Understanding the influence of multiple levels of governments on the development of inter-organizational systems

In the arena of international trade, multiple levels of governments (ranging from national to supranational) play an important role in regulating and controlling cross-border trade activities. Recently, they have also become powerful players in influencing decisions about inter-organizational systems (IOS). The influences of these multiple levels of governments on IOS are of an enormous scale and impact on businesses and national economies. Understanding them is a prerequisite for informed actions. From a theoretical point of view we contribute with the MLxMC framework, a conceptual framework, which combines a processual, multi-level approach with the motors of change. We use the MLxMC framework to explicitly highlight the influences of multiple levels of governments on IOS developments that take place in the highly regulated environment of international trade. We demonstrate how the framework can be applied to analyze such developments, extending the existing IOS research with models that explicitly acknowledge the role of government. The framework makes use of multi-level analysis by taking the political and institutional aspects into account. As an analytical tool, the framework can support business practitioners as well as policy-makers in their strategic choices of which level to engage at and with whom to collaborate in order to influence the debate.     

Proteomics in rheumatology: The beginning of a fairy tale?

One of the major challenges in proteome research is to translate its applications to the setting of human diseases. Proteomics in rheumatology is an area with marked potential including applications ranging from diagnostics, over therapeutic monitoring to discovery of new potential therapeutic targets. Biomarkers will be essential to discriminate between clinical similar rheumatic diseases, to monitor disease-states or to install the best appropriate therapy. Especially in the field of rheumatology, analysis of specific genes and/or their expression products by pharmacogenetics/-genomics or pharmacoproteomics could be necessary to enable an effective, patient-tailored therapy. In rheumatology, direct examination of proteins may be of utmost importance, as it is already known that PTMs, such as citrullination of proteins or peptides, may be involved in certain rheumatic diseases. The discovery and validation of antibodies directed against citrullinated proteins/peptides in rheumatic diseases using proteome analysis approaches has been described. Gel-free methods, SELDI-approaches and classic 2-DE approaches have been deployed on body fluids as well as on target tissues in different rheumatic diseases. Proteomics in rheumatology is on the rise and pilot studies have indicated that the application of proteomics-based technologies in rheumatic diseases appears to be an exciting example of translational research.     

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.     

On‐line LC‐NMR‐MS characterization of sesame oil extracts and assessment of their antioxidant activity

Sesame lignans were isolated by solvent extraction and subsequently purified by solvent crystallization from crude, unroasted sesame oil, and a sesame oil deodorizer distillate. In addition, an aliquot of the purified sesame oil extract was treated with camphorsulfonic acid to obtain a sesaminol‐enriched extract. The sesame lignan composition of the extracts was characterized by on‐line liquid chromatography nuclear magnetic resonance spectroscopy mass spectrometry coupling (LC‐NMR‐MS). The effect of the sesame oil extracts as well as pure sesame lignans and γ‐tocopherol on the oxidative stability of sunflower oil (lignan‐free) was studied by the Rancimat assay. The Rancimat assay revealed the following oxidative stability order: sesame oil extract < sesame oil deodorizer distillate < sunflower oil (no added sesame oil extracts) < sesamol < sesaminol‐enriched sesame oil extract. In addition, the radical‐scavenging capacity of these extracts was assessed by the Trolox® equivalent antioxidant capacity (TEAC) assay. The TEAC assay revealed a slightly different AOX activity order: sesamin < sesame oil extract < sesaminol‐enriched sesame oil extract < sesamol. In conclusion, the sesaminol‐enriched extract revealed strong antioxidant activity and is therefore suitable to increase the oxidative stability of edible oils high in polyunsaturated fatty acids.     

A critical comparison of systematic calibration protocols for activated sludge models: a SWOT analysis

Modelling activated sludge systems has gained an increasing momentum after the introduction of activated sludge models (ASMs) in 1987. Application of dynamic models for full-scale systems requires essentially a calibration of the chosen ASM to the case under study. Numerous full-scale model applications have been performed so far which were mostly based on ad hoc approaches and expert knowledge. Further, each modelling study has followed a different calibration approach: e.g. different influent wastewater characterization methods, different kinetic parameter estimation methods, different selection of parameters to be calibrated, different priorities within the calibration steps, etc. In short, there was no standard approach in performing the calibration study, which makes it difficult, if not impossible, to (1) compare different calibrations of ASMs with each other and (2) perform internal quality checks for each calibration study. To address these concerns, systematic calibration protocols have recently been proposed to bring guidance to the modeling of activated sludge systems and in particular to the calibration of full-scale models. In this contribution four existing calibration approaches (BIOMATH, HSG, STOWA and WERF) will be critically discussed using a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. It will also be assessed in what way these approaches can be further developed in view of further improving the quality of ASM calibration. In this respect, the potential of automating some steps of the calibration procedure by use of mathematical algorithms is highlighted.     

An accurate method for solving the Orr-Sommerfeld equation

Summary The method consists in integrating the Orr-Sommerfeld equation in the direction from the free stream toward the wall. In order to satisfy the boundary conditions at the wall, two linearly independent solutions have to be found. To prevent numerical solutions from becoming linearly dependent, the method of order reduction instead of repeated orthogonalization has been used. The method has been applied to calculate the neutral curve for the Blasius profile.     

A primal–dual semidefinite programming algorithm tailored to the variational determination of the two-body density matrix

The quantum many-body problem can be rephrased as a variational determination of the two-body reduced density matrix, subject to a set of N-representability constraints. The mathematical problem has the form of a semidefinite program. We adapt a standard primal–dual interior point algorithm in order to exploit the specific structure of the physical problem. In particular the matrix-vector product can be calculated very efficiently. We have applied the proposed algorithm to a pairing-type Hamiltonian and studied the computational aspects of the method. The standard N-representability conditions perform very well for this problem.     

An autonomous service‐platform to support distributed ontology‐based context‐aware agents

The use of semantic technology has recently witnessed a huge increase. One of the areas in which this technology is being used increasingly more often is that of context‐aware agents. However, the use of ontologies in general and reasoning in particular can rapidly become resource intensive. Certainly if the data set, called the A‐Box, used by these agents grows considerably over time. Moreover, in order to create context‐aware applications, taking into account a wide range of different data sets and context parameters, agents have to be provided to expose that data. The collaboration between the agents in the system is necessary to correlate the information and augment the intelligence and added value of the context‐aware agents. Therefore, there is a need to have a distributed approach by means of a service‐platform, where the different agents in a context‐aware environment can collaborate. The main focus of this article is on the research on the design of a service‐platform for semantic ontology‐based context‐aware collaboration. The platform architecture to allow the collaboration and scheduling, together with the associated algorithms, will be presented. The engineering and implementation details will be highlighted. By means of detailed UML sequence diagrams, we will present the workflow and collaboration between the different modules in the platform. Additionally, supporting developments, such as the meta‐ontology and our ontology generator, OTAGen, will be presented. Furthermore, we will detail how the platform can operate in an autonomous way, taking into account the changing context of the agents in the platform.