Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Start date: 2024Subject: search.filters.subject.Data analysis

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    An explorative analysis of gameplay data based on a serious game of climate adaptation in Aotearoa New Zealand
    (Elsevier B.V., 2025-08-27) Yang W; Harrison S; Blackett P; Allison A
    Serious games play a crucial role in educating and engaging the public on environmental management issues, such as climate change. These games also generate valuable data that can be used in understanding players' climate change decisions. However, there is a notable gap in the literature on serious game analytics to address the significance of scrutinising the usefulness of utilising gameplay data to explore player behaviours. This paper explores this gap through descriptive and quantitative analysis of gameplay data from ‘The Township Flooding Challenge’ in Aotearoa New Zealand to obtain data insights and data gaps in understanding players' behaviours and decisions on climate change adaptation. The findings suggest that gameplay data can offer insights into players' decisions on climate change adaptations amid uncertainty, but also highlights data gaps such as unclear definitions and incomplete data. Leveraging gameplay data can aid in data collection, decision-making modelling, and improving serious game design.
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    A Complete Analysis Pipeline for the Processing, Alignment and Quantification of HPLC–UV Wine Chromatograms
    (Springer-Verlag GmbH Germany, 2024-03) Ianeselli A; Longo E; Poggesi S; Montali M; Boselli E
    Elucidating the chemistry of wine would help defining its quality, chemical and sensory characteristics and optimise the wine-making processes. High-performance liquid chromatography coupled with UV–Vis spectroscopy (HPLC–UV–Vis) is a common analysis method used to obtain the molecular profile of wine samples. We propose a complete procedure for the analysis of wine chromatograms. Data are pre-processed using standard methods of down-sampling, smoothing and baseline subtraction. Multiple samples are then merged in a three-dimensional tensor, decomposed using parallel factor analysis (PARAFAC2) into three factors: (i) one reduced (rank-one) chromatogram per sample, (ii) an estimate of the samples’ spectral UV–Vis profile and (iii) an estimate of the samples’ concentrations. If the decomposition is performed on a single peak of the tensor, the second and third factors correspond to the representative wavelength spectrum and to the relative concentrations of the samples, respectively. Otherwise, when multiple peaks are analysed, further processing is required. In the latter case, the decomposed rank-one chromatograms are peak-detected and aligned, clustered and integrated. A table containing the concentration of the peaks at different retention times is obtained. The pipeline proposed in this study is a guideline for a quantitative and reproducible chemical analysis of wine, or other samples, via the HPLC–UV–Vis method.