The 10 most recently reported publications
Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users.
To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
A series of reactive binaphthyl‐diimine‐based dopants is prepared and investigated with respect to their potential for the chiral induction of structural coloration in nematic liquid crystal mixture E7 and the selective photonic sensing of nitrogen dioxide (NO2). Studies of the helical twisting power (HTP) in 4‐cyano‐4′‐pentylbiphenyl (5CB) reveal HTP values as high as 375 µm‐1 and the tremendous impact of structural compatibility and changes of the dihedral binaphthyl angle on the efficiency of the chiral transfer. Detailed investigation of the sensing capabilities of the systems reveals an extraordinarily high selectivity for NO2 and a response to concentrations as low as 100 ppm. The systems show a direct response to the analyte gas leading to a concentration‐dependent shift of the reflectance wavelength of up to several hundred nanometers. Incorporation of copper ions remarkably improves the sensor's properties in terms of sensitivity and selectivity, enabling the tailored tweaking of the system's properties.
In thyroid carcinoma cells, the soluble βgalactosidespecific lectin, galectin3, is extra and intracellularly expressed and plays a significant role in thyroid cancer diagnosis. The functional relevance of this molecule, particularly in its extracellular environment however, warrants further elucidation. To gain insight into this topic, the present study characterized principal functional properties of galectin3 in 3 commonly used thyroid carcinoma cell lines (BCPAP, Cal62 and FTC133) that express the molecule intra and extracellulary. Cellintrinsic galectin3 harbors a functional carbohydrate recognition domain as determined by affinity purification. Moreover, cell surface expressed galectin3 can be partially removed by treatment with lactose or asialofetuin, but not with sucrose. Thyroid carcinoma cells adhere to substratebound galectin3 in a βgalactosidespecific manner, whereby only cell adhesion, but not cell migration is promoted. Thus, thyroid tumor cells harbor functional active galectin3 that, inter alia, specifically interacts with cell surfaceexpressed molecular ligands in a βgalactosidedependent manner, whereby the molecule can at least interfere with cell adhesion. The modulation of galectin3 expression level or its ligands in such tumor cells could be of therapeutic interest and needs further experimental clarification.
With the digital transformation, software systems have become an integral part of our society and economy. In every part of our life, software systems are increasingly utilized to, e.g., simplify housework or to optimize business processes. All these applications are connected to the Internet, which already includes millions of software services consumed by billions of people. Applications which process such a magnitude of users and data traffic requires to be highly scalable and are therefore denoted as Ultra Large Scale (ULS) systems. Roy Fielding has defined one of the first approaches which allows designing modern ULS software systems. In his doctoral thesis, Fielding introduced the architectural style Representational State Transfer (REST) which builds the theoretical foundation of the web. At present, the web is considered as the world's largest ULS system. Due to a large number of users and the significance of software for society and the economy, the security of ULS systems is another crucial quality factor besides high scalability.
Off-lattice Boltzmann methods increase the flexibility and applicability of lattice Boltzmann methods by decoupling the discretizations of time, space, and particle velocities. However, the velocity sets that are mostly used in off-lattice Boltzmann simulations were originally tailored to on-lattice Boltzmann methods. In this contribution, we show how the accuracy and efficiency of weakly and fully compressible semi-Lagrangian off-lattice Boltzmann simulations is increased by velocity sets derived from cubature rules, i.e. multivariate quadratures, which have not been produced by the Gauss-product rule. In particular, simulations of 2D shock-vortex interactions indicate that the cubature-derived degree-nine D2Q19 velocity set is capable to replace the Gauss-product rule-derived D2Q25. Likewise, the degree-five velocity sets D3Q13 and D3Q21, as well as a degree-seven D3V27 velocity set were successfully tested for 3D Taylor-Green vortex flows to challenge and surpass the quality of the customary D3Q27 velocity set. In compressible 3D Taylor-Green vortex flows with Mach numbers Ma={0.5;1.0;1.5;2.0} on-lattice simulations with velocity sets D3Q103 and D3V107 showed only limited stability, while the off-lattice degree-nine D3Q45 velocity set accurately reproduced the kinetic energy provided by literature.
Hintergrund: Empirische Studien zeigen, dass mehr als zwei Drittel der Beschäftigten trotz Krankheit zur Arbeit gehen. Dieser sog. Präsentismus bringt sowohl gesundheitliche und motivationale Risiken für die Mitarbeiter als auch wirtschaftliche Risiken für die Organisation mit sich.
Ziel der Arbeit: Die durchgeführten Studien fokussieren Möglichkeiten zur Verringerung der negativen gesundheitlichen Effekte und entwickeln Maßnahmen zur generellen Vermeidung von Präsentismus am spezifischen Setting Hochschule.
Methode: An einer deutschen Hochschule erfolgte eine quantitative Befragung (n = 308) zur Prävalenz von Präsentismus, dessen Zusammenhang mit körperlichen Beschwerden untersucht wurde. Weiterhin wurden potenziell moderierende Effekte der Ressourcen Erholung, Achtsamkeit und Work-Life-Balance (WLB) betrachtet. Eine qualitative Studie explorierte auf Grundlage von Interviews (n = 11, qualitative Inhaltsanalyse) Gründe für Präsentismus und potenzielle Maßnahmen, um diesem entgegenzuwirken.
Ergebnisse: Die quantitativen Ergebnisse zeigen, dass Präsentismus im Hochschulkontext vertreten ist und körperliche Beschwerden begünstigt. Die Ressourcen Erholung, Achtsamkeit und WLB können bei hoher Ausprägung die negativen gesundheitlichen Effekte von Präsentismus abschwächen. Bei niedriger Ausprägung verstärken sie die Effekte. Die qualitative Analyse machte deutlich, dass quantitative Arbeitsbelastung, Pflichtgefühl sowie das Gefühl, noch leistungsfähig zu sein, zentrale Gründe für Präsentismus sind und zum Beispiel die Unterstützung eines gesundheitsförderlichen Organisationsklimas oder Vertretungsregelungen geeignete Gegenmaßnahmen darstellen.
Diskussion: Die Ergebnisse werden vor dem Hintergrund verhaltens- und verhältnispräventiver Maßnahmen diskutiert und praktische Implikationen abgeleitet.
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.
