Biodiversity of macrozoobenthos in a large river, the Austrian Danube, including quantitative studies in a free-flowing stretch below Vienna: a short review

The Danube is ca. 2850 km in length and is the second largest river in Europe. The Austrian part of the Danube falls 156 metres in altitude over its 351 km length and, since the early 1950s, the river has been developed into a power-generating waterway, so that the continuity of the river is now interrupted by ten impounded areas. Only two stretches of the original free-flowing river are left, the Wachau region (above river-km 2005, west of Vienna) and the region downstream from the impoundment at Vienna (river-km 1921). Most of the recent theories and concepts related to invertebrates, in the context of the ecology of running waters, are based on studies on small streams, whereas investigations of large rivers have played a minor role for a long time, mainly due to methodological difficulties. The authors' recent detailed studies on macroinvertebrates in the free-flowing section of the Danube below Vienna, provide an excellent opportunity to survey or restate scientific hypotheses on the basis of a large river. In this review the main interest focuses on the investigation of biodiversity, i.e. the number of species and their relative proportions in the whole invertebrate community, as well as major governing environmental factors. The article summarises the species composition, the important environmental variables at the river cross-section and the effect of upstream impoundment on the riverbed and its fauna

A novel deep-learning based approach to DNS over HTTPS network traffic detection

Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification

Leveraging Variational Autoencoders for Parameterized MMSE Channel Estimation

In this manuscript, we propose to utilize the generative neural network-based variational autoencoder for channel estimation. The variational autoencoder models the underlying true but unknown channel distribution as a conditional Gaussian distribution in a novel way. The derived channel estimator exploits the internal structure of the variational autoencoder to parameterize an approximation of the mean squared error optimal estimator resulting from the conditional Gaussian channel models. We provide a rigorous analysis under which conditions a variational autoencoder-based estimator is mean squared error optimal. We then present considerations that make the variational autoencoder-based estimator practical and propose three different estimator variants that differ in their access to channel knowledge during the training and evaluation phase. In particular, the proposed estimator variant trained solely on noisy pilot observations is particularly noteworthy as it does not require access to noise-free, ground-truth channel data during training or evaluation. Extensive numerical simulations first analyze the internal behavior of the variational autoencoder-based estimators and then demonstrate excellent channel estimation performance compared to related classical and machine learning-based state-of-the-art channel estimators.Comment: 13 pages, 12 figure

Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models

This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative models, specifically Gaussian mixture models (GMMs), mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These models effectively learn the unknown channel distribution inherent in radio propagation scenarios, providing valuable prior information. Conditioning on the latent variable of these generative models yields a locally Gaussian channel distribution, thus enabling the application of the well-known Bussgang decomposition. By exploiting the resulting conditional Bussgang decomposition, we derive parameterized linear minimum mean square error (MMSE) estimators for the considered generative latent variable models. In this context, we explore leveraging model-based structural features to reduce memory and complexity overhead associated with the proposed estimators. Furthermore, we devise necessary training adaptations, enabling direct learning of the generative models from quantized pilot observations without requiring ground-truth channel samples during the training phase. Through extensive simulations, we demonstrate the superiority of our introduced estimators over existing state-of-the-art methods for coarsely quantized systems, as evidenced by significant improvements in mean square error (MSE) and achievable rate metrics

Subjektive Repräsentation der Zusammenhänge zwischen Organisationsstruktur, soziomoralischer Atmosphäre und prosozialem Arbeitshandeln in demokratischen Unternehmen – eine Kreuzvalidierung

Theoretischer Hintergrund und Fragestellung Bei der Erforschung der individuellen moralischen Urteilskompetenz wurde zunehmend die Frage verfolgt, more about welche organisationalen Merkmale, buy Interaktionspraktiken und Interventionen in Institutionen sich fördernd bzw. hemmend auf die Genese von Kompetenzen und Handlungsorientierungen, die ethisch angemessenem Handeln zugrunde liegen, auswirken (z.B. Lempert, 2009; Oser & Althof, 2001; Power, Higgins, & Kohlberg, 1989

Endovascular therapy of direct dural carotid cavernous fistulas - A therapy assessment study including long-term follow-up patient interviews

Purpose Endovascular embolization nowadays is a well-established treatment option for direct carotid cavernous fistulas (dCCF, Barrow Type A). There are many publications on the complication and success rates of this method. However, little is known on the patients' opinion on the treatment result after several years. We report on this issue also including the "pioneer patients" treated almost two decades ago. Methods We retrospectively reviewed the records of all patient (n = 25) with a more than 24 months follow-up interval after endovascular treatment of a dCCF at our institution from 01/1999 to 08/2018. We determined primary therapy success, complication rate, state of the fistula in the last imaging follow-up and quoted the patient's subjective perception of the long-term treatment success using a standardized interview form. Results Occlusion rate in the last imaging follow up was 96% (24/25) with a complication rate of 8% (2/25). The response rate on our interview request was 96% (24/25) with a rate of considered feedback of 84% (21/25 patients). Duration of our observation interval for the patient reported outcome was 143 months / 11 years (median, range: 35-226 m/2-18 y). Most of them (21/25,84%) felt they benefited from the treatment. Conclusions Endovascular supply of dCCF is a highly effective treatment method leading to a sustainable therapy success with long-lasting stable subjective benefit even to our "pioneer patients" treated almost two decades ago

Pseudo-subarachnoid haemorrhage due to chronic hypoxaemia: case report and review of the literature

Background: The specificity of computed tomography (CT) for subarachnoid haemorrhage (SAH) is very high. However, physicians should be aware of rare false positive findings, also referred to as "pseudo-SAH". We present an unusual case in which such a finding was caused by chronic hypoxaemia. Case presentation: A 37-year-old male patient presented with headaches. His CT-scan showed multiple confluent subarachnoid hyperattenuations, which mimicked SAH. However, the headache was chronic and had no features typical for SAH. The patient suffered from severe chronic hypoxaemia due to congenital heart failure. On CT-angiography diffuse intracranial vessel proliferation was found and laboratory results revealed a highly raised level of haematocrit, which had both probably developed as compensatory mechanisms. A combination of these findings explained the subarachnoid hyperdensities. Magnetic resonance imaging (MRI) showed no signs of SAH and visualized hypoxaemia in cerebral veins. A diagnosis of pseudo-SAH was made. The patient's symptoms were likely due to a secondary headache attributed to hypoxia and/or hypercapnia. Therapy was symptomatic. Conclusions: Severe chronic hypoxaemia should be recognised as a rare cause of pseudo-SAH. Clinical evaluation and MRI help differentiate SAH from pseudo-SAH

Channel Estimation based on Gaussian Mixture Models with Structured Covariances

In this work, we propose variations of a Gaussian mixture model (GMM) based channel estimator that was recently proven to be asymptotically optimal in the minimum mean square error (MMSE) sense. We account for the need of low computational complexity in the online estimation and low cost for training and storage in practical applications. To this end, we discuss modifications of the underlying expectation-maximization (EM) algorithm, which is needed to fit the parameters of the GMM, to allow for structurally constrained covariances. Further, we investigate splitting the 2D time and frequency estimation problem in wideband systems into cascaded 1D estimations with the help of the GMM. The proposed cascaded GMM approach drastically reduces the complexity and memory requirements. We observe that due to the training on realistic channel data, the proposed GMM estimators seem to inherently perform a trade-off between saving complexity/parameters and estimation performance. We compare these low-complexity approaches to a practical and low cost method that relies on the power delay profile (PDP) and the Doppler spectrum (DS). We argue that, with the training on scenario-specific data from the environment, these practical baselines are outperformed by far with equal estimation complexity