RNA-sequencing elucidates the regulation of behavioural transitions associated with mating in honey bee queens

This study was funded by a BBSRC ISIS grant BB/J019453/1, a Royal Holloway Research Strategy Fund Grant, and a Leverhulme Grant F/07537/AK to MJFB. BPO was supported by Australian Research Council Discovery grants DP150100151 and DP120101915. FM was supported by a Marie Curie International Incoming Fellowship FP7-PEOPLE-2013-IIF-625487 to MJFB. We would like to thank Dave Galbraight (Penn State) and Alberto Paccanaro (RHUL) for support with analysis of RNAseq data and four anonymous reviewers for providing thoughtful insights that helped to improve the manuscript.Peer reviewedPublisher PD

Output Only Functional Series Time Dependent AutoRegressive Moving Average (FS-TARMA) Modelling of Tool Acceleration Signals for Wear Estimation

In this paper, tool vibration signals obtained from a turning process are used for tool wear estimation purposes. During the cutting process, tool acceleration signals are recorded for different levels of wear. Due to non-stationarity of tool/holder system's response, Time dependent time series model of Functional Series Time dependent AutoRegressive Moving Average (FS-TARMA) type is used for modelling the signals and extraction of wear sensitive features that will be exploited in a wear estimation algorithm. Results of the analysis through FS-TARMA, reveals its higher accuracy with respect to stationary type models, since it captures time dependent properties as well, which can be used in an online tool wear estimation algorithm