6,366 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Optical Remote Sensing of Oil Spills by using Machine Learning Methods in the Persian Gulf: A Multi-Class Approach

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    Marine oil spills are harmful for the environment and costly for society. Coastal areas are particularly vulnerable since they provide habitats for organisms, animals and marine ecosystems. This thesis studied machine learning methods to classify thick oil in a multi-class case, using remotely sensed multi-spectral data in the Persian Gulf. The study area covers a large area between United Arab Emirates (UAE) and Iran. The dataset is extracted from 10 Sentinel-2 tiles on six spectral bands between 492 nm to 2202 nm. These images were annotated for four classes, namely thick oil, thin oil, ocean water and turbid water by using the Bonn Agreement to analyse true color composite images. A variety of machine learning methods were trained and evaluated using this dataset. Then a robustness evaluation was done by using selected machine learning methods on an independent dataset. Initially multiple machine learning methods were included; three decision trees, six K-Nearest Neighbor (KNN) models, two Artificial Neural Network (ANN) models, two Naive bayes models, and two discriminant models. Two KNN models and two ANN models were then picked for further evaluation. The results show that the fine KNN approach with two nearest neighbors had the best performance based on the computed statistical measures. However, the robustness evaluation showed that the tri-layered NN performed better. This thesis has shown that supervised machine learning with a multi-class approach can be used for oil spill monitoring using multi-spectral remote sensing data in the Persian Gulf

    Advances in Methane Production from Coal, Shale and Other Tight Rocks

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    This collection reports on the state of the art in fundamental discipline application in hydrocarbon production and associated challenges in geoengineering activities. Zheng et al. (2022) report an NMR-based method for multiphase methane characterization in coals. Wang et al. (2022) studied the genesis of bedding fractures in Ordovician to Silurian marine shale in the Sichuan basin. Kang et al. (2022) proposed research focusing on the prediction of shale gas production from horizontal wells. Liang et al. (2022) studied the pore structure of marine shale by adsorption method in terms of molecular interaction. Zhang et al. (2022) focus on the coal measures sandstones in the Xishanyao Formation, southern Junggar Basin, and the sandstone diagenetic characteristics are fully revealed. Yao et al. (2022) report the source-to-sink system in the Ledong submarine channel and the Dongfang submarine fan in the Yinggehai Basin, South China Sea. There are four papers focusing on the technologies associated with hydrocarbon productions. Wang et al. (2022) reported the analysis of pre-stack inversion in a carbonate karst reservoir. Chen et al. (2022) conducted an inversion study on the parameters of cascade coexisting gas-bearing reservoirs in coal measures in Huainan. To ensure the safety CCS, Zhang et al (2022) report their analysis of available conditions for InSAR surface deformation monitoring. Additionally, to ensure production safety in coal mines, Zhang et al. (2022) report the properties and application of gel materials for coal gangue control

    Vulnerability of the Nigerian coast and communities to climate change induced coastal erosion

