Global Warming Induced Water-Cycle Changes and Industrial Production – A Scenario Analysis for the Upper Danube River Basin

Using the environmental decision support system DANUBIA, we analyze the effects ofclimate change on industry and compare the effectiveness of different adaptation strategies.The observed area covers Germany and Austria up to 2025. Since the main effects ofclimate change in this region are expected to be caused through changes in the watercycle,we place a special focus on the exemplary region of the upper Danube catchmentarea. Industry is the main regional user of water resources. Water is an essential productionfactor and is used in almost every production process of a manufactured good. We applyestimates of regional production functions, based on AFiD-panel micro-data for Germany,to calibrate regional industrial production and water usage within DANUBIA. Thus, weare able to simulate region-specific effects of climate change and the impact of socialscenarios using an unprecedented model of reciprocal influences of a huge network ofinterdisciplinary research areas. Simulation results show wide regional differences inproduction site reactions as well as between differing scenarios. Comparing scenarios ofmoderate and serious climate change, we are able to illustrate the severe environmentaleffects in some regions and to determine considerable economic effects on regionaleconomic growth.Environmental decision support system, climate change, water-cycle, river basin management

Nachhaltige Schweinezucht mittels angepasster BLUP Zuchtwertschätzung am Beispiel des Schwäbisch-Hällischen Schweins

Nowadays the loss of species is still a problem. Sustaining the genetic potential, especially of old domestic breeds, is an urgent matter. Possibilities for protection of domestic species are discussed and evaluated. The research is based on the rare pig breed “Schwäbisch Hällisches Schwein” (SH). In three steps, a BLUP breeding method developed to sustain the SH in its uniqueness, enabling breeders to use the positive attributes of this pig in the meat market. Breeding, according to this model, maintains and selects positive attributes of the breed in accordance with the demand of the premium meat market, the conditions of organic or low- input farming on market opportunities. These positive attributes also create an added value for farmers. The breed can be sustained, because the farmers can refinance their work by earnings from the meat market. The model is applicable for other endangered pig breeds and also other species. The research shows a new way of protecting rare breeds, almost completely independent from governmental support

Super-localised wave function approximation of Bose-Einstein condensates

This paper presents a novel spatial discretisation method for the reliable and efficient simulation of Bose-Einstein condensates modelled by the Gross-Pitaevskii equation and the corresponding nonlinear eigenvector problem. The method combines the high-accuracy properties of numerical homogenisation methods with a novel super-localisation approach for the calculation of the basis functions. A rigorous numerical analysis demonstrates superconvergence of the approach compared to classical polynomial and multiscale finite element methods, even in low regularity regimes. Numerical tests reveal the method's competitiveness with spectral methods, particularly in capturing critical physical effects in extreme conditions, such as vortex lattice formation in fast-rotating potential traps. The method's potential is further highlighted through a dynamic simulation of a phase transition from Mott insulator to Bose-Einstein condensate, emphasising its capability for reliable exploration of physical phenomena

Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging

Neural networks can be significantly compressed by pruning, leading to sparse models requiring considerably less storage and floating-point operations while maintaining predictive performance. Model soups (Wortsman et al., 2022) improve generalization and out-of-distribution performance by averaging the parameters of multiple models into a single one without increased inference time. However, identifying models in the same loss basin to leverage both sparsity and parameter averaging is challenging, as averaging arbitrary sparse models reduces the overall sparsity due to differing sparse connectivities. In this work, we address these challenges by demonstrating that exploring a single retraining phase of Iterative Magnitude Pruning (IMP) with varying hyperparameter configurations, such as batch ordering or weight decay, produces models that are suitable for averaging and share the same sparse connectivity by design. Averaging these models significantly enhances generalization performance compared to their individual components. Building on this idea, we introduce Sparse Model Soups (SMS), a novel method for merging sparse models by initiating each prune-retrain cycle with the averaged model of the previous phase. SMS maintains sparsity, exploits sparse network benefits being modular and fully parallelizable, and substantially improves IMP's performance. Additionally, we demonstrate that SMS can be adapted to enhance the performance of state-of-the-art pruning during training approaches.Comment: 9 pages, 5 pages references, 7 pages appendi

Parameter estimation for stochastic models of biochemical reactions

Parameter estimation is central for the analysis of models in Systems Biology. Stochastic models are of increasing importance. However, parameter estimation for stochastic models is still in the early phase of development and there is need for efficient methods to estimate model parameters from time course data which is intrinsically stochastic, only partially observed and has measurement noise. The thesis investigates methods for parameter estimation for stochastic models presenting one efficient method based on integration of ordinary differential equations (ODE) which allows parameter estimation even for models which have qualitatively different behavior in stochastic modeling compared to modeling with ODEs. Further methods proposed in the thesis are based on stochastic simulations. One of the methods uses the stochastic simulations for an estimation of the transition probabilities in the likelihood function. This method is suggested as an addition to the ODE-based method and should be used in systems with few reactions and small state spaces. The resulting stochastic optimization problem can be solved with a Particle Swarm algorithm. To this goal a stopping criterion suited to the stochasticity is proposed. Another approach is a transformation to a deterministic optimization problem. Therefore the polynomial chaos expansion is extended to stochastic functions in this thesis and then used for the transformation. The ODE-based method is motivated from a fast and efficient method for parameter estimation for systems of ODEs. A multiple shooting procedure is used in which the continuity constraints are omitted to allow for stochasticity. Unobserved states are treated by enlarging the optimization vector or using resulting values from the forward integration. To see how well the method covers the stochastic dynamics some test functions will be suggested. It is demonstrated that the method works well even in systems which have qualitatively different behavior in stochastic modeling than in modeling with ODEs. From a computational point of view, this method allows to tackle systems as large as those tackled in deterministic modeling