Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

Application of GA-PLS and GA-KPLS calculations for the prediction of the retention indices of essential oils

Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 117 diverse compounds in essential oils from 5 Pimpinella species gathered from central Turkey which were obtained by gas chromatography and gas chromatography-mass spectrometry. The square correlation coefficient leave-group-out cross validation (LGO-CV) (Q²) between experimental and predicted RI for training set by GA-PLS and GA-KPLS was 0.940 and 0.963, respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure-retention relationship (QSRR) studies.

Modelagem da distribuição geográfica de espécies lenhosas de cerrado no Estado de São Paulo

Atualmente vêm sendo desenvolvidas e utilizadas várias técnicas de modelagem de distribuição geográfica de espécies com os mais variados objetivos. Algumas dessas técnicas envolvem modelagem baseada em análise ambiental, nas quais os algoritmos procuram por condições ambientais semelhantes àquelas onde as espécies foram encontradas, resultando em áreas potenciais onde as condições ambientais seriam propícias ao desenvolvimento dessas espécies. O presente estudo trata do uso da modelagem preditiva de distribuição geográfica de espécies nativas, através da utilização de algoritmo genético, como ferramenta para auxiliar o entendimento dos padrões de distribuição do bioma cerrado no Estado de São Paulo. A metodologia empregada e os resultados obtidos foram considerados satisfatórios para a geração de modelos de distribuição geográfica de espécies vegetais, baseados em dados abióticos, para as regiões de estudo. A eficácia do modelo em predizer a ocorrência de espécies do cerrado é maior se forem utilizados apenas pontos de amostragem com fisionomias de cerrado, excluindo-se áreas de transição. Para minimizar problemas decorrentes da falta de convergência do algoritmo utilizado GARP ("Genetic Algorithm for Rule Set Production"), foram gerados 100 modelos para cada espécie modelada. O uso de modelagem pode auxiliar no entendimento dos padrões de distribuição de um bioma ou ecossistema em uma análise regional.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

Rôle du test génétique dans la cardiomyopathie arythmogène du ventricule droit. Étude sur une cohorte prospective unicentrique.

La cardiomyopathie/dysplasie arythmogène du ventricule droit (ARVC/D) est un désordre d’origine génétique caractérisé par le remplacement du myocarde par du tissus fibro-adipeux dans le ventricule droit. Ce désordre est responsable d’un grand pourcentage de mort subite, spécialement chez les plus jeunes. ARVC/D est difficile à diagnostiquer avec les outils cliniques actuels. Elle est causée en grande majorité par des mutations dans les protéines desmosomales. ARVC/D a donc des implications d’une grande importance chez les membres de la famille, qui peuvent sans le savoir, être aussi à risque de mort subite. Dans le but d’améliorer le diagnostique, un nouvel outil, le test génétique, est de plus en plus utilisé. Hypothèses: Dans le but d’évaluer la valeur du test génétique en complément du test clinique classique chez ARVC/D nous avons effectué une investigation clinique et génétique chez 23 cas-index atteints. Méthodes: Les cas-index sont diagnostiqué après une mort subite dans la famille ou après un examen clinique poussé pour arythmies. Le diagnostique d’ARVC/D a été fait avec les outils cliniques selon les critères. L’analyse génétique des protéines desmosomales associées à la maladie a été effectuée en séquençant leurs exons ainsi que les régions introniques nécessaires à l’épissage alternatif. Résultats: Le diagnostique clinique était clair dans 18/23 et incertain dans 5/23 des individus. Nous avons identifié 15 différentes mutations chez 10 cas-index. 64% des mutations n’avaient jamais été décrites. De plus, nous avons observé la présence de double ou triple mutant dans 40% des cas-index positifs. Les individus avec mutations sont plus jeunes et ont plus de symptômes que les individus sans mutation. Conclusion: Les tests génétiques sont positifs dans 43% des patients avec ARVC/D. L’utilisation de la technologie génétique basée sur l’identification de mutations connues a une valeur limitée vu le haut pourcentage des mutations nouvelles dans la maladie. La présence de double, même de triple mutant n’est pas associé avec un phénotype plus sévère, mais renforce l’idée de la nécessité d’un test génétique pour tous les gènes. Le test génétique est un outil fort utile à ajouter aux tests cliniques pour le diagnostique des patients qui ne remplissent pas tous les critères cliniques de la maladie. Mots clés: génétique, ARVC/D, mort subite, desmosome

Apports de la segmentation de la population des dirigeants au processus de gestion des ressources humaines : Le cas de l’administration algérienne

A partir des résultats d’une enquête effectuée en 2005 sur un échantillon de 203 dirigeants publics, une typologie floue de trois profils a été dégagée en vue de concevoir un système d’affectation des dirigeants en fonction de leur style du leadership, sens du travail, et leurs préoccupations de gestion des ressources humaines. En se basant sur cette typologie floue, des techniques empruntées à l’intelligence artificielle ont été appliquées pour apprendre des règles de classification. Ces techniques sont au nombre de quatre : le réseau neuronal (Neural Network), l’algorithme génétique (Genetic Algorithm), l’arbre de décision (Decision Tree) et la théorie des ensembles approximatifs (Rough Sets). Les résultats de l’étude ainsi que ses perspectives seront présentées et discutés tout au long de cette communication.

