University of Fribourg

About: University of Fribourg is a education organization based out in Fribourg, Freiburg, Switzerland. It is known for research contribution in the topics: Population & Glacier. The organization has 6040 authors who have published 14975 publications receiving 542500 citations. The organization is also known as: UNIFR & Universität Freiburg.

Depolarization of backscattered linearly polarized light.

Abstract: We formulate a quantitative description of backscattered linearly polarized light with an extended photon diffusion formalism taking explicitly into account the scattering anisotropy parameter g of the medium. From diffusing wave spectroscopy measurements, the characteristic depolarization length for linearly polarized light, lp, is deduced. We investigate the dependence of this length on the scattering anisotropy parameter g spanning an extended range from -1 (backscattering) to 1 (forward scattering). Good agreement is found with Monte Carlo simulations of multiply scattered light.

Reinforcement of Stereolithographic Resins for Rapid Prototyping with Cellulose Nanocrystals

Abstract: We report on the mechanical properties of optically curable stereolithographic resins (SLRs) which were reinforced through the addition of small amounts of cellulose nanocrystals (CNCs). The resin/filler mixtures are readily accessible via simple mixing processes. A detailed rheological investigation of such mixtures and the successful processing of these materials on a commercial SLR machine show that at low filler concentrations (below 5%) the processability of the materials is barely impacted. The storage modulus, E′, increased steadily with increasing CNC content in the regimes below and above the glass transition. A remarkable modulus enhancement was observed in the rubbery regime, where E′ increased by 166, 233, and 587% for CNC/SLR nanocomposites with 0.5, 1.0, and 5.0% w/w CNC, respectively. The modulus increase was less pronounced in the glassy state, where E′ increased by 21, 32 and 57%, for the same compositions. The increase in tensile strength was of similar magnitude. In comparison to previo...

Analysis of Ratios in Multivariate Morphometry

Abstract: The analysis of ratios of body measurements is deeply ingrained in the taxonomic literature. Whether for plants or animals, certain ratios are commonly indicated in identification keys, diagnoses, and descriptions. They often provide the only means for separation of cryptic species that mostly lack distinguishing qualitative characters. Additionally, they provide an obvious way to study differences in body proportions, as ratios reflect geometric shape differences. However, when it comes to multivariate analysis of body measurements, for instance, with linear discriminant analysis (LDA) or principal component analysis (PCA), interpretation using body ratios is difficult. Both techniques are commonly applied for separating similar taxa or for exploring the structure of variation, respectively, and require standardized raw or log-transformed variables as input. Here, we develop statistical procedures for the analysis of body ratios in a consistent multivariate statistical framework. In particular, we present algorithms adapted to LDA and PCA that allow the interpretation of numerical results in terms of body proportions. We first introduce a method called the "LDA ratio extractor," which reveals the best ratios for separation of two or more groups with the help of discriminant analysis. We also provide measures for deciding how much of the total differences between individuals or groups of individuals is due to size and how much is due to shape. The second method, a graphical tool called the "PCA ratio spectrum," aims at the interpretation of principal components in terms of body ratios. Based on a similar idea, the "allometry ratio spectrum" is developed which can be used for studying the allometric behavior of ratios. Because size can be defined in different ways, we discuss several concepts of size. Central to this discussion is Jolicoeur's multivariate generalization of the allometry equation, a concept that was derived only with a heuristic argument. Here we present a statistical derivation of the allometric size vector using the method of least squares. The application of the above methods is extensively demonstrated using published data sets from parasitic wasps and rock crabs.

Bike sharing station placement leveraging heterogeneous urban open data

Abstract: Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors for maximizing the utility of such systems is placing bike stations at locations that can best meet users' trip demand. Traditionally, urban planners rely on dedicated surveys to understand the local bike trip demand, which is costly in time and labor, especially when they need to compare many possible places. In this paper, we formulate the bike station placement issue as a bike trip demand prediction problem. We propose a semi-supervised feature selection method to extract customized features from the highly variant, heterogeneous urban open data to predict bike trip demand. Evaluation using real-world open data from Washington, D.C. and Hangzhou shows that our method can be applied to different cities to effectively recommend places with higher potential bike trip demand for placing future bike stations.