Power-law Properties Research Articles

Preserving Unique Structural Blocks of Targets in ISAR Imaging by Pitman–Yor Process

This article focuses on capturing and preserving unique structures in inverse synthetic aperture radar (ISAR) imaging procedure, such as structures with little quantity around the edges of targets, to further improve the performance of compressive sensing based ISAR imaging. Specifically, the newly proposed method utilizes the framework of extended block sparse Bayesian learning, and divides the two-dimension ISAR image into overlapping patches for capturing structures. Moreover, the method exploits Pitman-Yor process for learning and clustering space-variant local structures of the overlapping patches adaptively for preserving unique structures, since the power-law property of Pitman-Yor process helps to capture more unique variables in clustering procedure. Meanwhile, this article utilizes variational Bayesian inference for approximating the posterior of the hidden variables and estimating the parameters of the proposed model. Experiment results based on synthetic data, Electromagnetic simulated data, and real measured data, demonstrate the performance of the newly proposed method in obtaining ISAR images with high resolution using fewer measurements. And two metrics, correlation value and structural similarity, illuminate the performance of the method in preserving unique structures and weak scatterers in inherent structures in ISAR imaging.

Steady flow and heat transfer of the power-law fluid between two stretchable rotating disks with non-uniform heat source/sink

This study aims to analyze the heat transfer phenomenon of power-law fluid with the occurrence of non-uniform heat source/sink within two stretchable disks which are parted with the constant distance and are co-axially rotating. The thermal conductivity is obeying the similar properties of power-law as that of viscosity. Von Karman’s generalized similarity transformation has been used firstly to reduce the physically modeled partial differential equations to nonlinear coupled ordinary differential equations and then tackled numerically with shooting method by finding missing initial conditions with the help of Newton–Raphson method and then system of equations are handled by means of RK-method. The influence of physical parameters for instance rotation as well as stretching, power-law index, Prandtl number, heat sink/source parameters upon non-dimensional velocity and temperature profiles are studied profoundly, later on, comprehensive analysis is expressed in discussion and results segment. The results which are computed numerically illustrate that the emerging parameters have substantial influences on velocity and temperature fields. In addition, rotation enhances the velocity components but temperature is predicting two diverse behaviors for shear-thinning and shear-thickening fluids, whenever upper and lower disk stretching it leads to an upsurge in radial and axial velocities but causes a decline in tangential velocity and temperature. Moreover, velocity and temperature distributions are in increasing trend except for the tangential component of the velocity which is decreasing by boosting the index of power-law. Furthermore, temperature decreases along with the similarity variable with the increasing Prandtl number but enhances with the enhancement in heat source/sink parameters. Finally, the skin friction in radial direction and local Nusselt number are escalating along the stretching parameters and Prandtl number but skin friction in tangential direction plummeting.

Coupling effect of power-law fluid properties and scaled fractal characteristics on flow through fractured media

Non-Newtonian fluid flow through fractured media widely exists in both natural and man-made materials, where fractures have been shown to have self-similar and fractal characteristics. In this study, a new analytical approach is developed to calculate the flowrate of a power-law fluid through fractal fractures, in which fracture trace length follows a fractal distribution and the aperture and trace length are related by a scaled power-law function. The average fracture flowrate in the fractal fractures is obtained through integrating flows in all fractures. The interplay of the power-law properties and fractal characteristic parameters on the flow through the fractal fractures is quantified and discussed. The results demonstrate that the average flowrate decreases with the fractal dimension of fracture length and scaling exponent. The increase in the trace length range ratio leads to the increased flowrate. The power-law index plays a significant role in the flowrate through fractal fractures coupling with the effect of pressure head gradient. The increase in pressure head gradient amplifies the effect of power-law fluid index on the flowrate in fractal fractures.

