Manukau Institute of Technology
  • Auckland, New Zealand
Recent publications
This study examined the relationship between perceived bowling intensity, ball release speed and ground reaction force (measured by peak force, impulse and loading rate) in male pace bowlers. Twenty participants each bowled 36 deliveries, split evenly across three perceived intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. Peak force and loading rate were significantly different across the three perceived intensity zones in the horizontal and vertical directions (Cohen's d range = 0.14-0.45, p < 0.01). When ball release speed increased, peak force and loading rate also increased in the horizontal and vertical directions (ηp2 = 0.04-0.18, p < 0.01). Lastly, bowling at submaximal intensities (i.e., low - medium) was associated with larger decreases in peak horizontal force (7.9-12.3% decrease), impulse (15.8-21.4%) and loading rate (7.4-12.7%) compared to decreases in ball release speed (5.4-8.3%). This may have implications for bowling strategies implemented during training and matches, particularly for preserving energy and reducing injury risk.
This study examines how seven counselors and psychotherapists in Aotearoa New Zealand made sense of their self-transcendent experiences (STE), and discussed how they see these experiences influencing their therapeutic approaches. The term “self-transcendent experience” is defined as a short-lived peak event that achieves a perceived connection beyond one’s sense of self, which can be difficult to describe in words. It is assumed that when STE occurs in the lives of psychologically healthy individuals, its aftermath is associated with increased well-being. Applying interpretative phenomenological analysis (IPA) to seven semi-structured interviews, this study attempts to make interpretative sense of how participants understand transcendent phenomena. Results point to an interplay between participants’ perceived meaning of STE and its context, defined as the confluence of interpersonal and intrapersonal characteristics of lived experience preceding, during, and following the event. Participants couch ineffable phenomena of STE within more communicable narratives of grief and loss, shifting identity, struggling with insecurity, and undergoing transformational growth. Findings point to the role of intuitive states during therapy, where participants receive “pictures or sensations” and “pings of information.” Some participants reported sensing deep connection with clients, suggesting states of relational depth during therapy might be conceived of as low-key STE.
IntroductionSexuality and intimacy in residential aged care (RAC) are receiving increased research attention. In this article, porneia refers to access to sex workers, as well as online pornography, and masturbation by residents in RAC. Sex work is legal and regulated in Aotearoa New Zealand.Methods The present study was a two-arm mixed-method cross-sectional study using a concurrent triangulation design. A validated survey tool was developed. Data were collected in 2018–2019: 433 staff surveys were collected from 35 RAC across the country; 61 interviews were carried out with 77 staff, residents, and family members.ResultsStaff opinions about sex work and pornography were inconclusive. Nevertheless, access to sex workers occurs in many RAC facilities across the country. Interviews demonstrated a diversity of responses among the three groups; staff attitudes are paramount.Conclusions Some staff are prepared for resident requests for sex workers; others continue to look to policies and management for guidance, but such policies are often lacking. Most staff have adopted the language of needs vs. rights which dominates the literature.Policy ImplicationsStaff education on sexuality and facility policy is essential; education for residents and their families is also desirable. Facilities often over-notify third parties. Discourse about sexuality needs to move towards a person-centred, salutogenic approach.
This study examined the contextual factors that shape how young children come to value and use the visual arts in their learning. The research sought to understand more deeply, the impact of visual arts practices that are informed by sociocultural theories on children’s and their family’s perceptions and engagement with the visual arts in their learning. Recognising the profound impact of bidirectional relationships in the early years (Bronfenbrenner, 1979), this interpretive qualitative research focused on the interactions between children, teachers, and families at three early childhood settings and at six children’s homes in Auckland, New Zealand. The theoretical framework and study design were underpinned by sociocultural theories, bioecological theories, and by narrative inquiry. Participatory arts-based methods were fundamental as they allowed the research participants to play significant roles in telling their stories through textual and visual means. Through multi-layered analysis, a complex web of influences shaping how children engage in the visual arts emerged. A key finding was the impact of bi-directional interactions within settings and between settings. The teachers in this study wove together rich, contextualised visual arts curricula and actively engaged with children through the visual arts. They prioritised disseminating the value of these practices to their educational communities. As a result, parents recognised how visual arts can enrich and support their child’s learning. Teachers who actively role modelled enjoyment and expertise in the visual arts were a particularly potent influence. These findings demonstrate that developing shared values between settings in the microsystem can enrich children’s capacity to become imaginative visual researchers.
Background: Reflective practice is an integral part of modern healthcare. If done well, it can significantly improve the individual skills of health care practitioners. However, we hypothesize that extrapolating individual reflective practice into broader organization applications undermines its fundamental nature and inhibits objective benchmarking within the health sector. Methods: We reflect on the nature and use of the reflective practice in healthcare. Results: An organization that practices reflective practice may, in effect, create an environment where reflective practice is promoted but operates to homogenize thinking to a point where it turns into dysfunctional institutional navel-gazing. Homogenized thinking may inhibit the ability to move beyond practice to explore ideas that lead to change. Conclusions: The collective approach to reflective practice can subvert the underlying process of self-analysis, which allows the critical examination of individual values, priorities, and evaluations. It can inhibit individual growth, favouring a homogenizing effect which is the antithesis of an innovative organization when measured against the original intent and must therefore be used with care.