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    Improving coastal resilience to climate change hazards requires understanding past shoreline changes. As the coastal population grows, evaluation and monitoring of shoreline changes are essential for planning and development. Population growth increases exposure to sea level rise and coastal hazards. Nigeria, where the study is situated, is among the top fifteen countries in the world for coastal population exposure to sea level rise. This study provided a novel lens in establishing a link between social factors and the intensifying coastal erosion along the Akwa Ibom State study coast. The mixed-method approach used in the study to assess the vulnerability of the Nigerian coast and communities to climate change-induced coastal erosion proved to be essential in gathering a wide range of data (physical, socio economic, participatory GIS maps and social learning) that contributed to a more robust and holistic assessment of coastal erosion, which is a complex issue due to the interplay between the human and natural environments. Remotely sensed data was used to examine the susceptibility and coastal evolution of Akwa Ibom State over 36 years (1984 -2020). Longer-term (1984- 2020) and short-term (2015-2020) shoreline change analyses were used to understand coastal erosion and accretion. From 1984-2020, the total average linear regression rate (LRR) was - 2.7+0.18m/yr and from 2015-2020, it was -3.94 +1.28m/yr, demonstrating an erosional trend along the study coast. Although the rate of erosion varies along the study coast, the linear regression rates (LRR) results show a predominant trend of erosion in both the short and longer term. According to the 2022 Intergovernmental Panel on Climate Change report, loss of land, loss of assets, community disruption and livelihood, loss of environmental resources, ecosystem, loss of life, or adverse health impact are all potential risks along the African coast due to climate change – this study shows that these risks are already occurring today. To quantify the anticipated future coastal erosion risk by 2040 along the study coast, the findings in this study show an overall average LRR of -2.73+ 0.99 m/yr which anticipates that coastal erosion will still be prevalent along the coast by 2040. And, given the current global climate change situation, should be expected to be much higher than the current forecasting. This study re-conceptualised the European Environmental Agency Driver-Pressure StateImpact-Response (DPSIR) model to show Hazard-Driver-Pressure-State-Impact ResponseObservation causal linkages to coastal erosion hazards. The results showed how human activities and environmental interactions have evolved through time, causing coastal erosion. Removal of vegetation cover/backstop for residential and agricultural purposes, indicate that human activities significantly contribute to the study area's susceptibility, rapid shoreline changes, and vulnerability to coastal erosion, in addition to oceanic and climate change drivers such as sea level rise and storminess. Risk perception of coastal erosion in the study area was analysed using the rhizoanalytic method proposed by Deleueze. The method demonstrates how connections and movements can be related and how data can be used to show multiplicity, mark and unmark ideas, rupture pre-conceptions and make new connections. This study shows that coastal erosion awareness is insufficient to build a long-term management plan and sustain coastal resilience. The Hino's conceptual model which provides in-depth understanding on planned retreat was used to illustrate migratory and planned retreat for the study coast where relocation has already occurred due to coastal erosion. The result fell within the Self-Reliance quadrant, indicating that people left the risk zone without government backing or retreat plans. Other coastal residents who have not relocated fell within the Hunkered Down quadrant, showing that they are willing to stay in the risk zone and cope with the threat unless the government/environmental agencies relocate them. This study shows that coastal resilience requires adaptive capacity and government support. However, multilevel governance has inhibited government-community dialogue and involvement, increasing coastal erosion vulnerability. The coastal vulnerability index to coastal erosion was calculated using the Analytical Hierarchy Process weightings. It revealed that 67.55% of the study coast falls within the high-very high vulnerability class while 32.45% is within the very low-low vulnerability class. This study developed and combined a risk perception index to coastal erosion (RPIerosion) and participatory GIS (PGIS) mapping into a novel coastal vulnerability index called the integrated coastal erosion vulnerability index (ICEVI). The case study evaluation in Akata, showed an improvement in the overall vulnerability assessment to reflect the real-world scenario, which was consistent with field data. This study demonstrated not only the presence and challenges of coastal erosion in the research area but also the relevance of involvement between the local stakeholders, government and environmental agencies. Thus, showing the potential for the perspectives of the inhabitants of these regions to inform the understanding of the resilience capacity of the people impacted, and importantly to inform future co-design and/or selection of effective adaptation methods, to better support coastal climate change resilience in these communities. Overall, the study provides a useful contribution to coastal erosion vulnerability assessments in data-scarce regions more broadly, where the mixed-methods approach used here can be applied elsewhere

    Evidence of Sea Level Rise At the Peruvian Coast (1942-2019)

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    The present work aims to analyze the variability of the sea level of the Peruvian coast with time series over a long observation period (Seventy-eight years, from 1942 to 2019). Data came from the Talara, Callao and Matarani tide gauge stations located at the north, center and south of the coast. Variations of sea level as well as air and seawater surface temperature were analyzed. Among the different scenarios studied, a sea level rise of 6.79, 4.21 and 5.16 mm/year for Talara, Callao and Matarani, respectively was found during the 1979–1997 nodal cycle. However, these results decreased significantly during the next cycle (1998–2016) until values of 1.53, 2.16 and 1.0 mm/year for Talara, Callao and Matarani, respectively. Thus, it has been demonstrated that sea level rise are highly dependent on the time interval chosen. Moreover, large interannual changes of up to 200 mm/year are observed, due to recurring phenomena, such as “El Niño”. On the other hand, the trends obtained are slightly lower than those shown by the IPCC up until 2006 but significantly higher values have been observed. Finally, the results presented herein show the necessity of a local study of the sea level variability at the coastal areas

    2023-2024 Undergraduate Catalog

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    2023-2024 undergraduate catalog for Morehead State University

    A survey on artificial intelligence-based acoustic source identification

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    The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

    Islands at Risk - Analyzing Resource-use Dynamics from a Socio-metabolic Research Perspective

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    Our resource-use dynamics have contributed significantly to the improvement in global material standards of living through the provisioning of essential societal services. Nonetheless, these dynamics have also impacted on the already limited natural resource-base of the Earth system on which we depend. Moreover, the characteristics of a global self-perpetuating resource-use linearity, the growing demand for finite raw materials, the high waste generation that remains unrecovered, and the increasing negative effects of climate change further exacerbate the Earth system’s vulnerabilities and exposure to risks. As such, the resource-use dynamics is posited as an important example of complex systems in need for better understanding, particularly in advancing towards sustainability and build system’s resilience. For resource-stressed settings like small island nations, the analysis of these complex systems is not only crucial, but urgent. Small Island Developing States are often characterized by sustainability challenges like limited resource-bases, reduced waste absorption capacity, a strong dependency on external resources to meet their basic needs, geographic isolation from markets which impact connectivity and resource supply, and natural and built environment that is progressively been threatened by the negative effects of climate change, which amplify the pre-existing vulnerabilities and risks for these territories. Thus, dealing with sustainability would require a deeper understanding of the interactions and trade-offs between the resource-use dynamics and the influences that internal/external factors like climate change have over these. By doing so, the system will have the ability to both contribute to global environment change, but also determine their own vulnerability or resilience to those changes. This thesis analyzes resource-use dynamics from a socio-metabolic research perspective in the context of small islands to enhance resource security and build system’s resilience, by looking into the way in which natural resources are interconnected, influenced, and managed. The analysis is spread across three main empirical Chapters, each of which contribute to advancing the arguments that arise from this work. First, in Chapter 3, the thesis analyzes the shifting resource-baselines of water, energy, and food, emphasizing the intra- and interconnected nature between essential resources and socio-metabolic risk, which builds the foundations for deeper analysis on current and future sustainability in small islands. Then, in Chapter 4, the thesis analyzes and identifies the size and make-up of material and energy flows specific to an individual case study, bringing important quantitative and qualitative insights on the potentials that reconfigured resource-use patterns may offer to minimizing or reducing socio-metabolic risk in small islands. Next, in Chapter 5, the thesis analyzes the role that critical material stocks play in driving resource-use and in furthering sustainable development, emphasizing climate change adaptation strategies to build system’s resilience. The overall framework of this thesis has demonstrated how a better understanding of resource-use dynamics may offer an opportunity to achieve resource security and self-reliance as a resilience building measure in the island context. Finally, this thesis encourages for the development and application of holistic and long-term resource management strategies through inclusive, climate and nature-based solutions that consider the trade-offs and synergies between different resource-use dynamics

