1. Epidemic trajectories and awareness diffusion among unequals in simplicial complexesLijin Liu, Meiling Feng, Chengyi Xia, Dawei Zhao, Matjaž Perc, 2023, izvirni znanstveni članek Opis: The interplay between awareness diffusion and epidemic spreading has been an active topic of research in recent years. Studies have shown that group interactions are an important consideration in contagion processes, and that thus higher-order interactions should be introduced into epidemic modeling. Research has also shown that individual responses to an unfolding epidemic are often strongly heterogeneous. We therefore present a two-layer network model, where the diffusion of awareness unfolds over 2-simplicial complexes in one layer, and the actual epidemic spreading unfolds over pairwise physical contacts in the other layer. The model takes into account individual differences in the degree of acceptance of information and self-protection measures once the epidemic is perceived. We use the micro Markov chain approach to determine the epidemic threshold of the model, which agrees well with the results obtained by Monte Carlo simulations. We show that the synergistic reinforcement due to 2-simplicial complexes in the virtual layer can restrain epidemic spreading by facilitating awareness diffusion, and moreover, that individual heterogeneity in the physical layer can increase the epidemic threshold and decrease the size of epidemic transmission. However, heterogeneity in the perception can also have the opposite effect because it inhibits the diffusion of awareness. Our results reveal the intricate interplay between awareness diffusion and epidemic spreading, and we hope they can help determine effective control measures. Ključne besede: higher-order interactions, awareness diffusion, epidemic spreading, multiplex network, social physics Objavljeno v DKUM: 21.06.2024; Ogledov: 128; Prenosov: 6 Celotno besedilo (1,23 MB) Gradivo ima več datotek! Več... |
2. Turing patterns in simplicial complexesShupeng Gao, Lili Chang, Matjaž Perc, Zhen Wang, 2023, izvirni znanstveni članek Opis: The spontaneous emergence of patterns in nature, such as stripes and spots, can be mathematically explained by reaction-diffusion systems. These patterns are often referred as Turing patterns to honor the seminal work of Alan Turing in the early 1950s. With the coming of age of network science, and with its related departure from diffusive nearest-neighbor interactions to long-range links between nodes, additional layers of complexity behind pattern formation have been discovered, including irregular spatiotemporal patterns. Here we investigate the formation of Turing patterns in simplicial complexes, where links no longer connect just pairs of nodes but can connect three or more nodes. Such higher-order interactions are emerging as a new frontier in network science, in particular describing group interaction in various sociological and biological systems, so understanding pattern formation under these conditions is of the utmost importance. We show that a canonical reaction-diffusion system defined over a simplicial complex yields Turing patterns that fundamentally differ from patterns observed in traditional networks. For example, we observe a stable distribution of Turing patterns where the fraction of nodes with reactant concentrations above the equilibrium point is exponentially related to the average degree of 2-simplexes, and we uncover parameter regions where Turing patterns will emerge only under higher-order interactions, but not under pairwise interactions. Ključne besede: Turing pattern, higher-order network, nonlinear dynamics, pattern formation Objavljeno v DKUM: 31.05.2024; Ogledov: 148; Prenosov: 4 Celotno besedilo (1,57 MB) Gradivo ima več datotek! Več... |
3. Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networksDarja Kavšek, Adriána Bednárová, Miša Biro, Roman Kranvogl, Darinka Brodnjak-Vončina, Ernest Beinrohr, 2013, izvirni znanstveni članek Opis: Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbonand volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV. Ključne besede: Slovenian coal, higher heating value, HHV, regression, artificial neural network Objavljeno v DKUM: 03.04.2017; Ogledov: 29204; Prenosov: 369 Celotno besedilo (749,77 KB) Gradivo ima več datotek! Več... |