We explore a cosmology where the Big Bang singularity is replaced by a condensation event of interacting strings. We study the transition from an uncontrolled, chaotic soup (“before”) to a clearly interacting “real world”. Cosmological inflation scenarios do not fit current observations and are avoided. Instead, long-range interactions inside this crystallization event limit growth and crystal symmetries ensure the same laws of nature and basic symmetries over our domain. Tiny mis-arrangements present nuclei of superclusters and galaxies and crystal structure leads to the arrangement of dark (halo regions) and normal matter (galaxy nuclei) so convenient for galaxy formation. Crystals come and go, allowing an evolutionary cosmology where entropic forces from the quantum soup “outside” of the crystal try to dissolve it. These would correspond to dark energy and leads to a big rip scenario in 70 Gy. Preference of crystals with optimal growth and most condensation nuclei for the next generation of crystals may select for multiple self-organizing processes within the crystal, explaining “fine-tuning” of the local “laws of nature” (the symmetry relations formed within the crystal, its “unit cell”) to be particular favorable for self-organizing processes including life or even conscious observers in our universe. Independent of cosmology, a crystallization event may explain quantum-decoherence in general: The fact, that in our macroscopic everyday world we only see one reality. This contrasts strongly with the quantum world where you have coherence, a superposition of all quantum states. We suggest that a “real world” (so our everyday macroscopic world) happens only in our domain, i.e. inside a crystal. “Outside” of our domain and our observable universe there is the quantum soup of boiling quantum foam and superposition of all possibilities. In our crystallized world the vacuum no longer boils but is cooled down by the crystallization event and hence is 10**20 smaller, exactly as observed in our everyday world. As we live in a “solid” state, within a crystal, the different quanta which build our world have all their different states nicely separated. This theory postulates there are only n quanta and m states available for them (there is no Everett-like ever splitting multiverse after each decision). In the solid state we live in, there is decoherence, the states are nicely separated. The arrow of entropy for each edge of the crystal forms one fate, one worldline or clear development of a world, while the layers of the crystal are different system states. Some mathematical leads from loop quantum gravity point to required interactions and potentials. A complete mathematical treatment of this unified theory is far too demanding currently. Interaction potentials for strings or membranes of any dimension allow a solid state of quanta, so allowing decoherence in our observed world are challenging to calculate. However, if we introduce here the heuristic that any type of physical interaction of strings corresponds just to a type of calculation, there is already since 1898 the Hurwitz theorem showing that then only 1D, 2D, 4D and 8D (octonions) allow complex or hypercomplex number calculations. No other hypercomplex numbers and hence dimensions or symmetries are possible to allow calculations without yielding divisions by zero. However, the richest solution allowed by the Hurwitz theorem, octonions, is actually the observed symmetry of our universe, E8.  

In der Unterrichtsmethode Flipped Classroom sind schulische und häusliche Aktivitäten vertauscht. Instruktionale Elemente werden in online verfügbare Lernvideos ausgelagert, welche die Schüler als häusliche Vorbereitung ansehen. Im Unterricht stehen dann schülerzentrierte Tätigkeiten im Vordergrund, in denen die Schüler ihr Wissen anwenden und vertiefen können. Durch die Auslagerung von Inputphasen wandelt sich die Rolle des Lehrers vom Instructor zum Lernbegleiter. Die vorliegende quasi-experimentelle Studie im Pre-/Postdesign mit Kontrollgruppe untersuchte die Wirkungen des Flipped Classroom in Physikkursen der Oberstufe (Grundkursniveau) an zwei deutschen Gymnasien mit N = 151 Schülerinnen und Schülern. Acht Physikkurse der 11. Jahrgangsstufe nahmen an der Studie teil, die sich über einen Zeitraum von zwei Schuljahren erstreckte (2015/16 und 2016/17). Vier der fünf teilnehmenden Lehrkräfte unterrichteten sowohl einen Kontroll- als auch einen Treatmentkurs. Sämtliche Lernvideos wurden von den Lehrkräften selbst erstellt. Dabei integrierten sie reale Experimente, um dem Anspruch physikauthentischen Unterrichts gerecht zu werden. Die Forschungsfragen richteten sich sowohl auf die Leistung in einem Fachwissenstest als auch auf affektive Lernmerkmale wie die Motivation, das Interesse und das Selbstkonzept. Zusätzlich wurden die wahrgenommene Lehrerunterstützung und das Hausaufgabenverhalten untersucht. Die Anwendung von Flipped Classroom im Physikunterricht zeigte größtenteils positive Effekte. Die Schülerinnen und Schüler im Flipped Classroom hatten einen höheren kognitiven Lernzuwachs und ein besseres Selbstkonzept als ihre Mitschüler, die traditionell unterrichtet wurden. Das Leistungsniveau und das Geschlecht der Schülerinnen und Schüler hatten dabei keinen Einfluss auf diese Effekte. Während die Motivation, sich mit Physik zu beschäftigen, in der Kontrollgruppe sank, blieb sie in der Treatmentgruppe auf konstantem Niveau. Bei genauerem Blick zeigte sich, dass die Motivation bei Schülerinnen im Flipped Classroom anstieg, bei Schülerinnen im traditionellen Unterricht jedoch abnahm. Das Interesse am Unterrichtsfach Physik wurde in beiden Gruppen geringer. Sowohl die wahrgenommene Lehrerunterstützung als auch die Hausaufgabendauer blieben in beiden Gruppen zwischen Pre- und Posttest unverändert. Die Hausaufgabendisziplin war im Flipped Classroom jedoch deutlich höher, was zeigt, dass die Schülerinnen und Schüler eher bereit waren, sich instruktionale Lernvideos anzusehen als klassische Hausaufgaben zu bearbeiten.

