Improving the scalability of parallel N-body applications with an event driven constraint based execution model

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing.Comment: 11 figure

Survey: Women and California Law

This survey of California law, a regular feature of the Women\u27s Law Forum, summarizes recent California Supreme Court and Court of Appeal decisions of special importance to women. A briefanalysis of the issues pertinent to women raised in each case is provided

Survey: Women and California Law

This survey of California law, a regular feature of the Women\u27s Law Forum, summarizes recent California Supreme Court and Court of Appeal decisions of special importance to women. A briefanalysis of the issues pertinent to women raised in each case is provided

Serological survey for mycoplasma hyopneumoniae in free-living wild boars from Campos Gerais region, Paraná, Brasil.

The south region of Brazil was responsible for 80.3% of total pork meat export in the country in 2015 (2), with the state of Paraná accountable for 21% of the total pork meat production in that year. Pig farming represented 5.7% of the agricultural gross income of the state in 2016, and the Campos Gerais region accounted for 13.2% of that amount (2). Wild boars are the result of crossbreeding between boars (Sus scrofa scrofa) and domestic pigs (Sus scrofa domesticus). The total population of free-living wild boars in Brazil is unknown (11), but sightings are common in the crop fields and near livestock farms of different regions of Paraná state, including in Campos Gerais (9). The health status of pig herds is important in terms of maintenance and growth of pork production and exports and there are evidences that domestic pigs and wild boars share vulnerabilities in certain viral and bacterial pathogen infections (12). Mycoplasma hyopneumoniae (Mhyo) is a bacterial pathogen that causes porcine enzootic pneumonia, an economically important disease that affects both domestic pigs and wild boars. Mhyo was first isolated in 1965, simultaneously in the United Kingdom (UK) and in the United States of America (USA) (3; 7). Economic losses related to this pathogen and mycoplasmal pneumonia in pig herds are associated with decreased feed efficiency, reduced average of the daily weight gain, and increased medication costs. Thus, knowing the health status of free-living wild boars in the regards of this pathogen is important for the biosecurity of the pork production. The aim of this study was to investigate antibodies against Mhyo in serum samples of free-living wild boars in Campos Gerais region

“Salus Populi Suprema Lex”: Considerations on the Initial Response of the United Kingdom to the SARS-CoV-2 Pandemic

In several countries worldwide, the initial response to coronavirus disease 2019 (COVID-19) has been heavily criticized by general public, media, and healthcare professionals, as well as being an acrimonious topic in the political debate. The present article elaborates on some aspects of the United Kingdom (UK) primary reaction to SARS-CoV-2 pandemic; specifically, from February to July 2020. The fact that the UK showed the highest mortality rate in Western Europe following the first wave of COVID-19 certainly has many contributing causes; each deserves an accurate analysis. We focused on three specific points that have been insofar not fully discussed in the UK and not very well known outside the British border: clinical governance, access to hospital care or intensive care unit, and implementation of non-pharmaceutical interventions. The considerations herein presented on these fundamental matters will likely contribute to a wider and positive discussion on public health, in the context of an unprecedented crisis

Utility of the two-source energy balance (TSEB) model in vine and interrow flux partitioning over the growing season

For monitoring water use in vineyards, it becomes important to evaluate the evapotranspiration (ET) contributions from the two distinct management zones: the vines and the interrow. Often the interrow is not completely bare soil but contains a cover crop that is senescent during the main growing season (nominally May–August), which in Central California is also the dry season. Drip irrigation systems running during the growing season supply water to the vine plant and re-wet some of the surrounding bare soil. However, most of the interrow cover crop is dry stubble by the end of May. This paper analyzes the utility of the thermal-based two-source energy balance (TSEB) model for estimating daytime ET using tower-based land surface temperature (LST) estimates over two Pinot Noir (Vitis vinifera) vineyards at different levels of maturity in the Central Valley of California near Lodi, CA. The data were collected as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Local eddy covariance (EC) flux tower measurements are used to evaluate the performance of the TSEB model output of the fluxes and the capability of partitioning the vine and cover crop transpiration (T) from the total ET or T/ET ratio. The results for the 2014–2016 growing seasons indicate that TSEB output of the energy balance components and ET, particularly, over the daytime period yield relative differences with flux tower measurements of less than 15%. However, the TSEB model in comparison with the correlation-based flux partitioning method overestimates T/ET during the winter and spring through bud break, but then underestimates during the growing season. A major factor that appears to affect this temporal behavior in T/ET is the daily LAI used as input to TSEB derived from a remote sensing product. An additional source of uncertainty is the use of local tower-based LST measurements, which are not representative of the flux tower measurement source area footprint.info:eu-repo/semantics/acceptedVersio

A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks

: We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies by up to 80%

Network structure indexes to forecast epidemic spreading in real-world complex networks

Complex networks are the preferential framework to model spreading dynamics in several real-world complex systems. Complex networks can describe the contacts between infectious individuals, responsible for disease spreading in real-world systems. Understanding how the network structure affects an epidemic outbreak is therefore of great importance to evaluate the vulnerability of a network and optimize disease control. Here we argue that the best network structure indexes (NSIs) to predict the disease spreading extent in real-world networks are based on the notion of network node distance rather than on network connectivity as commonly believed. We numerically simulated, via a type-SIR model, epidemic outbreaks spreading on 50 real-world networks. We then tested which NSIs, among 40, could a priori better predict the disease fate. We found that the “average normalized node closeness” and the “average node distance” are the best predictors of the initial spreading pace, whereas indexes of “topological complexity” of the network, are the best predictors of both the value of the epidemic peak and the final extent of the spreading. Furthermore, most of the commonly used NSIs are not reliable predictors of the disease spreading extent in real-world networks