Fatigue testing has been conducted on welds of AA2024 (Al4.5Cu1.4Mg0.5Mn) alloy to Ti6Al4V alloy made using friction stir lap welding (FSLW). During FSLW, pin bottom aimed for touching the Ti6Al4V plate (dPin≈0), although it could readily penetrate (dPin>0). It has been found that fatigue limit of the AA2024/Ti6Al4V welds was slightly higher than the fatigue limits of the FSL Al-to-Al alloy welds reported in literature. Examination has demonstrated that forming a very thin interface layer during FSLW was the major mechanism responsible for the good fatigue strength. The diffusion weld distance outside the pin width in dPin>0 welds was significantly larger than that in dPin≈0 welds. Thus, the fatigue limit of dPin>0 welds was comparable to the fatigue limit of dPin≈0 welds despite of the mix stir zone (MSZ) in dPin>0 welds being a brittle one. The different thermomechanical conditions for the different diffusion-weld widths will be illustrated.
An electronic wallet (e-wallet) is the digital equivalent of a physical wallet that can support cashless and contactless payment, thereby enabling consumers' to meet the physical contact restrictions imposed to contain the spread of COVID-19. Hence, consistent with the increasing awareness of e-wallets, this study investigates consumers' intention to use e-wallets. Drawing on the motivation-ability-opportunity (MAO) framework, we investigated the factors of consumers' usage intention of e-wallets. The hypothesized model was tested using the survey data collected from 226 respondents in Malaysia. The results of partial modelling analysis of 226 respondents affirmed the significance of perceived COVID-19 risk, perceived government support, and facilitating conditions in influencing usage intention. However, effort expectancy was not a significant predictor. As hypothesized, facilitating conditions moderated the effects of effort expectancy and perceived government support on usage intention, but not that of perceived COVID-19 risk. Our findings demonstrated that motivation in terms of health risk avoidance and government incentives and opportunity in the form of facilitating conditions play significant roles in influencing the usage intention of e-wallets.
The traditional feature selection methods are not suitable for imbalanced data as they tend to be biased towards the majority class. This problem is particularly acute in the field of medical diagnostics and fraud detection where the class distribution is highly skewed. In this paper, we propose a novel filter approach using decision tree-based F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}-score. The F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}-score incorporates the accuracy with respect to the minority class data and hence is a good measure in the case of imbalanced data. In the proposed implementation, the F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}-score is calculated based on a 1-dimensional decision tree classifier resulting in a fast and effective feature evaluation method. Numerical experiments confirm that the proposed method achieves robust dimensionality reduction and accuracy results. In addition, the low computational complexity of the algorithm makes it a practical choice for big data applications.
One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.
The increasing usage of distributed emergency services (e.g. natural disaster or disaster caused by humans) in wireless local area networks requires the support of immediate channel access with strict quality of service (QoS) guarantee from a medium access control (MAC) protocol. The IEEE 802.11e/enhanced distributed channel access (EDCA) standard is the MAC enhancement for QoS. Unfortunately, 802.11e (EDCA) neither supports emergency traffic nor provides a strict QoS guarantee especially for a large number of users who report an emergency. To address this problem of achieving a strict QoS guarantee for emergency traffic, we previously developed and reported a multi-preemptive EDCA (MP-EDCA) protocol suitable for operating under low to medium traffic loads. However, MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g. ambulance calls) in highly loaded networks. In this paper, we provide a solution to the problem of achieving a strict QoS guarantee to life-saving emergency traffic under high traffic loads. To this end, we propose a preemptive admission control (PAC) mechanism for MP-EDCA called PAC-MP-EDCA. The proposed PAC-MP-EDCA protects ongoing life-saving emergency traffic flows by giving high priority; thus, assures a QoS guarantee (in terms of lower packet delays) to life-saving emergency traffic when a high number of nodes require immediate channel access during emergency time. The priorities are set by carefully adjusting the short inter-frame space and slot-time in the emergency frames. The performance of PAC-MP-EDCA is evaluated by Riverbed Modeler simulation. Results obtained show that the proposed PAC-MP-EDCA achieved up to 98% lower MAC delays for life-saving emergency nodes and about 15% higher throughput than MP-EDCA under high traffic loads.
