Complex dynamic behaviors of the complex Lorenz system

AbstractThis study compares the dynamic behaviors of the Lorenz system with complex variables to that of the standard Lorenz system involving real variables. Different methodologies, including the Lyapunov Exponents spectrum, the bifurcation diagram, the first return map to the Poincaré section and topological entropy, were used to investigate and compare the behaviors of these two systems. The results show that expressing the Lorenz system in terms of complex variables leads to more distinguished behaviors, which could not be achieved in the Lorenz system with real variables, such as quasi-periodic and hyper-chaotic behaviors

Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67 of dataset A and 80 of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination. © IMechE 2011

Initial results from a realtime FRB search with the GBT

We present the data analysis pipeline, commissioning observations and initial results from the GREENBURST fast radio burst (FRB) detection system on the Robert C. Byrd Green Bank Telescope (GBT) previously described by Surnis et al. which uses the 21~cm receiver observing commensally with other projects. The pipeline makes use of a state-of-the-art deep learning classifier to winnow down the very large number of false positive single-pulse candidates that mostly result from radio frequency interference. In our observations totalling 156.5 days so far, we have detected individual pulses from 20 known radio pulsars which provide an excellent verification of the system performance. We also demonstrate, through blind injection analyses, that our pipeline is complete down to a signal-to-noise threshold of 12. Depending on the observing mode, this translates to peak flux sensitivities in the range 0.14--0.89~Jy. Although no FRBs have been detected to date, we have used our results to update the analysis of Lawrence et al. to constrain the FRB all-sky rate to be $1140^{+200}_{-180}$ per day above a peak flux density of 1~Jy. We also constrain the source count index $\alpha=0.83\pm0.06$ which indicates that the source count distribution is substantially flatter than expected from a Euclidean distribution of standard candles (where $\alpha=1.5$). We discuss this result in the context of the FRB redshift and luminosity distributions. Finally, we make predictions for detection rates with GREENBURST, as well as other ongoing and planned FRB experiments.Comment: 9 pages, 7 figures, submitted to MNRA

The effect of corrective exercises on balance in elderly women with hyperkyphosis

Background: Women with hyperkyphosis have impaired balance and increased body sway, which can increase their risk of falls. Impaired balance and poor postural stability are the main risk factors for falling. This study aimed to study the effect of corrective exercises on balance in elderly women with hyperkyphosis. Methods and Materials: In this quasi experimental study, 30 adult women (age range, 60-75 years old) were selected using a purposive sampling and assigned randomly into the experimental and control groups. Participants in the experimental group took part in an 8-week (3 sessions a week) corrective exercise program. In this period, the control group did not receive any corrective exercise program. The flexicurve ruler and Berg balance scale were used in pre- and post-tests to measure kyphosis angle and balance, respectively. This study was conducted in Spring 2014 at Jahandideh Nursing Home in Arak city, Iran. The number of falls and fears of falling was also recorded. To analyze data, dependent t-test and covariate analysis at a significance level of

Recruiting the K-most influential prospective workers for crowdsourcing platforms

© 2018, Springer-Verlag London Ltd., part of Springer Nature. Viral marketing is widely used by businesses to achieve their marketing objectives using social media. In this work, we propose a customized crowdsourcing approach for viral marketing which aims at efficient marketing based on information propagation through a social network. We term this approach the social community-based crowdsourcing platform and integrate it with an information diffusion model to find the most efficient crowd workers. We propose an intelligent viral marketing framework (IVMF) comprising two modules to achieve this end. The first module identifies the K-most influential users in a given social network for the platform using a novel linear threshold diffusion model. The proposed model considers the different propagation behaviors of the network users in relation to different contexts. Being able to consider multiple topics in the information propagation model as opposed to only one topic makes our model more applicable to a diverse population base. Additionally, the proposed content-based improved greedy (CBIG) algorithm enhances the basic greedy algorithm by decreasing the total amount of computations required in the greedy algorithm (the total influence propagation of a unique node in any step of the greedy algorithm). The highest computational cost of the basic greedy algorithm is incurred on computing the total influence propagation of each node. The results of the experiments reveal that the number of iterations in our CBIG algorithm is much less than the basic greedy algorithm, while the precision in choosing the K influential nodes in a social network is close to the greedy algorithm. The second module of the IVMF framework, the multi-objective integer optimization model, is used to determine which social network should be targeted for viral marketing, taking into account the marketing budget. The overall IVMF framework can be used to select a social network and recruit the K-most influential crowd workers. In this paper, IVMF is exemplified in the domain of personal care industry to show its importance through a real-life case

Complex recurrent interactions in systems biology: from Henri Poincaré to Robert Rosen

In the systems biology era of the life sciences turning the vast amount of biological interaction data into meaningful knowledge requires indisputably methodological advances in data mining and system modelling. Modular hierarchy of complex molecular networks implies that modules have their own dynamics and interaction manifolds. However, when they hierarchically hook together, depending on different stable and unstable attractors of each module, a new organization of interaction with intertwined interacting region will be emerged which represent a complicated higher level complex manifold. To investigate how such higher level complex manifold emerges from integration of lower level modules, this paper presents an Event-Related Recurrent Modular Modelling Approach based on actual systems biology roots. The emphasis of this approach is on recurrence theorem, which is embedded in Henri Poincaré and Robert Rosen points of views as systems and biology roots, and it could be a conceptual strategy for systems identification and design methodology, in systems biology and synthetic biology, respectively. By the use of Iterated Maps, we explain how simple signaling pathways can be embedded in networks to generate more complex behaviours such as toggle switches and oscillators as the basic building blocks of cell cycle engine