Physical Sciences and Mathematics Commons^™

Convex Relaxations Of A Continuum Aggregation Model, And Their Efficient Numerical Solution, Mahdi Bandegi

Dissertations

In this dissertation, the global minimization of a large deviations rate function (the Helmholtz free energy functional) for the Boltzmann distribution is discussed. The Helmholtz functional arises in large systems of interacting particles — which are widely used as models in computational chemistry and molecular dynamics. Global minimizers of the rate function (Helmholtz functional) characterize the asymptotics of the partition function and thereby determine many important physical properties such as self-assembly, or phase transitions. Finding and verifying local minima to the Helmholtz free energy functional is relatively straightforward. However, finding and verifying global minima is much more difficult since the …

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Dimension Reduction Techniques For High Dimensional And Ultra-High Dimensional Data, Subha Datta

Dissertations

This dissertation introduces two statistical techniques to tackle high-dimensional data, which is very commonplace nowadays. It consists of two topics which are inter-related by a common link, dimension reduction.

The first topic is a recently introduced classification technique, the weighted principal support vector machine (WPSVM), which is incorporated into a spatial point process framework. The WPSVM possesses an additional parameter, a weight parameter, besides the regularization parameter. Most statistical techniques, including WPSVM, have an inherent assumption of independence, which means the data points are not connected with each other in any manner. But spatial data violates this assumption. Correlation between …

Reduction In Salt Deposition On Carbon Nano-Tube Immobilized Membrane During Desalination Via Membrane Distillation, Madihah Saud Humoud

Dissertations

As water scarcity increases globally under the stresses of increasing demand, aquifer depletion, and climate change, the market for efficient desalination technologies has grown rapidly to fill the void. One such developing technology, membrane distillation (MD), has found much interest in the scientific community. MD has also been powered by solar energy and waste heat resources because it can be operated at relatively low temperatures. Recent studies indicate that MD could potentially achieve the efficiencies of state-of-the-art mature thermal desalination technologies, although additional engineering and scientific challenges must first be overcome.

MD can be used to treat high salinity water …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Investigation Of Small-Scale Energy Release And Transfer Processes In The Solar Atmosphere With High-Resolution Observations In Infrared, Xu Yang

Dissertations

Solar spectrum in the infrared (IR) contains abundant information of solar activities, however, it has not spectral lines in the solar IR spectrum provide different tools to probe the solar atmosphere in various heights. This radiation band in such relatively long wavelength includes various atom and molecule spectral lines that are generated by relatively small energy level transitions. The temperature-sensitive and highly dynamic spectral lines could reveal the energy transmission process more easily than those in the visible wavelength of solar emission. Moreover, the better magnetic sensitivities for the infrared lines resulting from their longer wavelength make them detect the …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Amyloid Proteins And Fibrils Stability, Farbod Mahmoudinobar

Dissertations

Compared to globular proteins that have a stable native structure, intrinsically disordered peptides (IDP) sample an ensemble of structures without folding into a native conformation.One example of IDP is the amyloid-beta(Abeta) protein which is the main constituent of senile plaques in the brain of Alzheimer's patients.Understanding the process by which IDPs undergo structural changes to form oligomers that eventually aggregate into senile plaques/amyloid fibrils may significantly advance the development of novel therapeutic methods to treat neurodegenerative diseases, for which there is no cure to date. This dissertation has two main objectives. The first one is to investigate and identify structural …

Mitochondria Imaging And Targeted Cancer Treatment, Tinghan Zhao

Dissertations

Mitochondria are essential organelles as the site of respiration in eukaryotic cells and are involved in many crucial functions in cell life. Dysfunction of mitochondrial metabolism and irregular morphology have been frequently found in human cancers. The capability of imaging mitochondria as well as regulating their microenvironment is important both scientifically and clinically. Mitochondria penetrating peptides (MPPs), certain peptides that are composed of cationic and hydrophobic amino acids, are good candidates for mitochondria targeting. Herein, a novel MPP, D-argine-phenylalanine-D-argine-phenylalanine-D-argine-phenylalanine-NH₂ (rFrFrF), is conjugated with a rhodamine-based fluorescent chromophore (TAMRA). The TAMRA-rFrFrF probe exhibits advantageous properties for long-term mitochondria tracking of …

Topics On High Dimensional Selective Inference, Yan Zhang

Dissertations

In such applications as identifying differentially expressed genes in micro-array experiments or assessing safety and efficacy of drugs in clinical trials, researchers often report confidence intervals (CIs) and p-values only for the selected parameters, which is called selective inference. While constructing multiple CIs for the selected parameters, it is common practice to ignore issue of selection and multiplicity. Although protection against the effect of selection is sufficient in some cases, simultaneous coverage should be also needed in real applications. For example, in clinical trials, multiple endpoints are considered to assess effects of a drug and the ultimate decision often depends …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Dental Professionals Occupational Noise Exposure And Its Auditory And Non-Auditory Effects, Alexis Frees

