7,643 research outputs found
Topological Chern-Simons Sigma Model
We consider topological twisting of recently constructed Chern-Simons-matter
theories in three dimensions with N=4 or higher supersymmetry. We enumerate
physically inequivalent twistings for each N, and find two different twistings
for N=4, one for N=5,6, and four for N=8. We construct the two types of N=4
topological theories, which we call A/B-models, in full detail. The A-model has
been recently studied by Kapustin and Saulina. The B-model is new and it
consists solely of a Chern-Simons term of a complex gauge field up to
BRST-exact terms. We also compare the new theories with topological Yang-Mills
theories and find some interesting connections. In particular, the A-model
seems to offer a new perspective on Casson invariant and its relation to
Rozansky-Witten theory.Comment: 31 pages, no figure; v2. references adde
Influence of operating parameters on the biodegradation of steroid estrogens and nonylphenolic compounds during biological wastewater treatment processes
This document is the unedited author's version of a Submitted Work that was subsequently accepted for
publication in Environmental Science & Technology, copyright © American Chemical Society after peer
review. To access the final edited and published work see http://pubs.acs.org/doi/abs/10.1021/es901612v.This study investigated operational factors influencing the removal of steroid estrogens and nonylphenolic compounds in two sewage treatment works, one a nitrifying/denitrifying activated sludge plant and the other a nitrifying/denitrifying activated sludge plant with phosphorus removal. Removal efficiencies of >90% for steroid estrogens and for longer chain nonylphenol ethoxylates (NP4−12EO) were observed at both works, which had equal sludge ages of 13 days. However, the biological activity in terms of milligrams of estrogen removed per day per tonne of biomass was found to be 50−60% more efficient in the nitrifying/denitrifying activated sludge works compared to the works which additionally incorporated phosphorus removal. A temperature reduction of 6 °C had no impact on the removal of free estrogens, but removal of the conjugated estrone-3-sulfate was reduced by 20%. The apparent biomass sorption (LogKp) values were greater in the nitrifying/denitrifying works than those in the nitrifying/denitrifying works with phosphorus removal for both steroid estrogens and nonylphenolic compounds possibly indicating a different cell surface structure and therefore microbial population. The difference in biological activity (mg tonne−1 d−1) identified in this study, of up to seven times, suggests that there is the potential for enhancing the removal of estrogens and nonylphenols if more detailed knowledge of the factors responsible for these differences can be identified and maximized, thus potentially improving the quality of receiving waters.Public Utilities Board (Singapore), Anglian Water Ltd, Severn Trent Water Ltd, Thames Water Utilities Ltd, United Utilities 393 Plc and Yorkshire Water Services
Statistical Basis for Predicting Technological Progress
Forecasting technological progress is of great interest to engineers, policy
makers, and private investors. Several models have been proposed for predicting
technological improvement, but how well do these models perform? An early
hypothesis made by Theodore Wright in 1936 is that cost decreases as a power
law of cumulative production. An alternative hypothesis is Moore's law, which
can be generalized to say that technologies improve exponentially with time.
Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus.
These hypotheses have not previously been rigorously tested. Using a new
database on the cost and production of 62 different technologies, which is the
most expansive of its kind, we test the ability of six different postulated
laws to predict future costs. Our approach involves hindcasting and developing
a statistical model to rank the performance of the postulated laws. Wright's
law produces the best forecasts, but Moore's law is not far behind. We discover
a previously unobserved regularity that production tends to increase
exponentially. A combination of an exponential decrease in cost and an
exponential increase in production would make Moore's law and Wright's law
indistinguishable, as originally pointed out by Sahal. We show for the first
time that these regularities are observed in data to such a degree that the
performance of these two laws is nearly tied. Our results show that
technological progress is forecastable, with the square root of the logarithmic
error growing linearly with the forecasting horizon at a typical rate of 2.5%
per year. These results have implications for theories of technological change,
and assessments of candidate technologies and policies for climate change
mitigation
An application of hybrid life cycle assessment as a decision support framework for green supply chains
In an effort to achieve sustainable operations, green supply chain management has become an important area for firms to concentrate on due to its inherent involvement with all the processes that provide foundations to successful business. Modelling methodologies of product supply chain environmental assessment are usually guided by the principles of life cycle assessment (LCA). However, a review of the extant literature suggests that LCA techniques suffer from a wide range of limitations that prevent a wider application in real-world contexts; hence, they need to be incorporated within decision support frameworks to aid environmental sustainability strategies. Thus, this paper contributes in understanding and overcoming the dichotomy between LCA model development and the emerging practical implementation to inform carbon emissions mitigation strategies within supply chains. Therefore, the paper provides both theoretical insights and a practical application to inform the process of adopting a decision support framework based on a LCA methodology in a real-world scenario. The supply chain of a product from the steel industry is considered to evaluate its environmental impact and carbon ‘hotspots’. The study helps understanding how operational strategies geared towards environmental sustainability can be informed using knowledge and information generated from supply chain environmental assessments, and for highlighting inherent challenges in this process
Plasma S-adenosylmethionine, DNMT polymorphisms, and peripheral blood LINE-1 methylation among healthy Chinese adults in Singapore
10.1186/1471-2407-13-389BMC Cancer13-BCMA
HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection
We consider classification tasks in the regime of scarce labeled training
data in high dimensional feature space, where specific expert knowledge is also
available. We propose a new hybrid optimization algorithm that solves the
elastic-net support vector machine (SVM) through an alternating direction
method of multipliers in the first phase, followed by an interior-point method
for the classical SVM in the second phase. Both SVM formulations are adapted to
knowledge incorporation. Our proposed algorithm addresses the challenges of
automatic feature selection, high optimization accuracy, and algorithmic
flexibility for taking advantage of prior knowledge. We demonstrate the
effectiveness and efficiency of our algorithm and compare it with existing
methods on a collection of synthetic and real-world data.Comment: Proceedings of 8th Learning and Intelligent OptimizatioN (LION8)
Conference, 201
ZERO MEAN CURVATURE SURFACES IN LORENTZ-MINKOWSKI 3-SPACE WHICH CHANGE TYPE ACROSS A LIGHT-LIKE LINE
Automated detection of calcified plaque using higher-order spectra cumulant technique in computer tomography angiography images
Cardiovascular disease continues to be the leading cause of death globally. Often, it stems from atherosclerosis, which can trigger substantial variations in the coronary arteries, possibly causing coronary artery disease (CAD). Coronary artery calcification is known to be a strong and independent forecaster of CAD. Hence, coronary computer tomography angiography (CTA) has become a fundamental noninvasive imaging tool to characterize coronary artery plaques. In this article, an automated algorithm is presented to uncover the presence of a calcified plaque, using 2060 CTA images acquired from 60 patients. Higher-order spectra cumulants were extracted from each image, thereby providing 2448 descriptive features per image. The features were then reduced using numerous well-established techniques, and ranked according to t value. Subsequently, the reduced features were input to several classifiers to achieve the best diagnostic accuracy with a minimum number of features. Optimal results were obtained using the support vector machine with a radial basis function, having 22 features obtained with the multiple factor analysis feature reduction algorithm. The accuracy, positive predictive value, sensitivity, and specificity obtained were 95.83%, 97.05%, 94.54%, and 97.13%, respectively. Based on these results, the technique could be useful to automatically and accurately identify calcified plaque evident in CTA images, and may therefore become an important tool to help reduce procedural costs and patient radiation dose
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