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Articles 31 - 41 of 41
Full-Text Articles in Physical Sciences and Mathematics
Vegetation Sensitivity During The Mid-Holocene Warming In Western Ohio, Kristin Kopera
Vegetation Sensitivity During The Mid-Holocene Warming In Western Ohio, Kristin Kopera
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There has been a growing interest in prairie reconstruction in western Ohio, yet there are few recent academic sources supporting the claim that prairies appeared in western Ohio during the mid-Holocene. The hypsithermal was the warmest and driest part of the Holocene and occurred from 8,000-4,000 years ago in the Midwest. During the hypsithermal, the Prairie Peninsula appeared from Minnesota to eastern Ohio. If prairie did appear in Ohio, it occurred during the mid-Holocene hypsithermal. The goal of this study was to determine if western Ohio experienced a prairie period during the hypsithermal using pollen as a proxy for past …
Investigating An Apparent Structural High In Seismic Data In North Terre Haute, Indiana, Through First-Arrival Traveltime Tomography And Gravity Analysis, Daniel Grant Koehl
Investigating An Apparent Structural High In Seismic Data In North Terre Haute, Indiana, Through First-Arrival Traveltime Tomography And Gravity Analysis, Daniel Grant Koehl
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This study focuses on northern Terre Haute, Indiana, where seven 2D seismic reflection time sections were collected by CountryMark and donated to Wright State University. Geologically, the area is on the eastern margin of the Illinois Basin. Two of these seismic lines display significant relief along a continuous, high-amplitude horizon approximately 180 milliseconds in two-way traveltime depth. This horizon was previously interpreted by CountryMark to be a Silurian reef core of the type common in this region of the Illinois Basin; however, other seismic lines within the data set display no relief. Furthermore, borehole logs within the area show no …
Rules With Right Hand Existential Or Disjunction With Rowltab, Sri Jitendra Satpathy
Rules With Right Hand Existential Or Disjunction With Rowltab, Sri Jitendra Satpathy
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One hotly debated research topic is, “What is the best approach for modeling ontologies?”. In the earlier stages of modeling ontologies, researchers have favored the usage of description logic to capture knowledge. One such choice is the Web Ontology Language (OWL) that is based on description logic. Many tools were designed around this principle and are still widely being used to model and explore ontologies. However, not all users find description logic to be intuitive, at least not without an extensive background in formal logics. Due to this, researchers have tried to explore other ways that will enable such users …
Scalable Clustering For Immune Repertoire Sequence Analysis, Prem Bhusal
Scalable Clustering For Immune Repertoire Sequence Analysis, Prem Bhusal
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The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecule level. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how the immune system of a patient evolves over different stages of disease development. A recent study has shown that the hierarchical clustering (HC) algorithm gives the best results for B-cell clones analysis - an important type of immune repertoire sequencing (IR-Seq) analysis. However, due to the inherent complexity, the classical hierarchical clustering algorithm does not scale well to large sequence datasets. Surprisingly, no algorithms …
Paradoxical Behavior In Groundwater Levels In Response To Precipitation Events, Alexandra Shelters
Paradoxical Behavior In Groundwater Levels In Response To Precipitation Events, Alexandra Shelters
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Groundwater levels are expected to fluctuate with precipitation, rising when precipitation increases and falling when it decreases. However, observations often show that groundwater levels rise in months when precipitation has decreased from the previous month, or alternately, falls in months when precipitation has increased from the previous month. Such paradoxical behavior is documented in a 30-year record for a monitoring well in southwestern Ohio. This record was analyzed to evaluate the hypothesis that mass balance controls the change in groundwater level such that changes cannot be predicted solely from monthly changes in precipitation. Though precipitation may vary from one month …
Abusive And Hate Speech Tweets Detection With Text Generation, Abhishek Nalamothu
Abusive And Hate Speech Tweets Detection With Text Generation, Abhishek Nalamothu