Theses

The purpose of this study was to assess noise exposure and its auditory and non-auditory effects on workers in five clinical departments in the School of Dental Medicine at Rutgers Biomedical Health Sciences Campus in Newark, New Jersey. The study included environmental noise level measurement, dental instrument sound level measurement, personal noise dosimetry and a questionnaire survey to assess non-auditory effects. Octave band analysis of environmental noise levels showed that they are slightly above the standard noise criteria for clinics, and measurements from six dental instruments confirm that they contribute higher sound pressure levels at the frequencies of 1000, 2000, …

Effects Of Electro-Osmotic Consolidation Of Clays And Its Improvement Using Ion Exchange Membranes, Lucas Martin

Dissertations

Electro-osmosis is an established method of expediting consolidation of soft, saturated clayey soils compared to commonly used methods, such as preloading with wick drains. In electro-osmotic consolidation a direct current (DC) is applied via inserted electrodes. This causes hydrated ions in the interstitial fluid to migrate to oppositely charged electrodes. Because the clay particles have a negative surface charge, the majority of ions in the interstitial fluid are positively charged. Therefore, the net flow will be towards the negatively charged electrode (cathode), where the water can be removed and thus consolidation is achieved. Certain problems, such as pH changes in …

Engineering Of Escherichia Coli 2-Oxoglutarate Dehydrogenase Complex With Mechanistic And Synthetic Goals, Joydeep Chakraborty

Dissertations

The Escherichia coli 2-oxoglutarate dehydrogenase complex (OGDHc) compromises multiple copies of three enzymes - 2-oxoglutarate dehydrogenase (E1o), dihydrolipoyl succinyltransferase (E2o), and dihydrolipoyl dehydrogenase (E3). OGDHc is found in the Krebs cycle and catalyzes the formation of the all-important succinyl-Coenzyme A (succinyl-CoA). OGDHc was engineered to understand the catalytic mechanism and optimized for chemical synthetic goals.

Succinyl-CoA formation takes place within the catalytic domain of E2o via a transesterification reaction. The succinyl group from the thiol ester of S8-succinyldihydrolipoyl-E2o is transferred to the thiol group of CoA. Mechanistic studies were designed to investigate enzymatic transthioesterification. His375 and Asp374 was shown to …

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …

Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang

Dissertations

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization …

Model-Based Deep Autoencoders For Characterizing Discrete Data With Application To Genomic Data Analysis, Tian Tian

Dissertations

Deep learning techniques have achieved tremendous successes in a wide range of real applications in recent years. For dimension reduction, deep neural networks (DNNs) provide a natural choice to parameterize a non-linear transforming function that maps the original high dimensional data to a lower dimensional latent space. Autoencoder is a kind of DNNs used to learn efficient feature representation in an unsupervised manner. Deep autoencoder has been widely explored and applied to analysis of continuous data, while it is understudied for characterizing discrete data. This dissertation focuses on developing model-based deep autoencoders for modeling discrete data. A motivating example of …

Blind Separation For Intermittent Sources Via Sparse Dictionary Learning, Annan Dong

Dissertations

Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on …

Probabilistic Spiking Neural Networks : Supervised, Unsupervised And Adversarial Trainings, Alireza Bagheri

Dissertations

Spiking Neural Networks (SNNs), or third-generation neural networks, are networks of computation units, called neurons, in which each neuron with internal analogue dynamics receives as input and produces as output spiking, that is, binary sparse, signals. In contrast, second-generation neural networks, termed as Artificial Neural Networks (ANNs), rely on simple static non-linear neurons that are known to be energy-intensive, hindering their implementations on energy-limited processors such as mobile devices. The sparse event-based characteristics of SNNs for information transmission and encoding have made them more feasible for highly energy-efficient neuromorphic computing architectures. The most existing training algorithms for SNNs are based …

Supercapacitors With Gate Electrodes, Tazima Selim Chowdhury

Dissertations

A new approach to improve the capacitance of supercapacitors (SC) is proposed in this study. A typical SC is composed of an anode and a cathode; a separator in between them assures an unintentional discharge of the capacitor. The study focuses on a family of structured separators, either electronically active or passive which are called gates. An active structured separator layer has been fabricated and analyzed. The structured separator has characteristics of electrical diode and is fabricated out of functionalized carbon nanotubes (CNT). Improvement of the overall capacitance of SC, equipped with either active or passive structured separators demonstrated a …

Multi-Wavelength Investigation Of Energy Release And Chromospheric Evaporation In Solar Flares, Viacheslav M. Sadykov

Dissertations

For a comprehensive understanding of the energy release and chromospheric evaporation processes in solar flares it is necessary to perform a combined multi-wavelength analysis using observations from space-based and ground-based observatories, and compare the results with predictions of the radiative hydrodynamic (RHD) flare models. Initially, the case study of spatially-resolved chromospheric evaporation properties for an M 1.0-class solar flare (SOL2014-06-12T21:12) using data form IRIS (Interface Region Imaging Spectrograph), HMI/SDO (Helioseismic and Magnetic Imager onboard Solar Dynamics Observatory), and VIS/GST (Visible Imaging Spectrometer at Goode Solar Telescope), demonstrate a complicated nature of evaporation and its connection to the magnetic field topology. …

