Atmospheric Transport and Diffusion Modeling of Rocket Exhaust

Space launches at Vandenberg Air Force Base (VAFB) and the Cape Canaveral Air Station (CCAS) produce exhaust from the solid rocket boosters and liquid hypergolic fuels containing several toxic substances including hydrogen chloride and hydrazine. In order to estimate the health risk that would be imposed upon the public by proposed launches, range safety officials rely on the Rocket Exhaust Effluent Diffusion Model to predict where the exhaust chemicals will go after the launch and how strong the concentrations will be. The original REEDM program averaged the meteorological parameters (wind speed, wind direction, shear, etc.) across the entire mixing level and used these averages in its Gaussian calculations to predict instantaneous concentration, dose and time weighted average concentration. This thesis modified the model program to perform its meteorological parameter averaging using only those parameters which affected source material transport in diffusion, excluding from the averaging, those parameters which did not affect the calculation. The difference in before and after modification REEDM output was statistically, significantly different for maximum centerline instantaneous concentrations and maximum centerline doses. However, the magnitude of the difference may not be considered practically significant by launch safety officials

Polarizing Double Negation Translations

Double-negation translations are used to encode and decode classical proofs in intuitionistic logic. We show that, in the cut-free fragment, we can simplify the translations and introduce fewer negations. To achieve this, we consider the polarization of the formul{\ae}{} and adapt those translation to the different connectives and quantifiers. We show that the embedding results still hold, using a customized version of the focused classical sequent calculus. We also prove the latter equivalent to more usual versions of the sequent calculus. This polarization process allows lighter embeddings, and sheds some light on the relationship between intuitionistic and classical connectives

Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Plasmodium vivax but not Plasmodium falciparum blood-stage infection in humans is associated with the expansion of a CD8+ T cell population with cytotoxic potential

P. vivax and P. falciparum parasites display different tropism for host cells and induce very different clinical symptoms and pathology, suggesting that the immune responses required for protection may differ between these two species. However, no study has qualitatively compared the immune responses to P. falciparum or P. vivax in humans following primary exposure and infection. Here, we show that the two species differ in terms of the cellular immune responses elicited following primary infection. Specifically, P. vivax induced the expansion of a subset of CD8+ T cells expressing the activation marker CD38, whereas P. falciparum induced the expansion of CD38+ CD4+ T cells. The CD38+ CD8+ T cell population that expanded following P. vivax infection displayed greater cytotoxic potential compared to CD38- CD8+ T cells, and compared to CD38+ CD8+ T cells circulating during P. falciparum infection. We hypothesize that P. vivax infection leads to a stronger CD38+ CD8+ T cell activation because of its preferred tropism for MHC-I-expressing reticulocytes that, unlike mature red blood cells, can present antigen directly to CD8+ T cells. This study provides the first line of evidence to suggest an effector role for CD8+ T cells in P. vivax blood-stage immunity. It is also the first report of species-specific differences in the subset of T cells that are expanded following primary Plasmodium infection, suggesting that malaria vaccine development may require optimization according to the target parasite

Characterization of aluminum, aluminum oxide and titanium dioxide nanomaterials using a combination of methods for particle surface and size analysis

International audienceThe application of appropriate analytical techniques is essential for nanomaterial (NM) characterization. In this study, we compared different analytical techniques for NM analysis. Regarding possible adverse health effects, ionic and particulate NM effects have to be taken into account. As NMs behave quite differently in physiological media, special attention was paid to techniques which are able to determine the biosolubility and complexation behavior of NMs. Representative NMs of similar size were selected: aluminum (Al 0) and aluminum oxide (Al 2 O 3), to compare the behavior of metal and metal oxides. In addition, titanium dioxide (TiO 2) was investigated. Characterization techniques such as dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) were evaluated with respect to their suitability for fast characterization of nanoparticle dispersions regarding a particle's hydrodynamic diameter and size distribution. By application of inductively coupled plasma mass spectrometry in the single particle mode (SP-ICP-MS), individual nanoparticles were quantified and characterized regarding their size. SP-ICP-MS measurements were correlated with the information gained using other characterization techniques, i.e. transmission electron microscopy (TEM) and small angle X-ray scattering (SAXS). The particle surface as an important descriptor of NMs was analyzed by X-ray diffraction (XRD). NM impurities and their co-localization with biomolecules were determined by ion beam microscopy (IBM) and confocal Raman microscopy (CRM). We conclude advantages and disadvantages of the different techniques applied and suggest options for their complementation. Thus, this paper may serve as a practical guide to particle characterization techniques

Collective intelligence for promoting changes in behaviour: a case study on energy conservation

Climate change is one of the biggest challenges humanity faces today. Despite of high investments in technology, battling climate change is futile without the participation of the public, and changing their perception and habits. Collective intelligence tools can play an important role in translating this “distant” concept that is climate change into practical hints for everyday life. In this paper, we report a case study grounded on collective intelligence tools to collaboratively build knowledge around energy conservation. A preliminary study to raise energy awareness in an academic environment is summarised, setting the scene to a more ambitious initiative based on personal stories to transform energy awareness into behaviour change. The role of the collective intelligence tools and other technical artefacts involved are discussed, suggesting strategies and features that contributed (or not) to users’ engagement and collective awareness. Lessons learned from both studies are reported with a sociotechnical approach as implications for design pursuing behaviour change