Review of Benthic Invertebrate Fauna in Extremely Acidic Environments (pH ≤ 3)

Some benthic invertebrate species are able to colonise habitats in extremely acidic waters. We compiled a list of acid-resistant benthic invertebrates from the literature and extended it by studying extremely acidic mining lakes in eastern Germany. Acid-resistant species were registered for some habitats with pH ≤ 3, such as volcanic lakes, acid strip streams, and acidic mining lakes. Twenty nine taxa were found in waters with pH below 3. Diptera comprised 48.3% of the total number of taxa, followed by Coleoptera with 10.3%, Trichoptera 10.3%, Ephemeroptera, Megaloptera, and Plecoptera each with 6.9%, and Odonata, Hirudinea, and Acari each with 3.5%. Chironomus (Diptera: Chironomidae) were the most common genus in extremely acidic environments with 9 species. In Germany, Chironomus crassimanus was found to be the most abundant species in extremely acidic mining lakes. Generally, aquatic insects were the most common group of benthic invertebrates.     

Eigenvalue Beamforming Using a Multirank MVDR Beamformer and Subspace Selection

We derive eigenvalue beamformers to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown. The unknown orientation may be fixed, in which case the signal covariance is rank-1, or it may be random, in which case the signal covariance is multirank. We present a systematic treatment of such signal models and explain their relevance for modeling signal uncertainties. We then present a multirank generalization of the MVDR beamformer. The idea is to minimize the power at the output of a matrix beamformer, while enforcing a data dependent distortionless constraint in the signal subspace, which we design based on the type of signal we wish to resolve. We show that the eigenvalues of an error covariance matrix are fundamental for resolving signals of interest. Signals with rank-1 covariances are resolved by the largest eigenvalues of the error covariance, while signals with multirank covariances are resolved by the smallest eigenvalues. Thus, the beamformers we design are eigenvalue beamformers, which extract signal information from eigen-modes of an error covariance. We address the tradeoff between angular resolution of eigenvalue beamformers and the fraction of the signal power they capture.     

Range sidelobes blanking by comparing outputs of contrasting mismatched filters

Range sidelobes, a major shortcoming of radar pulse compression, are often reduced through the use of mismatched filters. The authors propose to blank the remaining sidelobes by using two or more mismatched filters, whose sidelobes are designed to peak at different delays. The authors show how to design such filters and present promising simulation results with two or more mismatched filters.     

Comparison of batch sorption tests, pilot studies, and modeling for estimating GAC bed life

Saint Paul Regional Water Services (SPRWS) in Saint Paul, MN experiences annual taste and odor episodes during the warm summer months. These episodes are attributed primarily to geosmin that is produced by cyanobacteria growing in the chain of lakes used to convey and store the source water pumped from the Mississippi River. Batch experiments, pilot-scale experiments, and model simulations were performed to determine the geosmin removal performance and bed life of a granular activated carbon (GAC) filter-sorber. Using batch adsorption isotherm parameters, the estimated bed life for the GAC filter-sorber ranged from 920 to 1241 days when challenged with a constant concentration of 100 ng/L of geosmin. The estimated bed life obtained using the AdDesignS model and the actual pilot-plant loading history was 594 days. Based on the pilot-scale GAC column data, the actual bed life (>714 days) was much longer than the simulated values because bed life was extended by biological degradation of geosmin. The continuous feeding of high concentrations of geosmin (100-400 ng/L) in the pilot-scale experiments enriched for a robust geosmin-degrading culture that was sustained when the geosmin feed was turned off for 40 days. It is unclear, however, whether a geosmin-degrading culture can be established in a full-scale filter that experiences taste and odor episodes for only 1 or 2 months per year. The results of this research indicate that care must be exercised in the design and interpretation of pilot-scale experiments and model simulations for predicting taste and odor removal in full-scale GAC filter-sorbers. Adsorption and the potential for biological degradation must be considered to estimate GAC bed life for the conditions of intermittent geosmin loading typically experienced by full-scale systems.     

Determination of selected organophosphate esters in the aquatic environment of Austria

Organophosphate esters are used in multitude of applications such as flame retardants, plasticizers and lubricants. In recent years concerns have been raised in regards to the environmental impact of some of the more commonly used phosphoric acid triesters, because many of them are toxic and persistent. Liquid-liquid extraction and ultrasound-assisted solvent extraction followed by liquid chromatography coupled to tandem mass spectrometry detection (LC-MS/MS) were used for the determination of nine priority organophosphate esters in waste and surface water, as well as in sediment samples for first time in Austria. Recoveries ranged from 63% to 94% in water and from 74 to 104% in sediment with estimated quantification limits between 2.6 and 7.9 ng/l in surface water, 4.1 and 13 ng/l in effluent waste water, and between 0.48 and 11 microg/kg in sediment. The validated method was applied to determine the occurrence of the selected organophosphorus flame retardants (OPFRs) and plasticizers in the aquatic environment of Austria. The impact of the discharge of waste water treatment plants into the receiving water bodies was also studied.     

