[Re]Constructing Community: A Strategy for Post-Disaster Recovery

This thesis focuses on exploring a viable solution for permanent housing after a natural disaster with an emphasis on community rebuilding in a southern coastal context. This region will continue to be affected by hurricanes and it is only a matter of time until another major disaster will happen. In any major disaster the fabric of the community is torn and takes a long time to recover. Communal places are vital to recovery after such disasters because they serve as a place where the people of the community can gather and provide support or receive support from each other and outside organizations. In current disaster response strategies, establishing shelter immediately after such an event is paramount, while community rebuilding is given secondary importance. Current practices in the aftermath of natural disasters, while well intended, often turn into poor solutions. A “quick-fix” solution to this issue often leaves the community out of the equation, both its members and the sense of community. As a result there is too much focus on housing and community places are only addressed later in the recovery process. Drawing from personal experience in the disaster recovery process post-Katrina, this thesis proposal challenges the existing disaster response strategies and focuses on simultaneous rebuilding of both housing and community places, approaching the problem of housing and community place as a synergetic issue. Housing is an integral part of a community, just as is community place, and by approaching the issue as if the two are one in the same, then community becomes the focus. A community is more than just its buildings, but its built environment provides a framework for community. By focusing on community rebuilding, both in housing and community space, the design solution will provide a greater quality of life for a community. The design proposal will have two aspects: housing and public community place. Both aspects will focus on community engagement and community rebuilding. The overall goal is to form a solution to reestablish the sense of community and address the need of adequate housing borne out through design

Influences Of Nesting Behaviors In Common Eiders (Somateria Mollissima Sedentaria) In The Western Hudson Bay

Reproductive decisions of birds are shaped by minimizing predation while meeting physiological needs of parents and offspring. As a result, birds must make decisions about investment in offspring that allows them to maximize their overall fitness. This often drives nesting behaviors and can provide insights into reproductive success. Common eiders (Somateria mollissima) are thought to have high nest attendance (time spent tending the nest) and use substantial energetic resources during breeding. However, the specific behaviors at the nest of these birds are not well studied, with even less known about the Hudson Bay common eider (S. m. sedentaria). The objectives of this study were to determine (1) if colony characteristics and nest age influence nest attendance patterns, (2) if these patterns influence the probability of predation from a specific predator, and (3) what invertebrate resources are available to eiders and what factors impact the availability of those resources. To address our first and second objectives we used data collected during the summers of 2014 to 2017 where we captured nest attendance patterns and their fates at 103 common eider nests using time-lapse imagery from trail cameras. We found eiders spend 95.65% of a day on the nest on average, take 1.4 recesses per day and are gone on recess for an average duration of 43.51 minutes. We did not find nest age or colony characteristics to be major drivers in attendance patterns. Common eider nest predators included arctic foxes (Vulpes lagopus), red foxes (Vulpes vulpes), grizzly bears (Ursus arctos), polar bears (Ursus maritimus), sandhill cranes (Antigone canadensis), herring gulls (Larus argentatus), and bald eagles (Haliaeetus leucocephalus). Annual variation was the most important factor driving nest success. When exploring what predator would consume a nest, we found the best predictor was timing within the breeding season. For our third objective, we collected aquatic invertebrates in June and July of 2016 and 2017 and compared our findings to data collected in 2003 and 2004. We created models and used AIC model selection to determine the factors that most simply and best explained abundance of individuals in each invertebrate. We found flow, season, substrate type and sediment depth appeared in most of our models. When comparing our findings to the initial collections in 2003 and 2004, we found increased sedimentation across our sampling sites. Finally, we used ANOVAs and discriminate analyses to investigate if stable isotope values of 13C and 15N collected from aquatic invertebrates were different among the four different water types they were collect in (Mast River freshwater, Wawao Creek freshwater, brackish, and marine). We found that no difference in 13C samples between our two freshwater sources, but did find variation in our 15N samples which are likely different from the proportions of different types of invertebrate categories collected. Our brackish samples to not be different from freshwater sources but did find marine samples to be unique from all other samples. Findings from this study provide important natural history information on the nesting ecology of the common eider and provide a foundation for future research exploring availability of invertebrate resources for breeding birds in the area

Accelerating Chemical Tool Discovery by Academic Collaborative Models

The development of chemical tool compounds becomes increasingly important for chemical biology research projects in many disciplines of life sciences. In addition, they form essential parts in both academic and industrial drug discovery efforts. The required expertise and technology platforms for the identification and optimization of these potent and target-selective small molecules often exceed the capabilities of academic groups and smaller companies. Over the years, several initiatives were created all over the world which address this issue by either creating networks or consortia of academic institutes, public-private partnerships with industry, or even dedicated new research infrastructures for chemical biology. Several of these organizations and their different access models will be described. We will focus in particular on the model of EU-OPENSCREEN ERIC, a new European Research Infrastructure which was founded in 2018 and consists of more than 20 partner institutes from eight countries

David Bowie - Serious Moonlight : Großbritannien/USA 1984

Nach fünfjähriger Tourneepause kehrte David Bowie 1983 mit seiner 'Serious Moonlight World Tour' zurück, die der Veröffentlichung seines kommerziell hoch erfolgreichen Albums 'Let's Dance' folgte. Am 12.9.1983 führte die Tour ihn auch nach Vancouver, Kanada, wo er vor 15.000 Zuschauern im National Exhibition Coliseum auftrat. Der Sender HBO wurde damit beauftragt, einen Mitschnitt des Konzertes zu erstellen, der die besondere Live-Atmosphäre einfangen sollte. Resultat war eine zwanzig Songs umfassende VHS-Kassette, die 1984 erstmals auf den Markt kam und in der neuesten Überarbeitung seit 2006 als offizielle DVD vorliegt

Element learning: a systematic approach of accelerating finite element-type methods via machine learning, with applications to radiative transfer

In this paper, we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations (PDEs). The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion. This map takes input of the element geometry and the PDEs' parameters on that element, and gives output of two operators -- (1) the in2out operator for inter-element communication, and (2) the in2sol operator (Green's function) for element-wise solution recovery. A significant advantage of this approach is that, once trained, this network can be used for the numerical solution of the PDE for any domain geometry and any parameter distribution without retraining. Also, the training is significantly simpler since it is done on the element level instead on the entire domain. We call this approach element learning. This method is closely related to hybridizbale discontinuous Galerkin (HDG) methods in the sense that the local solvers of HDG are replaced by machine learning approaches. Numerical tests are presented for an example PDE, the radiative transfer equation, in a variety of scenarios with idealized or realistic cloud fields, with smooth or sharp gradient in the cloud boundary transition. Under a fixed accuracy level of $10^{-3}$ in the relative $L^2$ error, and polynomial degree $p=6$ in each element, we observe an approximately 5 to 10 times speed-up by element learning compared to a classical finite element-type method

Pathogen and Toxin Entry - How Pathogens and Toxins Induce and Harness Endocytotic Mechanisms

Humans have been exposed to a plethora of pathogens (bacteria, viruses) ever since. Infectious diseases are among the leading causes of death worldwide. For example, in 2011, 1.34 million people died of tuberculosis, which is caused by an infection with Mycobacterium tuberculosis. Even more died of an infection by the human immunodeficiency virus (HIV; 1.78 million) or lower respiratory tract infection (3.46 million) [1]. In addition, recurring pandemic outbreaks of the influenza A virus, as in 2009, or an epidemic outbreak of enterohemorrhagic E. coli (EHEC) in Germany in 2011, show quite plainly that pathogens in the 21th century still are a severe health problem, not only in developing countries