To overcome the limitation in spatial and temporal resolution of methane oceanic measurements, sensors are needed that can autonomously detect CH4-concentrations over longer periods of time. The proposed project is aimed at:- Designing molecular receptors for methane recognition (cryptophane-A and -111) and synthesizing new compounds allowing their introduction in polymeric structure (Task 1; LC, France); - Adapting, calibrating and validating the 2 available optical technologies, one of which serves as the reference sensor, for the in-situ detection and measurements of CH4 in the marine environments (Task 2 and 3; GET, LAAS-OSE, IOW) Boulart et al. (2008) showed that a polymeric filmchanges its bulk refractive index when methane docks on to cryptophane-A supra-molecules that are mixed in to the polymeric film. It is the occurrence of methane in solution, which changes either the refractive index measured with high resolution Surface Plasmon Resonance (SPR; Chinowsky et al., 2003; Boulart et al, 2012b) or the transmitted power measured with differential fiber-optic refractometer (Boulart et al., 2012a; Aouba et al., 2012).- Using the developed sensors for the study of the CH4 cycle in relevant oceanic environment (the GODESS station in the Baltic Sea, Task 4 and 5; IOW, GET); GODESS registers a number of parameters with high temporal and vertical resolution by conducting up to 200 vertical profiles over 3 months deployment with a profiling platform hosting the sensor suite. - Quantifying methane fluxes to the atmosphere (Task 6); clearly, the current project, which aims at developing in-situ aqueous gas sensors, provides the technological tool to achieve the implementation of ocean observatories for CH4. The aim is to bring the fiber-optic methane sensor on the TRL (Technology Readiness Level) from their current Level 3 (Analytical and laboratory studies to validate analytical predictions) - to the Levels 5 and 6 (Component and/or basic sub-system technology validation in relevant sensing environments) and compare it to the SPR methane sensor, taken as the reference sensor (current TRL 5). This would lead to potential patent applications before further tests and commercialization. This will be achieved by the ensemble competences and contributions from the proposed consortium in this project.
Deviant behaviour on various levels of the food supply chain may cause food risks. It entails irregular technological procedures which cause (increased probabilities of) adverse outcomes for buyers and consumers. Besides technological hazards and hitherto unknown health threats, moral hazard and malpractice in food businesses represent an additional source of risk which can be termed 'behavioural food risk'. From a regulatory perspective, adverse outcomes associated with deviance represent negative externalities that are caused by the breaking of rules designed to prevent them. From a rational choice perspective, the probability of malpractice increases with the benefits for its authors. It decreases with the probability of detection and resulting losses. It also decreases with bonds to social norms that protect producers from yielding to economic temptations. The design of mechanisms that reduce behavioural risks and prevent malpractice requires an understanding of why food businesses obey or do not obey the rules. This project aims to contribute to a better understanding of malpractice on the restaurant/retail level through comparative case studies and statistical analyses of food inspection and survey data. Accounting for the complexity of economic behaviour, we will not only look at economic incentives but consider all relevant behavioural determinants, including social context factors.
Farm structures are often characterized by regional heterogeneity, agglomeration effects, sub-optimal farm sizes and income disparities. The main objective of this study is to analyze whether this is a result of path dependent structural change, what the determinants of path dependence are, and how it may be overcome. The focus is on the German dairy sector which has been highly regulated and subsidized in the past and faces severe structural deficits. The future of this sector in the process of an ongoing liberalization will be analyzed by applying theoretical concepts of path dependence and path breaking. In these regards, key issues are the actual situation, technological and market trends as well as agricultural policies. The methodology will be based on a participative use of the agent-based model AgriPoliS and participatory laboratory experiments. On the one hand, AgriPoliS will be tested as a tool for stakeholder oriented analysis of mechanisms, trends and policy effects. This part aims to analyze whether and how path dependence of structural change can be overcome on a sector level. In a second part, AgriPoliS will be extended such that human players (farmers, students) can take over the role of agents in the model. This part aims to compare human agents with computer agents in order to overcome single farm path dependence.
Dairy farming across Germany displays diverse production systems. Factor endowment, management, technology adoption as well as competitive dynamics in the local or regional land, agribusiness and dairy processing sectors contribute to this differentiation on farm level. These differences impact on the ability of dairy farms and regional dairy production systems to successfully respond to pressures arising from future market and policy changes. The overall objective of the research activities of which this project is a part of, is to develop a thorough understanding of the processes that govern the spatial dynamics of dairy farm development in different regions in Germany. The central hypothesis of this research project is that management system and technological choices differ systematically across local production and market conditions. The empirical approach will focus on the estimation of farm specific nonparametric cost functions for dairy farms located in across Germany differentiated by time and location. A spatially differentiated data base with information on input use, resource availability, as well as local market conditions for land and output markets will be compiled. The nonparametric approach is specifically suited to disclose a more accurate representation of dairy production system heterogeneity across locations and time compared to parametric concepts as it provides the necessary flexibility to accommodate non-linearities relevant for a wide domain of explanatory variables. The methodology employed goes beyond the state of the art of the literature as it combines kernel density estimation with a Bayesian sampling approach to provide theory consistent parameters for each farm in the data sample.The specific methodological hypothesis is that the nonparametric approach is superior to current parametric techniques and this hypothesis is tested using statistical model evaluation. Regarding the farm management and technological choices, we hypothesize that land suitability for feed production determines the farm intensity of dairy production and thus management and technological choices. With respect to the ability of farms to successfully respond to market pressures we hypothesize that farms at the upper and lower tail of the intensity distribution both can generate positive returns from dairy production. These last two hypotheses will be tested using the estimated spatially differentiated farm specific costs and marginal costs.The expected outcomes are of relevance for the agricultural sector and the food supply chain economy as a whole as fundamental market structure changes in the dairy sector are ongoing due to the abolition of the quota regulation in the years 2014/2015. Thus, exact knowledge about differences and development of dairy cost heterogeneity of farms within and between regions are an important factor for the actors involved in the market as well as the political support of this process.