Relationship between rheological properties and actin filaments of single cells investigated by atomic force microscopy

We investigated how rheological properties of single cells are spatially correlated with actin filaments (F-actin) by atomic force microscopy (AFM). This was achieved by measuring the power-law rheological properties of mouse fibroblast cells adhered on micropatterned substrate by multi-frequency force modulation AFM in the combination of the immunofluorescent observation of F-actin. We observed that G0, a scale factor of the modulus at a certain frequency, of cells was decreased as the F-actin was disrupted with a chemical treatment, showing that the spatial-averaged stiffness is associated with the F-actin formation, as observed in various cell types and microrheological methods. Interestingly, we found that G0 measured in local locations within cells displayed a negative correlation with the corresponding F-actin density. The results indicate that the local cell stiffness is not necessarily correlated with the F-actin density, but rather depending on how network structures of F-actin are formed in local regions.

Changing structures of B2B networks in the Japanese textile and apparel industry

The aim of the present study was to evaluate how the business-to-business (B2B) networks in the Japanese textile and apparel industry changed between 2005 and 2010 using data on 200 companies. Network analysis was used to study the properties of the B2B networks, and how their structures changed was characterized using the USD/JPY exchange rate. The network analysis revealed power-law properties of the B2B networks, and the core networks characterized by the largest degree centrality exhibited positive correlations with the USD/JPY exchange rate. By contrast, the peripheral networks characterized by the network path length exhibited the negative correlations with the exchange rate USD/JPY. Therefore, the changes that occurred in the B2B networks are explained as the complementarity of comparative advantages originating in the USD/JPY exchange rate. Moreover, the USD/JPY exchange rate affected the B2B networks through not only the complementarity of importing and exporting but also by changing the structures.

Contact Analysis of a Soft-Coated Asperity During Loading–Unloading Process in Consideration of Strain Hardening Effects

Abstract In this paper, the flattening process of a coated single asperity by a rigid flat is studied based on a two-dimensional finite element model under different contact interferences. The soft coating and hard substrate materials of the main bearing shell in a medium-speed vehicle diesel engine are both considered to follow the power-law hardening elastic-plastic properties. For both loading and unloading processes, the effects of geometrical and material properties, including the coating thickness, Young’s modulus, Poisson’s ratio, yielding stress, and hardening exponent, on the contact behaviors are studied in a wide range to cover the real material properties. The von Mises stress on the interface is also analyzed in order to improve the bonding strength between coating and substrate. The main contribution of this paper is to provide a method to determine the contact properties caused by different material and geometric properties of soft coating and substrate materials, which follow the power-law hardening properties.

Stability and Flow Properties of Oil-Based Foam Generated by CO2

Summary Foam flooding is an important method used to protect oil reservoirs and increase oil production. However, the research on foam fluid is generally focused on aqueous foam, and there are a few studies on the stability mechanism of oil-based foam. In this paper, a compound surfactant consisting of Span® 20 and a fluorochemical surfactant is determined as the formula for oil-based foam. The foam volume and half-life in the bulk phase are measured to be 275 mL and 302 seconds, respectively, at room temperature and atmospheric pressure. The stability mechanism of oil-based foam is proposed by testing the interfacial tension (IFT) and interfacial viscoelasticity. The lowest IFT of 18.5 mN/m and the maximum viscoelasticity modulus of 16.8 mN/m appear at the concentration of 1.0 wt%, resulting in the most-stable oil-based foam. The effect of oil viscosity and temperature on the properties of oil-based foam is studied. The foam stability increases first and then decreases with the rising oil viscosity, and the stability decreases with rising temperature. The apparent viscosity of oil-based foam satisfies the power-law non-Newtonian properties, and this viscosity is much higher than that of the phases of oil and CO2. The flow of oil-based foam in porous media is studied through microscopic-visualization experiments. Bubble division, bubble merging, and bubble deformation occur during oil-based-foam flow in porous media. The oil-recovery efficiency of the oil-based-foam flooding is 78.3%, while the oil-recovery efficiency of CO2 flooding is only 28.2%. The oil recovery is enhanced because oil-based foam reduces CO2 mobility, inhibits gas channeling, and improves sweep efficiency. The results are meaningful for CO2 mobility control and for the application of foam flooding for enhanced oil recovery (EOR).

Nonclassical Systemics of Quasicoherence: From Formal Properties to Representations of Generative Mechanisms. A Conceptual Introduction to a Paradigm-Shift

In this article, we consider how formal models and properties of emergence, e.g., long-range correlations, power laws, and self-similarity are usually platonically considered to represent the essence of the phenomenon, more specifically, their acquired properties, e.g., coherence, and not their generative mechanisms. Properties are assumed to explain, rather than represent, real processes of emergence. Conversely, real phenomenological processes are intended to be approximations or degenerations of their essence. By contrast, here, we consider the essence as a simplification of the phenomenological complexity. It is assumed to be acceptable that such simplification neglects several aspects (e.g., incompleteness, inhomogeneities, instabilities, irregularities, and variations) of real phenomena in return for analytical tractability. Within this context, such a trade-off is a kind of reductionism when dealing with complex phenomena. Methodologically, we propose a paradigmatic change for systems science equivalent to the one that occurred in Physics from object to field, namely, a change from interactional entities to domains intended as extensions of fields, or multiple fields, as it were. The reason to introduce such a paradigm shift is to make nonidealist approaches suitable for dealing with more realistic quasicoherence, when the coherence does not consistently apply to all the composing entities, but rather, different forms of coherence apply. As a typical general interdisciplinary case, we focus on so-called collective behaviors. The goal of this paper is to introduce the concepts of domain and selection mechanisms which are suitable to represent the generative mechanisms of quasicoherence of collective behavior. Domains are established by self-tracking entities such as financial or are effectively GPS-detectable. Such domains allow the profiling of collective behavior. Selection mechanisms are based on learning techniques or cognitive approaches for social systems.

The structure of collaboration networks: Findings from three decades of co-invention patents in Chile

Abstract Collaboration networks have primarily been studied in developed economies. In this paper, we present a comprehensive analysis of co-inventor networks in a small but thriving OECD economy in Latin America: Chile. Using a dataset for patents granted in the country between 1989 and 2018, we study a variety of statistical properties for five different co-inventor networks. We show that these networks exhibit power-law, small-world, and preferential attachment properties, all of which have been used to describe networks in more advanced economies. We also highlight some apparent differences in collaboration patterns between networks (and inventors) and we reveal the precarious nature of the network of local inventors by confirming the community structure property. Evidence for strong positive autocorrelation between highly and lowly productive inventors is presented via methods used in spatial econometrics. Our results show that the inventor system in Chile is highly fragmented, which emphasizes the need for strong government intervention in steering research funding, and for more collaboration between inventors, especially local ones.

Centralized Business-to-Business Networks in the Japanese Textile and Apparel Industry: Using Network Analysis and an Agent-Based Model

This study deals with the estimation of the changes that occur in the Business-to-Business (B2B) networks in the Japanese textile and apparel industry by applying datasets of about 2000 companies from 2011/2012 to 2015/2016. Network analysis was used to examine the properties of the B2B networks. A factor of innovation in information and communications technology (ICT) and logistics technology was introduced into an agent-based model to demonstrate changes occurring in the related structures of B2B networks. The agent-based model was designed and tested based on qualitative information on Japanese textile and apparel industries. Consequently, network analysis revealed power-law properties and the structures of centralized hub companies. Moreover, in the simulation experiments, the centralizations of the networks generated by the agent-based model due to innovation in ICT and logistics technology were illustrated. Therefore, one of the predicted cases regarding changes that occur in the B2B networks was explained as centralizations to hub companies.

PowerHash: a hybrid grouping scheme by leveraging power-law properties of data

We study GroupBy implementation scheme which is widely used in distributed systems and databases. The GroupBy operation partitions a set of out-of-order records into groups. Due to the massive data size, many I/O-efficient grouping schemes that exploit external memory have been proposed. In this paper, we observe that the group sizes of many real data exhibit power-law property and the grouping schemes’ performance varies a lot for data with different group sizes. The indexing–filling approach prefers data with big group size, while the partitioned hash approach prefers data with small group size. Based on this observation, we propose a hybrid approach, PowerHash, which invokes different grouping schemes for different data. The group size information is approximately estimated by the count-min sketch so that the big groups and small groups can be distinguished from each other. With a given memory budget, our results show that PowerHash can improve performance by up to six times over the existing GroupBy implementations.

Consequences of Long-Distance Dispersal for Epidemic Spread: Patterns, Scaling, and Mitigation.

Epidemics caused by long-distance dispersed pathogens result in some of the most explosive and difficult to control diseases of both plants and animals (including humans). Yet the factors influencing disease spread, especially in the early stages of the outbreak, are not well-understood. We present scaling relationships, of potentially widespread relevance, that were developed from more than 15 years of field and in silico single focus studies of wheat stripe rust spread. These relationships emerged as a consequence of accounting for a greater proportion of the fat-tailed disease gradient that may be frequently underestimated in disease spread studies. Leptokurtic dispersal gradients (highly peaked and fat-tailed) are relatively common in nature and they can be represented by power law functions. Power law scale invariance properties generate patterns that repeat over multiple spatial scales, suggesting important and predictable scaling relationships between disease levels during the first generation of disease outbreaks and subsequent epidemic spread. Experimental wheat stripe rust outbreaks and disease spread simulations support theoretical scaling relationships from power law properties and suggest that relatively straightforward scaling approximations may be useful for projecting the spread of disease caused by long-distance dispersed pathogens. Our results suggest that, when actual dispersal/disease data are lacking, an inverse power law with exponent = 2 may provide a reasonable approximation for modeling disease spread. Furthermore, our experiments and simulations strongly suggest that early control treatments with small spatial extent are likely to be more effective at suppressing an outbreak caused by a long-distance dispersed pathogen than would delayed treatment of a larger area. The scaling relationships we detail and the associated consequences for disease control may be broadly applicable to plant and animal pathogens characterized by non-exponentially bound, fat-tailed dispersal gradients.

Time-varying volatility and the power law distribution of stock returns

Abstract While many studies find that the tail distribution of high frequency stock returns follows a power law, there are only a few explanations for this finding. This study presents evidence that time-varying volatility can account for the power law property of high frequency stock returns. In particular, one finds that a conditional normal model with nonparametric volatility provides a strong fit. Specifically, a cross-sectional regression of the power law coefficients obtained from stock returns on the coefficients implied by the nonparametric volatility model yields a slope close to one. Further, for most of the stocks in the sample taken individually, the model-implied coefficient falls within the 95 percent confidence interval for the coefficient estimated from returns data.

Time-Varying Volatility and the Power Law Distribution of Stock Returns

Abstract While many studies find that the tail distribution of high frequency stock returns follows a power law, there are only a few explanations for this finding. This study presents evidence that time-varying volatility can account for the power law property of high frequency stock returns. In particular, one finds that a conditional normal model with nonparametric volatility provides a strong fit. Specifically, a cross-sectional regression of the power law coefficients obtained from stock returns on the coefficients implied by the nonparametric volatility model yields a slope close to one. Further, for most of the stocks in the sample taken individually, the model-implied coefficient falls within the 95 percent confidence interval for the coefficient estimated from returns data.

Non-Linear Diffusion and Power Law Properties of Heterogeneous Systems: Application to Financial Time Series.

In this work, we show that it is possible to obtain important ubiquitous physical characteristics when an aggregation of many systems is taken into account. We discuss the possibility of obtaining not only an anomalous diffusion process, but also a Non-Linear diffusion equation, that leads to a probability distribution, when using a set of non-Markovian processes. This probability distribution shows a power law behavior in the structure of its tails. It also reflects the anomalous transport characteristics of the ensemble of particles. This ubiquitous behavior, with a power law in the diffusive transport and the structure of the probability distribution, is related to a fast fluctuating phenomenon presented in the noise parameter. We discuss all the previous results using a financial time series example.

