Search results for "Ruth"

Phenothiazine-based dyes for efficient dye-sensitized solar cells

2016

As an emerging photovoltaic technology, dye-sensitized solar cells (DSSCs) have attracted a great deal of academic and industrial interest due to their reasonably high power conversion efficiency, low material cost and facile fabrication process. Metal-free organic dyes, as one of the key components of DSSCs, play a pivotal role in light harvesting and electron injection. Among the various species of organic dyes, easily tunable 10H-phenothiazine-based dyes hold a large proportion. The electron-rich nitrogen and sulfur atoms render 10H-phenothiazine a stronger donor character than other amines, even better than triphenylamine, tetrahydroquinoline, carbazole and iminodibenzyl. On the other h…

Synthesis of β- and γ-carbolines via ruthenium and rhodium catalysed [2+2+2] cycloadditions of yne-ynamides with methylcyanoformate

2011

A flexible approach towards substituted β- and γ-carbolines based on transition metal catalysed [2+2+2] cycloaddition reactions between functionalised yne-ynamides and methylcyanoformate is described. The versatility of this new reaction sequence is demonstrated by its application in the total synthesis of the marine natural product eudistomin U.

Fuzzy Logic, Vagueness and Uncertainty

2009

3D Ruthenium Nanoparticle Covalent Assemblies from Polymantane Ligands for Confined Catalysis

2020

International audience; The synthesis of metal nanoparticle (NP) assemblies stabilized by functional molecules is an important research topic in nanoscience, and the ability to control interparticle distances and positions in NP assemblies is one of the major challenges in designing and understanding functional nanostructures. Here, two series of functionalized adamantanes, bis-adamantanes, and diamantanes, bearing carboxylic acid or amine functional groups, were used as building blocks to produce, via a straightforward method, networks of ruthenium NPs. Both the nature of the ligand and the Ru/ligand ratio affect the interparticle distance in the assemblies. The use of 1,3-adamantanedicarb…

Selection of Subjunctors in Turkic Non-Finite Complement Clauses

2013

The topic of the paper is Turkic clausal complementation: the syntactic and semantic behavior of complement clauses, the subjunctors that mark them, and the roles of various predicate types in selecting them. Two main types of bound complementizers serve as subjunctors in complement clauses: a participial and an infinitival type, both usually corresponding to the English complimentizer that. Traditionally, the semantic behavior of the complement clauses has been thought to depend on a distinction between factive and non-factive verbs. Complement clauses provided with participial subjunctors have been described as factive in contrast to non-factive complement clauses provided with infinitiva…

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…

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

2020

 To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…

Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy.

2014

Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After …

Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation

2018

Part 8: Pattern Recognition and Image Processing; International audience; Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugat…

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

2010

Fuel feeding and inhomogeneity of fuel typically cause fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate the fluctuations, the whole plant will suffer from dynamics that is reinforced by the closed-loop controls. This phenomenon causes reducing efficiency and the lifetime of process components. In this paper we address the problem of online mass flow prediction, which is a part of control. Particularly, we consider the problem of learning an accurate predictor with explicit detection of abrupt concept drift and noise handling mechanisms. We emphasize the importance of having domain knowledge concerning the considered case and constructing the…