Department of Informatics and Enabling Technologies

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The Department of Informatics and Enabling Technologies involves using computing technology to solve real world problems.

Current interests include: Visualisation, Computer Graphics, Image Processing, Data modelling and management, Simulation and Modelling, and Computing and Education.

Recent Submissions

Now showing 1 - 5 of 124
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    Using accounting information systems to benefit micro businesses : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
    (Lincoln University, 2024) Benbow, Pamela
    Ninety percent of all businesses in New Zealand are micro businesses, defined as having zero to five employees. This sector is critical to New Zealand’s economy. Micro businesses create opportunities for new entrepreneurial talents, provide employment and offer consumers choice and variety including specialist goods and services. Central to all businesses is the need for information, managed by the accounting information system (AIS). The AIS supports decision-making, achieving business objectives and managing limited resources. Prior studies and government reports call for further research of micro businesses so that this sector of the economy can be strengthened. This research addresses this call by exploring the benefits of using AIS in micro businesses using multiple methods, including desk-based research, semi-structured interviews with professional accountants, a survey of micro business and finally semi-structured interviews of micro business owners. Findings show that a variety of tools are used, ranging from manual record keeping, to spreadsheets, to computerised AIS, and including a mixture of these tools. The majority of microbusinesses use computerised AIS tools, of which two software providers dominate. Some accounting firms specialise their practice either through industry or choice of AIS. Other accountants accommodate any AIS approach, focusing on the individual micro business needs. AIS use by micro businesses is primarily focused on monitoring cash flow, sales and income activities and compliance reporting (GST and income tax). The greatest utilisation of computerised AIS and add-on tools are observed with these activities. Micro businesses could utilise other features more, especially reporting, as a basis for decision-making. The decision to adopt computerised AIS includes factors affecting the individual business owner (generation, individual knowledge and skill and personal attitude to technology), internal business factors (financial costs, time costs and the business purpose and future) and external business factors (supply chain, regulatory bodies and supporting services). The benefits of using computerised AIS include connectivity, autofill, automated calculations and drilldown. Connectivity through cloud technology provides accessibility to a single version of the data between users regardless of location. Autofill populates data entry screens with information previously captured, reducing the need for typing. Automated calculations automatically completes basic arithmetic in the creation of invoices, supplier bills and reports. Finally, drilldown enables direct access to supporting detail for information provided on screen. These benefits may not be available in older versions of computerised AIS, or versions that only include a subset of the features. This research increases the understanding of factors impacting micro businesses in their decision to implement computerised AIS, and the benefits from doing so. The findings support accountants, government agencies and AIS software developers to devise strategies to support micro businesses. Findings from this research are applicable to micro businesses throughout New Zealand and more globally and will benefit other small businesses outside of the micro definition, both locally and globally.
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    Body composition estimation in breeding ewes using live weight and body parameters utilising image analysis : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
    (Lincoln University, 2023) Shalaldeh, Ahmad
    Farmers are continually looking for new reliable, objective and non-invasive methods for estimating ewe body condition. Live weight (LW) in combination with body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Where LW is a crucial indicator of body composition, body condition can be evaluated by determining the amount of fat in the animal. This amount plays a key role in ewes’ health condition and animal productivity. The body condition score is used to monitor animals to ensure the best condition and is a measure between 1 (low condition) and 5 (high condition). If an ewe has a condition below 2 this is considered poor, whereas above 3 is regarded as good condition and ready for breeding. The current method is subjective (relies on professional judgment from farm handlers) as such it can introduce an element of error when estimating the fat, which makes it difficult to monitor the animal condition. A quick, objective, and accurate method of body composition estimation is required to improve farm management. If such a method could be devised, many farmers around the world would utilize it to assist in managing sheep farms. In addition, image processing and body measurements have not been used before to estimate body composition for ewes during the production cycle using a comprehensive, repeatable, non-invasive method. Image processing and body parameter measurements have been widely used to estimate ewe body size and weight. The objective of this thesis was to establish a relationship between body parameters of body length, width, depth and height as independent variables and body fat, lean, bone and carcass (total weight of body fat, lean and bone) as dependent variables. The aim was to use these easily obtained body parameters to predict body composition. Two full experiments at weaning and pre-mating were conducted to establish the relationship between body composition and body parameters using measurements automatically determined by an image processing application at Lincoln University sheep farm for 88 Coopworth ewes. Computerised Tomography (CT) technology was used as a benchmark to validate the predicted body composition. A trial run, wool test, uncertainty test, repeat test and carcass test were also conducted to minimise uncertainty and test the experiment setup. The image processing application used techniques from OpenCV library such as image extraction, convert to HSV colour, erode, dilate and smooth filter to remove the ewe’s head, legs (for side image) and extract the body to calculate the body parameters in an automated method. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were used to analyse the relationship between independent and dependent variables to predict body fat, lean, bone and carcass. The artificial neural network method was found to be the best method to show how much variance of the dependent variables is explained by a set of independent variables. The result showed a correlation between fat, lean, bone and carcass weight determined by CT and the fat, lean, bone, carcass weight and percentage of fat–carcass weight estimated by live weight and body parameters calculated in an automated method using the image processing application with R2 values of 0.88, 0.85, 0.72, 0.97 and 0.94, respectively for the training data of 138 ewes with a root mean square error (RMSE) less than 2.5. A new set test data of was used to test the accuracy of the results of multivariate linear regression, neural networks and regression tree. The neural networks model provided the highest R2 for total fat prediction with R2=0.90 and RMSE=1.01 with a maximum difference of 2.7 kg and a minimum difference of 0.018 kg between the predicted value and the actual value, lean prediction with an R2 of 0.72 and RMSE=1.03, bone prediction with an R2 of 0.50 and RMSE=1.21, carcass prediction with an R2 of 0.95 and RMSE=1.31 and percentage of fat – carcass weight with an R2 of 0.90 and RMSE=1.30, respectively. ANNs also showed the lowest RMSE for fat with a value of 1.01 and for carcass with a value of 1.31. The image processing application calculations showed an uncertainty of -9.43 to 9.22 mm for chest width. The result also showed that many ewes had the same body condition score but different fat and chest widths, which confirmed that the body condition score may not provide an accurate indication of fat. The results showed an optimal fat of 9.37% of LW for ewes during the production cycle. If the percentage of fat is less than or more than 9.37%, farmers must take action to improve the conditions of the animals to ensure the best performance during weaning and ewe and lamb survival during the next lambing. The new method can be used to determine body composition on sheep farms as an alternative to BCS since it showed more accurate results. This method can also lead to the use of new image analysis technologies and more research on using image processing on-farms. The accuracy of the new method is slightly less than CT but it takes less time and cost than the CT. It can be used on-farm at any stage during ewe production cycle and can be applied on animals with wool or after shearing the wool.
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    Implementing Multi Agent Systems (MAS)-based trust and reputation in smart IoT environments : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
    (Lincoln University, 2022) Al-Shamaileh, Mohammad
    The Internet of Things (IoT) provides advanced services by interconnecting a huge number of heterogeneous smart things (virtual or physical devices) through existing interoperable information and communication technologies. As IoT devices become more intelligent, they will have the ability to communicate and cooperate with each other. In doing so, enormous amount of sensitive data will flow within the network such as a credit card information, medical data, factory details, pictures and videos. With sensitive data flowing through the network, privacy becomes one of most important issues facing IoT. Studies of data sensitivity and privacy indicate the importance of evaluating the trustworthiness of IoT participants to maximize the satisfaction and the performance of the IoT applications. It is also important to maintain successful collaboration between the devices deployed in the network and ensure all devices operate in a trustworthy manner. This research aims to determine: How to select the best service provider in an IoT environment based on the trustworthiness and the reputation of the service provider? To achieve this, we proposed an IoT agent-based decentralized trust and reputation model IoT-CADM (Comprehensive Agent-based Decision-making Model for IoT) to select the best service providers for a particular service based on multi-context quality of services. IoT-CADM as a novel trust and reputation model, is developed for the smart multi-agent IoT environment to gather information from entities and score them using a new trust and reputation scoring mechanism. IoT-CADM aims to ensure that the service consumers are serviced by the best service providers in the IoT environment which in turn maximizes the service consumers’ satisfaction, which lead the IoT entities to operate and make-decisions on behalf of its owner in a trustworthy manner. To evaluate the performance of the proposed model against some other well-known models like ReGreT, SIoT, and R-D-C, we implemented a scenario based on the SIPOC Supply Chain approach developed using an agent development framework called JADE. This research used the TOPSIS approach to compare and rank the performance of these models based on different parameters that have been chosen carefully for fair comparison. The TOPSIS result confirmed that the proposed IoT-CADM has the highest performance. In addition, the model can be tuned to its parameters weight to adapt to varying scenarios in honest and dishonest agents’ environments.
