Journal Articles

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

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Author: search.filters.author.Zhao XStart date: 2020

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Now showing 1 - 10 of 14
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    Early evolution of beetles regulated by the end-Permian deforestation.
    (eLife Sciences Publications Ltd, 2021-11-08) Zhao X; Yu Y; Clapham ME; Yan E; Chen J; Jarzembowski EA; Zhao X; Wang B; Perry GH
    The end-Permian mass extinction (EPME) led to a severe terrestrial ecosystem collapse. However, the ecological response of insects-the most diverse group of organisms on Earth-to the EPME remains poorly understood. Here, we analyse beetle evolutionary history based on taxonomic diversity, morphological disparity, phylogeny, and ecological shifts from the Early Permian to Middle Triassic, using a comprehensive new dataset. Permian beetles were dominated by xylophagous stem groups with high diversity and disparity, which probably played an underappreciated role in the Permian carbon cycle. Our suite of analyses shows that Permian xylophagous beetles suffered a severe extinction during the EPME largely due to the collapse of forest ecosystems, resulting in an Early Triassic gap of xylophagous beetles. New xylophagous beetles appeared widely in the early Middle Triassic, which is consistent with the restoration of forest ecosystems. Our results highlight the ecological significance of insects in deep-time terrestrial ecosystems.
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    Simulating human behavior under earthquake early warning
    (Elsevier Ltd, 2025-02-08) Wood M; McBride SK; Zhao X; Baldwin D; Cochran ES; Zhang X; Luco N; Lovreglio R; Cova T
    Earthquakes are a rapid-onset hazard where advance planning and learning plays a key role in mitigating injuries and death to individuals. Recent advances in earthquake detection have resulted in the development of earthquake early warning (EEW) systems. These systems can provide advance warning to predetermined geographic regions that an earthquake is in progress, which may result in individuals receiving warning seconds before significant shaking is felt at their location. This additional time could allow individuals to take more effective protective actions during the immediate disaster. To demonstrate this, we created an agent-based simulation of a basic apartment that allowed us to randomly and repeatedly simulate an individual receiving and responding to an EEW message. The results of our preliminary simulation show that, in our study environment, earthquake early warning alerts have the potential to allow for sufficient time for individuals to take protective actions.
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    Unveiling the Effects of Phosphorus on the Mineral Nutrient Content and Quality of Alfalfa (Medicago sativa L.) in Acidic Soils
    (MDPI (Basel, Switzerland), 2024-10-02) Li Z; Hao Y; Wang X; He J; Zhao X; Chen J; Gu X; Zhang M; Yang F; Dong R; Yang J
    Alfalfa (Medicago sativa L.) grown in acidic soils is often affected by phosphorus (P) deficiency, which results in reduced mineral nutrient content and forage quality. In this context, the effects of phosphorus (P) fertiliser remain unclear. In this study, we analysed the effects of P application on mineral nutrient content and forage quality in aluminium (Al)-sensitive (Longzhong) and Al-tolerant (Trifecta) alfalfa cultivars cultivated in two acidic soil environments. Mineral nutrient content and quality were affected by genotype, soil type, and P treatment concentration (p < 0.001). In limestone soil, for Longzhong and Trifecta, the optimal potassium (K), calcium (Ca), and magnesium (Mg) contents as well as crude protein content (CP) and ether extract (EE) values were observed at 20 mg P kg−1, that of the P content was observed at 40 mg P kg−1, and the minimum neutral detergent fibre (NDF) acid detergent lignin (ADL) values were observed at 40 mg P kg−1. In yellow soil, the maximum K, Ca, Mg, and P contents in Longzhong and Trifecta were observed at 40 mg P kg−1, whereas the maximum CP, EE, and ADL values were observed at 20 mg P kg−1. Our study provides an empirically based framework for optimising alfalfa fertilisation programmes in acidic soils.
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    The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester.
    (Frontiers Media S.A., 2022-12-02) Chen X; Zhao X; Jones MB; Harper A; de Seymour JV; Yang Y; Xia Y; Zhang T; Qi H; Gulliver J; Cannon RD; Saffery R; Zhang H; Han T-L; Baker PN; Zhou N
    BACKGROUND: Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort. METHODS: A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites. RESULTS: Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM2.5, PM10, SO2, NO2, CO and the exposure estimates of PM2.5 and NO2, and positively associated with O3. CONCLUSIONS: This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.
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    A Semi-automatic Diagnosis of Hip Dysplasia on X-Ray Films
    (Frontiers Media S.A., 2020-12-17) Yang G; Jiang Y; Liu T; Zhao X; Chang X; Qiu Z; Gao X
    Background: Diagnosis of hip joint plays an important role in early screening of hip diseases such as coxarthritis, heterotopic ossification, osteonecrosis of the femoral head, etc. Early detection of hip dysplasia on X-ray films may probably conduce to early treatment of patients, which can help to cure patients or relieve their pain as much as possible. There has been no method or tool for automatic diagnosis of hip dysplasia till now. Results: A semi-automatic method for diagnosis of hip dysplasia is proposed. Considering the complexity of medical imaging, the contour of acetabulum, femoral head, and the upper side of thigh-bone are manually marked. Feature points are extracted according to marked contours. Traditional knowledge-driven diagnostic criteria is abandoned. Instead, a data-driven diagnostic model for hip dysplasia is presented. Angles including CE, sharp, and Tonnis angle which are commonly measured in clinical diagnosis, are automatically obtained. Samples, each of which consists of these three angle values, are used for clustering according to their densities in a descending order. A three-dimensional normal distribution derived from the cluster is built and regarded as the parametric model for diagnosis of hip dysplasia. Experiments on 143 X-ray films including 286 samples (i.e., 143 left and 143 right hip joints) demonstrate the effectiveness of our method. According to the method, a computer-aided diagnosis tool is developed for the convenience of clinicians, which can be downloaded at http://www.bio-nefu.com/HIPindex/. The data used to support the findings of this study are available from the corresponding authors upon request. Conclusions: This data-driven method provides a more objective measurement of the angles. Besides, it provides a new criterion for diagnosis of hip dysplasia other than doctors' experience deriving from knowledge-driven clinical manual, which actually corresponds to very different way for clinical diagnosis of hip dysplasia.
