Improving Markov-based TCP Traffic Classification

This paper presents an improved variant of our Markov-based TCP traffic classifier and demonstrates its performance using traffic captured in a university network. Payload length, flow direction, and position of the first data packets of a TCP connection are reflected in the states of the Markov models. In addition, we integrate a new "end of connection" state to further improve the classification accuracy. Using 10-fold cross validation, we identify appropriate settings for the payload length intervals and the number of data packets considered in the models. Finally, we discuss the classification results for the different applications

In Situ Experiments To Reveal the Role of Surface Feature Sidewalls in the Cassie–Wenzel Transition

Waterproof and self-cleaning surfaces continue to attract much attention as they can be instrumental in various different technologies. Such surfaces are typically rough, allowing liquids to contact only the outermost tops of their asperities, with air being entrapped underneath. The formed solid–liquid–air interface is metastable and, hence, can be forced into a completely wetted solid surface. A detailed understanding of the wetting barrier and the dynamics of this transition is critically important for the practical use of the related surfaces. Toward this aim, wetting transitions were studied in situ at a set of patterned perfluoropolyether dimethacrylate (PFPEdma) polymer surfaces exhibiting surface features with different types of sidewall profiles. PFPEdma is intrinsically hydrophobic and exhibits a refractive index very similar to water. Upon immersion of the patterned surfaces into water, incident light was differently scattered at the solid–liquid–air and solid–liquid interface, which allows for distinguishing between both wetting states by dark-field microscopy. The wetting transition observed with this methodology was found to be determined by the sidewall profiles of the patterned structures. Partial recovery of the wetting was demonstrated to be induced by abrupt and continuous pressure reductions. A theoretical model based on Laplace’s law was developed and applied, allowing for the analytical calculation of the transition barrier and the potential to revert the wetting upon pressure reduction

A Generic Liquid Chromatography-Tandem Mass Spectrometry Exposome Method for the Determination of Xenoestrogens in Biological Matrices.

We are constantly exposed to a variety of environmental contaminants and hormones, including those mimicking endogenous estrogens. These highly heterogeneous molecules are collectively referred to as xenoestrogens and hold the potential to affect and alter the delicate hormonal balance of the human body. To monitor exposure and investigate potential health implications, comprehensive analytical methods covering all major xenoestrogen classes are needed but not available to date. Herein, we describe a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the simultaneous determination of multiple classes of endogenous as well as exogenous estrogens in human urine, serum, and breast milk to enable proper exposure and risk assessment. In total, 75 analytes were included, whereof a majority was successfully in-house validated in the three matrices. Extraction recoveries of validated analytes ranged from 71% to 110% and limits of quantification from 0.015 to 5 μg/L, 0.03 to 14 μg/L, and 0.03 to 4.6 μg/L in urine, serum, and breast milk, respectively. The applicability of the novel method was demonstrated in proof-of-principle experiments by analyzing urine from Austrian individuals and breast milk from Austrian and Nigerian individuals. Thereby, we proved the methods' feasibility to identify and quantify different classes of xenoestrogens simultaneously. The results illustrate the general importance of multiclass exposure assessment in the context of the exposome paradigm. Specifically, they highlight the need for estimating total estrogenic burden rather than single analyte or chemical class measurements and its potential impact in endocrine disruption and hormone related diseases including cancers

Benchmarking a DEM‐CFD Model of an Optical Belt Sorter by Experimental Comparison

A DEM-CFD (discrete element method - computational fluid dynamics) model of an optical belt sorter was extensively compared with experiments of a laboratory-scale sorter to assess the model\u27s accuracy. Brick and sand-lime brick were considered as materials. First, the transport characteristics on the conveyor belt, involving mass flow, lateral particle distribution and proximity, were compared. Second, sorting results were benchmarked for varying mixture proportions at differing mass flows. It was found that the numerical model is able to reproduce the experimental results with high accuracy