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According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online harassment detection is vital for securing and sustaining the popularity and viability of online social networks. Machine learning techniques play a crucial role in automatic harassment detection. One of the challenges of using supervised approaches is training data imbalance. Existing text generation techniques can help augment the training data, but they are still inadequate and ineffective. This research explores the role of domain-specific knowledge to complement the limited training data available for training a text …
Automated Vehicle Electronic Control Unit (Ecu) Sensor Location Using Feature-Vector Based Comparisons, Gregory S. Buthker
Automated Vehicle Electronic Control Unit (Ecu) Sensor Location Using Feature-Vector Based Comparisons, Gregory S. Buthker
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In the growing world of cybersecurity, being able to map and analyze how software and hardware interact is key to understanding and protecting critical embedded systems like the Engine Control Unit (ECU). The aim of our research is to use our understanding of the ECU's control flow attained through manual analysis to automatically map and identify sensor functions found within the ECU. We seek to do this by generating unique sets of feature vectors for every function within the binary file of a car ECU, and then using those feature sets to locate functions within each binary similar to their …
Ammonium Cycling And Nitrifier Community Composition In Eutrophic Waters Affected By Cyanobacterial Harmful Algal Blooms, Justyna J. Hampel
Ammonium Cycling And Nitrifier Community Composition In Eutrophic Waters Affected By Cyanobacterial Harmful Algal Blooms, Justyna J. Hampel
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Non-point source nitrogen (N) from agriculture is a main driver of eutrophication in aquatic systems, which often manifests as toxin producing cyanobacterial harmful algal blooms (cyanoHABs). Non-N2 fixing cyanobacteria, such as Microcystis, thrive on chemically reduced N forms (e.g., ammonium (NH4+) and urea) used as the main N form in fertilizer. NH4+ turnover rates are important components of the aquatic N cycle in eutrophic lakes affected by cyanoHABs. Regeneration of NH4+ can contribute to the internal cycling of NH4+, which can sustain cyanoHABs when external loads are low. Additionally, NH4+ uptake by cyanobacteria competes directly with nitrification, another important pathway …
Recognition Of Incomplete Objects Based On Synthesis Of Views Using A Geometric Based Local-Global Graphs, Michael Christopher Robbeloth
Recognition Of Incomplete Objects Based On Synthesis Of Views Using A Geometric Based Local-Global Graphs, Michael Christopher Robbeloth
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The recognition of single objects is an old research field with many techniques and robust results. The probabilistic recognition of incomplete objects, however, remains an active field with challenging issues associated to shadows, illumination and other visual characteristics. With object incompleteness, we mean missing parts of a known object and not low-resolution images of that object. The employment of various single machine-learning methodologies for accurate classification of the incomplete objects did not provide a robust answer to the challenging problem. In this dissertation, we present a suite of high-level, model-based computer vision techniques encompassing both geometric and machine learning approaches …
Knowledge Graph Reasoning Over Unseen Rdf Data, Bhargavacharan Reddy Kaithi
Knowledge Graph Reasoning Over Unseen Rdf Data, Bhargavacharan Reddy Kaithi
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In recent years, the research in deep learning and knowledge engineering has made a wide impact on the data and knowledge representations. The research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. Semantic Web Technologies and Deep Learning have an interest in creating intelligent artifacts. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Deep learning is in- creasingly employed to analyze various knowledge representations mentioned in Semantic Web and provides better results for Semantic …
Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat
Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat
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Information Extraction (IE) techniques are developed to extract entities, relationships, and other detailed information from unstructured text. The majority of the methods in the literature focus on designing supervised machine learning techniques, which are not very practical due to the high cost of obtaining annotations and the difficulty in creating high quality (in terms of reliability and coverage) gold standard. Therefore, semi-supervised and distantly-supervised techniques are getting more traction lately to overcome some of the challenges, such as bootstrapping the learning quickly. This dissertation focuses on information extraction, and in particular entities, i.e., Named Entity Recognition (NER), from multiple domains, …