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    Holistic Boolean model of cell cycle and investigation of related diseases through perturbation studies : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
    (Lincoln University, 2022) Pasha, Mustafa Kamal
    The cell cycle is the mechanism by which organisms develop and grow by cell division where a mother cell produces two daughter cells with exact copies of DNA. In the past few decades, much progress has been made in the field of systems biology in studying the complexity of the molecular regulation of cell cycle. However, most recent computational models have focused only on fewer aspects of the cell cycle because of its challenging complexity. Specifically, some core regulatory processes involved in DNA replication are modelled in most studies. However, a very limited attempt has been made to model the other crucial aspect – consistent volume growth during cell cycle to accommodate two sets of DNA and produce two daughter cells. Volume is particularly important because a number of debilitating diseases, including cancer, Alzheimer’s, Parkinson’s, and Down’s syndrome have been attributed to the aberrant cell cycle due to volume dysregulation. DNA replication and volume growth are highly regulated concurrent processes in the cell cycle. This study proposes to develop a holistic computational model of G1/S phase of cell cycle, integrating volume and DNA replication processes in a temporal Boolean model to gain insights into the mammalian cell cycle more comprehensively. It contributes towards the first most comprehensive cell cycle model integrating volume. Additionally, it explores the robustness of cell cycle from a perspective of integrated operations with multiple processes involved in cell division. It also explores the robustness of the cell cycle against single mutations. Further, this study probes into the causes of cell cycle diseases (i.e., volume, neurodegenerative, and cancers) and potential avenues for their understanding, elimination and therapeutics. These aspects along with temporal Boolean modelling are major novel contributions in the proposed study. The cell cycle consists of four main phases, G1, S, G2, and M, representing two Growth phases (G1 and G2) and DNA Synthesis (S) and Mitosis (M) or DNA segregation phases. This study focuses on G1 phase where the cell accomplishes the first volume growth and prepares the conditions (Cyclins and proteins) necessary for DNA synthesis in S phase. Our investigation revealed that these two processes are tightly interlinked and concurrently regulated in G1. The proposed model captures these features to accurately represent this phase of the cell cycle. We focus only on the G1 phase primarily because we realised that closer attention to G1 is needed to bring a clearer picture of some of the crucial aspects of this interlinking that we have uncovered. A cell maintains its volume within close bounds in normal operation and doubles its size in the cell cycle. Volume is increased through osmosis and ion channel operations that bring water into the cell from outside due to gradients in ionic concentrations. Therefore, in cell volume growth, a cell adjusts ion gradients through the operation of a large number of ion channels located on its plasma membrane, which also signifies the important role of bio-electricity (membrane polarisation) in cell cycle. In particular, cell continuously adjusts membrane polarisation to activate the required ion channels throughout volume regulation. Studies found that a large number of ion channels involved in volume regulation in G1 are associated in normal and cancerous cell proliferation. This implication of volume involvement is another reason to keep our focus on G1. Further, during large-scale volume changes, a cell concurrently reorganises its cytoskeleton (CS) to accommodate volume growth. This is achieved primarily through elevated Ca+2 which is established early in G1 phase through the K+ mediated hyperpolarisation of membrane (Vmem) that activates Ca+2 channels to bring Ca+2 into the cell. Ca+2 depolymerises the cytoskeleton and further contributes to increasing Vmem required to activate Cl− channels. This changes the ionic gradient causing the efflux of water through Aquaporin (AQ) and Taurine channels leading to shrinkage of the cell. The purpose of cell shrinkage appears to be primarily to relax the cytoskeleton before swelling. Once the shrinkage has stopped, the swelling process starts through the operation of Cl− and Osmolyte channels activated through increased Vmem for water influx and concurrent repolymerisation of CS that cause the cell to swell. Swelling