Next-generation power systems aim at optimizing the energy consumption of household appliances by utilising computationally intelligent techniques, referred to as load monitoring. Non-intrusive load monitoring (NILM) is considered to be one of the most cost-effective methods for load classification. The objective is to segregate the energy consumption of individual appliances from their aggregated energy consumption. The extracted energy consumption of individual devices can then be used to achieve demand-side management and energy saving through optimal load management strategies. Machine learning (ML) has been popularly used to solve many complex problems including NILM. With the availability of the energy consumption datasets, various ML algorithms have been effectively trained and tested. However, most of the current methodologies for NILM employ neural networks only for a limited operational output level of appliances and their combinations (i.e., only for a small number of classes). On the contrary, this work depicts a more practical scenario where over a hundred different combinations were considered and labelled for the training and testing of various machine learning algorithms. Moreover, two novel concepts—i.e., thresholding/occurrence per million (OPM) along with power windowing—were utilised, which significantly improved the performance of the trained algorithms. All the trained algorithms were thoroughly evaluated using various performance parameters. The results shown demonstrate the effectiveness of thresholding and OPM concepts in classifying concurrently operating appliances using ML.
This study examined whether an inertial measurement unit (IMU) and machine learning models could accurately measure bowling volume (BV), ball release speed (BRS), and perceived intensity zone (PIZ). Forty-four male pace bowlers wore a high measurement range, research-grade IMU (SABELSense) and a consumer-grade IMU (Apple Watch) on both wrists. Each participant bowled 36 deliveries, split into two different PIZs (Zone 1 = 70–85% of maximum bowling effort, Zone 2 = 100% of maximum bowling effort). BRS was measured using a radar gun. Four machine learning models were compared. Gradient boosting models had the best results across all measures (BV: F-score = 1.0; BRS: Mean absolute error = 2.76 km/h; PIZ: F-score = 0.92). There was no significant difference between the SABELSense and Apple Watch on the same hand when measuring BV, BRS, and PIZ. A significant improvement in classifying PIZ was observed for IMUs located on the dominant wrist. For all measures, there was no added benefit of combining IMUs on the dominant and non-dominant wrists.
In this paper, an investigation into the dynamics flow behaviour of a humidifier unit is carried out using the numerical simulation method. The Computational Aeroacoustics (CAA) with a hybrid approach is conducted in the ANSYS software environment. The CFD simulations were used to investigate the internal flow behaviours of the humification fluid model. The predicted results have shown the internal flow characteristics combined with scatters, circulations, and separation behaviours. These behaviours are due to the installation of a baffle unit inside the humification unit. Turbulent and pressure fluctuations were used to predict the noise level generated within the system. The predicted results are compared with the experimental results for validation. The comparison shows the predicted results agreed with the experimental result with some differences in frequency analysis. In summary, the numerical model was developed for the CPAP humidifier unit to study the internal flow behaviours and the potential noise source location. The predicted results have explained and identified some locations of interest. These findings were used as guidelines to optimise and further develop to improve the future product. It also concludes that the CAA approach can be considered as a potential cost-effective tool in the early product development process for a better product design.
Background The ethical complexity of residential care is especially apparent for staff responding to residents’ inappropriate sexual expression, particularly when directed towards care workers as these residents are typically frail, often cognitively impaired, and require ongoing care. Objectives To explore staff accounts of how they made meaning of and responded to residents' unwanted sexual behaviours directed towards staff. This exploration includes whether staff appeared to accept harassment as a workplace hazard to be managed, or an unacceptable workplace violation, or something else. Methods These qualitative data are drawn from a national two-arm mixed method study in Aotearoa New Zealand undertaken in 35 residential care facilities. Semi-structured interviews were conducted with 77 staff, residents and family members. Interpretive description was used to analyse the data. Results Staff had numerous ways they used to respond to behaviours: (1) minimisation, deflection and de-escalation, where staff used strategies to minimise behaviours without requiring any accountability from residents; (2) holding residents accountable, where staff to some degree addressed the behaviour directly with residents; (3) blurred boundaries and complexities in intimate long-term care, where staff noted that in a context where touch is common-place, cognitive function was diminished and relationships were long-term, boundaries were easily breached; (4) dehumanising and infantilising residents’ behaviours, where staff appeared to assert control through diminishing the residents’ identity as an older person. It was evident that staff had developed considerable practice wisdom focused on preserving the care relationship although few referred to policy and education guiding practice. Conclusions Staff appeared to be navigating a complex ethical terrain with thoughtfulness and skill. Care workers seemed reluctant to label resident behaviour as sexual harassment, and the term may not fit for staff where they perceive residents are frail and cognitively impaired. Implications for practice Policy, education and clinical leadership are recommended to augment practice wisdom and ensure staff and resident safety and dignity and to determine how best to intervene with residents' unwanted sexual behaviours.
Background: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. Objective: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. Methods: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs' performance with other prominent machine learning algorithms. Results: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. Conclusion: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.
Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation. Non-intrusive load monitoring (NILM) offers many promising applications in the context of energy efficiency and conservation. Load classification is a key component of NILM that relies on different artificial intelligence techniques, e.g., machine learning. This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis. Moreover, this study also analyzes the role of input feature space dimensionality in the context of classification performance. For the above purposes, an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households. Based on the presented analysis, it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data. The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier. Furthermore, it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.
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Keith Macky
  • Faculty of Business and Information Technology
Sayan Kumar Ray
  • School of Digital Technologies
John Young
  • School of Business
Auckland, New Zealand