26800 research outputs found
Sort by
Low-Altitude sUAS Flights for Remote Sensing of Submillimeter Hairline Cracks: A Case Study
No full textRecent advancements in small uncrewed aircraft systems (sUAS) have enhanced their use in infrastructure monitoring, targeting distresses such as cracks, spalling, delamination, rutting, and rust. While automatic sUAS flights are typically operated at 15-30 m altitudes with RGB sensors (12-61 megapixels), capturing visible distresses, detecting submillimeter hairline cracks remains challenging. This study explores the feasibility of achieving submillimeter-resolution orthophotos from low-altitude sUAS flights to detect hairline cracks on bridge decks. Data were collected from two bridge decks: a newly constructed deck with hairline cracks averaging 0.25 mm in width and a 1-year-old deck with grooved cracks (0.35-0.7 mm) at altitudes of 4.6, 10.7, and 12.2 m. Findings show that while low-altitude sUAS flights are challenging to execute, the processed orthophotos provide sufficient resolution for fine crack detection, extending their potential in high-precision infrastructure inspections
The Damage Assessment of 2019 Eurobodalla Shire Wildfire to the Endangered Ecological Communities Using Change Detection and Object-Based Classification
No full textThe data for the project - The Damage Assessment of 2019 Eurobodalla Shire Wildfire to the Endangered Ecological Communities Using Change Detection and Object-Based Classificatio
Stratified hydrogen combustion with various mixing processes
No full textHydrogen is recognized as a key alternative fuel for mitigating greenhouse-gas emissions owing to its high fuel efficiency and carbon-free combustion. In the stratified charge combustion (SCC) mode, ensuring optimal air-fuel mixing in the combustion chamber is crucial because the local equivalence ratio has a dominant influence on combustion characteristics. Therefore, this study aims to build a detailed understanding of stratified hydrogen combustion under various local equivalence ratios. Laser-induced breakdown spectroscopy (LIBS) was used to measure the local equivalence ratios in hydrogen jets at different mixture-formation times (MFTs) and laser-ignition points (LIPs). The results showed that shorter MFTs induced highly stratified mixtures with elevated local equivalence ratios exceeding 2.0, enhancing the laminar flame speed and maximizing the conversion of chemical energy into pressure gain, resulting in a representative total heat release over three times higher compared to longer MFTs. Furthermore, ignition near the injector tip produced leaner mixtures with equivalence ratios around 0.3, whereas downstream LIPs generated peak local equivalence ratios around 2.0, facilitating rapid flame propagation and increased heat release by 25 %
ANALYSIS OF LAVA FLOW PATHS USING REMOTE SENSING AND GEOMORPHOLOGICAL TECHNIQUES
No full textLava flows have severe impacts on the Earth’s surface, communities, and natural habitats. Accurate modeling of lava flow paths is crucial for volcanic hazard assessment and risk mitigation. This research integrated remote sensing, geographic information systems, geomorphological techniques, and hydrologic analysis to numerically determine the optimal ground sample distance (GSD) of digital elevation models (DEMs) for delineating lava flow paths. Two volcanic regions were analyzed: the Afar Region of Ethiopia (Alu, Dalaffilla, and Erta Ale volcanoes) and the Reykjanes Peninsula of Iceland (Fagradalsfjall volcano). Forward and backward modeling methods were used to simulate potential lava flow paths. Remote sensing data, including multispectral and thermal images as well as DEMs, were utilized for analysis. A large-scale mean-shift image segmentation method was applied to identify lava objects and extract the main centerline for analysis. The forward model was applied to the fissures between the Alu and Dalaffilla volcanoes. The main lava centerline was derived from a 2008 ASTER image, while flow paths were simulated using the 2000 SRTM DEM. An optimal GSD of 35 m resulted in the lowest root mean square error (RMSE) of 146.89 m. This was validated using the 2017 Erta Ale eruption, confirming 35 m as a suitable GSD for delineating a major lava polygon. In the backward model, hydrologic analysis on resampled DEMs using TauDEM identified an optimal GSD of 38 m with an RMSE of 123.44 m. In the study of the Fagradalsfjall volcano, an optimal GSD of 71 m with an RMSE of 98.84 m was obtained, while the backward model yielded an optimal GSD of 33 m with an RMSE of 173.56 m. Statistical analyses were conducted to validate the results. This research contributed to volcanic hazard assessment by providing a framework that integrated geospatial technologies into lava flow modeling. Initiating simulation from the observed lava outlet, the forward model addressed a key limitation of the backward model. Results indicate that lava flow path accuracy was sensitive to the GSD of a DEM. The findings further demonstrate that the forward model may aid emergency response planning and risk mitigation strategies in volcanic regions
UNDERSTANDING THE THERMODYNAMICS AND MASS TRANSFER OF VIRUSES IN AQUEOUS TWO-PHASE SYSTEMS
No full textMillions of lives and significant productivity are lost globally each year to vaccine-preventable illnesses. For example, seasonal influenza alone causes millions of cases and tens of thousands of deaths annually in the United States. While vaccines offer effective protection, manufacturing deficiencies, particularly in adapting downstream purification to continuous processing, limit their impact. Traditional methods like ultracentrifugation and chromatography struggle with continuous operation and often yield less than 30% for viral particles. Aqueous two-phase systems (ATPS) present a promising solution: they adapt naturally to continuous processing, achieve high yields and purity, and can reduce capital and operating costs by an order of magnitude compared to chromatography-based processes.
