Survival Regression Models With Dependent Bayesian Nonparametric Priors

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data. Supplementary materials for this article are available online

Hierarchical Self-Assembly of Halogen-Bonded Block Copolymer Complexes into Upright Cylindrical Domains

Self-assembly of block copolymers into well-defined, ordered arrangements of chemically distinct domains is a reliable strategy for preparing tailored nanostructures. Microphase separation results from the system, minimizing repulsive interactions between dissimilar blocks and maximizing attractive interactions between similar blocks. Supramolecular methods have also achieved this separation by introducing small-molecule additives binding specifically to one block by noncovalent interactions. Here, we use halogen bonding as a supramolecular tool that directs the hierarchical self-assembly of low-molecular-weight perfluorinated molecules and diblock copolymers. Microphase separation results in a lamellar-within-cylindrical arrangement and promotes upright cylindrical alignment in films upon rapid casting and without further annealing. Such cylindrical domains with internal lamellar self-assemblies can be cleaved by solvent treatment of bulk films, resulting in separated and segmented cylindrical micelles stabilized by halogen-bond-based supramolecular crosslinks. These features, alongside the reversible nature of halogen bonding, provide a robust modular approach for nanofabricatio

Magnetic Anisotropy of Maghemite Nanoparticles Probed by RF Transverse Susceptibility

AbstractWe present radio frequency magnetic transverse susceptibility measurements on γ-Fe2O3 nanoparticles, which yield an estimation of their effective anisotropy constant, Keff as a function of nanoparticle size. The resulting values range from 4 to 8 × 104 erg/cm3, being on the order of the magnetocrystalline anisotropy in bulk maghemite. Keff values increase as the particle diameter increases. Evidences of anisotropy field distribution given by the size distribution in the samples, and interparticle interactions that increase as the particle size increases, are also observed in the TS measurements. The effects of such interparticle interaction overcome those of thermal fluctuations, in contrast with the behavior of other iron oxide particles

Temperature dependence of antiferromagnetic susceptibility in ferritin

We show that antiferromagnetic susceptibility in ferritin increases with temperature between 4.2 K and 180 K (i. e. below the N\'{e}el temperature) when taken as the derivative of the magnetization at high fields ($30\times10^4$ Oe). This behavior contrasts with the decrease in temperature previously found, where the susceptibility was determined at lower fields ($5\times10^4$ Oe). At high fields (up to $50 \times10^4$ Oe) the temperature dependence of the antiferromagnetic susceptibility in ferritin nanoparticles approaches the normal behavior of bulk antiferromagnets and nanoparticles considering superantiferromagnetism, this latter leading to a better agreement at high field and low temperature. The contrast with the previous results is due to the insufficient field range used ($< 5 \times10^4$ Oe), not enough to saturate the ferritin uncompensated moment.Comment: 7 pages, 7 figures, accepted in Phys. Rev.

Regional-scale vegetation heterogeneity in northeastern Patagonia: Environmental and spatial components

Our aim was to describe vegetation heterogeneity at a regional scale in northeastern Patagonia and to identify the environmental variables associated to it. The study area encompasses 13 144 km2 and is characterized by a mixture of species typical of Patagonian steppes and Monte Desert. We performed 48 vegetation relevés, which were randomly assigned to a training set and to a validation set (32 and 16 relevés, respectively). Training set was subjected to cluster analysis, which allowed the identification of two plant communities one related to Patagonian steppes and another to the Monte desert. We derived 3 attributes of the seasonal curve of the NDVI as indicators of ecosystem function: the seasonal amplitude (SA), the date of the maximum (DOM), and the large seasonal integral (LSI). We explored the relationship between NDVI attributes and communities by classification tree analysis. LSI was the strongest predictor among NDVI attributes, separating both communities without misclassification errors. Patagonian steppes occupy areas with higher LSI. The partial RDA analysis explained 38.1% of total data variation, of which 16.5% was ascribed to environment, 7.9% to space, and 13.7% to spatial component of environment. Patagonian steppes are closer to the coast, in areas exhibiting higher annual precipitation and lower annual temperature range than Monte deserts. Our results indicate the occurrence of two plant communities in the study area and highlight the significance of climatic variables to explain their spatial distribution. As most scenarios of future climate predict greater annual thermal amplitude in the study area, the limit between both communities could be displaced eastward

Continuous improvement integrating technological tools to assertively accelerate decision-making of logistics. Case implemented in a construction materials supplier company

Considering that many of the logistics infrastructure designs around the world are often sup-ported by studies with various computational tools, but most of these solutions are using in isolation and little understandably. Therefore, it is proposed, to develop this research based on a Logistics Reference Model, which will allow, visualize, manage and analyze the different processes and logistical scenarios of the system, with in aim to execute the best cost-benefit strategy in a company dedicated to the distribution of construction materials. By implementing this methodology, the management of the company studied was able to make the best decision for the structuring of its processes in the area of picking and dispatch. The results showed a 50% re-duction in inventory review time, equal an increase in reliability 7% that leaves the company in around location close to 85.68%; a decrease in cycle time in each order between 20% and 40% which positively impacted the customer service level. In addition, a decrease in lead times for the receipt of materials to suppliers was achieved between 15% and 30%, and a decrease in the number of warehouses, went having from 5 independent to maintain one single distribution center

Anatomic and computed tomographic atlas of the head of the newborn bottlenose dolphin (Tursiops truncatus)

The head of a newborn dolphin (Tursiops truncatus), that died shortly after birth was imaged using computed tomography (CT). Gross cross-sectional slices of the head were compared with the CT images to identify normal structures of the cranium, brain, and respiratory and digestive pathways. Labelled transverse CT images of the dolphin head are presented sequentially as a reference for normal anatomy