Optimizing feature selection through binary charged system search

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.

Efeito do meio diluidor e da dose inseminante sobre a congebilidade e fertilidade do sêmen bovino utilizado em programas de inseminação artificial em tempo-fixo(LATF)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Effect of nAellore cows ciclicity on conception and pregnant rates after synchronization protocols for fixed timed artificial insemination

Palhano H.B., Jesus V.L.T., Abidu-Figueiredo M., Baldrighi J.M. & Mello M.R.B. [Effect of nAellore cows ciclicity on conception and pregnant rates after synchronization protocols for fixed timed artificial insemination]. Efeito da ciclicidade de vacas Nelore sobre as taxas de concepcao e de prenhez apos protocolos de sincronizacao para inseminacao artificial em tempo fixo. Revista Brasileira de Medicina Veterinaria, 34(1):63-68, 2012. Departamento de Biologia Animal, Universidade Federal Rural do Rio de Janeiro, BR 465 km 7, Seropedica, RJ 23890-000, Brasil. Email: hbpalhano@gmail.com The present study evaluated the effect on conception and pregnancy rates of Nellore cows selected for Fixed Timed Artificial Insemination (FTAI) program, submitted to four synchronization protocols. Four hundred and ninety lactating females were used and assigned to eight groups: I-OvSynch, n=68, with selection of cycling cows; II-OvSynch + progesterone (P-4), n=67, after selection of non-cycling animals; III-OvSynch, without selection, n=68; IV-OvSynch + P-4, without selection, n=67; V-Co-Synch, n=55, with selection of cycling cows; VI-Co-Synch + P-4, n=55, with selection non-cycling cows; VII- Co-Synch without selection, n=55; VIII- Co-Synch + P-4, without selection, n=55. The conception and pregnancy rates were, respectively, 45.6%, 27.9% and 82.4%, 48.5% for groups I and III; 61.2%, 37.3% and 85.1%, 58.2% for groups II and IV; 43.6%, 25.5% and 80%, 41.8% for groups V and VII; 52.7%, 32.7% and 83.6%, 50.9% for groups VI and VIII. When compared these rates, the results after chi-square test showed significant difference (P < 0.05) among protocols with or without selection. There was no significant difference (P > 0.05) between OvSynch and Co-Synch protocols, with or without P-4 and with selection, considering Co-Synch a viable option for optimization of FTAI. In conclusion, the selection of cows before FTAI program contributed significantly to improve the conception and pregnancy rates.

Controlled reproduction in Anguilla anguilla (L.): advanced studies on broodstock management, spawning techniques and system design for artificial seed production.

The thesis aims to expose the advances achieved in the practices of captive breeding of the European eel (Anguilla anguilla). Aspects investigated concern both approaches livestock (breeding selection, response to hormonal stimulation, reproductive performance, incubation of eggs) and physiological aspects (endocrine plasma profiles of players), as well as engineering aspects. Studies conducted on various populations of wild eel have shown that the main determining factor in the selection of wild females destined to captive breeding must be the Silver Index which may determine the stage of pubertal development. The hormonal induction protocol adopted, with increasing doses of carp pituitary extract, it has proven useful to ovarian development, with a synchronization effect that is positively reflected on egg production. The studies on the effects of photoperiod show how the condition of total darkness can positively influence practices of reproductions in captivity. The effects of photoperiod were also investigated at the physiological level, observing the plasma levels of steroids ( E2, T) and thyroid hormones (T3 and T4) and the expression in the liver of vitellogenin (vtg1 and vtg2) and estradiol membrane receptor (ESR1). From the comparison between spontaneous deposition and insemination techniques through the stripping is inferred as the first ports to a better qualitative and quantitative yield in the production of eggs capable of being fertilized, also the presence of a percentage of oocytes completely transparent can be used to obtain eggs at a good rate of fertility. Finally, the design and implementation of a system for recirculating aquaculture suited to meet the needs of species-specific eel showed how to improve the reproductive results, it would be preferable to adopt low-flow and low density incubation.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

Consciousness, Action Selection, Meaning and Phenomenic Anticipation

Phenomenal states are generally considered the ultimate sources of intrinsic motivation for autonomous biological agents. In this article, we will address the issue of the necessity of exploiting these states for the design and implementation of robust goal-directed artificial systems. We will provide an analysis of consciousness in terms of a precise definition of how an agent "understands" the informational flows entering the agent and its very own action possibilities. This abstract model of consciousness and understanding will be based in the analysis and evaluation of phenomenal states along potential future trajectories in the state space of the agents. This implies that a potential strategy to follow in order to build autonomous but still customer-useful systems is to embed them with the particular, ad hoc phenomenality that captures the system-external requirements that define the system usefulness from a customer-based, requirements-strict engineering viewpoint.