Improved modeling of free power-law liquid sheets by weighted-residual approximations

The linear temporal instability of planar power-law liquid sheets surrounded by viscous gas medium is investigated theoretically for symmetric disturbances. The linear stability analysis is improved by method of the weighted residual approach using an appropriate projection basis, instead of the traditional Kármán's momentum integral method which is known to make the resulting model inaccurate near the stability threshold. Two different ways are used to deal with linear stability analysis with the existence of the viscous gas medium: inviscid gas pressure approximation and spectral collocation method. A system of two equations in relation to the film thickness h and the local flow rate q is obtained through the first way and then the first-order dispersion equation is deduced. The results are analyzed to investigate how the power-law index, consistency coefficient, gas boundary-layer thickness and other rheological parameters affect the atomization mechanism of planar power-law sheets. Meanwhile, comparisons of the results obtained by the two ways have been made to verify rationality of the inviscid gas pressure approximation. Furthermore, the competition between the power-law characteristic property and aerodynamic instability on sheet instability has been examined. Finally, the theoretical calculations have been compared with previous experimental results and show relatively good agreement.

Utilizing the average node degree to assess the temporal growth rate of Twitter

Several models have been proposed that describe the evolution of the graph properties of many online social networks (OSNs) and explain the behavior of their users. These models are essential for understanding the growth dynamics of the underlying social graph. One of the most prominent OSNs is Twitter, since it covers a significant part of the online worldwide population. Nevertheless, investigating the validity of these models on Twitter entails many difficulties. The size of Twitter and the limitations of its access API make extremely difficult the estimation of many graph properties and therefore the evaluation of the proposed models. In this study, we present a simple and efficient method to fit an already existing model, which describes the densification power law property of modern OSNs. This model states that the average degree of an OSN increases over time. In a case study, we assess this model in two large samples of Twitter, and we demonstrate how it can portray the altering growth periods of Twitter. Finally, we make some remarks on several events during the early period of Twitter that may have affected its growth rates.

Cooperation of improved HK networks based on prisoner dilemma game

To model the cooperative behavior under the realistic world, we put forward a high-clustering network model with the property of adjustable power-law, and analyze the evolution of the prisoner dilemma game on this improved HK network model. Using simulation experiments, we investigate the effect of high clustering property of this network on the cooperative behavior. Extensive simulations indicate that a high value of the clustering coefficient may greatly contribute to the emergence of cooperative behavior. We also find that with the increase in the temptation parameter, the level of cooperation will decrease, but the variation range is not wide. Altogether, this evolutionary game model can promote the emergence of cooperative phenomenon by resisting the spread of the betrayal strategy.

Fluctuations in email size

The purpose of this study is to explain fluctuations in email size. We have previously investigated the long-term correlations between email send requests and data flow in the system log of the primary staff email server at a university campus, finding that email size frequency follows a power-law distribution with two inflection points, and that the power-law property weakens the correlation of the data flow. However, the mechanism underlying this fluctuation is not completely understood. We collected new log data from both staff and students over six academic years and analyzed the frequency distribution thereof, focusing on the type of content contained in the emails. Furthermore, we obtained permission to collect “Content-Type” log data from the email headers. We therefore collected the staff log data from May 1, 2015 to July 31, 2015, creating two subdistributions. In this paper, we propose a model to explain these subdistributions, which follow log-normal-like distributions. In the log-normal-like model, email senders --consciously or unconsciously-- regulate the size of new email sentences according to a normal distribution. The fitting of the model is acceptable for these subdistributions, and the model demonstrates power-law properties for large email sizes. An analysis of the length of new email sentences would be required for further discussion of our model; however, to protect user privacy at the participating organization, we left this analysis for future work. This study provides new knowledge on the properties of email sizes, and our model is expected to contribute to the decision on whether to establish upper size limits in the design of email services.

Post-buckling behavior and nonlinear vibration analysis of a fluid-conveying pipe composed of functionally graded material

The post-buckling behavior and nonlinear vibration of a fluid-conveying pipe composed of a functionally graded material were analytically studied. The power-law material property was considered as continuously varying across the direction of the pipe wall thickness. A nonlinear governing equation for the pipe and relevant boundary conditions were derived based on Hamilton’s principle. The post-bucking configurations of the pipe were analytically predicted. The closed-form expression of the nonlinear free vibration of the pipe was determined using the homotopy analysis method. Numerical results are presented to display the dependence of the flow velocity, fluid density, and the initial stress on the post-buckling configurations. It was concluded that the statics and dynamics are significantly changed by the material properties, which suggests that the dynamic behavior of pipes may be tailored by use of man-made functionally graded materials.