1,720,968 research outputs found
2D-PAGE Maps Analysis
No full textDue to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature
Artificial neural networks applications in the field of separation science optimisation
No full textOptimisation procedures in chromatography usually exploit "hard" model approaches or methods based on the coupling of experimental design techniques and surface response methods. A powerful alternative has been recently provided by Artificial Neural Networks (ANNs), which allow to obtain "soft" models, not based on the a-priori knowledge of the mechanisms involved in the separation, and permit to model non-linear relationships. Most of ANNs applications in chromatography regard multivariate calibration and prediction or studies on structure-activity relationships. They have also been recently applied to the optimisation of process and mobile phase composition parameters: in these applications they are usually coupled to response surface methods and/or experimental design techniques. This review reports the main applications of ANNs to the optimisation of different separation techniques: high-performance liquid-chromatography, ion and gas chromatography, electro-separation methods. A section describing the main experimental designs and the theory of ANNs is also present
Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks
No full textA Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set. Then, a "leave-more-out" approach, based on the use of a training set, a test set, and a production set, was adopted. The training set was used to build the models, the test set was used to select the number of latent variables or the neural network training endpoint, and the production set was used to produce genuine predictions. ANNs proved to be much more effective in prediction with respect to PLS and PCR and, at least in the case of SO2 and dust, provided a predictive ability comparable with the experimental estimated uncertainty of the response. This showed that it is possible to satisfactorily predict the two responses. Such a prediction will result in the prevention of environmental and legal problems connected to the polluting emissions
Optimization of the setting parameters of a probe analyzer used for quality assessment of the interlace level and variation of textile polyester fibers
No full textOptimization of the experimental settings of a laser probe analyzer used for monitoring the interlace level of yarns in the textile industry was performed to reduce large number of errors that concern the reading of the interlace level of internal reference materials. The aim was to obtain a repeatable and accurate reading of the interlace level for two classes of products: texturized and stretched yarns. The experimental design techniques allowed to build a regression model for each class, relating the instrument reading and the experimental parameters of the control analyzer. The use of the optimal settings suggested by the regression models ensured great improvement in the accuracy of the readings furnished by the probe. This improvement ranged from 8% to 10% in the case of the texturized threads and from 15% to 32% in the case of the stretched ones, bringing about a relevant improvement in the quality control process
Optimisation of sensitivity in the multi-elemental determination of 83 isotopes by ICP-MS as a function of 21 instrumental operative conditions by modified simplex, principal component analysis and partial least squares
No full textThe optimisation of the sensitivity in the ICP-MS determination of 83 isotopes, as a function of 21 operative
parameters was performed by generating an initial experimental design that was used to define, by
principal component analysis, the multi-criteria target function. The first PC, which contained an overall
evaluation of the signal intensity of all isotopes, was used to rank the experiments. The modified simplex
optimisation techniquewas then applied on the ranked experiments. The increase in signal intensitywas,
on the average, 3.9 times for the isotopes considered for the simplex procedure. When finally convergence
was achieved, a PLS regression model calculated on the available experiments allowed to investigate the
effect played by each factor on the experimental response. Simplex and PCA proved to be extremely effective
to obtain the optimisation and to generate the multi-criteria target function: they can be suggested
as an automatic method to perform the optimisation of the instrumental operative conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Use of Legendre moments for the fast comparison of two-dimensional polyacrylamide gel electrophoresis maps images
No full textIn this paper, Legendre moments are calculated to extract the global information from a set of two-dimensional polyacrylamide gel electrophoresis map images. The dataset contains 18 samples belonging to two different cell lines (PACA44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of this work was to obtain the correct classification of the 18 samples, using the Legendre moments as discriminant variables. For each image the Legendre moments up to a maximum order of 100 were computed. The stepwise linear discriminant analysis (LDA) was performed in order to select the moments with the highest discriminating power. The results demonstrate that the Legendre moments can be successfully applied for fast classification purposes and similarity analysis
- …
