Physical Sciences and Mathematics Commons^™

Synthesis And Characterization Of 2,6-Diisopropylphenoxy Tetrapyrazinoporphyrazines As Potential Molecular Qubits, Benjamin Marx

Masters Theses

This thesis reports the synthesis of metal-free and metal-containing phthalocyanine derivatives using methods typical for that class of functional dyes. A mixture of the target compounds prepared here, one paramagnetic and one diamagnetic tetrapyrazinoporphyrazine, will be tested for their potential use in spintronics – the application of spin-active species in molecular electronics. Phthalocyanines and their derivatives are well-known for their ease of preparation and tunability; they have been utilized as sensitizers for photodynamic therapy of cancer and solar cells, as well as in catalysis and chemical sensing. Tetrapyrazinoporphyrazine complexes are compared with the parent phthalocyanine as their chemistry is similar …

Numerical Simulation Of Electroosmotic Flow Of Viscoelastic Fluid In Microchannel, Jianyu Ji

Mechanical & Aerospace Engineering Theses & Dissertations

Electroosmotic flow (EOF) has been widely used in various biochemical microfluidic applications, many of which often involve the use of viscoelastic non-Newtonian fluids. Due to the existence of the elastic effect, the viscoelastic EOF develops into chaotic flow under extremely low Reynolds numbers, which is known as elastic turbulence. The mechanism of elastic turbulence in electroosmotic flow remains unclear. Numerical simulation plays an important role in understanding the mechanisms of elastic turbulence. This dissertation is aimed to study the EOF of viscoelastic fluids in constriction microchannels under various direct current (DC) and alternating current (AC) electric fields. First, the EOF …

Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy

Engineering Management & Systems Engineering Theses & Dissertations

The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container …

Physical Controls On The Hydrology Of Perennially Ice-Covered Lakes, Taylor Valley, Antarctica (1996-2013), J. M. Cross, Andrew G. Fountain, M. J. Hoffman, M. K. Obryk

Geology Faculty Publications and Presentations

The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed-watershed, perennially ice-covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. We …

Internal Waves Of Port Everglades: Experimental And Numerical Studies To Characterize Ambient Magnetic Signals, Johanna Riley Evans

Theses and Dissertations

Ocean dynamics are an important contributing factor to measurable environmental magnetic noise. Seawater is conductive and as an internal wave oscillates across Earth’s magnetic field, a small yet detectable induced magnetic field develops. A significant source of underwater magnetic noise off the southeast coast of Florida comes from internal waves. Characterizing the magnetic signals present required a combination of computational modeling effort and field experiments. For this research, undersea sensors were monitored as a continuous field experiment and a computational fluid dynamic (CFD) magneto-hydrodynamic (MHD) model was customized to simulate internal wave-induced magnetic fields at the site. I developed the …

Unified Model Of Lightning & Electrostatic Discharges Across The Solar System, Annelisa Blackwell Esparza

Theses and Dissertations

As we increase the number of probes on other objects within our solar system, it becomes all the timelier to understand the physics of electrostatic discharges (ESDs). Since these probes will have sensitive instruments on-board, we will need to apply our existing knowledge of ESDs to extraterrestrial environments to protect these investments from deterioration. On Earth, even small-scale ESDs can permanently destroy critical components within electronics, while large-scale ESDs (e.g., lightning strikes) are capable of debilitating entire power grids. The characteristic timescale of an ESD is small compared to the timescales of cloud charging and storm evolution. This lets us …

Investigating The O’Connell Effect In Kepler Eclipsing Binaries, Matthew Frederick Knote

Theses and Dissertations

The O’Connell effect – the presence of unequal maxima in eclipsing binaries – remains an unsolved riddle in the study of close binary systems. The Kepler space telescope produced high precision photometry of nearly 3,000 eclipsing binary systems, providing a unique opportunity to study the O’Connell effect in a large sample and in greater detail than in previous studies. I have characterized the observational properties – including temperature, luminosity, and eclipse depth – of a set of 212 systems (7.3% of Kepler eclipsing binaries) that display a maxima flux difference of at least 1%, representing the largest sample of O’Connell …

Non-Negative Matrix Factorization In The Identification Of Co-Mutations, Michael Robert Kolar

Theses and Dissertations

One of the difficulties of genetic research is the asymmetrical relationship between data collection techniques and data analysis techniques. The goal of this research was to test a novel application of non-negative matrix factorization, which would allow researchers to more easily identify co-mutations. Those co-mutations then can then be further verified by frequency analysis. This pruning process allows researchers to identify more fruitful research opportunities, saving time, energy, and funding. Past research has utilized non-negative matrix factorization to extract factors which meaningfully express underlying data features. This study extends the depth of non-negative matrix factorization knowledge in various ways. First, …

Design Of Ethical Autonomous Agents For Unmanned Aerial Vehicles Using Fuzzy Logic, Gavin Giovanni Smith

