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Performance-based architectural design pursues building performance objectives like energy efficiency to guide design decisions. Nevertheless, these informed decisions are usually made according to performance simulation software results, which are quite effort-consuming and tedious. Meanwhile, this low feedback efficiency blocks architects' mind flows from creating new design alternatives. Therefore, the current authors develop a digital platform by coupling Internet of Things (IoT) and Mixed Reality (MR). It streams and visualizes real-time sensory data of indoor illuminance into an MR glass HoloLens. It enables students to intuitively observe spatio-temporal illuminance fluctuation when manually modifying the physical shading device model. Therefore, they can quickly learn how design decisions influence indoor illuminances. At last, in the validation experiment with first and second year students in an architecture school, we compare this platform with two other teaching methods and platforms. Experimental results identify that the IoT-MR platform can effectively attract students and increase their learning effects.

Abstract Background Accounting for and quantifying the associative effect of each animal could improve both welfare of animals and response to selection. Because of the limitation of REML, Gibbs Sampling could be an alternative technique to estimate the variance component of the associative effect. The objective of this study was to investigate the estimation accuracy of the variance component of associative effect by using simulation via Gibbs Sampling. The simulated data comprised five generations of pigs. The breeding animals of each generation were selected randomly. In the simulation, variations were introduced for the methods of assigning pens (random, mixed sib and full sib), the number of pigs per pen (5 or 10), the number of breeding animals per generation (162 or 324) and the correlation between genetic direct effect and genetic associative effect (-0.5, 0.1 or +0.5). Each set of simulation was run for 30 replications. Results Random assignment or mixed sib assignment resulted in bias of estimated variance components in only 3 of 24 combinations. Furthermore, these 3 cases occurred with 162 breeding animals. With full sib assignment, 9 out of 12 groups of estimates significantly deviated from the true parameter value. The Root Mean Square Errors obtained with the full sib assignment were higher than with the other two methods of pen assignment in most of the cases. The Root Mean Square Errors obtained with datasets with 324 breeding animals were notably smaller than the datasets from 162 breeding animals. Within each method of pen assignment, the relative bias of the associative effect was significantly smaller with group size 10 than with group size 5. Conclusion Full sib assignment caused difficulties to estimate variance components in most of the cases, due to a lack of identifiability. With random and mixed assignment, most data structures yielded unbiased results but increasing the number of breeding animals or group size improves the estimation. Thus to get identifiable and unbiased estimates of the genetic associative effect, it is recommended to avoid close genetic relationship between animals within one pen and to use sufficient numbers of breeding animals and sufficient group sizes.

Aims To examine the frequency of axillary lymph node (ALN) invasion of operable breast cancers by their combined oestrogen receptor (ER), progesterone receptor (PR) and HER-2 status. Methods 2227 recently operated cases in one centre were retrieved from the Multidisciplinary Breast Centre database and stratified according to their combined immunohistochemical (IHC) expression of ER/PR/HER-2 status. An equivocal HER-2 status was further analysed by Fluorescence in situ Hybridisation (FISH). The following 6 groups were considered: ER−PR−HER-2− (NNN; triple negative), ER−PR−HER-2+ (NNP), ER+PR−HER-2− (PNN), ER+PR−HER-2+ (PNP), ER+PR+HER-2− (PPN), ER+PR+HER-2+ (PPP; triple positive). For ALN, the following variables were tested in uni- and multivariate models: age at diagnosis (years), tumour size (mm), tumour grade, ER, PR, HER-2 and the combined steroid receptor and HER-2 status. Likelihood ratio χ2-tests were used for univariate analysis and logistic regression for multivariate analysis. Results Triple positive tumours had a higher likelihood of being ALN positive than others (56.2% versus 35.7%; P < 0.0001). Univariate logistic regression also withheld age, size, grade and HER-2 as predictors of ALN involvement. Final multivariate logistic regression revealed age, size, grade and PPP versus non-PPP to be independent predictors of ALN involvement; the odds ratio (OR) and 95% CI for PPP versus non-PPP tumours was 2.169 (1.490–3.156). Conclusion Our data provide insight into the natural history of triple positive breast carcinomas. Such tumours are more likely ALN positive than those with another steroid receptor and HER-2 status. How these findings correlate with breast cancer prognosis remains to be investigated. [ABSTRACT FROM AUTHOR]