Two important but almost never related beliefs

Engelmann believes in two general strong beliefs. On one side, Engelmann believes in probabilistic skepticism. That the existence has its maximum probability or there is no existence at all is not possible. The other probabilities of existence are always possible. On the other hand, instead of elements that constitute a beginning arrived at by an unknown division, the Gestalt theory begins with wholes or Gestalten. These Gestalten can be divided in parts but each part can be only a part of his specific Gestalt. The majority of probabilistic skeptics and the majority of Gestalt theory followers do not believe they can be together accepted. On the contrary Engelmann is a member of a small group that both beliefs occur simultaneously. Bibace`s questions were each answered by Engelmann. In some questions Engelmann harmonize with Bibace, in others they disagree.

Exact field-driven interface dynamics in the two-dimensional stochastic Ising model with helicoidal boundary conditions

We investigate the interface dynamics of the two-dimensional stochastic Ising model in an external field under helicoidal boundary conditions. At sufficiently low temperatures and fields, the dynamics of the interface is described by an exactly solvable high-spin asymmetric quantum Hamiltonian that is the infinitesimal generator of the zero range process. Generally, the critical dynamics of the interface fluctuations is in the Kardar-Parisi-Zhang universality class of critical behavior. We remark that a whole family of RSOS interface models similar to the Ising interface model investigated here can be described by exactly solvable restricted high-spin quantum XXZ-type Hamiltonians. (C) 2012 Elsevier B.V. All rights reserved.

The chemiluminescent peroxyoxalate system: state of the art almost 50 years from its discovery

Almost fifty years after the discovery of the peroxyoxalate reaction by E. A. Chandross in the early nineteen sixties, this review article intends to give a general overview on mechanistic aspects of this system and to describe the principles of its analytical application. After a short general introduction on the principles of chemiluminescence and the history of peroxyoxalate discovery, mechanistic aspects of high-energy intermediate formation, its structure and its reaction with an activator in the peroxyoxalate system are discussed. Finally, analytical applications of peroxyoxalate chemiluminescence are exemplified using representative recent examples, including oxalic acid detection in biological samples.

Association of single nucleotide polymorphisms in the bovine leptin and leptin receptor genes with growth and ultrasound carcass traits in Nellore cattle

Given the important role of leptin in metabolism, we looked for a possible association of leptin and leptin receptor polymorphisms with carcass and growth traits in Nellore cattle. We examined associations of leptin and leptin receptor SNPs with ultrasound carcass (longissimus dorsi muscle area (ribeye area), backfat thickness and rump fat thickness and growth traits (weaning weight adjusted to 210 days of age, yearling weight adjusted to 550 days of age, weight gain of weaning to yearling and scrotal circumference adjusted to 550 days of age) of 2162 Bos primigenius indicus (Nellore) animals. Allele and genotypic frequencies were calculated for each marker. Allele substitution, additive and dominance effects of the polymorphisms were also evaluated. Some alleles of the molecular markers had low frequencies, lower than 1%, in the sample analyzed, although the same polymorphisms described for B. p. taurus cattle were found. Due to very low allelic frequencies, the E2JW, A59V and UASMS2 markers were not included in the analysis, because they were almost fixed. E2FB was found to be significantly associated with weight gain, ribeye area and backfat thickness. The promoter region markers, C963T and UASMS1, were also found to be significantly associated with ribeye area. T945M was significantly associated with weight gain. We conclude that the leptin and receptor gene markers would be useful for marker-assisted selection.

Almost p-compact groups

We show that if p is a selective ultrafilter, then for each cardinal alpha <= omega(1), there exists a topological group G such that G(beta) is almost p-compact (in particular, countably compact), for beta < alpha, but G(alpha) is not countably compact. If in addition, we assume Martin's Axiom, then the result above holds for every alpha < c. (C) 2012 Elsevier By. All rights reserved.

Collective Almost Synchronisation in Complex Networks

This work introduces the phenomenon of Collective Almost Synchronisation (CAS), which describes a universal way of how patterns can appear in complex networks for small coupling strengths. The CAS phenomenon appears due to the existence of an approximately constant local mean field and is characterised by having nodes with trajectories evolving around periodic stable orbits. Common notion based on statistical knowledge would lead one to interpret the appearance of a local constant mean field as a consequence of the fact that the behaviour of each node is not correlated to the behaviours of the others. Contrary to this common notion, we show that various well known weaker forms of synchronisation (almost, time-lag, phase synchronisation, and generalised synchronisation) appear as a result of the onset of an almost constant local mean field. If the memory is formed in a brain by minimising the coupling strength among neurons and maximising the number of possible patterns, then the CAS phenomenon is a plausible explanation for it.