Despite their potential, two barriers prevent the industrial adoption of ATPS: unpredictable separations and poor understanding of scale-up. This dissertation addresses both challenges. First, a comprehensive literature review of current experimental, statistical, and mechanistic approaches to optimizing biomolecule separations in aqueous two-phase systems is presented. This review emphasizes the need to replace the existing norm of iterative optimization with more universal predictive models and highlights artificial neural networks and molecular dynamics as key drivers of this transition. Importantly, the discussion closes by considering how predictive models, once developed, may be leveraged by flowcharts to optimize separations with minimal experiments. Second, to facilitate ATPS scale-up for continuous manufacturing, a microfluidic method is developed to inform a two-resistance mass transport model. Predicting the mass flux of products and contaminants across the ATPS interface will inform the design of mixer-settler or column contactor systems to maximize recovery and minimize processing time. Finally, a study of protein and virus aggregation in ATPS used for viral separations connects these two investigations, exploring how aggregation and non-equilibrium behavior contributes both to separations and mass transfer. Collectively, this work bridges critical gaps, moving ATPS closer to industrial implementation and ultimately aiming to reduce costs and enable the production of better, more effective vaccines
Graph Neural Network-Based Approaches for Protein Function Prediction
No full textProtein functions often involve a dynamic interplay that covers a variety of molecular interactions that can be represented and analyzed in a 3-dimensional space. To this end, researchers have applied graph neural networks (GNNs) that effectively model such spaces as a promising methodology to predict protein functions. We discuss the graph-based representations of proteins that are applied to different prediction tasks, which include graphs at various levels of granularity: atomic, residue, and multi-scale. We also review various protein function prediction tools that rely on GNN architectures that learn representations from protein graphs, specifically in the context of the Gene Ontology prediction and protein-protein interaction prediction. GNN-based methods leverage the underlying structural knowledge and offer a promising future in improving the quality of the protein function predictions
Peak policy lab or chasing windmills? The overlooked issue of misaligned policy design
No full textPolicy innovation labs (PILs) are relatively new policy actors and are part of a larger global “labification” movement. They are touted as spaces for the novel development and testing of policy solutions. PILs have evolved into various forms–including those at different levels of government (central, sub-national, and local), sectoral (such as food, transportation, and environment), and cross-sectoral labs (social innovation and data labs). After a decade, some practitioners lament the effectiveness of their efforts and question if policy labs are indeed engines of innovation and change. We argue that the approach to policy design by PILs, in part, is an explanation for their perceived ineffectiveness. It is unclear what their role is in the policy design process. From a sample of 149 PILs worldwide, we employ Cashore and Howlett’s (2007) 3 × 3 nine-dimensional hierarchical policy classification framework characterized by policy focus (abstract goals, program objectives, and micro policy goal targets) and policy means (instrumental logic, program mechanism, and tool calibration). Our website content analysis found that key PIL characteristics, namely their broad focus and oversight, had little to no influence on their policy design activity. We develop five policy design typologies from the above policy mix framework, namely “Classic Policy Designers,” “Advisors,” “Dreamers,” “Planners,” and “Technicians.” The remaining labs\u27 policy design foci were too broad or misaligned
Iterative Driven Competency-Based Assessment in a First-Year Engineering Computation Module
No full textThis Complete Evidence Based Practice paper will explore one tool for supporting competency based assessment in a first-year engineering course. Competency-based assessment in a first-year engineering computation module offers a pathway to improve student engagement and enhance learning outcomes. Shifting the focus from traditional one-try assessment to a more dynamic evaluation of core computational skills—such as algorithmic loops, plotting, and functions—can enable deeper personalized learning experiences. The primary challenge is creating a more responsive, interactive relationship with every student, regardless of their previous content knowledge. Autograding systems can play a pivotal role in this relationship by providing instant, real-time feedback on students\u27 efforts. One approach to autograding systems is to allow autograding to occur during the assessment in an iterative process. To be effective, these systems must be designed to not only evaluate correctness but also analyze visual outputs like graphs