University of Maribor

About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.

Using Ontologies in the Domain Analysis of Domain-Specific Languages

Abstract: The design stage of domain-specific language development, which includes domain analysis, has not received as much attention compared to the subsequent stage of language implementation. This paper investigates the use of ontology in domain analysis for the development of a domain-specific language. The standard process of ontology development is investigated as an aid to determine the pertinent information regarding the domain (e.g., the conceptualization of the domain and the common and variable elements of the domain) that should be modeled in a language for the domain. Our observations suggest that ontology assists in the initial phase of domain understanding and can be combined with further formal domain analysis methods during the development of a domain-specific language.

Coupled Simulations of Nozzle Flow, Primary Fuel Jet Breakup, and Spray Formation

Abstract: Presented are two approaches for coupled simulations of the injector flow with spray formation. In the first approach the two-fluid model is used within the injector for the cavitating flow. A primary breakup model is then applied at the nozzle orifice where it is coupled with the standard discrete droplet model. In the second approach the Eulerian multi-fluid model is applied for both the nozzle and spray regions. The developed primary breakup model, used in both approaches, is based on locally resolved properties of the cavitating nozzle flow across the orifice cross section. The model provides the initial droplet size and velocity distribution for the droplet parcels released from the surface of a coherent liquid core. The major feature of the predictions obtained with the model is a remarkable asymmetry of the spray. This asymmetry is in agreement with the recent observations at Chalmers University where they performed experiments using a transparent model scaled-up injector. The described model has been implemented into AVL FIRE computational fluid dynamics code which was used to obtain all the presented results. Copyright

Complex networks and deep learning for EEG signal analysis

Abstract: Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.

Discrepancies among Scopus, Web of Science, and PubMed coverage of funding information in medical journal articles

Abstract: Objective: The overall aim of the present study was to compare the coverage of existing research funding information for articles indexed in Scopus, Web of Science, and PubMed databases. Methods: The numbers of articles with funding information published in 2015 were identified in the three selected databases and compared using bibliometric analysis of a sample of twenty-eight prestigious medical journals. Results: Frequency analysis of the number of articles with funding information showed statistically significant differences between Scopus, Web of Science, and PubMed databases. The largest proportion of articles with funding information was found in Web of Science (29.0%), followed by PubMed (14.6%) and Scopus (7.7%). Conclusion: The results show that coverage of funding information differs significantly among Scopus, Web of Science, and PubMed databases in a sample of the same medical journals. Moreover, we found that, currently, funding data in PubMed is more difficult to obtain and analyze compared with that in the other two databases. This article has been approved for the Medical Library Association’s Independent Reading Program .