A knowledge-based system for the strategic control level of robots in flexible manufacturing cells

The strategic control level synthesis for robots is related to a hierarchical robot control problem. The main control problem at the strategic control level is to select the model and algorithm to be used by the lower control level to execute the given robot task. Usually there are several lower control level models and algorithms that can be used by the robot control system for every robot task. Strategic control level synthesis depends on the particular robot system application. In a typical application, when the robot system is used in a flexible manufacturing system for manipulating various part types, the robot tasks executed by the robot system depend on the manufacturing processes in the system. If the robot system is applied in another flexible manufacturing system, dedicated to other manufacturing processes, another set of robot tasks might be needed to perform the necessary operations. Therefore, the quantity and the kind of knowledge required in the system for the strategic control level differ from one application to another. Such a fact creates the appropriate conditions for employing some artificial intelligence techniques. This article describes a knowledge-based system approach to the strategic control level synthesis problem.     

Natural selection as an emergent process: instructional implications

Student reasoning about cases of natural selection is often plagued by errors that stem from miscategorising selection as a direct, causal process, misunderstanding the role of randomness, and from the intuitive ideas of intentionality, teleology and essentialism. The common thread throughout many of these reasoning errors is a failure to apply ‘population thinking’. Students fail to recognise that natural selection refers to changes in the distribution of certain traits at the population level, the collective, resulting from interactions between individual organisms and their environment at the next lower level in the system. Processes like selection are emergent processes in hierarchical systems, where patterns in a collective are generated by interactions at the lower level. By helping students develop an emergent process schema that enables them to recognise that even random interactions at one level in a system can generate predictable patterns at a higher level, their understanding of natural selection should improve. Some studies have shown this to be an effective approach for teaching other emergent processes. Instructional recommendations based on these studies are presented here, but more research is needed to determine the full extent to which this approach can improve students’ understanding.     

Organizational levels analysis: a key to understanding processes in natural systems

Recent research conducted on a hypothetical four-parameter system generated by two stochastic processes has shown that errors of interpretation are likely to result when the relative importance of an underlying generating process is inferred from the analysis of an emergent pattern of a natural system. This paper presents an approach which substantially reduces that error for the hypothetical system, taken as an analog for a natural system. This approach is based on recognition of the organizational levels composing the system and utilizes observations on structure at a lower organizational level to resolve the properties of the generating processes at the lower level. The resulting knowledge of those underlying structures and the processes which created them are then used to determine the relative importance of those processes in affecting the emergent pattern of the system at a higher organizational level. It is concluded that in order to understand the role of a process in a natural system, measurements of system structure must be performed at a frequency which isolates the process from the other processes in the system.     

Profiles of random change during aging contain hidden information about longevity and the aging process.

Many different morphological and physiological changes occur during the yeast replicative lifespan. It has been proposed that change is a cause rather than an effect of aging. It is difficult to ascribe causality to processes that manifest themselves at the level of the entire organism, because of their global nature. Although causal connections can be established for processes that occur at the molecular level, their exact contributions are obscured, because they are immersed in a highly interactive network of processes. A top-down approach that can isolate crucial features of aging processes for further study may be a productive avenue. We have mathematically depicted the complicated and random changes that occur in cellular spatial organization during the lifespan of individual yeast cells. We call them budding profiles. This has allowed us to demonstrate that budding profiles are a highly individual characteristic, and that they are correlated with an individual cell's longevity. Additional information can be extracted from our model, indicating that random budding is associated with longevity. This expectation was confirmed, providing new avenues for exploring causal factors in yeast aging. The methodology described here can be readily applied to other aspects of aging in yeast and in higher organisms.     

Network Theory and the Formation of Groups Without Evolutionary Forces

This paper presents a modified random network model to illustrate how groups can form in the absence of evolutionary forces, assuming groups are collections of entities at any level of organization. This model is inspired by the Zero Force Evolutionary Law, which states that there is always a tendency for diversity and complexity to increase in any evolutionary system containing variation and heredity. That is, in the absence of evolutionary forces, the expectation is a continual increase in diversity and complexity at any level of biological hierarchy. I show that, when modeled, this expectation of increasing variation results not only in the formation of groups, but also in a higher probability of group formation than is found in a model that is purely random.     

Stochastic analysis of predator–prey type ecosystems

A stochastic model for the predator–prey type ecosystems in a random environment is proposed and investigated. The model is a variation of the Lotka–Volterra type with an additional self-competition mechanism within the prey population. Two different situations are considered: (1) saturation of predators, and (2) competition among predators. Changes in the birth rate of the preys and the death rate of the predators are modeled as random processes. The stochastic averaging procedure of Stratonovich and Khasminskii is applied to obtain the probability distribution of the system state variables at the state of statistical stationarity. Asymptotic behaviors of the system are also investigated. Effects on the ecosystem behaviors are evaluated of (1) prey self-competition, (2) predator saturation and predator competition, (3) random variation in the prey birth rate, and (4) random variation in the predator death rate.     

Trophic interactions and the relationship between species diversity and ecosystem stability

Several theoretical studies propose that biodiversity buffers ecosystem functioning against environmental fluctuations, but virtually all of these studies concern a single trophic level, the primary producers. Changes in biodiversity also affect ecosystem processes through trophic interactions. Therefore, it is important to understand how trophic interactions affect the relationship between biodiversity and the stability of ecosystem processes. Here we present two models to investigate this issue in ecosystems with two trophic levels. The first is an analytically tractable symmetrical plant-herbivore model under random environmental fluctuations, while the second is a mechanistic ecosystem model under periodic environmental fluctuations. Our analysis shows that when diversity affects net species interaction strength, species interactions--both competition among plants and plant-herbivore interactions--have a strong impact on the relationships between diversity and the temporal variability of total biomass of the various trophic levels. More intense plant competition leads to a stronger decrease or a lower increase in variability of total plant biomass, but plant-herbivore interactions always have a destabilizing effect on total plant biomass. Despite the complexity generated by trophic interactions, biodiversity should still act as biological insurance for ecosystem processes, except when mean trophic interaction strength increases strongly with diversity.     

Dynamics of asynchronous random Boolean networks with asynchrony generated by stochastic processes

An asynchronous Boolean network with N nodes whose states at each time point are determined by certain parent nodes is considered. We make use of the models developed by Matache and Heidel [Matache, M.T., Heidel, J., 2005. Asynchronous random Boolean network model based on elementary cellular automata rule 126. Phys. Rev. E 71, 026232] for a constant number of parents, and Matache [Matache, M.T., 2006. Asynchronous random Boolean network model with variable number of parents based on elementary cellular automata rule 126. IJMPB 20 (8), 897-923] for a varying number of parents. In both these papers the authors consider an asynchronous updating of all nodes, with asynchrony generated by various random distributions. We supplement those results by using various stochastic processes as generators for the number of nodes to be updated at each time point. In this paper we use the following stochastic processes: Poisson process, random walk, birth and death process, Brownian motion, and fractional Brownian motion. We study the dynamics of the model through sensitivity of the orbits to initial values, bifurcation diagrams, and fixed-point analysis. The dynamics of the system show that the number of nodes to be updated at each time point is of great importance, especially for the random walk, the birth and death, and the Brownian motion processes. Small or moderate values for the number of updated nodes generate order, while large values may generate chaos depending on the underlying parameters. The Poisson process generates order. With fractional Brownian motion, as the values of the Hurst parameter increase, the system exhibits order for a wider range of combinations of the underlying parameters.     

Interpretation of concentration-dependence in aggregation kinetics

Aggregation processes are analyzed by two kinetic models, the random polymerization model and the nucleation-dependent polymerization model. A kinetic equation for the random polymerization model can be derived analytically, revealing the relation between the initial monomer concentration ([M]0), the rate constant (k(a)), time (t), the yield of detectable aggregate ([F]), and the critical aggregation number (m). However, time-course curves for the nucleation-dependent polymerization model can be obtained by numerical calculation. It is found that lag time (t(d)) and half-time (t1/2) are proportional to [M](-1) in the random polymerization model, while t(d) and t1/2 are proportional to [M1](-s) (1 < s < n; n is nucleus size) at the lower concentration and are less dependent on [M1] at the higher concentration in the nucleation-dependent polymerization model.     

Water: the bloodstream of the biosphere

Water, the bloodstream of the biosphere, determines the sustainability of living systems. The essential role of water is expanded in a conceptual model of energy dissipation, based on the water balance of whole landscapes. In this model, the underlying role of water phase changes--and their energy-dissipative properties--in the function and the self-organized development of natural systems is explicitly recognized. The energy-dissipating processes regulate the ecological dynamics within the Earth's biosphere, in such a way that the development of natural systems is never allowed to proceed in an undirected or random way. A fundamental characteristic of self-organized development in natural systems is the increasing role of cyclic processes while loss processes are correspondingly reduced. This gives a coincidental increase in system efficiency, which is the basis of growing stability and sustainability. Growing sustainability can be seen as an increase of ecological efficiency, which is applicable at all levels up to whole landscapes. Criteria for necessary changes in society and for the design of the measures that are necessary to restore sustainable landscapes and waters are derived.     

