Narrow Results

Daisyworld is a reduced-order ecological model that establishes biological homeostasis of the global biota and environment. This work proposes a novel adaptive Daisyworld by considering the classical environment–life interactions, incorporating devastation, greenhouse gas effects and mutations. Climate variability is described by means of the external stimuli that represent Sun’s luminosity. Different mutation mechanisms are investigated considering the temperature-induced changes that define the gray daisies. Either deterministic or random aspects are of concern while incorporating chance on the biota interactions. Numerical simulations are performed using a nonlinear dynamics perspective. Distinct scenarios are of concern while evaluating the different mutation mechanisms and conditions. Complex dynamics are investigated showing the chance and chaos related to the Daisyworld behavior.

A secret sharing scheme is a method which allows a dealer to share a secret among a set of participants in such a way that only qualified subsets of participants can recover the secret. The collection of subsets of participants that can reconstruct the secret in this way is called access structure. The rank of an access structure is the maximum cardinality of a minimal qualified subset. A secret sharing scheme is perfect if unqualified subsets of participants obtain no information regarding the secret. The dealer's randomness is the number of random bits required by the dealer to setup a secret sharing scheme. The efficiency of the dealer's randomness is the ratio between the amount of the dealer's randomness and the length of the secret. Because random bits are a natural computational resource, it is important to reduce the amount of randomness used by the dealer to setup a secret sharing scheme. In this paper, we propose some decomposition constructions for perfect secret sharing schemes with access structures of constant rank. Compared with the best previous results, our constructions have some improved upper bounds on the dealer's randomness and on the efficiency of the dealer's randomness.

In order to test the multicanonical approach in simulations of the spin systems with quenched bond randomness, I simulated the q = 8 state Potts model in two dimensions with various degrees of randomness. It appears quite feasible to simulate spin systems with quenched bond randomness by multicanonical algorithm, but extra care is needed with increasing randomness.

The model of Bonabeau explains the emergence of social hierarchies from the memory of fights in an initially egalitarian society. Introducing a feedback from the social inequality into the probability to win a fight, we find a sharp transition between an egalitarian society at low population density and a hierarchical society at high population density.

We reinvestigate the model of Bonabeau et al.¹ of self-organizing social hierarchies by including a distribution of attractive sites. Agents move randomly except in the case where an attractive site is located in its neighborhood. We find that the transition between an egalitarian society at low population density and a hierarchical one at high population density strongly depends on the distribution and percolation of the valuable sites. We also show how agent diffusivity is closely related to social hierarchy.

Monte Carlo simulations using the recently proposed Wang–Landau algorithm are performed to the q = 8 state Potts model in two dimension with various degrees of randomness. We systematically studied the effect of quenched bond randomness to system which has first-order phase transition. All simulations and measurements were done from pure case r = 1 to r = 0.4. Physical quantities such as energy density and ground-state entropy were evaluated at all temperatures. We have also obtained probability distributions of energy to monitor softening of transitions. It appears quite feasible to simulate spin systems with quenched bond randomness by Wang–Landau algorithm.

The Schelling model of 1971 is a complicated version of a square-lattice Ising model at zero temperature, to explain urban segregation, based on the neighbor preferences of the residents, without external reasons. Various versions between Ising and Schelling models give about the same results. Inhomogeneous "temperatures" T do not change the results much, while a feedback between segregation and T leads to a self-organization of an average T.

We have studied the influence of the distribution of bimodal bonds on the phase transition in two-dimensional 8-state Potts model by the recently proposed Wang–Landau (WL) and the Swendsen–Wang (SW) algorithm. All simulations and measurements are done for r = 0.5. Physical quantities such as energy density and specific heat are evaluated at all temperatures. We have also obtained the probability distributions of the energy in order to monitor the transitions. We have observed that some cases of the periodically arranged bond distributions show a single peak, and some cases show double or triple peaks in the specific heat. Besides, it seems that the appearing of these peaks in the specific heat relates to a blocking procedure for periodicity. When the number of interaction pairs between the bimodal bonds is increased on the lattice with the blocking procedure, one can observe a single peak, otherwise, one can observe a double or triple peaks in the specific heat. From the point of view of simulation methods, the WL algorithm also works efficiently in the simulation of the system for a periodically arranged bond distribution as well as the SW algorithm.

