Digitally reconstructing Van Gogh's Field with Irises near Arles part 3: Determining the original colors

In earlier articles, we determined the spatial distributions and concentrations of all pigments used by Van Gogh in his painting Field with Irises near Arles. The colors of some pigments are expected to have changed over time, especially those of chrome yellow, cochineal, and eosin lake. For all pigments in this painting, we made physical paint reconstructions by following historical sources on raw materials and production processes, and we determined their optical properties. We combined this with pigment concentration maps to reconstruct the original colors of the painting digitally. When substituting the reconstruction paints into the calculations, we found that technical‐scientific data was not sufficient to resolve several issues. In those cases, discussions within the broad interdisciplinary team allowed us to make informed decisions. These issues refer to the representation of the sky area, and the original contributions of the red lake pigments to local colors. The digitally reconstructed colors of the painting show that due to discoloring of red lake pigments, the irises in the field have changed from a warm purple to purplish blue, and many pink spots in the field have turned to white. The range of yellows in the field has decreased and partly turned to dark brown. The digital reconstruction gives a better understanding of the color scheme used by Van Gogh when compared to remarks the artist made in letters when describing this painting. Also, the original color composition is seen to be aligned with color theories on which Van Gogh based his work.     

Effect of Pre-heat and Inert Gas Species on Cryogenic Aerosol Cleaning Performance in Semiconductor Manufacturing

Cryogenic aerosol cleaning is a dry cleaning method used in the back end of line (BEOL) semiconductor manufacturing to remove defects from planar hydrophobic surfaces such as SiCOH and SiCxNyHz. Cryogenic aerosol cleaning is preferred over conventional wet cleaning methods as it is a non-contact cleaning method, which uses inert gases to generate sub-micrometer-sized solid aerosol particles that physically remove nanometer-sized contaminants on wafer surfaces. Particle removal mechanism involves detachment of the particles upon impact with aerosol, diffusion, and finally entrainment away from the wafer. In BEOL metal line patterning, particles on the dielectric isolation surfaces translate through the subsequent lithography and copper fill steps in to single or multiple metal line open defects that are yield killers. In this study, we show that the particle removal performance of the standard aerosol cleaning can be enhanced by pre-heating the wafer and use of a higher molecular weight inert gas, namely Ar, for aerosol generation. Both the addition of a Pre-heat step and the use of Ar as the aerosol source showed 47–52% reduction in single and multiple line opens detected through wafer electrical tests during high volume semiconductor manufacturing process.     

Developing an Updated Strategy for Estimating the Free-Energy Parameters in RNA Duplexes

For the last 20 years, it has been common lore that the free energy of RNA duplexes formed from canonical Watson–Crick base pairs (bps) can be largely approximated with dinucleotide bp parameters and a few simple corrective constants that are duplex independent. Additionally, the standard benchmark set of duplexes used to generate the parameters were GC-rich in the shorter duplexes and AU-rich in the longer duplexes, and the length of the majority of the duplexes ranged between 6 and 8 bps. We were curious if other models would generate similar results and whether adding longer duplexes of 17 bps would affect the conclusions. We developed a gradient-descent fitting program for obtaining free-energy parameters—the changes in Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS), and the melting temperature (Tm)—directly from the experimental melting curves. Using gradient descent and a genetic algorithm, the duplex melting results were combined with the standard benchmark data to obtain bp parameters. Both the standard (Turner) model and a new model that includes length-dependent terms were tested. Both models could fit the standard benchmark data; however, the new model could handle longer sequences better. We developed an updated strategy for fitting the duplex melting data.     

Modelling the aqueous extraction of soluble substances from ground roasted coffee

This work aims to study and model the extraction of soluble substances from coffee grounds having different particle sizes. The results demonstrate that coffee brewing cannot be considered a simple diffusion mechanism as reported in the literature. The phenomenon has two phases: a washing phase where surface soluble substances instantaneously dissolve and are transferred to the extract by a convection mechanism, and a diffusion phase involving the solubles within the porous particles. A model was set up to calculate extraction yield as the addition of the two phases. It was also demonstrated that the effect of washing shows a linear correlation with the external surface area of particles and that the composition of the extracts does not depend on extraction time.     

Highly Active Dinuclear Copper Catalysts for Homogeneous Azide–Alkyne Cycloadditions

The main feature of the herein presented class of molecularly defined catalysts for the copper‐catalyzed azide–alkyne cycloaddition reaction is the presence of two copper centres in one catalyst molecule. We report the facile three‐step synthesis of two representative bis‐NHC‐dicopper complexes as well as their catalytic performance in the azide–alkyne cycloaddition. A screening with one of these complexes has proved its wide applicability and excellent performance as homogeneous catalyst in various organic solvents and with different alkyne and azide substrates.     

Designing resilient networks using multicriteria metaheuristics

The paper deals with the design of resilient networks that are fault tolerant against link failures. Usually, fault tolerance is achieved by providing backup paths, which are used in case of an edge failure on a primary path. We consider this task as a multiobjective optimization problem: to provide resilience in networks while minimizing the cost subject to capacity constraint. We propose a stochastic approach, which can generate multiple Pareto solutions in a single run. The feasibility of the proposed method is illustrated by considering several network design problems using a single weighted average of objectives and a direct multiobjective optimization approach using the Pareto dominance concept.     

Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of lymphocytes from days 1 to 15 after admission to hospital) are associated with relapse rates and evaluations of COVID-19 infections. Accordingly, the severity of COVID-19 is classified in two categories, serious and non-serious. The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.     

Topic Identification in Dynamical Text by Complexity Pursuit

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream. This revised version was published online in June 2006 with corrections to the Cover Date.