Atomic trajectory characterization in a fountain clock based on the spectrum of a hyperfine transition

We describe a new method to determine the position of the atomic cloud during its interaction with the microwave field in the cavity of a fountain clock. The positional information is extracted from the spectrum of the F=3,mF=0 to F=4,mF=-1 hyperfine transition, which shows a position dependent asymmetry when the magnetic C-field is tilted by a few degrees with respect to the cavity axis. Analysis of this spectral asymmetry provides the horizontal center-of-mass position for the ensemble of atoms contributing to frequency measurements. With an uncertainty on the order of 0.1 mm, the obtained information is useful for putting limits on the systematic uncertainty due to distributed cavity phase gradients. The validity of the new method is demonstrated through experimental evidence.Comment: 6 figures, submitted to PR

Decomposed description of Ramsey spectra under atomic interactions

We introduce a description of Ramsey spectra under atomic interactions as a sum of decomposed components with differing dependence on interaction parameters. This description enables intuitive understanding of the loss of contrast and asymmetry of Ramsey spectra. We derive a quantitative relationship between the asymmetry and atomic interaction parameters, which enables their characterization without changing atom density. The model is confirmed through experiments with a Yb optical lattice clock

Towards better understanding of rapid internationalisation : the significance of the entrepreneur

Siirretty Doriast

Bewertung der Erfassungswahrscheinlichkeit für globales Biodiversitäts-Monitoring: Ergebnisse von Sampling GRIDs aus unterschiedlichen klimatischen Regionen

