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They usually consist of two components that derive from biological olfactory principles: (1) cross-reactive sensing arrays that generate patterned responses upon odor exposure and (2) analytical algorithms that categorize these patterned response signatures.
One potential way to improve laboratory surveillance would be to employ molecular methods and analytical algorithms that are pathogen-agnostic.
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Unlike past pseudo-analytical planar-layered modeling algorithms that impose parallelism between the formation's bed junctions, our method involves judicious employment of Transformation Optics techniques to address challenges related to modeling relative slope (i.e., tilting) between said junctions (including arbitrary azimuth orientation of each junction).
This functionality greatly facilitates the training of gene prediction algorithms that particularly pose an analytical challenge (Cantarel et al., 2008).
By leveraging the analytical results obtained in our previous works, we design low complexity algorithms that are capable of estimating transmission distortion accurately.
The analytical pipelines in general have a predefined order of analysis steps and built-in algorithms that cannot be easily modified and/or replaced.
The calculation of the aforementioned factors takes place using analytical algorithms, formulated in such a way that have led to the development of the integrated, holistic decision-making support tool, namely ib3at.
Analytical algorithms have the intrinsic, limiting assumption that measured data are perfectly consistent with the object, which is never true in practice due to noise and other physical factors (e.g. attenuation).
It should be noted that many of the analytical algorithms share these characteristics as well.
It involves a simple and computationally feasible Markov Chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function.
Note that almost all the aforementioned attempts are constrained to the use of analytical algorithms only to generate a predictive model of students' success and failure.
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