The National Farmers Federation (NFF) is a peak producer organisation. Its executive has purported to represent all Australian farmers with a unified voice. This thesis argues that primary producers are too heterogeneous a group ever to have developed much solidarity in articulation of or action for the furtherance of common interests and that this fact is reflected in the NFF. Heterogeneity results from farm size, product specialisation, level of technology adopted, geographical location and special needs. Successive farm organisations and the National Party (and predecessors) have attempted to encompass these differences since the 1890s. Producer differences either have led to secession or to unification when political and economic circumstances have warranted it. This diversity has prevented farm groups becoming united. The lack of unity at first prevented all farmers joining in one organisation, and when they did, they kept on splitting up. The charisma of Ian McLachlan allowed farmers to get together, but the diversity meant that the getting together benefited some not only without the others, but sometimes at the expense of others. This thesis explores the heterogeneity of the agricultural sector within the political and economic context of Australian agriculture and discusses its consequences, in the constant re-forming of farm organisations and the institutional framework of the NFF in the context of politicisation of agricultural interest groups. This thesis concludes that producer differences in terms of size and product specialisation determine how effectively they are represented. Corporate farmers have fared much better than family and family-plus farmers, who would probably be better represented by a small business organisation, with which they have more in common, rather than a farming organisation.

No fault found (NFF) errors, i.e. errors which origin has not been established, irregularly occur in electronic devices. The actual cause of such errors varies but one, possibly more prominent, source for these soft errors is atmospheric radiation. The overarching aim of this thesis is to demonstrate: 1) the importance of keeping the atmospheric radiation environment in mind when designing robust airborne systems, 2) how to take this environment into consideration when applying mitigation techniques which may drastically reduce the risk of SEEs (Single Event Effects) which can cause NFF errors. To achieve these goals, Part 1 of this thesis describes how cosmic rays affect electronics (i.e. what kind of errors may be induced), which types of devices are susceptible to radiation, and why this subject is of extra importance for airborne systems. In addition, soft error mitigation techniques, which may be applied at different design levels to reduce the soft error rate (SER) or the impact of soft errors, are also presented. The aim is further corroborated by Part 2. Five subsystems of a modern aircraft are studied and real examples of failures potentially induced by atmospheric radiation are presented. For each of the five systems, all errors that have been reported for these (in the past few years) have been studied, and the number of errors found to be potentially induced by cosmic radiation has been listed and compared to number of expected soft errors based on calculations and previous experimental tests.

Due to the successful application in internet related fields, Extensible Markup Language (XML) and its related technologies are being explored as a revolutionary software file format technology used to provide increased interoperability in the discrete-event simulation (DES) arena. The National Institute of Standards and Technology (NIST) has developed an XML-based information model (XSD) called the Shop Data Model (SDM), which is used to describe the contents of a neutral file format (NFF) that is being promoted as a means to make manufacturing simulation technology more accessible to a larger group of potential customers. Using a two step process, this thesis evaluates the NIST SDM information model in terms of its ability to encapsulate the informational requirements of one vendor's simulation model information conceptually and syntactically in order to determine its ability to serve as an NFF for the DES industry. ProModel Corporation, a leading software vendor in the DES industry since 1988, serves as the test case for this evaluation. The first step in this evaluation is to map the contents of ProModel's information model over to an XML schema file (XSD). Next, the contents of this new XSD file are categorized and compared to the SDM information model in order to evaluate compatibility. After performing this comparison, observations are made in relation to the challenges that simulation vendors might encounter when implementing the proposed NIST SDM. Two groups of limitations are encountered which cause the NIST SDM to support less than a third of the ProModel XSD elements. These two groups of limitations are: paradigm differences between the two information models and limitations posed due to the incomplete status of the NIST SDM specification. Despite these limitations, this thesis shows by comparison that XML technology does not pose any limitation which would invalidate its ability to syntactically represent a common information model or associated XML NFF. While only 28% of the ProModel element are currently supported by the SDM, appropriate changes to the SDM would allow the information model to serve as a foundation upon which a common information model and neutral file format for the DES industry could be built using XML technologies.