Theses and Dissertations

Autonomous systems have, over the years become part of our everyday lives. These systems have been deployed to executed a diverse range of applications in different industries; finance, healthcare, military, and in particular, the flight industry. With the rise of UAVs, new opportunities arose, but with those opportunities came new pitfalls within any industry. For UAVs, one of the pitfalls came in the form of ethical decisionmaking, which led to a variety of questions. Can the Autonomous systems within UAVs be designed with ethics in mind? Which ethical guidelines would we use to implement such a system? How would we …

Cloud Image Classification Using Machine Learning, Marcus Peter Cote

Theses and Dissertations

Machine learning is a rapidly expanding technology that has proven to be highly useful for image classification. Ground-based camera networks are an emerging resource for aviation weather information with near real-time imagery available online for public viewing and download. While raw web camera imagery can be analyzed by aviators, high pilot workload motivates the use of machine learning to autonomously interpret cloud type information from images that is relevant to aviation weather hazards. In particular, transfer learning is a machine learning approach by which elements of a pre-trained machine learning model are refitted for new tasks. By employing transfer learning …

Understanding Ecological Relationships To Improve Biofouling Management Practices In The Marine Environment, Ann Catherine Wassick

Theses and Dissertations

Biofouling, the growth of benthic invertebrates on anthropogenic surfaces, impacts many industries and is a major vector of non-native species worldwide. Understanding the ecological relationships between biofouling organisms and their environment is necessary to assist management practices. This study investigates the biofouling communities at Port Canaveral, Florida, in order to potentially refine biofouling mitigation practices. Recruitment panels were used to document the temporal changes in community composition, biofouling pressure, recruitment, and growth rates. These factors were also used to assess relationships with temperature and salinity. A second component of this work provided documentation on the variation in non-native species abundance …

Modeling Settlement And Filtration Of The Indian River Lagoon Benthic Community, With An Emphasis On Living Dock Restoration Mats, Sandra Lisette Rech

Theses and Dissertations

The Indian River Lagoon (IRL) is one of the most biologically diverse estuaries in North America. An increase in population and anthropogenic effects have led to eutrophication, a decline in water quality, and overall degradation. The Living Docks program is a citizen science initiative started at the Florida Institute of Technology for lagoon restoration. In this program, public and private docks are volunteered to become Living Docks, where oyster mats made of plastic mesh and dead, dried oyster shells are attached to dock pilings in an effort to provide a natural substrate for benthic organism growth. The community which develops …

Growth And Mortality Of American Oysters (Crassostrea Virginica) For Several Years In The Mississippi Sound – Effects Of Freshwater Influence, Taylor Slade

Honors Theses

The American oyster, Crassostrea virginica, is a filter feeding bivalve native to the Gulf of Mexico, and an essential part of the Mississippi Sound estuarine ecosystem. In recent years, influx of freshwater into the Mississippi Sound as a result of frequent rainfall events has altered the water chemistry in several ways that are detrimental to the oyster populations. In this thesis, I examine the growth rate of C. virginica over the last five years in association with salinity fluctuations in the Mississippi Sound. Given diminishing populations and limited recruitment, researchers have facilitated citizen-scientist managed oyster gardens along the coast …

Machine Learning-Based Event Generator, Yasir Alanazi

Computer Science Theses & Dissertations

Monte Carlo-based event generators have been the primary source for simulating particle collision experiments for the study of interesting physics scenarios. Monte Carlo generators rely on theoretical assumptions, which limit their ability to capture the full range of possible correlations between particle’s momenta. In addition, the simulations of the complete pipeline often take minutes to generate a single event even with the help of supercomputers.

In recent years, much attention has been devoted to the development of machine learning event generators. They demonstrate attractive advantages, including fast simulations, data compression, and being agnostic of theoretical assumptions. However, most of the …

Oak Savanna Restoration And Climate Change Mitigation Through Silvopasture In Minnesota, James Siems

Environmental Studies Student Work

Reducing emissions of world food systems will be critical to combatting climate change. Silvopasture systems, which integrate managed forests with pastureland, have been shown to be a more sustainable alternative to traditional livestock production and have the ancillary benefit of diversifying the sources of income for farmers. Silvopasture may also have the capacity to serve as a mechanism for ecological restoration. This paper combines existing literature with an interview of a farmer who is engaging in silvopasture to evaluate the potential of silvopasture as means to restore Minnesota’s disappearing oak savanna ecosystem, while improving the sustainability of food production within …

What Technologies And Barriers Are Present In Sustainable Heating And Cooling, Mason D. Nibbe

Environmental Studies Student Work

Sustainable heating and cooling practices can save homeowners money and reduce their carbon emissions. In the cold upper Midwest, Minnesota specifically, there are ways houses can be more efficient in terms of heating. The most efficient and practical are heat pumps systems, passive solar designs, and proper insulation of the building. This study is targeted at both existing buildings and the construction of new buildings. This project focuses on what sustainable heating and cooling practices, technologies, and barriers are present in the heating, ventilation, and air-conditioning (HVAC) industry. I find that using any sustainable solution is helpful, but multiple solutions …

