26800 research outputs found
Sort by
Registration/Breakfast Opens
No full textWe extend our sincere gratitude to Meemic Insurance for sponsoring our Breakfast Session. Their generous support helps us bring our community together to share ideas, connect, and start the day with inspiration. We truly appreciate their commitment to making this event possible
COMPREHENSIVE ANALYSIS OF KRAUKLIS WAVES AND LONG PERIOD SEISMIC EVENTS IN FLUID-FILLED FRACTURES VIA INTEGRATED PHYSICAL AND NUMERICAL MODELING
No full textLong-period (LP) seismic events, commonly recorded in volcanic and hydrothermal environments, are widely attributed to the resonance of fluid-filled fractures and the excitation of Krauklis waves along the solid-fluid interface. These low-velocity, dispersive waves are sensitive to both fluid dynamics and fracture geometry, offering critical insights into subsurface fluid transport, fracture evolution, and seismic source processes. Despite robust theoretical treatments, experimental validation under realistic geological conditions has remained limited. To address this, I aimed to develop a series of laboratory-based experimental models that systematically investigate the key physical mechanisms driving LP signal generation. In the first set of experiments, I have examined how steady-state fluid flow within a tri-layer aluminum crack model affects Krauklis wave propagation. Our results reveal that flow direction and rate affect the wave velocity, amplitude, resonance frequency, and quality factor, highlighting the role of dynamic fluid conditions in shaping LP characteristics. Building on this, we constructed a large-scale concrete slab embedded with a customized crack model to study the influence of fluid viscosity, crack stiffness, and triggering location on resonance behavior. These experiments confirm theoretical predictions and help isolate parameter-specific effects on LP generation. Finally, we expanded the investigation to consider the role of fracture orientation by comparing horizontally and vertically aligned cracks using waveform analysis and Moment Tensor inversion. This revealed orientation-dependent frequency content and spatial distribution of resonance modes, providing a nuanced view of source geometry effects. Together, these three studies constitute a unified experimental and modeling framework that bridges the gap between theory and observation in LP seismology. By isolating the influence of fluid xvii flow, fracture properties, and source orientation, our work provides new benchmarks for interpreting LP events in complex geological settings such as active volcanoes, geothermal systems, and hydraulically fractured reservoirs
COMBINING DNA METABARCODING AND STABLE ISOTOPE ANALYSIS TO EXAMINE DIET AND ECOLOGY IN UNIQUELY ADMIXED GHOST WOLVES
No full textGenetic admixture offers insight into how hybridization influences ecological traits such as feeding ecology. Along the Louisiana and Texas Gulf Coast, admixed canids, descendants of coyotes (Canis latrans) and the extinct-in-the-wild red wolf (Canis rufus), form a unique population known as “ghost wolves.” We examined how retained red wolf ancestry may shape their diet using DNA metabarcoding and stable isotope analysis of noninvasive scat samples collected in 2023-2024 from southwest Louisiana and east Texas. Nutria (Myocastor coypus) and rats (Rattus spp.) were the most frequent prey items, occurring in 68.8% and 54.8% of scat, respectively. Dietary niche breadth did not differ significantly between urban and rural habitats or between summer and winter. These findings highlight the consistent foraging strategy of Gulf Coast canids and establish a foundation for future studies on how admixture influences diet and ecological niche across the region
Water and Fuego: An Interdisciplinary Characterization of Lahar Activity on Volcán de Fuego, Guatemala
No full textLahars are a type of volcanic hazard common in tropical stratovolcanoes. They occur when large amounts of water remobilize unconsolidated volcanic sediments, forming a mixture that flows violently downstream. In the case of Volcán de Fuego, Guatemala, lahars are mainly triggered by intense precipitation during the local rainy season. With dozens of these flows reported on Fuego during the rainy months, they pose a significant risk to people living near active lahar channels. This study aims to characterize critical aspects of lahar activity on Fuego using geophysical records, rainfall measurements, video observations, and computational simulations that help constrain the initiation and propagation mechanisms of these flows. This work shows that measured seismic energy increases and signal frequency content decreases while lahars move from proximal to distal areas of the volcano’s flanks. However, in the long term, seismic characteristics remain unchanged. This information supported the development of a machine learning-based framework to automatically detect lahar activity using geophysical monitoring. These results, combined with rainfall information, also allowed us to describe the control of precipitation on lahar activity. Statistically, rainfall at higher elevations is highly correlated with seismic parameters associated with flow size (e.g., cumulative power amplitude) farther downstream and least correlated with spectral characteristics of lahar signals (e.g., dominant frequencies), meaning rainfall alone is not sufficient to predict internal flow dynamics. This is consistent with a set of hydrologic outputs showing the significance that rainfall at higher elevations has in model accuracy. These results improve our knowledge of such ubiquitous types of volcanic processes and related hazards and can potentially be relevant for the development of mitigation strategies benefiting the communities around Fuego