Matched direction detectors and estimators for array processing with subspace steering vector uncertainties

In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Crame/spl acute/r-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations.     

Using machine learning to predict the density profiles of surface-densified wood based on cross-sectional images

Over the past decades, the surface densification of solid wood has received increased attention. However, the inhomogeneous density distribution in the densification direction might be a challenge with regard to process control within a large-scale production process, as the density profile governs many relevant properties of surface-densified wood. Currently, the measurement of density profiles relies on sensitive X-ray equipment and is difficult to integrate into an on-line process. Hence, in this study, three machine learning approaches were applied to predict the density profiles of surface-densified Scots pine specimens, only based on visual image acquisition—a technology that is ubiquitous in the wood industry: partial least squares (PLS) regression, artificial neural networks (ANN), and convolutional neural networks (CNN). The machine learning models were trained on images of the specimen cross-sections as input data, and X-ray density profiles as output data. There were 1850 observations, and the model performance was evaluated on external test sets. The models had mean absolute percentage errors of the predicted values between 9 and 18%; the CNN achieving the smallest error (9.24%). A deeper analysis of the data revealed that the ANN approach performed inconsistently between observations. PLS regression predicted the main density peak to a high accuracy but could not model other features. Only the CNN could reliably model the main density peak, wide growth rings, and the important region between the specimen surface and the main density peak. The ability of the models to generalise to untypical new data was improved by augmentation of the training data.

     

Microstructure alterations in the base material, heat affected zone and weld metal of a 440-VVER-reactor pressure vessel caused by high fluence irradiation during long term operation; material: 15 Ch2MFA ≈0.15 C–2.5 Cr–0.7 Mo–0.3 V

Within the scope of the EC research project Tacis ’91 (‘RPV-Embrittlement’), trepans were taken from the highly irradiated circumferential RPV-weld of the Novovoronesh power plant unit-2 of the type VVER-440/230. The cumulated fast fluence level in this position reaches up to 6.5×10¹⁹/cm² (E>0.5 MeV). In a joint research work, the mechanical properties, the chemical composition, and the microstructure of the base material, the heat affected zone (HAZ), and the weld metal have been investigated in order to study the influence of irradiation, and of post irradiation heat treatment (475°C, 560°C) on the properties. The examination of the microstructure performed by analytical transmission electron microscopy (200 kV) shows the existence of dislocation loops (‘black dots’), irradiation induced precipitates, and segregation of copper in the carbides. These changes in microstructure, which are due to service affection (neutron irradiation, temperature) have occurred more pronounced in the weld metal and the HAZ than in the base material.     

A process and load adjusted coating system for metallic inserts in hybrid composites

According to the concept of an intrinsic hybrid composite, adhesive bonding is designed for generating the connection between the applied fiber reinforced polymer and a metallic insert. To induce adhesive bonding, a metallic insert, made of aluminum, is coated. This contribution focusses on the development of a suitable coating system. To this end, the coating system must meet certain requirements. On one hand, demands on the coating like ductility can be deduced from analyzing the manufacturing process. On the other hand, requirements like corrosion protection as well as high static and dynamic strength arise from specific applications under considerations. The utilized coating system is based on organically modified silicate layers (Ormosil) applied using a sol–gel process. To prove that this coating system fulfils the requirements, the corrosion protection is analysed by impedance spectroscopy. Furthermore, different mechanical experimental investigations are performed to verify the ductility of the coating as well as the strength of the resulting interface. Hence, it is shown that the considered coating system can be applied for the analysed intrinsic hybrid composite manufactured in series.     

Transfusion Cost Containment for Abdominal Surgery with Neural Networks

Typing and crossmatching blood is a significant cost for most hospitals, regardless of whether the blood is actually transfused. Many hospitals have implemented a Maximum Surgical Blood Order Schedule, MSBOS, to control over-ordering of blood units for surgery. The research presented in this article examines the use of neural networks for predicting the quantity of blood required by individual patients undergoing abdominal surgery (e.g. splenectomy). A comparison is made between the neural network predictions at a particular hospital versus the current MSBOS methodology for ordering surgical blood, by using the crossmatch to transfusion ratio. Results from the neural network transfusion predictions for the abdominal aortic aneurysm (AAA) surgery imply that neural networks can significantly improve the transfusion efficiency of hospitals. However, further examination of neural network capabilities for predicting the transfusion needs of patients undergoing other types of abdominal surgeries indicates that for operations other than the AAA, neural networks tend to under-predict the transfusion requirements of ten percent of the patients. Even if not used to limit over-ordering of blood for surgical transfusions, neural networks may be used as an intelligent decision support system to evaluate the current efficiency of hospital transfusion practices and to indicate beneficial changes to current MSBOS values.