Hintergrund: Nach Schätzungen des GRC (German Resuscitation Council) erleiden jährlich allein in Deutschland 50.000 Menschen außerhalb eines Krankenhauses einen Herz-Kreislaufstillstand. In jeder Minute ohne Herzdruckmassage sinkt die Überlebenswahrscheinlichkeit um 10%. Bis zum Eintreffen professioneller Helfer müssen die Basismaßnahmen (Basic-Life-Support - BLS) der Reanimation durch nicht-medizinische Laien durchgeführt werden. Laien werden durch Basic-Life-Support-Kurse in den Basismaßnahmen geschult. Diese Basismaßnahmen sind als Qualitätsparameter messbar. Fragestellung: In der vorliegenden Arbeit wurde untersucht, ob ein standardisierter BLS-Kurs die Basismaßnahmen positiv beeinflussen kann und ob dieser Effekt auch noch nach 6 Monaten nachweisbar ist. Besonderer Wert wurde dabei auf den klinisch und prognostisch wichtigen Qualitätsparameter der No-Flow-Fraktion (NFF) gelegt. Methodik: Diese Prospektive Studie umfasste eine Kontrollgruppe aus Laien, die einmal in ihren Leben einen Erste-Hilfe-Kurs besucht hatten und nun ohne Schulung drei mal einem gleichen standardisierten Reanimationsszenario (5min Dauer) zugeführt wurden. Die Reanimationsleistungen wurden stets als Paarleistung im Rahmen der 2-Helfer-Methode erfasst. Dem gegenüber standen die Teilnehmer der Interventionsgruppe, welche ebenfalls Laien waren, jedoch im Rahmen ihrer Stellung als 'Betriebsersthelfer' alle 2 Jahre in Reanimation geschult wurden. Auch diese Teilnehmer wurden drei Mal dem Reanimationsszenario ausgesetzt: Einmal vor dem Auffrischungskurs, unmittelbar nach dem BLS-Kurs und erneut ohne vorherige Re-Schulung nach 6 Monaten. Die erfassten Reanimationsleistungen wurden statistisch aufgearbeitet und verschiedenen Analysen unterzogen. Ergebnis: Ein standardisierter BLS-Kurs kann die meisten Qualitätsparameter der Reanimation signifikant verbessern. Der Effekt ist auch noch nach 6 Monaten nachweisbar, lässt jedoch bereits wieder nach. Trotz regelmäßiger Schulung zeigten die Teilnehmer der Interventiontionsgruppe vor der Re-Schulung vergleichbar schlechte Reanimationsleistungen, wie die Teilnehmder der Kontrollgruppe. Das optimale Schulungsintervall bleibt somit weiter unklar. Der klinisch relevante Parameter NFF wird durch den BLS-Kurs drastisch verbessert, bleibt jedoch unzureichend hoch.

