Flow and wakes in complex terrain and offshore. Model development and verification in UPWIND

The paper presents research conducted in the Flow workpackage of the EU funded UPWIND project which focuses on improving models for flow within and downwind of large wind farms in complex terrain and offshore. The main activity is modelling the behaviour of wind turbine wakes in order to improve power output predictions.

Sensitivity analysis on turbulence models for the ABL in complex terrain

Análisis de sensibilidad de modelos de turbulencia para un modelo CFD de viento aplicados a un emplazamiento en terreno complejo. Validación con datos de viento y turbulencia registrados a 3 alturas en 3 torres de medida.

Simulating Metabolic Processes Using an Architecture Based on Networks of Bio-inspired Processors.

In this work, we propose the Networks of Evolutionary Processors (NEP) [2] as a computational model to solve problems related with biological phenomena. In our first approximation, we simulate biological processes related with cellular signaling and their implications in the metabolism, by using an architecture based on NEP (NEP architecture) and their specializations: Networks of Polarized Evolutionary Processors (NPEP) [1] and NEP Transducers (NEPT) [3]. In particular, we use this architecture to simulate the interplay between cellular processes related with the metabolism as the Krebs cycle and the malate-aspartate shuttle pathway (MAS) both being altered by signaling by calcium.

Sensitisation to Act 2d in patientsallergic to Alternaria alternanta: an epiphenomenom without clinical significance?

In the last few years, the introduction of microarrays in the diagnosis of type I allergy is allowing the clinicians to have a much more accurate picture of their allergenic profile. However, the simultaneous measurement of specific IgE to multiple molecules can show unexpected sensitisations, without knowing their clinical relevance. For instance, we have been observing a high prevalence (74%) of sensitisation to Act d 2 (the thaumatin of kiwifruit) in patients sensitised to Alt a 1 (major allergen of Alternaria alternata) with a confirmed allergy to this mould. The aim of the present study was to clarify if there was any clinical relevance in this finding.

Enriched mannose glycosylation contributes to Act d 2 allergenicity.

Allergens are responsible for the Th2 response in patients as part of complex mixtures of proteins, fatty acids and other molecules. Plant allergens have hitherto been included in several protein families that share no common biochemical features. Their physical, biochemical and immunological characteristics have been widely studied, but no definite conclusion has been reached about what makes a protein an allergen. N-glycosylation is characteristic of plant allergen sources but is not present in mammals.

Modulatory impact of cinnamaldehyde on immune cells is partly due to induction of apoptosis.

Cinnamaldehyde (CA) has been reported to have antiinflammatory, anti-bacterial, anti-fungal, chemoprotective and anti-carcinogenic activity. Here, we further investigated the immune-modulating capacity of CA.

Finite Element Model of the Innovation Diffusion: An Application to Photovoltaic Systems

This paper presents a Finite Element Model, which has been used for forecasting the diffusion of innovations in time and space. Unlike conventional models used in diffusion literature, the model considers the spatial heterogeneity. The implementation steps of the model are explained by applying it to the case of diffusion of photovoltaic systems in a local region in southern Germany. The applied model is based on a parabolic partial differential equation that describes the diffusion ratio of photovoltaic systems in a given region over time. The results of the application show that the Finite Element Model constitutes a powerful tool to better understand the diffusion of an innovation as a simultaneous space-time process. For future research, model limitations and possible extensions are also discussed.

What role does climate change play in agricultural market uncertainty? An integrated assessment taking into account market-driven adjustments

Recent studies point to climate change being one of the long-term drivers of agricultural market uncertainty. To advance in the understanding of the influence of climate change on future agricultural market developments, we compare a reference scenario for 2030 with alternative simulation scenarios that differ regarding: (1) emission scenarios; (2) climate projections; and (3) the consideration of carbon fertilization effects. For each simulation scenario, the CAPRI model provides global and EU-wide impacts of climate change on agricultural markets. Results show that climate change would considerably affect agrifood markets up to 2030. Nevertheless, market-driven adaptation strategies (production intensification, trade adjustments) would soften the impact of yield shocks on supply and demand. As a result, regional changes in production would be lower than foreseen by other studies focused on supply effects.