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Signal propagation in complex networks
Peng Ji, Jiachen Ye, Yu Mu, Wei Lin, Yang Tian, Chittaranjan Hens, Matjaž Perc, Yang Tang, Jie Sun, Jürgen Kurths, 2023, pregledni znanstveni članek

Opis: Signal propagation in complex networks drives epidemics, is responsible for information going viral, promotes trust and facilitates moral behavior in social groups, enables the development of misinformation detection algorithms, and it is the main pillar supporting the fascinating cognitive abilities of the brain, to name just some examples. The geometry of signal propagation is determined as much by the network topology as it is by the diverse forms of nonlinear interactions that may take place between the nodes. Advances are therefore often system dependent and have limited translational potential across domains. Given over two decades worth of research on the subject, the time is thus certainly ripe, indeed the need is urgent, for a comprehensive review of signal propagation in complex networks. We here first survey different models that determine the nature of interactions between the nodes, including epidemic models, Kuramoto models, diffusion models, cascading failure models, and models describing neuronal dynamics. Secondly, we cover different types of complex networks and their topologies, including temporal networks, multilayer networks, and neural networks. Next, we cover network time series analysis techniques that make use of signal propagation, including network correlation analysis, information transfer and nonlinear correlation tools, network reconstruction, source localization and link prediction, as well as approaches based on artificial intelligence. Lastly, we review applications in epidemiology, social dynamics, neuroscience, engineering, and robotics. Taken together, we thus provide the reader with an up-to-date review of the complexities associated with the network’s role in propagating signals in the hope of better harnessing this to devise innovative applications across engineering, the social and natural sciences as well as to inspire future research.
Ključne besede: signal propagation, complex networks, nonlinear dynamic
Objavljeno v DKUM: 03.06.2024; Ogledov: 46; Prenosov: 3
.pdf Celotno besedilo (14,80 MB)
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The IHI Rochester Report 2022 on healthcare informatics research : Resuming after the CoViD-19
Carlo Combi, Julio C. Facelli, Peter Haddawy, John H. Holmes, Sabine Koch, Hongfang Liu, Jochen Meyer, Mor Peleg, Giuseppe Pozzi, Gregor Štiglic, Pierangelo Veltri, Christopher C. Yang, 2023, pregledni znanstveni članek

Opis: In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th–11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics—IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.
Ključne besede: biomedical and health informatics, artificial intelligence in medicine, research trends, CoViD-19
Objavljeno v DKUM: 03.04.2024; Ogledov: 256; Prenosov: 235
.pdf Celotno besedilo (1,73 MB)
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Estimating piping potential in earth dams and levees using generalized neural networks
Xinhua Xue, Xingguo Yang, Xin Chen, 2014, izvirni znanstveni članek

Opis: Internal erosion and piping in embankments and their foundations is the main cause of failures and accidents to embankment dams. To estimate the risks of dam failure phenomenon, it is necessary to understand this phenomenon and to develop scientifically derived analytical models that are simpler, easier to implement, and more accurate than traditional methods for evaluation of piping potential. In this study, a generalized regression neural network (GRNN) technique has been applied for the assessment of piping potential, as well, due to its ability to fit complex nonlinear models. The performance of GRNN has been cross validated using the k-fold cross validation method technique. The GRNN model is found to have very good predictive ability and is expected to be very reliable for evaluation of piping potential.
Ključne besede: piping, generalized neural network, cross validation, BP neural network
Objavljeno v DKUM: 14.06.2018; Ogledov: 1170; Prenosov: 64
.pdf Celotno besedilo (463,20 KB)
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An analysis of interaction effects of China–South Korea and China–Australia FTAs and the expanding TPP
Sun Yuhong, Mu Yifei, Jun Yang, 2016, izvirni znanstveni članek