INFRES is a three year EU FP7 funded study focused on developing innovative technology and logistics for the forest residual biomass supply chain. The goal is to improve the overall competitiveness of forest residue biomass throughout the EU by accelerating technological (equipment and software) development and demonstrations. Following the directive for renewable energy targets in the EU, forest residue will continue to play an leading role in the renewable energy sector. This overall study focuses on the efficient collection, processing and delivery of woody feed stock for heating, power and emerging bio-refining uses. This research and demonstration project involves 23 partners, including nine research universities and organizations and 14 SMEs FELIS: FELIS will be leading Work Package 3, titled 'Business innovations and adaptations of forestry practices to bioenergy supply'. This research effort first evaluates the future customers and markets of forest biomass to 2050 and then focuses on how existing forestry practices may be modified in order to enhance and improve biomass recovery. Partnering with both software companies and regional bioenergy firms, this work task develops business and service innovations in order to improve organization efficiency, lowering cost and improving service.
There is enormous economic potential for the application of embedded optimization technologies in embedded systems design. Recent advances in the performance of embedded hardware platforms, in combination with fundamental improvements in optimization theory and algorithms, have opened the door to widespread applications over the next decade. Embedded optimization will enable huge energy and resource savings, increased safety, and improved fault detection across a wide a range of industrial applications in the mechatronic, automotive, process control and aerospace sectors. In order to realize the full potential of optimization in embedded systems, their design must also be supported by a focussed set of tools enabling the rapid transfer of novel high-performance algorithms to practical applications. The EMBOCON consortium will enable widespread application of real-time optimization in embedded systems through: - Tailoring of customized numerical algorithms to increase their robustness and efficiency on embedded systems - Enabling real-time optimization on cheap industry-standard hardware platforms - Defining a common user interface for optimization technologies to facilitate technology transfer to industry - Performing challenging case studies in cooperation with industrial partners to demonstrate technological maturity. The EMBOCON consortium will strengthen a network of world-leading academic and industrial partners with complementary expertise in control, optimization and embedded systems in a range of industrial applications. Particular emphasis is placed on close collaboration between mathematical algorithm developers, control theorists, hardware specialists and industrial application engineers. The network will consolidate and extend Europe's position as the world research leader in these areas and foster strong collaborative links between European academia and industry. Prime Contractor: University London, Imperial College of Science, Technology and Medicine, Faculty of Engineering, Level 2 Faculty; London; United Kingdom.
Small molecule natural products are a prolific source of inspiration for the development of new drugs, and essential tools in basic biomedical research as probes of biological functions. The contribution of academic laboratories in natural products discovery has been essential. The limiting factor of traditional approaches in bioactivity-directed natural product research has been the tedious process of purification and identification of active molecules from a highly complex extract matrix. Recent technological advances enable substantial improvements in efficiency via a consequential miniaturization of the screening and discovery process, and automation of certain process steps. The aim of the project is to discover small molecule natural products leads from plants and fungi acting against clinically relevant and/or emerging targets in important disease areas. The targets have been selected on the basis of specific criteria, such as (i) novelty and importance of target; (ii) lack of specific/selective inhibitors; (iii) need for enhancement of structural diversity of ligands; (iv) difficulty/impossibility to use rational drug discovery approaches; (v) access to animal models. Indications include CNS (selective GABA-A receptor agonists), inflammation and cancer (modulation of angiogenesis and lymphangiogenesis, inhibition of PI3 kinases). In addition, a screening for hERG channel inhibition will be carried out as the currently most critical anti-target in drug discovery & development. An extract library and a technology platform for the miniaturized discovery of natural products will be used. The library consists of currently 1000 plant and fungal extracts. An ethnomedicine-based focussed sub-library will be specifically tested for GABAA receptor agonistic properties. All process steps in the screening and consecutive lead identification are miniaturized, in part automated, and based on the 96-well microtiter footprint. Most of the assays are via external collaborations, and some assays involving cell signalling are established in-house. Prioritized extracts are submitted to HPLC-based activity profiling with microtiter-based fractionation of column effluent, and simultaneous on-line spectroscopic (PDA, ion-trap ESI and APCI-MS, and ESI-TOF) analysis. Compound dereplication and identification is supported by off-line microprobe NMR spectroscopy. Around the active target molecules, structurally related compounds will be characterized to generate small 'virtual' libraries for preliminary structure activity relationships. Calculation of physico-chemical data and secondary bioassays will characterize leads, and shortlisted compounds will be tested in vivo for proof of concept. For this purpose, compounds of interest are isolated in a targeted manner in amounts of up to several hundred mg.
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