7,063 research outputs found
Warabandi in Pakistan's canal irrigation systems: Widening gap between theory and practice
Irrigation scheduling / Irrigation systems / Irrigation canals / Privatization / Water rights / Social aspects / Economic aspects / Watercourses / Water supply / Equity / Water distribution / Water users' associations / Pakistan / Punjab
Institutional perspectives of land reclamation operations in Punjab: A case study of the Lower Chenab Canal (East) Circle Area
Land reclamationSoil salinityIrrigated sitesIrrigation canalsIrrigation waterInstitutionsLegal aspectsIrrigation schedulingWater availabilityIrrigated farmingCase studies
An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data
High-throughput spectrometers are capable of producing data sets containing
thousands of spectra for a single biological sample. These data sets contain a
substantial amount of redundancy from peptides that may get selected multiple
times in a LC-MS/MS experiment. In this paper, we present an efficient
algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass
spectrometry data which increases both the sensitivity and confidence of
spectral assignment. CAMS utilizes a novel metric, called F-set, that allows
accurate identification of the spectra that are similar. A graph theoretic
framework is defined that allows the use of F-set metric efficiently for
accurate cluster identifications. The accuracy of the algorithm is tested on
real HCD and CID data sets with varying amounts of peptides. Our experiments
show that the proposed algorithm is able to cluster spectra with very high
accuracy in a reasonable amount of time for large spectral data sets. Thus, the
algorithm is able to decrease the computational time by compressing the data
sets while increasing the throughput of the data by interpreting low S/N
spectra.Comment: 4 pages, 4 figures, Bioinformatics and Biomedicine (BIBM), 2012 IEEE
International Conference o
Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data
This paper measures social media activity of 15 broad scientific disciplines
indexed in Scopus database using Altmetric.com data. First, the presence of
Altmetric.com data in Scopus database is investigated, overall and across
disciplines. Second, the correlation between the bibliometric and altmetric
indices is examined using Spearman correlation. Third, a zero-truncated
negative binomial model is used to determine the association of various factors
with increasing or decreasing citations. Lastly, the effectiveness of altmetric
indices to identify publications with high citation impact is comprehensively
evaluated by deploying Area Under the Curve (AUC) - an application of receiver
operating characteristic. Results indicate a rapid increase in the presence of
Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A
zero-truncated negative binomial model is implemented to measure the extent to
which different bibliometric and altmetric factors contribute to citation
counts. Blog count appears to be the most important factor increasing the
number of citations by 38.6% in the field of Health Professions and Nursing,
followed by Twitter count increasing the number of citations by 8% in the field
of Physics and Astronomy. Interestingly, both Blog count and Twitter count
always show positive increase in the number of citations across all fields.
While there was a positive weak correlation between bibliometric and altmetric
indices, the results show that altmetric indices can be a good indicator to
discriminate highly cited publications, with an encouragingly AUC= 0.725
between highly cited publications and total altmetric count. Overall, findings
suggest that altmetrics could better distinguish highly cited publications.Comment: 34 Pages, 3 Figures, 15 Table
A Scalable Algorithm for Locating Distribution Centers on Real Road Networks
The median problem is a type of network location problem that aims at finding a node with the total minimum demand weighted distance to a set of demand nodes in a weighted graph. In this research, an algorithm for solving the median problem on real road networks is proposed. The proposed algorithm, referred to as the multi-threaded Dijkstra’s (MTD) algorithm, is then used to optimally locate Wal-Mart distribution centers on the 28-million node road network of the United States with the objective of minimizing the total demand weighted transportation cost. The resulting optimal location configuration of Wal-Mart distribution centers improves the total transportation cost by 40%
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