Electromagnetic design search and optimisation of photonic bandgap devices on distributed computational resources

Photonic crystals are devices with periodically modulated dielectric constant, designed to exhibit band gaps in a frequency spectrum in which electromagnetic waves cannot propagate. Tuning the properties of these structures to achieve precise band gaps before fabrication is of high interest to photonic crystal manufacturers. In this paper, we present the process of finding and optimising a photonic crystal design using a high-throughput Condor-based compute cluster and transparent database technology for easy storage, retrieval and reuse of the created designs. We also demonstrate how a band gap diagram can easily be obtained on a compute cluster when using the developed user interface technology. The optimisation process can easily be adapted to other problem area

Promoting Hope, Healing, and Wellness: Catholic Interventions in Behavioral Health Care

In this chapter, we will outline, highlight, and review some of the Catholic traditions and pastoral tools that can be integrated into any professional clinical practice in behavioral health care. We will focus our attention on six tools in particular that are particularly popular and unique within the Catholic faith tradition. We will also offer brief case illustrations to provide examples of how these Catholic tools can be effectively integrated into professional clinical practice

ESTIMATES OF THE EXTERNAL AND SUSTAINABILITY COSTS OF CLIMATE CHANGE

social cost of carbon, climate change

PREDICTION OF CROP YIELDS ACROSS FOUR CLIMATE ZONES IN GERMANY: AN ARTIFICIAL NEURAL NETWORK APPROACH

This paper shows the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. The method may later be used to construct regional time series of agricultural output under climate change, based on the highly resolved output of the global circulation models and regional models. Three 30-year combined historical data sets of rural district yields (oats, spring barley and silage maize), daily temperatures (mean, maximum, dewpoint) and precipitation were constructed. They were used with artificial neural network technology to investigate, simulate and predict historical time series of crop yields in four climate zones of Germany. Final neural networks, trained with data sets of three climate zones and tested against an independent northern zone, have high predictive power (0.83global change, agriculture, artificial neural networks, yield prediction