Characterization of spray paints used in street art graffiti by a non‐destructive multi‐analytical approach

Often associated with acts of vandalism, graffiti can also be identified with the so‐called street art movement. Moreover, in the historical context of visual arts from the 20th and 21st century, graffiti spray paints feature among the materials employed in the work of representative artists such as Lucio Fontana, Richard Hamilton, Yves Klein, or David Alfaro Siqueiros. In this study, a large number of artist spray paints were analyzed by means of X‐Ray Fluorescence (XRF), Fourier Transform Infrared Spectroscopy (FTIR), spectrophotometry, and Hyperspectral Imaging (HSI). The aim of the study was to provide a chemical characterization of the main organic and inorganic components present within the spray paint formulations by means of a complementary non‐destructive approach. Titanium white, zinc white, bismuth vanadate yellow, ultramarine, strontium sulfide, iron, and copper oxides, along a series of pigments of the azo, phthalocyanine, and quinacridone classes could be identified. High amounts of barium sulfate as well as calcium‐based extenders were also detected. FTIR analysis provided important information regarding the binder composition, mainly modified alkyd resins being identified. Additional information related to the existing chromophores as well as specific binder‐pigments interactions could also be highlighted within the HSI data sets. Overall results provide new insights on the complex chemistry of this new range of materials, which could help future investigations carried on street art graffiti, contemporary murals, or mixed‐media artworks.     

Biosynthesis of silver nanoparticles in collagen gel improves their medical use in periodontitis treatment

Silver nanoparticles (AgNP) suspensions were biosynthesized by silver ions reduction in the presence of collagen, a nontoxic, organic polymer, intending to improve their medical use in periodontitis treatment. Spectrophotometric measurements showed a time- and concentration-dependent increase of AgNP formation in each suspension variant. Transmission electron microscopy revealed spherical morphology of AgNP in collagen and their mean diameter size was around 30?nm. The particle size distribution and zeta potential values of AgNP in collagen were determined by dynamic light scattering measurements. The surface charge of AgNP in collagen was positive, while commercial AgNP stabilized in citrate had negative surface charge. In vitro cytotoxicity testing of AgNP in collagen showed that they were biocompatible with human gingival fibroblasts in a wider range of concentrations than commercial nanoparticles. The antibacterial activity of AgNP in collagen against two pathogenic strains present in the periodontal pocket was dose-dependent and higher than that of AgNP in citrate. All these results demonstrated that AgNP prepared in collagen gel had improved properties, like small diameter, positive surface charge, high biocompatibility in human gingival fibroblasts, efficiency against bacterial growth and, thus, better therapeutic potential in periodontal disease treatment.     

Proportional-delayed controllers design for LTI-systems: a geometric approach

This paper focuses on the design of P-δ controllers for single-input-single-output linear time-invariant systems. The basis of this work is a geometric approach allowing to partitioning the parameter space in regions with constant number of unstable roots. This methodology defines the hyper-planes separating the aforementioned regions and characterises the way in which the number of unstable roots changes when crossing such a hyper-plane. The main contribution of the paper is that it provides an explicit tool to find P-δ gains ensuring the stability of the closed-loop system. In addition, the proposed methodology allows to design a non-fragile controller with a desired exponential decay rate σ. Several numerical examples illustrate the results and a haptic experimental set-up shows the effectiveness of P-δ controllers.     

PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks

With the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of assumptions such as local smoothness to tackle this ill‐posed problem. However, such priors lead to loss of important features and geometric detail. Instead, we propose a novel data‐driven approach for point cloud consolidation via a convolutional neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons to the state‐of‐the‐art.     

Test and Reliability in Approximate Computing

This paper presents an overview of test and reliability approaches for approximate computing architectures. We focus on how specific methods for test and reliability can be used to improve the characteristics of approximate computing in terms of power consumption, area, life expectancy and precision. This paper does not address specification and design of approximate hardware/software/algorithms, but provides an in-depth knowledge on how the reliability and test related techniques can be efficiently used to maximize the benefits of approximate computing.     

Determination of R‐Deprenyl using a Maltodextrin‐Based Enantioselective,Potentiometric Membrane Electrode

Abstract

The enantioselective, potentiometric membrane electrode (EPME) based on maltodextrin with dextrose equivalence (DE) 16.5–19.5 is proposed for the assay of R‐deprenyl. The linear concentration range for the proposed electrode is 10^?10?10^?3 mol L^?1. The slope of the electrode is 53.1 mV per decade of concentration. The detection limit is 3.6×10^?11 mol L^?1. The proposed electrode could be reliably employed for the assay of R‐deprenyl raw material and its pharmaceutical formulation, Lentogesic tablets.