Una aproximación evolucionista para la generación automática de sentencias SQL a partir de ejemplos

En la actualidad, el uso de las tecnologías ha sido primordial para el avance de las sociedades, estas han permitido que personas sin conocimientos informáticos o usuarios llamados “no expertos” se interesen en su uso, razón por la cual los investigadores científicos se han visto en la necesidad de producir estudios que permitan la adaptación de sistemas, a la problemática existente dentro del ámbito informático. Una necesidad recurrente de todo usuario de un sistema es la gestión de la información, la cual se puede administrar por medio de una base de datos y lenguaje específico, como lo es el SQL (Structured Query Language), pero esto obliga al usuario sin conocimientos a acudir a un especialista para su diseño y construcción, lo cual se ve reflejado en costos y métodos complejos, entonces se plantea una pregunta ¿qué hacer cuando los proyectos son pequeñas y los recursos y procesos son limitados? Teniendo como base la investigación realizada por la universidad de Washington[39], donde sintetizan sentencias SQL a partir de ejemplos de entrada y salida, se pretende con esta memoria automatizar el proceso y aplicar una técnica diferente de aprendizaje, para lo cual utiliza una aproximación evolucionista, donde la aplicación de un algoritmo genético adaptado origina sentencias SQL válidas que responden a las condiciones establecidas por los ejemplos de entrada y salida dados por el usuario. Se obtuvo como resultado de la aproximación, una herramienta denominada EvoSQL que fue validada en este estudio. Sobre los 28 ejercicios empleados por la investigación [39], 23 de los cuales se obtuvieron resultados perfectos y 5 ejercicios sin éxito, esto representa un 82.1% de efectividad. Esta efectividad es superior en un 10.7% al establecido por la herramienta desarrollada en [39] SQLSynthesizer y 75% más alto que la herramienta siguiente más próxima Query by Output QBO[31]. El promedio obtenido en la ejecución de cada ejercicio fue de 3 minutos y 11 segundos, este tiempo es superior al establecido por SQLSynthesizer; sin embargo, en la medida un algoritmo genético supone la existencia de fases que amplían los rangos de tiempos, por lo cual el tiempo obtenido es aceptable con relación a las aplicaciones de este tipo. En conclusión y según lo anteriormente expuesto, se obtuvo una herramienta automática con una aproximación evolucionista, con buenos resultados y un proceso simple para el usuario “no experto”.

Identification et étude de mécanismes régulant l’expression de MAPK

Les fichiers accompagnant le document sont en format Microsoft Excel 2010.

A Coupled 2 × 2D Babcock–Leighton Solar Dynamo Model. I. Surface Magnetic Flux Evolution

The need for reliable predictions of the solar activity cycle motivates the development of dynamo models incorporating a representation of surface processes sufficiently detailed to allow assimilation of magnetographic data. In this series of papers we present one such dynamo model, and document its behavior and properties. This first paper focuses on one of the model's key components, namely surface magnetic flux evolution. Using a genetic algorithm, we obtain best-fit parameters of the transport model by least-squares minimization of the differences between the associated synthetic synoptic magnetogram and real magnetographic data for activity cycle 21. Our fitting procedure also returns Monte Carlo-like error estimates. We show that the range of acceptable surface meridional flow profiles is in good agreement with Doppler measurements, even though the latter are not used in the fitting process. Using a synthetic database of bipolar magnetic region (BMR) emergences reproducing the statistical properties of observed emergences, we also ascertain the sensitivity of global cycle properties, such as the strength of the dipole moment and timing of polarity reversal, to distinct realizations of BMR emergence, and on this basis argue that this stochasticity represents a primary source of uncertainty for predicting solar cycle characteristics.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Design of an Edge -coupled dual -ring Split-ring resonator

Electric permittivity and magnetic permeability control electromagnetic wave propagation th rough materials. I n naturally occu rring materials, these are positive. Artificial materials exhi b iting negative material properties have been reported : they are referred to as metamaterials. This paper concentrates on a ring-type split-ring resonator (SRR) exhibiting negative magnetic permeability. The design and synthesis of the SRR using the genetic-algorithm approach is explained in detail. A user-friendly g raphical user i nterface (G U I ) for an SRR optim izer and estimator using MATLAB TM is also presented