Massey Research Online
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Non-destructive and on-site estimation of grape total soluble solids by field
spectroscopy and stack ensemble learning
(Elsevier, 2025-02-20) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.
(BioMed Central Ltd, 2025-02-08) Liyanagedera ND; Bareham CA; Kempton H; Guesgen HW
This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.
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Understanding mariners’ tsunami information needs and decision-making contexts: A post-event case study of the 2022 Tonga eruption and tsunami
(Elsevier Inc, 2025-02-21) Harrison SE; Lawson RV; Kaiser L; Potter SH; Johnston D
The 15 January 2022, Hunga Tonga Hunga Ha'apai volcanic eruption generated a tsunami that spread across the Pacific Ocean and prompted a tsunami advisory in Aotearoa New Zealand (NZ). Concurrently, a severe weather warning was issued for ex-Tropical Cyclone Cody, passing east of NZ and producing heightened swells along the North Island coast. Numerous boats were significantly damaged or sunk in Tūtūkākā Marina, Northland, NZ. Mariners raised concerns over the perceived lack of tsunami warnings. We interviewed mariners in Tūtūkākā to understand their experiences on the night of 15 January 2022 and their needs and expectations of tsunami warnings. The complexity of the multi-hazard event made it difficult to assess and convey the severity of the expected tsunami. We found that mariners require information about expected wave height and arrival time, weather, and sea conditions to inform their anticipatory mitigation actions. This event shows the importance of multi-hazard risk assessments to produce effective warnings and action advice.
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Do you remember? Consumer reactions to health-related information on snacks in repeated exposure
(Elsevier Ltd, 2025-05) Stickel L; Poggesi S; Grunert KG; Lähteenmäki L; Hort J
Health-related information on pre-packed food products can enhance purchase intention and healthy choices. However, retained positive influence of health-related information on product liking is necessary to help consumers make informed decisions about a healthy diet in the long term. According to information-reduction theory, consumers reduce the amount of information that is processed in repeated exposure. Hence, increasing familiarity with a product could lead to increased levels of ignoring health-related information and an increasing reliance on product experience-based associations. These effects were tested in a laboratory study, involving actual food tasting and repeated exposure across two sessions. Participants (N = 154) were invited to evaluate yoghurts with and without health-related information with a screen representation of the product packaging. Differences in product evaluations and attention paid to health-related information between the two sessions were recorded using both implicit and explicit methods. Findings reveal that, despite a decrease in visual attention to health-related information, the perceived healthiness of the products remained stable. However, consumers reported lower perceived tastiness when health-related information was present. The findings underscore the importance of compelling taste experiences, as taste beliefs, in contrast to health beliefs, consistently influenced product liking. Finally, the findings emphasised the need for a comprehensive understanding of consumer reactions to healthier food products that considers both implicit and explicit responses.
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Residual methane emissions in grazing lactating dairy cows
(Taylor and Francis Group, 2023-12-13) Starsmore K; Lahart B; Villalobos-Lopez N; Egan M; Herron J; Burke JB; Shalloo L
Residual methane emission (RME) is a trait that has previously been identified as being independent of animal production traits. The objective of this study was to investigate the effect of ranking grazing dairy cows by RME on animal productivity and enteric methane (CH4) emissions. Milk production, dry matter intake (DMI), liveweight (LWT) and CH4 were recorded on grazing late lactation dairy cows at Teagasc Moorepark, Ireland. The dairy cows were producing 352 g CH4/day, while consuming 16.6 kg DM. The mean methane yield was 20.79 g CH4/kg DMI. Residual methane emission was calculated as the difference between measured CH4 yield and New Zealand emission factor (21.6 g CH4/kg DMI). These dairy cows were ranked based on their RME and classified into groups. The low RME group produced 15% less CH4 than the high RME group while maintaining milk production and feed conversion efficiency. The low RME group had lower methane yield, and methane intensity. There are no significant phenotypic correlations between RME and animal production traits such as energy corrected milk yield, or LWT. These results indicate that RME has the ability to select and rank low emitting grazing dairy cows while being independent from animal productivity traits.