Combining and choosing case base maintenance algorithms

Case-Based Reasoning (CBR) uses past experiences to solve new problems. The quality of the past experiences, which are stored as cases in a case base, is a big factor in the performance of a CBR system. The system's competence may be improved by adding problems to the case base after they have been solved and their solutions verified to be correct. However, from time to time, the case base may have to be refined to reduce redundancy and to get rid of any noisy cases that may have been introduced. Many case base maintenance algorithms have been developed to delete noisy and redundant cases. However, different algorithms work well in different situations and it may be difficult for a knowledge engineer to know which one is the best to use for a particular case base. In this thesis, we investigate ways to combine algorithms to produce better deletion decisions than the decisions made by individual algorithms, and ways to choose which algorithm is best for a given case base at a given time. We analyse five of the most commonly-used maintenance algorithms in detail and show how the different algorithms perform better on different datasets. This motivates us to develop a new approach: maintenance by a committee of experts (MACE). MACE allows us to combine maintenance algorithms to produce a composite algorithm which exploits the merits of each of the algorithms that it contains. By combining different algorithms in different ways we can also define algorithms that have different trade-offs between accuracy and deletion. While MACE allows us to define an infinite number of new composite algorithms, we still face the problem of choosing which algorithm to use. To make this choice, we need to be able to identify properties of a case base that are predictive of which maintenance algorithm is best. We examine a number of measures of dataset complexity for this purpose. These provide a numerical way to describe a case base at a given time. We use the numerical description to develop a meta-case-based classification system. This system uses previous experience about which maintenance algorithm was best to use for other case bases to predict which algorithm to use for a new case base. Finally, we give the knowledge engineer more control over the deletion process by creating incremental versions of the maintenance algorithms. These incremental algorithms suggest one case at a time for deletion rather than a group of cases, which allows the knowledge engineer to decide whether or not each case in turn should be deleted or kept. We also develop incremental versions of the complexity measures, allowing us to create an incremental version of our meta-case-based classification system. Since the case base changes after each deletion, the best algorithm to use may also change. The incremental system allows us to choose which algorithm is the best to use at each point in the deletion process.

Investigation of lactic acid bacteria mediated bioprotection with applications in cereal industry. Case-study: malting process

Antifungal compounds produced by Lactic acid bacteria (LAB) metabolites can be natural and reliable alternative for reducing fungal infections pre- and post-harvest with a multitude of additional advantages for cereal-base products. Toxigenic and spoilage fungi are responsible for numerous diseases and economic losses. This thesis includes an overview of the impact fungi have on aspects of the cereal food chain. The applicability of LAB in plant protection and cereal industry is discussed in detail. Specific case studies include Fusarium head blight, and the impact of fungi in the malting and baking industry. The impact of Fusarium culmorum infected raw barley on the final malt quality was part of the investigation. In vitro infected barley grains were fully characterized. The study showed that the germinative energy of infected barley grains decreased by 45% and grains accumulated 199 μg.kg-1 of deoxynivalenol (DON). Barley grains were subsequently malted and fully characterized. Fungal biomass increased during all stages of malting. Infected malt accumulated 8-times its DON concentration during malting. Infected malt grains revealed extreme structural changes due to proteolytic, (hemi)-cellulolytic and starch degrading activity of the fungi, this led to increased friability and fragmentation. Infected grains also had higher protease and β-glucanase activities, lower amylase activity, a greater proportion of free amino and soluble nitrogen, and a lower β-glucan content. Malt loss was over 27% higher in infected malt when compared to the control. The protein compositional changes and respective enzymatic activity of infected barley and respective malt were characterized using a wide range of methods. F. culmorum infected barley grains showed an increase in proteolytic activity and protein extractability. Several metabolic proteins decreased and increased at different rates during infection and malting, showing a complex F. culmorum infection interdependence. In vitro F. culmorum infected malt was used to produce lager beer to investigate changes caused by the fungi during the brewing processes and their effect on beer quality attributes. It was found, that the wort containing infected malt had a lower pH, a higher FAN, higher β-glucan and a 45% increase in the purging rate, and led to premature yeast flocculation. The beer produced with infected malt (IB) had also a significantly different amino acid profile. IB flavour characterization revealed a higher concentration of esters, fusel alcohols, fatty acids, ketones, and dimethylsulfide, and in particular, acetaldehyde, when compared to the control. IB had a greater proportion of Strecker aldehydes and Maillard products contributing to an increased beer staling character. IB resulted in a 67% darker colour with a trend to better foam stability. It was also found that 78% of the accumulated mycotoxin deoxynivalenol in the malt was transferred into beer. A LAB cell-freesupernatant (cfs), produced in wort-base substrate, was investigated for its ability to inhibit Fusarium growth during malting. Wort was a suitable substrate for LAB exhibiting antifungal activity. Lactobacillus amylovorus DSM19280 inhibited 104 spores.mL-1 for 7 days, after 120 h of fermentation, while Lactobacillus reuteri R29 inhibited 105 spores.mL-1 for 7 days, after 48 h of fermentation. Both LAB cfs had significant different organic acid profiles. Acid-base antifungal compounds were identified and, phenyllactic, hydroxy-phenyllactic, and benzoic acids were present in higher concentrations when compared to the control. A 3 °P wort substrate inoculated With L. reuteri R29 (cfs) was applied in malting and successfully inhibited Fusarium growth by 23%, and mycotoxin DON by 80%. Malt attributes resulted in highly modified grains, lower pH, higher colouration, and higher extract yield. The implementation of selected LAB producing antifungal compounds can be used successfully in the malting process to reduce mould growth and mycotoxin production.

