Asymmetrical awnings : a way to increase daylight in buildings without increasing the overheating

Different shapes of asymmetric awnings for east and west windows are investigated mathematically as well as by measurement in a model. A box with 90 cm side and a 30x30 cm window was placed outdoor in overcast weather and the daylight factor was measured at the bottom of the box when the window was unshaded or equipped with different awnings. The average daylight factor in the box decreased from 4.6% for the unshaded window to 1.0% when a full awning was used. With “the best” asymmetrical awning, the average daylight factor was 80% larger than with the full awing. Using Dutch climate, calculation of the energy from direct radiation transmitted through the window during the cooling season showed that this was decreased from 100% as an annual mean for the unshaded window down 22% with a full awing. With “the best” asymmetrical awning, 26% of the energy was transmitted. Calculation of the indoor temperature in a hypothetical row house in Netherlands show that the use of either normal or asymmetrical awnings considerable decrease the indoor temperature during the hot season. Therefore the use of asymmetrical awnings for east or west faced windows considerable can increase the daylight in buildings, with almost no change in overheating, compared to if traditional awnings are used.

Increasing the power of genome wide association studies in natural populations using repeated measures : evaluation and implementation

1. Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long-term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single-nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful. 2. Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long-term study of collared flycatchers in Sweden. 3. The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response. 4. The method is available in the R package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long-term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.