Massey Research Online
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Nutrient-Level Evaluation of Meals Provided on the Government-Funded School Lunch Program in New Zealand
(MDPI (Basel, Switzerland), 2022-12) de Seymour J; Stollenwerk Cavallaro A; Wharemate-Keung L; Ching S; Jackson J; Maeda-Yamamoto M
Approximately 1 in 6 children in New Zealand are living in households facing poverty and 14% of the population is food insecure. The Ka Ora, Ka Ako|Healthy School Lunches program aims to reduce food insecurity by providing access to a nutritious lunch every school day. This study analyzed the nutritional content of Ka Ora, Ka Ako meals and compared them to national and international standards. Meals were selected at random from approved menus. The suppliers covered by the 302 meals analyzed provide 161,699 students with a lunch (74.9% of students on the program). The meals were analyzed using Foodworks 10 nutrient analysis software. The nutrient content was compared against the New Zealand/Australia Nutrient Reference Values (NRVs) and to nutrient-level standards for international school lunch programs. A total of 77.5% of nutrients analyzed exceeded 30% of the recommended daily intakes. Protein, vitamin A and folate met the NRV targets and a majority of the international standards (55/57). Energy, calcium, and iron were low compared to NRVs and international standards (meeting 2/76 standards). Carbohydrates were low compared to international standards. The findings have been used to inform the development of revised nutrition standards for the program, which will be released in 2022.
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A review of climate change impact assessment and methodologies for urban sewer networks
(Elsevier B V, 2025-06) Karimi AM; Jelodar MB; Susnjak T; Sutrisna M
Understanding how climate change affects urban sewer networks is essential for the sustainable management of these infrastructures. This research uses a systematic literature review (PRISMA) to critically review methodologies to assess the effects of climate change on these systems.
A scientometric analysis traced the evolution of research patterns, while content analysis identified three primary research clusters: Climate Modelling, Flow Modelling, and Risk and Vulnerability Assessment. These clusters, although rooted in distinct disciplines, form an interconnected framework, where outputs of climate models inform flow models, and overflow data from flow models contribute to risk assessments, which are gaining increasing attention in recent studies. To enhance risk assessments, methods like Gumbel Copula, Monte Carlo simulations, and fuzzy logic help quantify uncertainties. By integrating these uncertainties with a Bayesian Network, which can incorporate expert opinion, failure probabilities are modelled based on variable interactions, improving prediction.
The study also emphasises the importance of factors, such as urbanisation, asset deterioration, and adaptation programs in order to improve predictive accuracy. Additionally, the findings reveal the need to consider cascading effects from landslides and climate hazards in future risk assessments. This research provides a reference for methodology selection, promoting innovative and sustainable urban sewer management.
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Transfer learning on transformers for building energy consumption forecasting—A comparative study
(Elsevier B V, 2025-06-01) Spencer R; Ranathunga S; Boulic M; van Heerden AH; Susnjak T
Energy consumption in buildings is steadily increasing, leading to higher carbon emissions. Predicting energy consumption is a key factor in addressing climate change. There has been a significant shift from traditional statistical models to advanced deep learning (DL) techniques for predicting energy use in buildings. However, data scarcity in newly constructed or poorly instrumented buildings limits the effectiveness of standard DL approaches. In this study, we investigate the application of six data-centric Transfer Learning (TL) strategies on three Transformer architectures—vanilla Transformer, Informer, and PatchTST—to enhance building energy consumption forecasting. Transformers, a relatively new DL framework, have demonstrated significant promise in various domains; yet, prior TL research has often focused on either a single data-centric strategy or older models such as Recurrent Neural Networks. Using 16 diverse datasets from the Building Data Genome Project 2, we conduct an extensive empirical analysis under varying feature spaces (e.g., recorded ambient weather) and building characteristics (e.g., dataset volume). Our experiments show that combining multiple source datasets under a zero-shot setup reduces the Mean Absolute Error (MAE) of the vanilla Transformer model by an average of 15.9 % for 24 h forecasts, compared to single-source baselines. Further fine-tuning these multi-source models with target-domain data yields an additional 3–5 % improvement. Notably, PatchTST outperforms the vanilla Transformer and Informer models. Overall, our results underscore the potential of combining Transformer architectures with TL techniques to enhance building energy consumption forecasting accuracy. However, careful selection of the TL strategy and attention to feature space compatibility are needed to maximize forecasting gains.
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Hyperspectral Data Can Classify Plant Functional Groups
Within New Zealand Hill Farm Pasture
(MDPI AG, 2025-03-21) Cushnahan T; Grafton M; Pearson D; Ramilan T; Hasenauer H
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Enhancing Health and Exercise Consultation through Scenario-Based Learning: An Approach for Interpersonal Skill Development. A Practice Report
(University of Southern Queensland Library, 2025-03-04) Cochrane D
This practice report describes the application of scenario-based learning to improve awareness of interpersonal skills in sport and exercise students. Thirty second-year undergraduate students over two consecutive academic years engaged in three scenario-based learning activities that simulated client interviews and consultations. The consensus among the students was that the scenarios increased their awareness of active listening, recognising physical client cues, and understanding the intricate dynamics of the ‘client-practitioner’ interaction. The implementation of scenario-based health and exercise consultations provided students with an immersive and effective learning experience, which promoted the development of the interpersonal skills required for successful client consultations.