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

Computational Fluid Dynamics Expert System using Artificial Neural Networks

The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations. As a results of this, Computational Fluid Dynamic (CFD) solvers are widely used in the aeronautical field. These solvers require the correct selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on the proper choice of these parameters. In this paper we create an expert system capable of making an accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver. Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time required for the convergence of a CFD solver.

Monitorización de estructuras aeronáuticas mediante técnicas de inteligencia artificial

Una de las barreras para la aplicación de las técnicas de monitorización de la integridad estructural (SHM) basadas en ondas elásticas guiadas (GLW) en aeronaves es la influencia perniciosa de las condiciones ambientales y de operación (EOC). En esta tesis se ha estudiado dicha influencia y la compensación de la misma, particularizando en variaciones del estado de carga y temperatura. La compensación de dichos efectos se fundamenta en Redes Neuronales Artificiales (ANN) empleando datos experimentales procesados con la Transformada Chirplet. Los cambios en la geometría y en las propiedades del material respecto al estado inicial de la estructura (lo daños) provocan cambios en la forma de onda de las GLW (lo que denominamos característica sensible al daño o DSF). Mediante técnicas de tratamiento de señal se puede buscar una relación entre dichas variaciones y los daños, esto se conoce como SHM. Sin embargo, las variaciones en las EOC producen también cambios en los datos adquiridos relativos a las GLW (DSF) que provocan errores en los algoritmos de diagnóstico de daño (SHM). Esto sucede porque las firmas de daño y de las EOC en la DSF son del mismo orden. Por lo tanto, es necesario cuantificar y compensar el efecto de las EOC sobre la GLW. Si bien existen diversas metodologías para compensar los efectos de las EOC como por ejemplo “Optimal Baseline Selection” (OBS) o “Baseline Signal Stretching” (BSS), estas, se emplean exclusivamente en la compensación de los efectos térmicos. El método propuesto en esta tesis mezcla análisis de datos experimentales, como en el método OBS, y modelos basados en Redes Neuronales Artificiales (ANN) que reemplazan el modelado físico requerido por el método BSS. El análisis de datos experimentales consiste en aplicar la Transformada Chirplet (CT) para extraer la firma de las EOC sobre la DSF. Con esta información, obtenida bajo diversas EOC, se entrena una ANN. A continuación, la ANN actuará como un interpolador de referencias de la estructura sin daño, generando información de referencia para cualquier EOC. La comparación de las mediciones reales de la DSF con los valores simulados por la ANN, dará como resultado la firma daño en la DSF, lo que permite el diagnóstico de daño. Este esquema se ha aplicado y verificado, en diversas EOC, para una estructura unidimensional con un único camino de daño, y para una estructura representativa de un fuselaje de una aeronave, con curvatura y múltiples elementos rigidizadores, sometida a un estado de cargas complejo, con múltiples caminos de daños. Los efectos de las EOC se han estudiado en detalle en la estructura unidimensional y se han generalizado para el fuselaje, demostrando la independencia del método respecto a la configuración de la estructura y el tipo de sensores utilizados para la adquisición de datos GLW. Por otra parte, esta metodología se puede utilizar para la compensación simultánea de una variedad medible de EOC, que afecten a la adquisición de datos de la onda elástica guiada. El principal resultado entre otros, de esta tesis, es la metodología CT-ANN para la compensación de EOC en técnicas SHM basadas en ondas elásticas guiadas para el diagnóstico de daño. ABSTRACT One of the open problems to implement Structural Health Monitoring techniques based on elastic guided waves in real aircraft structures at operation is the influence of the environmental and operational conditions (EOC) on the damage diagnosis problem. This thesis deals with the compensation of these environmental and operational effects, specifically, the temperature and the external loading, by the use of the Chirplet Transform working with Artificial Neural Networks. It is well known that the guided elastic wave form is affected by the damage appearance (what is known as the damage sensitive feature or DSF). The DSF is modified by the temperature and by the load applied to the structure. The EOC promotes variations in the acquired data (DSF) and cause mistakes in damage diagnosis algorithms. This effect promotes changes on the waveform due to the EOC variations of the same order than the damage occurrence. It is difficult to separate both effects in order to avoid damage diagnosis mistakes. Therefore it is necessary to quantify and compensate the effect of EOC over the GLW forms. There are several approaches to compensate the EOC effects such as Optimal Baseline Selection (OBS) or Baseline Signal Stretching (BSS). Usually, they are used for temperature compensation. The new method proposed here mixes experimental data analysis, as in the OBS method, and Artificial Neural Network (ANN) models to replace the physical modelling which involves the BSS method. The experimental data analysis studied is based on apply the Chirplet Transform (CT) to extract the EOC signature on the DSF. The information obtained varying EOC is employed to train an ANN. Then, the ANN will act as a baselines interpolator of the undamaged structure. The ANN generates reference information at any EOC. By comparing real measurements of the DSF against the ANN simulated values, the damage signature appears clearly in the DSF, enabling an accurate damage diagnosis. This schema has been applied in a range of EOC for a one-dimensional structure containing single damage path and two dimensional real fuselage structure with stiffener elements and multiple damage paths. The EOC effects tested in the one-dimensional structure have been generalized to the fuselage showing its independence from structural arrangement and the type of sensors used for GLW data acquisition. Moreover, it can be used for the simultaneous compensation of a variety of measurable EOC, which affects the guided wave data acquisition. The main result, among others, of this thesis is the CT-ANN methodology for the compensation of EOC in GLW based SHM technique for damage diagnosis.

