247,197 research outputs found

    A generalized trajectories-based evaluation approach for pedestrian evacuation models

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    The fundamental diagram and self-organized phenomena in crowds are widely used to test the applicability of evacuation models. These benchmarks are good indicators for the validity of a model, whereas they are insufficient descriptors for the realistic microscopic behaviors of pedestrians. In recent years, the rapid increase of the trajectory datasets which benefits from the development of recognition technologies open the door to new possibilities for an extensive quantitative validation of the models. In this work, a trajectories-based analysis approach which contains types of indexes is proposed. The indexes are a mix of macroscopic type (fundamental diagram index, speed choice index, and direction choice index) and microscopic type (trajectories pattern index), distribution type (route length distribution index, travel time distribution index) and time-series type (starting position distance time-series index, destination position distance time-series index. Moreover, the Kolmogorov-Smirnov (K-S) test as well as the dynamic time warping (DTW) method are introduced to quantify the similarities of results on different types of indexes. In brief, by comparing experimental and simulation trajectories, we can measure a set of performance scores in different perspectives. Here, the Social Force Model (SFM) and Heuristics Model (HM) are respectively introduced and evaluated. According to the proposed evaluation approach, we show that the HM performs better than the SFM. Our analysis approach is model agnostic and is defined in a general way, such that it can be applied for trajectory sets from different experiment settings. This work can help to improve the accuracy of simulation models, and the pedestrian safety in crowd activities and autonomous vehicle navigation will be benefited

    Argatroban promotes recovery of spinal cord injury by inhibiting the PAR1/JAK2/STAT3 signaling pathway

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    Argatroban is a synthetic thrombin inhibitor approved by U.S. Food and Drug Administration for the treatment of thrombosis. However, whether it plays a role in the repair of spinal cord injury is unknown. In this study, we established a rat model of T10 moderate spinal cord injury using an NYU Impactor Moder III and performed intraperitoneal injection of argatroban for 3 consecutive days. Our results showed that argatroban effectively promoted neurological function recovery after spinal cord injury and decreased thrombin expression and activity in the local injured spinal cord. RNA sequencing transcriptomic analysis revealed that the differentially expressed genes in the argatroban-treated group were enriched in the JAK2/STAT3 pathway, which is involved in astrogliosis and glial scar formation. Western blotting and immunofluorescence results showed that argatroban downregulated the expression of the thrombin receptor PAR1 in the injured spinal cord and the JAK2/STAT3 signal pathway. Argatroban also inhibited the activation and proliferation of astrocytes and reduced glial scar formation in the spinal cord. Taken together, these findings suggest that argatroban may inhibit astrogliosis by inhibiting the thrombin-mediated PAR1/JAK2/STAT3 signal pathway, thereby promoting the recovery of neurological function after spinal cord injury

    Establishment risk of invasive golden mussel in a water diversion project: An assessment framework

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    Inter-basin water diversion projects have led to accelerated colonization of aquatic organisms, including the freshwater golden mussel (Limnoperna fortunei), exacerbating global biofouling concerns. While the influence of environmental factors on the mussel's invasion and biofouling impact has been studied, quantitative correlations and underlying mechanisms remain unclear, particularly in large-scale inter-basin water diversion projects with diverse hydrodynamic and environmental conditions. Here, we examine the comprehensive impact of environmental variables on the establishment risk of the golden mussel in China's 1432-km-long Middle Route of the South-to-North Water Diversion Project. Logistic regression and multiclass classification models were used to investigate the environmental influence on the occurrence probability and reproductive density of the golden mussel. Total nitrogen, ammonia nitrogen, water temperature, pH, and velocity were identified as crucial environmental variables affecting the biofouling risk in the project. Logistic regression analysis revealed a negative correlation between the occurrence probability of all larval stages and levels of total nitrogen and ammonia nitrogen. The multiclass classification model showed that elevated levels of total nitrogen hindered mussel reproduction, while optimal water temperature enhanced their reproductive capacity. Appropriate velocity and pH levels were crucial in maintaining moderate larval density. This research presents a quantitative analytical framework for assessing establishment risks associated with invasive mussels, and the framework is expected to enhance invasion management and mitigate biofouling issues in water diversion projects worldwide

    Quantifying cloud chemical processes and aerosol optical properties using a particle–resolved aerosol model

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    Aerosol particles exert substantial radiative effects on the Earth's climate directly by scattering and absorbing incoming solar radiation, and indirectly by interacting with clouds. These climate effects depend on particle size distributions and chemical composition, and these properties evolve as particles are transported in the atmosphere. As an important aging process, cloud processing changes particle size and composition through cloud chemistry and in-cloud coagulation. These processes are highly affected by per-particle properties, by determining which particles can be activated and which reactions occur within each droplet. It is challenging for global or regional models with simplified aerosol representations to accurately capture these processes. The aim of the first part of this thesis was to (1) quantify the changes of aerosol mixing state and microphysical properties after cloud processing (2) quantify the role of coagulation between the interstitial particles and cloud droplets for mixing state of the aerosol. By coupling an aqueous chemistry mechanism to the particle-resolved model PartMC-MOSAIC, the new model was able to track the evolution of compositions and sizes of individual aerosol particles in the cloud without averaging their composition within size bins or modes. Aqueous-phase chemistry processes caused aerosol populations to be more internally mixed, and cloud condensation nuclei concentrations increased substantially after cloud processing for supersaturation levels lower than the maximum cloud supersaturation. Coagulation within clouds had a negligible impact on aerosol mixing state. The aim of the second part of the thesis was to systematically quantify the impact of aerosol mixing state on aerosol optical properties. To this end, I created a reference scenario library with aerosol populations of a wide range of mixing states using the particle-resolved model PartMC-MOSAIC. The impact of aerosol mixing state on optical properties was quantified by comparing the reference populations to populations with the same number and mass size distributions but with averaged aerosol composition in prescribed size bins. Particle absorption coefficients were universally overestimated after using internal mixture assumptions, with the overestimation reaching up to 70% for externally-mixed populations. In contrast, scattering coefficients were underestimated, with a maximum error of -32%. Overall, this led to an underestimation in single scattering albedo of up to -22%. The environmental relative humidity and associated aerosol water uptake only had a small impact on the magnitude of these errors.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste
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