[HbA1c revisited (again and again ...)]

The "International Federation of Clinical Chemistry" (IFCC) has developed a new international reference measurement system as anchor for worldwide standardization of HbA1c determinations. The use of IFCC-referenced methods results in "true" HbA1c-values that are 1 - 2% lower compared to traditional methods. This leads to potential risks of clinical misjudgement. For the evaluation of glycaemic control it is, therefore, important to know the method used, its normal range as well as possibilities of mathematical conversion of results. To reduce the risk of wrong interpretation of results the Swiss Society of Endocrinology and Diabetes recommends that reports of HbA1c clearly indicate whether the values are "IFCC-" or "DCCT-traceable". "IFCC"-results should best be reported in mmol/mol. In addition, the normal range has to be indicated properly.

Admissibility in Finitely Generated Quasivarieties

Checking the admissibility of quasiequations in a finitely generated (i.e., generated by a finite set of finite algebras) quasivariety Q amounts to checking validity in a suitable finite free algebra of the quasivariety, and is therefore decidable. However, since free algebras may be large even for small sets of small algebras and very few generators, this naive method for checking admissibility in Q is not computationally feasible. In this paper, algorithms are introduced that generate a minimal (with respect to a multiset well-ordering on their cardinalities) finite set of algebras such that the validity of a quasiequation in this set corresponds to admissibility of the quasiequation in Q. In particular, structural completeness (validity and admissibility coincide) and almost structural completeness (validity and admissibility coincide for quasiequations with unifiable premises) can be checked. The algorithms are illustrated with a selection of well-known finitely generated quasivarieties, and adapted to handle also admissibility of rules in finite-valued logics.

The Emergence of Partnership Networks in the Enterprise Application Development Industry - A Global Corporation Perspective

Currently, dramatic changes are happening in the IS development industry. The incumbent system developers (hubs) are embracing partnerships with less well established companies (spokes), acting in specific niches. This paper seeks to establish a better understanding of the motives for this strategy. Relying on existing work on strategic alliance formation, it is argued that partnering is particularly attractive, if these small companies possess certain capabilities that are difficult to obtain through other arrangements than partnering. Again drawing on the literature, three categories of capabilities are identified: the capability to innovate within their niche, the capability to provide a specific functionality that can be integrated with the incumbents’ systems, and the capability to address novel markets. These factors are analyzed through a case study. The case represents a market leader in the global IS development industry, which fosters a network of smaller partner firms. The study reveals that temporal dynamics between the identified factors are playing a dominant role in these networks. A cyclical partnership model is developed that attempts to explain the life cycle of a partnership within such a network.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.