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Kijko et al. (2016) present various methods to estimate parameters that are relevant for probabilistic seismic-hazard assessment. One of these parameters, although not the most influential, is the maximum possible earthquake magnitude m(max). I show that the proposed estimation of m(max) is based on an erroneous equation related to a misuse of the estimator in Cooke (1979) and leads to unstable results. So far, reported finite estimations of m(max) arise from data selection, because the estimator in Kijko et al. (2016) diverges with finite probability. This finding is independent of the assumed distribution of earthquake magnitudes. For the specific choice of the doubly truncated Gutenberg-Richter distribution, I illustrate the problems by deriving explicit equations. Finally, I conclude that point estimators are generally not a suitable approach to constrain m(max).
DOES AGE INFLUENCE BRAIN POTENTIALS DURING AFFECTIVE PICTURE PROCESSING IN MIDDLE-AGED WOMEN?
(2017)
THE P300 AND THE LC-NE SYSTEM: NEW INSIGHTS FROM TRANSCUTANEOUS VAGUS NERVE STIMULATION (TVNS)
(2017)
Predicting macroscopic elastic rock properties requires detailed information on microstructure
(2017)
Predicting variations in macroscopic mechanical rock behaviour due to microstructural changes, driven by mineral precipitation and dissolution is necessary to couple chemo-mechanical processes in geological subsurface simulations. We apply 3D numerical homogenization models to estimate Young’s moduli for five synthetic microstructures, and successfully validate our results for comparable geometries with the analytical Mori-Tanaka approach. Further, we demonstrate that considering specific rock microstructures is of paramount importance, since calculated elastic properties may deviate by up to 230 % for the same mineral composition. Moreover, agreement between simulated and experimentally determined Young’s moduli is significantly improved, when detailed spatial information are employed.
As a potentially toxic agent on nervous system and bone, the safety of aluminium exposure from adjuvants in vaccines and subcutaneous immune therapy (SCIT) products has to be continuously reevaluated, especially regarding concomitant administrations. For this purpose, knowledge on absorption and disposition of aluminium in plasma and tissues is essential. Pharmacokinetic data after vaccination in humans, however, are not available, and for methodological and ethical reasons difficult to obtain. To overcome these limitations, we discuss the possibility of an in vitro-in silico approach combining a toxicokinetic model for aluminium disposition with biorelevant kinetic absorption parameters from adjuvants. We critically review available kinetic aluminium-26 data for model building and, on the basis of a reparameterized toxicokinetic model (Nolte et al., 2001), we identify main modelling gaps. The potential of in vitro dissolution experiments for the prediction of intramuscular absorption kinetics of aluminium after vaccination is explored. It becomes apparent that there is need for detailed in vitro dissolution and in vivo absorption data to establish an in vitro-in vivo correlation (IVIVC) for aluminium adjuvants. We conclude that a combination of new experimental data and further refinement of the Nolte model has the potential to fill a gap in aluminium risk assessment. (C) 2017 Elsevier Inc. All rights reserved.
The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.
The maximum entropy method is used to derive an alternative gravity model for a transport network. The proposed method builds on previous methods which assign the discrete value of a maximum entropy distribution to equal the traffic flow rate. The proposed method however, uses a distribution to represent each flow rate. The proposed method is shown to be able to handle uncertainty in a more elegant way and give similar results to traditional methods. It is able to incorporate more of the observed data through the entropy function, prior distribution and integration limits potentially allowing better inferences to be made.
Background: Evidence that home telemonitoring (HTM) for patients with chronic heart failure (CHF) offers clinical benefit over usual care is controversial as is evidence of a health economic advantage. Therefore the CardioBBEAT trial was designed to prospectively assess the health economic impact of a dedicated home monitoring system for patients with CHF based on actual costs directly obtained from patients’ health care providers.
Methods: Between January 2010 and June 2013, 621 patients (mean age 63,0 ± 11,5 years, 88 % male) with a confirmed diagnosis of CHF (LVEF ≤ 40 %) were enrolled and randomly assigned to two study groups comprising usual care with and without an interactive bi-directional HTM (Motiva®). The primary endpoint was the Incremental Cost-Effectiveness Ratio (ICER) established by the groups’ difference in total cost and in the combined clinical endpoint “days alive and not in hospital nor inpatient care per potential days in study” within the follow up of 12 months. Secondary outcome measures were total mortality and health related quality of life (SF-36, WHO-5 and KCCQ).
Results: In the intention-to-treat analysis, total mortality (HR 0.81; 95% CI 0.45 – 1.45) and days alive and not in hospital (343.3 ± 55.4 vs. 347.2 ± 43.9; p = 0.909) were not significantly different between HTM and usual care. While the resulting primary endpoint ICER was not positive (-181.9; 95% CI −1626.2 ± 1628.9), quality of life assessed by SF-36, WHO-5 and KCCQ as a secondary endpoint was significantly higher in the HTW group at 6 and 12 months of follow-up.
