Narrow Results

We study a family of correlated one-dimensional random walks with a finite memory range M. These walks are extensions of the Taylor's walk as investigated by Goldstein, which has a memory range equal to one. At each step, with a probability p, the random walker moves either to the right or to the left with equal probabilities, or with a probability q = 1 -p performs a move, which is a stochastic Boolean function of the M previous steps. We first derive the most general form of this stochastic Boolean function, and study some typical cases which ensure that the average value <R_n> of the walker's location after n steps is zero for all values of n. In each case, using a matrix technique, we provide a general method for constructing the generating function of the probability distribution of R_n; we also establish directly an exact analytic expression for the step–step correlations and the variance of the walk. From the expression of , which is not straightforward to derive from the probability distribution, we show that, for n approaching infinity, the variance of any of these walks behaves as n, provided p > 0. Moreover, in many cases, for a very small fixed value of p, the variance exhibits a crossover phenomenon as n increases from a not too large value. The crossover takes place for values of n around 1/p. This feature may mimic the existence of a nontrivial Hurst exponent, and induce a misleading analysis of numerical data issued from mathematical or natural sciences experiments.

The population in the sexual Penna aging model is first separated into several reproductively isolated groups. Then, after equilibration, sexual mixing between the groups is allowed. We study the changes in the population size due to this mixing and interpret them through a counterplay of purifying selection and of haplotype complementarity.

To evaluate the efficacy of Chinese medicine (CM) acupuncture for chronic neck pain (CNP), a single blind, controlled, crossover, clinical trial was undertaken. Twenty-nine volunteers with CNP were randomly recruited into two groups. Both groups received two phases of treatment with a washout period between the two phases. Group A (14 volunteers) received CM acupuncture in the first phase and sham acupuncture in the second, while Group B (15 volunteers) received sham in the first and real in the second. CM acupuncture was individualized and consisted of nine sessions on both local and distal points. Manual twisting of the needle was applied on all points plus strong electrical stimulation of distal points in CM acupuncture. Sham acupoints (lateral to the real) and sham (weak) electrical stimulation was used in the control group. Comparison of subjective and objective measures between the two groups was made at different periods, including baseline, after each phase of treatment, after washout, and after the 16th week follow-up. The subjective measures included pain intensity, duration per day, analgesic medication count, visual analogue scales (VAS) and neck disability index (NDI). The objective measures consisted of neck range of motion (ROM) and pain threshold (PT). Both the real and sham treatments significantly reduced subjective pain, without significant differences between groups for most subjective measures. Objective measures showed no significant change for either group before and after each period or by inter-groups analysis. A minimum 16-week effect of both real and sham acupuncture was found for subjective measures in the follow-up periods. Further study is recommended with an increased sample size, a longer washout period, and a longer baseline period.

In the framework of truncated Dyson–Schwinger equation for fermion propagator, we investigate the chiral susceptibility with the rise of temperature in QED₃, while it demonstrates a phase transition characteristic in the chiral limit and illustrates a crossover beyond chiral limit in this Abelian system.

In this paper, we use the two-flavor Nambu–Jona-Lasinio (NJL) model to study the quantum chromodynamics (QCD) chiral phase transition. To deal with the ultraviolet (UV) issue, we adopt the popular proper time regularization (PTR), which is commonly used not only for hadron physics but also for the studies with magnetic fields. This regularization scheme can introduce the infrared (IR) cutoff to include quark confinement. We generalize the PTR to zero temperature and finite chemical potential case use a completely new method, and then study the chiral susceptibility, both in the chiral limit case and with finite current quark mass. The chiral phase transition is second-order in $\mu =0$ and $T=0$ and crossover at $\mu \ne 0$ and $T=0$. Three sets of parameters are used to make sure that the results do not depend on the parameter choice.

In this paper, we study the 2-flavor equation of state of the quantum chromodynamics at zero temperature and finite chemical potentials with a modified Nambu–Jona–Lasinio model, where the beta equilibrium and electric charge neutrality conditions of the system (including u, d quarks, electrons and muons) are considered. The related chiral phase transition is also discussed in this paper. For comparison, we show the results with four different parameter sets, and find only quantitative differences. As chemical potential increases, the crossover instead of first-order chiral phase transition happens. Finally, we calculate the binding energy per baryon for different parameter sets, and find that the 2-flavor quark system with a smaller $G_1$ (or a larger m) possesses the lower binding energy per baryon, indicated to be more stable than the other case.

The crossover of dilution in the critical exponent associated with the thermodynamic properties in the magnetic materials via Monte Carlo simulations is observed. The obtained results of critical exponent associated with the magnetic susceptibility, specific heat and correlation length for the ZnCr_2xAl_2-2xS₄, Cd_1-yCr_2-2xIn_y+2xSe₄ and Zn_1-xMn_xTe systems are comparable with those given by the experiment results.

