Farm Level Risk Assessment Using Downside Risk Measures

Recent and presumable future developments tend to increase the risk associated with farming activities. This causes an increasing importance of risk management. Farmers have a wide variety of possibilities to influence the risk exposure of their operations. Among them are the choice of the production program as well as marketing activities including forward pricing and hedging with futures and options. In total all these opportunities comprise a portfolio of activities which must be selected as to match the resources of the farm as well as the farmer's attitudes towards risk. The paper addresses this issue using a whole farm stochastic optimisation approach based on a risk-value framework. The paper starts with a discussion of risk-value models and the relationship between them and the expected utility hypothesis. In the second part the approach is incorporated in a whole farm model that optimizes a portfolio of production activities and risk management instruments. A case study is used to analyse the possibilities and limitations of the approach and to illustrate the effects of yield and production risk on decision making.downside risk, risk management, risk measure, risk-value models, stochastic optimisation, Risk and Uncertainty,

Adaptive evolution of transcription factor binding sites

The regulation of a gene depends on the binding of transcription factors to specific sites located in the regulatory region of the gene. The generation of these binding sites and of cooperativity between them are essential building blocks in the evolution of complex regulatory networks. We study a theoretical model for the sequence evolution of binding sites by point mutations. The approach is based on biophysical models for the binding of transcription factors to DNA. Hence we derive empirically grounded fitness landscapes, which enter a population genetics model including mutations, genetic drift, and selection. We show that the selection for factor binding generically leads to specific correlations between nucleotide frequencies at different positions of a binding site. We demonstrate the possibility of rapid adaptive evolution generating a new binding site for a given transcription factor by point mutations. The evolutionary time required is estimated in terms of the neutral (background) mutation rate, the selection coefficient, and the effective population size. The efficiency of binding site formation is seen to depend on two joint conditions: the binding site motif must be short enough and the promoter region must be long enough. These constraints on promoter architecture are indeed seen in eukaryotic systems. Furthermore, we analyse the adaptive evolution of genetic switches and of signal integration through binding cooperativity between different sites. Experimental tests of this picture involving the statistics of polymorphisms and phylogenies of sites are discussed.Comment: published versio

Jumping Through Loops: On Soft Terms from Large Volume Compactifications

We subject the phenomenologically successful large volume scenario of hep-th/0502058 to a first consistency check in string theory. In particular, we consider whether the expansion of the string effective action is consistent in the presence of D-branes and O-planes. Due to the no-scale structure at tree-level, the scenario is surprisingly robust. We compute the modification of soft supersymmetry breaking terms, and find only subleading corrections. We also comment that for large-volume limits of toroidal orientifolds and fibered Calabi-Yau manifolds the corrections can be more important, and we discuss further checks that need to be performed.Comment: 57 page

SEE Test and Data Analysis for Complex FPGA Systems

Critical space applications require knowledge of single event upset (SEU) susceptibility (mission survivability). Generic SEU test and analysis techniques do not provide adequate data for survivability analysis. This presentation provides information on how to: (1) Investigate (test for) SEU susceptibilities of tactical (mission specific) designs that are implemented in a SRAM-based FPGA; and (2) Analyze SEU cross-sections for use in survivability prediction