Online)

ABSTRACT Context Skeletal muscle weakness and impaired gait function are common risk factors for disease and even death. Therefore, identification of the modifiable causes of skeletal muscle weakness should have high priority. Knowledge regarding optimal vitamin D treatment in cases of pancreatic insufficiency is scarce. Case report We report a case of a slow decrease in ability to walk distances more than 100 m during the previous 6 months. Low exocrine pancreatic function resulting in phosphorus, magnesium and vitamin D deficiency was found. Medical treatment with peroral pancreatic enzymes, phosphorus, magnesium and i.m. injections of ergocalciferol (vitamin D 2 ) was initiated. Gait function gradually increased to a walking distance of 1,500-3,000 m along with the normalization of the vitamin D and mineral blood levels. Conclusions Vitamin D deficiency due to exocrine pancreatic insufficiency should be kept in mind as one of the reasons for impaired gait and skeletal muscle weakness

A comparison of six metamodeling techniques applied to building performance simulations

Building performance simulations (BPS) are used to test different designs and systems with the intention of reducing building costs and energy demand while ensuring a comfortable indoor climate. Unfortunately, software for BPS is computationally intensive. This makes it impractical to run thousands of simulations for sensitivity analysis and optimization. Worse yet, millions of simulations may be necessary for a thorough exploration of the high-dimensional design space formed by the many design parameters. This computational issue may be overcome by the creation of fast metamodels. In this paper, we aim to find suitable metamodeling techniques for diverse outputs from BPS. We consider five indicators of building performance and eight test problems for the comparison six popular metamodeling techniques – linear regression with ordinary least squares (OLS), random forest (RF), support vector regression (SVR), multivariate adaptive regression splines, Gaussian process regression (GPR), and neural network (NN). The methods are compared with respect to accuracy, efficiency, ease-of-use, robustness, and interpretability. To conduct a fair and in-depth comparison, a methodological approach is pursued using exhaustive grid searches for model selection assisted by sensitivity analysis. The comparison shows that GPR produces the most accurate metamodels, followed by NN and MARS. GPR is robust and easy to implement but becomes inefficient for large training sets compared to NN and MARS. A coefficient of determination, R 2, larger than 0.9 have been obtained for the BPS outputs using between 128 and 1024 training points. In contrast, accurate metamodels with R 2 values larger than 0.99 can be achieved for all eight test problems using only 32–256 training points. </p