Possible black universes in a brane world

A black universe is a nonsingular black hole where, beyond the horizon, there is an expanding, asymptotically isotropic universe. Such spherically symmetric configurations have been recently found as solutions to the Einstein equations with phantom scalar fields (with negative kinetic energy) as sources of gravity. They have a Schwarzschild-like causal structure but a de Sitter infinity instead of a singularity. It is attempted to obtain similar configurations without phantoms, in the framework of an RS2 type brane world scenario, considering the modified Einstein equations that describe gravity on the brane. By building an explicit example, it is shown that black-universe solutions can be obtained there in the presence of a scalar field with positive kinetic energy and a nonzero potential.Comment: 8 pages, 5 figures, gc styl

The impact of negative selection on thymocyte migration in the medulla

Developing thymocytes are screened for self-reactivity before they exit the thymus, but how thymocytes scan the medulla for self antigens is unclear. Using two-photon microscopy, we observed that medullary thymocytes migrated rapidly and made frequent, transient contacts with dendritic cells. In the presence of a negative selecting ligand, thymocytes slowed, became confined to areas of approximately 30 mum in diameter and had increased contact with dendritic cells surrounding confinement zones. One third of polyclonal medullary thymocytes also showed confined, slower migration and may correspond to autoreactive thymocytes. Our data suggest that many autoreactive thymocytes do not undergo immediate arrest and death after encountering a negative selecting ligand but instead adopt an altered migration program while remaining in the medullary microenvironment

A Synthesis of Pseudo-Boolean Empirical Models by Precedential Information

The problem of decision-making based on partial, precedential information is the most important when the creation of articial intelligence systems. According to the results of observations over the behaviour of external objects or systems it is necessary to synthesize or, more precisely, extract from the data a mathematical model of optimization of the object on the basis of accumulated empirical information in the form of a nite set of triples: "a state vector, the value of the quality of functioning of the object, a binary indicator of the admissibility of this state". The aim of the work is to create and substantiate mathematical methods and algorithms that allow to synthesize models of scalar pseudoBoolean optimization with a constraint in the form of disjunctive normal form (DNF) using this precedential information. The peculiarity of pseudo-Boolean optimization models with separable objective functions and DNF constraint, which has a bounded constant length, is their polynomial solvability. However, the complexity of bringing the problem to the form with a DNF constraint in general case is exponential. When extracting the model from the data the DNF constraint is synthesized approximately but with polynomial complexity and the number of conjunctions in the extracted DNF does not exceed the number of examples in the initial precedential information. In the paper is shown how to use binary decision trees to construct a disjunctive constraint, proposed the methods to identify the properties of monotonicity and linearity of the partially dened objective functions, and developed algorithms for solving problems pseudo-Boolean scalar optimization in the presence of incomplete, precedential initial information. The scope of application of the obtained results includes intelligent control systems, intelligent agents. Although the control models derived from the data are approximate, their application can be more successful than the use of less realistic, inconsistent with the objects models which are chosen on the base of subjective considerations.Проблема принятия решений по частичной, прецедентной информации является важнейшей при создании систем искусственного интеллекта. По результатам наблюдений над поведением внешних объектов или систем необходимо на основе накопленной информации в виде конечного множества троек: ' вектор состояния, значение качества функционирования объекта, бинарный индикатор допустимости этого состояния' синтезировать или, точнее, извлечь из данных математическую модель оптимизации объекта. Целью работы является создание и обоснование математических методов и алгоритмов, позволяющих синтезировать модели скалярной псевдобулевой оптимизации с ограничением в виде дизъюнктивной нормальной формы (ДНФ), используя указанную прецедентную информацию. Особенностью псевдобулевых оптимизационных моделей с сепарабельными целевыми функциями и ДНФ ограничением, имеющим ограниченную константой длину, является их полиномиальная разрешимость. Однако сложность приведения задачи к форме с ДНФ ограничением в общем случае является экспоненциальной. При извлечении модели из данных ДНФ ограничение синтезируется приближенно, и сложность его аппроксимации оказывается полиномиальной, а число конъюнкций в извлеченной ДНФ не превышает числа примеров в начальной прецедентной информации. В статье показано, как использовать для построения дизъюнктивного ограничения бинарные решающие деревья. Предложены методы выявления свойств монотонности и линейности частично заданной целевой функции и алгоритмы решения задач псевдобулевой скалярной оптимизации при наличии неполной, прецедентной начальной информации. Область применения полученных результатов - системы интеллектуального управления, интеллектуальные агенты. Несмотря на то, что модели управления, извлеченные из данных, являются приближенными, их применение может быть более успешным, чем использование менее реалистичных, не согласованных с моделируемым объектом и выбранных из субъективных соображений моделей

Tar formation influence on fixed bed air-blown biomass gasification process efficiency

The paper is devoted to reduced biomass gasification tar formation model. According to this model, fixed volatiles part escapes fuel in the form of tars. Tar content influence on the cold gas efficiency is analyzed. It is shown that tar formation in is key process that determines gasification process efficiency

MATHEMATICAL MODELLING OF WOOD GASIFICATION WITH TARRY PRODUCTS DECOMPOSITION ON ACTIVE MATERIAL PARTICLES

The work is devoted to the numerical study of the process of downdraft fixed-bed gasification of woody biomass. Such processes are used to produce combustible gases at lowcapacity power plants. To improve the quality of the produced gas, it is proposed to use a mixture of wood fuel with a non-combustible material that can exhibit catalytic activity in the decomposition of undesired tary products. Adding a non-combustible material leads to lower heat value of fuel mixture, but contributes to a deeper gas purification. The aim of the study is to select the optimal "active material / wood fuel" ratio and to determine the minimum material activity at which its addition to the fuel becomes effective

On the smart trees and competence areas based decision forest

In this paper, we propose a new approach to decision forest building based on the use of the areas of competence of individual decision trees which are thoroughly trained. We offer two main strategies for the use of a set of decision trees. The first strategy is to use the best decision tree, except the case of his incompetence. The second strategy uses a weighted voting of trees which is organized by a special way