Photorefractive properties of lithium niobate crystals doped with manganese

The photorefractive properties of lithium niobate crystals doped with manganese (Mn) have been investigated. It is found that the effect of dark decay due to electron tunneling, which is the limiting factor of the highest practical doping level, is less in LiNbO_3:Mn than in LiNbO_3:Fe , and higher doping levels can be used in LiNbO_3:Mn to achieve larger dynamic range and sensitivity for holographic applications. The highest practical doping level in LiNbO_3:Mn has been found to be ~0.5 wt.% MnCO_3, and refractive-index changes and sensitivities up to 1.5X10^-3 and 1.3 cm/J are measured for extraordinarily polarized light of the wavelength 458 nm. It has been found that, in terms of both dynamic range (or refractive-index change) and sensitivity, the optimal oxidation state is highly oxidized. The distribution coefficient of Mn has been determined to be ~1. Absorption measurements are used to obtain more information about charge-transport parameters. The material is excellently suited for holographic recording with blue light. The hologram quality is outstanding because holographic scattering is much weaker compared with that in, e.g., iron-doped lithium niobate. Thermal fixing has been successfully demonstrated in LiNbO_3:Mn crystals

The implicit equation of a canal surface

A canal surface is an envelope of a one parameter family of spheres. In this paper we present an efficient algorithm for computing the implicit equation of a canal surface generated by a rational family of spheres. By using Laguerre and Lie geometries, we relate the equation of the canal surface to the equation of a dual variety of a certain curve in 5-dimensional projective space. We define the \mu-basis for arbitrary dimension and give a simple algorithm for its computation. This is then applied to the dual variety, which allows us to deduce the implicit equations of the the dual variety, the canal surface and any offset to the canal surface.Comment: 26 pages, to be published in Journal of Symbolic Computatio

The devil is in the detail:tobacco industry political influence in the Dutch implementation of the 2001 EU Tobacco Products Directive

Introduction - The Dutch implementation of the black border provision in the 2001 European Union Tobacco Products Directive (TPD) is studied to examine the implications of tobacco industry involvement in the implementation phase of the policy process. Methods - A qualitative analysis was conducted of Dutch government documents obtained through Freedom of Information Act requests, triangulated with in-depth interviews with key informants and secondary data sources (publicly available government documents, scientific literature, and news articles). Results - Tobacco manufacturers’ associations were given the opportunity to set implementation specifications via a fast-track deal with the government. The offer of early implementation of the labelling section of the TPD was used as political leverage by the industry, and underpinned by threats of litigation and arguments highlighting the risks of additional public costs and the benefits to the government of expediency and speed. Ultimately, the government agreed to the industry's interpretation, against the advice of the European Commission. Conclusions - The findings highlight the policy risks associated with corporate actors’ ability to use interactions over technical product specifications to influence the implementation of health policy and illustrate the difficulties in limiting industry interference in accordance with Article 5.3 of the Framework Convention on Tobacco Control (FCTC). The implementation phase is particularly vulnerable to industry influence, where negotiation with industry actors may be unavoidable and the practical implications of relatively technical considerations are not always apparent to policymakers. During the implementation of the new TPD 2014/40/EU, government officials are advised to take a proactive role in stipulating technical specifications

Sotagliflozin, a Dual SGLT1 and SGLT2 Inhibitor, as Adjunct Therapy to Insulin in Type 1 Diabetes

To assess the safety and efficacy of dual sodium–glucose cotransporter (SGLT) 1 and SGLT2 inhibition with sotagliflozin as adjunct therapy to insulin in type 1 diabetes

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages