Redefining military memorials and commemoration and how they have changed since the 19th century with a focus on Anglo-American practice

This thesis is a study of military memorials and commemoration with a focus on Anglo-American practice. The main question is: How has history defined military memorials and commemoration and how have they changed since the 19th century. In an effort to resolve this, the work examines both historic and contemporary forms of memorials and commemoration and establishes that remembrance in sites of collective memory has been influenced by politics, conflicts and religion. Much has been written since the Great War about remembrance and memorialization; however, there is no common lexicon throughout the literature. In order to better explain and understand this complex subject, the work includes an up-to-date literature review and for the first time, terminologies are properly explained and defined. Particular attention is placed on recognizing important military legacies, being familiar with spiritual influences and identifying classic and new signs of remembrance. The thesis contends that commemoration is composed of three key principles – recognition, respect and reflection – that are intractably linked to the fabric of memorials. It also argues that it is time for the study of memorials to come of age and proposes Memorialogy as an interdisciplinary field of study of memorials and associated commemorative practices. Moreover, a more modern, adaptive, General Classification System is presented as a means of identifying and re-defining memorials according to certain groups, types and forms. Lastly, this thesis examines how peacekeeping and peace support operations are being memorialized and how the American tragic events of 11 September 2001 and the war in Afghanistan have forever changed the nature of memorials and commemoration within Canada and elsewhere. This work goes beyond what has been studied and written about over the last century and provides a deeper level of analysis and a fresh approach to understanding the field of Memorialogy.

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

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.