8413 research outputs found
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Modulation of Sterile Inflammatory Responses in Uterine Smooth Muscle for Preterm Labor Drug Development
No full textExploring the growth factor role of insulin-like peptides in the western tarnished plant bug, Lygus hesperus
No full textExploring Behavioral Impact of Chemosensory Genes in the Western Tarnished Plant Bug, Lygus hesperus
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Advancing Coupled Fire-Atmosphere Simulation: Fuel Representation and Fire Spotting
No full textWildland fires can lead to substantial ecological, social, and economical losses as well as health burdens due to smoke produced from biomass burning. Accurate wildland fire simulation is an essential capability in predicting and managing these losses, benefiting both pre-fire risk mitigation and preparedness as well as active-fire emergency response management. The goal of this research is to assess the performance and limitations of a state-of-the-art coupled wildland fire-atmosphere simulation platform, known as WRF-Fire, and advance the fire simulation capabilities by developing models and parametrization to introduce key missing physics in the current platforms. First, the performance of WRF-Fire is evaluated in simulating historic fires including the 2018 Camp Fire. Simulation results are compared with high temporal-resolution fire perimeters derived from weather radar observations, depicting non-negligible discrepancies between the simulated and observed rate of spread (ROS) and spread direction. Then, the sensitivity of the simulation to different modeling parameters and assumptions are investigated. While the sensitivity analyses show that refining the atmospheric grid in complex terrains improves fire prediction capabilities, the model still comprises systematic sources of modeling errors including errors in the fuel bed representation and lack of fire spotting capability. These two elements are the focal point of the advancements in the next step.
Wildland fuels not only drive the fire propagation, but also affect the generated fire heat, which leads to localized fire-induced weather conditions in the atmosphere. The preliminary simulation results of historic fires indicate the likely inaccurate characterization of the biomass fuel load, which results in the underestimation of heat fluxes in the simulation. WRF-Fire coupled fire-atmosphere models currently only consider the effects of surface fuels for heat flux calculation. Thus, the fuel bed representation in the platform is enhanced by adding canopy fuel load and a new heat release scheme. The results show that while the improved fuel bed and heat release scheme have limited impacts on the simulated fire perimeters, the resulting plume structure better agrees with the observations. This study provides guidance on the next level of scientific research and computational developments needed to further address the fuel characterization and heat flux calculation.
Another source of wildland fire modeling error is the lack of fire spotting simulation. Fire spotting is a process during which new fires are ignited in front of the active fire line by firebrands. For this process, a fire spotting framework capable of simulating firebrand generation, transport, and ignition is developed and implemented in the WRF-Fire recently. The objective of this work is to advance the current implementation and develop simple yet robust models for firebrand generation and spot fire ignition. For firebrand generation, a data-driven model is developed based on the existing experimental data. The model predicts distribution of firebrand mass and projected area given fuel characteristics and wind speed. Validation studies against independent experimental data show reasonable accuracy of the firebrand generation model.
Unlike the firebrand generation, limited experimental data are available for spot fire ignition due to its complex nature. Therefore, to develop a spot fire ignition model, a high-fidelity combustion-based fire simulation platform, known as Fire Dynamics Simulator (FDS), is utilized to simulate heat flux from smoldering firebrands with different properties. Energy balance and heat conduction theories are utilized to simulate the heat transfer between the firebrand and recipient fuel bed and its temperature increase due to firebrand heat flux. After validating the framework with experimental data, a dataset of recipient fuel ignition by firebrands given varying firebrand properties, as well as recipient fuel bed characteristics are generated. This dataset is then utilized to train machine learning models to enable spot fire ignition prediction given firebrand and fuel bed characteristics. The firebrand generation and spot fire ignition models are essential contributions to advance the coupled wildland fire-atmosphere simulation capabilities.