    Simulating the gravimetric detection of submarines, calculating high-accuracy terrain corrections using LIDAR elevation data and performing a microgravity survey in the Campsie Fells

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    The work in this thesis relates to the field of gravimetry, the measurement of gravitational fields and their variations, which is carried out using highly-sensitive accelerometers known as gravimeters. By using gravimeters to measure the changes in gravitational field strength from place to place, it is possible to detect differences in the concentration of mass around the gravimeter and this has historically been used to monitor geophysical activity (such as variations in groundwater, volcanic activity or glacial mass), for geological exploration (such as searching for mineral or hydrocarbon resources) and many other applications. This work covers a range of topics in gravimetry, starting with the use of computer programs to simulate the gravitational fields that would be generated when a submarine travelled past a stationary gravimeter, or array of gravimeters, situated underwater. This is done with the aim of estimating the efficacy of a new gravimeter known as the ‘Wee-g’ under development at the University of Glasgow at the time of writing and also has applications to the gravitational detection of submarines more generally. The gravitational field of a 100m-long submarine is simulated, using a simplified one-dimensional density profile approximating the real density variations along the length of a large submarine. The simulated gravity field is then compared to the sensitivity of a prototype Wee-g gravimeter of 5”Gal/ √ Hz to give an initial estimate of the maximum detection range of such a signal by the Wee-g, which is found to be approximately 20m. Then, synthetic noisy signals are made by combining the simulated gravity signals with real Wee-g sensor noise data and digital signal processing methods are used to try and recover the corrupted signal from the noise in a way that maximises the detection range. Matched filtering is applied which uses foreknowledge of the signal being searched for to significantly increase the signal to noise ratio (SNR) in the noisy data by an order of magnitude, which increases the Wee-g’s detection range of the modelled submarine to ∌ 30m. In addition, computer programs are made that determine a quantity known as the terrain correction at a given gravity survey point using digitised elevation data describing the surrounding topography. Terrain correction is the effect that the presence of surrounding hills and valleys has on the gravitational field strength at a location and, if it is not accounted for, substantial variations in gravity (and hence, potentially useful information) can be partially or completely obscured. Methods already exist to calculate the terrain correction but these are either slow and laborious, inaccurate (in comparison to contemporary gravimeter performance) or both, while the program presented in this work makes use of modern computing speed and high-accuracy elevation maps to improve on these. The terrain correction program presented here analyses terrain out to a distance of 166.735km from the survey point, using 1m-resolution LiDAR elevation data to describe the nearest 2km2 , and can calculate terrain correction values in approximately 9s when run on a computer with 8GB of RAM. Terrain at all distances from the survey point is modelled using many flat-topped rectangular prisms and the gravitational field strength due to each prism is calculated using an already existing analytic solution. An in-depth analysis of the terrain correction computation of the innermost 2km is carried out to compare the accuracy of the method used with simple analytic solutions. This analysis concludes that terrain corrections can be calculated with an uncertainty of 2”Gal or less when using 1m2 -resolution elevation data, provided the terrain immediately around the survey point has an incline of less than 10◩ . Finally, two gravity surveys carried out in January of 2020 by the author with a Scintrex CG-5 commercial gravimeter are described: one in the Campsie Fells — a range of hills roughly 10km north of Glasgow — and the second in the cloisters of the Gilbert-Scott building on the University of Glasgow campus. The Campsies survey is compared with a gravimeter survey of the same region carried out in 1969 and discrepancies of up to a few mGal are observed, understood to be due to terrain correction inaccuracies in the older survey. Results from the survey in the cloisters are compared to the gravitational field due to underfloor air ducts described by plans of the building but little correlation is found. This is suspected to be the result of either inaccuracies in the building plans or the impact of environmental noise on the measurements
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