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Information integration across company borders becomes increasingly important for the success of product lifecycle management in industry and complex supply chains. Semantic technologies are about to play a crucial role in this integrative process. However, cross-company data exchange requires mechanisms to enable fine-grained access control definition and enforcement, preventing unauthorized leakage of confidential data across company borders. Currently available semantic repositories are not sufficiently equipped to satisfy this important requirement. This paper presents an infrastructure for controlled sharing of semantic data between cooperating business partners. First, we motivate the need for access control in semantic data federations by a case study in the industrial service sector. Furthermore, we present an architecture for controlling access to semantic repositories that is based on our newly developed SemForce security service. Finally, we show the practical feasibility of this architecture by an implementation and several performance experiments.
Auswirkungen und Schäden
(2015)
In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the Substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process
Coal transitions - part 1
(2021)
A rapid coal phase-out is needed to meet the goals of the Paris Agreement, but is hindered by serious challenges ranging from vested interests to the risks of social disruption. To understand how to organize a global coal phase-out, it is crucial to go beyond cost-effective climate mitigation scenarios and learn from the experience of previous coal transitions. Despite the relevance of the topic, evidence remains fragmented throughout different research fields, and not easily accessible. To address this gap, this paper provides a systematic map and comprehensive review of the literature on historical coal transitions. We use computer-assisted systematic mapping and review methods to chart and evaluate the available evidence on historical declines in coal production and consumption. We extracted a dataset of 278 case studies from 194 publications, covering coal transitions in 44 countries and ranging from the end of the 19th century until 2021. We find a relatively recent and rapidly expanding body of literature reflecting the growing importance of an early coal phase-out in scientific and political debates. Previous evidence has primarily focused on the United Kingdom, the United States, and Germany, while other countries that experienced large coal declines, like those in Eastern Europe, are strongly underrepresented. An increasing number of studies, mostly published in the last 5 years, has been focusing on China. Most of the countries successfully reducing coal dependency have undergone both demand-side and supply-side transitions. This supports the use of policy approaches targeting both demand and supply to achieve a complete coal phase-out. From a political economy perspective, our dataset highlights that most transitions are driven by rising production costs for coal, falling prices for alternative energies, or local environmental concerns, especially regarding air pollution. The main challenges for coal-dependent regions are structural change transformations, in particular for industry and labor. Rising unemployment is the most largely documented outcome in the sample. Policymakers at multiple levels are instrumental in facilitating coal transitions. They rely mainly on regulatory instruments to foster the transitions and compensation schemes or investment plans to deal with their transformative processes. Even though many models suggest that coal phase-outs are among the low-hanging fruits on the way to climate neutrality and meeting the international climate goals, our case studies analysis highlights the intricate political economy at work that needs to be addressed through well-designed and just policies.
We investigate the cononsolvency effect of poly(N-isopropylacrylamide) (PNIPAM) in mixtures of water and methanol. Two systems are studied: micellar solutions of polystyrene-b-poly(N-isopropylacrylamide) (PS-b-PNIPAM) diblock copolymers and, as a reference, solutions of PNIPAM homopolymers, both at a concentration of 20 mg/mL in DO. Using a stopped-flow instrument, fully deuterated methanol was rapidly added to these solutions at volume fractions between 10 and 20%. Time-resolved turbidimetry revealed aggregate formation within 10-100 s. The structural changes on mesoscopic length scales were followed by time-resolved small-angle neutron scattering (TR-SANS) with a time resolution of 0.1 s. In both systems, the pathway of the aggregation depends on the content of deuterated methanol; however, it is fundamentally different for homopolymer and diblock copolymer solutions: In the former, very large aggregates (>150 nm) are formed within the dead time of the setup, gradient appears at their surface in the late stages. In contrast, the growth of the aggregates in the latter system features different regimes, and the final aggregate size is 50 nm, thus much smaller than for the homopolymer. For the diblock copolymer, the time dependence of the aggregate radius can be described by two models: In the initial stage, the diffusion-limited coalescence model describes the data well; however, the resulting coalescence time is unreasonably high. In the late stage, a logarithmic coalescence model based on an energy barrier which is proportional to the aggregate radius is successfully applied. and a concentration
Das Hochwasser im Juni 2013
(2015)
Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.
Growth-regulating factors (GRFs) are plant-specific transcription factors that were originally identified for their roles in stem and leaf development, but recent studies highlight them to be similarly important for other central developmental processes including flower and seed formation, root development, and the coordination of growth processes under adverse environmental conditions. The expression of several GRFs is controlled by microRNA miR396, and the GRF-miRNA396 regulatory module appears to be central to several of these processes. In addition, transcription factors upstream of GRFs and miR396 have been discovered, and gradually downstream target genes of GRFs are being unraveled. Here, we review the current knowledge of the biological functions performed by GRFs and survey available molecular data to illustrate how they exert their roles at the cellular level.
Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.