Designing tools that enhance communication and understanding between multiple stakeholders: the case of mobile payment value networks

The pervasive use of mobile technologies has provided new opportunities for organisations to achieve competitive advantage by using a value network of partners to create value for multiple users. The delivery of a mobile payment (m-payment) system is an example of a value network as it requires the collaboration of multiple partners from diverse industries, each bringing their own expertise, motivations and expectations. Consequently, managing partnerships has been identified as a core competence required by organisations to form viable partnerships in an m-payment value network and an important factor in determining the sustainability of an m-payment business model. However, there is evidence that organisations lack this competence which has been witnessed in the m-payment domain where it has been attributed as an influencing factor in a number of failed m-payment initiatives since 2000. In response to this organisational deficiency, this research project leverages the use of design thinking and visualisation tools to enhance communication and understanding between managers who are responsible for managing partnerships within the m-payment domain. By adopting a design science research approach, which is a problem solving paradigm, the research builds and evaluates a visualisation tool in the form of a Partnership Management Canvas. In doing so, this study demonstrates that when organisations encourage their managers to adopt design thinking, as a way to balance their analytical thinking and intuitive thinking, communication and understanding between the partners increases. This can lead to a shared understanding and a shared commitment between the partners. In addition, the research identifies a number of key business model design issues that need to be considered by researchers and practitioners when designing an m-payment business model. As an applied research project, the study makes valuable contributions to the knowledge base and to the practice of management.

Exploring alternatives for milk quality improvement and more efficient dairy production in a smallholder farming context – case study: Mantaro Valley (Peru)

Consumer demand is revolutionizing the way products are being produced, distributed and marketed. In relation to the dairy sector in developing countries, aspects of milk quality are receiving more attention from both society and the government. However, milk quality management needs to be better addressed in dairy production systems to guarantee the access of stakeholders, mainly small-holders, into dairy markets. The present study is focused on an analysis of the interaction of the upstream part of the dairy supply chain (farmers and dairies) in the Mantaro Valley (Peruvian central Andes), in order to understand possible constraints both stakeholders face implementing milk quality controls and practices; and evaluate “ex-ante” how different strategies suggested to improve milk quality could affect farmers and processors’ profits. The analysis is based on three complementary field studies conducted between 2012 and 2013. Our work has shown that the presence of a dual supply chain combining both formal and informal markets has a direct impact on dairy production at the technical and organizational levels, affecting small formal dairy processors’ possibilities to implement contracts, including agreements on milk quality standards. The analysis of milk quality management from farms to dairy plants highlighted the poor hygiene in the study area, even when average values of milk composition were usually high. Some husbandry practices evaluated at farm level demonstrated cost effectiveness and a big impact on hygienic quality; however, regular application of these practices was limited, since small-scale farmers do not receive a bonus for producing hygienic milk. On the basis of these two results, we co-designed with formal small-scale dairy processors a simulation tool to show prospective scenarios, in which they could select their best product portfolio but also design milk payment systems to reward farmers’ with high milk quality performances. This type of approach allowed dairy processors to realize the importance of including milk quality management in their collection and manufacturing processes, especially in a context of high competition for milk supply. We concluded that the improvement of milk quality in a smallholder farming context requires a more coordinated effort among stakeholders. Successful implementation of strategies will depend on the willingness of small-scale dairy processors to reward farmers producing high milk quality; but also on the support from the State to provide incentives to the stakeholders in the formal sector.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.