Bewertungsgrundlagen zur Beurteilung der Lebensraumfunktion kontaminierter Böden an Hand von mikrobiellen Gasumsätzen

The application of biological tests for the assessment of the habitat function of soils is of great importance. Soil respiration measured as oxygen consumption or carbon dioxide release is used frequently for the evaluation of contaminated sites. The oxygen and carbon dioxide turn over in soil respiration is composed of different metabolic activities, for example aerobic respiration, nitrification and urease activity. First of all the influence of pollutants on basal respiration and substrate induced respiration was investigated with special consideration of nutrient supply (nitrogen and phosphorus). For the derivation of evaluation criteria, respiration of uncontaminated soils was compared to the respiration of contaminated soils. It was shown that the following criteria can be used for the assessment of the habitat function: Cumulated basal respiration, maximum of substrate induced respiration rate, maximum slope of substrate induced respiration rate, the space of time, which is needed to reach the maximum substrate induced respiration rate. Furthermore it was possible to detect organic pollutants, which are degradable under aerobic conditions, by high cumulated basal respiration and by the influence of nitrogen supply for the time span, which is needed to reach the maximum substrate induced respiration rate (D tN and D tNP). The symptoms, which are caused by a lack of nutrients like ammonia and phosphate, are comparable to the effects caused by pollutants. For this reason the nutrient supply of a soil sample has to be considered for evaluation of soil respiration data. The positive (promotion) or negative (inhibition) influence of nutrients can be quantitatively estimated using the limitation quotients for basal respiration (qBNPn) and for substrate induced respiration (qSXn). Limitation quotients (qSNO2 and qSNCO2) for soils which had high contents of heavy metals were significantly higher than for soils without heavy metal contamination. Standard values and a strategy for a combined evaluation were proposed for the above mentioned criteria, which allows for an exact interpretation of the achieved data. A reliable quantitative measurement of ammonia oxidation by oxygen consumption could not be realized, because the specific process of ammonia oxidation could not be separated from soil respiration. The photometrical test with the specific inhibitor allylthiourea showed that the production of nitride in a soil sample could be stopped completely, but the inhibition of the oxygen consumption caused by ammonia oxidation could not definitely be proved. But the direct comparison of oxygen consumption and carbon dioxide release offers the possibility of a qualitative measurement of ammonia oxidation. In most cases the results of the two methods were corresponding. One exception occured with the soil BMKW 1a, which showed a very high nitrogen limitation of basal respiration. Therefore the result of measuring oxygen consumption seemed to be a very high ammonia oxidation, but the result of the measurement of nitride production was very low. For these reasons the measurement of oxygen consumption in testing ammonia oxidation can only be used as complementary investigation for soil respiration: Much longer incubation time, no reliable quantification possible and in special cases wrong results are obtained. The urease activity of soils could be measured by the carbon dioxide release. By using NBPT as specific inhibitor, it was possible to seperate the carbon dioxide which was produced by urease from the carbon dioxide which was produced by soil respiration. The developed method offers some important advantages in comparison to the photometrical measurement of ammonia: Incubation of a soil sample without using buffer solution, no extraction of ammonia from the soil sample necessary, less chemical consumption and better reproduction of results. Urease activity has proved to be a sensitive parameter for the assessment of microbial habitat function, because most of the contaminated soil samples showed low or no urease activity. Soils, which were classified as not critically influenced relative to the ammonia oxidation measured as nitride production, were graded as critical by the urease activity measured as carbon dioxide release. The reverse case could be observed, too. Therefore the use of both assays avoids the possibility of achieving misleading results if the influence of pollutants on the microbial habitat function has to be tested. As consequence the widespread use of soil respiration and ammonia oxidation should be completed by the urease activity measured as carbon dioxide release. The complete validation of the proposed assays, the validity of criteria of evaluation and proposed standard values have to be seen as basis for future investigations and have to be proved by investigating a greater amount of soil samples

Machine Learning Methods for Anomaly Detection in BACnet Networks

In recent years, the volume and the complexity of data in Building Automation System networks have increased exponentially. As a result, a manual analysis of network traffic data has become nearly impossible. Even automated but supervised methods are problematic in practice since the large amount of data makes manual labeling, required to train the algorithms to differentiate between normal traffic and anomalies, impractical. This paper introduces a framework which allows the characterization of BACnet network traffic data by means of unsupervised machine learning techniques. Specifically, we use clustering, random forests, one-class support vector machines and support vector classifier, after a pre-processing step that includes principal components analysis for dimensionality reduction. We compare the effectiveness of the methods in detecting anomalies by performing experiments on BACnet network traffic data from various sources. We describe which of these unsupervised methods work best in specific scenarios since each method has its distinct advantages and disadvantages. In particular, we discuss which method is best suited to detect new types of anomalies (novelty detection), or which method most reliably and efficiently finds new attacks of a type that has been captured in the data previously

Virtual reality for assessing stereopsis performance and eye characteristics in Post-COVID

Abstract In 2019, we faced a pandemic due to the coronavirus disease (COVID-19), with millions of confirmed cases and reported deaths. Even in recovered patients, symptoms can be persistent over weeks, termed Post-COVID. In addition to common symptoms of fatigue, muscle weakness, and cognitive impairments, visual impairments have been reported. Automatic classification of COVID and Post-COVID is researched based on blood samples and radiation-based procedures, among others. However, a symptom-oriented assessment for visual impairments is still missing. Thus, we propose a Virtual Reality environment in which stereoscopic stimuli are displayed to test the patient’s stereopsis performance. While performing the visual tasks, the eyes’ gaze and pupil diameter are recorded. We collected data from 15 controls and 20 Post-COVID patients in a study. Therefrom, we extracted features of three main data groups, stereopsis performance, pupil diameter, and gaze behavior, and trained various classifiers. The Random Forest classifier achieved the best result with 71% accuracy. The recorded data support the classification result showing worse stereopsis performance and eye movement alterations in Post-COVID. There are limitations in the study design, comprising a small sample size and the use of an eye tracking system