Opis: On 5 October 2015, the Trans-Pacific Partnership Agreement (TPP) led by the U.S. was signed. Already, 12 countries1 have joined the agreement, but China has not. Thus, lots of research has focused on the negative effect of the TPP on China’s foreign trade. On the other hand, China is moving forward in its own efforts to establish bilateral free trade agreements (FTAs) and free trade zones. In June 2015, China-South Korea and China-Australia signed bilateral FTAs which went into effect in December 2015. Several questions were raised: Since South Korea and Australia are the major trade partners in the Pacific area and the bilateral FTAs will be effective before the TPP, will these FTAs’ positive effects on China’s foreign trade offset some of the negative effects of the TPP? If China and the U.S. adopted a competitive trade policy, which countries would benefit? If China and the U.S. adopted a cooperative trade policy, how would the trade value and economic welfare change? This paper simulates and analyses the mutual effects of China-South Korea and China-Australia FTAs and the enlarging TPP using the computable general equilibrium model. The major conclusions drawn suggest that China-South Korea and China-Australia FTAs will significantly offset the TPP’s negative effect on China’s foreign trade. If China is not included, the U.S. economic benefit from the TPP will be limited. The economic welfare for a country like Australia, which joined both the bilateral FTA and the TPP, will be increased the most. In the long run, China joining the TPP would be the most beneficial decision for its national interest. However, if the TPP cannot be approved by the US congress, the U.S.’s economic indicators and export would be decreasing sharply. China’s economy and export will benefit from FTAs.
Ključne besede: China-South Korea FTA, China-Australia FTA, TPP, interaction effects
Objavljeno v DKUM: 03.05.2018; Ogledov: 1109; Prenosov: 292
.pdf Celotno besedilo (285,29 KB)
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Regulation of irregular neuronal firing by autaptic transmission
Daqing Guo, Shengdun Wu, Mingming Chen, Matjaž Perc, Yangsong Zhang, Jingling Ma, Yan Cui, Peng Xu, Yang Xia, Dezhong Yao, 2016, izvirni znanstveni članek

Opis: The importance of self-feedback autaptic transmission in modulating spike-time irregularity is still poorly understood. By using a biophysical model that incorporates autaptic coupling, we here show that self-innervation of neurons participates in the modulation of irregular neuronal firing, primarily by regulating the occurrence frequency of burst firing. In particular, we find that both excitatory and electrical autapses increase the occurrence of burst firing, thus reducing neuronal firing regularity. In contrast, inhibitory autapses suppress burst firing and therefore tend to improve the regularity of neuronal firing. Importantly, we show that these findings are independent of the firing properties of individual neurons, and as such can be observed for neurons operating in different modes. Our results provide an insightful mechanistic understanding of how different types of autapses shape irregular firing at the single-neuron level, and they highlight the functional importance of autaptic self-innervation in taming and modulating neurodynamics.
Ključne besede: neuronal dynamics, autapse, regulation, excitation
Objavljeno v DKUM: 23.06.2017; Ogledov: 1229; Prenosov: 410
.pdf Celotno besedilo (2,65 MB)
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Towards the novel reasoning among particles in PSO by the use of RDF and SPARQL
Iztok Fister, Xin-She Yang, Karin Ljubič Fister, Dušan Fister, Janez Brest, Iztok Fister, 2014, izvirni znanstveni članek

Opis: The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.
Ključne besede: resource definition framework, SPARQL, best solutions, RDF, PSO
Objavljeno v DKUM: 15.06.2017; Ogledov: 1596; Prenosov: 418
.pdf Celotno besedilo (1,67 MB)
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A hybrid bat algorithm
Iztok Fister, Dušan Fister, Xin-She Yang, 2013, izvirni znanstveni članek

Ključne besede: swarm intelligence, bat algorithm, differential evolution, optimization
Objavljeno v DKUM: 21.12.2015; Ogledov: 2063; Prenosov: 27
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Memetic self-adaptive firefly algorithm
Iztok Fister, Xin-She Yang, Janez Brest, Iztok Fister, 2013, samostojni znanstveni sestavek ali poglavje v monografski publikaciji

Ključne besede: firefly algorithms, memetic algorithms, self-adaptation, local search, graph 3-coloring problem
Objavljeno v DKUM: 10.07.2015; Ogledov: 1831; Prenosov: 105
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Iztok Fister, 2013, magistrsko delo

Opis: Swarm intelligence is a modern and efficient mechanism for solving hard problems in computer science, engineering, mathematics, economics, medicine and optimization. Swarm intelligence is the collective behavior of decentralized and self-organized systems. This research area is a branch of artificial intelligence and could be viewed as some kind of family relationship with evolutionary computation because both communities share a lot of common characteristics. To date, a lot of swarm intelligence algorithms have been developed and applied to several real-world problems. The main focus of this thesis is devoted to the bat algorithm which is a member of the swarm intelligence community, as developed recently. In line with this, a comprehensive analysis of papers was performed tackling this algorithm. Some hybridizations of the original algorithm were proposed because the preliminary results of this algorithm regarding the optimization of benchmark functions with higher dimensions had not too promising. Extensive experiments showed that the hybridizing the original bat algorithm has beneficial effects on the results of the original bat algorithm. Finally, an experimental study was performed during which we researched for the dependence of an applied randomized method on the results of the original bat algorithm. The results of this study showed that selecting the randomized method had a crucial impact on the results of the original bat algorithm and that the bat algorithm using Levy flights is also suitable for solving the harder optimization problems.
Ključne besede: swarm intelligence, evolutionary computation, bat algorithm, hybridization, review
Objavljeno v DKUM: 06.09.2013; Ogledov: 3391; Prenosov: 326
.pdf Celotno besedilo (8,69 MB)

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