The GRB 030328 host: another case of a blue starburst galaxy

We present for the first time the detection of the GRB 030328 host galaxy in four optical bands equivalent to UBRI. The host galaxy spectral energy distribution is consistent with a low extinction (E(B-V) < 0.21) starburst galaxy. The restframe B-band magnitude of the host is M_B ~ -20.4Comment: 4 pages, 2 figures, accepted for publication in Il nuovo cimento (4th Workshop Gamma-Ray Bursts in the Afterglow Era, Rome, 18-22 October 2004

On the Drivers of Potential Customers' Interest in Long-term Care Insurance: Evidence from Switzerland

As the risks associated with aging start to materialize, societies become more aware of the financial consequences of long-term care (LTC). While limited coverage is available through social insurance in many countries, attractive offers of private products barely exist and a lack of knowledge about LTC insurance persists. Based on a novel survey on aging, health, and dependence conducted in Switzerland, this study aims to comprehend the key drivers that make individuals interested in buying care insurance products for themselves. Using models that combine features from both classical statistics and machine learning techniques, we depict the characteristics of potential buyers based on key economic, social, demographic, and political factors. We find that factors relating to the awareness and understanding of LTC are extremely relevant. Self-perceived health, behavior, and trust relationships between customers and insurers are important. Socio-economic factors only play a secondary role in the decision-making process. Our findings are relevant beyond the academic community and for policymakers and private insurers alike

Evaluating recent methods to overcome spatial confounding

The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995-2001 and lip cancer incidence data in Scotland between the years 1975-1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value

A scalable approach for short-term disease forecasting in high spatial resolution areal data

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatio-temporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed ``divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatio-temporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error and interval score when forecasting cancer mortality one, two and three years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace (INLA) estimation technique

Surface Effects on the Mechanical Elongation of AuCu Nanowires: De-alloying and the Formation of Mixed Suspended Atomic Chains

We report here an atomistic study of the mechanical deformation of AuxCu(1-x) atomic-size wires (NWs) by means of high resolution transmission electron microscopy (HRTEM) experiments. Molecular dynamics simulations were also carried out in order to obtain deeper insights on the dynamical properties of stretched NWs. The mechanical properties are significantly dependent on the chemical composition that evolves in time at the junction; some structures exhibit a remarkable de-alloying behavior. Also, our results represent the first experimental realization of mixed linear atomic chains (LACs) among transition and noble metals; in particular, surface energies induce chemical gradients on NW surfaces that can be exploited to control the relative LAC compositions (different number of gold and copper atoms). The implications of these results for nanocatalysis and spin transport of one-atom-thick metal wires are addressed.Comment: Accepted to Journal of Applied Physics (JAP

A Farm-Level Evaluation of Conditions Under Which Farmers Will Supply Biomass Feedstocks for Energy Production

This study evaluated the risk management potential of including biomass crops as a diversification strategy for a grain farm in northwest Tennessee. Results indicate that adding biomass crops to the farm enterprise mix could improve mean net revenues and reduced net revenue variability.Resource /Energy Economics and Policy,

A one-step spatial+ approach to mitigate spatial confounding in multivariate spatial areal models

Ecological spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a one-step version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyze two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses