Search results for "dolo"

Compartmentalization of gypsum and halite associated with cyanobacteria in saline soil crusts

2016

The interface between biological and geochemical components in surface crust of a saline soil was investigated using X-Ray Diffraction (XRD), and variable pressure scanning electron microscopy (SEM) in combination with Energy Dispersive X-ray Spectrometry (EDS). Mineral compounds such as halite and gypsum were identified crystallized around filaments of cyanobacteria. A total of 92 genera were identified from the bacterial community based on 16S gene pyrosequencing analysis. The occurrence of the gypsum crystals, their shapes and compartmentalization suggested that they separated NaCl from the immediate microenvironment of the cyanobacteria, and that some cyanobacteria and communities of su…

A biproportional filter to compare technical and allocation coefficient variations

1997

International audience; In input-output analysis there are two alternate possibilities between Leontief's mechanism (fixed technical coefficients) and Ghosh's mechanism (fixed allocation coefficients). Testing the long term consistency of these mechanisms entails comparing input-output matrices over time. This paper challenges the value of proportional filters (separate comparison of column and row coefficients) and introduces the biproportional filter which allows simultaneous comparison of column and rows. An application is proposed using French input-output tables for 1980 and 1993. The stability of column coefficients cannot be taken for granted and generally, for any sector, both rows …

Six Sigma in small- and medium-sized enterprises: a Black Belt project in the Swedish steel industry

2014

Many big companies around the world have solid Six Sigma infrastructures and it is easy to find in literature successful case studies. Conversely, small and medium-sized enterprises (SMEs) generally suffer lower attention in the literature. This happens because SMEs have only recently approached the methodology; they have weaker connection with the academia; or they do not rigorously pursue the frameworks. This paper is based on a Six Sigma project in a Swedish medium-sized company that produces steel tubes mainly for hydraulic applications. The project focused on the improvement of warehouse activities related to cutting processes. This Black Belt project was part of a Six Sigma education …

Sparse Deconvolution Using Support Vector Machines

2008

Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise. Publicado

A sensor-data-based denoising framework for hyperspectral images

2015

Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term…

Subclinical atherosclerosis and history of cardiovascular events in Italian patients with rheumatoid arthritis: Results from a cross-sectional, multi…

2017

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Mass movements (landslides) and environmental features in an area Of Alto Belice Destro (High Right Belice) Western Sicily - Italy

2007

Processing of rock core microtomography images: Using seven different machine learning algorithms

2016

The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…

Alternating model trees

2015

Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose a method for growing alternating model trees, a form of option tree for regression problems. The motivation is that alternating decision trees achieve high accuracy in classification problems because they represent an ensemble classifier as a single tree structure. As in alternating decision trees for classification, our alternating model trees for regression contain splitter and prediction nodes, but we use simple linear regression functions as opposed to constant predicto…

Bagging and Boosting with Dynamic Integration of Classifiers

2000

One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The co-operation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine learning techniques which derive base classifiers. Boosting uses a kind of weighted voting and bagging uses equal weight voting as a combining method. Both do not take into account the local aspects that the base classifiers may have inside the problem space. We have proposed a dynamic integration tech…