Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

The emerging strategic partnership between the European Union and the African Union

Many among the emerging generation of political elites in Africa see the role the European Union (EU) plays in the maintenance of an unprecedented period of peace in Western Europe as an inspirational example of the manner in which the African Union (AU) can contribute to peace and stability in Africa. This doctoral thesis examines security cooperation between the EU and the AU, with a particular focus on the nature and substance of that cooperation. It suggests that despite the establishment of various EU–AU institutions and ties with a role in security policy and cooperation, such security cooperation is limited in substance. This study argues that EU–AU security cooperation is especially constrained by the emergence of alternative partners, most notably China, and by failures of implementation and follow-through. Two case studies, the first dealing with EU–AU cooperation in peacekeeping, and the second addressing the silent water crisis along with the link between water and security, have been analysed in detail to determine the effectiveness and sustainability of the EU–AU partnership. A number of important lessons for regionalism, interregionalism and multilateralism are drawn from the bond between the EU and the AU. This doctoral thesis will prove that, despite an emphasis on the problematic term ‘strategic’ by both EU and AU policymakers, EU–AU cooperation is limited and somewhat lacking in strategic direction. The cooperation between the EU and the AU focuses mainly on EU financial support for AU peacekeeping and specific projects in Africa (e.g. in the water sector), as well as on a limited political dialogue. Nonetheless, the EU–AU link represents the most comprehensive partnership the AU has with any non-African actor. This study will furthermore demonstrate that the United Nations (UN) is an indispensable third-party to their relationship and it is therefore more appropriate to speak of the AU–EU–UN nexus. This doctoral thesis concludes that the AU–EU–UN nexus is an important example of interregionalism in a global context and that such interregionalism is an important emerging part of global governance.

Towards a situation-awareness-driven design of operational business intelligence & analytics systems

With the swamping and timeliness of data in the organizational context, the decision maker’s choice of an appropriate decision alternative in a given situation is defied. In particular, operational actors are facing the challenge to meet business-critical decisions in a short time and at high frequency. The construct of Situation Awareness (SA) has been established in cognitive psychology as a valid basis for understanding the behavior and decision making of human beings in complex and dynamic systems. SA gives decision makers the possibility to make informed, time-critical decisions and thereby improve the performance of the respective business process. This research paper leverages SA as starting point for a design science project for Operational Business Intelligence and Analytics systems and suggests a first version of design principles.