Conclusions: The first simultaneous assessment of clinical and economic outcome of HTM in patients with CHF did not demonstrate superior incremental cost effectiveness compared to usual care. On the other hand, quality of life was improved. It remains open whether the tested HTM solution represents a useful innovative approach in the recent health care setting.
The keynote article (Mayberry & Kluender, 2017) makes an important contribution to questions concerning the existence and characteristics of sensitive periods in language acquisition. Specifically, by comparing groups of non-native L1 and L2 signers, the authors have been able to ingeniously disentangle the effects of maturation from those of early language exposure. Based on L1 versus L2 contrasts, the paper convincingly argues that L2 learning is a less clear test of sensitive periods. Nevertheless, we believe Mayberry and Kluender underestimate the evidence for maturational factors in L2 learning, especially that coming from recent research.
This paper discusses a new approach for designing and deploying Security-as-a-Service (SecaaS) applications using cloud native design patterns. Current SecaaS approaches do not efficiently handle the increasing threats to computer systems and applications. For example, requests for security assessments drastically increase after a high-risk security vulnerability is disclosed. In such scenarios, SecaaS applications are unable to dynamically scale to serve requests. A root cause of this challenge is employment of architectures not specifically fitted to cloud environments. Cloud native design patterns resolve this challenge by enabling certain properties e.g. massive scalability and resiliency via the combination of microservice patterns and cloud-focused design patterns. However adopting these patterns is a complex process, during which several security issues are introduced. In this work, we investigate these security issues, we redesign and deploy a monolithic SecaaS application using cloud native design patterns while considering appropriate, layered security counter-measures i.e. at the application and cloud networking layer. Our prototype implementation out-performs traditional, monolithic applications with an average Scanner Time of 6 minutes, without compromising security. Our approach can be employed for designing secure, scalable and performant SecaaS applications that effectively handle unexpected increase in security assessment requests.
The ionospheric delay of global navigation satellite systems (GNSS) signals typically is compensated by adding a single correction value to the pseudorange measurement of a GNSS receiver. Yet, this neglects the dispersive nature of the ionosphere. In this context we analyze the ionospheric signal distortion beyond a constant delay. These effects become increasingly significant with the signal bandwidth and hence more important for new broadband navigation signals. Using measurements of the Galileo E5 signal, captured with a high gain antenna, we verify that the expected influence can indeed be observed and compensated. A new method to estimate the total electron content (TEC) from a single frequency high gain antenna measurement of a broadband GNSS signal is proposed and described in detail. The received signal is de facto unaffected by multi-path and interference because of the narrow aperture angle of the used antenna which should reduce the error source of the result in general. We would like to point out that such measurements are independent of code correlation, like in standard receiver applications. It is therefore also usable without knowledge of the signal coding. Results of the TEC estimation process are shown and discussed comparing to common TEC products like TEC maps and dual frequency receiver estimates.
S-test results for the USGS and RELM forecasts. The differences between the simulated log-likelihoods and the observed log-likelihood are labelled on the horizontal axes, with scaling adjustments for the 40year.retro experiment. The horizontal lines represent the confidence intervals, within the 0.05 significance level, for each forecast and experiment. If this range contains a log-likelihood difference of zero, the forecasted log-likelihoods are consistent with the observed, and the forecast passes the S-test (denoted by thin lines). If the minimum difference within this range does not contain zero, the forecast fails the S-test for that particular experiment, denoted by thick lines. Colours distinguish between experiments (see Table 2 for explanation of experiment durations). Due to anomalously large likelihood differences, S-test results for Wiemer-Schorlemmer.ALM during the 10year.retro and 40year.retro experiments are not displayed. The range of log-likelihoods for the Holliday-et-al.PI forecast is lower than for the other forecasts due to relatively homogeneous forecasted seismicity rates and use of a small fraction of the RELM testing region.
Massive Open Online Courses (MOOCs) have left their mark on the face of education during the recent years. At the Hasso Plattner Institute (HPI) in Potsdam, Germany, we are actively developing a MOOC platform, which provides our research with a plethora of e-learning topics, such as learning analytics, automated assessment, peer assessment, team-work, online proctoring, and gamification. We run several instances of this platform. On openHPI, we provide our own courses from within the HPI context. Further instances are openSAP, openWHO, and mooc.HOUSE, which is the smallest of these platforms, targeting customers with a less extensive course portfolio. In 2013, we started to work on the gamification of our platform. By now, we have implemented about two thirds of the features that we initially have evaluated as useful for our purposes. About a year ago we activated the implemented gamification features on mooc.HOUSE. Before activating the features on openHPI as well, we examined, and re-evaluated our initial considerations based on the data we collected so far and the changes in other contexts of our platforms.
Dielectrophoretic functionalization of nanoelectrode arrays for the detection of influenza viruses
(2017)
E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.