Submission of content to a digital object repository using a configurable workflow system

The prototype of a workflow system for the submission of content to a digital object repository is here presented. It is based entirely on open-source standard components and features a service-oriented architecture. The front-end consists of Java Business Process Management (jBPM), Java Server Faces (JSF), and Java Server Pages (JSP). A Fedora Repository and a mySQL data base management system serve as a back-end. The communication between front-end and back-end uses a SOAP minimal binding stub. We describe the design principles and the construction of the prototype and discuss the possibilities and limitations of work ow creation by administrators. The code of the prototype is open-source and can be retrieved in the project escipub at http://sourceforge.ne

Record occurrence and record values in daily and monthly temperatures

We analyze the occurrence and the values of record-breaking temperatures in daily and monthly temperature observations. Our aim is to better understand and quantify the statistics of temperature records in the context of global warming. Similar to earlier work we employ a simple mathematical model of independent and identically distributed random variables with a linearly growing expectation value. This model proved to be useful in predicting the increase (decrease) in upper (lower) temperature records in a warming climate. Using both station and re-analysis data from Europe and the United States we further investigate the statistics of temperature records and the validity of this model. The most important new contribution in this article is an analysis of the statistics of record values for our simple model and European reanalysis data. We estimate how much the mean values and the distributions of record temperatures are affected by the large scale warming trend. In this context we consider both the values of records that occur at a certain time and the values of records that have a certain record number in the series of record events. We compare the observational data both to simple analytical computations and numerical simulations. We find that it is more difficult to describe the values of record breaking temperatures within the framework of our linear drift model. Observations from the summer months fit well into the model with Gaussian random variables under the observed linear warming, in the sense that record breaking temperatures are more extreme in the summer. In winter however a significant asymmetry of the daily temperature distribution hides the effect of the slow warming trends. Therefore very extreme cold records are still possible in winter. This effect is even more pronounced if one considers only data from subpolar regions.Comment: 16 pages, 20 figures, revised version, published in Climate Dynamic

Principal types for object-oriented languages

Object-oriented languages can he translated into a lambda-calculus with records. Therefore, type inference for record languages is one aspect of the yet unsolved problem of inferring types for object-oriented languages. In order to obtain the necessary flexibility for such a type system, we can either introduce a general subtyping notion or use extensible record types. Subtyping, especially in combination with imperative features, poses many hard problems. Therefore, the second approach is promising. The problem is that, in previous type inference systems that used extensible record types, principal types could not be inferred. We have found that, for an object-oriented language where classes are not first-class citizens, we could greatly simplify the underlying record language. We show that, for our simple record language, there exists a type inference algorithm that infers principal types

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system “Mittelfristige Klimaprognose” (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability and with respect to the temporal evolution at the 26° N latitude

Acceleration sensors based on polymer-electronic materials

AbstractThe paper focuses on acceleration sensors which are based on polymer-electronic materials. The sensors have been fabricated by using ink-jet technologies on flexible substrates. Amongst details of the fabrication process, reached parameters and a comparison to FEM-simulations are presented

Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering

Probabilistic spatial reconstructions of past climate states are valuable to quantitatively study the climate system under different forcing conditions because they combine the information contained in a proxy synthesis into a comprehensible product. Unfortunately, they are subject to a complex uncertainty structure due to complicated proxy–climate relations and sparse data, which makes interpolation between samples difficult. Bayesian hierarchical models feature promising properties to handle these issues, like the possibility to include multiple sources of information and to quantify uncertainties in a statistically rigorous way. We present a Bayesian framework that combines a network of pollen and macrofossil samples with a spatial prior distribution estimated from a multi-model ensemble of climate simulations. The use of climate simulation output aims at a physically reasonable spatial interpolation of proxy data on a regional scale. To transfer the pollen data into (local) climate information, we invert a forward version of the probabilistic indicator taxa model. The Bayesian inference is performed using Markov chain Monte Carlo methods following a Metropolis-within-Gibbs strategy. Different ways to incorporate the climate simulations into the Bayesian framework are compared using identical twin and cross-validation experiments. Then, we reconstruct the mean temperature of the warmest and mean temperature of the coldest month during the mid-Holocene in Europe using a published pollen and macrofossil synthesis in combination with the Paleoclimate Modelling Intercomparison Project Phase III mid-Holocene ensemble. The output of our Bayesian model is a spatially distributed probability distribution that facilitates quantitative analyses that account for uncertainties

