ovaj proces, kreirani skup podataka dijeli se na skup podatka za treniranje i na skup za testiranje. Glavnu prepreku u ostvarenju cilja predstavlja neuravnoteženost klasa jer je broj pozadinskih objekata uvelike, približno 14 puta, manji od signalnih. Obrađena su dva pristupa problemu neuravnoteženosti podataka. Prvi pristup je težinsko treniranje koje mijenja težine funkciji pogreške. Drugi pristup je balansiranje klasa dodavanjem ili izbacivanjem objekata skupa podataka za treniranje.In this thesis, the identification of objects obtained by simulation of the Drell-Yan process is made. The reconstructed objects were divided into two classes: the signal object class and the background object class, which were then stored in a data set. Signal objects are muons most likely formed in the primary vertex, while background objects are inaccurately reconstructed as muons. Data analysis at CERN often use so-called cut-based ID methods to identify objects, therefore this paper considers the possibility of replacing this approach with a machine learning strategy. Machine learning teaches the model to distinguish objects by training on already classified data to be able to identify the signal in the experimental data collected by the operation of the CMS detector. Wanting to mimic this process, the created data set is divided into a training data set and a test data set. In achieving the goal, the main impediment were the imbalance of classes which means the number of background objects is much, approximately 14 times, less than the signal. Two approaches to the problem of data imbalance are discussed. The first approach is weight balancing which changes the weights of the error function. The second approach is to balance classes by adding or removing objects from a training data set

Intrabody communication using pulse modulation techniques

Prijenos signala ljudskim tijelom (eng. Intrabody communication, IBC) je relativno mlada tehnologija koja koristi tijelo kao medij za prijenos informacija. Začetnik ove vrste prijenosa signala je T. G. Zimmerman, koji je 1996. godine konstruirao prvi uređaj za prijenos signala ljudskim tijelom. IBC sustav sastoji se od odašiljača i prijamnika koji su sa tijelom spojeni preko signalne i referentne elektrode. Signalna elektroda služi za prijenos signala po ljudskom tijelu dok referentna služi da bi se zatvorio povratni put signala. Obje elektrode su postavljene na tijelo te galvanski odvojene od tijela. Sustav pokazan u ovome radu sastoji se od odašiljača, prijamnika te bežičnog modula za vezu prema računalu. Odašiljač šalje Manchester kodirani signal direktno u tijelo gdje se zbog visokopropusne karakteristike ljudskog tijela rastući brid Manchester kodiranog signala pretvara u pozitivni impuls, a padajući brid Manchester kodiranog signala u negativni impuls. Zadaća prijamnika je filtrirati primljene impulse, pojačati ih te rekonstruirati u poslani Manchester kodirani signal. Obrađeni primljeni signali se zatim šalju na računalo putem bežičnog modula NRF24L01Intrabody communication (IBC) is relatively new method which uses the human body as a signal transmission medium. The founder of this method is T.G. Zimmerman who constructed the first intrabody device in 1996. The parts of IBC system are transmitter and receiver, which are connected to the human body via signal and referent electrodes. The signal electrode is used to transfer signal through the body and the referent electrode is used to close signal return path. Both electrodes are placed on body and galvanically isolated from the human body. The IBC system developed in this master thesis consists of transmitter, receiver and wireless module for connection with a PC. Transmitter sends Manchester coded signals directly into the body, where the rising edge of a Manchester signal is transformed into the positive impulse while the falling edge is transformed into the negative impulse, due to the high-pass characteristic of the human body. Receiver must filter incoming impulses then amplify them before they can be reconstructed back in Manchester coded signal. Reconstructed signal is then sent to PC via NRF24L01 wireless module