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Während sich Unternehmen die Möglichkeiten der vernetzten Informations- und Kommunikationstechnologie für die gemeinsame Entwicklung neuer Produkte zunutze machen, stellen sich gleichzeitig Fragen nach dem Schutz von vertraulichen Informationen und Wissen. Um ihre ungewollte Verbreitung zu vermeiden, benötigen Unternehmen in verschiedenen Branchen klare Strategien zur Risikominimierung. Durch den aktuellen Wandel hin zur Elektromobilität herrscht in der Automobilindustrie ein harter Wettbewerb und die Gefahr von Nachahmung ist groß. Wir präsentieren eine explorative Fallstudie in einem führenden europäischen Automobilunternehmen und untersuchen das Verhalten am Arbeitsplatz und die damit verbundenen Informationssicherheit bei der Entwicklung neuer Produkte. Die Ergebnisse zeigen, dass das Bewusstsein zum Schutz von Informationen in Organisationsroutinen verwurzelt ist und dass bestehende Sicherheitsmaßnahmen eine schwache Wirkung haben. Unpraktische formale Sicherheitsrichtlinien sowie hierarchische Spannungen und Zeitdruck führen zu riskanten Verhaltensweisen der Mitarbeiter. Unsere Ergebnisse weisen auf die Bedeutung von Transparenz und Flexibilität von Wissenssicherheitsrichtlinien hin. Darüber hinaus bieten wir eine neue Perspektive auf den Wissenstransfer, indem wir die sog. Sichtbarkeitslinie für Informationen einführen und einen Sicherheitsrahmen für die Praxis vorstellen.

ABSTRACTBusiness process management distinguishes the actual “as-is” and a prescribed “to-be” state of a process. In practice, many different causes trigger a process’s drifting away from its to-be state. For instance, employees may “workaround” the proposed systems to increase their effectiveness or efficiency in day-to-day work. So far, ethnography or critical incident techniques are used to identify how and why workarounds emerge. We design a deep-learning-based method that helps detect different workaround types in event logs. Our method tracks indications of potential workarounds in the early stages of their emergence among deviating behaviour. Our evaluation based on four real-life event logs reveals that our method performs well and works best for business processes with fewer variations and a higher number of different activities. The proposed method is one of the first information technology artefacts to bridge the boundaries between the complementing research disciplines of organisational routines and business processes management.

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein–ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.

A precise understanding of DNA methylation dynamics is of great importance for a variety of biological processes including cellular reprogramming and differentiation. To date, complex integration of multiple and distinct genome-wide datasets is required to realize this task. We present GwEEP (genome-wide epigenetic efficiency profiling) a versatile approach to infer dynamic efficiencies of DNA modifying enzymes. GwEEP relies on genome-wide hairpin datasets, which are translated by a hidden Markov model into quantitative enzyme efficiencies with reported confidence around the estimates. GwEEP predicts de novoand maintenance methylation efficiencies of Dnmts and furthermore the hydroxylation efficiency of Tets. Its design also allows capturing further oxidation processes given available data. We show that GwEEP predicts accurately the epigenetic changes of ESCs following a Serum-to-2i shift and applied to Tet TKO cells confirms the hypothesized mutual interference between Dnmts and Tets.

Systems of stochastic chemical kinetics are modeled as infinite level-dependent quasi-birth-and-death (LDQBD) processes. For these systems, in contrast to many other applications, levels have an increasing number of states as the level number increases and the probability mass may reside arbitrarily far away from lower levels. Ideas from Lyapunov theory are combined with existing matrix-analytic formulations to obtain accurate approximations to the stationary probability distribution when the infinite LDQBD process is ergodic. Results of numerical experiments on a set of problems are provided.

A variety of 3chlorosalicylates were prepared by onepotcyclizations of the first reported 4chloro1,3bistrimethylsilyloxybuta1,3dienes with various 1,3dielectrophiles. These include cyclizations with 3silyloxy2en1ones, 3aryl3silyloxy2en1ones, 1methoxy1en3ones, 1,1bismethylthio1en3ones, 3oxo orthoesters, 1,1,3,3tetraethoxypropanes, 1,1diacetylcyclopropane, and 3formylchromones. © WileyVCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2009

TmsOTf‐mediated coupling is followed by treatment with NEt3to provide azaxanthones and/or highly functionalized biaryls depending on the diene structure.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Highlights • Complexity reduction: one PDE per mode instead of one ODE per state. • Analytical solution for a very general case of a self-regulatory gene. • Accurate results of hybrid approximation scheme if molecule counts sufficiently large. Abstract A widely used approach to describe the dynamics of gene regulatory networks is based on the chemical master equation, which considers probability distributions over all possible combinations of molecular counts. The analysis of such models is extremely challenging due to their large discrete state space. We therefore propose a hybrid approximation approach based on a system of partial differential equations, where we assume a continuous-deterministic evolution for the protein counts. We discuss efficient analysis methods for both modeling approaches and compare their performance. We show that the hybrid approach yields accurate results for sufficiently large molecule counts, while reducing the computational effort from one ordinary differential equation for each state to one partial differential equation for each mode of the system. Furthermore, we give an analytical steady-state solution of the hybrid model for the case of a self-regulatory gene. [ABSTRACT FROM AUTHOR]