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    Does student sampling impact our understanding of argumentativeness and verbal aggressiveness?
    (Taylor and Francis Group on behalf of the American Forensic Association, 2024-08-17) Croucher SM; Kelly S; Elers P; Jackson K; Nguyen T
    Student samples are regularly used in research. While student samples are convenient and easy to access, the use of such samples has been criticized for exposing theories and research to internal validity threats, as students are not representative of the general population. Using argumentativeness and verbal aggressiveness as contexts for analysis, this study explores the extent to which student and non-student samples differ in published empirical research. We found that in the case of the original verbal aggression and argumentativeness measures, sample type did not moderate the means among argumentativeness and verbal aggressiveness studies. We discuss the implications of these findings in terms of student vs. non-student samples.
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    Contributions to food safety acceptance sampling plans : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics, Massey University, School of Mathematical and Computer Sciences
    (Massey University, 2023) Thevaraja, Mayooran
    An appropriate sampling inspection method is an essential tool for risk assessment in the food industry. A representative sampling approach will be helpful to reduce risk while minimising the sampling costs. Consequently, food manufacturers are employing efficient sampling approaches to assure food safety. In the food safety field, microbiological or other contamination often spreads unevenly across the production. Many factors are involved in the microbial risk assessment, such as (1) the amount of sample used for inspection, (2) what sampling methods were applied, (3) laboratory testing procedures, (4) physical sampling of materials from lots/batches of products and (5) the mixing of initially collected samples. This study focusses on improved sampling inspection approaches to reduce microbiological risk in food products. Part of this research also included developing open-source R packages to generate graphical displays for probabilistic risk assessment for practitioners. A single “wrapper” package is also provided to install all the newly developed packages in a single step.
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    Robotic capsule for sampling gut microbiota : design, development and evaluation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Palmerston North, New Zealand
    (Massey University, 2022) Rehan, Muhammad
    In this research, a pill-sized robotic capsule was developed that can collect gut microbiota both from the gut lumen (capsule surroundings) and intestinal wall (mucosa layer). Initially, the peristaltic forces exerted on the robotic capsule inside the gut were quantified so the working environment of the capsule could be understood. Secondly, a unique sampling mechanism was developed that could gently scrape the content from the gut lining and could provide a full length assessment of microbiota after capsule retrieval. Thirdly, the design of shape memory alloy (SMA) spring actuator was realised that could apply sufficient force to overcome peristaltic and frictional forces for sample collection at the target-site. Furthermore, an actuation system was devised by tackling the high-drain current requirement of SMAs. Fourthly, a sealing mechanism was developed to secure the collected sample from cross contamination and to assure successful encapsulation. Fifthly, the robotic capsule was rigorously tested in various in vitro simulators replicating the gut environment and a dedicated gut simulator that mimicked the in-vivo environment to ensure successful and safe travel of the capsule along the gastrointestinal tract. Finally, an in vitro experimental setup that kept an intestine alive for 6 hours was used to optimise the sample collection process. The robotic capsule collected sufficient quantities of sample (more than 100 µL) for microbiota analysis from living intestines of three animal species (pig, sheep and cow) during the trials. The study of gut microbiota is gaining increasing attention due to its direct impact on human health. Gut microbiota can provide comprehensive information about the health of a host, and it can help in the early diagnosis of diseases like cancer, diabetes, obesity, etc. The robotic capsule prototype, developed in this work, has a potential to become a vital apparatus for clinicians and scientists to sample human and animal gut in the future.
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    Redox characteristics of shallow groundwater in the Tararua Ground Water Management Zone : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Earth Science at Massey University, Manawatū, New Zealand
    (Massey University, 2018) McGowan, Peter Grant
    Groundwater redox conditions have a major influence on transport and transformation of nutrients such as nitrate from farms to rivers and lakes. This study focused on measurement and analysis of chemical and physical characteristics of groundwater to determine the spatial distribution of redox characteristics across the Tararua Ground Water Management Zone in the Manawatu River catchment. The influence of catchment characteristics such as soil texture and drainage, and rock type have on groundwater chemistry and its redox characteristics across the Tararua GWMZ is investigated using multivariate statistical analysis. Existing geographical information was collated and analysed to map spatial distributions of land use, soil characteristics and lithologies across the study area. This information was utilised to identify potential site locations for sampling and analysis of shallow groundwater in the Tararua GWMZ. A direct-push system capable of penetrating a range of substrates including deep, imbricated, and coarse gravels was developed. Using this system, shallow groundwater samples were recovered from contrasting hydrogeological settings, areas where water wells are rarely installed; such as along the margins of the axial ranges, and from areas considered not to have groundwater; e.g. the mudstone country on the east of the Tararua District. Data collected with the direct-push method was combined with similar data collected from existing wells by Rivas et al. (2017) and classified according to redox status. The data was subjected to multivariate statistical assessment using Hierarchical Cluster Analysis to determine the water type, and Principal Component Analysis to determine the influence of discrete catchment characteristics on redox reactions occurring in shallow groundwater of the Tararua GWMZ. The in-field and chemical analysis revealed significant variation of groundwater quality parameters and redox characteristics across the Tararua GWMZ. The regional trend was for reducing conditions in gravel aquifers in the north western areas of the Tararua GWMZ and oxidising in gravel aquifers of the south western; although statistically significant variations of redox characteristics is also recognised within these areas. Groundwater samples were collected from mudstone where little, if any, groundwater research has been conducted previously. Groundwater characteristics from mudstone are generally classified as anoxic and strongly reducing, with very high specific conductivity and analyte levels such as bromide, chlorine, sodium, fluorine, dissolved inorganic carbon and magnesium. Identifying the influence of discrete catchment characteristics on groundwater chemistry and redox characteristics was complex and difficult to quantify. Extrapolation of the principal component inferred to be associated with redox characteristics provides a useful means to evaluate the influence of discrete catchment characteristics on redox conditions in shallow groundwater of the Tararua GWMZ. The direct-push method provided an opportunity to compare groundwater chemistry between samples collected proximal and distal to production wells. Statistically significant differences in redox related parameters such as DOC, Eh, Fe2+, Mn2+, NH4+-N, and N02--N were detected in groundwater samples collected from existing wells compared to groundwater samples collected with the direct-push method. Factors contributing to this effect were explored but found to be difficult to isolate.
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    Acceptance sampling for food quality assurance : this dissertation is submitted for the degree of Doctor of Philosophy in Statistics, Institute of Fundamental Sciences, Massey University
    (Massey University, 2017) Santos-Fernández, Edgar
    Acceptance sampling plays a crucial role in food quality assurance. However, safety inspection represents a substantial economic burden due to the testing costs and the number of quality characteristics involved. This thesis presents six pieces of work on the design of attribute and variables sampling inspection plans for food safety and quality. Several sampling plans are introduced with the aims of providing a better protection for the consumers and reducing the sample sizes. The effect of factors such as the spatial distribution of microorganisms and the analytical unit amount is discussed. The quality in accepted batches has also been studied, which is relevant for assessing the impact of the product in the public health system. Optimum design of sampling plans for bulk materials is considered and different scenarios in terms of mixing efficiency are evaluated. Single and two-stage sampling plans based on compressed limits are introduced. Other issues such as the effect of imperfect testing and the robustness of the plan have been also discussed. The use of the techniques is illustrated with practical examples. We considered numerous probability models for fitting aerobic plate counts and presence-absence data from milk powder samples. The suggested techniques have been found to provide a substantial sampling economy, reducing the sample size by a factor between 20 and 80% (when compared to plans recommended by the international Commission on Microbiological Specification for Food (ICMSF) and the CODEX Alimentarius). Free software and apps have been published, allowing practitioners to design more stringent sampling plans. Keywords: Bulk material, Composite samples, Compressed limit, Consumer Protection, Double sampling plan, Food safety, Measurement errors, Microbiological testing, Sampling inspection plan.
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    Capturing loft : adding value to New Zealand wool bedding products through textile design innovation : an exegesis in partial fulfilment towards a Master of Design, Massey University.