From Disaster To Conservation: Geoheritage Potential of the 2024 Wayanad Landslide, India
No full textThe 2024 Wayanad landslide in the Western Ghats of India, which occurred on 30 July, is one of the most significant and devastating landslides in India, and stands as a compelling candidate for designation as a geoheritage site. This landslide, initiated as a rockslide and transformed into a massive debris flow, travelled 8 km causing widespread destruction across three villages, altering the course of the Punnapuzha River, and resulting in over 266 fatalities. This tragic event underscores the urgent need for management of natural hazards, particularly rain-induced landslides in the Western Ghats region. Thus, the Wayanad landslide site offers a unique opportunity to establish a field segment for guided exploration and a museum segment for research and education on landslide dynamics and geological processes, benefiting students, researchers, and disaster management professionals. Hence, this study evaluates the geoheritage potential of the site through a combined approach of Strengths, Weaknesses, Opportunities, and Challenges (SWOC) analysis, and a comprehensive stakeholder-informed survey. Results from the survey revealed especially high educational and tourism potential, despite major social, environmental, and economic disruptions. Recommendations include establishing real-time monitoring systems, controlled public access, and a digital platform to engage broader audiences. By recognizing the geoheritage significance, it could serve as a platform for scientific investigation, public awareness, and disaster risk reduction strategies. Furthermore, such a designation would foster geotourism, supporting sustainable development and benefitting the local community. Thus, this study aims to highlight the scientific value of the 2024 Wayanad landslide site and its potential to educate future generations while promoting both conservation and geotourism, aligning with UNESCO’s initiatives to preserve dynamic geomorphosites that reveal Earth’s active geological history
Large Language Models for Construction Risk Classification: A Comparative Study
No full textRisk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited
SARCOCYSTIS INFECTIONS IN RIVER OTTER (LONTRA CANADENSIS) IN MICHIGAN
No full textSarcocystis infections are common in the muscles of herbivores but were, until recently, considered relatively rare in carnivores. Little is known of sarcocysts in the muscles of river otters in the United States. In a previous epidemiologic study of Toxoplasma gondii infections in North American river otters (Lontra canadensis) from Michigan in the 2018 and 2019 harvest season, Sarcocystis DNA was found in 34 (27.4%) of 124 otter muscles. Tongues from these 34 PCR-positive samples were further examined here for Sarcocystis species. An additional batch of frozen 62 samples collected at the end of the season was also tested for Sarcocystis herein. Morphologically, sarcocysts were studied in 23 otters (13 of 34 PCR-positive samples from the first batch and 10 of 62 samples of batch 2) in compression smears and paraffin-embedded histologic sections stained with hematoxylin and eosin. Morphologically, at least 2 different kinds of sarcocysts were identified, 1 with a smooth sarcocyst wall and the second with villar protrusions. By transmission electron microscopy, sarcocysts from 1 otter were similar to Sarcocystis caninum. Morphologically, sarcocysts from the river otter were different from the European otter (Lutra lutra). Sequencing amplification products from 18S rRNA, 28S rRNA, and cox1 genes, suggested S. caninum–like, Sarcocystis svanai–like, and Sarcocystis sp. We detected a third, potentially undescribed species, in 3 otters. Genetic markers for conclusive differentiation of Sarcocystis spp. from mustelids should be developed. The samples in the present study had degraded; better preserved samples are needed for further morphologic studies. This is the first report of S. caninum–like, S. svanai–like, and Sarcocystis sp. in the river otter in the United States
Analysis of a saline dust storm from the Aralkum Desert - Part 1: Consistency between multisensor satellite aerosol products