Bildungscontrolling in der Schule? Möglichkeiten und Grenzen des Prozess-, Output- und Transfercontrollings am Beispiel eines innovativen Unterrichtsprojekts

Educational controlling (Bildungscontrolling) is an economically influenced concept of quality development in educational institutions, which originated in the context of corporate further education. This article asks to what degree approaches of process, output, and transfer controlling are suitable for adaptation in publicly financed education, and what kind of adjustments are necessary in contexts such as schools. As a case example the article presents the evaluation of the innovative school project “Tatfunk”, which aims at fostering students’ entrepreneurial skills and thinking. Methods and results of the educational controlling process within the project are reported and analysed in regard to the goals of the evaluation. The results show that the innovative concept of the project was successfully implemented in practice and that the central project goals could be achieved. Concluding, the article discusses which main premises of process, output, and transfer controlling need adjustment in the context of publicly financed education. Additionally, it is shown which surplus value can be derived from the perspective of educational controlling for the overall educational quality discourseBildungscontrolling ist ein ökonomisch geprägtes Konzept der Qualitätsarbeit in Bildungsinstitutionen, das seinen Ursprung in der betrieblichen Weiterbildung hat. Der Beitrag geht der Frage nach, inwiefern Ansätze des Prozess-, Output- und Transfercontrollings sich zur Übernahme in den Bereich der öffentlich finanzierten Bildung wie etwa der Schule eignen und welche Anpassungsleistungen dabei notwendig werden. Als Fallbeispiel dient die Evaluation des innovativen Schulprojekts „Tatfunk“, das die Förderung des unternehmerischen Denkens und Handelns in der Schule zum Ziel hat. Methoden und Ergebnisse des Bildungscontrollings im Projekt werden dargestellt und in Bezug auf die Ziele der Evaluation analysiert. Die Ergebnisse belegen, dass das innovative Projektkonzept gut in der Praxis realisiert werden konnte und dass wesentliche Projektziele erreicht wurden. Abschließend wird diskutiert, inwiefern zentrale Prämissen des Prozess-, Output- und Transfercontrollings im Bereich öffentlich finanzierter Bildung der Adaption bedürfen und welchen Mehrwert die Perspektive des Bildungscontrollings in den allgemeinen Qualitätsdiskurs einbringen kan

An abstract machine for an object-oriented language with top-level classes

Object-oriented programming languages where classes are top-level, i.e. not first-class citizens, are better suited for compilation than completely dynamic languages like SMALLTALK or SELF. In O\u27SMALL, a language with top-level classes, the compiler can statically determine the inheritance hierarchy. Due to late binding, the class of the receiver of a message must be determined at run time. After that a direct jump to the corresponding method is possible. Method lookup can thus be done in constant time. We present an abstract machine for O\u27SMALL based on these principles. It is a concise description of a portable O\u27SMALL implementation

Teaching AI competencies in engineering using projects and open educational resources

A major challenge in engineering education is to empower students to use their acquired technical skills to solve real-world problems. In particular, methods of Artificial Intelligence (AI) need to be studied as tools in their respective application contexts. This puts pressure on university lecturers concerning the didactical design and elaboration of a course, and requires them to move towards a practice-based learning approach. Moreover, working on real-world problems leads to uncertainties for the lecturer and their students. Before and during the course, it is not always clear which methods will be used to solve the problem, respectively which competencies the participants need to acquire. Therefore, we propose to combine two established approaches: a project-based learning approach and the use of digital, curated learning content provided by Open Education Resources (OERs). We hypothesise that a practical study project solving a real-world problem using a combination of OERs and project-based learning is beneficial to AI education. Furthermore, we show implementations of our concept in three different courses. The first results indicate that student-centred tasks lead to high intrinsic motivation. At the same time, lecturers have to deal with a modified and extended role: They are no longer the broadcaster of knowledge but rather a guide within the learning process. Using the combination of OERs and project-based learning, the courses are attractive and exciting for students and lecturers without becoming unmanageable