is stopped upon reaching a volume threshold sensed by sensor protein mTorC1. This sensing defines the volume checkpoint in cell cycle. Ca+2 is a crucial player in controlling and linking both volume regulation and preparation of machinery for DNA replication. Specifically, while contributing to regulation of cell volume as described above, Ca+2 also plays a concurrent role in initiating the DNA replication machinery by activating Immediate Early Genes (IEG) that leads to the production of the first cell cycle Cyclin, Cyclin D. The preparation of the machinery for DNA replication mainly refers to the preparation of Cyclins required for DNA synthesis that takes place in S phase. Cyclins are the drivers of DNA replication, which themselves are tightly regulated by the cell itself through production and degradation processes. The production of Cyclins happens in G1 where they control the transition from G1 to S phase. G1 phase is important for Cyclin production, preparing three Cyclins, Cyclin D, E and A. Among them, Cyclin D is needed to partially release E2F transcription factor which is needed for the production of Cyclin E and A. The first cell cycle Cyclin, Cyclin D, hypophosphorylates Retinoblastoma protein (Rb) bound to partially release E2F for CycE production. This process marks the first checkpoint of the system called Rc, in our proposed model. This and the volume checkpoint are two major checkpoints introduced in our model. The Volume and DNA-replication-related processes further coincide during cell’s passage through Rg-volume checkpoint. Passing of the Rg coincides with complete release of E2F by Cyclin D and Cyclin E. The complete release of E2F factor for Cyclin E synthesis in bulk is done by Cyclin D and E together. During this process, the volume sensor protein, mTorC1, after having ensured that the cell passes Rg, then plays a key role in helping to complete the full release of E2F from Rb to aid bulk CycE synthesis and subsequently CycA. The role of Cyclin E is to assemble the DNA replication machinery on the DNA in late G1; therefore, adequate preparation of CycE signifies the transition from G1 to S phase. We introduce subsystems to integrate the volume regulatory processes, for example, membrane polarisation, CS adjustment, Ca+2, and checkpoints with DNA replicationrelated processes in a holistic system of G1 phase and represent the system in a temporal Boolean model. In particular, our investigation of the literature revealed the two G1 checkpoints mentioned above. One to ensure adequate cell growth (Rg) and the other to ensure the readiness for preparation of Cyclin E (Rc). Only the latter checkpoint Rc has been studied in past computational models and we show in our model how these two checkpoints are operated integrally. This constitutes another important contribution of the study. The goal of the study was to develop and study G1/S network as a holistic system with multiple subsystems. For this, we curated a temporal Boolean core regulatory model of G1 phase of cell cycle with 34 nodes and 42 Boolean Eqs., simplified from over 100 elements. The network model contained six subsystems: signal initiation, Calcium establishment, volume regulation, cytoskeletal regulations, Cyclin synthesis, and checkpoints. The model was implemented on MATLAB. An important aspect of our model is that it incorporates realistic times for the activation and operation of proteins extracted from an extensive literature survey. This gives the model temporal sense while avoiding spurious trajectories commonly found in Boolean models with random asynchronous updates of protein states. This marks another important contribution of this research as other existing Boolean cell cycle models lack time stamps while representing only the DNA-related process. We conducted a comprehensive study of the model to answer a number of questions: (i) Does the model resemble reality? We built the model from an extensive and exhaustive literature survey from which we distilled information for rigorous model building. Still, it is important to assess the correctness of model logic and how well the model represents reality in order to gain valuable insights from the model about the holistic operation of cell cycle and to ensure that the model is realistic to study its response to various perturbations. The model simulation reveals the seamless operation of the subsystems to accomplish the G1/S transfer. Specifically, it correctly unfolds how Vmem, CS, and Ca+2 regulate volume and how volume regulatory processes collaborate with the DNA replication-related processes and how these two strands of activities intricately control the two checkpoints. (ii) How