Self-tuning Position Control for the Linear Long-stroke, Compound Switched Reluctance Conveyance Machine

This paper proposes a long-stroke linear switched reluctance machine (LSRM) with a primary and a secondary translator for industrial conveyance applications. The secondary one can translate according to the primary one so that linear compound motions can be achieved. Considering the fact that either one translator imposes a time-variant, nonlinear disturbance onto the other, the self-tuning position controllers are implemented for the compound machine and experimental results demonstrate that the absolute steady-state error values can fall into 0.03 mm and 0.05 mm for the secondary and primary translator, respectively. A composite absolute precision of less than 0.6 mm can be achieved under the proposed control strategy.

Two more pieces of the colibactin genotoxin puzzle from Escherichia coli show incorporation of an unusual 1-aminocyclopropanecarboxylic acid moiety

Biosynthetic pathway intermediates related to genotoxin colibactin formation: a linear compound3bearing a rare 7-methyl-4-azaspiro[2.4]hept-6-en-5-one residue.

Peptide-based rotaxanes and catenanes: an emerging class of supramolecular chemistry systems

Abstract[2]Rotaxanes are interlocked molecular systems in which the cavity of a macrocycle (wheel) is threaded by a linear compound (axle). The axle is held in place principally by a sterically demanding moiety (stopper) at each end. If the two ends of a rotaxane are covalently linked together, then an assembly of two intertwined macrocycles (termed [2]catenane) is generated. Although rotaxanes and catenanes have been extensively investigated by several research groups, only a limited amount of studies has been devoted to these systems when a peptide molecule characterizes either the axle or the wheel(s) (or both). The purpose of this short conceptual overview is to summarize recent findings on synthetic and naturally occurring peptide-based [2]rotaxanes, [3]rotaxanes, and [2]catenanes and discuss future prospects for the research in this emerging area of supramolecular chemistry.

Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression

Based on the single kernel function relevance vector machine(RVM) models，a multiple load-forecasting model has been established and simulated with several compound kernel functions, including Gauss kernel, Laplace, linear compounded by Gauss and Laplace, Gauss and polynomial kernel. Each model gained comparatively reasonable results in simulation .Moreover, multi linear-compound kernel RVMs performed better than single kernel RVMs in terms of most evaluating indicators, which prove that RVM is an appropriate machine learning method in monitoring status of components of wind turbines.