Grazing behavior of two Holstein dairy cow strains under organic farming conditions in Switzerland

The aim of the thesis was to test if concentrate supplementation is required in an organic, pasture-based feeding system and if concentrate supplementation influences grazing behavior. The study consisted of two trials, both with a crossover design performed on an organic farm in Switzerland with 12 Swiss Holstein cows and 12 Holstein cows of New Zealand origin. In the first trial the focus was on the impact of concentrate supplementation on milk yield and composition, grazing and rumination behavior, physical activity, and blood metabolites and the differences between the two cow strains. In the second trial the focus laid on the estimation of plant species selection by dairy cows with plant wax markers and whether differences exist between concentrates supplemented and non-supplemented cows in selection behavior. Concentrate supplementation had an impact on milk yield and composition, the time animals spent grazing, herbage dry matter intake and physical activity, but no on rumination behavior. Supplemented cows had a more stable energy status, but no indices for strong negative energy balance were recorded for non-supplemented cows, for both cow strains. In the second trial the main focus was on the estimation of herbage composition of grazing dairy cows with plant wax markers, namely alkanes, long-chain fatty acids and long-chain alcohols (LCOH). Concentrate samples, feces samples from each cow and samples from each paddock were taken and plant species were manually separated. All plant species, concentrate and feces samples were analyzed for their marker contents. Corrections of fecal recovery were calculated in relation to dosed ytterbium. The estimations of diet composition were performed with the software “EatWhat” based on non-negative least squares. Results were compared to the botanical composition with the Aitchison distance. The most accurate diet composition estimation was achieved with alkanes, LCOH and a correction of fecal recovery. No differences in selected plant composition between cow strains were recorded, but supplemented cows selected more Trifolium repens compared to non-supplemented cows. However, further studies are required to confirm the feasibility of the approach and validate the calculation of fecal recovery. Understanding the grazing behavior and the consequences of concentrate supplementation may lead to management measures that increase production efficiency and ensure animal welfare. Minor differences between cow strains indicated that both are suitable for pasture-based feeding systems. However, short-term trials cannot give a conclusion for the whole lactation, and fertility and health traits should be included

Open Source In The Clouds - How Organizational Ambidexterity Shapes and is Shaped by Disruptive Innovation in an Open Source Software Provider

How do incumbent firms effectively respond to disruptive innovations? The extant literature shows that incumbent firms, while often excelling at incremental innovation, usually fare poorly in the face of disruptive innovation. Even firms that have been the direct beneficiaries of disruptive innovations in the past can fall prey to more agile competitors during these periods of upheaval. Organizational Ambidexterity – the idea of striking the right balance between the exploitation of existing resources and the exploration of new capabilities – can be used as a theoretical framework to investigate how firms adapt and change in the face of disruptive innovation. In this study, we use ambidexterity as a lens to study Red Hat, a leader in Open Source Software, during the company’s transition through a period of disruptive innovation – namely Cloud Computing. The study reveals a number of interesting insights. The first is that the nature of the disruptive innovation itself shaped Red Hat’s organizational response. The second is that Red Hat demonstrated a high level of contextual ambidexterity in its response which, in turn, led Red Hat to selectively adopt structural ambidexterity principles. The third is that Red Hat’s history as a successful Open Source Software company enabled it to implicitly become ambidextrous by adopting and implementing key Open Source cultural values. In conclusion we discuss the implications of these findings for theory and practice

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry\u27s effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions\u27 variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available

Urteile von Schülern über Verhaltensmerkmale Jugendlicher anderer Nationalitäten

200 männliche und 198 weibliche Schüler an polytechnischen Oberschulen der DDR wurden 1989 zu ihren Einschätzungen von Jugendlichen aus den Nationalitäten Polen, UdSSR, USA, Bundesrepublik Deutschland, DDR, Vietnam und Afrika befragt. Einzelne Untersuchungergebnisse wurden mit Werten einer Untersuchung aus dem Jahre 1978 verglichen. Einige Ergebnisse: Das Urteil über Jugendliche aus Polen, der UdSSR und der DDR erfuhr eine deutliche Negativierung, während die Beurteilungen für die USA und die BRD deutlich positiver ausfielen. Die sowjetischen Jugendlichen werden nach wie vor relativ positiv eingeschätzt; im Vergleich der Nationalitäten erhalten sie insgesamt das beste Urteil. Die BRD-Jugendlichen rangieren mit Platz zwei noch vor der DDR. Durchweg äußerten sich die Schüler kritischer und auch differenzierter als früher, allerdings war auch der Anteil der Urteilsenthaltungen mit mehr als 29 Prozent sehr hoch.(psz