Atlantic Surfclam (Spisula Solidissima) Population Demographics And Distribution Along The Middle Atlantic Bight, Mauricio González Díaz

OES Theses and Dissertations

The Atlantic surfclam (Spisula solidissima) is a long-lived benthic biomass dominant organism that occurs on the Middle Atlantic Bight (MAB) continental shelf between 10 m and 50 m. Trends in Atlantic surfclam population specific growth and mortality rates were analyzed using four decades of age and length observations obtained from NOAA stock surveys from the 1980s to 2010s in six regions distributed along the MAB. Atlantic surfclam specific growth rates and asymptotic lengths were estimated from the age and length observations using the von Bertalanffy growth model. The analysis showed that the Atlantic surfclam median asymptotic length in …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

Qualitative Analysis For A Two-Component Peakon System With Cubic Nonlinearity, Shaojie Yang, Zhijun Qiao

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

This paper is devoted to studying a two-component peakon system with cubic nonlinearity, which is a two-component extension of the cubic Camassa–Holm equation. We first discuss the local well-posedness for the Cauchy problem of the system. Then, in light of a fine structure of the system, we present the precise blow-up scenario for strong solutions to the system and derive a new blow-up result with respect to initial data. Finally, peakon solutions are discussed as well.

A Relevance Model For Threat-Centric Ranking Of Cybersecurity Vulnerabilities, Corren G. Mccoy

Computer Science Theses & Dissertations

The relentless and often haphazard process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge they face is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the single point of failure in an otherwise formidable defense. This means one of the biggest challenges in vulnerability management relates to prioritization. Given that so few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely …

Towards Privacy And Security Concerns Of Adversarial Examples In Deep Hashing Image Retrieval, Yanru Xiao

Computer Science Theses & Dissertations

With the explosive growth of images on the internet, image retrieval based on deep hashing attracts spotlights from both research and industry communities. Empowered by deep neural networks (DNNs), deep hashing enables fast and accurate image retrieval on large-scale data. However, inheriting from deep learning, deep hashing remains vulnerable to specifically designed input, called adversarial examples. By adding imperceptible perturbations on inputs, adversarial examples fool DNNs to make wrong decisions. The existence of adversarial examples not only raises security concerns for real-world deep learning applications, but also provides us with a technique to confront malicious applications.

In this dissertation, we …

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Jianli Fan, Lingxiao Jiang, Daniel Wai Kiat Lim

Research Collection School Of Computing and Information Systems

Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyber- attack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the above-mentioned anomalies. In this paper, we propose a novel, deep …

Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

This paper aims to provide differentiated security protection for infotainment data commu- nication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication technologies to enable information exchange between pedestrians, cars, and urban infrastructure. Negligence on the security of infotainment data commu- nication in IoV networks can unintentionally open an easy access point for social engineering attacks. The attacker can spread false information about traffic conditions, mislead drivers in their directions, and interfere with traffic management. Such attacks can also cause distractions to the driver, which has a potential implication …

Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao

Research Collection School Of Computing and Information Systems

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, a huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our …

Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati

Research Collection School Of Computing and Information Systems

In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …

Towards Reinterpreting Neural Topic Models Via Composite Activations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Most Neural Topic Models (NTM) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. These topics are interpreted through the selection of the top activated words via the weights or reconstructed vector of the decoder that are directly connected to each neuron. In this paper, we present a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. Firstly, building on the original information from a trained NTM, we generate a pool of potential candidate “composite topics” by exploiting possible co-occurrences within the original set of …

Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo

Research Collection School Of Computing and Information Systems

Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given …

Deep Just-In-Time Defect Localization, Fangcheng Qiu, Zhipeng Gao, Xin Xia, David Lo, John Grundy, Xinyu Wang

Research Collection School Of Computing and Information Systems

During software development and maintenance, defect localization is an essential part of software quality assurance. Even though different techniques have been proposed for defect localization, i.e., information retrieval (IR)-based techniques and spectrum-based techniques, they can only work after the defect has been exposed, which can be too late and costly to adapt to the newly introduced bugs in the daily development. There are also many JIT defect prediction tools that have been proposed to predict the buggy commit. But these tools do not locate the suspicious buggy positions in the buggy commit. To assist developers to detect bugs in time …

Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters. In this paper, we propose a novel lightweight fully convolutional architecture, called DialogConv, for response selection. DialogConv is exclusively built on top of convolution to extract matching features of context and response. Dialogues are modeled in 3D views, where DialogConv performs convolution operations on embedding view, word view and utterance view to capture richer semantic information from multiple contextual views. On the four benchmark datasets, …