Theoretical aspects of sexual selection: A generalized model of mating behaviour

A model of mate selection is described in which females mate preferentially according to their probability of encounter with the males they prefer. In this model, different thresholds of response to the courtship of different male phenotypes determine the female mating preferences. Females with a lower threshold toward particular males require fewer encounters before mating with these males and more encounters before mating with any of the others. Such females mate preferentially if they encounter a male they prefer before they have been stimulated to the level of the higher threshold. At the higher threshold they mate at random. The number of the extra encounters required to raise the females' level of stimulation from the lower to the higher threshold is a parameter of the model. The frequency of the preferred males then determines the probability that a female encounters and mates with one of them before she has been sufficiently stimulated to mate at random. Sexual selection by differences in male courtship can also be described in terms of this model.The preferred characters may be determined either by dominant and recessive alleles or by each different genotype. When only one extra encounter is required before the females mate at random, the preferred males only gain a slight frequency-dependent advantage: Stable polymorphisms can only be maintained if the heterozygotes have the greater preference in their favor. When more than one extra encounter is required before random mating, the males gain a negative frequency-dependent advantage: Stable polymorphisms are generally maintained.The models are fitted to published data on the mating success of male Drosophila at varying frequencies and provide an explanation of the “rare male” effect in which less common males gain a mating advantage.     

Effects of parameters of the higher nervous activity on stress in students during exams

The complex research of visceral responses of the students in conditions of examination stress was conducted. The main parameters of hemodynamic and heart rate variability were investigated depending on a type of higher nervous activity and self-evaluation of the students. It is shown, that at the students with a high level of steadiness of nervous processes at examination the increased vagal activity is marked, and in the students with a high level of self-evaluation at examination the lower activation of a sympathetic system is revealed.     

Quantum-like model of processing of information in the brain based on classical electromagnetic field

We propose a model of quantum-like (QL) processing of mental information. This model is based on quantum information theory. However, in contrast to models of "quantum physical brain" reducing mental activity (at least at the highest level) to quantum physical phenomena in the brain, our model matches well with the basic neuronal paradigm of the cognitive science. QL information processing is based (surprisingly) on classical electromagnetic signals induced by joint activity of neurons. This novel approach to quantum information is based on representation of quantum mechanics as a version of classical signal theory which was recently elaborated by the author. The brain uses the QL representation (QLR) for working with abstract concepts; concrete images are described by classical information theory. Two processes, classical and QL, are performed parallely. Moreover, information is actively transmitted from one representation to another. A QL concept given in our model by a density operator can generate a variety of concrete images given by temporal realizations of the corresponding (Gaussian) random signal. This signal has the covariance operator coinciding with the density operator encoding the abstract concept under consideration. The presence of various temporal scales in the brain plays the crucial role in creation of QLR in the brain. Moreover, in our model electromagnetic noise produced by neurons is a source of superstrong QL correlations between processes in different spatial domains in the brain; the binding problem is solved on the QL level, but with the aid of the classical background fluctuations.     

Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence

Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter that weights the relative fraction of an individual''s neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.     

Partitioning the effects of deterministic and stochastic processes on species extinction risk

Species populations are subjected to deterministic and stochastic processes, both of which contribute to their risk of extinction. However, current understanding of the relative contributions of these processes to species extinction risk is far from complete. Here, we address this knowledge gap by analyzing a suite of models representing species populations with negative intrinsic growth rates, to partition extinction risk according to deterministic processes and two broad classes of stochastic processes – demographic and environmental variance. Demographic variance refers to random variations in population abundance arising from random sampling of events given a particular set of intrinsic demographic rates, whereas environmental variance refers to random abundance variations arising from random changes in intrinsic demographic rates over time. When the intrinsic growth rate was not close to zero, we found that deterministic growth was the main driver of mean time to extinction, even when population size was small. This contradicts the intuition that demographic variance is always an important determinant of extinction risk for small populations. In contrast, when the intrinsic growth rate was close to zero, stochastic processes exerted substantial negative effects on the mean time to extinction. Demographic variance had a greater effect than environmental variance at low abundances, with the reverse occurring at higher abundances. In addition, we found that the combined effects of demographic and environmental variance were often substantially lower than the sum of their effects in isolation from each other. This sub-additivity indicates redundancy in the way the two stochastic processes increase extinction risk, and probably arises because both processes ultimately increase extinction risk by boosting variation in abundance over time.     