This dissertation contributes to developing solutions to address two key sources of modeling error within the existing wildland fire simulation platforms, namely heat simulation from non-surface fuels and fire spotting simulation, with the goal of improving the existing platforms accuracy in landscape-scale fire simulations. Specifically, this dissertation develops innovative and novel models for crown fire heat release, firebrand generation, and spot fire ignition of vegetative fuel beds. Moreover, the work contributes to shedding light on the critical scientific and technical challenges that can pave the way for future research and technical development for wildland fire simulation platforms
Ranging-Image-Based Methodologies to Enhance LiDAR Processing Efficiency
No full textRoadside-LiDAR enhanced traffic monitoring represents a promising roadside sensing solution for Intelligent Transportation Systems (ITS), extracting microscopic traffic trajectories from high-precision, high-resolution 3D point cloud streams for informed traffic decision-making and studies. The efficiency in the roadside-LiDAR data processing framework is a paramount factor to be considered. In real-time applications, reduced computational requirements enable the deployment of cost-effective hardware solutions, facilitating broader system implementation across transportation networks while maintaining operational reliability. For offline applications involving huge data volumes, enhanced processing efficiency accelerates processing outcomes and enables more comprehensive analyses within practical time constraints. Designing a data processing framework with less computational complexity not only reduces infrastructure costs but also promotes scalability and accessibility of roadside-LiDAR traffic monitoring solutions with varying resource capacities. This dissertation presents an efficient roadside-LiDAR data processing framework based on the ranging image, a spherical angular point cloud encoding method for conventional 10Hz mechanical LiDAR sensors. The research focuses on designing and developing a systematic suite of data processing components built upon the ranging image data structure to reduce the computational complexity by leveraging the fast searching of laser spatial relationships offered by ranging image encoding. Both online and offline processing frameworks were developed, demonstrating real-time operational performance during peak traffic hours with high amount of tracking targets while maintaining reliable trajectory quality. The online framework development begins at the raw data packet parsing level. Rather than parsing packets into point cloud coordinates, the proposed framework directly encodes the packets into ranging images according to mechanical LiDAR communication protocols. Critical processing components, including background filtering and clustering algorithm, were redesigned to achieve lower time complexity compared to conventional solutions while enabling parallelization for enhanced performance. The online framework achieves processing efficiency ranging from 67.29 to 91.46 ms/frame on a cost-effective hardware equipped with a 2.4GHz CPU during normal to extreme traffic conditions. The offline framework incorporates deep learning technologies to capture more complex traffic context information and designed to handle occlusion problems, a persistent challenge in dense urban traffic scenarios that affect trajectory quality. The framework maintains computational efficiency through an innovative lane occupancy embedding technique based on ranging image data structure to encode collective platoon behaviors. This approach facilitates the reconstruction of occluded traffic information using LSTM time sequential neural networks. Performance evaluations under GPU environment demonstrate an 18.75 ms/frame processing efficiency for in-lane trajectory extraction and a 95.3% occlusion reconstruction rate. Both frameworks present fast processing efficiency, far-reaching the real-time processing level. In the trajectory quality assessment, 1,478 trajectories are manually annotated for trajectory quality evaluation. The experimental results revealed that the online and offline frameworks can correctly identify 82.6% and 95.3% of the annotated real-world trajectories respectively without trajectory disconnection and ID-switching issues. This dissertation provides a foundational study demonstrating the feasibility and potential possibility of high-efficiency roadside LiDAR data processing frameworks using cost-effective computing hardware, and new opportunities to occlusion handling. The efficiency improvements and hardware accessibility demonstrated have profound implications for the widespread adoption of roadside-LiDAR traffic monitoring systems. By reducing the technical barriers to implementation, this research paves the way for larger-scale deployments of roadside LiDAR systems across extensive transportation networks. The framework's capability to handle both real-time operations and extensive data volumes with high efficiency positions it as a valuable tool for transportation agencies, urban planners, and researchers. Furthermore, the methodologies developed in this study establish a new paradigm for processing high-resolution traffic data, contributing to the advancement of intelligent transportation systems and smart city initiatives. As cities worldwide continue to prioritize data-driven traffic safety, operation, management and planning, the efficient processing framework presented in this dissertation offers a practical and scalable solution for next-generation transportation systems
Scalar Curvature Constraints on Symplectic 4-Manifolds
No full textThis thesis presents a remarkable result, that showcases an unexpected interplaybetween Riemannian geometry and symplectic topology on 4-manifolds. These two
branches of mathematics belong to different worlds: Where geometry concerns it-
self with distances, angles, areas, curvatures, etc., all of which typically vary from
one point on the manifold to the next, topology studies properties of manifolds that
are unaltered and remain constant when varying the metric. In this thesis we first
provide a general overview of spin geometry on Riemannian manifolds and then es-
tablish a fundamental restriction on the scalar curvature induced by the topology of
a symplectic manifold
Decision-Making in Mental Health Courts: A Mixed-Methods Analysis of Legal and Extra-Legal Factors in Evaluations of Mentally Ill Defendants
No full textMental health courts (MHCs) are specialized programs that aim to divert individuals with serious mental illness away from incarceration and into treatment. Their goal is to reduce recidivism and promote rehabilitation by addressing the underlying mental health needs that can contribute to criminal behavior. As the number of MHCs continues to increase, more research is needed to understand the processes and operations of these courts. Issues of disparity and equal access to these programs is one area in need of such research. This research describes a mixed-methods analysis grounded in classical social psychological theories of attribution and heuristics in addition to the focal concerns framework from the criminal justice literature. This dissertation describes three studies investigating decision-making among MHC workgroups. The first study describes ethnographic observations of three remote MHCs in the United States. Using a flexible coding strategy, this qualitative analysis focused on violation and termination hearings as important instances of decision-making within these programs. The second study describes another qualitative analysis of in-depth interviews with MHC workgroup members. These interviews focused on the referral and admission process as an important instance of decision making in addition to the work group members’ perceptions of disparity within their programs. Finally, the third study describes an experimental survey using vignettes to investigate the effects of defendant race, gender, and offense type on MHC diversion decisions
TEST Blood Falls: A Novel
No full textWhen Richard Freeley decided to give a presentation on the controversial subject of Antarctic sea ice expansion at a conference, he never expected a rival to humiliate him onstage and backlash to follow from every corner of the media landscape. Though eccentric energy executive and billionaire Jed Baton offers Richard a chance at redemption through a lavishly funded private research expedition to the Antarctic, Richard would rather decline, but his wife, a struggling filmmaker named Julie Asgard, has other plans. The couple join Baton and an idiosyncratic team on the icebreaker ship Sarmiento. Soon they discover that someone is sabotaging their work, leading to perilous incidents on board the ship. As the voyage heads inexorably toward a mysterious Antarctic glacier called Blood Falls, nothing is what it seems, and Richard and Julie find themselves pitted against each other in a conspiracy which could upend the global economy