Global environmental change leads to the emergence of new human health risks. As a consequence, transmission opportunities of environment-related diseases are transformed and human infection with new emerging pathogens increase. The main motivation for this study is the considerable demand for disease surveillance and monitoring in relation to dynamic environmental drivers. Remote sensing (RS) data belong to the key data sources for environmental modelling due to their capabilities to deliver spatially continuous information repeatedly for large areas with an ecologically adequate spatial resolution. A major research gap as identified by this study is the disregard of the spatial mismatch inherent in current modelling approaches of profiling disease risk using remote sensing data. Typically, epidemiological data are aggregated at school or village level. However, these point data do neither represent the spatial distribution of habitats, where disease-related species find their suitable environmental conditions, nor the place, where infection has occurred. As a consequence, the prevalence data and remotely sensed environmental variables, which aim to characterise the habitat of disease-related species, are spatially disjunct. The main objective of this study is to improve RS-based disease risk models by incorporating the ecological and spatial context of disease transmission. Exemplified by the analysis of the human schistosomiasis disease in West Africa, this objective includes the quantification of the impact of scales and ecological regions on model performance. In this study, the conditions that modify the transmission of schistosomiasis are reviewed in detail. A conceptual underpinning of the linkages between geographical RS measures, disease transmission ecology, and epidemiological survey data is developed. During a field-based analysis, environmental suitability for schistosomiasis transmission was assessed on the ground, which is then quantified by a habitat suitability index (HSI) and applied to RS data. This conceptual model of environmental suitability is refined by the development of a hierarchical model approach that statistically links school-based disease prevalence with the ecologically relevant measurements of RS data. The statistical models of schistosomiasis risk are derived from two different algorithms; the Random Forest and the partial least squares regression (PLSR). Scale impact is analysed based on different spatial resolutions of RS data. Furthermore, varying buffer extents are analysed around school-based measurements. Three distinctive sites of Burkina Faso and Côte d’Ivoire are specifically modelled to represent a gradient of ecozones from dry savannah to tropical rainforest including flat and mountainous regions. The model results reveal the applicability of RS data to spatially delineate and quantitatively evaluate environmental suitability for the transmission of schistosomiasis. In specific, the multi-temporal derivation of water bodies and the assessment of their riparian vegetation coverage based on high-resolution RapidEye and Landsat data proofed relevant. In contrast, elevation data and water surface temperature are constraint in their ability to characterise habitat conditions for disease-related parasites and freshwater snail species. With increasing buffer extent observed around the school location, the performance of statistical models increases, improving the prediction of transmission risk. The most important RS variables identified to model schistosomiasis risk are the measure of distance to water bodies, topographic variables, and land surface temperature (LST). However, each ecological region requires a different set of RS variables to optimise the modelling of schistosomiasis risk. A key result of the hierarchical model approach is its superior performance to explain the spatial risk of schistosomiasis. Overall, this study stresses the key importance of considering the ecological and spatial context for disease risk profiling and demonstrates the potential of RS data. The methodological approach of this study contributes substantially to provide more accurate and relevant geoinformation, which supports an efficient planning and decision-making within the public health sector.

The glaciers in Norway exert a strong influence on Norwegian economy and society. Unlike many glaciers elsewhere and despite ongoing climate change and warming, many of them showed renewed advances and positive net mass changes in the 1980's and 1990's, followed by rapid retreats and mass losses since 2000. This difference in behaviour may be attributed to differences and shifts in the glaciological regime - the differences in the magnitude of impacts of climatic and non-climatic geographical factors on the glacier mass. This study investigates the influence of various atmospheric variables on mass balance changes of a selection of glaciers in Norway by means of Pearson correlation analyses and cross-validated stepwise multiple regression analyses. The analyses are carried out for three time periods (1949-2008, 1949-1988, 1989-2008) separately in order to take into consideration the possible shift in the glaciological regime in the 1980's. The atmospheric variables are constructed from ERA40 and NCEP/NCAR re-analysis datasets and include regional means of seasonal air temperature and precipitation rates and atmospheric circulation indices. The multiple regression models trained in these time periods are then applied to predictors reconstructed from the CMIP3 climate model dataset to generate an estimate for mass changes from the year 1950 to 2100. The temporal overlap of estimates and observations is used for calibration. Finally, observed atmospheric states in seasons that are characterised by a particularly positive or negative mass balance are categorised into time periods of modelled climate by the application of a Bayesian classification procedure. The strongest influence on winter mass balance is exerted by different indices of the North Atlantic Oscillation (NAO), Northern Annular Mode (NAM) and precipitation. The correlation coefficients and explained variances determined from the multiple regression analyses reveal an East-West gradient, suggesting a weaker influence of the NAO and NAM on glaciers underlying a more continental regime. The highest correlation coefficients and explained variances were obtained for the 1989-2008 time period, which might be due to a strong and predominantly positive phase of the NAO. Multi-model ensemble means of the estimates show a mass loss for all three eastern glaciers, while the estimates for the more maritime glaciers are ambivalent. In general, the estimates show a greater sensitivity to the training time period than to the greenhouse gas emission scenarios according to which the climates were simulated. The average net mass change by the end of 2100 is negative for all glaciers except for the northern Engabreen. For many glaciers, the Bayesian classification of observed atmospheric states into time periods of modelled climate reveals a decrease in probability of atmospheric states favouring extremes in winter, and an increase in probability of atmospheric states favouring extreme mass loss in summer for the distant future (2071-2100). This pattern of probabilities for the ablation season is most pronounced for glaciers underlying a continental and intermediate regime.