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    Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires
    (Elsevier B.V., 2024-09-10) Zhang X; Zhao X; Xu Y; Nilsson D; Lovreglio R
    Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. To tackle this research gap, the study develops a new methodological framework for modeling highly granular spatiotemporal trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested using a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are weekend indicator, population change, evacuation order/warning information, and proximity to fire, which are consistent with behavioral theories and empirical findings. SA-MGCRN can be directly used in future wildfire events to assist real-time decision-making and emergency management.
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    Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids.
    (Hindawi Limited, 2021-03-08) Zhao Z; Liu T; Zhao X; Haber RE
    Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.
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    Social vulnerabilities and wildfire evacuations: A case study of the 2019 Kincade fire
    (Elsevier B.V., 2024-05-31) Sun Y; Forrister A; Kuligowski ED; Lovreglio R; Cova TJ; Zhao X
    Vulnerable populations (e.g., populations with lower income or disabilities) are disproportionately impacted by natural hazards like wildfires. It is crucial to develop equitable and effective evacuation strategies to meet their unique needs. While existing studies offer valuable insights, we need to improve our understanding of how vulnerabilities affect wildfire evacuation decision-making, as well as how this varies spatially. The goal of this study is to conduct an in-depth analysis of the impacts of social vulnerabilities on aggregated evacuation decisions, including evacuation rates, delay in departure time, and evacuation destination distance by leveraging large-scale GPS data generated by mobile devices. Specifically, we inferred evacuation decisions at the level of the census block group, a geographic unit defined by the U.S. Census, utilizing GPS data. We then employed ordinary least squares and geographically weighted regression models to investigate the impacts of social vulnerabilities on evacuation decisions. We also used Moran's I to test if these impacts were consistent across different block groups. The 2019 Kincade Fire in Sonoma County, California, was used as the case study. The impacts of social vulnerabilities on evacuation rates show significant spatial variations across block groups, whereas their effects on the other two decision types do not. Additionally, unemployment, a factor under-explored in previous studies, was identified as contributing to both an increased delay in departure time and a reduction in destination distance of evacuees at the aggregate level. Furthermore, upon comparing the significant factors across different models, we observed that some of the vulnerabilities contributing to evacuation rates for all residents differed from those affecting the delay in departure time and destination distance, which only applied to evacuees. These new insights can guide emergency managers and transportation planners to enhance equitable wildfire evacuation planning and operations.
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    Analyzing Risk Perception, Evacuation Decision and Delay Time: A Case Study of the 2021 Marshall Fire in Colorado
    (Elsevier B.V., 2023-12-11) Forrister A; Kuligowski ED; Sun Y; Yan X; Lovreglio R; Cova TJ; Zhao X
    Climate change is increasing the threat of wildfires to populated areas, especially those within the wildland-urban interface (WUI). The 2021 Marshall fire forced the evacuation of over 30,000 people in Boulder, Jefferson and Adams Counties in Colorado, US. To improve our understanding of wildfire evacuation response, we surveyed individuals affected by the Marshall fire to analyze their evacuation decisions and resulting behavior. We used linear and logistic regression models to determine the factors influencing individuals’ risk perceptions, their decisions to evacuate or stay, and the associated evacuation delay times. We found higher levels of risk perception at the time of the evacuation decision were associated with higher levels of pre-fire perceived risk, having mid-level household income, the receipt of fire cues and having a medical condition. Increased pre-event risk perception increased the likelihood of evacuating, along with gender (female-identified), being aged between 55 and 64 years, and having a higher household income. On the other hand, having a prior awareness of wildfires had a negative effect on evacuation likelihood. Additionally, having previous experience with fire damage, owning their home, having a larger household size and being alerted later in the fire event reduced the delay time; whereas engaging in preparation activities and having children in the home led to longer delay times. These research findings can be used by emergency managers to better prepare WUI communities for future wildfire events.
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    A highway vehicle routing dataset during the 2019 Kincade Fire evacuation.
    (Springer Nature Limited, 2022-10-07) Xu Y; Zhao X; Lovreglio R; Kuligowski E; Nilsson D; Cova TJ; Yan X
    As the threat of wildfire increases, it is imperative to enhance the understanding of household evacuation behavior and movements. Mobile GPS data provide a unique opportunity for studying evacuation routing behavior with high ecological validity, but there are little publicly available data. We generated a highway vehicle routing dataset derived from GPS trajectories generated by mobile devices (e.g., smartphones) in Sonoma County, California during the 2019 Kincade Fire that started on October 23, 2019. This dataset contains 21,160 highway vehicle routing records within Sonoma County from October 16, 2019 to November 13, 2019. The quality of the dataset is validated by checking trajectories and average travel speeds. The potential use of this dataset lies in analyzing and modeling evacuee route choice behavior, estimating traffic conditions during the evacuation, and validating wildfire evacuation simulation models.