Search results for "Mining"
showing 10 items of 1730 documents
Reply to 'Healthy dietary indices and risk of depressive outcomes: a systematic review and meta-analysis of observational studies'.
2019
The assessment of environmental pollution caused by mining and metallurgy wastes from highly polluted post-industrial regions in Southern Poland
2012
Stored metallurgy and mining wastes contain relatively high amounts of potentially toxic elements. To monitor the distribution of contaminants originating from dumps, the chemical and physical properties of the wastes must be characterised. In this study, the chemical properties of wastes deposited in two different locations in Southern Poland (Szklary and Zloty Stok) were evaluated. Heaps located in Zloty Stok contain wastes from gold mineralisation comprising arsenic while wastes in Szklary originate from a factory that produced an iron-nickel alloy. In Szklary the total concentrations of Ca, Mg, Fe, Zn, Mn, Cr, Co, Cu, Ni, Tl, Ag, Cd and Pb were determined, while in Zloty Stok also As is…
Endovascular stentectomy using the snare over stent-retriever (SOS) technique: An experimental feasibility study
2017
PLoS one 12(5), e0178197 (2017). doi:10.1371/journal.pone.0178197
Clinical Predictors of Immunotolerance in Heart Transplantation
2010
Abstract Background and Aim The drugs routinely administered to prevent rejection often cause lethal side effects. Tolerant patients, therefore, should be identified to minimize these problems. The aim of this analysis was to identify clinical variables that may be associated with tolerance. Methods We recruited 522 heart transplants (HT), excluding combined procedures, retransplantations, pediatric recipients, and subjects who died in the first year to obtain a cohort of 375 patients. Two groups were distinguished by the presence of echocardiographic, clinical, or pathological evidence of rejection in the first year (15 echocardiograms and 10 protocol biopsies per patient); 99 tolerant pat…
Semisupervised nonlinear feature extraction for image classification
2012
Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…
A survey and comparison of transformation tools based on the transformation tool contest
2014
Model transformation is one of the key tasks in model-driven engineering and relies on the efficient matching and modification of graph-based data structures; its sibling graph rewriting has been used to successfully model problems in a variety of domains. Over the last years, a wide range of graph and model transformation tools have been developed – all of them with their own particular strengths and typical application domains. In this paper, we give a survey and a comparison of the model and graph transformation tools that participated at the Transformation Tool Contest 2011. The reader gains an overview of the field and its tools, based on the illustrative solutions submitted to a Hello…
Domain Adaptation of Landsat-8 and Proba-V Data Using Generative Adversarial Networks for Cloud Detection
2019
Training machine learning algorithms for new satellites requires collecting new data. This is a critical drawback for most remote sensing applications and specially for cloud detection. A sensible strategy to mitigate this problem is to exploit available data from a similar sensor, which involves transforming this data to resemble the new sensor data. However, even taking into account the technical characteristics of both sensors to transform the images, statistical differences between data distributions still remain. This results in a poor performance of the methods trained on one sensor and applied to the new one. In this this work, we propose to use the generative adversarial networks (G…
Robust Principal Component Analysis of Data with Missing Values
2015
Principal component analysis is one of the most popular machine learning and data mining techniques. Having its origins in statistics, principal component analysis is used in numerous applications. However, there seems to be not much systematic testing and assessment of principal component analysis for cases with erroneous and incomplete data. The purpose of this article is to propose multiple robust approaches for carrying out principal component analysis and, especially, to estimate the relative importances of the principal components to explain the data variability. Computational experiments are first focused on carefully designed simulated tests where the ground truth is known and can b…