A new luminescent Ru(terpy) complex incorporating a 1,2,4-triazole based σ-donor ligand

The mononuclear compound [Ru(terpy)L], where H2L is 2,6-bis(1,2,4-triazol-3-yl)pyridine, shows an emission lifetime of 65 ns, about 300 times longer than that observed for the parent [Ru(terpy)3]2+ complex

Molecular causes of primary microcephaly and related diseases: a report from the UNIA Workshop

The International University of Andalucía (UNIA) Current Trends in Biomedicine Workshop on Molecular Causes of Primary Microcephaly and Related Diseases took place in Baeza, Spain, November 18–20, 2019. This meeting brought together scientists from Europe, the USA and China to discuss recent advances in our molecular and genetic understanding of a group of rare neurodevelopmental diseases characterised by primary microcephaly, a condition in which head circumference is smaller than normal at birth. Microcephaly can be caused by inherited mutations that affect key cellular processes, or environmental exposure to radiation or other toxins. It can also result from viral infection, as exemplified by the recent Zika virus outbreak in South America. Here we summarise a number of the scientific advances presented and topics discussed at the meeting

A real time bolometer tomographic reconstruction algorithm in nuclear fusion reactors

In tokamak nuclear fusion reactors, one of the main issues is to know the total emission of radiation, which is mandatory to understand the plasma physics and is very useful to monitor and control the plasma evolution. This radiation can be measured by means of a bolometer system that consists in a certain number of elements sensitive to the integral of the radiation along straight lines crossing the plasma. By placing the sensors in such a way to have families of crossing lines, sophisticated tomographic inversion algorithms allow to reconstruct the radiation tomography in the 2D poloidal cross-section of the plasma. In tokamaks, the number of projection cameras is often quite limited resulting in an inversion mathematic problem very ill conditioned so that, usually, it is solved by means of a grid-based, iterative constrained optimization procedure, whose convergence time is not suitable for the real time requirements. In this paper, to illustrate the method, an assumption not valid in general is made on the correlation among the grid elements, based on the statistical distribution of the radiation emissivity over a set of tomographic reconstructions, performed off-line. Then, a regularization procedure is carried out, which merge highly correlated grid elements providing a squared coefficients matrix with an enough low condition number. This matrix, which is inverted offline once for all, can be multiplied by the actual bolometer measures returning the tomographic reconstruction, with calculations suitable for real time application. The proposed algorithm is applied, in this paper, to a synthetic case study

Forecasting-Aided Monitoring for the Distribution System State Estimation

In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid

Ni(0) catalysed homo-coupling reactions: a novel route towards the synthesis of multinuclear ruthenium polypyridine complexes featuring made-to-order properties.

A new synthetic procedure for the efficient preparation of dinuclear ruthenium(II) polypyridyl complexes is reported. The compounds synthesised are [(bpy)2Ru(BPBT)Ru(bpy)2](PF6)2 and [(bpy)2Ru(BPZBT)Ru(bpy)2](PF6)2 (bpys2,29-bipyridine; H2BPBTs5,59- bis(pyridin-2-yl)-3,39-bis(1,2,4-triazole); H2BPZBTs5,59-bis(pyrazin-2-yl)-3,39-bis(1,2,4-triazole). Electrochemical experiments show that the two dinuclear systems investigated exhibit pH switchable intercomponent interactions

Neural reflectance transformation imaging

Reflectance transformation imaging (RTI) is a computational photography technique widely used in the cultural heritage and material science domains to characterize relieved surfaces. It basically consists of capturing multiple images from a fixed viewpoint with varying lights. Handling the potentially huge amount of information stored in an RTI acquisition that consists typically of 50\u2013100RGB values per pixel, allowing data exchange, interactive visualization, and material analysis, is not easy. The solution used in practical applications consists of creating \u201crelightable images\u201d by approximating the pixel information with a function of the light direction, encoded with a small number of parameters. This encoding allows the estimation of images relighted from novel, arbitrary lights, with a quality that, however, is not always satisfactory. In this paper, we present NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. Using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, especially in the case of challenging glossy materials. We also address the problem of validating the relight quality on different surfaces, proposing a specific benchmark, SynthRTI, including image collections synthetically created with physical-based rendering and featuring objects with different materials and geometric complexity. On this dataset and as well on a collection of real acquisitions performed on heterogeneous surfaces, we demonstrate the advantages of the proposed relightable image encoding