Bayesian Hierarchical Modelling for Uncertainty Quantification in Operational Thermal Resistance of LED Systems

Remaining useful life (RUL) prediction is central to prognostics and reliability assessment of light-emitting diode (LED) systems. Their unknown long-term service life remaining when subject to specific operating conditions is affected by various sources of uncertainty stemming from production of individual system components, application of the whole system, measurement and operation. To enhance the reliability of model-based predictions, it is essential to account for all of these uncertainties in a systematic manner. This paper proposes a Bayesian hierarchical modelling framework for inverse uncertainty quantification (UQ) in LED operation under thermal loading. The main focus is on the LED systems’ operational thermal resistances, which are subject to system and application variability. Posterior inference is based on a Markov chain Monte Carlo (MCMC) sampling scheme using the Metropolis–Hastings (MH) algorithm. Performance of the method is investigated for simulated data, which allow to focus on different UQ aspects in applications. Findings from an application scenario in which the impact of disregarded uncertainty on RUL prediction is discussed highlight the need for a comprehensive UQ to allow for reliable predictions

IPO: a tool for automated optimization of XCMS parameters

BACKGROUND: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. RESULTS: We implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.IPO optimizes XCMS peak picking parameters by using natural, stable 13C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. CONCLUSIONS: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO

DFG Funding Programme Open Access Publishing - Report about the Funding

The report "Open Access Publishing" contains extensive information from an empirical evaluation of the funding programme with which the DFG has been supporting the gold route of open access since 2010. The study is based on a bibliometric analysis carried out by Forschungszentrum J&uuml;lich. An online survey was also conducted among 82 funded and non-funded institutions, which shared both their experiences of funding and assessments of future needs in relation to open access funding from the DFG. The surveys were supplemented by more detailed interviews. This part of the evaluation was carried out by JOANNEUM RESEARCH. Between 2010 and 2016, 45 universities received funding through the programme. During this period, these institutions published almost 12,000 articles through gold open access with the help of the programme (including the universities&#39; own contributions). Most of the funded articles were in the life sciences. The number of articles funded each year rose steadily throughout the period. The effects of funding in the Open Access Publishing programme can be seen in the organisation and structures of the institutions. In addition to the establishment of workflows and funds to cover costs, the funding promoted measures to record and monitor publication figures. Over half of the funding recipients surveyed used the DFG funds to bring acquisition and open access organisationally closer together or merge them, and to stimulate the transformation from a subscription to an open access model.</p