1. Network representation of multicellular activity in pancreatic islets : Technical considerations for functional connectivity analysisMarko Šterk, Yaowen Zhang, Viljem Pohorec, Eva Paradiž, Jurij Dolenšek, Richard K. P. Benninger, Andraž Stožer, Vira Kravets, Marko Gosak, 2024, izvirni znanstveni članek Opis: Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.
Ključne besede: islets of Langerhans, beta cells, calcium signaling, intercellular communication, functional networks, myosin model
Objavljeno v DKUM: 09.12.2024; Ogledov: 0; Prenosov: 1
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2. Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patternsOleg Maslennikov, Matjaž Perc, Vladimir Nekorkin, 2024, izvirni znanstveni članek Opis: In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor-Purpura distance. We apply algebraic topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically calculating the persistence barcodes and Betti curves. By comparing the features of dierent types of output patterns, we uncover the complex relations between low-dimensional target signals and the underlying multidimensional spike trains.
Ključne besede: topological features, neural networks, spatiotemporal patterns, nonlinear dynamics
Objavljeno v DKUM: 27.11.2024; Ogledov: 0; Prenosov: 0
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3. A bibliometric network analysis of green information technology and green information systems researchAnja Žnidaršič, Daria Maltseva, Alenka Brezavšček, Matjaž Maletič, Alenka Baggia, 2021, izvirni znanstveni članek Opis: Background: In recent years, the concepts of Green Information Technology and Green Information Systems (Green IT/IS) have attracted the attention of many researchers. Several environmental and sustainability studies have suggested that smart usage of Green IT/IS is one of the most important enablers for sustainable development in organizations and plays an essential role in greening the planet.
Objectives: This paper aims to examine the development of the Green IT/IS field based on the published works. The focus is on analysing the keywords of related papers obtained from the Web of Science database.
Methods/Approach: Based on the twomode network of papers and keywords, the analysis of co-occurrence of keywords is provided. The most frequent keywords discovered by the temporal network analysis are presented from the perspective of the most prominent journals.
Results: The main pillars of knowledge in Green IT/IS research are highlighted, and a chronological map of the field is provided.
Conclusions: Green IT/IS's studied field shows constant growth in the last decades, and the results indicate the trends of future development in the field. The paper is one of the first studies that apply the bibliographic network analysis approach to the field of Green IT/IS.
Ključne besede: green information system, green information technology, bibliographic networks
Objavljeno v DKUM: 14.11.2024; Ogledov: 0; Prenosov: 4
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4. Evolutionary games on multilayer networks : a colloquiumZhen Wang, Lin Wang, Attila Szolnoki, Matjaž Perc, 2015, pregledni znanstveni članek Opis: Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.
Ključne besede: kooperacija, sociološke mreže, evolucijske igre, kolektivni pojav, samo-organizacija, fazni prehodi, recipročnost, fizika socioloških sistemov, cooperation, social networks, evolutionary games, collective phenomena, self-organization, phase transitions, reciprocity, physics of social systems
Objavljeno v DKUM: 17.09.2024; Ogledov: 0; Prenosov: 1
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5. Dynamics on higher-order networks: a reviewSoumen Majhi, Matjaž Perc, Dibakar Ghosh, 2022, pregledni znanstveni članek Opis: Network science has evolved into an indispensable platform for studying complex systems. But recent research has identified limits of classical networks, where links connect pairs of nodes, to comprehensively describe group interactions. Higher-order networks, where a link can connect more than two nodes, have therefore emerged as a new frontier in network science. Since group interactions are common in social, biological and technological systems, higher-order networks have recently led to important new discoveries across many fields of research. Here, we review these works, focusing in particular on the novel aspects of the dynamics that emerges on higher-order networks. We cover a variety of dynamical processes that have thus far been studied, including different synchronization phenomena, contagion processes, the evolution of cooperation and consensus formation. We also outline open challenges and promising directions for future research.
Ključne besede: higher-order networks, synchronization, cooperation, dynamics
Objavljeno v DKUM: 17.09.2024; Ogledov: 0; Prenosov: 0 |
6. Universal productivity patterns in research careersAndre S. Sunahara, Matjaž Perc, Haroldo V. Ribeiro, 2023, izvirni znanstveni članek Opis: A common expectation is that career productivity peaks rather early and then gradually declines with seniority. But whether this holds true is still an open question. Here we investigate the productivity trajectories of almost 8500 scientists from over 50 disciplines using methods from time-series analysis, dimensionality reduction, and network science, showing that there exist six universal productivity patterns in research. Based on clusters of productivity trajectories and network representations where researchers with similar productivity patterns are connected, we identify constant, u-shaped, decreasing, periodic-like, increasing, and canonical productivity patterns, with the latter two describing almost three-fourths of researchers. In fact, we find that canonical curves are the most prevalent, but contrary to expectations, productivity peaks occur much more frequently around midcareer rather than early. These results outline the boundaries of possible career paths in science and caution against the adoption of stereotypes in tenure and funding decisions.
Ključne besede: scientific networks, research career, social physics, universality
Objavljeno v DKUM: 13.09.2024; Ogledov: 38; Prenosov: 7
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7. Rapid assessment of steel machinability through spark analysis and data-mining techniquesGoran Munđar, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, Uroš Župerl, 2024, izvirni znanstveni članek Opis: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting
forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive
and costly. This study presents a novel methodology to rapidly determine steel machinability using
spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including
various low-alloy and high-alloy steels, with most samples being calcium steels known for their
superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic
grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured
during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15
values, which were measured using the standard ISO 3685 test. Our results demonstrate that the
created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While
some samples exhibited high MAPE values, the method overall provided accurate machinability
predictions. Compared to the standard ISO test, which takes several hours to complete, our method is
significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective
and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Ključne besede: steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Objavljeno v DKUM: 12.09.2024; Ogledov: 15; Prenosov: 8
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8. Hierarchically porous polyacetylene networks : adsorptive photocatalysts for efficient bisphenol a removal from waterDavid Šorm, Jiří Brus, Albin Pintar, Jan Sedláček, Sebastijan Kovačič, 2024, izvirni znanstveni članek Ključne besede: polyacetylenes, emulsion-templating, π-conjugated networks, macroporous polymers, heterogeneous photocatalysis
Objavljeno v DKUM: 29.08.2024; Ogledov: 73; Prenosov: 3
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9. A waste separation system based on sensor technology and deep learning: a simple approach applied to a case study of plastic packaging wasteRok Pučnik, Monika Dokl, Yee Van Fan, Annamaria Vujanović, Zorka Novak-Pintarič, Kathleen B. Aviso, Raymond R. Tan, Bojan Pahor, Zdravko Kravanja, Lidija Čuček, 2024, izvirni znanstveni članek Ključne besede: waste management, smart waste bin system, central post-sorting, sensor technology, deep learning, convolutional neural networks
Objavljeno v DKUM: 23.08.2024; Ogledov: 51; Prenosov: 7
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10. Decision-making in sustainable energy transition in Southeastern Europe : probabilistic network-based modelNena Hribar, Goran Šimić, Simonida Vukadinović, Polona Šprajc, 2021, izvirni znanstveni članek Opis: Background: Sustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding.
Methods: Research was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx—Microsoft Research’s Bayesian Network Authoring and Evaluation Tool.
Results: Due to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks.
Conclusion: Testing of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.
Ključne besede: sustainable energy transition, SE Europe, decision-making, Bayesian networks
Objavljeno v DKUM: 19.08.2024; Ogledov: 88; Prenosov: 10
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