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A small PET system has been built up with two multichannel photomultipliers, which are attached to a matrix of 64 single LSO crystals each. The signal from each multiplier is being sampled continuously by a 12 bit ADC at a sampling frequency of 40 MHz. In case of a scintillation pulse a subsequent FPGA sends the corresponding set of samples together with the channel information and a time mark to the host computer. The data transfer is performed with a rate of 20 MB/s. On the host all necessary information is extracted from the data. The pulse energy is determined, coincident events are detected and multiple hits within one matrix can be identified. In order to achieve a narrow time window the pulse starting time is refined further than the resolution of the time mark (=25 ns) would allow. This is possible by interpolating between the pulse samples. First data obtained from this system will be presented. The system is part of developments for a much larger system and has been created to study the feasibility and performance of the technique and the hardware architecture.
Within the developments for the Crystal Clear small animal PET project (CLEARPET) a dual head PET system has been established. The basic principle is the early digitization of the detector pulses by free running ADCs. The determination of the γ-energy and also the coincidence detection is performed by data processing of the sampled pulses on the host computer. Therefore a time mark is attached to each pulse identifying the current cycle of the 40 MHz sampling clock. In order to refine the time resolution the pulse starting time is interpolated from the samples of the pulse rise. The detector heads consist of multichannel PMTs with a single LSO scintillator crystal coupled to each channel. For each PMT only one ADC is required. The position of an event is obtained separately from trigger signals generated for each single channel. An FPGA is utilized for pulse buffering, generation of the time mark and for the data transfer to the host via a fast I/O-interface.
A novel scheme for precise diagnostics and effective stabilization of currents in a fuel cell stack
(2010)
BACKGROUND
Currently, several techniques exist for the downstream processing of protein, phytic acid and sinapic acid from rapeseed and rapeseed meal, but no technique has been developed to separate all of the components in one process. In this work, two new downstream processing strategies focusing on recovering sinapic acid, phytic acid and protein from rapeseed meal were established.
RESULTS
The sinapic acid content was enhanced by a factor of 4.5 with one method and 5.1 with the other. The isolation of sinapic acid was accomplished using a zeolite-based adsorbent with high adsorptive and optimal desorption characteristics. Phytic acid was isolated using the anion-exchange resin Purolite A200®. In addition, the processes resulted in two separated protein fractions. The ratios of globulin and albumin ratio to the total protein were 59.2% and 40.1%, respectively. The steps were then combined in two different ways: (a) a ‘sequential process’ using the zeolite and A200 in batch processes; and (b) a ‘parallel process’ using only A200 in a chromatographic system to separate all of the compounds.
CONCLUSIONS
It can be concluded that isolation of all three components was possible in both processes. These could enhance the added value of current processes using rapeseed meal as a protein source. © 2015 Society of Chemical Industry
Advances in polymer science have significantly increased polymer applications in life sciences. We report the use of free-standing, ultra-thin polydimethylsiloxane (PDMS) membranes, called CellDrum, as cell culture substrates for an in vitro wound model. Dermal fibroblast monolayers from 28- and 88-year-old donors were cultured on CellDrums. By using stainless steel balls, circular cell-free areas were created in the cell layer (wounding). Sinusoidal strain of 1 Hz, 5% strain, was applied to membranes for 30 min in 4 sessions. The gap circumference and closure rate of un-stretched samples (controls) and stretched samples were monitored over 4 days to investigate the effects of donor age and mechanical strain on wound closure. A significant decrease in gap circumference and an increase in gap closure rate were observed in trained samples from younger donors and control samples from older donors. In contrast, a significant decrease in gap closure rate and an increase in wound circumference were observed in the trained samples from older donors. Through these results, we propose the model of a cell monolayer on stretchable CellDrums as a practical tool for wound healing research. The combination of biomechanical cell loading in conjunction with analyses such as gene/protein expression seems promising beyond the scope published here.
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.