Multifemale nests and social behavior in Euglossa melanotricha (Hymenoptera,Apidae, Euglossini)

The nesting biology and social behavior of the euglossine bee species Euglossa melanotricha was analyzed based on the monitoring of eight nests found in man-made cavities and transferred to observation boxes. Euglossa melanotricha females usually construct their nests in cavities in the ground, in buildings, or in mounds. In this study, we present new data on the nesting biology of E. melanotricha. The process of reactivation of nests was commonly observed with one to three females participating in the reactivation. The duration of the process of reactivation ranged from 10 to 78 days (n = 31) and were longer during the rainy season. Time spent (in days) for provisioning, oviposition and closing a single cell was higher in reactivations that occurred during the dry period. 151 emergences were observed (39 males and 112 females). 90 (80.3%) of the emerged females returned to the natal nest, but only 35(38.9%) remained and actively participated in the construction and provisioning of cells. The other 55 abandoned the nests after several days without performing any work in the nest. Matrifilial nest structure was regulated by dominance-subordinate aggressive behavior among females, where the dominant female laid almost all eggs. Task allocation was recognized by behavioral characteristics, namely, agonism and oophagy in cells oviposited by other females. Euglossa melanotricha is multivoltine and its nesting is asynchronous with respect to season. Our observations suggest a primitively eusocial organization. These observations of E. melanotricha provide valuable information for comparison with other species of Euglossa in an evolutionary context.

A spatial stochastic model for rumor transmission

We consider an interacting particle system representing the spread of a rumor by agents on the d-dimensional integer lattice. Each agent may be in any of the three states belonging to the set {0,1,2}. Here 0 stands for ignorants, 1 for spreaders and 2 for stiflers. A spreader tells the rumor to any of its (nearest) ignorant neighbors at rate lambda. At rate alpha a spreader becomes a stifler due to the action of other (nearest neighbor) spreaders. Finally, spreaders and stiflers forget the rumor at rate one. We study sufficient conditions under which the rumor either becomes extinct or survives with positive probability.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Stochastic gravito-inertial modes discovered by CoRoT in the hot Be star HD51452

Context. Be stars are rapidly rotating stars with a circumstellar decretion disk. They usually undergo pressure and/or gravity pulsation modes excited by the kappa-mechanism, i.e. an effect of the opacity of iron-peak elements in the envelope of the star. In the Milky Way, p-modes are observed in stars that are hotter than or equal to the B3 spectral type, while g-modes are observed at the B2 spectral type and cooler. Aims. We observed a B0IVe star, HD51452, with the high-precision, high-cadence photometric CoRoT satellite and high-resolution, ground-based HARPS and SOPHIE spectrographs to study its pulsations in great detail. We also used the lower resolution spectra available in the BeSS database. Methods. We analyzed the CoRoT and spectroscopic data with several methods: CLEAN-NG, FREQFIND, and a sliding window method. We also analyzed spectral quantities, such as the violet over red (V/R) emission variations, to obtain information about the variation in the circumstellar environment. We calculated a stellar structure model with the ESTER code to test the various interpretation of the results. Results. We detect 189 frequencies of variations in the CoRoT light curve in the range between 0 and 4.5 c d(-1). The main frequencies are also recovered in the spectroscopic data. In particular we find that HD51452 undergoes gravito-inertial modes that are not in the domain of those excited by the kappa-mechanism. We propose that these are stochastic modes excited in the convective zones and that at least some of them are a multiplet of r-modes (i.e. subinertial modes mainly driven by the Coriolis acceleration). Stochastically excited gravito-inertial modes had never been observed in any star, and theory predicted that their very low amplitudes would be undetectable even with CoRoT. We suggest that the amplitudes are enhanced in HD51452 because of the very rapid stellar rotation. In addition, we find that the amplitude variations of these modes are related to the occurrence of minor outbursts. Conclusions. Thanks to CoRoT data, we have detected a new kind of pulsations in HD51452, which are stochastically excited gravito-inertial modes, probably due to its very rapid rotation. These modes are probably also present in other rapidly rotating hot Be stars.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Robustness of the non-Markovian Alzheimer walk under stochastic perturbation

The elephant walk model originally proposed by Schutz and Trimper to investigate non-Markovian processes led to the investigation of a series of other random-walk models. Of these, the best known is the Alzheimer walk model, because it was the first model shown to have amnestically induced persistence-i.e. superdiffusion caused by loss of memory. Here we study the robustness of the Alzheimer walk by adding a memoryless stochastic perturbation. Surprisingly, the solution of the perturbed model can be formally reduced to the solutions of the unperturbed model. Specifically, we give an exact solution of the perturbed model by finding a surjective mapping to the unperturbed model. Copyright (C) EPLA, 2012

PERFORMANCE EVALUATION OF RESOURCE-AWARE BUSINESS PROCESSES USING STOCHASTIC AUTOMATA NETWORKS

In this work, we study the performance evaluation of resource-aware business process models. We define a new framework that allows the generation of analytical models for performance evaluation from business process models annotated with resource management information. This framework is composed of a new notation that allows the specification of resource management constraints and a method to convert a business process specification and its resource constraints into Stochastic Automata Networks (SANs). We show that the analysis of the generated SAN model provides several performance indices, such as average throughput of the system, average waiting time, average queues size, and utilization rate of resources. Using the BP2SAN tool - our implementation of the proposed framework - and a SAN solver (such as the PEPS tool) we show through a simple use-case how a business specialist with no skills in stochastic modeling can easily obtain performance indices that, in turn, can help to identify bottlenecks on the model, to perform workload characterization, to define the provisioning of resources, and to study other performance related aspects of the business process.