As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

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Abstract-In most embedded and general purpose architectures, stack data and non-stack data is cached together, meaning that writing to or loading from the stack may expel non-stack data from the data cache. Manipulation of the stack has a different memory access pattern than that of non-stack data, showing higher temporal and spatial locality. We propose caching stack and non-stack data separately and develop four different stack caches that allow this separation without requiring compiler support. These are the simple, window, and prefilling with and without tag stack caches. The performance of the stack cache architectures was evaluated using the SimpleScalar toolset where the window and prefilling stack cache without tag resulted in an execution speedup of up to 3.5% for the MiBench benchmarks, executed on an out-oforder processor with the ARM instruction set.

A B S T R A C TTemperature and head movement are relevant parameters when analyzing the farrowing behavior of sows. Obtaining these body parameters in a way that is nonintrusive to animals is a major challenge in the harsh farrowing pen environment. Due to the presence of large amounts of metal as well as the unpredictable behavior of animals, such environments are not ideal for wired communication platforms. Intrusive measuring equipment may cause animals to deviate from their normal behavioral patterns invalidating gathered sensor data. Using lightweight, highly mobile and wireless sensor equipment is thus essential for unobtrusive measurements. This article discusses the challenges involved in developing a lightweight and flexible wireless sensor network infrastructure platform used in the analysis of sow behavior during farrowing. The platform is based on the customizable wireless sensor platform Shimmer and the open source software frameworks TinyOS and SPINE. Embedded in hot melt adhesive, the Shimmer modules were used as ear tags providing biologists with head movement and temperature data throughout six months. Focus is on the technical aspects of developing a system faced with mutually exclusive and changing requirements in an iterative and progressive research project, drawing upon the experiences from several stages of live experiments with farrowing sows.