The ground-state properties in one-dimensional Hubbard model with on-site attraction and repulsion of electrons in the presence of magnetic field h are calculated by means of the exact Bethe-ansatz formalism and the generalized self-consistent field (GSCF) approach for general electron concentrations n and arbitrary interaction strength. The ground-state properties, including the energy, the average spin (magnetization) and the kinetic energy are compared over a wide range of parameter space. The GSCF theory is in qualitative and in some cases in good quantitative agreement with the exact results. The GSCF theory at U≤0 (or U≥0) differentiates the spin (or charge) energy gap from the BCS (or antiferromagnetic) order parameter and suggests a smooth crossover from the phase with the itinerant BCS-like behavior to the Bose condensation regime of the local pairs.

The influence of high pressure on the conductivity in the ab-plane of the oxygen deficient YBa₂Cu₃O_7-δ single crystals is investigated. It is determined that excess conductivity Δσ(T) in the YBa₂Cu₃O_7-δ single crystals in a wide temperature interval (T_c < T < T*, where T_c is the critical temperature and T* is the temperature that the PG regime begins) obeys an exponential temperature-dependence. The description of the excess conductivity with ) can be interpreted in terms of mean field theory. The temperature-dependence of the pseudo-gap is well described in the framework of the Bardeen–Cooper–Schrieffer (BCS) to the Bose–Einstein condensate (BEC) crossover theory. Increasing the applied pressure leads to the effect of narrowing the temperature range of implementation of the PG regime, thus extending the area of linear ρ(T) dependence in the ab-plane.

In this work we study the influence of the constant magnetic field up to 14 kOe to the temperature dependence of the electrical conductivity in the ab-plane of aluminium doped YBa₂Cu₃O_7-δ single crystals with an unidirectional twin boundaries system. The temperature dependence of the excess para-conductivity is interpreted within the Aslamazov–Larkin theoretical model of the fluctuation conductivity. It was shown that the lack of fan-shaped expansion of the resistive transitions in the magnetic field in these samples may be due to the lack of the non-pinning vortex liquid phase.

In this paper, we investigate the temperature dependence of the electrical resistivity of a two-dimensional electron system in n-channel Si-MOSFETs at zero magnetic field down to 0.2 K. At low electron densities, near the metal–insulator transition point from the insulating side, our results show the existence of a crossover, from Efros–Shklovskii variable range hopping (ES-VRH), which is consistent with the existence of a Coulomb gap, where ρ = ρ₀exp(T_ES/T)^1/2 to Mott regime, where ρ = ρ₀exp(T_M/T)^1/3. With ρ₀ is a pre-exponential factor that is found to be close to 2 (h/e²), this crossover occurs when T ~ 1 K.

This paper presents a structural distance-based crossover for neural network classifiers, which is applied as part of a Memetic Algorithm (MA) for evolving simultaneously the structure and weights of neural network models applied to multiclass problems. Previous researchers have shown that this simultaneous evolution is a way to avoid the noisy fitness evaluation. The MA incorporates a crossover operator that shows to be useful for ameliorating the permutation problem of the network representation (i.e. different genotypes can be used to represent the same neural network phenotype), increasing the structural diversity of the individuals and improving the accuracy of the results. Instead of a recombination probability, the crossover operator considers a similarity parameter (the minimum structural distance), which allows to maintain a trade-off between global and local search. The neural network models selected in this work are the product-unit neural networks (PUNNs), due to their increasing relevance in those classification problems which show a high order relationship between the input variables. The proposed MA is intended to reduce the possible overtraining problems which can raise in some datasets for this kind of models. The evolutionary system is applied to eight classification benchmarks and the results of an analysis of variance contrast (ANOVA) show the effectiveness of the structural-based crossover operator and the capacity of our algorithm to obtain evolved PUNNs with a higher classification accuracy than those obtained using other evolutionary techniques. On the other hand, the results obtained are compared with popular effective machine learning classification methods, resulting in a competitive performance.

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-Gravitational Search Algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images from databases holding numerous image categories. The experimental outcome shows that our proposed CBMIRVC technique outperforms other conventional image retrieval techniques by effectively retrieving similar images from large databases.

The Detrended Fluctuation Analysis (DFA) and its extensions (MF-DFA) have been proposed as robust techniques to determine possible long-range correlations in self-affine signals. However, many studies have reported the susceptibility of DFA to trends which give rise to spurious crossovers and prevent reliable estimations of the scaling exponents. Lately, several modifications of the DFA method have been reported with many different techniques for eliminating the monotonous and periodic trends. In this study, a smoothing algorithm based on the Orthogonal V-system (OVS) is proposed to minimize the effect of power-law trends, periodic trends, assembled trends and piecewise function trends. The effectiveness of the new method is demonstrated on monofractal data and multifractal data corrupted with different trends.

We use an atomic ratchet realized by applying short pulses of an optical standing-wave to a Bose–Einstein condensate to study the crossover between classical and quantum dynamics. The signature of the ratchet is the existence of a directed current of atoms, even though there is an absence of a net bias force. Provided that the pulse period is close to one of the resonances of the system, the ratchet behavior can be understood using a classical like theory which depends on a single variable containing many of the experimental parameters. Here we show that this theory is valid in both the true classical limit, when the pulse period is close to zero, as well as regimes when this period is close to other resonances where the usual scaled Planck's constant is nonzero. By smoothly changing the pulse period between these resonances we demonstrate how it is possible to tune the ratchet between quantum and classical types of behavior.

Text summarization is one of the most discussed topic in the field in information exchange and retrieval. Recently, the need for local language based text summarization methods are increasing. In this paper, a method for text summarization in Hindi language is plotted with help of extraction methods. The proposed approach is uses three major algorithms, fuzzy classifier, neural network and global search optimization (GSO). The fuzzy classifier and neural network are used for generating sentence score. The GSO algorithm is used with the neural network, in order to optimize the weights in the neural network. A hybrid score is generated from fuzzy method and neural network for each input sentences. Finally, based on the hybrid score from fuzzy classifier and neural network, the summary of the given input records are generated. An experimental analysis of the proposed approach will subjected based on the evaluation parameters precision, recall. Later on experimental analysis are conducted on the proposed approach in order to evaluate the performance. According to the experimental analysis, the proposed approach achieved an average precision rate 0.90 and average recall rate of 0.88 for compression rate 20%. The comparative analysis also provided reasonable results to prove the efficiency of the proposed approach.

Homologous recombination is important for DNA repair and for increasing genetic variation, whereby it enriches the gene pool and keeps populations viable. In eukaryotes, genetic recombination takes place during meiosis. For genes on different chromosomes, mixing of paternal and maternal genes is achieved through the random formation of different chromosome configurations at metaphase I of meiosis. This process accounts for the genetic principle of the independent assortment of unlinked genes. Recombination of paternal and maternal genes on different members of the homologous chromosome pair, on the other hand, can only be achieved through the exchange of genetic material between nonsister chromatids, resulting in increased genetic variation. The molecular mechanisms of homologous recombination are complicated and often difficult for students to understand. The objective of this research is to develop an interactive computer program for teaching this important biological process. The software program, along with related computer-based genetics learning programs — for mitosis and meiosis, as well as for a cytogenetics laboratory — will be useful for genetics education at the high school and university levels.

Assuming no interference, a multi-locus genetic likelihood is implemented based on a mathematical model of the recombination process in meiosis that accounts for events up to double crossovers in the genetic interval for any specified order of genetic markers. The mathematical model is realized with a straightforward algorithm that implements the likelihood computation process. The time complexity of the straightforward algorithm is exponential without bound in the number of genetic markers and implementation of the model for more than 7 genetic markers is not feasible, motivating the need for a novel algorithm. A recursive linking algorithm is proposed that decomposes the pool of genetic markers into segments and renders the model implementable for a large number of genetic markers. The recursive algorithm is shown to reduce the order of time complexity from exponential to linear. The improvement in time complexity is shown theoretically by a worst-case analysis of the algorithm and supported by run time results using data on linkage group-I of the fungal genome Neurospora crassa.

This chapter introduces the basic working principles of genetic algorithm (GA). This method was tested on typical used products collection problem in the context of remanufacturing. It was observed that genetic algorithm was able to find the shortest travel plan, while keeping the fuel consumption rate at the lowest possible level. Furthermore, GA was used to update the finite element (FE) model to better reflect the measured data. The results obtained indicated that GA was able to successfully update the FE model resulting in better reflection of the measured data.

The Detrended Fluctuation Analysis (DFA) and its extensions (MF-DFA) have been proposed as robust techniques to determine possible long-range correlations in self-affine signals. However, many studies have reported the susceptibility of DFA to trends which give rise to spurious crossovers and prevent reliable estimations of the scaling exponents. Lately, several modifications of the DFA method have been reported with many different techniques for eliminating the monotonous and periodic trends. In this study, a smoothing algorithm based on the Orthogonal V-system (OVS) is proposed to minimize the effect of power-law trends, periodic trends, assembled trends and piecewise function trends. The effectiveness of the new method is demonstrated on monofractal data and multifractal data corrupted with different trends.