DEVELOPMENT OF A MATHEMATICAL MODEL FOR PERFORMANCE PREDICTION OF PLANING CATAMARAN IN CALM WATER

In this paper, an attempt has been made to predict the performance of a planing catamaran using a mathematical model. Catamarans subjected to a common hydrodynamic lift, have an extra lift between the two asymmetric half bodies. In order to develop a mathematical model for performance prediction of planing catamarans, existing formulas for hydrodynamic lift calculation must be modified. Existing empirical and semi-empirical equations in the literature have been implemented and compared against available experimental data. Evaluation of lift in comparison with experimental data has been documented. Parameters influencing the interaction between demi-hulls and separation effects have been analyzed. The mathematical model for planing catamarans has been developed based on Savitsky’s method and results have been compared against experimental data. Finally, the effects of variation in hull geometry such as deadrise angle and distance between two half bodies on equilibrium trim angle, resistance and wetted surface have been examined.

Controlling the Uncontrolled Variation in the Diet Induced Obese Mouse by Microbiomic Characterization

C57BL/6NTac (B6NTac) and C57BL/6NRj (B6NRj) mice were fed a high calorie diet and treated with liraglutide. 42 mice would have been needed in an ordinary one-way ANOVA to show a reduction in glycosylated hemoglobin (HbA1c) in B6NTac mice, but incorporating the sequenced fecal microbiota in a two-way ANOVA reduced the group size needed to obtain a statistical significance to 12 mice. In B6NRj mice there was no impact of liraglutide on HbA1c neither with or without microbiota incorporation. In both sub-strains, the liraglutide effect on glucose tolerance and body weight was powered by incorporation of microbiota clusters. Although B6NTac mice were genetically far more conform than the B6NRj mice, incorporation of a genetic characterization by short tandem repeats had only little impact on the outcome of data evaluation. In conclusion, incorporation of microbiota characterisation powers data evaluation in diet induced obesity mouse strains, which are influenced by the microbiota.

High-Performance VOC Quantification for IAQ Monitoring Using Advanced Sensor Systems and Deep Learning

With air quality being one target in the sustainable development goals set by the United Nations, accurate monitoring also of indoor air quality is more important than ever. Chemiresistive gas sensors are an inexpensive and promising solution for the monitoring of volatile organic compounds, which are of high concern indoors. To fully exploit the potential of these sensors, advanced operating modes, calibration, and data evaluation methods are required. This contribution outlines a systematic approach based on dynamic operation (temperature-cycled operation), randomized calibration (Latin hypercube sampling), and the use of advances in deep neural networks originally developed for natural language processing and computer vision, applying this approach to volatile organic compound measurements for indoor air quality monitoring. This paper discusses the pros and cons of deep neural networks for volatile organic compound monitoring in a laboratory environment by comparing the quantification accuracy of state-of-the-art data evaluation methods with a 10-layer deep convolutional neural network (TCOCNN). The overall performance of both methods was compared for complex gas mixtures with several volatile organic compounds, as well as interfering gases and changing ambient humidity in a comprehensive lab evaluation. Furthermore, both were tested under realistic conditions in the field with additional release tests of volatile organic compounds. The results obtained during field testing were compared with analytical measurements, namely the gold standard gas chromatography mass spectrometry analysis based on Tenax sampling, as well as two mobile systems, a gas chromatograph with photo-ionization detection for volatile organic compound monitoring and a gas chromatograph with a reducing compound photometer for the monitoring of hydrogen. The results showed that the TCOCNN outperforms state-of-the-art data evaluation methods, for example for critical pollutants such as formaldehyde, achieving an uncertainty of around 11 ppb even in complex mixtures, and offers a more robust volatile organic compound quantification in a laboratory environment, as well as in real ambient air for most targets.