GNSS and InSAR based water vapor tomography: A Compressive Sensing solution

An accurate knowledge of the three-dimensional (3D) distribution of water vapor in the atmosphere is a key element for weather forecasting and climate research. In addition, a precise determination of water vapor is also required for accurate positioning and deformation monitoring using Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). Several approaches for 3D tomographic water vapor reconstruction from GNSS-based Slant Wet Delay (SWD) estimates exist. Yet, due to the usually sparsely distributed GNSS sites and due to the limited number of visible GNSS satellites, the tomographic system usually is ill-posed and needs to be regularized, e.g. by means of geometric constraints that risk to over-smooth the tomographic refractivity estimates. Therefore, this work develops and analyzes a Compressive Sensing (CS) approach for neutrospheric water vapor tomographies benefiting of the sparsity of the refractivity estimates in an appropriate transform domain as a prior for regularization. The CS solution is developed because it does not include any geometric smoothing constraints as applied in common Least Squares (LSQ) approaches and because the sparse CS solution containing only a few non-zero coefficients may be determined, at a constant number of observations, based on less parameters than the corresponding LSQ solution. In addition to the developed CS solution, this work introduces SWDs obtained from both GNSS and InSAR into the tomographic system in order to dispose of a better spatial distribution of the observations. The novelties of this approach are 1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and 2) the solution of the tomographic system by means of Compressive Sensing. In addition, 3) the quality of the CS reconstruction is compared with the quality of common LSQ approaches to water vapor tomography. The tomographic reconstruction is performed, on the one hand, based on a real data set using GNSS and InSAR SWDs and, on the other hand, based on three different synthetic SWD data sets generated using wet refractivity information from the Weather Research and Forecasting (WRF) model. Thus, the validation of the achieved results focuses, on the one hand, on radiosonde profiles and, on the other hand, on a comparison of the refractivity estimates with the input WRF refractivities. The real data set resp. the first synthetic data set compares the reconstruction quality of the developed CS approach with LSQ approaches to water vapor tomography and investigates in how far the inclusion of InSAR resp. synthetic InSAR SWDs increases the accuracy and precision of the refractivity estimates. The second synthetic data set is designed in order to analyze the general effect of the observing geometry on the quality of the refractivity estimates. The third synthetic data set places a special focus on the sensibility of the tomographic reconstruction to different numbers of GNSS sites, varying voxel discretization, and different orbit constellations. In case of the real data set, for both the GNSS only solution and a combined GNSS and InSAR solution, the refractivities estimated by means of the LSQ and CS methodologies show a consistent behavior, although the two solution strategies differ. The synthetic data sets show that CS can yield very precise and accurate results, if an appropriate tomographic setting is chosen. The reconstruction quality mainly depends on i) the accuracy of the functional model relating the SWD estimates to the refractivity parameters and to the distances passed by the rays within the voxels, ii) the number of available GNSS sites, iii) the voxel discretization, and iv) the variety of ray directions introduced into the tomographic system. The sizes of the study areas associated to the real resp. to the synthetic data sets are about 120 × 120 km2 and about 100 × 100 km2, respectively. In the real data set, a total of eight GNSS sites is available and SWD estimates of GPS and InSAR are introduced. In the synthetic data sets, different numbers of sites are defined and a variety of ray directions is tested

Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments

The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pose from a point cloud, absolute pose regression (APR) methods learn a semantic understanding of the environment through neural networks. However, both fields face challenges caused by the environment such as motion blur, lighting changes, repetitive patterns, and feature-less structures. This study aims to address these challenges by incorporating additional information and regularizing the absolute pose using relative pose regression (RPR) methods. The optical flow between consecutive images is computed using the Lucas-Kanade algorithm, and the relative pose is predicted using an auxiliary small recurrent convolutional network. The fusion of absolute and relative poses is a complex task due to the mismatch between the global and local coordinate systems. State-of-the-art methods fusing absolute and relative poses use pose graph optimization (PGO) to regularize the absolute pose predictions using relative poses. In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction. We evaluate eight different recurrent units and construct a simulation environment to pre-train the APR and RPR networks for better generalized training. Additionally, we record a large database of different scenarios in a challenging large-scale indoor environment that mimics a warehouse with transportation robots. We conduct hyperparameter searches and experiments to show the effectiveness of our recurrent fusion method compared to PGO

Vitamin D receptor, Retinoid X receptor and peroxisome proliferator-activated receptor γ are overexpressed in BRCA1 mutated breast cancer and predict prognosis

Background: BRCA1 mutated breast cancers are commonly diagnosed as negative for classical hormone receptors i.e. estrogen receptor, progesterone receptor and/or Her2. Due to these common targets being absent the application of anti-endocrine therapies is rather limited and a certain focus has been set on discovering alternative target molecules. We recently highlighted thyroid hormone receptors (TRs) to predict prognosis in breast cancer patients that had been diagnosed a BRCA1 germline mutation. Vitamin D Receptor (VDR), Retinoid X Receptor (RXR) and Peroxisome Proliferator-activated Receptor γ (PPARγ) are known to interact with TRs by forming functional heterodimers. Whether VDR, RXR or PPARγ are expressed in BRCA1 mutated breast cancer or may even be present in case of triple negativity is not known. Hence the current study aimed to investigate VDR, RXR and PPARγ in BRCA1 mut breast cancer and to test whether any of the three may be associated with clinico-pathological criteria including overall survival. Methods: This study analyzed VDR, RXR and PPARγ by immunohistochemistry in BRCA1 associated (n = 38) and sporadic breast cancer (n = 79). Receptors were quantified by applying an established scoring system (IR-score) and were tested for association with clinico-pathological variables. Results: VDR, RXR and PPARγ were detected in over 90% of triple negative BRCA1 mut breast cancer and were significantly (VDR: p < 0.001, RXR: p = 0.010, PPARγ: p < 0.001) overexpressed in BRCA1 mutated as compared to sporadic cancer cases. VDR and RXR positivity predicted prolonged overall survival only in BRCA1 mutated cases while such association was not observed in sporadic breast cancer. Conclusions: In conclusion, this is the first study to describe VDR, RXR and PPARγ in BRCA1 mutated breast cancer. Based on the data presented here these receptors may be hypothesized to potentially evolve as interesting markers or even targets in hereditary breast cancer. However, independent studies are indispensable thus to confirm this hypothesis

Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SWD dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SWDs. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SWDs into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SWDs into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