Pseudo-random bit sequence have a wide range of applications in the field of cryptography and communications. For the good chaotic dynamical properties of chaotic systems sequence such as randomness and initial sensitivity, chaotic systems have a strong advantage in generating the pseudo-random bit sequence. However, in practical use, the dynamical properties of chaotic systems will be degraded because of the limited calculation accuracy and it even could cause a variety of security issues. To improve the security, in full analyses of the pseudo-random bit generator proposed in our former paper, we point out some problems in our former design and redesign a better pseudo-random bit generator base on it. At the same time, we make some relevant theoretical and experimental analyses on it. The experiments show that the design proposed in this paper has good statistical properties and security features.

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

In this study, we introduce a new method to control continuously the frustration caused by the quenched interaction in a given lattice, and we examine how the phenomenon changes by continuous control of the frustration, especially examining a continuous change of phase transition points. For this purpose, we introduce many-body correlation among original spin-interactions using a distribution function to express the correlations, which plays a role of a parameter to control the frustration. The transition points have been obtained by the cluster effective field theory. As a result, an interesting phase diagram has been obtained for the continuous change of frustration. Furthermore, phase transitions of various models can be analyzed systematically in a fixed lattice from this phase diagram with a variable of continuous change of frustration. In addition, it is understood that the spin-glass transition points do not depend on the disorder of the interaction and are decided only by the frustration.

We propose an order index, $\varphi$, which quantifies the notion of “life at the edge of chaos” when applied to genome sequences. It maps genomes to a number from 0 (random and of infinite length) to 1 (fully ordered) and applies regardless of sequence length and base composition. The 786 complete genomic sequences in GenBank were found to have $\varphi$ values in a very narrow range, 0.037 ± 0.027. We show this implies that genomes are halfway towards being completely random, namely, at the edge of chaos. We argue that this narrow range represents the neighborhood of a fixed-point in the space of sequences, and genomes are driven there by the dynamics of a robust, predominantly neutral evolution process.

The quaternary alloy (QA) is simulated on the Bethe lattice (BL) in the form of AB_pC_qD_r and its phase diagrams are calculated by using the exact recursion relations (ERR) for the coordination number z = 3. The QA is designed on the BL by placing A atoms (spin-1/2) on the odd shells and randomly placing B (spin-3/2), C (spin-5/2) or D (spin-1) atoms with probabilities p, q and r, respectively, on the even shells. A compact form of formulation for the QA is obtained in the standard-random approach which can easily be reduced to ternary alloy (TA) and mixed-spin models by the appropriate values of the random variables p, q and r. The phase diagrams are calculated on the temperature and ratio of bilinear interaction parameter planes for given values of probabilities.

In this work, the ternary alloy (TA) of the form $A{B}_p{C}_{1-p}$ with spin-$\frac{3}{2}$, spin-2 and spin-$\frac{5}{2}$, respectively, is studied on the Bethe lattice in terms of exact recursion relations in the standard random approach. The bilinear interaction parameter $J_{AB}$ is assumed to be ferromagnetic between the nearest-neighbor spins with spin-$\frac{3}{2}$ and spin-2, while $J_{AC}$ is taken to be antiferromagnetic between spin-$\frac{3}{2}$ and spin-$\frac{5}{2}$. The possible phase diagrams are obtained from the thermal analysis of the order parameters for the given coordination numbers z = 3,4,5 and 6. This analysis has also revealed that the model gives both second- and first-order phase transitions in addition to the compensation temperatures.

The mixed spin-1/2 and spin-3/2 Blume–Capel (BC) model is considered on the Bethe lattice (BL) with randomly changing coordination numbers (CN) and examined in terms of exact recursion relations. A couple of two different CNs are changed randomly on the shells of the BL in terms of a standard–random approach to obtain the phase diagrams on possible planes of the system parameters. It is found from the thermal analysis of the order-parameters that the model only gives the second-order phase transitions as in the regular mixed case. As the probability of having larger CN increases, the temperatures of the critical lines also increase as expected.

We generalize the exact solution to the Bernoulli shift map. Under certain conditions, the generalized functions can produce unpredictable dynamics. We use the properties of the generalized functions to show that certain dynamical systems can generate random dynamics. For instance, the chaotic Chua's circuit coupled to a circuit with a noninvertible I–V characteristic can generate unpredictable dynamics. In general, a nonperiodic time-series with truncated exponential behavior can be converted into unpredictable dynamics using noninvertible transformations. Using a new theoretical framework for chaos and randomness, we investigate some classes of coupled map lattices. We show that, in some cases, these systems can produce completely unpredictable dynamics. In a similar fashion, we explain why some well-known spatiotemporal systems have been found to produce very complex dynamics in numerical simulations. We discuss real physical systems that can generate random dynamics.