The purpose of this master thesis was to investigate if using both X-ray images and patient's register data could increase the performance of a neural network in discrimination of two types of fractures in the thigh bone, called atypical femoral fractures (AFF) and normal femoral fractures (NFF). We also examined and evaluated how the fusion of the two data types could be done and how different types of fusion affect the performance. Finally, we evaluated how the number of variables in the register data affect a network's performance. Our image dataset consisted of 1,442 unique images from 580 patients (16.85% of the images were labelled AFF corresponding to 15.86% of the patients). Since the dataset is very imbalanced, sensitivity is a prioritized evaluation metric. The register data network was evaluated using five different versions of register data parameters: two (age and sex), seven (binary and non-binary) and 44 (binary and non-binary). Having only age and sex as input resulted in a classifier predicting all samples to class 0 (NFF), for all tested network architectures. Using a certain network structure (celled register data model 2), in combination with the seven non-binary parameters outperforms using both two and 44 (both binary and non-binary) parameters regarding mean AUC and sensitivity. Highest mean accuracy is obtained by using 44 non-binary parameters. The seven register data parameters have a known connection to AFF and includes age and sex. The network with X-ray images as input uses a transfer learning approach with a pre-trained ResNet50-base. This model performed better than all the register data models, regarding all considered evaluation metrics.        Three fusion architectures were implemented and evaluated: probability fusion (PF), feature fusion (FF) and learned feature fusion (LFF). PF concatenates the prediction provided from the two separate baseline models. The combined vector is fed into a shallow neural network, which are the only trainable part in this architecture. FF fuses a feature vector provided from the image baseline model, with the raw register data parameters. Prior to the concatenation both vectors were normalized and the fused vector is then fed into a shallow trainable network. The final architecture, LFF, does not have completely frozen baseline models but instead learns two separate feature vectors. These feature vectors are then concatenated and fed into a shallow neural network to obtain a final prediction. The three fusion architectures were evaluated twice: using seven non-binary register data parameters, or only age and sex. When evaluated patient-wise, all three fusion architectures using the seven non-binary parameters obtain higher mean AUC and sensitivity than the single modality baseline models. All fusion architectures with only age and sex as register data parameters results in higher mean sensitivity than the baseline models. Overall, probability fusion with the seven non-binary parameters results in the highest mean AUC and sensitivity, and learned feature fusion with the seven non-binary parameters results in the highest mean accuracy.

Radomes were invented to protect sensitive antenna systems from the disturbing environment. Estimation of their permittivity is a vital step for their design. This project studies the free- space method to measure the permittivity of radome materials. The free space method has the advantages of non-contacting and free-of-reshaping, which are suitable for sheet-shape samples. In this method, the sample is placed between two antennas, and the permitivity of the material is determined based on the S-parameters between the two antennas. To avoid the influence of energy diffraction at the edges of samples, a near field focus(NFF) antenna is used. NFF antennas have their focal point in the near field. Consequently, the energy diffraction at the edges is reduced if the sample is placed at the focal point of the NFF antenna. The measurement results are processed with the genetic algorithm. This method starts from a group of assumption values of permittivity. An optimal value is obtained after multiple iterations. Compared with the results from a cavity resonant method provided in the datasheet of the materials, the free space setup in this thesis measurement provides an average 2.80% error in permittivity, and an average 34.14% error in loss tangent. Therefore, the genetic algorithm is suitable for this free space setup. The error of this thesis measurement is affected by several factors, such as thickness value error and inaccurate operation. / Radom används för att skydda känsliga antennsystem från omgivningen. Det är viktigt att estimera permittiviteten av radom innan de designas. Med korrekt permittivitet kan radomets inverkan på systemet räknas ut. I det här projektet används en fri-rymdsmetod för att mäta permittivitet. Fri-rymdsmetoden har fördelarna av att vara kontaktfri och icke-invasiv, vilket gör den lämplig för permittivitetmätning av materialskivor. Med den här metoden placeras en materialskiva mellan två antenner. Permittiviteten kan beräknas från S-parametrarna mellan antennerna. För att undvika diffraktion från kanten av materialskivan används närfältsfokuserade (NFF) antenner i fri-rymdsmetoden. NFF antenner har en fokalpunkt i närfältet. Kantdiffraktionen minskas om materialskivan placeras i fokalpunkten av NFF antennen. Mätningarna efterbehandlas med en genetisk algoritm (GA). Denna metod gissar värden på permittiviteten. Ett optimalt värde räknas fram iterativt. Jämfört med specificerad permittivitet från tillverkaren skiljer sig fri-rymdsmätningarna i denna avhandling i medeltal 2.8% i realdelen för den relativa permittiviteten och 34.14% i förlusttangent. Felet i mätningen påverkas av flera faktorer, som fel i mätningen av materialskivans tjocklek och fel i mätuppställningen.

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