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Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on.
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Many papers describing methods of characterizing columns were published to solve this issue [ 3– 14], while others are under development [ 15, 16].
For decades, numerous papers have been published in order to solve this problem using the same basic idea: prepare a compound film while the target remains non-poisoned.
Recently, a new amplification protocol has been published that solves this problem [ 39].
Approximately 50 books were published describing how to solve the puzzle of Rubik's Cube.
Then, in 2005, a study was published that seemed to solve the mystery once and for all.
Several studies attempting to solve this controversy have been published, but none of them were set up as prospective randomized trials.
Two studies attempting to solve this question have been published in recent years, with Arabidopsis thaliana used as experimental material (Johannes et al. 2009; Reinders et al. 2009).
The [adiabatic] algorithms that have been published were strongly connected to [solving] optimization problems.
A great deal of literature on algorithms for solving SEP has been published, most of which are projection methods [1 3, 6 11].
A rather precise idea of the structure of the network exists, several kinetic models of the network dynamics are available [(Bettenbrock et al., 2005; Kotte et al., 2010) and references therein], and recently a high-throughput dataset containing the required information for solving Problem 1 has been published (Ishii et al., 2007).
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