Feed-forward control of a redundant motor system

We describe a model of feed-forward control of a redundant motor system and validate it using, as examples, tasks of multi-finger force production. The model assumes the existence of two input signals at an upper level of the control hierarchy, related and unrelated to a task variable. Knowledge of the Jacobian of the system is assumed at the level of generation of elemental variables (variables at the level of effectors). Variance at the level of elemental variables is considered as the sum of two components, related and unrelated to variability in the task variable. An index of stabilization of the task variable is similarly introduced as to how it was done in several studies using the framework of the uncontrolled manifold hypothesis. Several phenomena have been simulated including data point distributions corresponding to presence and absence of force-stabilizing synergies in two-finger tasks, changes in synergies with practice, and changes in synergy indices in preparation to a fast action. The model is discussed in comparison to other models of control of multi-element systems based on feedback processes. It shows that patterns of structured variability in the space of elemental variables can result from feed-forward processes. Relations of the model to the equilibrium-point hypothesis are also discussed.     

Semiparametric models and inference for biomedical time series with extra-variation

Biomedical trials often give rise to data having the form of time series of a common process on separate individuals. One model which has been proposed to explain variations in such series across individuals is a random effects model based on sample periodograms. The use of spectral coefficients enables models for individual series to be constructed on the basis of standard asymptotic theory, whilst variations between individuals are handled by permitting a random effect perturbation of model coefficients. This paper extends such methodology in two ways: first, by enabling a nonparametric specification of underlying spectral behaviour; second, by addressing some of the tricky computational issues which are encountered when working with this class of random effect models. This leads to a model in which a population spectrum is specified nonparametrically through a dynamic system, and the processes measured on individuals within the population are assumed to have a spectrum which has a random effect perturbation from the population norm. Simulation studies show that standard MCMC algorithms give effective inferences for this model, and applications to biomedical data suggest that the model itself is capable of revealing scientifically important structure in temporal characteristics both within and between individual processes.     

On the accuracy of a diffusion approximation to a discrete state–space Markovian model of a population

The traditional Kolmogorov equations treat the size of a population as a discrete random variable. A model is introduced that extends these equations to incorporate environmental variability. Difficulties with this discrete model motivate approximating the population size as a continuous random variable through the use of diffusion processes. The set of cumulants for both the population size and the environmental factors affecting the population size characterize the population–environmental system. The evolution of this set, as predicted by the diffusion approximation, closely matches the corresponding predictions for the discrete model. It is also noted that the simulation estimates of the cumulants against which the predictions of the diffusion model are checked can vary considerably between simulations — despite averaging over a large number of simulation runs. The precision of the simulation estimates–both over time and with differing cumulant order–is discussed.     

On the accuracy of a diffusion approximation to a discrete state–space Markovian model of a population

The traditional Kolmogorov equations treat the size of a population as a discrete random variable. A model is introduced that extends these equations to incorporate environmental variability. Difficulties with this discrete model motivate approximating the population size as a continuous random variable through the use of diffusion processes. The set of cumulants for both the population size and the environmental factors affecting the population size characterize the population–environmental system. The evolution of this set, as predicted by the diffusion approximation, closely matches the corresponding predictions for the discrete model. It is also noted that the simulation estimates of the cumulants against which the predictions of the diffusion model are checked can vary considerably between simulations — despite averaging over a large number of simulation runs. The precision of the simulation estimates–both over time and with differing cumulant order–is discussed.     

Statistics of the Underwater Light Field: An Empirical Model

The underwater light field is described as a stochastic process, with the vertical attenuation as a random variable. It is shown that the vertical attenuation integral is well described as a Normal process with uncorrelated increments. The attenuation processes within a water body are found to be quite independent of the incoming irradiance. Thus, the relative light intensity at any depth can be approximated by a Lognormal random variable. Based upon the expectation of this Lognormal variable, and the mean value of the incident irradiance, the irradiance delivered at any depth can be estimated, as well as the statistical distribution of the irradiance. Light data from a number of Norwegian soft-water lakes showed good fit to the model. Two productive lakes included in this study also had a light regime well described by the statistical model. However, the model should be extended to cater for seasonal variations in these applications.