Advertising and Business Cycle Fluctuations

This paper provides new empirical evidence for quarterly U.S. aggregate advertisingexpenditures, showing that advertising has a well defined pattern over the BusinessCycle. To understand this pattern we develop a general equilibrium model wheretargeted advertising increases the marginal utility of the advertised good. Advertisingintensity is endogenously determined by profit maximizing firms. We embed thisassumption into an otherwise standard model of the business cycle withmonopolistic competition. We find that advertising affects the aggregate dynamics ina relevant way, and it exacerbates the welfare costs of fluctuations for the consumer.Finally, we provide estimates of our setup using Bayesian techniques.Advertising, DSGE model, Business Cycle fluctuations, Bayesian

Study of the slowing down of high energy proton shots through metals via a Monte Carlo simulation of the Fokker-Planck equation

The aim of this work is to analyze the diffusion and the slowing down of high energy proton shots through a target. Analyzing the phenomenon rigorously with the full transport equations, means tack ling many difficulties, most of which arise from the long range nature of the Coulomb interactions involving more than one particle simultaneously. The commonly used approach of neglecting the multi-body collisions, though correct for rarefied neutral gases, of ten leads to very poor approximations when charged particles moving through dense matter are considered. Here we present a Monte Carlo simulation of the Fokker-Planck equation where the multi-body collisions are taken into account. The model al lows the calculation of a point-wise distribution of energy and momentum transferred to the tar get

Making Sense of Gov 2.0 Strategies: "No Citizens, No Party"

One of the main factors contributing to the limited impact of eParticipation projects is the presence of a high level of social complexity that has been identified by Macintosh as one of the five challenges in the implementation of eParticipation practices. How to make sense of social complexity is still an open issue as well as the way governments can take benefit from the wealth of information that is already available on their constituencies' collective behaviour. In this paper, we contend that the presence of a considerable variance in terms of political interests, educational level and technological skills makes it very difficult to design workable and effective systems to support participation. A modular strategy is then recommended requiring policy designers to make a step towards citizens rather than expecting the citizenry to move their content production activity onto the "official" spaces created for ad hoc participation

Let’s stick together for continuous flow biocatalysis

Fusion systems have been designed linking enzymes to cofactors and immobilization modules through appropriate synthetic spacers. These modular biocatalysts (assembling catalysis, cofactor provision/regeneration, and assisted immobilization) are suited for heterogeneous biocatalysis systems and can be efficiently used in continuous flow reactors

Genetic Strategies to Enhance Plant Biomass Yield and Quality- Related Traits for Bio-Renewable Fuel and Chemical Productions

Owing to the increasing concerns on the environment, climate change, and limited natural resources, there are currently considerable efforts applied to produce chemicals and materials from renewable biomass. While initial emphasis has been placed on biofuel production from food plant sugars, the competition between crop usage for food and non-food applications has promoted research efforts to genetically improve yield and quality-related traits for biorefining applications. This chapter summarizes the potential of genetic and biotechnological strategies for improving plant biomass yields and quality-related traits and for breeding varieties more suitable to meet biorefining applications. Attempts were also made to provide a description on the genetic and molecular mechanisms affecting starch, cell wall composition and architecture, and oils synthesis and deposition, including genetic strategies to modify these traits. Similarly, the chapter covers the genetic strategies to improve yields by emphasizing the efforts done to identifying genetic variation and gene(s) governing critical morphological, structural, and physiological traits that in turn influence biomass yields. Finally, in the chapter it is suggested that knowledge of plant biosynthetic pathways will eventually provide valuable opportunities for metabolic engineering, as well as access to chemical transformations unique to plants for breeding varieties with built-in new traits

Search for massive protostar candidates in the southern hemisphere: II. Dust continuum emission

In an ongoing effort to identify and study high-mass protostellar candidates we have observed in various tracers a sample of 235 sources selected from the IRAS Point Source Catalog, mostly with dec < -30 deg, with the SEST antenna at millimeter wavelengths. The sample contains 142 Low sources and 93 High, which are believed to be in different evolutionary stages. Both sub-samples have been studied in detail by comparing their physical properties and morphologies. Massive dust clumps have been detected in all but 8 regions, with usually more than one clump per region. The dust emission shows a variety of complex morphologies, sometimes with multiple clumps forming filaments or clusters. The mean clump has a linear size of ~0.5 pc, a mass of ~320 Msolar for a dust temperature Td=30 K, an H_2 density of 9.5E5 cm-3, and a surface density of 0.4 g cm-2. The median values are 0.4 pc, 102 Msolar, 4E4 cm-3, and 0.14 g cm-2, respectively. The mean value of the luminosity-to-mass ratio, L/M ~99 Lsolar/Msolar, suggests that the sources are in a young, pre-ultracompact HII phase. We have compared the millimeter continuum maps with images of the mid-IR MSX emission, and have discovered 95 massive millimeter clumps non-MSX emitters, either diffuse or point-like, that are potential prestellar or precluster cores. The physical properties of these clumps are similar to those of the others, apart from the mass that is ~3 times lower than for clumps with MSX counterpart. Such a difference could be due to the potential prestellar clumps having a lower dust temperature. The mass spectrum of the clumps with masses above M ~100 Msolar is best fitted with a power-law dN/dM proportional to M-alpha with alpha=2.1, consistent with the Salpeter (1955) stellar IMF, with alpha=2.35.Comment: 83 pages, 10 figures, 3 tables. Accepted for publication by A&A. The full paper, including Fig.2 with the maps of all the individual regions, complete Tables 1 and 2 can be found at http://www.arcetri.astro.it/~starform/publ2005.ht

Can multiple segmentation methods enhance deep learning networks generalization? A novel hybrid learning paradigm

Deep learning methods are the state-of-the-art for medical imaging segmentation tasks. Still, numerous segmentation algorithms based on heuristic-based methods have been proposed with exceptional results. To validate segmentation algorithms, manual annotations are typically considered as ground truth. However, manual annotations often suffer from inter/intra-operator variability and can also be occasionally inaccurate, especially when considering time-consuming and precise tasks. A sample case is the manual delineation of the lumen-intima (LI) and media-adventitia (MA) borders for intima-media thickness (IMT) measurement in B-mode ultrasound images. In this work, a novel hybrid learning paradigm which combines manual segmentations with the automatic segmentation of a dynamic programming technique for ground truth determination is presented. A profile consensus strategy is proposed to construct the hybrid ground truth. Two open-source datasets (n=2576) were employed for training four deep learning networks using the hybrid learning paradigm and three single source training targets as a comparison. The pipeline was fixed across the four tests and included a Faster R-CNN detection network to locate the carotid artery and then subsequent division into patches which were segmented using a UNet. The validation of the results was performed on an external test set comparing the predictions of the four different models to the annotations of three independent manual operators. The hybrid learning paradigm showed the best overall segmentation results (Dice=0.907±0.037, p<0.001) and demonstrated an exceptional correlation between the mean of three operators and the automatic measure (ICC(2,1)=0.958), demonstrating how the incorporation of heuristic-based segmentation methods within the learning paradigm of a deep neural network can enhance and improve final segmentation performance results

Social Innovation and Entrepreneurship: Case Studies, Practices and Perspectives

This book was developed to bring together a collection of papers on social entrepreneurship that have been presented at the European Conference for Innovation and Entrepreneurship (ECIE) and International Conference for Innovation and Entrepreneurship (ICIE). The long-running ECIE, now into it 9th year has been a forum to discuss and share case studies, practices and perspectives on social entrepreneurship and in this book we have carefully selected with the potential reader in mind, whether it is an entrepreneurship educator, researcher or practitioner