scholarly journals Analysis of the determinism of time-series extracted from social and biological systems

2010 ◽  
Vol 6 (2) ◽  
pp. 180-185
Author(s):  
Luigi Fortuna ◽  
Frasca Mattia ◽  
Gambuzza Valentina ◽  
Angelo Sarra ◽  
Ramzy Ali ◽  
...  
Keyword(s):  
Time Series ◽  
Order Parameter ◽  
Biological Systems ◽  
Biological Data ◽  
Central Question ◽  
Analyze Data ◽  
Self Organizing

Self-organizing systems arise in many different fields. In this work we analyze data from social and biological systems. A central question is to demonstrate the presence of the determinism in time-series extracted from such systems that appear apparently not correlated but that are two good benchmarks for the study of complexity in real systems. We will apply the Kaplan test and we will define an order parameter for the biological data to characterize the complexity of the system.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Specific ion effects in liquids, in biological systems, and at interfaces

2006 ◽  
Vol 78 (8) ◽  
pp. 1611-1617 ◽  
Author(s):  
Werner Kunz
Keyword(s):  
Current Knowledge ◽  
Biological Systems ◽  
Electrolyte Solutions ◽  
Short Review ◽  
Colloidal Systems ◽  
Theoretical Approaches ◽  
Specific Ion Effects ◽  
Ion Effects ◽  
Self Organizing

A short review is given of current knowledge of ion effects in solutions, at interfaces, and in complex colloidal systems. Further to some key experiments, recent and new theoretical approaches are discussed and references of most important papers are given. Finally, an example of a dissipative, self-organizing system involving electrolyte solutions is mentioned.

scholarly journals Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

2021 ◽  
Author(s):  
Roozbeh Manshaei
Keyword(s):  
Unique Solution ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Biological Systems ◽  
Network Reconstruction ◽  
Biological Data ◽  
Tangible Interaction ◽  
Survival Prediction ◽  
New Approach ◽  
Gene Regulatory

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can inform and reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

scholarly journals Phenomena preceding major earthquakes interconnected through a physical model

2019 ◽  
Vol 37 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Panayiotis A. Varotsos ◽  
Nicholas V. Sarlis ◽  
Efthimios S. Skordas
Keyword(s):  
Time Series ◽  
Physical Model ◽  
Order Parameter ◽  
Tohoku Earthquake ◽  
Entropy Change ◽  
Natural Time ◽  
Earth Magnetic Field ◽  
Preceding Period ◽  
Magnetic Field Variations

Abstract. The analysis of earthquake time series in a new time domain termed natural time enables the uncovering of hidden properties in time series of complex systems and has been recently employed as the basis of a method to estimate seismic risk. Natural time also enables the determination of the order parameter of seismicity, which is a quantity by means of which one can identify when the system approaches the critical point (the mainshock occurrence is considered the new phase). Applying this analysis, as an example, to the Japanese seismic data from 1 January 1984 until the super-giant M 9 Tōhoku earthquake on 11 March 2011, we find that almost 3 months before its occurrence the entropy change of seismicity under time reversal is minimized on 22 December 2010, which signals an impending major earthquake. On this date the order parameter fluctuations of seismicity exhibit an abrupt increase. This increase is accompanied by various phenomena; e.g., from this date the horizontal GPS azimuths start to become gradually oriented toward the southern direction, while they had random orientation during the preceding period. Two weeks later, a minimum of the order parameter fluctuations of seismicity appears accompanied by anomalous Earth magnetic field variations and by full alignment of the orientations of GPS azimuths southwards leading to the most intense crust uplift. These phenomena are discussed and found to be in accordance with a physical model which seems to explain on a unified basis anomalous precursory changes observed either in ground-based measurements or in satellite data.

Stickiness in the order parameter time-series as a signature of criticality

2020 ◽  
Vol 544 ◽  
pp. 123508
Author(s):  
Y.F. Contoyiannis ◽  
S.M. Potirakis ◽  
F.K. Diakonos
Keyword(s):  
Time Series ◽  
Order Parameter

5. Robots and artificial life

2018 ◽  
Author(s):  
Margaret A. Boden
Keyword(s):  
Artificial Life ◽  
Biological Systems ◽  
Evolutionary Programming ◽  
Self Organization ◽  
Big Business ◽  
Practical Applications ◽  
Living Organisms ◽  
Self Organizing

Artificial life (A-Life) models biological systems. Like AI, it has both technological and scientific aims. ‘Robots and artificial life’ explains that A-Life is integral to AI, because all the intelligence we know about is found in living organisms. AI technologists turn to biology in developing practical applications of many kinds, including robots, evolutionary programming, and self-organizing devices. Robots are quintessential examples of AI, having high visibility and being hugely ingenious—and very big business, too. Evolutionary AI, although widely used, is less well known. Self-organizing machines are even less familiar. Nevertheless, in the quest to understand self-organization, AI has been as useful to biology as biology has been to AI.

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