Rare Event Sampling In Applied Stochastic Dynamical Systems, Yiming Yu

Dissertations

Predicting rare events is a challenging problem in many complex systems arising in physics, chemistry, biology, and materials science. Simulating rare events is often prohibitive in such systems due to their high dimensionality and the numerical cost of their simulation, yet analytical expressions for rare event probabilities are usually not available. This dissertation tackles the problem of approximation of the probability of rare catastrophic events in optical communication systems and spin-torque magnetic nanodevices. With the application of the geometric minimum action method, the probability of pulse position shifts or other parameter changes in a model of an actively mode-locked laser …

Speciation Of Gaseous Oxidized Mercury Molecules Relevant To Atmospheric And Combustion Environments, Francisco J. Guzman

Dissertations

Mercury is a pervasive and highly toxic environmental pollutant. Major anthropogenic sources of mercury emissions include artisanal gold mining, cement production, and combustion of coal. These sources release mostly gaseous elemental mercury (GEM), which upon entering the atmosphere can travel long distances before depositing to environmental waters and landforms. The deposition of GEM is relatively slow, but becomes greatly accelerated when GEM is converted to gaseous oxidized mercury (GOM) because the latter has significantly higher water solubility and lower volatility. Modeling GOM deposition requires the knowledge of its molecular identities, which are poorly known because ultra-trace (tens to hundreds part …

Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan

Dissertations

Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.

First, dependencies among discrete, continuous and repeated observations are studied …

N8- Polynitrogen Stabilized On Carbon-Based Supports As Metal-Free Electrocatalyst For Oxygen Reduction Reaction In Fuel Cells, Zhenhua Yao

Dissertations

The sluggish oxygen reduction reaction (ORR) kinetics at the cathode is one of the key factors limiting the performance of polymer electrolyte membrane fuel cell (PEMFC). Platinum-based materials are the most widely studied catalysts for this ORR reaction while their large-scale practical application in fuel cells is hindered due to their scarcity and low stability. Therefore, highly active, low cost and robust non-Pt catalysts are being developed to overcome the drawbacks. Recently, a novel polynitrogen N8- (PN) stabilized on multiwall carbon nanotube (MWNT) was synthesized under ambient condition for the first time by our group and demonstrated high ORR activities. …

Surface-Enhanced Raman Detection Of Glucose On Several Novel Substrates For Biosensing Applications, Laila Saad Alqarni

Dissertations

The small normal Raman cross-section of glucose is considered to be a major challenge for its detection by Surface Enhanced Raman Spectroscopy (SERS) for medical applications. These applications include blood glucose level monitoring of diabetic patients and evaluation of patients with other medical conditions, since glucose is a marker for many human diseases. This dissertation focuses on Surface-Enhanced Raman Scattering primarily for the detection of glucose. Some experiments also are carried out for the detection of the corresponding enzyme glucose oxidase that is used in electrochemical glucose sensors and in biofuel cells. This project explores the possibility of utilizing Surface …

Workload Allocation In Mobile Edge Computing Empowered Internet Of Things, Qiang Fan

Dissertations

In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart "brain" to process and store the big data generated from …

Fouling And Aging In Membrane Filtration : Hybrid Afm-Based Characterization, Modelling And Reactive Membrane Design, Wanyi Fu

Dissertations

Membrane filtration has been extensively used in water and wastewater treatment, desalination, dairy making, and biomass/water separation. However, membrane fouling, aging and insufficient removal efficiency for dissolved organic matters remain major challenges for wider industrial applications. In order to tackle these challenges, this doctoral dissertation investigates mechanisms of membrane fouling and development of antifouling membrane filtration technologies. Specifically, four major research areas are explored: (i) nanoscale physicochemical characterization of the chemically modified polymeric membranes; (ii) quantitative modelling between membrane properties and membrane fouling and defouling kinetics; (iii) development of quantitative structure-activity relationships for membranes that undergo thermal and chemical aging …

A Comparative Study Of Russian Trolls Using Several Machine Learning Models On Twitter Data, Kannan Neten Dharan Kannan Neten Dharan

Theses

Ever since Russian trolls have been brought into light, their interference in the 2016 US Presidential elections has been monitored and studied thoroughly. These Russian trolls have fake accounts registered on several major social media sites to influence public opinions. Our work involves trying to discover patterns in these tweets and classifying them by using different machine learning approaches such as Support Vector Machines, Word2vec and neural network models, and then creating a benchmark to compare all the different models. Two machine learning models are developed for this purpose. The first one is used to classify any given specific tweet …

A Study Of Machine Learning And Deep Learning Models For Solving Medical Imaging Problems, Fadi G. Farhat

Theses

Application of machine learning and deep learning methods on medical imaging aims to create systems that can help in the diagnosis of disease and the automation of analyzing medical images in order to facilitate treatment planning. Deep learning methods do well in image recognition, but medical images present unique challenges. The lack of large amounts of data, the image size, and the high class-imbalance in most datasets, makes training a machine learning model to recognize a particular pattern that is typically present only in case images a formidable task.

Experiments are conducted to classify breast cancer images as healthy or …