    (Massey University, 2014) Olatunji, Kelly Rimkeit
    This design-led research project was developed in collaboration with the Christchurch-based bedding manufacturing firm FibreTech New Zealand Limited. It explored the potential of an innovative wool-fill product developed by FibreTech. This new wool-fill maximises loft and bulk, both key factors for warmth and comfort in bedding. Loft is an active, three-dimensional feature of bedding, controlled through processes of compression and release. Retaining and managing loft was vital. The designer provided a holistic approach, using a textile design perspective to explore functionality and aesthetics in relation to the structure of the fill and outer membrane layers of bedding products. Through material sampling the project assessed how FibreTech’s new wool product could be layered and bonded with other textiles. The technical processes of needle punching, fusing and stitch bonding were used to explore the loft and compression relationship within the textiles. It was found that ratios of loft and compression could be altered to improve the efficiency of manufacturing; while at the same time optimising functionality and aesthetics. Textile design, wool knops, knoppy web, New Zealand wool, loft, overbody bedding, underbody bedding, sampling, research and development, digital quilting, computer-aided design Key words: Using the existing manufacturing process of digital quilting, stitch paths were redesigned to create an innovative range of bedding products for use over and under the body. The resulting textiles revealed a departure from classic bedding construction, with a new focus on controlling the stitch line through computer-aided design (CAD) technology. This hard-edged stitch line was a digital imposition that contrasted with the organic nature of soft, lofted materials. This visual and haptic tension was identified as key design interplay for both overbody and underbody approaches. Strategies were created towards lightweight overbody bedding and engineered shaping of underbody bedding. These new digital quilting strategies captured loft in distinctly different, yet functional ways. This project provides evidence that a textile designer can be a key contributor in the manufacturing industry, along with other disciplines such as science and engineering to add value to research and development in the New Zealand wool textile manufacturing industry. The design research progressed as a Callaghan Innovation Postgraduate Fellowship project and represents the development of a new aesthetic for wool bedding products.
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    Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012
    (Massey University, 2012) Anwar, Muhammad Nafees
    The class imbalance problem is a challenge in the statistical, machine learn- ing and data mining domains. Examples include fraud/intrusion detection, medical diagnosis/monitoring, bioinformatics, text categorization, insurance claims, and target marketing. The problem with imbalanced data sets is that the conventional classifiers (both statistical and machine learning algorithms) aim at maximizing overall accuracy, which is often achieved by allocating all, or almost all, cases to the majority class. Thus there tends to be bias against the minority class in class imbalance situations. Despite numerous algorithms and re-sampling techniques proposed in the last few decades to tackle imbalanced classification problems, there is no consistent winning strategy for all data sets (neither in terms of sampling, nor learning algorithm). Special attention needs to be paid to the data in hand. In doing so, one should take into account several factors simultaneously: the imbalance rate, the data complexity, the algorithms and their associated parameters. As suggested in the literature, mining such datasets can only be improved by algorithms tailored to data characteristics; therefore it is important and necessary to do data exploratory analysis before deciding on a learning algorithm or re-sampling techniques. In this study, we have developed a framework "Complexity Measurement" (CM) to explore the connection between the imbalanced data problem and data complexity. Our study shows that CM is an ideal candidate to be recognized as a "goodness criterion" for various classifiers, re-sampling and feature selection techniques in the class imbalance framework. We have used CM as a meta-learner to choose the classifier and under-sampling strategy that best fits the situation. We design a systematic over-sampling technique, Over-sampling using Complexity Measurement (OSCM) for dealing with class overlap. Using OSCM, we do not need to search for an optimal class distribution in order to get favorable accuracy for the minority class, since the amount of over-sampling is determined by the complexity; ideally using CM would detect fine structural differences (class-overlap and small disjunct) between different classes.Existing feature selection techniques were never meant for class imbalanced data. We propose Feature Selection using Complexity Measurement (FSCM), which can specifically focus on the minority class, hence those features (and multivariate interactions between predictors) can be selected, which form a better model for the minority class. Methods developed have been applied to real datasets. The results from imbalanced datasets show that CM, OSCM and FSCM are effective as a systematic way of correcting class imbalance/overlap and improving classifier performance. Highly predictive models were built; discriminating patterns were discovered, and automated optimization was proposed. The methodology proposed and knowledge discovered will benefit exploratory data analysis for imbalanced datasets. It may be taken as a judging criterion for new algorithms and re-sampling techniques. Moreover, new data sets may be evaluated using our CM criterion in order to build a sensible model.