Improving the resolution of Non-Invasive Time Domain Reflectometry

Non invasive time domain reflectometry (TDR) may be used to estimate the volumetric moisture content, θᵥ, with depth for a variety of sample materials. The forward physical model is couched in terms of a moments method where integration is performed over a discretised sample space to estimate the measured propagation time, tp down a pair of parallel transmission lines. We show that inverse solution to this, which recovers relative permittivity and thus θᵥ, is greatly facilitated by a simplification of the system geometry via, 1) realistically modeling the prior density of the sample, 2) using this prior with the inherent system symmetry to reduce the number of required discretisation cells, and 3) determining a physically meaningful reduction operator to allow a coarse discretisation mesh to be used. The observational equation is expressed in the Bayesian paradigm with the most accurate and robust solution obtained using the conditional mean of the posterior distribution constructed via a Monte Carlo method. Results of simulation show that the method is capable of providing accurate estimates of the moisture density profile down to a depth of 100 mm with an error < 4%. Further, the reduction in the number of discretised cells required to accurately estimate these profiles means that the inversion procedure is quick enough to enable the real time application of the equipment, a fundamental requirement in the development

An IIoT-Device for Acquisition and Analysis of High-Frequency Data Processed by Artificial Intelligence

This publication presents the development of an Industrial-Internet-of-Things device. The device is capable of completing several tasks, such as the acquisition of high-frequency measurement data and evaluating data via machine learning methods in an artificial intelligence application. The installed measurement technology generates data which is comparable to data generated by costly laboratory equipment, meaning that it can be used as a low-budget and open-source alternative. A workflow method has been designed that promotes experimental work and simplifies the effort required to implement artificial intelligence solutions. At the end of this paper, the results of the experiment, which aimed to collect measurement data, extract suitable features, and train artificial intelligence models, are presented. Techniques from vibration analysis were used for feature extraction, and concepts for the extrapolation and enhancement of data sets were investigated. The test results have proven that the development is comparable with high-end laboratory equipment. The created application has demonstrated sufficient accuracy in predictions, and the designed process can be used for arbitrary, artificial intelligence-based rapid prototyping

High-Sulfated Glycosaminoglycans Prevent Coronavirus Replication

Coronaviruses (CoVs) are common among humans and many animals, causing respiratory or gastrointestinal diseases. Currently, only a few antiviral drugs against CoVs are available. Especially for SARS-CoV-2, new compounds for treatment of COVID-19 are urgently needed. In this study, we characterize the antiviral effects of two high-sulfated glycosaminoglycan (GAG) derivatives against SARS-CoV-2 and bovine coronaviruses (BCoV), which are both members of the Betacoronavirus genus. The investigated compounds are based on hyaluronan (HA) and chondroitin sulfate (CS) and exhibit a strong inhibitory effect against both CoVs. Yield assays were performed using BCoV-infected PT cells in the presence and absence of the compounds. While the high-sulfated HA (sHA3) led to an inhibition of viral growth early after infection, high-sulfated CS (sCS3) had a slightly smaller effect. Time of addition assays, where sHA3 and sCS3 were added to PT cells before, during or after infection, demonstrated an inhibitory effect during all phases of infection, whereas sHA3 showed a stronger effect even after virus absorbance. Furthermore, attachment analyses with prechilled PT cells revealed that virus attachment is not blocked. In addition, sHA3 and sCS3 inactivated BCoV by stable binding. Analysis by quantitative real-time RT PCR underlines the high potency of the inhibitors against BCoV, as well as B.1-lineage, Alpha and Beta SARS-CoV-2 viruses. Taken together, these results demonstrated that the two high-sulfated GAG derivatives exhibit low cytotoxicity and represent promising candidates for an anti-CoV therapy