Background The hemostatic balance in patients with coronavirus disease 2019 (COVID-19) seems to be shifted toward a hypercoagulable state. The aim of the current study was to assess the associated coagulation alterations by point-of-care-diagnostics, focusing on details of clot formation and lysis in these severely affected patients. Methods The authors’ prospective monocentric observational study included critically ill patients diagnosed with COVID-19. Demographics and biochemical data were recorded. To assess the comprehensive hemostatic profile of this patient population, aggregometric (Multiplate) and viscoelastometric (CloPro) measures were performed in the intensive care unit of a university hospital at a single occasion. Coagulation analysis and assessment of coagulation factors were performed. Data were compared to healthy controls. Results In total, 27 patients (21 male; mean age, 60 yr) were included. Impedance aggregometry displayed no greater platelet aggregability in COVID-19 in comparison with healthy controls (area under the curve [AUC] in adenosine diphosphate test, 68 ± 37 U vs. 91 ± 29 U [−27 (Hodges–Lehmann 95% CI, −48 to −1); P = 0.043]; AUC in arachidonic acid test, 102 ± 54 U vs. 115 ± 26 U [−21 (Hodges–Lehmann 95% CI, −51 to 21); P = 0.374]; AUC in thrombin receptor activating peptide 6 test, 114 ± 61 U vs. 144 ± 31 U [−31 (Hodges–Lehmann 95% CI, −69 to −7); P = 0.113]). Comparing the thromboelastometric results of COVID-19 patients to healthy controls, the authors observed significant differences in maximum clot firmness in fibrin contribution to maximum clot firmness assay (37 ± 11 mm vs. 15 ± 4 mm [21 (Hodges–Lehmann 95% CI, 17 to 26); P < 0.001]) and lysis time in extrinsic activation and activation of fibrinolysis by tissue plasminogen activator assay (530 ± 327 s vs. 211 ± 80 s [238 (Hodges–Lehmann 95% CI, 160 to 326); P < 0.001]). Conclusions Thromboelastometry in COVID-19 patients revealed greater fibrinolysis resistance. The authors did not find a greater platelet aggregability based on impedance aggregometric tests. These findings may contribute to our understanding of the hypercoagulable state of critically ill patients with COVID-19. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

A variety of real-world applications heavily rely on an adequate analysis of transient data streams. Due to the rigid processing requirements of data streams, common analysis techniques as known from data mining are not directly applicable. A fundamental building block of many data mining and analysis approaches is density estimation. It provides a well-defined estimation of a continuous data distribution, a fact which makes its adaptation to data streams desirable. A convenient method for density estimation utilizes kernels. The computational complexity of kernel density estimation, however, renders its application to data streams impossible. In this paper, we tackle this problem and propose our Cluster Kernel approach, which provides continuously computed kernel density estimators over streaming data. Not only do Cluster Kernels meet the rigid processing requirements of data streams, but they also allocate only a constant amount of memory, even with the opportunity to adapt it dynamically to changing system resources. For this purpose, we develop an intelligent merge scheme for Cluster Kernels and utilize continuously collected local statistics to resample already processed data. We validate the efficacy of Cluster Kernels for a variety of real-world data streams in an extensive experimental study.

Modern steam power plants must operate safely at extremely low loads, known as windage, in which the low pressure (LP) turbine runs with decreased or even zero flow. Windage is characterized by a strongly unsteady three-dimensional (3D) flow field leading to possible aerodynamic excitations. Extensive flow field measurements were performed in an LP steam turbine test rig during windage, using pneumatic probes in the last stage and a diffuser. The flow field of the whole turbine was also calculated with steady 3D computational fluid dynamics (ANSYS CFX). Good agreement is found between the simulations and the measurements of the flow field, and the characteristic vortex structures behind the last rotor row are captured. The numerically predicted trends of power output, pressure ratio, and temperature of the last turbine blade row closely match the experimental data. The complex vortex flow in the stage is interpreted using both numerical and experimental results.

In order to support continuous queries over data streams, a plethora of suitable techniques as well as prototypes have been developed and evaluated in recent years. In particular, it is of utmost importance to confirm their necessity and feasibility in real-world applications. For that reason, we have successfully coupled our infrastructure for data stream processing (PIPES) with an industrial Production-to-Business software (i-Plant) dedicated to highly automated manufacturing processes. PIPES: Stream Processing PIPES (Public Infrastructure for Processing and Exploring Streams)[3] is a powerful toolkit providing the essential components to build and execute continuous queries over data streams. The library character of PIPES substantially facilitates the construction of a data stream management system (DSMS) tailored to the particular requirements of an application domain. Our demonstration validates this approach by showing up the intuitive and fruitful composition of PIPES and i-Plant.PIPES relies on a sound semantics [4] which is compatible with the Continuous Query Language (CQL). Its core is a push-based, time-interval operator algebra. Continuous queries are implemented as a directed acyclic operator graph which enables PIPES to gain from subquery sharing.In addition, PIPES provides flexible frameworks for the runtime components, e. g., scheduler, memory manager, and query optimizer. Each component is parameterized by a strategy in order to guarantee runtime adaptivity. Appropriate strategies are usually based on a cost model [1] which incorporates continuously gathered runtime metadata such as stream rates, memory usage, and operator costs. As more complex metadata, PIPES offers online, resource-adaptive estimations of data distributions based on complex statistical methods, like wavelet-based density estimators [2].