A Concept for a New Budget Rule for Germany

Öffentlicher Haushalt, Öffentliche Finanzplanung, Deutschland, Public Budget, Public Budgeting, Germany

The Second Chvatal Closure Can Yield Better Railway Timetables

We investigate the polyhedral structure of the Periodic Event Scheduling Problem (PESP), which is commonly used in periodic railway timetable optimization. This is the first investigation of Chvatal closures and of the Chvatal rank of PESP instances. In most detail, we first provide a PESP instance on only two events, whose Chvatal rank is very large. Second, we identify an instance for which we prove that it is feasible over the first Chvatal closure, and also feasible for another prominent class of known valid inequalities, which we reveal to live in much larger Chvatal closures. In contrast, this instance turns out to be infeasible already over the second Chvatal closure. We obtain the latter result by introducing new valid inequalities for the PESP, the multi-circuit cuts. In the past, for other classes of valid inequalities for the PESP, it had been observed that these do not have any effect in practical computations. In contrast, the new multi-circuit cuts that we are introducing here indeed show some effect in the computations that we perform on several real-world instances - a positive effect, in most of the cases

The expansion of supermarkets and the establishment of delivery systems and intermediaries for fresh fruit and vegetables in the Global South – the case of Kenya and Tanzania

In the last three decades, supermarket chains from the countries of the Global North expanded in the Global South. The regions of interest were, in particular, those countries in which new market potentials resulted from economic development. There is also the trend that domestic supermarket chains are developing in the countries of the Global South. A number of studies in the Global South analyzes the impact on agricultural producers by incorporating them into delivery systems to supermarkets. However, little evidence exists yet on how the delivery systems are organized by intermediaries between agricultural producers and the supermarket chains in the Global South. Especially for fresh produce (vegetables, fruit) special challenges occur., e.g. concerning infrastructures This article will examine the relationship between the spatial and temporal expansion of supermarket chains and the establishment of delivery systems/intermediaries using the example of fresh produce in the countries of Kenya and Tanzania

Bedingte Kaufpreisanpassungen, Informationsasymmetrien und Shareholder Value: Eine empirische Analyse deutscher Unternehmensübernahmen

Gerade in Zeiten der globalen Finanzkrise eignen sich Earn-out-Vereinbarungen in besonderem Maße, um den gesteigerten Informationsasymmetrien im Rahmen einer Unternehmensübernahme begegnen zu können. Doch wie stark profitieren die Käufer vom Einsatz eines solchen Instrumentes und was bestimmt die Intensität und Richtung? Im Rahmen einer branchenübergreifenden Untersuchung wird der Einfluss von Earn-out-Regelungen auf den Unternehmenswert deutscher Käufer untersucht. Die Ergebnisse zeigen, dass der deutsche Kapitalmarkt den Einsatz von Earn-outs grundsätzlich positiv bewertet. Bei näherer Betrachtung zeigt sich, dass neben dem Abbau von Informationsasymmetrien, auch ein höheres Earn-out-Ratio, eine nied-rigere Earn-out-Laufzeit und ein geringerer Verschuldungsgrad einen positiven Effekt haben. Der Einfluss des Earn-out-Ratios wird allerdings durch eine längere Laufzeit be-grenzt und ins Gegenteil verkehrt.Unternehmensübernahme, Earn-out, Ereignisstudie, Shareholder Value, Informationsasymmetrien

In-depth characterization of genome-scale network reconstructions for the in vitro synthesis in cell-free systems

Cell‐free systems containing multiple enzymes are becoming an increasingly interesting tool for one‐pot syntheses of biochemical compounds. To extensively explore the enormous wealth of enzymes in the biological space, we present methods for assembling and curing data from databases to apply them for the prediction of pathway candidates for directed enzymatic synthesis. We use Kyoto Encyclopedia of Genes and Genomes to establish single organism models and a pan‐organism model that is combining the available data from all organisms listed there. We introduce a filtering scheme to remove data that are not suitable, for example, generic metabolites and general reactions. In addition, a valid stoichiometry of reactions is required for acceptance. The networks created are analyzed by graph theoretical methods to identify a set of metabolites that are potentially reachable from a defined set of starting metabolites. Thus, metabolites not connected to such starting metabolites cannot be produced unless new starting metabolites or reactions are introduced. The network models also comprise stoichiometric and thermodynamic data that allow the definition of constraints to identify potential pathways. The resulting data can be directly applied using existing or future pathway finding tools

3-D Printed Protective Equipment during COVID-19 Pandemic

While the number of coronavirus cases from 2019 continues to grow, hospitals are reporting shortages of personal protective equipment (PPE) for frontline healthcare workers. Furthermore, PPE for the eyes and mouth, such as face shields, allow for additional protection when working with aerosols. 3-D printing enables the easy and rapid production of lightweight plastic frameworks based on open-source data. The practicality and clinical suitability of four face shields printed using a fused deposition modeling printer were examined. The weight, printing time, and required tools for assembly were evaluated. To assess the clinical suitability, each face shield was worn for one hour by 10 clinicians and rated using a visual analogue scale. The filament weight (21-42 g) and printing time (1:40-3:17 h) differed significantly between the four frames. Likewise, the fit, wearing comfort, space for additional PPE, and protection varied between the designs. For clinical suitability, a chosen design should allow sufficient space for goggles and N95 respirators as well as maximum coverage of the facial area. Consequently, two datasets are recommended. For the final selection of the ideal dataset to be used for printing, scalability and economic efficiency need to be carefully balanced with an acceptable degree of protection

ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species

Waveguide properties recovered from shallow diffractions in common offset GPR

Near-surface heterogeneities produce diffractions in common offset ground-penetrating radar (GPR) data from the Gnangara Groundwater Mound, north of Perth, Western Australia. These diffracted wavefields can be enhanced and show a dispersion pattern if they propagate along a waveguide caused by a low velocity surface layer, such as moist sand on top of dry sand. Until now, GPR waveguide dispersion has been analyzed and inverted using common midpoint data. Using numerical modeling, we demonstrate that the same dispersion information can also be recovered from a diffracted electromagnetic wavefield recorded with common offset geometry. Frequency-slowness analysis of shallow diffractions in common offset GPR field data reveals high resolution dispersion curves. Inverting picked dispersion maxima to modeled curves (i.e., modal wave propagation in waveguide layer) allows estimation of waveguide height and velocities of waveguide and the underlying material. Data analysis in the frequency-wavenumber domain provides an alternative technique for extracting dispersion curves. Preliminary results validate this approach, which could be favorable in large-scale applications due to minimal processing requirement and inherent yet adjustable spatial averaging. The differences between waveguide parameters recovered from two surveys appear to be consistent with seasonal changes in moisture content and lateral changes due to variations in depositional environment.Our approach presents a new method to quantify the shallow dielectric permittivity structure of the subsurface from common offset gathers—the most commonly acquired type of GPR data. Potential applications of this method include estimation of shallow moisture distribution, early target identification for unexploded ordnance (UXO) detection, concrete slab characterization, pedological investigations, or planetary exploration