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The small animal PET scanners developed by the Crystal Clear Collaboration (ClearPETtrade) detect coincidences by analyzing timemarks which are attached to each event. The scanners are able to save complete single list mode data which allows analysis and modification of the timemarks after data acquisition. The timemarks are obtained from the digitally sampled detector pulses by calculating the baseline crossing of the rising edge of the pulse which is approximated as a straight line. But the limited sampling frequency causes a systematic error in the determination of the timemark. This error depends on the phase of the sampling clock at the time of the event. A statistical method that corrects these errors will be presented
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.
Thin film chemical sensors based on chalcogenide glasses for „electronic tongue“ applications
(2001)
This study addresses a proof-of-concept experiment with a biocompatible screen-printed carbon electrode deposited onto a biocompatible and biodegradable substrate, which is made of fibroin, a protein derived from silk of the Bombyx mori silkworm. To demonstrate the sensor performance, the carbon electrode is functionalized as a glucose biosensor with the enzyme glucose oxidase and encapsulated with a silicone rubber to ensure biocompatibility of the contact wires. The carbon electrode is fabricated by means of thick-film technology including a curing step to solidify the carbon paste. The influence of the curing temperature and curing time on the electrode morphology is analyzed via scanning electron microscopy. The electrochemical characterization of the glucose biosensor is performed by amperometric/voltammetric measurements of different glucose concentrations in phosphate buffer. Herein, systematic studies at applied potentials from 500 to 1200 mV to the carbon working electrode (vs the Ag/AgCl reference electrode) allow to determine the optimal working potential. Additionally, the influence of the curing parameters on the glucose sensitivity is examined over a time period of up to 361 days. The sensor shows a negligible cross-sensitivity toward ascorbic acid, noradrenaline, and adrenaline. The developed biocompatible biosensor is highly promising for future in vivo and epidermal applications.
In this article, we present an overview on the thermocatalytic reaction of hydrogen peroxide (H₂O₂) gas on a manganese (IV) oxide (MnO₂) catalytic structure. The principle of operation and manufacturing techniques are introduced for a calorimetric H₂O₂ gas sensor based on porous MnO₂. Results from surface analyses by X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM) of the catalytic material provide indication of the H₂O₂ dissociation reaction schemes. The correlation between theory and the experiments is documented in numerical models of the catalytic reaction. The aim of the numerical models is to provide further information on the reaction kinetics and performance enhancement of the porous MnO₂ catalyst.