Browsing by Author "Xu Y"

Now showing 1 - 7 of 7
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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.
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    Effects of alertness on perceptual detection and discrimination
    (Elsevier B.V., 2025-09) Xu Y; Wokke M; Noreika V; Bareham C; Jagannathan S; Georgieva S; Trentin C; Bekinschtein T
    The level of alertness fluctuates throughout the day, exerting modulatory effects on human cognitive processes at any moment. However, our knowledge of how alertness level interacts with specific cognitive demands and perceptual rules of a task is still limited. Here we used perceptual decision-making paradigms to explore this issue. We analysed data from four different experiments involving a total of 113 participants: 1) auditory masking detection, 2) sensorimotor detection, 3) auditory spatial discrimination, and 4) auditory phoneme discrimination. We examined participant performance during the natural transition from awake (high alertness) to drowsy (low alertness). First, we fitted psychometric functions to the performance in EEG-defined high and low alertness metastable states. Second, we modelled slope and threshold from the fitted sigmoidal curves as well as signal detection theory measures, including perceptual sensitivity (d’) and response bias (criterion). We found lower detection and discrimination sensitivity to stimuli as alertness level decreases, signalled by a shallower slope and a lower d’, while the threshold increases slightly and equivalently across experiments. We observed no change in criterion during the transition. Zooming in, we observed that the decrease in sensitivity measured by slope was stronger for discrimination than for detection decisions, indicating that lower alertness impairs the precision of decisions in discriminating alternatives more than in identifying the presence of a stimulus around the threshold. Taken together, these results suggest that alertness has a common effect on perceptual decision-making and differentially modulates detection and discrimination decisions.
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    Integrated transcriptome and proteome analyses reveal potential mechanisms in Stipa breviflora underlying adaptation to grazing
    (John Wiley and Sons Australia, Ltd on behalf of Chinese Grassland Society and Lanzhou University, 2024-03-14) Liu Y; Sun S; Zhang Y; Song M; Tian Y; Lockhart PJ; Zhang X; Xu Y; Dang Z; Matthew C
    Background: Long-term overgrazing has led to severe degradation of grasslands, posing a significant threat to the sustainable use of grassland resources. Methods: Based on the investigation of changes in functional traits and photosynthetic physiology of Stipa breviflora under no grazing, moderate grazing, and heavy grazing treatments, the changes in expression patterns of genes and proteins associated with different grazing intensities were assessed through integrative transcriptomic and proteomic analyses. Results: Differentially expressed genes and proteins were identified under different grazing intensities. They were mainly related to RNA processing, carbon metabolism, and secondary metabolite biosynthesis. These findings suggest that long-term grazing leads to molecular phenotypic plasticity, affecting various biological processes and metabolic pathways in S. breviflora. Correlation analysis revealed low correlation between the transcriptome and the proteome, indicating a large-scale regulation of gene expression at the posttranscriptional and translational levels during the response of S. breviflora to grazing. The expression profiles of key genes and proteins involved in photosynthesis and phenylpropanoid metabolism pathways suggested their synergistic response to grazing in S. breviflora. Conclusions: Our study provides insight into the adaptation mechanisms of S. breviflora to grazing and provides a scientific basis for the development of more efficient grassland protection and utilization practices.
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    Nanoengineered polymers and other organic materials in lung cancer treatment: Bridging the gap between research and clinical applications
    (Elsevier Ltd, 2024-03-25) Jin X; Heidari G; Hua Z; Lei Y; Huang J; Wu Z; Paiva-Santos AC; Guo Z; Karimi Male H; Neisiany RE; Sillanpää M; Prakash C; Wang X; Tan Y; Makvandi P; Xu Y
    Cancer remains a major global health challenge, with increasing incidence and mortality rates projected for the coming years. Lung cancer, in particular, poses significant obstacles due to late-stage diagnosis and limited treatment options. While advancements in molecular diagnostics have been made, there is a critical need to connect the dots between laboratory and hospital for better lung cancer treatment. Systemic therapy plays a crucial role in treating advanced-stage lung cancer, and recent efforts have focused on developing innovative drug delivery techniques. Nanoparticles (NPs) have emerged as a promising approach to lung cancer treatment, offering enhanced drug delivery, active targeting, and reduced toxicity. Organic-based nanomaterials, like polymeric nanoparticles, solid lipid nanoparticles, and liposomes hold great potential in this field. This review examines the application of NPs in lung cancer treatment, highlights current therapies, explores organic nanoparticle-based approaches, and discusses limitations and future perspectives in clinical translation.
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    Quality improvement of the cured fish by replacing nitrite with chromogenic microorganisms: Insight into the effect on residual nitrosamine levels, color, and flavor characteristics
    (Elsevier Ltd, United Kingdom, 2025-04-01) Fang D; Xu Y; Zhang X; Tian HS
    To achieve enhanced red color and improved safety of cured fish, the three nitrite-alternative strains, namely, Staphylococcus saprophyticus CICC 24365 (SS), Staphylococcus carnosus ATCC 51365 (SP), and Limosilactobacillus fermentum CICC 25124 (LF), were employed in this study. Furthermore, the hemoglobin in grass carp muscle was largely retained by directly removing the head of fish to increase heme levels in fish muscle. The results demonstrated that the nitroso-heme contents in the SS and SP groups were 7.40 and 6.53 mg/kg, respectively, which exhibited a comparable level to that observed in the nitrite group (8.70 mg/kg). Compared to the nitrite group, the nitrite levels in the SS and SP groups decreased by 91.70 % and 89.97 %, respectively, while there was a notable decrease of 70.33 % and 74.64 % in the total amount of nitrosamines. In addition, inoculation fermentation can effectively enhance the quality of cured fish by augmenting the levels of free amino acids and aromatic compounds.
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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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    To Achieve Carbon Neutrality, What Do Individual Residents Say? A Case Study of Yunnan Province of China Based on Spatial Analysis
    (SAGE Publishing, 2024-10-01) Yang W; Lu Y; Wang L; Xu Y
    This study aims to explore factors that affect individual residents’ behaviors contributing to reducing carbon emissions (low-carbon behaviors), based on the empirical analysis of the choice of adoption and the extent of adoption of low-carbon practices, such as using low-carbon transportation and energy-saving, in Kunming, China. We use spatial econometric regression models to consider positive spillover of low-carbon behaviors amongst residents as people tend to obtain knowledge and learn good actions from those located nearby. The results show the existence of positive spillover effects of low-carbon behaviors across several types of low-carbon practices. We find that location effects, such as access to parks, residents’ knowledge of carbon neutrality, and science communications in the local community are the most important determinants of residents’ low-carbon behaviors. The findings may provide insights into designing supporting policies to incentivize residents’ low-carbon behaviors and contribute to the pathway toward carbon neutrality from the micro-perspective.