Artificial boundary conditions for the Stokes and Navier-Stokes equations in domains that are layer-like at infinity

Artificial boundary conditions are presented to approximate solutions to Stokes- and Navier-Stokes problems in domains that are layer-like at infinity. Based on results about existence and asymptotics of the solutions v^infinity, p^infinity to the problems in the unbounded domain Omega the error v^infinity - v^R, p^infinity - p^R is estimated in H^1(Omega_R) and L^2(Omega_R), respectively. Here v^R, p^R are the approximating solutions on the truncated domain Omega_R, the parameter R controls the exhausting of Omega. The artificial boundary conditions involve the Steklov-Poincare operator on a circle together with its inverse and thus turn out to be a combination of local and nonlocal boundary operators. Depending on the asymptotic decay of the data of the problems, in the linear case the error vanishes of order O(R^{-N}), where N can be arbitrarily large.

Studies on arbuscular mycorrhiza (AM) in the Alentejo (Portugal) using pea mutants resistant to AM fungi as a control tool for field conditions

The utilization and management of arbuscular mycorrhiza (AM) symbiosis may improve production and sustainability of the cropping system. For this purpose, native AM fungi (AMF) were sought and tested for their efficiency to increase plant growth by enhanced P uptake and by alleviation of drought stress. Pot experiments with safflower (Carthamus tinctorius) and pea (Pisum sativum) in five soils (mostly sandy loamy Luvisols) and field experiments with peas were carried out during three years at four different sites. Host plants were grown in heated soils inoculated with AMF or the respective heat sterilized inoculum. In the case of peas, mutants resistant to AMF colonization were used as non-mycorrhizal controls. The mycorrhizal impact on yields and its components, transpiration, and P and N uptake was studied in several experiments, partly under varying P and N levels and water supply. Screening of native AMF by most probable number bioassays was not very meaningful. Soil monoliths were placed in the open to simulate field conditions. Inoculation with a native AMF mix improved grain yield, shoot and leaf growth variables as compared to control. Exposed to drought, higher soil water depletion of mycorrhizal plants resulted in a haying-off effect. The growth response to this inoculum could not be significantly reproduced in a subsequent open air pot experiment at two levels of irrigation and P fertilization, however, safflower grew better at higher P and water supply by multiples. The water use efficiency concerning biomass was improved by the AMF inoculum in the two experiments. Transpiration rates were not significantly affected by AM but as a tendency were higher in non-mycorrhizal safflower. A fundamental methodological problem in mycorrhiza field research is providing an appropriate (negative) control for the experimental factor arbuscular mycorrhiza. Soil sterilization or fungicide treatment have undesirable side effects in field and greenhouse settings. Furthermore, artificial rooting, temperature and light conditions in pot experiments may interfere with the interpretation of mycorrhiza effects. Therefore, the myc- pea mutant P2 was tested as a non-mycorrhizal control in a bioassay to evaluate AMF under field conditions in comparison to the symbiotic isogenetic wild type of var. FRISSON as a new integrative approach. However, mutant P2 is also of nod- phenotype and therefore unable to fix N2. A 3-factorial experiment was carried out in a climate chamber at high NPK fertilization to examine the two isolines under non-symbiotic and symbiotic conditions. P2 achieved the same (or higher) biomass as wild type both under good and poor water supply. However, inoculation with the AMF Glomus manihot did not improve plant growth. Differences of grain and straw yields in field trials were large (up to 80 per cent) between those isogenetic pea lines mainly due to higher P uptake under P and water limited conditions. The lacking N2 fixation in mutants was compensated for by high mineral N supply as indicated by the high N status of the pea mutant plants. This finding was corroborated by the results of a major field experiment at three sites with two levels of N fertilization. The higher N rate did not affect grain or straw yields of the non-fixing mutants. Very efficient AMF were detected in a Ferric Luvisol on pasture land as revealed by yield levels of the evaluation crop and by functional vital staining of highly colonized roots. Generally, levels of grain yield were low, at between 40 and 980 kg ha-1. An additional pot trial was carried out to elucidate the strong mycorrhizal effect in the Ferric Luvisol. A triplication of the plant equivalent field P fertilization was necessary to compensate for the mycorrhizal benefit which was with five times higher grain yield very similar to that found in the field experiment. However, the yield differences between the two isolines were not always plausible as the evaluation variable because they were also found in (small) field test trials with apparently sufficient P and N supply and in a soil of almost no AMF potential. This similarly occurred for pea lines of var. SPARKLE and its non-fixing mycorrhizal (E135) and non-symbiotic (R25) isomutants, which were tested in order to exclude experimentally undesirable benefits by N2 fixation. In contrast to var. FRISSON, SPARKLE was not a suitable variety for Mediterranean field conditions. This raises suspicion putative genetic defects other than symbiotic ones may be effective under field conditions, which would conflict with the concept of an appropriate control. It was concluded that AMF resistant plants may help to overcome fundamental problems of present research on arbuscular mycorrhiza, but may create new ones.

