Iskanje po repozitoriju

A+ | A- | | SLO | ENG

Opis: Magistrska naloga vsebuje statistično analizo pogostosti potresov v svetovnem merilu. V nalogi predstavimo zbiranje podatkov o potresnih dejavnostih, ki se nahajajo na spletni strani ameriške znanstvene agencije. Z uporabo programskega jezika Python zbrane podatke očistimo, jih analiziramo in predstavimo grafično. Z uporabo knjižnice GeoPandas posamezne lokacije potresov prestavimo tudi na zemljevidu. Za vodilo analize uporabimo zastavljene hipoteze s katerimi skušamo preveriti korelacijo med magnitudo, globino, posamezno lokacijo in časovno komponento potresov. Pri tem se osredotočimo na potresno zanimive lokacije, obravnavamo tudi Slovenijo.
Ključne besede: potresi, seizmologija, analiza velikih količin podatkov, magnituda, globina, gručenje
Objavljeno v ReVIS: 19.07.2023; Ogledov: 687; Prenosov: 81
Celotno besedilo (3,83 MB)

Opis: The thesis touches on important concepts of innovation and regional development. Their empirical relevance for economic and social growth is a subject of active debate. Studies revealed that interactions have a major impact, de facto, on the capacity to innovate. Diversifying knowledge sources, joint ventures and co-creational practices can explain much of the locational factors and innovation in a-spatial networks. With the rise of globalisation, scholars found that Multinational Corporations and their global production chains are integrating more and more into regions worldwide. They share practices and engage local stakeholders in their innovation projects. This research aims to explore the importance of locational and global factors in this process. The goal is to see how these two forces contribute to innovation separately and when intertwined regionally, closing the gap between regional and global innovation frameworks. The thesis contributes to science by developing a new synthesis model based on mainstream Innovation theories. It includes the endogenous and exogenous forces that create environmental pressures and opportunities, pushing economic agents to innovate. The central aspect of this model is the acknowledgement of the multiscalar nature of innovation. Local institutions, mindsets and influence from the production chains create conditions and pressures in the network, which motivates companies to innovate. Depending on the local competencies and the needs of production networks, it creates various environments for companies. It is up to enterprises to use created opportunities, which can be local, national or global. Data collected in eight (distinct) regions in Central and Eastern Europe (active in automotive and electronics industries) was used in qualitative and logical analysis to observe patterns in regional behaviour. The results revealed that endogenous and exogenous forces act circumstantially in each region, partially proving all the hypotheses on endogenous and exogenous factors and validating the model. Each region displays different rates of local and regional engagement and, thus, different patterns of innovation, determining their competitive potential. The research also revealed additional factors (e.g., the importance of trust, particularities of information diffusion, etc.) relevant to innovation in Central and Eastern Europe.
Ključne besede: innovation, regional development, transnational value chains, networks, institutions, cognitive mindset, regional-global co-operation
Objavljeno v ReVIS: 25.10.2022; Ogledov: 724; Prenosov: 73
Celotno besedilo (5,96 MB)

Opis: The modern computer resources and the data analysis methods allow for a biomedical data to be examined in a more detail than ever. The heart rate variability (HRV) is an easily accessible vital signal that offers a range of useful information about the person under a study. One such application regards an automatical determining whether a person is awake or asleep from the HRV data only. This is of an importance not just for medical but also for practical applications, such as a traffic safety or smart homes. In this doctoral work we study the HRV data of 75 healthy individuals of a varying age and sex, recorded with a microsecond precision. We employ the empirical fact that heart and respiration cycles couples differently during a sleep and awake period. Namely, a respiratory modulation of a heart rhythm or a respiratory sinus arrhythmia (RSA) is more pronounced while asleep, as both sleep and RSA are connected to a strong vagal activity. Therefore, the onset of sleep can be recognized or perhaps even predicted by a carefully examining the cardio-respiratory coupling. We show that the above can indeed be used, at least in principle, to design an algorithmic method to automatically determine the state of a person's consciousness from the HRV data only. On the methodological front we rely on quantifying the (self)similarity among the shapelets, the short chunks of the HRV time series, that allow for a consistent comparison among and within the time series. To establish a better benchmark, we also carry out a comprehensive analysis of the overall HRV dynamics depending on age and sex. Our results include: (i) that a distinctive patterns of the HRV dynamics are consistent across age and sex, (ii) that they are not only an indicative of sleep and awake, but allow to pinpoint the change from awake to sleep and vice versa almost immediately, (iii) that the shapelet analysis is a viable tool to examine these data with a great precision. We conclude that a more systematic analysis involving more subjects could lead to a practical method for the prediction of the onset of sleep.
Ključne besede: algoritem, EKG, Holter, vzorčna entropija, razmerje signal-šum, klasifikacija
Objavljeno v ReVIS: 18.09.2020; Ogledov: 2288; Prenosov: 220
Celotno besedilo (5,09 MB)

Opis: This PhD thesis is mainly devoted to the study of different problems in nonequilibrium systems that undergo certain phase transitions. The first part of this thesis constitutes a brief review of fundamental concepts in statistical physics. Many of them, of course, will be useful in the remaining of the thesis. My research is thus articulated in three main chapters. The second chapter focuses on a numerical and experimental evidence of an absorbing phase transition, so far associated with spatiotemporal dynamics provided in a purely temporal optical system. We provide a numerical and experimental study of an effective model for a bistable semiconductor laser, with long-delayed opto-electronic feedback and multiplicative noise showing the peculiar features of a critical phenomenon belonging to the directed percolation universality class. In the third part of the thesis we present a random network of heterogeneous phase oscillators in which the links mediating the interactions are constantly rearranged with a characteristic timescale and an extremely low instantaneous connectivity. We will show that, provided strong coupling and fast enough rewiring are considered, the network is able to reach partial synchronisation even in the vanishing connectivity limit. The last part is dedicated to a systematic test of an effective thermodynamics approach proposed for the identification of critical phase transitions in nonequilibrium systems by making a formal analogy with equilibrium systems. When the authors apply it to experimental data of neurons, this method seems to bring a signature of a "special critical point". However, the approach has never been tested on synthetic data and for this reason we will test it on out of equilibrium toy models that display critical transition in a known range of parameters. In the concluding section I summarise and briefly comment my main results and sketch some directions for further research.
Ključne besede: statistična fizika, ravnotežna statistična fizika, neenakomerna statistična fizika, termodinamika, kritični pojavi, kritična točka, Kuramoto model, skupina za renormalizacijo, samoorganizirana kritičnost omrežja, Zipfov zakon, efektivni termodinamični pristop
Objavljeno v ReVIS: 28.05.2019; Ogledov: 2688; Prenosov: 172
Celotno besedilo (14,36 MB)

Opis: Complex systems are omnipresent in nature, society as well as in human culture. Last few decades saw an increase of interest for their study, particularly by using graph-theoretic methodologies. By identifying systems' units as nodes and modelling interactions between the units as links, the study of complex networks spread to a number of disciplines including sociology, biology and linguistics, to just mention a few. The research done in this doctoral dissertation falls in this context. The core of this doctoral work is the data-driven multilevel analysis of major human languages, which was done in two stages. First, we looked at the speed of growth of Wikipedias in 26 different languages over the span of 15 years. This involved creating and analysing a dataset with 14962 articles, each of which exists in all 26 languages. We found six well-defined clusters of Wikipedias that share common growth patterns, with their make-up robust against the method used for their determination. Interestingly, the identified clusters were found to have little correlation with the respective language families. Rather, our results suggest that growth of Wikipedias is primarily governed by an intricate set of other factors, from culture to information literacy. Second, to approach human languages at another independent level, we gathered a dataset comprising a list of syllables and a list of syllables words in 10 different languages, specifically: English, Dutch, German, Russian, Slovenian, Croatian, French, Spanish, Latin and Basque. These datasets were obtained from recognized repositories for each language and benchmarked in the same way. Syllable networks were created by looking at pairs of syllables that jointly compose at least one word. We then carried out a systematic network analysis, relying on both standard network analysis methods and more recent techniques, such as K-core analysis and graphlet statistics. Research revealed striking similarities between the architectures of syllable networks that belong to the same language family, along with expected differences between the families. Indeed, structures of syllable networks were found to well quantify the linguistic similarities among these 10 languages, exactly as known from classical linguistics. Most interestingly, we found that Basque language, whose classification is as of today still unknown, bares a strong resemblance to Latin, at least when syllable network representation is concerned. Earlier stages of this doctoral work involved comparing the performance of network alignment algorithms, used in bioinformatics for studying protein networks. Several alignment algorithms were compared by scoring their performance on standard protein datasets. It was found that three algorithms, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. Due to the change of doctoral adviser, this research topic was abandoned in favour of language/syllable networks. In sum, this doctoral work involved two distinct directions of research in network science, one related to developing the methodology of network analysis (alignment algorithms), and the other devoted to extracting new information from specifically designed datasets (syllable networks). Therefore, the original contribution of this work to science includes both theory and methodology. Future research avenues include advancement along both directions, most interesting being the application of network alignment methods to syllable datasets, which could reveal more precise quantification of structural differences among syllable networks.
Ključne besede: computational statistics, biostatistics, bioinformatics, machine learning, computational linguistics
Objavljeno v ReVIS: 21.12.2018; Ogledov: 3178; Prenosov: 139
Celotno besedilo (17,51 MB)