Full text linkThe Aralkum Desert presents a challenging environment for satellite aerosol observations due to its very bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on region-specific aerosol properties. We survey current global satellite algorithms capable of detecting the presence, column burden, and elevation of airborne dust over the Aral Sea basin. Discrepancies and potential biases in retrieved UV aerosol index (UVAI), mid-visible and thermal infrared optical depth (AOD), and layer height due to different assumptions on surface and aerosol properties are assessed. The results indicate that (1) UVAI products consistently delineate dust plume extent but show large positive values over turbid waters and salt flats due to enhanced surface absorption. (2) MODIS and VIIRS total and coarse-mode AOD retrievals show strong agreement over the Caspian Sea despite using different aerosol optical models. Over desert surfaces, all operational AOD products misclassify fresh dust plumes as clouds and exhibit strong nonlinear relationships. The NOAA EPS algorithm retrieves significantly lower AOD than others, although the agreement improves when a dust optical model is used. The MISR research algorithm produces higher, more consistent AOD and improved particle property retrievals compared to the MISR operational product. (3) Among four IASI infrared products, the LMD algorithm performs best in detecting dust plume features over both desert and water surfaces. (4) The EPIC aerosol optical centroid height (AOCH) product overestimates dust layer altitude under low aerosol loadings but exhibits good agreement with CALIOP in detecting the elevated dust characterized by well-defined upper boundaries. MISR height retrievals also align well with CALIOP and EPIC. IASI infrared retrievals are about 0.4 km higher than EPIC over dust-laden scenes. This study underscores the value of a synergistic, multisensor approach leveraging the complementary strengths of satellite aerosol products and calls for their appropriate application and careful interpretation when characterizing saline dust from the Aralkum Desert
Clinical phenotypes among patients that underwent cardiac resynchronization therapy using unsupervised learning integrating gated SPECT
No full textBACKGROUND: Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure when left ventricular mechanical dyssynchrony (LVdys) is present, yet approximately 30-40% of patients do not respond to therapy. The purpose of this study is to use unsupervised learning to identify phenotypes of patients with a better response rate.
METHODS: Unsupervised learning integrating gated single-photon emission computed tomography (SPECT) was used to identify clinical phenotypes among patients undergoing CRT. We utilized hierarchical clustering analysis to group 217 patients based on 49 pretreatment variables, including demographic, clinical, and phase analysis of gated SPECT data. Fibrosis was measured by the percentage of pixels with less than 50% of maximum relative counts. LVdys was evaluated by phase SD \u3e43° and phase bandwidth \u3e135°.
RESULTS: We identified three phenotypes of patients: two with similar response rates (86.2 and 87.0%) but with different characteristics, one presenting borderline LVdys, low fibrosis and nondilated heart and the other high LVdys, moderate fibrosis and a dilated heart, the third phenotype represents patients with moderate LVdys, substantial amounts of cardiac fibrosis and a dilated heart that do not have a good response to CRT (55.9%).
CONCLUSION: Our results suggest that evaluating cardiac dyssynchrony, fibrosis, and remodeling through phase analysis of gated SPECT is relevant in characterizing the phenotype of good responders. Patients with substantial amounts of cardiac fibrosis have less benefit from CRT. This work suggests that CRT recommendations based on customized selection criteria associated with gated SPECT can lead to higher response rates
Impact of Detergent Type, Detergent Concentration, and Friction Modifiers on PM-PN Emissions in an SI Engine Using EEPS
Full text linkThree TOP TIERTM gasoline deposit control additives (DCAs) of differing chemistries were tested for their impact on particulate matter emissions in terms of particulate mass (PM) and particle number (PN) at operating conditions representative of road load, cold start, and high load on a 2.0 L, 4-cylinder, gasoline direct injection (GDI) spark ignition (SI) engine. The PM-PN emissions were measured using an Exhaust Emissions Particle Sizer (EEPS). Deposit control additives or detergents are gasoline additives used to prevent and clean combustion chamber and injector deposits in gasoline spark ignition (SI) engines. All three gasoline additives were tested at each operating condition at three different treatment rates. In addition, one of the additives was tested with a fuel-based friction modifier (FM). The results showed that of the treatment rates tested, the lowest allowable concentration (LAC) for all additives requires the least time for the emissions to settle. However, the impact of the gasoline additives on PM-PN emissions is not linear and changes with additive concentration depending on the additive chemistry and operating conditions. The additive with the friction modifier resulted in an increase of over 19% particle number and over 30% particulate mass at the road load operating condition, while the increase at high load was over 27% for particle number and 11% for particle mass