19,579 research outputs found
Modeling Emotion Influence from Images in Social Networks
Images become an important and prevalent way to express users' activities,
opinions and emotions. In a social network, individual emotions may be
influenced by others, in particular by close friends. We focus on understanding
how users embed emotions into the images they uploaded to the social websites
and how social influence plays a role in changing users' emotions. We first
verify the existence of emotion influence in the image networks, and then
propose a probabilistic factor graph based emotion influence model to answer
the questions of "who influences whom". Employing a real network from Flickr as
experimental data, we study the effectiveness of factors in the proposed model
with in-depth data analysis. Our experiments also show that our model, by
incorporating the emotion influence, can significantly improve the accuracy
(+5%) for predicting emotions from images. Finally, a case study is used as the
anecdotal evidence to further demonstrate the effectiveness of the proposed
model
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Pass-back chain extension expands multimodular assembly line biosynthesis.
Modular nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) enzymatic assembly lines are large and dynamic protein machines that generally effect a linear sequence of catalytic cycles. Here, we report the heterologous reconstitution and comprehensive characterization of two hybrid NRPS-PKS assembly lines that defy many standard rules of assembly line biosynthesis to generate a large combinatorial library of cyclic lipodepsipeptide protease inhibitors called thalassospiramides. We generate a series of precise domain-inactivating mutations in thalassospiramide assembly lines, and present evidence for an unprecedented biosynthetic model that invokes intermodule substrate activation and tailoring, module skipping and pass-back chain extension, whereby the ability to pass the growing chain back to a preceding module is flexible and substrate driven. Expanding bidirectional intermodule domain interactions could represent a viable mechanism for generating chemical diversity without increasing the size of biosynthetic assembly lines and challenges our understanding of the potential elasticity of multimodular megaenzymes
Integration by differentiation: new proofs, methods and examples
Recently, new methods were introduced which allow one to solve ordinary
integrals by performing only derivatives. These studies were originally
motivated by the difficulties of the quantum field theoretic path integral, and
correspondingly, the results were derived by heuristic methods. Here, we give
rigorous proofs for the methods to hold on fully specified function spaces. We
then illustrate the efficacy of the new methods by applying them to the study
of the surprising behavior of so-called Borwein integrals.Comment: Match published versio
An investigation into reducing the spindle acceleration energy consumption of machine tools
Machine tools are widely used in the manufacturing industry, and consume large amount of energy. Spindle acceleration appears frequently while machine tools are working. It produces power peak which is highly energy intensive. As a result, a considerable amount of energy is consumed by this acceleration during the use phase of machine tools. However, there is still a lack of understanding of the energy consumption of spindle acceleration. Therefore, this research aims to model the spindle acceleration energy consumption of computer numerical control (CNC) lathes, and to investigate potential approaches to reduce this part of consumption. The proposed model is based on the principle of spindle motor control and includes the calculation of moment of inertia for spindle drive system. Experiments are carried out based on a CNC lathe to validate the proposed model. The approaches for reducing the spindle acceleration energy consumption were developed. On the machine level, the approaches include avoiding unnecessary stopping and restarting of the spindle, shortening the acceleration time, lightweight design, proper use and maintenance of the spindle. On the system level, a machine tool selection criterion is developed for energy saving. Results show that the energy can be reduced by 10.6% to more than 50% using these approaches, most of which are practical and easy to implement
A uniformly accurate (UA) multiscale time integrator pseudospectral method for the Dirac equation in the nonrelativistic limit regime
We propose and rigourously analyze a multiscale time integrator Fourier
pseudospectral (MTI-FP) method for the Dirac equation with a dimensionless
parameter which is inversely proportional to the speed of
light. In the nonrelativistic limit regime, i.e. , the
solution exhibits highly oscillatory propagating waves with wavelength
and in time and space, respectively. Due to the rapid
temporal oscillation, it is quite challenging in designing and analyzing
numerical methods with uniform error bounds in . We
present the MTI-FP method based on properly adopting a multiscale decomposition
of the solution of the Dirac equation and applying the exponential wave
integrator with appropriate numerical quadratures. By a careful study of the
error propagation and using the energy method, we establish two independent
error estimates via two different mathematical approaches as
and ,
where is the mesh size, is the time step and depends on the
regularity of the solution. These two error bounds immediately imply that the
MTI-FP method converges uniformly and optimally in space with exponential
convergence rate if the solution is smooth, and uniformly in time with linear
convergence rate at for all and optimally with
quadratic convergence rate at in the regimes when either
or . Numerical results are
reported to demonstrate that our error estimates are optimal and sharp.
Finally, the MTI-FP method is applied to study numerically the convergence
rates of the solution of the Dirac equation to those of its limiting models
when .Comment: 25 pages, 1 figur
Experimental study on energy consumption of computer numerical control machine tools
Machining processes are responsible for substantial environmental impacts due to their great energy consumption. Accurately characterizing the energy consumption of machining processes is a starting point to increase manufacturing energy efficiency and reduce their associated environmental impacts. The energy calculation of machining processes depends on the availability of energy supply data of machine tools. However, the energy supply can vary greatly among different types of machine tools so that it is difficult to obtain the energy data theoretically. The aim of this research was to investigate the energy characteristics and obtain the power models of computer numerical control (CNC) machine tools through an experimental study. Four CNC lathes, two CNC milling machines and one machining center were selected for experiments. Power consumption of non-cutting motions and material removal was measured and compared for the selected machine tools. Here, non-cutting motions include standby, cutting fluid spraying, spindle rotation and feeding operations of machine tools. Material removal includes turning and milling. Results show that the power consumption of non-cutting motions and milling is dependent on machine tools while the power consumption of turning is almost independent from the machine tools. The results imply that the energy saving potential of machining processes is tremendous
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