Wirtschaftlichkeit ökologischer Milchviehhaltung bei unterschiedlichem Weideumfang – 5-jährige Auswertung

Eine 5-jährige Auswertung von 50 Öko-Milchviehbetrieben in Norddeutschland mit unterschiedlichem Weideumfang zeigte: Bei gleicher Kuhzahl sind Betriebe mit viel Weidegang zu 72 % überdurchschnittlich wirtschaftlich, ansonsten nur zu 42 – 45 % und dass, obwohl Betriebe mit viel Weidegang eine um 1054 kg ECM/Kuh niedrigere Jahresmilchleistung haben. Betriebe mit weniger Kühen können bei viel Weidegang deshalb genau so wirtschaftlich sein wie solche mit mehr Kühen aber wenig Weidegang. Betriebe die viel weiden können sollten deshalb aus ökonomischer Sicht diese Art der Milchviehhaltung voll nutzen. Einer näheren Untersuchung bedürfen die höheren Produktionskosten bei mittlerem Weideumfang

Extending error bounds for radial basis function interpolation to measuring the error in higher order Sobolev norms

Radial basis functions (RBFs) are prominent examples for reproducing kernels with associated reproducing kernel Hilbert spaces (RKHSs). The convergence theory for the kernel-based interpolation in that space is well understood and optimal rates for the whole RKHS are often known. Schaback added the doubling trick, which shows that functions having double the smoothness required by the RKHS (along with complicated, but well understood boundary behavior) can be approximated with higher convergence rates than the optimal rates for the whole space. Other advances allowed interpolation of target functions which are less smooth, and different norms which measure interpolation error. The current state of the art of error analysis for RBF interpolation treats target functions having smoothness up to twice that of the native space, but error measured in norms which are weaker than that required for membership in the RKHS. Motivated by the fact that the kernels and the approximants they generate are smoother than required by the native space, this article extends the doubling trick to error which measures higher smoothness. This extension holds for a family of kernels satisfying easily checked hypotheses which we describe in this article, and includes many prominent RBFs. In the course of the proof, new convergence rates are obtained for the abstract operator considered by Devore and Ron, and new Bernstein estimates are obtained relating high order smoothness norms to the native space norm

Coherent control of a nanomechanical two-level system

The Bloch sphere is a generic picture describing a coupled two-level system and the coherent dynamics of its superposition states under control of electromagnetic fields. It is commonly employed to visualise a broad variety of phenomena ranging from spin ensembles and atoms to quantum dots and superconducting circuits. The underlying Bloch equations describe the state evolution of the two-level system and allow characterising both energy and phase relaxation processes in a simple yet powerful manner. Here we demonstrate the realisation of a nanomechanical two-level system which is driven by radio frequency signals. It allows to extend the above Bloch sphere formalism to nanoelectromechanical systems. Our realisation is based on the two orthogonal fundamental flexural modes of a high quality factor nanostring resonator which are strongly coupled by a dielectric gradient field. Full Bloch sphere control is demonstrated via Rabi, Ramsey and Hahn echo experiments. This allows manipulating the classical superposition state of the coupled modes in amplitude and phase and enables deep insight into the decoherence mechanisms of nanomechanical systems. We have determined the energy relaxation time T1 and phase relaxation times T2 and T2*, and find them all to be equal. This not only indicates that energy relaxation is the dominating source of decoherence, but also demonstrates that reversible dephasing processes are negligible in such collective mechanical modes. We thus conclude that not only T1 but also T2 can be increased by engineering larger mechanical quality factors. After a series of ground-breaking experiments on ground state cooling and non-classical signatures of nanomechanical resonators in recent years, this is of particular interest in the context of quantum information processing

Signatures of two-level defects in the temperature-dependent damping of nanomechanical silicon nitride resonators

The damping rates of high quality factor nanomechanical resonators are well beyond intrinsic limits. Here, we explore the underlying microscopic loss mechanisms by investigating the temperature-dependent damping of the fundamental and third harmonic transverse flexural mode of a doubly clamped silicon nitride string. It exhibits characteristic maxima reminiscent of two-level defects typical for amorphous materials. Coupling to those defects relaxes the momentum selection rules, allowing energy transfer from discrete long wavelength resonator modes to the high frequency phonon environment

Is very high energy emission from the BL Lac 1ES 0806+524 centrifugally driven?

We investigate the role of centrifugal acceleration of electrons in producing the very high energy (VHE) radiation from the BL Lac object 1ES 0806+524, recently detected by VERITAS. The efficiency of the inverse Compton scattering (ICS) of the accretion disk thermal photons against rotationally accelerated electrons is examined. By studying the dynamics of centrifugally induced outflows and by taking into account a cooling process due to the ICS, we estimate the maximum attainable Lorentz factors of particles and derive corresponding energetic characteristics of the emission. Examining physically reasonable parameters, by considering the narrow interval of inclination angles (0.7^o-0.95^o) of magnetic field lines with respect to the rotation axis, it is shown that the centrifugally accelerated electrons may lead to the observational pattern of the VHE emission, if the density of electrons is in a certain interval.Comment: 6 pages, 2 figure

Composting of domestic refuse and sewage sludge. I. Evolution of temperature, pH, C/N ratio and cation-exchange capacity

Peer reviewe

MBE Growth of Al/InAs and Nb/InAs Superconducting Hybrid Nanowire Structures

We report on \textit{in situ} growth of crystalline Al and Nb shells on InAs nanowires. The nanowires are grown on Si(111) substrates by molecular beam epitaxy (MBE) without foreign catalysts in the vapor-solid mode. The metal shells are deposited by electron-beam evaporation in a metal MBE. High quality supercondonductor/semiconductor hybrid structures such as Al/InAs and Nb/InAs are of interest for ongoing research in the fields of gateable Josephson junctions and quantum information related research. Systematic investigations of the deposition parameters suitable for metal shell growth are conducted. In case of Al, the substrate temperature, the growth rate and the shell thickness are considered. The substrate temperature as well as the angle of the impinging deposition flux are explored for Nb shells. The core-shell hybrid structures are characterized by electron microscopy and x-ray spectroscopy. Our results show that the substrate temperature is a crucial parameter in order to enable the deposition of smooth Al layers. Contrary, Nb films are less dependent on substrate temperature but strongly affected by the deposition angle. At a temperature of 200{\deg}C Nb reacts with InAs, dissolving the nanowire crystal. Our investigations result in smooth metal shells exhibiting an impurity and defect free, crystalline superconductor/InAs interface. Additionally, we find that the superconductor crystal structure is not affected by stacking faults present in the InAs nanowires.Comment: 8 pages, 10 figures, 1 tabl

The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis