The activity and number of protocerebral neurosecretory neurons of the dorsolateral group (L1, L2 and L2'), as well as the size of the corpora allata were investigated in 5th instar larvae of the gypsy moth (Lymantria dispar) from two populations (oak and locust-tree forests) fed one of two host-plants (oak is a suitable and locust-tree an unsuitable host-plant).
The monitoring of activity related cytological parameters and the number of protocerebral dorsolateral neurosecretory neurons revealed that differently adapted populations respond to nutritive stress differently. The activity of the L1 neurosecretory neurons in the protocerebra of the caterpillars is low in those from the locust-tree forest regardless of host-plant quality. The L2 neurosecretory neurons in the oak population become active when they were fed the unsuitable host-plant whereas their activity was high in locust-tree caterpillars regardless of the host-plant. A decrease in the number of neurosecretory neurons in a response to a novel food was noticed in both populations. The activity of the L2' neurosecretory neurons was similar in all caterpillars, but their number was increased in those from the locust-tree forest. The corpora allata of the locust-tree caterpillars were large whereas those of the oak forest caterpillars only increased in size when they were fed locust-tree leaves.
It is obvious that nutritive stress results in neurosecretory reorganization and changes in the titre of hormones that modulate the morphogenetic programme.
In this paper is proved a weighted inequality for Riesz potential similar to the classical one by D. Adams. Here the gain of integrability is not always algebraic, as in the classical case, but depends on the growth properties of a certain function measuring some local potential of the weight.
This paper presents a novel error-feedback practical solution for real-time implementation of nonlinear output regulation. Sufficient and necessary conditions for both state- and error-feedback output regulation have been established for linear and nonlinear systems several decades ago. In their most general form, these solutions require solving a set of nonlinear partial differential equations, which may be hard or even impossible to solve analytically. In recent years, a methodology for dynamic calculation of the mappings required for state-feedback regulation has been put forward; following the latter, an error-feedback extension is hereby provided which, when combined with design conditions in the form of linear matrix inequalities, becomes suitable for real-time setups. Real-time results are presented for a nonlinear twin rotor MIMO system. Issues concerning the implementation as well as the solutions adopted, are discussed.
The existence of a positive solution for the generalized predator-prey model for two species $$ \begin{gathered} \Delta u + u(a + g(u,v)) = 0\quad \mbox {in}\ \Omega ,\\ \Delta v + v(d + h(u,v)) = 0\quad \mbox {in} \ \Omega ,\\ u = v = 0\quad \mbox {on}\ \partial \Omega , \end{gathered} $$ are investigated. The techniques used in the paper are the elliptic theory, upper-lower solutions, maximum principles and spectrum estimates. The arguments also rely on some detailed properties of the solution of logistic equations.
In this survey we consider superlinear parabolic problems which possess both blowing-up and global solutions and we study a priori estimates of global solutions.
We present an approach for probabilistic contour prediction within the framework of an object tracking system. We combine level-set methods for image segmentation with optical flow estimations based on probability distribution functions (pdfs) calculated at each image position. Unlike most recent level-set methods that consider exclusively the sign of the level-set function to determine an object and its background, we introduce a novel interpretation of the value of the level-set function that reflects the confidence in the contour. To this end, in a sequence of consecutive images, the contour of an object is transformed according to the optical flow estimation and used as the initial object hypothesis in the following image. The values of the initial level-set function are set according to the optical flow pdfs and thus provide an opportunity to incorporate the uncertainties of the optical flow estimation in the object contour prediction.
The aim of this paper is to propose a new approach to probability density function (PDF) estimation which is based on the fuzzy transform (F-transform) introduced by Perfilieva in \cite{Perfilieva:FSS06}. Firstly, a smoothing filter based on the combination of the discrete direct and continuous inverse F-transform is introduced and some of the basic properties are investigated. Next, an alternative approach to PDF estimation based on the proposed smoothing filter is established and compared with the most used method of Parzen windows. Such an approach can be of a great value mainly when dealing with financial data, i. e. large samples of observations.