Artificial micro RNA system in Dictyostelium discoideum

Dictyostelium discoideum is a social amoeba that serves as a model system for RNA interference and related mechanisms. Its position between plants and animals enables evolutionary snapshot of mechanisms and protein machinery involved in investigated subjects. MiRNAs are small regulatory RNAs that are evolutionary conserved and present in animals, plants, viruses and some prokaryotes. They have roles in development, cell growth and differentiation, apoptosis and their miss-regulation is associated with many diseases such as cancer, neurodegenerative disorders and diabetes. Recently, through sequencing of DNA libraries miRNAs have been discovered in D. discoideum. In this work, it has been shown that heterologues miRNA let-7 can be expressed and processed in D. discoideum. Expression of let-7 miRNA in social amoeba resulted in a strong developmental phenotype suggesting an overload of the processing/silencing system or/and endogenous targets. The various effects on prel-7 strain have been observed and characterized, serving as a background for postulation of miRNA roles. An artificial miRNA system has been established and imposed to D. discoideum, showing that miRNAs in Dictyostelium could mediate gene expression on the level of mRNA stability and on the posttranscriptional level. Furthermore, presence of translational inhibition as a type of gene control was shown for the first time in this organism. Due to it new structures representing co-localities of miRNA and target mRNA have been detected. Taken together, this work shows functional artificial miRNA system and postulates roles of endogenous small RNA in social amoeba.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

A Colony Architecture for an Artificial Creature

This report describes a working autonomous mobile robot whose only goal is to collect and return empty soda cans. It operates in an unmodified office environment occupied by moving people. The robot is controlled by a collection of over 40 independent "behaviors'' distributed over a loosely coupled network of 24 processors. Together this ensemble helps the robot locate cans with its laser rangefinder, collect them with its on-board manipulator, and bring them home using a compass and an array of proximity sensors. We discuss the advantages of using such a multi-agent control system and show how to decompose the required tasks into component activities. We also examine the benefits and limitations of spatially local, stateless, and independent computation by the agents.

Building Grounded Abstractions for Artificial Intelligence Programming

Most Artificial Intelligence (AI) work can be characterized as either ``high-level'' (e.g., logical, symbolic) or ``low-level'' (e.g., connectionist networks, behavior-based robotics). Each approach suffers from particular drawbacks. High-level AI uses abstractions that often have no relation to the way real, biological brains work. Low-level AI, on the other hand, tends to lack the powerful abstractions that are needed to express complex structures and relationships. I have tried to combine the best features of both approaches, by building a set of programming abstractions defined in terms of simple, biologically plausible components. At the ``ground level'', I define a primitive, perceptron-like computational unit. I then show how more abstract computational units may be implemented in terms of the primitive units, and show the utility of the abstract units in sample networks. The new units make it possible to build networks using concepts such as long-term memories, short-term memories, and frames. As a demonstration of these abstractions, I have implemented a simulator for ``creatures'' controlled by a network of abstract units. The creatures exist in a simple 2D world, and exhibit behaviors such as catching mobile prey and sorting colored blocks into matching boxes. This program demonstrates that it is possible to build systems that can interact effectively with a dynamic physical environment, yet use symbolic representations to control aspects of their behavior.