and assess the intermediate steps of computation. This immediate iterative feedback loop follows the techniques content experts often deploy to solve challenging problems. This technique guides students to identify and correct mistakes as they learn, fostering deeper engagement with the material. By integrating real-time feedback driven evaluations, educators can create a more engaging learning environment that promotes essential computational reasoning skills. However, crafting automated feedback is time intensive and cost prohibitive, especially the first time. Collaborating problem sets, documenting observations and improvements we can aid to reduce these negative obstacles for broad implementation. In this paper we document the process implemented to transition a first-year engineering class MATLAB assessment into an autograded environment. We will demonstrate techniques to evaluate the components of a proper figure, and ways to randomize a problem in the commercial Mathworks Grader environment. We will compare student performance on the assessment, student’s perception on the experience and explore the effect on uniqueness in submissions. The students\u27 performance will be compared with a prior year\u27s standard assessment results, and students\u27 perception will be compared with a common end of course survey. Uniqueness of submissions will be evaluated with a tool to identify a percentage of similar lines of code. In the process of running an autograded environment educators are exposed to every early submission, so a metric of identifying which assessment objectives are the most challenging is collected as well. Through implementation of autograded assignments, our courses have identified a decrease in the time to engage with a challenging problem and ask questions. One core issue identified in deployment is the challenge in creating multiple problem sets or banks and the difficulty in writing broad validation code. The anticipated survey and performance results will discuss observed student performance, perception and the amount of non-unique submissions. This approach supports individual learning needs and better prepares students for future computational engineering challenges by making assessment a more dynamic and impactful part of their educational experience
Salinity adaption and toxicity of harmful algal blooms in three bays of Great Salt Lake (USA)
No full textCyanobacterial blooms can be harmful to animals and humans exposed to their toxins; however, their environmental drivers and boundaries still need to be elucidated. Salinity has been demonstrated to be an important driver of community composition that sets boundaries of species migration and survival. The filamentous cyanobacteria Nodularia spumigena forms dense blooms in estuaries around the world, produces the hepatotoxin nodularin, and has been thought to not survive or fix nitrogen (N) in high salinities. From 2005–2009 we studied three bays of Great Salt Lake (USA), two of which are estuaries with salinities ranging from 0 to \u3e90 g L-1 while the third, Gilbert Bay, had a salinity near 160 g L-1. Bear River Bay and the larger Gilbert Bay were meso‑eutrophic, while Farmington Bay, which receives direct inputs of secondary-treated sewage, was hypereutrophic with mean chlorophyll concentrations of 149 µg L-1 and dense blooms of N. spumigena. Cell densities were \u3e500 times those of Nodularia studied in the Baltic Sea. In Farmington Bay blooms occur at salinities ranging from 8–50 g L-1, which are much higher than usually reported for this taxon. Concentrations of the cyanotoxin nodularin reached 660 µg L-1 (mean = 41 µg L-1), far above critical thresholds for contact recreation and above those causing bird mortalities elsewhere. The mean N2 fixation rate of Nodularia measured over a salinity range of 14 to 52 g L-1 was 47 mg N m-2 D-1, which is among the highest reported values for freshwater and marine ecosystems. The local adaptation of Nodularia to the extreme salinity conditions in Great Salt Lake furthers our understanding of salinity adaptation and the potential spread of this species to new regions
Warming induces unexpectedly high soil respiration in a wet tropical forest
No full textTropical forests are a dominant regulator of the global carbon cycle, exchanging more carbon dioxide with the atmosphere than any other terrestrial biome. Climate models predict unprecedented climatic warming in tropical regions in the coming decades; however, in situ field warming studies are severely lacking in tropical forests. Here we present results from an in situ warming experiment in Puerto Rico, where soil respiration responses to +4 oC warming were assessed half-hourly for a year. Soil respiration rates were 42-204% higher in warmed relative to ambient plots, representing some of the highest soil respiration rates reported for any terrestrial ecosystem. While respiration rates were significantly higher in the warmed plots, the temperature sensitivity (Q10) was 71.7% lower, pointing to a mechanistic shift. Even with reduced Q10, if observed soil respiration rates persist in a warmer world, the feedback to future climate could be considerably greater than previously predicted or observed