robust is cell cycle design and how vulnerable it is to mutations? We conducted a comprehensive robustness study covering a number of investigations: a) Impact of mutation through element perturbations (Knock on/off), to identify elements and subsystems that crucially impact the main processes of G1: volume regulation, CS and Ca+2, membrane polarisation (MP), checkpoints and G1/S transfer. We found that each subsystem works independently and collaboratively towards the achievement of the G1/S goal. Any failure in one subsystem would either halt the cell cycle progression at various points in G1, or would lead towards cell death. We further found that the minimum working requirement for a subsystem is to achieve its local goal, i.e. to activate the neighbouring subsystem. Moreover, each subsystem has more than one element which can cause total system failure. b) What effects mutations have on volume related diseases, i.e, Alzheimer’s (AD), Parkinson’s (PD), Down’s Syndrome (DS) and other related diseases. We found that components of a sub-system of a larger network can be manipulated to effectively study the impact on the overall disease development. We confirmed through mutational studies that there are components available in the subsystem which can potentially be exploited to stop disease progression, or even eradicate if detected during the early onset of disease. More specifically, the Calcium and volume systems play a role in these disease progression. c) What are the most crucial elements responsible for cancer development and cell cycle-related diseases including, AD, PD and DS. The results showed that Cyclins, checkpoints (including volume checkpoint = Rg), and a few individual nodes from swelling, Calcium, and ion channels can contribute towards cancer development and other major cell cycle-related diseases and therefore be potential interests. These add a wealth of new information to the literature. Further to this, other results are in concert with the existing literature. This first comprehensive G1 model of the cell cycle is a major contribution to cell cycle modelling with its holistic coverage. It has made novel contributions by integrating volume and DNA-related processes and probing the model for cell cycle robustness against mutations, temporal sensitivity, and gaining insights into cancers, other diseases, and system failures. Major Contributions: 1. Extensive Literature Review (over 300) including over 90 Computational Studies 2. Multi-level regulatory Network Building, capturing concurrent regulation of volume increase and preparation of the crucial drivers (Cyclins) of DNA replication 3. Inclusion of volume Sensing, and introduction of the volume checkpoint Rg, and its integration with Cyclin Synthesis Checkpoint Rc 4. Flexible Boolean Logic Synthesis for a Comprehensive representation of the Complex and Dynamic G1/S System 5. Inclusion of Temporal data from in-vitro Expression Studies for Transforming the Standard Boolean Model into a Temporal Boolean Model 6. Investigational Studies via Perturbations on Cell Cycle Robustness, Cancer Development and Cell Cycle Related Diseases
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    Study of learning in small groups with an emphasis on facilitating effective learning in small groups in university programmes: A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Applied Science
    (Lincoln University, 1993) Keown, A. M.
    A study of student expectations and perceptions of learning in small groups in the university was carried out. A class of senior students was approached, and volunteers sought. The students were interviewed, and the interviews were recorded on audio cassette. They also completed a questionnaire giving their demographic details. The students identity remained anonymous in the analysis of their replies. The students had clear expectations of the leader and member roles within a group. They expected the leader to define the task, suggest solution for completing the task and hold the group together. They expected the members to fully contribute and participate in the group activity. Not surprisingly, their experience of working in small groups was similar to their expectations. The leaders role was as they expected, except in the leaderless groups that some of the students were involved in, where the leadership role was not required. The students could identify the task and maintenance needs of the group, but they had no perception of either their own individual needs, or the needs of the other individuals in the group. This showed that the students did not have any perception of the group processes working within their groups.