The production of proton-rich isotopes beyond iron: The ?-process in stars

© 2016 World Scientific Publishing Company. Beyond iron, a small fraction of the total abundances in the Solar System is made of proton-rich isotopes, the p-nuclei. The clear understanding of their production is a fundamental challenge for nuclear astrophysics. The p-nuclei constrain the nucleosynthesis in core-collapse and thermonuclear supernovae. The γ-process is the most established scenario for the production of the p-nuclei, which are produced via different photodisintegration paths starting on heavier nuclei. A large effort from nuclear physics is needed to access the relevant nuclear reaction rates far from the valley of stability. This review describes the production of the heavy proton-rich isotopes by the γ-process in stars, and explores the state of the art of experimental nuclear physics to provide nuclear data for stellar nucleosynthesis

Topological characterization of antireflective and hydrophobic rough surfaces: are random process theory and fractal modeling applicable?

The random process theory (RPT) has been widely applied to predict the joint probability distribution functions (PDFs) of asperity heights and curvatures of rough surfaces. A check of the predictions of RPT against the actual statistics of numerically generated random fractal surfaces and of real rough surfaces has been only partially undertaken. The present experimental and numerical study provides a deep critical comparison on this matter, providing some insight into the capabilities and limitations in applying RPT and fractal modeling to antireflective and hydrophobic rough surfaces, two important types of textured surfaces. A multi-resolution experimental campaign by using a confocal profilometer with different lenses is carried out and a comprehensive software for the statistical description of rough surfaces is developed. It is found that the topology of the analyzed textured surfaces cannot be fully described according to RPT and fractal modeling. The following complexities emerge: (i) the presence of cut-offs or bi-fractality in the power-law power-spectral density (PSD) functions; (ii) a more pronounced shift of the PSD by changing resolution as compared to what expected from fractal modeling; (iii) inaccuracy of the RPT in describing the joint PDFs of asperity heights and curvatures of textured surfaces; (iv) lack of resolution-invariance of joint PDFs of textured surfaces in case of special surface treatments, not accounted by fractal modeling.Comment: 21 pages, 13 figure

Sampling Sup-Normalized Spectral Functions for Brown-Resnick Processes

Sup-normalized spectral functions form building blocks of max-stable and Pareto processes and therefore play an important role in modeling spatial extremes. For one of the most popular examples, the Brown-Resnick process, simulation is not straightforward. In this paper, we generalize two approaches for simulation via Markov Chain Monte Carlo methods and rejection sampling by introducing new classes of proposal densities. In both cases, we provide an optimal choice of the proposal density with respect to sampling efficiency. The performance of the procedures is demonstrated in an example.Comment: 11 pages, 2 figure

Eco-efficient supply chain networks: Development of a design framework and application to a real case study

© 2015 Taylor & Francis. This paper presents a supply chain network design framework that is based on multi-objective mathematical programming and that can identify 'eco-efficient' configuration alternatives that are both efficient and ecologically sound. This work is original in that it encompasses the environmental impact of both transportation and warehousing activities. We apply the proposed framework to a real-life case study (i.e. Lindt & Sprüngli) for the distribution of chocolate products. The results show that cost-driven network optimisation may lead to beneficial effects for the environment and that a minor increase in distribution costs can be offset by a major improvement in environmental performance. This paper contributes to the body of knowledge on eco-efficient supply chain design and closes the missing link between model-based methods and empirical applied research. It also generates insights into the growing debate on the trade-off between the economic and environmental performance of supply chains, supporting organisations in the eco-efficient configuration of their supply chains

A Comparison of Big Data Frameworks on a Layered Dataflow Model

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models, for which only informal (and often confusing) semantics is generally provided, all share a common underlying model, namely, the Dataflow model. The Dataflow model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on High-Level Parallel Programming and Applications (HLPP), July 4-5 2016, Muenster, German

Affine parameterization of the dark sector: costraints from WMAP5 and SDSS

We study a set of universe models where the dark sector is described by a perfect fluid with an affine equation of state $P=P_0+\alpha \rho$, focusing specifically on cosmological perturbations in a flat universe. We perform a Monte Carlo Markov Chain analysis spanning the full parameter space of the model using the WMAP 5 years data and the SDSS LRG4 survey. The affine fluid can either play the role of a unified dark matter (UDM), accounting for both dark matter and a cosmological constant, or work alongside cold dark matter (CDM), as a form of dark energy. A key ingredient is the sound speed, that depends on the nature of the fluid and that, for any given background model, adds a degree of freedom to the perturbations: in the barotropic case the square of the sound speed is simply equal to the affine parameter $\alpha$; if entropic perturbations are present the effective sound speed has to be specified as an additional parameter. In addition to the barotropic case, we consider the two limiting cases of effective sound speed equal to 0 or 1. For $\alpha=c_s^2=0$ our UDM model is equivalent to the standard $\Lambda$CDM with adiabatic perturbations. Apart of a trivial subcase, all models considered satisfy the data constraints, with quite standard values for the usual cosmological parameters. In general our analysis confirms that cosmological datasets require both a collisionless massive and cold component to form the potential wells that lead to structure formation, and an effective cosmological constant that drives the late accelerated expansion.Comment: 10 pages, 9 figure

Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 201