News-driven business cycles in small open economies

The focus of this paper is on news-driven business cycles in small open economies. We make two significant contributions. First, we develop a small open economy model where the presence of financial frictions permits the replication of business cycle co-movements in response to news shocks. Second, we use VAR analysis to identify news shocks using data on four advanced small open economies. We find that expected shocks about the future Total Factor Productivity generate business cycle co-movements in output, hours, consumption and investment. We also find that news shocks are associated with countercyclical current account dynamics. Our findings are robust across a number of alternative identification schemes

CAC-TCP cross-layer interaction in a HAPS-satellite integrated scenario

The integration of a satellite system with a HAPS segment appears very suitable to provide communication services, including Internet access, for a large set of applications. In fact, the-satellite capability to provide wide coverage and broadband access can be enhanced by the use of cost-effective, mobile/portable and low-power consuming user terminals, when HAPS acts as an intermediate repeater. Moreover, also TCP-based applications, which suffer from long latency introduced by the satellite link and in general by errors, can get benefits in terms of end-to-end performance. In this frame, this paper deals with the introduction, on board the HAPS, of an efficient CAC scheme in order to guarantee an optimal utilization of the precious radio resources. In particular, we propose an innovative TCP driven CAC algorithm, which shall take into account not only the QoS requirements, but also TCP statistics obtained through a proxy installed on the HAPS. Results show that the overall system performance in terms of both average throughput and blocking probability is significantly improved

News-driven business cycles in small open economies

The focus of this paper is on news-driven business cycles in small open economies. We make two significant contributions. First, we develop a small open economy model where the presence of financial frictions permits the replication of business cycle co-movements in response to news shocks. Second, we use VAR analysis to identify news shocks using data on four advanced small open economies. We find that expected shocks about the future Total Factor Productivity generate business cycle co-movements in output, hours, consumption and investment. We also find that news shocks are associated with countercyclical current account dynamics. Our findings are robust across a number of alternative identification schemes

Metabonomic Investigation of Liver Profiles of Nonpolar Metabolites Obtained from Alcohol-Dosed Rats and Mice Using High Mass Accuracy MSn Analysis

Alcoholism is a complex disorder that, in man, appears to be genetically influenced, although the underlying genes and molecular pathways are not completely known. Here the intragastric alcohol feeding model in rodents was used together with high mass accuracy LC/MS(n) analysis to assess the metabonomic changes in nonpolar metabolite profiles for livers from control and alcohol treated rats and mice. Ion signals with a peak area variance of less than 30% (based on repeat analysis of a pooled quality control sample analysed throughout the batch) were submitted to multivariate statistical analysis (using principal components analysis, PCA). PCA revealed robust differences between profiles from control and alcohol-treated animals from both species. The major metabolites seen to differ between control and alcohol-treated animals were identified using high accuracy MS(n) data and verified using external search engines (http://www.lipidmaps.org; http://www.hmdb.ca; http://www.genome.jp/kegg/) and authentic standards. The main metabolite classes to show major changes in the alcoholic liver-derived samples were fatty acyls, fatty acid ethyl esters, glycerolipids and phosphatidylethanol homologues. Significant metabolites that were up-regulated by alcohol treatment in both rat and mouse livers included fatty acyls, metabolites such as octadecatrienoic acid and eicosapentaenoic acid, a number of fatty acid ethyl esters such as ethyl arachidonate, ethyl docosahexaenoic acid, ethyl linoleate and ethyl oleate and phosphatidylethanol (PEth) homologues (predominantly PEth 18:0/18:2 and PEth 16:0/18:2; PEth homologues are currently considered as potential biomarkers for harmful and prolonged alcohol consumption in man). A number of glycerophospholipids resulted in both up-regulation (m/z 903.7436 [M+H](+) corresponding to a triglyceride) and down-regulation (m/z 667.5296 [M+H](+) corresponding to a diglyceride). Metabolite profiles were broadly similar in both mouse and rat models. However, there were a number of significant differences in the alcohol-treated group particularly in the marked down-regulation of retinol and free cholesterol in the mouse compared to the rat. Unique markers for alcohol treatment included ethyl docosahexaenoic acid. Metabolites were identified with high confidence using predominantly negative ion MS(n) data for the fatty acyl components to match to www.lipidmaps.org MS and MS/MS databases; interpreting positive ion data needed to take into account possible adduct ions which may confound the identification of other lipid classes. The observed changes in lipid profiles were consistent with alcohol induced liver injury in humans

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Neurology

Contains reports on six research projects.U. S. Public Health Service (B-3055-4, B-3090-4, MH-06175-02)U. S. Air Force (AF49(638)-1313)U.S. Navy. Office of Naval Research (Nonr-1841(70)

Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results

A Partitioning Methodology for Accelerating Applications in Hybrid Reconfigurable Platforms *

Abstrac

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics