Novel measurement system for respiratory aerosols and droplets in indoor environments

The SARS-CoV-2 pandemic has created a great demand for a better understanding of the spread of viruses in indoor environments. A novel measurement system consisting of one portable aerosol-emitting mannequin (emitter) and a number of portable aerosol-absorbing mannequins (recipients) was developed that can measure the spread of aerosols and droplets that potentially contain infectious viruses. The emission of the virus from a human is simulated by using tracer particles solved in water. The recipients inhale the aerosols and droplets and quantify the level of solved tracer particles in their artificial lungs simultaneously over time. The mobile system can be arranged in a large variety of spreading scenarios in indoor environments and allows for quantification of the infection probability due to airborne virus spreading. This study shows the accuracy of the new measurement system and its ability to compare aerosol reduction measures such as regular ventilation or the use of a room air purifier.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

Investigation of the action of flame retardants in cellulose. I. Investigation of the flame retardant action of 2,2′-oxybis(5,5-dimethyl-1,3,2-dioxaphosphorinane-2,2′-disulfide) in cellulose

Pyrolysis experiments were performed in high vacuum and under reduced air pressure (100 Pa). The volatile products of pure cellulose and cellulose containing various amounts of flame retardant 2,2′-oxybis (5,5-dimethyl-1,3,2-dioxaphosphorinane-2,2′-disulfide), i.e., Sandoflam 5060 of Sandoz AG, were studied by means of gas chromatography in combination with mass spectroscopy. The volatile products were characterized with infrared spectroscopy. The studied revealed that the incorporation of the flame retardant enhanced the water release and shifted the onset of this reaction to lower temperature. On the basis of these findings an explanation for the mechanism of flame retardancy in generated cellulose fibers modified with this particular flame retardant is attempted. From experiments with different residual air pressure the influence of oxygen on the primary processes of the pyrolytic degradation of cellulose is being discussed.     

Influence of Reaction Parameters on the Hydrogenolysis of Hydroxymatairesinol Over Carbon Nanofibre Supported Palladium Catalysts

The density functional theory (DFT/B3LYP) calculations were applied to investigate the interaction of a Pt₆ particle with the ZSM-5 zeolite framework. The electronic structure of the metal particle is strongly affected by the interaction with basic framework oxygens and acid sites of the zeolite support. Adsorption on basic sites (E_ads = 6 kcal/mol) favors the formation of the electron enriched metal cluster. Interaction of the platinum cluster with the acid site characterized by stabilization energy of 47 kcal/mol results in oxidation of the metal particle and suppression of Brønsted acidity of the support. The hypothesis is put forward that the oxidized platinum particle can function as an active site for the alkane isomerisation on platinum supported high silica zeolites.     

Pruning Algorithms for Rule Learning

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.     

The value of long tendons in design of tunnel layout and support

A project was initiated from hypotheses that the use of longer and different types of tendon support could lead to improved control of tunnel deformation in changing stress fields. An extensive literature study was conducted. Subsequently a tunnel was supported with various lengths and types of cable tendons. The tunnel was subjected to mining-induced stress changes. Tunnel deformations were measured using instrumentation. Results from the literature study and tunnel deformation measurements indicated within context that: (1) long tendons or other support systems cannot substitute for good tunnel layout; (2) long tendons effect a retain-and-hold function while shorter tendons strengthen and reinforce highly fractured tunnel walls; (3) larger boreholes or weakergrout reduce support system stiffness; (4) cable tendons have high shear resistance and debonding tendency; (5) long tendons are beneficial in sidewalls but not the hangingwall, (6) long tendons induce a more stable sidewall deformation distribution; (7) the updip sidewall deformed less than downdip; (8) more deformation occurred with stress decrease than with stress increase.     

Neural architecture for robotic vision sensor that discounts uneven illumination in a manufacturing environment

Computer vision algorithms for inspection or pick-and-place operations often depend on spatially uniform illumination of a workplace. This necessitates expensive lighting fixtures. To discount effects of uneven illumination we designed and tested a neural network that can adaptively control light sensitivity at the photosensor level. Our neural network architecture consists of multiple layers with hexagonally arranged nodes. All nodes have partially overlapping receptive fields of different spatial frequencies. Feedforward connections are excitatory while feedback pathways subserve lateral inhibition. The outputs of these nodes are combined so as to maximize the signal-to-noise ratio while constantly resetting thresholds to maintain high sensitivity. Our connectionist architecture can account for many characteristics attributed to the lightness constancy phenomenon observed in biological systems. The results suggest that our module maintains high sensitivity over the whole domain of intensities without interfering with transmission of visual information embedded in spatial discontinuities of intensity.