In systems theory and science, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems [Aziz-Alaoui & Bertelle, 2009]. In this paper, we use the emergent property of the ultra weak multidimensional coupling of p 1-dimensional dynamical chaotic systems which leads from chaos to randomness.

Generation of random or pseudorandom numbers, nowadays, is a key feature of industrial mathematics. Pseudorandom or chaotic numbers are used in many areas of contemporary technology such as modern communication systems and engineering applications. More and more European or US patents using discrete mappings for this purpose are obtained by researchers of discrete dynamical systems [Petersen & Sorensen, 2007; Ruggiero et al., 2006]. Efficient Chaotic Pseudo Random Number Generators (CPRNG) have been recently introduced. They use the ultra weak multidimensional coupling of p 1-dimensional dynamical systems which preserve the chaotic properties of the continuous models in numerical experiments. Together with chaotic sampling and mixing processes, ultra weak coupling leads to families of (CPRNG) which are noteworthy [Hénaff et al., 2009a, 2009b, 2009c, 2010].

In this paper we improve again these families using a double threshold chaotic sampling instead of a single one.

We analyze numerically the properties of these new families and underline their very high qualities and usefulness as CPRNG when very long series are computed. Moreover, a determining property of such improved CPRNG is the high number of parameters used and the high sensitivity to the parameters value which allows choosing it as cipher-keys. It is why we call these families multiparameter chaotic pseudo-random number generators (M-p CPRNG).

Vibration-based energy harvesting is of increasing importance and there is a current challenge to improve energy harvesting capacity exploiting nonlinear and random effects. This article investigates random effects in a nonlinear energy harvesting system. The system is represented by a magnetoelastic structure with two piezoceramic layers attached to the root of a cantilever beam, obtaining a bimorph generator. The energy harvesting system is subjected to three excitation conditions: pure harmonic, pure random and a combination of harmonic and random excitations. Noise-to-Signal Ratio (NSR) is employed to quantify different combinations of the forcing terms, establishing a procedure to evaluate the system performance. This approach is based on Power Spectral Density (PSD) of input and output signals. Numerical simulations are carried out, identifying the better combinations of harmonic and random excitations for energy harvesting purposes. Discussions about the influence of the kind of response are carried out evaluating the differences between periodic and chaotic motions. Conclusions show that both random and nonlinear effects can be tuned in order to enhance energy harvesting capacity.

Thanks to the simplicity and robustness of its calculation methods, algorithmic (or Kolmogorov) complexity appears as a useful tool to reveal chaotic dynamics when experimental time series are too short and noisy to apply Takens’ reconstruction theorem. We measure the complexity in chaotic regimes, with and without extreme events (sometimes called optical rogue waves), of three different all-solid-state lasers: Kerr lens mode locking femtosecond Ti:Sapphire (“fast” saturable absorber), Nd:YVO₄$+$ Cr:YAG (“slow” saturable absorber) and Nd:YVO₄ with modulated losses. We discuss how complexity characterizes the dynamics in an understandable way in all cases, and how it provides a correction factor of predictability given by Lyapunov exponents. This approach may be especially convenient to implement schemes of chaos control in real time.

Regression testing is a very time-consuming and expensive testing activity. Many test case prioritization techniques have been proposed to speed up regression testing. Previous studies show that no one technique is always best. Random strategy, as the simplest strategy, is not always so bad. Particularly, when a test suite has higher fault detection capability, the strategy can generate a better result. Nevertheless, due to the randomness, the strategy is not always as satisfactory as expected. In this context, we present a test case prioritization approach using fixed size candidate set adaptive random testing algorithm to reduce the effect of randomness and improve fault detection effectiveness. The distance between pair-wise test cases is assessed by exclusive OR. We designed and conducted empirical studies on eight C programs to validate the effectiveness of the proposed approach. The experimental results, confirmed by a statistical analysis, indicate that the approach we proposed is more effective than random and the total greedy prioritization techniques in terms of fault detection effectiveness. Although the presented approach has comparable fault detection effectiveness to ART-based and the additional greedy techniques, the time cost is much lower. Consequently, the proposed approach is much more cost-effective.