A system dynamics approach for modelling a lead-market-based export potential

For knowledge-intensive goods, foreign trade performance also depends on the quality of the technology. Important factors to consider are technological capa-bilities, various market factors which influence the chances of a country devel-oping a lead-market position, innovation-friendly regulation and the existence of internationally competitive complementary industry clusters. In order to model these aspects, various feedback mechanisms between these factors have to be taken into account, among them knowledge spillovers from the export success which lead to an erosion of a lead-market position over time. A system dynam-ics framework is used for a first implementation of a simulation model for wind energy technology exports from Germany. The empirical results show the ex-pected dynamics of the system and underline the importance of the various feedback loops. --Renewable energy technologies,exports,first-mover advantage,lead markets

Spontaneous bleeding of an Abrikossoff's tumor - a case report

Abrikossoff tumors are a rare tumor entity. The complication of a hemothorax has not been described in the literature so far. A 24-year-old patient presented with repeated hemoptysis and right thoracic pain. The initial CT-scan revealed a solid tumor mass in the right lower bronchus. After further diagnostics, the patient was discharged and surgical intervention was planned. He was readmitted 4 days after discharge with a spontaneous hemothorax. After the right lower lobectomy and an uneventful course the patient recovered well

Hazard uncertainty and related damage potentials of extra-tropical storms

Extra--tropical winter windstorms are among the most loss--intensive natural hazards in Europe. This thesis is dedicated to advance the understanding of these hazardous events and their uncertainty in various aspects. These aspects include the serial clustering and spatial variability of storm events, the seasonal predictability of extreme wind speeds associated with windstorms and an impact assessment of windstorms both in a climatological as well as from a loss--related perspective. The recurring element in all studies are large--scale drivers (e.g. North Atlantic Oscillation - NAO) which are linked to different features of extra--tropical windstorms, e.g. the inter--annual variability. It can be shown that large--scale drivers are able to explain a considerable amount of variability of windstorms. Seasonal forecast ensemble hindcasts are used to create a physical consistent virtual reality of more than 1500 years. Thus, the uncertainty of these extreme events can be estimated more accurately compared to using century--long reanalysis. This large sample size can also be used to estimate potential extremes with respect to intensity and severity of windstorms more accurately. The findings of these studies are presented in five scientific papers which are included as five chapters in this submitted thesis

Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-valued Model Output

How can we quantify uncertainty if our favorite computational tool - be it a numerical, a statistical, or a machine learning approach, or just any computer model - provides single-valued output only? In this article, we introduce the Easy Uncertainty Quantification (EasyUQ) technique, which transforms real-valued model output into calibrated statistical distributions, based solely on training data of model output-outcome pairs, without any need to access model input. In its basic form, EasyUQ is a special case of the recently introduced Isotonic Distributional Regression (IDR) technique that leverages the pool-adjacent-violators algorithm for nonparametric isotonic regression. EasyUQ yields discrete predictive distributions that are calibrated and optimal in finite samples, subject to stochastic monotonicity. The workflow is fully automated, without any need for tuning. The Smooth EasyUQ approach supplements IDR with kernel smoothing, to yield continuous predictive distributions that preserve key properties of the basic form, including both, stochastic monotonicity with respect to the original model output, and asymptotic consistency. For the selection of kernel parameters, we introduce multiple one-fit grid search, a computationally much less demanding approximation to leave-one-out cross-validation. We use simulation examples and forecast data from weather prediction to illustrate the techniques. In a study of benchmark problems from machine learning, we show how EasyUQ and Smooth EasyUQ can be integrated into the workflow of neural network learning and hyperparameter tuning, and find EasyUQ to be competitive with conformal prediction, as well as more elaborate input-based approaches

Die Bedeutung veganer Bioprodukte für die ökologische Landwirtschaft

Vegane Lebensmittel haben in den letzten Jahren deutlich an Stellenwert gewonnen. Im Projekt wurde untersucht, welche Bedeutung der Vegan-Trend für die Öko-Branche hat und wie diese auf den Vegan-Trend reagieren kann. Hierzu wurden Befragungen und Workshops mit Akteuren entlang der Wertschöpfungskette durchgeführt. In einer Verbraucherbefragung wurden 503 Personen mit unterschiedlichen Ernährungsstilen nach ihren Einstellungen zu veganen Lebensmitteln und ihrer Zahlungsbereitschaft für diese befragt. Für Personen, die auch Fleisch essen, spielten Umweltvorteile veganer Lebensmittel kaum eine Rolle und der Geschmack veganer Lebensmittel stellte ein Kaufhemmnis dar. Auch wenn die Befragten vegane Lebensmittel eher als teuer wahrnahmen, waren viele Verbraucher bereit, mehr Geld für vegane Produkte zu bezahlen. Öko-Hersteller und -händler sahen das Potenzial für vegane Produkte noch nicht ausgeschöpft, wenngleich eine fehlende Vernetzung ein Hemmnis darstellte. Eine Analyse von Webseiten zu veganen Lebensmitteln zeigte, dass auf diesen der gute Geschmack veganer Lebensmittel und Vorteile für Gesundheit und Umwelt betont werden. Bei einer veganen Landbewirtschaftung besteht besonders hinsichtlich der langfristigen Auswirkungen auf Nährstoff- und Humusgehalte der Böden noch Forschungsbedarf. Für Produkte aus veganem Anbau waren Verbraucher bereit, Preisaufschläge zu bezahlen, wenn ihnen zuvor die Besonderheiten der Anbauform vermittelt wurden. Herstellern veganer Öko-Lebensmittel ist u.a. zu empfehlen, in Kooperation mit dem Handel eine gemeinsame Marketingkampagne für vegane Öko-Lebensmittel zu starten. Landwirtschaftliche Öko-Betriebe können sich mit veganem Öko-Landbau im Wettbewerb abheben und Pioniervorteile erzielen. Im Bereich der Außer-Haus-Verpflegung sollte die Kompetenz der Köche im Umgang mit veganen Lebensmitteln gestärkt werden

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.Comment: 27 pages, 9 figures, 4 table

Nephrin and CD2AP associate with phosphoinositide 3-OH kinase and stimulate AKT-dependent signaling

Mutations of NPHS1 or NPHS2, the genes encoding nephrin and podocin, as well as the targeted disruption of CD2-associated protein (CD2AP), lead to heavy proteinuria, suggesting that all three proteins are essential for the integrity of glomerular podocytes, the visceral glomerular epithelial cells of the kidney. It has been speculated that these proteins participate in common signaling pathways; however, it has remained unclear which signaling proteins are actually recruited by the slit diaphragm protein complex in vivo. We demonstrate that both nephrin and CD2AP interact with the p85 regulatory subunit of phosphoinositide 3-OH kinase (PI3K) in vivo, recruit PI3K to the plasma membrane, and, together with podocin, stimulate PI3K-dependent AKT signaling in podocytes. Using two-dimensional gel analysis in combination with a phosphoserine-specific antiserum, we demonstrate that the nephrin-induced AKT mediates phosphorylation of several target proteins in podocytes. One such target is Bad; its phosphorylation and inactivation by 14-3-3 protects podocytes against detachment-induced cell death, suggesting that the nephrin-CD2AP-mediated AKT activity can regulate complex biological programs. Our findings reveal a novel role for the slit diaphragm proteins nephrin, CD2AP, and podocin and demonstrate that these three proteins, in addition to their structural functions, initiate PI3K/AKT-dependent signal transduction in glomerular podocytes