International Journal of Modern Physics C

People with obstructive sleep apnea hypopnea syndrome (OSAHS) are affected by disruption in normal breathing patterns during sleep. In the literature, it is common to find acquisition of Thoracic (THO) and abdominal (ABD) movements with piezo-electric bands included in a full polysomnography. These movements convey valuable information related to sleep apnea events, and for this reason, contactless methods, such as the Pressure Bed Sensor (PBS), have been developed to extract this information. The main goal of this study is to analyze apnea and hypopnea fluctuations based on the spectral analysis of nasal airflow measure (as a reference signal), thoraco–abdominal effort and PBS respiration signal. To this end, features from the respiratory spectrum such as entropy, Gaussian modeling and instantaneous frequency were computed. These spectral properties were evaluated in three windows for each sensor: control point (CP) which is a window randomly extracted for the sleep time without apnea event, before event (BE) a window before an apnea episode and during event (DE) a window during an apnea episode. Apnea and hypopnea events were analyzed separately. According to a database of seventeen subjects, DE windows showed significant differences with respect to the CP window in most of the computed indices for both apnea and hypopnea events for all sensors. Significant differences were also found when DE and BE windows were compared in the case of apnea for all the sensors. In conclusion, the analyzed spectral characteristics could be a good tool to detect apnea and hypopnea. Finally, PBS signal which is a unobtrusive sensor, maintains the spectral properties of the standard respiratory effort measurements, and the use of this sensor could be useful for the monitoring outside of a clinical environment, simplifying the acquisition process.

Individuals that share common properties self-organize into communities. Therefore, community analysis is an important way to ascertain whether or not a complex system consists of sub-structures with different properties. In this paper, we give a two-level community structure analysis for the SSCI journal system by most similar node pairs. Five different strategies for the selection of node pairs are introduced. The efficiency is checked by normalized mutual information (NMI) technique. Statistical properties and comparisons of the community results show that both of the two level detections could give instructional information for the community structure in complex systems. Further comparisons of the five strategies show more indications. Firstly, it is always efficient to assign individuals with maximum similarity into the same community whether the interaction information is complete or not; secondly, rational random selection plays an important role in community diversity, and it is not a good idea to keep two much information for rational random selection; finally, random selection generates small-world community structure with no inside order. These results give valuable indication for efficient and stable community detection for huge complex systems with big interaction data.

Signal-tuned Gabor functions — Gaussian-modulated sinusoids whose parameters are determined by a spatial or spectral “tuning signal” — have previously been shown to provide a plausible model for the stimulus-dependent receptive fields and responses of the simple and complex cells of the primary visual cortex (V1). The signal-tuned responses obey Schrödinger equations, which has led to the proposal of a quantum-like model for V1 cells: by considering the squared magnitude of a particular signal-tuned wave function as a probability density, one arrives at a Poisson spiking process which appears consistent with the neurophysiological findings. Here, by incorporating Hermite-polynomial factors to the signal-tuned Gabor functions, we obtain a generalized quantum-like signal-tuned model for which further relevant properties are demonstrated, such as receptive-field coding of the stimulus and its derivatives, saturating spatial summation curves and half-wave rectification of the simple cell responses. Although only a one-dimensional approach is considered here, such properties will carry over to a two-dimensional model, in which case, as our preliminary analysis indicates, end-stopping — another important feature of cortical cells — can also be accommodated.

This paper explores the deep-zoom properties of the chaotic $k$-logistic map, in order to propose an improved chaos-based cryptosystem. This map was shown to enhance the random features of the Logistic map, while at the same time reducing the predictability about its orbits. We incorporate its strengths to security into a previously published cryptosystem to provide an optimal pseudo-random number generator (PRNG) as its core operation. The result is a reliable method that does not have the weaknesses previously reported about the original cryptosystem.

A new traffic flow model is proposed based on cellular automata and Naive Bayes theory to effectively describe the traffic flow velocity and flow state of the road. On the basis of NaSch model, the safety distance is fully considered in this model, and random deceleration and inflow rules of a vehicle are introduced. At the same time, vehicle acceleration, deceleration and lane change are optimized with Naive Bayes theory. Finally, experimental platform is used for numerical analysis, and the relationship between such parameters as average velocity of traffic flow, maximum velocity of traffic flow, number of inflow vehicles and random deceleration probability, etc. is studied in depth. The results show that the maximum velocity has a great effect on the traffic flow state, and when the vehicle inflow probability is lower, random deceleration probability has less effect on the average velocity and the number of vehicles waiting for inflow; on the contrary, the higher the random deceleration probability is, the more obvious the tendency of road congestion.

User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.

The minimum-energy configurations of $N(2\le N\le 160)$ identical classical point charges confined within a square under the effect of a Coulombic $1∕r$ potential have been determined by performing steepest-descent simulations. The energies of the final optimized configurations are given, along with their corresponding structural charge distributions. When all systems evolve in time to reach a minimum-energy configuration, local or global, they are all seen to be in quest of symmetry.

The permutation entropy (PE) is a statistical measure which can describe complexity of time series. In recent years, the research on PE is increasing gradually. As part of its application, the complexity–entropy causality plane (CECP) and weighted CECP (WCECP) have been recently used to distinguish the stage of stock market development. In this paper, we focus on weighted Rényi entropy causality plane (WRECP), and then extend WCECP and WRECP into multiscale WCECP (MWCECP) and multiscale WRECP (MWRECP) by introducing a new parameter scale. By data simulating and analyzing, we show the power of WRECP. Besides, we discuss the MWCECP and the MWRECP of adjacent scales. It reveals a gradual relationship between adjacent weighted scale entropies.

In this study, the deformations and trajectories of elastic fresh tea leaf in a simple straight channel model are investigated using the combined immersed boundary–lattice Boltzmann method (IB–LBM). The objective is to qualitatively analyze the effects of gravity, diameter and the Reynolds number (Re) on the physical characteristics of flexible fresh tea leaf, which is driven by Poiseuille airflow in a channel model. The LBM is used to simulate the fluid domain with regular Eulerian grid, while the IB method is employed to model the fluid–membrane interaction, with a set of Lagrangian moving grids being adopted for the fresh tea leaf. Our results mainly reveal that a tea leaf undergoes deformation due to the shearing effect of the Poiseuille flow, resulting in lifting of the leaf toward the channel center. Under the influence of gravity, the leaf performs a tumbling motion with clockwise rotation and preserves an oscillating stable state. Furthermore, the diameter has a far greater influence on the dimensionless shape parameters than Re. For a leaf of a certain size and position, a series of relations between $L∕W$ and Re are established at various ratios of fresh leaves by least square method. Based on the above findings, such studies provide useful data and insights to obtain high-quality green tea by selecting mechanical-plucked fresh tea leaves according to shape consistency.

A rod-shaped conductor in a uniform electric field is analyzed based on finite difference method. Most induction charges distribute at the two hemisphere ends, which is similar with two connected conducting spheres in a uniform field. Therefore, the boundary potentials are calculated by multiple image method for the system with two connected conducting spheres. Comparing the results from our method with those from traditional method with constant boundary potentials, our method can improve calculation evidently. Results prove that this method is available and credible, and it can give electric field with high precision.