ItemImplementing 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, MohammadThe 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. ItemHolistic 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 KamalThe 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 ItemStudy 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. ItemDesigning and testing holistic computational frameworks for identification of the most effective vaccine and drug targets against human and bovine tuberculosis : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University(Lincoln University, 2021) Pawar, PoojaThe World Health Organization has considered tuberculosis (TB) a threat with a significant mortality and morbidity rate worldwide. TB is caused by notorious Mycobacterium tuberculosis, which has evolved with successful survival strategies leading to the emergence of drug-resistant TB strains making drugs (first-line TB drugs) and vaccine (BCG) ineffective. The global emergence of tuberculosis is threatening to make one of humankind’s most lethal infectious diseases incurable, with an estimated 10.0 million new TB cases and 1.4 million deaths in 2019. Further, TB affects animals too; bovine tuberculosis primarily affects cattle and it is caused by the etiological agent Mycobacterium bovis. Twenty to thirty per cent of the global livestock population is potentially affected by bovine TB, leading to annual economic losses of more than USD 3 billion globally (Kuria, 2019). This study conducts an in-depth investigation into pathogen-human interactions to gain deeper insights into the evolution of pathogen and their drug resistance mechanisms and uses this understanding to provide potential solutions for effective vaccine and drug development for human and bovine tuberculosis. Our research begins with gaining an understanding of the pathogenesis of human and bovine TB, the interaction of TB bacteria with its host, the host defence mechanism, bacterial survival strategies in evading the host immune response, and in-depth knowledge of the mechanisms of TB drug resistance. The current drug treatment regimen has not changed in nearly 40 years. Although the first-line drugs play a pivotal role in combating TB, the emergence of resistant TB strains due to different survival mechanisms of TB bacteria such as reduced permeability of cell wall preventing drug entry into the cells, mutations in the drug target protein (major hurdle in TB treatment), inactivation of drug molecules with the help of bacterial enzymes, and a transmembrane drug efflux system to expel the drug out from the bacterial cell has heightened the burden of TB globally. BCG is the only licensed vaccine available and has been around for almost a hundred years. BCG (Bacillus Calmette-Guérin) is prepared from a live-attenuated strain of Mycobacterium bovis and it has shown protection in babies and young children. The inefficiency of BCG in not reducing the prevalence of disease and not protecting adults is so far not understood. Some of the crucial factors might include Mycobacterium bovis is less virulent and not a primary causative agent of TB, diversity in TB strains and over-attenuation of presently used BCG strain. The low efficacy of BCG, the emergence of the drug-resistant Mycobacterium tuberculosis strains, and challenges in developing drugs and vaccines have generated an urgent requirement for a powerful and effective therapeutic approach for TB treatment. This study introduces three holistic strategies/frameworks for developing new and effective therapeutic methods for fighting TB. ItemCharacterising sheep vocals using a machine learning algorithm : A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Applied Science at Lincoln University(Lincoln University, 2021) Kayani, Bilal NawazNew Zealand’s economy is mainly dependent on the farming sector and the sheep sector is one of the most important farming sectors, playing a backbone role to the agricultural industry and placing New Zealand among the top five sheep exporter countries in the world. International consumer trends show concerns over the well-being of animals before slaughter and research also indicates potential negative effects on meat quality of stressed animals. Indicators for sheep well-being have largely been limited to physical weight gain and visually observable behaviour and appearance. There has been recent interest but little substantive research on sheep vocalisation as a means of monitoring sheep well-being. This assumes that sheep vocalisation can be classified as representing different states of well-being. Therefore, this thesis investigated the potential to be able to classify sheep vocalisations in a way that would enable automated assessment of the well-being of New Zealand sheep using recorded vocalisations. A supervised machine learning approach was used to classify the sheep vocals into happy and unhappy classes. Sheep sounds were collected from a New Zealand Ryeland sheep stud farm and online databases. After collection, these sounds were labelled by an expert, pre-processed to make them clean from unwanted background sound noises and features were extracted and selected for classification. Models were built and trained and tested. Model use in this research shows that sheep sounds were classified into happy and unhappy classes with an accuracy of 87.5%, for the sheep vocals used in this research. Through demonstrating the ability for automated classification of sheep vocalisations this research opens the door for further study on the well-being of sheep through their vocalisations. Future researchers could also collect larger vocal data sets across different breeds to test for breed-related variance in vocalisations.. This may enable future sheep well-being certification systems to be established to assure consumers of the well-being of pre-slaughter sheep life.