This thesis provides important input for the development of a cost-effective global biodiversity assessment and monitoring system. The study is embedded in a larger project to evaluate possibilities of multiple-species surveys using biodiversity GRIDs. As a pilot study six GRIDs in diverse ecosystem settings are sampled. Sampling methods used for animal species are point transects for birds and trapping webs for arthropods; additionally a line transects add-on protocol is used at some study areas for amphibians, reptiles and butterflies. Within this framework the task is taken over to develop predictive models for sampled animal species with Random Forests. Additionally the data is analyzed to derive abundance estimates with multiple covariate DISTANCE sampling and occupancy estimates through the software PRESENCE. A total of 5,007 observations from six study areas from all over the world are analyzed in detail. Total sampling time is about 12 weeks. High quality non-random predictive models with a ROC value > 0.5 are gained with Random Forests analysis for 116 described animal narratives. Half of these observations origin from point transect sampling, the other half from trapping web catches. The line transects add-on protocol results in another 3 predictive models. Abundance and occupancy estimates are derived from the data for 46 animal narratives, 23 of those for point transect data, 22 for trapping web data, and 1 for line transect data. Predictive modeling with Random Forests proves to be a very powerful tool. DISTANCE sampling estimates from this study show large confidence interval ranges, but are extremely cost-efficient to gather initial information for multiple species rapidly. PRESENCE estimates are partly unsatisfying because of a large portion of animal narratives with perfect occupancy estimates (Psi = 1.0). It is assumed that this is an effect of small sampling size which will not be problematic for larger amounts of data. This has to be kept in mind when comparing DISTANCE and PRESENCE results. Correlation between DISTANCE and PRESENCE detection probability estimates is negative, while correlation between DISTANCE abundance estimates and PRESENCE occupancy estimates is positive for all but one study area. It is recommended to repeat the comparison when data from more plots is available. On one hand the results, the cost-effectiveness of the study, and possibilities opened by this kind of multiple-species multi-method sampling are promising, on the other hand funding for this visionary approach was not available.1 Introduction ........................................................................................................................ 9 1.1 Global Biodiversity Crisis and Biodiversity Monitoring ........................................... 9 1.2 Goals of the Study .................................................................................................... 11 2 Methods ............................................................................................................................ 13 2.1 Study Area................................................................................................................ 13 2.1.1 Study Area 1CR: La Suerte Station, Costa Rica .............................................. 14 2.1.2 Study Area 2Ni: Ometepe Island, Nicaragua ................................................... 15 2.1.3 Study Area 3AK: Fairbanks, Alaska ................................................................ 16 2.1.4 Study Area 4Ru: Verengery Sakhalin Island, Russia....................................... 17 2.1.5 Study Area 5PG: Bismarck Range, Papua New-Guinea.................................. 18 2.1.6 Study Area 6Ba: Barrow, Alaska ..................................................................... 19 2.2 Sampling Methods.................................................................................................... 20 2.2.1 Biodiversity GRID ........................................................................................... 20 2.2.2 Budget Constraints ........................................................................................... 21 2.2.3 Animal Species Data Collection ...................................................................... 22 2.2.4 Vegetation & Environment .............................................................................. 23 2.3 Analysis Methods ..................................................................................................... 25 2.3.1 Random Forests................................................................................................ 26 2.3.2 DISTANCE Sampling...................................................................................... 28 2.3.3 PRESENCE / Occupancy................................................................................. 29 3 Results .............................................................................................................................. 30 3.1 General Overview .................................................................................................... 30 3.2 Predictive Modeling with Random Forests .............................................................. 36 3.2.1 ROC Values by Region and Model.................................................................. 37 3.2.2 Randomly Selected vs. Systematically Selected Plots ..................................... 44 3.2.3 Aural vs. Visual Bird Detections...................................................................... 48 3.2.4 Biological Family and Order as Analysis Targets ........................................... 51 3.2.5 Covariates Identified as Important ................................................................... 54 3.3 DISTANCE Sampling.............................................................................................. 60 3.3.1 DISTANCE Sampling Results: Bird Point Transects ...................................... 60 3.3.2 DISTANCE Sampling Results: Trapping Web Catches .................................. 69 3.3.3 DISTANCE Sampling Results: Line Transect Counts .................................... 78 3.3.4 DISTANCE Sampling Results: Randomly vs. Systematically Selected Plots. 79 3.3.5 DISTANCE Sampling Results: Aural vs. Visual Bird Detections................... 82 3.3.6 DISTANCE Sampling Results: Biological Family and Order......................... 83 3.4 PRESENCE / Occupancy......................................................................................... 86 3.4.1 PRESENCE Results: Occupancy Estimates .................................................... 86 3.4.2 PRESENCE Results: Randomly vs. Systematically Selected Plots................. 89 3.4.3 PRESENCE Results: Aural vs. Visual Bird Detections................................... 90 3.4.4 PRESENCE Results: Biological Family and Order ......................................... 91 3.5 Comparing DISTANCE and PRESENCE Results................................................... 94 3.5.1 Comparing Point Transect Results ................................................................... 94 3.5.2 Comparing Trapping Web Results ................................................................... 97 4 Discussion ...................................................................................................................... 100 4.1 Discussion of Results ............................................................................................. 100 4.2 Discussion of the GRID Approach......................................................................... 103 4.3 Discussion of Sampling Methods........................................................................... 104 4.4 Discussion of Analysis Methods ............................................................................ 108 - 4 -5 6 7 Conclusions .................................................................................................................... 110 References ...................................................................................................................... 111 Appendix ........................................................................................................................ 117 7.1 Data: Biodiversity GRID Fieldsheets..................................................................... 117 7.2 Covariates by Study Area....................................................................................... 123 7.3 DISTANCE Sampling Model Definitions ............................................................. 124 7.4 PRESENCE Model Definitions ............................................................................. 146 7.5 Detailed Species Lists (Valid ITIS Taxonomy) ..................................................... 150 7.6 Random Forests Models with Hightest ROC Values............................................. 166 7.7 Allocation of Narrative Names to Biological Order/Family.................................. 184 7.8 Best Models (DISTANCE Sampling) .................................................................... 189 7.9 Best Models (PRESENCE) .................................................................................... 193 8 Declaration ..................................................................................................................... 19

Increasing longevity and life satisfaction: is there a catch to living longer?

Human longevity is rising rapidly all over the world, but are longer lives more satisfied lives? This study suggests that the answer might be no. Despite a substantial increase in months of satisfying life, people’s overall life satisfaction declined between 1985 and 2011 in West Germany due to substantial losses of life satisfaction in old age. When compared to 1985, in 2011, elderly West Germans were, on average, much less satisfied throughout their last five years of life. Moreover, they spent a larger proportion of their remaining lifetime in states of dissatisfaction, on average. Two important mechanisms that contributed to this satisfaction decline were health and social isolation. Using a broad variety of sensitivity tests, I show that these results are robust to a large set of alternative explanations

Discrete Morse Theory by Vector Fields: A Survey and New Directions

We synthesize some of the main tools in discrete Morse theory from various sources. We do this in regards to abstract simplicial complexes with an emphasis on vector fields and use this as a building block to achieve our main result which is to investigate the relationship between simplicial maps and homotopy. We use the discrete vector field as a catalyst to build a chain homotopy between chain maps induced by simplicial maps

Attitudes and perceptions of general education students toward students who receive additional education services

Includes bibliographical references