Study of natural ventilation for a modular façade system in wind tunnel tests

Wind tunnel tests are a reliable tool to determine the effect of natural ventilation on buildings. This paper presents results of wind tunnel tests conducted to evaluate the influence of ventilation modules positioning on a façade system. Modules positioning was modified, resulting in different façade configurations. The tests were carried out with the use of a model, varying the position of the ventilation modules in the façade configuration. The cases tested were six ventilation modules positioned below the window-sill (ventilated window-sill), and three ventilation modules positioned above and below the façade. The façade system proposed was movable and interchangeable so that the same basic model could be used to test the possibilities for ventilation. Wind speed measurements were taken inside and outside the model for the different façades configurations to evaluate the best performance in relation to natural ventilation. Singleâ sided and Cross ventilation were considered for wind speed measurements. Results show the use of six ventilation modules positioned below the window-sill, forming "a ventilated window-sill" is the best solution in terms of natural ventilation.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

In-line rheo-optical microstructural characterization of complex polymer systems

Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos.

Motilidad Orientada de Células Neurales: La comunicación celular a distancia. Oriented neural cell motility: Cell comunication at a distance.

La motilidad celular orientada (o mecanismo quimiotáctico de orientación), es una respuesta celular a señales moleculares de su micro-ambiente necesaria para modular la distribución celular en sitios específicos y con elevada precisión. Nuestra hipótesis establece que la migración orientada de células neurales es regulada por gradientes de concentración de moléculas solubles liberadas por sus regiones "blanco". Este proyecto es continuación del estudio de la quimiotaxis de células de cresta neural (CCN) y de neuronas ventriculares (NV) inducida respectivamente por factores difusibles de la región del futuro ganglio ciliar y del bulbo olfatorio.En el sistema de CCN, hemos caracterizado como moléculas quimiotácticas a la quimioquina Stromal Cell-Derived Factor-1, y los factores tróficos Stem Cell Factor y Neurotrophic Factor-3, habiendo determinado la expresión de sus respectivos receptores CXCR4, TrkC y p75 en la población de CCN mesencefálicas de ambrión de pollo. Actualmente, estamos desarrollando experimentos con el Ciliary Neurotrophic Factor y factores de la familia Bone Morphogenetic Proteins. Además de la estrategia experimental in vitro, hemos determinado en el embrión entero la expresión de las moléculas quimioatractantes mediante hibridación in situ del ARNm y la presencia de las respectivas proteínas mediante inmunocitoquímica. En el sistema de NV, estamos analizando la motilidad celular en relación con moléculas liberadas por el bulbo olfatorio. En los dos sistemas biológicos, estamos analizando elementos de la transducción de señales y cambios en el citoesqueleto, en ambos casos asociados con la respuesta temprana en la orientación quimiotáctica de la célula. Asimismo, en ambos sistemas biológicos, evaluamos los efectos del etanol sobre la migración y distribución celular, en condiciones equivalentes a las que inducen el Sindrome Fetal Alcohólico en mamíferos.En base a resultados ya obtenidos en experimentos in vitro, en la presente etapa intentaremos su caracterización in vivo mediante el bloqueo funcional de las moléculas quimiotácticas (y/o sus receptores) sobre embriones enteros, mediante silenciamiento con ARNsi (o morfolinos específicos) mediante electroporación, y posterior determinación de la distribución celular mediante marcadores específicos anti-CCN (o lipofílicos de tipo DiI).Los resultados permitirán mejorar el conocimiento del mecanismo de la migración celular orientada y aportar al diseño de recursos diagnósticos, terapéuticos o de control de anomalías embrionarias o patologías tumorales por mala distribución celular como las Neurocristopatías, o inducidas por tóxicos exógenos como el Sindrome Fetal Alcohólico.

Numerical simulation of combined heat transfer phenomena (conduction, convection, and radiation). Application to he optimisation of thermal systems

Thermal systems interchanging heat and mass by conduction, convection, radiation (solar and thermal ) occur in many engineering applications like energy storage by solar collectors, window glazing in buildings, refrigeration of plastic moulds, air handling units etc. Often these thermal systems are composed of various elements for example a building with wall, windows, rooms, etc. It would be of particular interest to have a modular thermal system which is formed by connecting different modules for the elements, flexibility to use and change models for individual elements, add or remove elements without changing the entire code. A numerical approach to handle the heat transfer and fluid flow in such systems helps in saving the full scale experiment time, cost and also aids optimisation of parameters of the system. In subsequent sections are presented a short summary of the work done until now on the orientation of the thesis in the field of numerical methods for heat transfer and fluid flow applications, the work in process and the future work.

Explicit methods for Hilbert modular forms

We exhibit algorithms to compute systems of Hecke eigenvalues for spaces of Hilbert modular forms over a totally real field. We provide many explicit examples as well as applications to modularity and Galois representations.

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy

Biophysical requirements of power-supply systems: nuclear energy and fossil energy

The report presents a grammar capable of analyzing the process of production of electricity in modular elements for different power-supply systems, defined using semantic and formal categories. In this way it becomes possible to individuate similarities and differences in the process of production of electricity, and then measure and compare “apples” with “apples” and “oranges” with “oranges”. For instance, when comparing the various unit operations of the process of production of electricity with nuclear energy to the analogous unit operations of the process of production of fossil energy, we see that the various phases of the process are the same. The only difference is related to characteristics of the process associated with the generation of heat which are completely different in the two systems. As a matter of facts, the performance of the production of electricity from nuclear energy can be studied, by comparing the biophysical costs associated with the different unit operations taking place in nuclear and fossil power plants when generating process heat or net electricity. By adopting this approach, it becomes possible to compare the performance of the two power-supply systems by comparing their relative biophysical requirements for the phases that both nuclear energy power plants and fossil energy power plants have in common: (i) mining; (ii) refining/enriching; (iii) generating heat/electricity; (iv) handling the pollution/radioactive wastes. This report presents the evaluation of the biophysical requirements for the two powersupply systems: nuclear energy and fossil energy. In particular, the report focuses on the following requirements: (i) electricity; (ii) fossil-fuels, (iii) labor; and (iv) materials.

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Interacting software-agents to support a kind of expert supervision systems

Expert supervision systems are software applications specially designed to automate process monitoring. The goal is to reduce the dependency on human operators to assure the correct operation of a process including faulty situations. Construction of this kind of application involves an important task of design and development in order to represent and to manipulate process data and behaviour at different degrees of abstraction for interfacing with data acquisition systems connected to the process. This is an open problem that becomes more complex with the number of variables, parameters and relations to account for the complexity of the process. Multiple specialised modules tuned to solve simpler tasks that operate under a co-ordination provide a solution. A modular architecture based on concepts of software agents, taking advantage of the integration of diverse knowledge-based techniques, is proposed for this purpose. The components (software agents, communication mechanisms and perception/action mechanisms) are based on ICa (Intelligent Control architecture), software middleware supporting the build-up of applications with software agent features

Quality coding by neural populations in the early olfactory pathway: analysis using information theory

In this article, we analyze the ability of the early olfactory system to detect and discriminate different odors by means of information theory measurements applied to olfactory bulb activity images. We have studied the role that the diversity and number of receptor neuron types play in encoding chemical information. Our results show that the olfactory receptors of the biological system are low correlated and present good coverage of the input space. The coding capacity of ensembles of olfactory receptors with the same receptive range is maximized when the receptors cover half of the odor input space - a configuration that corresponds to receptors that are not particularly selective. However, the ensemble's performance slightly increases when mixing uncorrelated receptors of different receptive ranges. Our results confirm that the low correlation between sensors could be more significant than the sensor selectivity for general purpose chemo-sensory systems, whether these are biological or biomimetic.

Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements

Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy.

Neural regulation of pancreatic islet cell mass and function.

Intracellular glucose signalling pathways control the secretion of glucagon and insulin by pancreatic islet α- and β-cells, respectively. However, glucose also indirectly controls the secretion of these hormones through regulation of the autonomic nervous system that richly innervates this endocrine organ. Both parasympathetic and sympathetic nervous systems also impact endocrine pancreas postnatal development and plasticity in adult animals. Defects in these autonomic regulations impair β-cell mass expansion during the weaning period and β-cell mass adaptation in adult life. Both branches of the autonomic nervous system also regulate glucagon secretion. In type 2 diabetes, impaired glucose-dependent autonomic activity causes the loss of cephalic and first phases of insulin secretion, and impaired suppression of glucagon secretion in the postabsorptive phase; in diabetic patients treated with insulin, it causes a progressive failure of hypoglycaemia to trigger the secretion of glucagon and other counterregulatory hormones. Therefore, identification of the glucose-sensing cells that control the autonomic innervation of the endocrine pancreatic and insulin and glucagon secretion is an important goal of research. This is required for a better understanding of the physiological control of glucose homeostasis and its deregulation in diabetes. This review will discuss recent advances in this field of investigation.

Noise and periodic modulations in neural excitable medium

We have analyzed the interplay between noise and periodic modulations in a mean field model of a neural excitable medium. For this purpose, we have considered two types of modulations, namely, variations of the resistance and oscillations of the threshold. In both cases, stochastic resonance is present, irrespective of whether the system is monostable or bistable.

Stimulus statistics shape oscillations in nonlinear recurrent neural networks.

Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands.