17,721 research outputs found
The context-dependence of mutations: a linkage of formalisms
Defining the extent of epistasis - the non-independence of the effects of
mutations - is essential for understanding the relationship of genotype,
phenotype, and fitness in biological systems. The applications cover many areas
of biological research, including biochemistry, genomics, protein and systems
engineering, medicine, and evolutionary biology. However, the quantitative
definitions of epistasis vary among fields, and its analysis beyond just
pairwise effects remains obscure in general. Here, we show that different
definitions of epistasis are versions of a single mathematical formalism - the
weighted Walsh-Hadamard transform. We discuss that one of the definitions, the
backgound-averaged epistasis, is the most informative when the goal is to
uncover the general epistatic structure of a biological system, a description
that can be rather different from the local epistatic structure of specific
model systems. Key issues are the choice of effective ensembles for averaging
and to practically contend with the vast combinatorial complexity of mutations.
In this regard, we discuss possible approaches for optimally learning the
epistatic structure of biological systems.Comment: 6 pages, 3 figures, supplementary informatio
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
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Intelligent Learning Algorithms for Active Vibration Control
YesThis correspondence presents an investigation into the
comparative performance of an active vibration control (AVC) system
using a number of intelligent learning algorithms. Recursive least square
(RLS), evolutionary genetic algorithms (GAs), general regression neural
network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS)
algorithms are proposed to develop the mechanisms of an AVC system.
The controller is designed on the basis of optimal vibration suppression
using a plant model. A simulation platform of a flexible beam system
in transverse vibration using a finite difference method is considered to
demonstrate the capabilities of the AVC system using RLS, GAs, GRNN,
and ANFIS. The simulation model of the AVC system is implemented,
tested, and its performance is assessed for the system identification models
using the proposed algorithms. Finally, a comparative performance of the
algorithms in implementing the model of the AVC system is presented and
discussed through a set of experiments
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