16,959 research outputs found
ESTIMATING THE ECONOMIC LOSSES FROM DISEASES AND EXTENDED DAYS OPEN WITH A FARM-LEVEL STOCHASTIC MODEL
This thesis improved a farm-level stochastic model with Monte Carlo simulation to estimate the impact of health performance and market conditions on dairy farm economics. The main objective of this model was to estimate the costs of seven common clinical dairy diseases (mastitis, lameness, metritis, retained placenta, left displaced abomasum, ketosis, and milk fever) in the U.S. An online survey was conducted to estimate veterinary fees, treatment costs, and producer labor data. The total disease costs were higher in multiparous cows than in primiparous cows. Left displaced abomasum had the greatest costs in all parities (555.79 in multiparous cows). Milk loss, treatment costs, and culling costs were the largest three cost categories for all diseases. A secondary objective of this model was to evaluate the dairy cow’s value, the optimal culling decision, and the cost of days open with flexible model inputs. Dairy cow value under 2013 market conditions was lower than previous studies due to the high slaughter and feed price and low replacement price. The first optimal replacement moment appeared in the middle of the first parity. Furthermore, the cost of days open was considerably influenced by the market conditions
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by HPC applications,
significantly reducing data size is critical because of limited capacity of
storage space and potential bottlenecks on I/O or networks in writing/reading
or transferring data. SZ and ZFP are the two leading lossy compressors
available to compress scientific data sets. However, their performance is not
consistent across different data sets and across different fields of some data
sets: for some fields SZ provides better compression performance, while other
fields are better compressed with ZFP. This situation raises the need for an
automatic online (during compression) selection between SZ and ZFP, with a
minimal overhead. In this paper, the automatic selection optimizes the
rate-distortion, an important statistical quality metric based on the
signal-to-noise ratio. To optimize for rate-distortion, we investigate the
principles of SZ and ZFP. We then propose an efficient online, low-overhead
selection algorithm that predicts the compression quality accurately for two
compressors in early processing stages and selects the best-fit compressor for
each data field. We implement the selection algorithm into an open-source
library, and we evaluate the effectiveness of our proposed solution against
plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results
on three data sets representing about 100 fields show that our selection
algorithm improves the compression ratio up to 70% with the same level of data
distortion because of very accurate selection (around 99%) of the best-fit
compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio
Study on space-time structure of Higgs boson decay using HBT correlation Method in ee collision at =250 GeV
The space-time structure of the Higgs boson decay are carefully studied with
the HBT correlation method using ee collision events produced through
Monte Carlo generator PYTHIA 8.2 at =250GeV. The Higgs boson jets
(Higgs-jets) are identified by H-tag tracing. The measurement of the Higgs
boson radius and decay lifetime are derived from HBT correlation of its decay
final state pions inside Higgs-jets in the ee collisions events with an
upper bound of fm and fs. This result is consistent with CMS data.Comment: 7 pages,3 figure
Characterizing and Modeling the Dynamics of Activity and Popularity
Social media, regarded as two-layer networks consisting of users and items,
turn out to be the most important channels for access to massive information in
the era of Web 2.0. The dynamics of human activity and item popularity is a
crucial issue in social media networks. In this paper, by analyzing the growth
of user activity and item popularity in four empirical social media networks,
i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links
between users and items are more likely to be created by active users and to be
acquired by popular items, where user activity and item popularity are measured
by the number of cross links associated with users and items. This indicates
that users generally trace popular items, overall. However, it is found that
the inactive users more severely trace popular items than the active users.
Inspired by empirical analysis, we propose an evolving model for such networks,
in which the evolution is driven only by two-step random walk. Numerical
experiments verified that the model can qualitatively reproduce the
distributions of user activity and item popularity observed in empirical
networks. These results might shed light on the understandings of micro
dynamics of activity and popularity in social media networks.Comment: 13 pages, 6 figures, 2 table
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