An artificial cell-cycle inhibitor isolated from a combinatorial library

Understanding the genetic networks that operate inside cells will require the dissection of interactions among network members. Here we describe a peptide aptamer isolated from a combinatorial library that distinguishes among such interactions. This aptamer binds to cyclin-dependent kinase 2 (Cdk2) and inhibits its kinase activity. In contrast to naturally occurring inhibitors, such as p21Cip1, which inhibit the activity of Cdk2 on all its substrates, inhibition by pep8 has distinct substrate specificity. We show that the aptamer binds to Cdk2 at or near its active site and that its mode of inhibition is competitive. Expression of pep8 in human cells retards their progression through the G1 phase of the cell cycle. Our results suggest that the aptamer inhibits cell-cycle progression by blocking the activity of Cdk2 on substrates needed for the G1-to-S transition. This work demonstrates the feasibility of selection of artificial proteins to perform functions not developed during evolution. The ability to select proteins that block interactions between a gene product and some partners but not others should make sophisticated genetic manipulations possible in human cells and other currently intractable systems.

Making artificial antibodies: A format for phage display of combinatorial heterodimeric arrays

The gene VII protein (pVII) and gene IX protein (pIX) are associated closely on the surface of filamentous bacteriophage that is opposite of the end harboring the widely exploited pIII protein. We developed a phagemid format wherein antibody heavy- and light-chain variable regions were fused to the amino termini of pVII and pIX, respectively. Significantly, the fusion proteins interacted to form a functional Fv-binding domain on the phage surface. Our approach will be applicable to the display of generic peptide and protein libraries that can form combinatorial heterodimeric arrays. Consequently, it represents a first step toward artificial antibodies and the selection of novel biological activities.

A new method for generating point mutations in bacterial artificial chromosomes by homologous recombination in Escherichia coli

Bacterial artificial chromosomes (BACs) and P1 artificial chromosomes (PACs), which contain large fragments of genomic DNA, have been successfully used as transgenes to create mouse models of dose-dependent diseases. They are also potentially valuable as transgenes for dominant diseases given that point mutations and/or small rearrangements can be accurately introduced. Here, we describe a new method to introduce small alterations in BACs, which results in the generation of point mutations with high frequency. The method involves homologous recombination between the original BAC and a shuttle vector providing the mutation. Each recombination step is monitored using positive and negative selection markers, which are the Kanamycin-resistance gene, the sacB gene and temperature-sensitive replication, all conferred by the shuttle plasmid. We have used this method to introduce four different point mutations and the insertion of the β-galactosidase gene in a BAC, which has subsequently been used for transgenic animal production.