Electrical Design: A Problem for Artificial Intelligence Research

This report outlines the problem of intelligent failure recovery in a problem-solver for electrical design. We want our problem solver to learn as much as it can from its mistakes. Thus we cast the engineering design process on terms of Problem Solving by Debugging Almost-Right Plans, a paradigm for automatic problem solving based on the belief that creation and removal of "bugs" is an unavoidable part of the process of solving a complex problem. The process of localization and removal of bugs called for by the PSBDARP theory requires an approach to engineering analysis in which every result has a justification which describes the exact set of assumptions it depends upon. We have developed a program based on Analysis by Propagation of Constraints which can explain the basis of its deductions. In addition to being useful to a PSBDARP designer, these justifications are used in Dependency-Directed Backtracking to limit the combinatorial search in the analysis routines. Although the research we will describe is explicitly about electrical circuits, we believe that similar principles and methods are employed by other kinds of engineers, including computer programmers.

Entorn de programació pel robot Stäubli mitjantçant MatLab, aplicació a un sistema de recol·lecció de peces basat en visió artificial

Els objectius del projecte són: realitzar un intèrpret de comandes en VAL3 que rebi les ordres a través d’una connexió TCP/IP; realitzar una toolbox de Matlab per enviar diferents ordres mitjançant una connexió TCP/IP; adquirir i processar mitjançant Matlab imatges de la càmera en temps real i detectar la posició d’objectes artificials mitjançant la segmentació per color i dissenyar i realitzar una aplicació amb Matlab que reculli peces detectades amb la càmera. L’abast del projecte inclou: l’estudi del llenguatge de programació VAL3 i disseny de l’ intèrpret de comandes, l’estudi de les llibreries de Matlab per comunicació mitjançant TCP/IP, per l’adquisició d’imatges, pel processament d’imatges i per la programació en C; el disseny de la aplicació recol·lectora de peces i la implementació de: un intèrpret de comandes en VAL3, la toolbox pel control del robot STAUBLI en Matlab i la aplicació recol·lectora de peces mitjançant el processament d’imatges en temps real també en Matlab

Inteligencia artificial para la detección y el diagnóstico de fallos

L' ús de tècniques de la intel·ligència artificial per a la detecció, la diagnòsi i control d' errors

¿Pueden pensar las máquinas? Una evaluación del problema de la inteligencia artificial mediante el equilibrio refractivo

Revisión del problema de la filosofía de la Inteligencia Artificial a la vista del Equilibrio refractivo. La revisión del problema se lleva a cabo para mostrar como "¿pueden pensar las máquinas?" sólo se ha evaluado en los terminos humanos. El equilibrio refractivo se plantea como una herramienta para definir conceptos de tal modo que la experiencia y los preceptos se encuentren en equilibrio, para con él construir una definición de pensar que no esté limitada exclusivamente a "pensar tal y como lo hacen los humanos".

Artificial Intelligence

Pearon's resource from the Brookshear Chapter

Intelúligència artificial. 'Inteligencia artificial'.

El autor comenta los avances que se han producido en la inteligencia de los ordenadores y la compara con la inteligencia humana. Nos explica detalladamente la diferencia entre las dos maneras de 'pensar'.

An artificial economy based on reinforcement learning and agent based modeling

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.

Inteligencia artificial y artificios intelectuales.

Valorar críticamente el desarrollo de la investigación en inteligencia y elaborar un modelo general de inteligencia. El objeto de estudio es el constructo de inteligencia y conceptos afines (inteligencia artificial, sistemas de procesamiento de información). Consta de tres partes. La primera consiste una revisión del marco teórico de la inteligencia desde la perspectiva de la Psicología de la Inteligencia. La segunda consiste en una revisión de las aportaciones de la investigación de la inteligencia artificial. Y la tercer consta de la descripción de los elementos teóricos de las dos disciplinas estudiadas que son coioncidentes, con el objeto de elaborar un modelo general de la inteligencia. Bibliografía, tests. Comparación, principios factoriales, análisis, epistemológico, correlaciones. Al ser una investigación básicamente epistemológica es difícil describir los resultados y comparaciones que va realizando. Por una parte realiza la identificación de los defectos e irregularidades de la investigación clásica en inteligencia y por otra argumenta la definición de un nuevo modelo de inteligencia más general. A través de argumentaciones, descripciones y comparaciones llega a la conclusión que las diferentes líneas de investigación en inteligencia son unificables, reestructurando el valor de las variables conducta, procesamiento y sujetos potencialmente inteligentes, en un modelo multidisciplinar de inteligencia.